"\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 59-64 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2020.59.64 \n\n\n\nCite the Article: Jimoh, M.T., Bolarinwa, A.T, T. O. Kolawole (2020). Mobility And Redistribution Of Major Elements In Weathered Profile Developed On \nPegmatite At Kitibi-Iwoye, Southwestern Nigeria. Malaysian Journal of Geosciences, 4(2): 59-64.\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2020.59.64\n\n\n\nMOBILITY AND REDISTRIBUTION OF MAJOR ELEMENTS IN WEATHERED PROFILE \n\n\n\nDEVELOPED ON PEGMATITE AT KITIBI-IWOYE, SOUTHWESTERN NIGERIA  \n\n\n\nJimoh, M.Ta., Bolarinwa, A.Tb, T. O. Kolawolec\n\n\n\na Department of Earth Sciences, Ladoke Akintola University of Technology, Ogbomosho. \nb Department of Geology, University of Ibadan. \nc Department of Geological Sciences, Osun State University, Oshogbo. \n\n\n\n*Corresponding author email: mtjimoh@lautech.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 11 February 2020 \nAccepted 13 March 2020 \nAvailable online 10 April 2020\n\n\n\nGeochemical studies of weathering profiles around Kitibi-Iwoye revealed redistribution of elements from \nparental pegmatite to the regolith. Geological mapping showed that the pegmatite intrudes diorite and \nmigmatite gneiss. Weathered samples from B-horizon were air-dried, pulverised and analysed using X-ray \nFluorescence Spectrometry (XRF) in the Department of Geological Sciences, University of Cape Town, South \nAfrica. Chemical Index of Alteration (CIA), Weathering Index of Parker (WIP), Mass balance calculation and \nAl2O3 \u2013 CaO+Na2O \u2013 K2O (A\u2013CN\u2013K) ternary plot were employed to determine elemental mobility and \ndistribution caused by weathering. SiO\u2082 (74.2 and 43.4 wt %), CaO (0.43 and 0.03 wt. %), Na2O (7.14 and 0.04 \nwt. %), K2O (1.90 and 0.67 wt. %), MnO (0.11 and 0.03 wt. %) and P2O5 (0.20 and 0.05wt. %) displayed \ndepletion from parent rock to the regolith respectively. But Al\u2082O\u2083 (15.5 wt. % and 33.5 wt. %), Fe2O3 (0.39 and \n3.40 wt. %), TiO2 (0.04 and 0.35 wt. %) and MgO (0.08 and 0.11 wt. %) showed enrichment from parent rock \nto the regolith respectively. Fe\u2082O\u2083 (3.19) is the most enriched whereas Na2O (-99.8), CaO (-98.9), P2O5 (-95.3), \nK2O (-89.5), SiO2 (-81.9), MgO (-73.1), MnO (-64.5) and Al2O3 (-23.6) are progressively depleted. Mean CIA \nvalue of 97.8 revealed that weathering has almost reached its completion whereas CIA of 62.1 for the \npegmatite suggested that the parent rock is at incipient stage of weathering. Pegmatite had a WIP of 110.5 \nwhereas the weathered samples with WIP ranging from 2.66, 3.88, 6.03, 6.23, 6.92, 8.08, 9.08, 9.76 and 14.6 \nrespectively showed decreasing trend of weathering. This study confirmed contrasting behaviour of CIA and \nWIP. A-CN-K diagram suggested strongly weathered samples plotted at the apex of Al2O3 field whereas \npegmatite plots along the A-CN line.  \n\n\n\nKEYWORDS \n\n\n\nWeathering profile, Pegmatite, B-horizon, Mass balance, Parent rock.\n\n\n\n1. INTRODUCTION \n\n\n\nMobility of elements in a particular geological setting is mostly triggered \nby chemical reactions within a rock body after its crystallisation. These \nreactions often occur when the rock mass interacts with an invading fluid \n(Rollinson, 1993). There is direct relationship between mobility and \nredistribution of elements in rock alteration processes. Cramer and \nNesbitt noted that redistribution of elements is dependent on the mobility \nof these elements during the interaction of rock with meteoric or connate \nwater (Cramer and Nesbitt, 1983). Natural processes such as weathering, \nwater-rock interaction, hydrothermal alteration, groundwater mixing, \nevolution and magmatic crystallisation can disrupt the pattern of mobility \nand fractionation of elements (Yusoff et al., 2013). \n\n\n\nSuch elemental mobility and fractionation (redistribution) provide a \nveritable tool for comprehending processes that lead to the formation of \nweathered products and their abundance. These processes are directly \ninfluenced by climatic and geomorphological conditions of tropical region \n(Voicu et al., 1997). The redistribution of elements in rock profile is \nstrongly controlled by the interaction between water, minerals, oxidizing \n\n\n\nenvironments and organic acids, so weathering in the soil and unsaturated \nzone above the groundwater table may be faster than below the \ngroundwater table. This interaction will be more influential on less stable \nand less resistant minerals, causing them to leach elements (Harnois, \n1988). \n\n\n\nP egmatites are important sources of economic minerals such as \ntourmaline, beryl, tantalite and kaolin which are extracted from \nweathered pegmatite during mining. In recent times there has been \nrenewed interest in the study of pegmatites globally because of their \nattractive economic potentials (Adekeye and Akintola, 2007; Garba, 2003; \nPrice and Velbel, 2003. Alteration of high temperature phyllosilicates such \nas muscovite and biotite into clay minerals follows a different pattern with \nthat of plagioclase and alkali feldspar (Velde and Meunier, 2008). \nTransformation trend in phyllosilicates proceeds in steps because of \nstrong similarity in the structure of unstable primary phase and newly \nformed secondary minerals. Major and trace elements hosted by rock \nforming minerals are mostly liberated during chemical weathering (Van \nder Weijden and Van der Weijden, 1995). Their mobilization and \nredistribution during weathering is particularly complicated because \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 59-64 \n\n\n\nCite the Article: Jimoh, M.T., Bolarinwa, A.T, T. O. Kolawole (2020). Mobility And Redistribution Of Major Elements In Weathered Profile Developed On \nPegmatite At Kitibi-Iwoye, Southwestern Nigeria. Malaysian Journal of Geosciences, 4(2): 59-64.\n\n\n\nthese elements are affected by various processes such as dissolution of \nprimary minerals, formation of secondary phases, redox processes, \ntransportation of materials, co-precipitation and ion exchange of various \nminerals (Islam et al., 2002; White et al., 2000). Direct evidence of past \nweathering conditions can be obtained from weathered soil through \ncombination of field observation, petrography, X-ray Diffractometry and \nwhole rock (Bahlburg and Dobrzingki, 2009). Saprolite retained original \ncomposition and texture of the pegmatite in some parts of the profiles. It \nwas noted that saprolites are open system as they are easily affected by \nvegetation, rainfall, soil processes and oxygen (Nesbitt and Young, 1982). \nThis research is targeted at studying geochemistry of weathered profile \ndeveloped on pegmatites at Kitibi-Iwoye and Awo mining sites. Proportion \nof each major element present in the profile will be revealed. Issues \npertaining to mobilization and redistribution of the elements in the source \nrock and overlying regolith caused by weathering are also addressed. \nVarious indices such as mass balance model, chemical index of alteration, \nweathering index of parker and Al2O3 \u2013 CaO+Na2O \u2013 K2O (A\u2013CN\u2013K) \nternary diagrams were employed to interpret rate of weathering \nprocesses. The Chemical Index of Alteration (CIA) proposed and the \nWeathering Index of Parker (WIP) which was first introduced and \ndeveloped are the two most commonly applied indices (Nesbitt and Young, \n1982; Parker, 1970; Hamdan and Burnham, 1996).  \n\n\n\n1.1 Local Geology and Description of Weathering Profiles \n\n\n\nThe study areas (Kitibi-Iwoye and Awo) are located within the \nPrecambrian basement complex of southwestern Nigeria. Vegetation of \nthe study area shows a transition from humid tropical forest to savannah \nwoodland. The geomorphological and topographical features revealed \nthat the study area had suffered various degrees of weathering which had \ntransformed the underlying bedrock into weathered saprolites of varying \ndepth and thickness.Various rock types identified in the area are \nmigmatite gneiss, biotite gneiss, banded gneiss, diorite, granodiorite, \ngranite, and pegmatite (Figure 1).  \n\n\n\nFigure 1: Geological Map of the study area \n\n\n\nPegmatite is the predominant rock type which intrudes older lithology \nsuch as migmatite gneiss. In some places, their veins occur as cross-cutting \ndiscordant dykes in rock units such as migmatite, banded gneisses and \ndiorite. These dykes and veins range from few centimeters to tens of \nmeters. Most of the pegmatite bodies seldom exceed 200-300 m in length, \n1-2 m wide and suffer varying degree of tropical weathering. Hand \nspecimen examination of un-weathered samples showed coarse grained \nmicrocline and plagioclase, quartz, muscovite and biotite as the major \nconstituent minerals whereas accessory minerals such as zircon, apatite, \nmagnetite, tourmaline and beryl occur in varying proportion (Figure 2).  \n\n\n\nFigure 2: Garnet-bearing pegmatite at Kitibi-Iwoye showing extremely \n\n\n\ncoarse crystal of feldspar \n\n\n\nPegmatites within the vicinity of the mining site have been deeply \nweathered revealing the resistant, fractured quartz. Evidence of \nweathering is noticeable as the less resistant feldspars display variegated \ncolours which is between red and off-white. The weathering profiles are \nlocated on a gently sloping hill of about 300 m above the sea level, the \ndepth of the profiles ranges from about 12m at the center of the hilltop to \n3m at the edge of the hill. There are numerous drilling pits where various \neconomic minerals have been worked. The top soil (20-50 cm deep) which \nconsists of unconsolidated humic and soil materials have been grossly \ndisturbed by farming and indiscriminate piling up of mine tailings. This \nlayer is directly underlain by gravelly lateritic layer of about 40-65 cm. The \ngravelly materials have sharp abraded edges with sizes ranging from 1-5 \ncm. Saprolite developed over the bedrock consists of disoriented mixture \nof white to light gray weathered materials which are soft, wet and friable. \nThe samples are gritty to touch due to presence of detrital quartz. Relicts \nof primary constituent minerals such as quartz, feldspar and muscovite \nare conspicuous at the boundary between the regolith and underlain \nbedrock (Figure 3). The weathering profile of the study area which is \ndirectly underlain by pegmatite has been considerably altered through \ninteraction between solution, rock and their weathering residues. \n\n\n\nFigure 3: Weathered profile at a road section along Iwo-Oshogbo road  \n\n\n\n1.2 Mineralogical Composition of the Pegmatite and weathered \n\n\n\nmaterials \n\n\n\nDetailed information on the mineralogy of pegmatite and weathered \nmaterials around Kitibi-Iwoye was provided (Jimoh et al., 2016). The \npegmatite consists of quartz, feldspar, muscovite, biotite, opaques and \naccessory minerals such as tourmaline and beryl. X-ray diffraction \npatterns of quartz- feldspar- muscovite-rich pegmatite further identified \nalbite, anorthite, microcline, quartz, muscovite \u00b1 biotite and accessory \nminerals (Figure 4). Phases such as kaolinite, illite, quartz, phlogopite, \nmuscovite and goethite were present in the weathered mass. \n\n\n\nFigure 4: Photomicrograph of pegmatite showing quartz (Qtz), \n\n\n\nmuscovite (Ms), myrmekite (My) wrapped within K-feldspar (K-fsp) and \n\n\n\nplagioclase (Pg), and Cross polars, x2.5 \n\n\n\n2. METHODOLOGY \n\n\n\n2.1 Sampling and Sample Preparation \n\n\n\nPegmatite within the precincts of the mining site were sampled and \nstudied for its geological and textural features. In the laboratory, \nrepresentative samples were crushed in a jaw- crusher with tungsten \ncarbide jaw blades and subsequently pulverised in a tungsten carbide ball-\nmill. Samples of the pulverised rock were thoroughly mixed in a Turbula \nmixer and aliquots of these samples were used for chemical analyses. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 59-64 \n\n\n\nCite the Article: Jimoh, M.T., Bolarinwa, A.T, T. O. Kolawole (2020). Mobility And Redistribution Of Major Elements In Weathered Profile Developed On \nPegmatite At Kitibi-Iwoye, Southwestern Nigeria. Malaysian Journal of Geosciences, 4(2): 59-64.\n\n\n\nWeathered materials developed as regolith on the pegmatite were \npreferably collected at designated depths of horizons B (3.5-5.0 m) and C \n(6.0-6.5 m) using hand auger. Samples were collected from these horizons \nbecause they contain chemically altered rock materials with identifiable \ntextural features of its parental pegmatitic protolith. The weathered \nprofiles are derived from the same parental source but at different \ntopographic positions of the study area. Changes in depth, structure, \ntextural distribution, colour, grain types and arrangements of the dried \nsamples were examined. The feldspathic components of the horizons have \nsuffered certain degrees of weathering whereas muscovite and quartz \ncomponents are relatively resistant. The samples were packed into \nvarious cellophane bags and air dried at the base camp. The samples were \nalso pulverized prior to chemical analyses.  \n\n\n\n2.2 Laboratory Techniques and Chemical Analyses \n\n\n\nChemical analyses of major elements such as Si, Ti, Al, Fe, Mn, Mg, Ca, Na, \nK and P were done by X-ray Fluorescence Spectrometry (XRF) in the \nlaboratory of Department of Geological Sciences, University of Cape Town, \nSouth Africa. 2 g of pulverized rock and weathered samples were weighed \nand put in the oven at 110\u02daC for at least 4 hours. Upon cooling, the samples \nwere weighed and roasted in a furnace overnight at 850\u02daC to drive off \nexcess moisture content and to convert all Fe in the samples to Fe2O3, \nhence Fe does not occur as total Fe but as Fe2O3. The samples were \nhomogenized by measuring 0.7 g of the samples from 2 g of dried samples \ninto glass discs and 6 g of X-ray flux was added to reduce the temperature \nat which the samples melt for homogenization to complete. The samples \nwere thereafter fused in a gas fusion chamber (Model: CLAISSE-M4 GAS \nFUSION) which contains platinum crucibles. The X-ray flux contains 35.3% \nlithium tetraborate (Li2B4O7) and 64.7% lithium metaborate (LiBO2). \nLithium bromide (LiBr) was also added to release the fused discs from the \nplatinum crucible in the gas fusion chamber. The operational settings for \nthe XRF spectrometry was set at 4 kW, 60 kV (160 mA). \n\n\n\n2.3 Data Evaluation and Assessment \n\n\n\nData obtained were evaluated using mass balance evaluation, chemical \nindex of alteration (CIA), Weathering index of Parker (WIP) and A-CN-K \nternary plot. \n\n\n\n2.3.1 Mass Balance Model \n\n\n\nMass balance model is the percentage loss or gain in the concentration of \nweathered samples in each element compared with its concentration in \nthe parent rock. In order to determine the extent of weathering, values of \nweathered samples and the parent rock were compared using mass \nbalance equation (Cramer and Nesbitt, 1983). Mass balance equation was \nused to evaluate distribution of elements during supergene weathering. \nThe equation is represented by: \n\n\n\n% (loss or gain) = (\n(\n\ud835\udc4b\ud835\udc34\n\n\n\n\ud835\udc4b\ud835\udc35\n)\n\n\n\n(\n\ud835\udc4c\ud835\udc34\n\n\n\n\ud835\udc4c\ud835\udc35\n)\n\n\n\n\u2044 )\u2212 1 \u2217100 Eqn 1 \n\n\n\nWhere XA is concentration of element X in weathered sample and XB is \n\n\n\nconcentration of element X in parent rock. \n\n\n\nYA is concentration of immobile element Y in weathered sample and YB is \n\n\n\nconcentration of immobile element Y in parent rock.   \n\n\n\nA group researchers observed that immobile elements are either \naccumulated within the residual phases or adsorbed by newly formed \nsecondary minerals (Eggleton and Buseck, 1980). It was further noted that \nCa, Na, P, K, Si, Ba, Rb, Mg, Pb, Ni, Zn, Cr and Co are mobile elements that \nare products of minerals such as feldspars, micas and apatite that are \nsusceptible to weathering. Mobile and immobile elements are \ngeochemically distributed throughout the regolith during weathering \n(Tijani et al., 2006).  \n\n\n\nLow concentration of elements or concentration below detection limits in \nparent rocks posed difficulties in choosing the immobile element to be \nused in the calculation of percentage loss or gain. Following the approach, \nTi was the preferred immobile element because of its availability in all \nigneous rocks in concentrations at wt. % levels (Cramer and Nesbitt, \n1983). Other elements are present at concentration (ppm or ppb levels) \nnear to their detection limits in the parent materials.   \n\n\n\n2.3.2 Chemical Index of Alteration  \n\n\n\nChemical Index of Alteration (CIA) is a measure of the extent of feldspars \nalteration to clays it is the most widely applied and most indicative of the \n\n\n\navailable weathering indices (Rollinson, 1993; Nesbitt and Young, 1984; \nNesbitt and Young, 1989). It represents a ratio of predominantly immobile \nAl2O3 to the mobile cations Na+, K+ and Ca2+ given as oxides (Bahlburg and \nDobrzinski, 2009). According to a study, CIA is represented by the \nmolecular proportions of (Nesbitt and Young, 1989). \n\n\n\nCIA= (\ud835\udc34 \ud835\udc34 + \ud835\udc36 + \ud835\udc41 +\ud835\udc3e\u2044 ) \u2217 100\u2026Eqn 2 \n\n\n\nWhere A = Al2O3, C = CaO, N = Na2O and K = K2O \n\n\n\n2.3.3 Weathering Index of Parker \n\n\n\nWeathering index of Parker (WIP) for silicate rocks such as acid, \nintermediate and basic igneous rock was introduced (Parker, 1970). The \nproportions of mobile elements such as Na+, K+, Mg2+ and Ca2+ were \nconsidered, this is because of their high mobility among the major \nelements. In WIP, the value of Ca2+ is implicitly assumed to be contained in \nsilicate minerals (Bahlburg and Dobrzinski, 2009; Price and Velbel, 2003). \nWIP is represented by: \n\n\n\n(100)[(2\ud835\udc41\ud835\udc4e2\ud835\udc42. 0.35\u2044 ) +](\ud835\udc40\ud835\udc54\ud835\udc42 0.9\u2044 ) + (2\ud835\udc3e2\ud835\udc42. 0.25\u2044 ) + (\ud835\udc36\ud835\udc4e\ud835\udc42 0.7\u2044 )\u2026Eqn 3 \n\n\n\n2.3.4 Ternary plot \n\n\n\nTernary plot consists of a triangle whose three apices represent a \ncomposition such as Al2O3, CaO + Na2O and K2O. Ternary diagram of Al2O3-\nCaO+Na2O-K2O (A-CN-K) was employed to show the trend and the degree \nof silicate weathering. It also plays a vital role in evaluating composition of \nthe parent rock (Nesbitt and Young, 1989; Fedo et al., 1995; Li and Yang, \n2010). \n\n\n\n3. RESULT AND DISCUSSION \n\n\n\nResult of major oxide mobilization and redistribution in pegmatite and \nweathered samples of Kitibi-Iwoye is presented in table 1. The \ngeochemical data showed that SiO\u2082 displayed steady depletion from the \nbedrock to the regolith, 74.2 wt % and mean value of 43.4 wt % were \nrecorded for SiO2 in pegmatitic bedrock and weathered samples \nrespectively. The values compared relatively with what was reported for \nsimilar geological unit (72.2 wt %) but higher for the mean value obtained \nfor the regolith (69.4 wt %) (Tijani et al., 2006). Oxides such as CaO (0.43 \nand 0.03 wt. %), Na2O (7.14 and 0.04 wt. %), K2O (1.90 and 0.67 wt. %), \nMnO (0.11 and 0.03 wt. %) and P2O5 (0.20 and 0.05wt. %) showed \ndecreasing trends for the parent rock and regolith respectively. The \namount of Al\u2082O\u2083 increases from 15.5 wt. % to a mean value of 33.5 wt. % \nin the weathered samples. This clearly show alumina enrichment within \nthe regolith compared to the parent rock. In other words, during \nweathering, more of Al\u2082O\u2083 accumulated as the rock disintegrates.  \n\n\n\nOxides such as Fe2O3 (0.39 and 3.40 wt. %), TiO2 (0.04 and 0.35 wt. %), \nMgO (0.08 and 0.11 wt. %) followed trends similar to that of alumina \n(Table.1) and Loss on Ignition (LOI) which represents the weight (wt%) of \nvolatiles and oxides (H2O+, CO2, F, Cl and S) lost upon heating the sample \nto 850\u02daC for more than four hours revealed parent rock and regolith values \nof 0.52 and 18.24 wt. % respectively. Fe\u2082O\u2083 showed general increase from \nthe pegmatites to the regolith. The pegmatites had concentration of about \n0.39 wt. % and mean concentration of 3.39 wt. % in the weathered \nsamples. WS\u2087 showed more reddish brown colouration compared to what \nwas observed in other weathered samples, this was due to high content of \nFe\u2082O\u2083 (7.00 wt. %). It also showed that during weathering, more of Fe\u2082O\u2083 \naccumulates in the Saprolite. It is suggestive of haematite abundance \ntowards the upper part of the weathering profile. Information obtained \nfrom the data showed mobilization pattern in the major oxides. Some \noxides are significantly mobilized while some sparingly mobilized or \nimmobilized during the weathering of pegmatitic bedrock. It is observed \nthat individual oxide increases in concentration with depth which is a clear \nindication of leaching from surface and accumulation towards the \nbedrock. TiO2 and Zr are universally used for quantifying mass losses for \nmajor and trace elements in soils and other residual weathering products \nby assuming that they are immobile during weathering processes (Nesbitt, \n1979). A group researchers preferred to use TiO2 instead of Zr, on the basis \nthat the latter is not always evenly distributed in samples, and TiO2 is more \nabundant (Nesbitt, 1979; Nesbitt and Markovics, 1997).  \n\n\n\n3.1 Mass balance Model \n\n\n\nMass balance which represents percentage (%) loss or gain was calculated \nusing equation 1 and presented in table. 3. Physical and chemical \nweathering of bedrock led to the formation of Saprolite. Extensive leaching \nof the primary constituent minerals is mostly restricted to the major \nelements whose depletion occurred in the parent rock towards the B-\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 59-64 \n\n\n\nCite the Article: Jimoh, M.T., Bolarinwa, A.T, T. O. Kolawole (2020). Mobility And Redistribution Of Major Elements In Weathered Profile Developed On \nPegmatite At Kitibi-Iwoye, Southwestern Nigeria. Malaysian Journal of Geosciences, 4(2): 59-64.\n\n\n\nhorizon. Positive and negative values obtained for the major elements \nconsidered are indication of enrichment and depletion respectively.  \n\n\n\nAccording to some researcher, stability of Ti has been proved in studies \nrelating to soil genesis and continuity of soil profile (Kabata-Pendia and \nPendias, 2001). Using these criteria, and according to a study, Ti was the \nimmobile element used to calculate the mass balance in this study (Nesbitt \nand Wilson, 1992). Data obtained from calculating the mass balance (table \n3), shows that most of the major elements have been highly depleted \nexcept for Fe\u2082O\u2083 (3.19) whose depletion was not as much as other major \nelements. Na2O with an average mass balance value of -99.8 is the most \ndepleted or least enriched while Fe2O3 with mean mass balance 3.19 is the \nmost enriched and least depleted (Table 3). CaO (-98.9), K2O (-89.5), P2O5 \n\n\n\n(-95.3) and SiO2 (-81.9) are significantly depleted whereas MgO (-73.1) \nand MnO (-64.5) are moderately depleted while Al2O3 (-23.6) is barely \ndepleted. \n\n\n\nLeaching of the major elements is mainly restricted to Mg, Ca, Na, K and Si. \nThese elements show considerable depletion in the soil when mass \nbalance was calculated. Significant leaching and depletion of the elements \nare due to weathering of primary minerals such as plagioclase, alkali \nfeldspars, biotite and muscovite. It also depicts that the major elements \nare being gradually reduced from the bedrock towards the topsoil in the \nB-horizon. Mobility characteristics of the major elements around the study \narea showed that weathering profile has definitely been disturbed by \nerosion and mass movement. \n\n\n\nSome researcher noticed a similar trend in the distribution of elements in \nthe soil which showed that the bedrock was obliterated to some extent by \nredistribution of the elements in the soil by weathering, erosion and \ndifferent rates of dissolution and mobility of the elements in the secondary \nenvironment (Adekeye and Akintola, 2007). The result shows that \nelements in the bedrock tend to be of lower concentration compared to \nthe soil samples which indicates that there is high elemental mobility. \nAlthough these elements are been worked on, they tend to be of higher \nconcentration to what is found in the bedrock. \n\n\n\n3.2 Chemical Index of Alteration \n\n\n\nChemical Index of Alteration (CIA) of the rock and weathered samples \nwere calculated using equation 2, the result is presented in Table 2. An \naverage value of 97.8 % was obtained for the weathered samples which \nobviously showed that the feldspar has been extensively weathered. A \ngroup researchers stated that kaolinite with CIA of 100 has reached the \nhighest degree of weathering (Nesbitt and Young, 1982; Fedo et al., 1995; \nBahlburg and Dobrzinski, 2009). Illite is between 75 and 90, muscovite at \n75, the feldspars at 50. Fresh basalts have values between 30 and 45, fresh \ngranites and granodiorites of 45 to 55 (Bahlburg and Dobrzinski, 2009). \nThe value of 97.8 obtained for the study area revealed that the weathering \nhas almost reached its completion stage and the product is kaolinite \n(Jimoh et al., 2016). Whereas CIA of 62.1 obtained for the pegmatite parent \nrock indicated a value close to that obtained for fresh granite and \ngranodiorite but suggested that the parent rock has just commenced its \nalteration processes which accounts for its slightly higher value.  \n\n\n\nThe behavioural pattern of the values implied that the higher the CIA the \nmore the intensity of weathering. The values obtained for pegmatite (58.3) \nand weathered mass (73.4) was compared with the CIA calculated in this \nstudy for pegmatite (62.1) and weathered mass (97.8), it shows that \nweathering is almost completed in this study due to the relatively high CIA \nbut the values of unaltered pegmatite are almost similar in this study and \nthat obtained (Tijani et al., 2006). The proposition was also supported the \nCIA values of approximately 45\u201355 indicate no weathering, while a value \nof 100 indicates intense weathering after the complete removal of alkali \nand alkaline earth elements from the parent rocks (Taylor and Eggleton, \n2001; Tijani et al., 2006; Mclennen, 1993). Generally, the larger CIA values \nmean stronger silicate weathering.  \n\n\n\n3.3 Weathering Index of Parker \n\n\n\nWeathering Index of Parker (WIP) was calculated using equation 3 and the \nresult was presented in Table 2. The more weathered rocks have the least \nvalues of WIP. Its value ranges commonly between \u2265100 and 0. Fresh and \nunweathered pegmatite (RS) had a WIP value of 110.5 which shows that \nRS has not suffered from much weathering. The least values of WIP for the \nstudy area are WS6 (2.66) and WS9 (3.88) while moderately high values \nwere reported for WS7 (6.03), WS8 (6.23), WS3 (6.92), WS1 (8.08), WS2 \n(9.08) and WS5 (9.76) whereas WS4 (14.6) had the highest WIP value. It \nis implied from these values that the most weathered sample is WS6 while \nthe least weathered is WS4. \n\n\n\nComparative analysis was drawn from the results obtained for CIA and \nWIP as shown in Table 2. Both models showed contrasting behavioural \npattern in which the values of CIA are increasing as weathering is \nprogressing whereas the values of WIP were correspondingly decreasing \n(Kingsley and Ekwuene, 2009). Their values were also plotted on a binary \nplot (Figure 5). The rate of weathering is increasing as CIA increases while \nthe rate is increasing as WIP decreases. Fresh and unweathered pegmatite \nplots at the top left corner of Figure 5 whereas the mostly weathered \nsamples plot at the bottom right corner. \n\n\n\n3.4 Ternary plot \n\n\n\nThe concentration (wt. %) of Al2O3, CaO, Na2O and K2O (A-CN-K) in fresh \npegmatite and weathered samples are presented in Tables 1 and 2. All \nvalues plotted fell into the apical portion of Al2O3 field (Figure 6). Using the \napproach of the samples have been strongly weathered whereas a value \nplotted at the middle along the A-CN line showed weak weathering which \nsuggests that the rock sample is at the incipient stage of weathering \n(Figure 6) (Shao et al., 2012). According to a study, fresh and unaltered \ngranitic rock such as pegmatite has a CIA value of 50 but the CIA of \nunaltered pegmatite in this study is 62.1 which indicate that the pegmatite \nhas been weakly weathered (Tijani et al., 2006). Other CIA values which \nplotted within the apex of Al2O3 field revealed that those samples had been \nstrongly weathered into kaolinite (Jimoh et al., 2016). This observation is \ncorroborated in Figure 6 which also confirms that most of the samples \nhave been intensely weathered. \n\n\n\nFigure 5: Relationship between CIA and WIP (Bahlburg and Dobrzinski, \n\n\n\n2009). Where U.R is unweathered rock \n\n\n\nFigure 6: The Al2O3-CaO+Na2O-K2O diagram of fresh and weathered \nsamples around the study area (Bahlburg and Dobrzinski, 2009; Shao et \n\n\n\nal., 2012). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 59-64 \n\n\n\nCite the Article: Jimoh, M.T., Bolarinwa, A.T, T. O. Kolawole (2020). Mobility And Redistribution Of Major Elements In Weathered Profile Developed On \nPegmatite At Kitibi-Iwoye, Southwestern Nigeria. Malaysian Journal of Geosciences, 4(2): 59-64.\n\n\n\nTable 1: Major oxides concentration (wt. %) of analysed samples at Kitibi-Iwoye \n\n\n\nWS1 WS2 WS3 WS4 WS5 WS6 WS7 WS8 WS9 Mean RS \n\n\n\nSiO2 42.1 46.8 44.9 45.5 43.6 44.6 38.7 43.2 41.4 43.4 74.2 \n\n\n\nAl2O3 33.21 37.68 36.92 29.54 29.05 34.69 29.37 35.82 35.44 33.50 15.50 \n\n\n\nFe2O3 2.62 0.27 2.27 5.14 5.92 2.28 7.00 2.80 2.21 3.39 0.39 \n\n\n\nCaO 0.02 0.01 0.01 0.07 0.02 0.02 0.02 0.02 0.06 0.03 0.43 \n\n\n\nMgO 0.11 0.06 0.11 0.14 0.16 0.08 0.18 0.08 0.09 0.11 0.08 \n\n\n\nNa2O 0.03 0.03 0.03 0.07 0.06 0.11 0.02 0.02 0.02 0.04 7.14 \n\n\n\nK2O  0.70 0.82 0.58 0.67 0.80 1.24 0.38 0.54 0.29 0.67 1.90 \n\n\n\nMnO 0.01 - - 0.06 0.03 0.06 0.03 0.01 - 0.03 0.11 \n\n\n\nTiO2 0.22 0.03 0.18 0.48 0.69 0.24 0.73 0.26 0.28 0.35 0.04 \n\n\n\nP2O5 0.01 0.03 0.03 0.06 0.07 0.04 0.06 0.05 0.07 0.05 0.20 \n\n\n\nLOI 21.48 14.35 15.34 17.51 18.66 15.54 23.29 17.35 20.60 18.24 0.52 \n\n\n\nSUM 100.6 100.1 100.5 99.24 99.06 98.9 99.78 100.2 100.5 99.85 100.5 \n\n\n\nTable 2: CIA and WIP values calculated for selected elements \n\n\n\nWS1 WS2 WS3 WS4 WS5 WS6 WS7 WS8 WS9 Mean RS \n\n\n\nAl2O3 33.21 37.68 36.92 29.54 29.05 34.69 29.37 35.82 35.44 33.50 15.50 \n\n\n\nCaO 0.02 0.01 0.01 0.07 0.02 0.02 0.02 0.02 0.06 0.03 0.43 \n\n\n\nMgO 0.11 0.06 0.11 0.14 0.16 0.08 0.18 0.08 0.09 0.11 0.08 \n\n\n\nNa2O 0.03 0.03 0.03 0.07 0.06 0.11 0.02 0.02 0.02 0.04 7.14 \n\n\n\nK2O \n\n\n\nCIA%  \n\n\n\nWIP% \n\n\n\n0.70 \n\n\n\n  97.8 \n\n\n\n8.08 \n\n\n\n0.82 \n\n\n\n97.8 \n\n\n\n9.08 \n\n\n\n0.58 \n\n\n\n98.3 \n\n\n\n6.92 \n\n\n\n0.67 \n\n\n\n97.3 \n\n\n\n14.6 \n\n\n\n0.80 \n\n\n\n97.1 \n\n\n\n9.76 \n\n\n\n1.24 \n\n\n\n96.2 \n\n\n\n2.66 \n\n\n\n0.38 \n\n\n\n98.6 \n\n\n\n5.05 \n\n\n\n0.54 \n\n\n\n98.1 \n\n\n\n6.23 \n\n\n\n0.29 \n\n\n\n98.9 \n\n\n\n3.88 \n\n\n\n0.67 \n\n\n\n97.8 \n\n\n\n7.36 \n\n\n\n1.90 \n\n\n\n62.1 \n\n\n\n110.5 \n\n\n\nTable 3: Mass Balance Calculation of Major Elements at Kitibi-Iwoye \n\n\n\nMajor Elements WS1 WS2 WS3 WS4 WS5 WS6 WS7 WS8 WS9 Mean \n\n\n\nSiO2 -89.7 -0.16 -86.5 -94.9 -96.6 -90.0 -97.1 -91.0 -92.0 -81.9 \n\n\n\nAl2O3 -61.0 224.1 -47.1 -84.1 -89.1 -62.7 -89.6 64.5 -67.3 -23.6 \n\n\n\nFe2O3 22.2 -7.73 29.3 9.83 -12.0 -2.57 -1.64 10.5 -19.1 3.19 \n\n\n\nCaO -99.2 -96.9 -99.5 -98.7 -99.7 -99.2 -99.8 -99.3 -98.0 -98.9 \n\n\n\nMgO -75.0 0.00 -69.5 -85.4 -88.4 -83.3 -87.7 -84.6 -83.9 -73.1 \n\n\n\nNa2O -99.9 -99.5 -99.9 -99.9 -99.9 -99.8 -99.9 -99.9 -99.9 -99.8 \n\n\n\nK2O -93.3 -42.5 -93.2 -97.1 -97.6 -89.1 -98.9 -95.6 -97.9 -89.5 \n\n\n\nMnO -98.4 0.00 0.00 -95.5 -98.4 -90.9 -98.5 -98.6 0.00 -64.5 \n\n\n\nP2O5 -99.1 -80.0 -96.7 -97.5 -98.0 -96.7 -98.4 -96.2 -95.0 -95.3 \n\n\n\n4. CONCLUSION \n\n\n\nThis investigation shows how major elements of Kitibi-Iwoye are released \nand redistributed during weathering. Al, Fe and Na are strongly mobilized \nand redistributed in weathering profile. Al and Fe showed up-profile \nenrichment whereas Na displayed up-profile depletion. Mass balance \nassessment obtained from the major elements showed that elements such \nas Na2O, CaO, K2O, P2O5, SiO2, MgO and MnO have been depleted in \nconcentration compared to what was observed in the parent rock. \nTherefore, these element decreases towards the top soil. The mean CIA \nvalue of 97.8 revealed that weathering has almost reached its completion \nand the product is kaolinite whereas CIA of 62.1 obtained for the \npegmatite parent rock suggested that the parent rock has just commenced \nits alteration processes which accounts for its slightly higher value.  \n\n\n\nFresh pegmatite with a WIP value of 110.5 had not suffered much from \nweathering. The WIP ranged from weakly weathered body through \nmoderate weathering up to strongly weathered body. Comparative \nanalysis of CIA and WIP showed contrasting behaviour in which the values \nof CIA are increasing as weathering is progressing whereas the values of \nWIP were correspondingly decreasing. The mass balance calculations are \ncategorized into three on the basis of depletion/enrichment factor. (i) \nstrong depletion but poor enrichment for Na, Ca, K and P (ii) moderate \ndepletion profiles for Si, Mg and Mn (iii) poor depletion but strong \nenrichment for Al and Fe. The Al2O3-CaO+Na2O-K2O diagram of fresh \npegmatite and weathered samples corroborated CIA, WIP and mass \nbalance model that most of the samples have been intensely weathered \nand that the weathering product is kaolinite. \n\n\n\nREFERENCES \n\n\n\nAdekeye, J.I.D., Akintola, O.F., 2007. Geochemical features of rare metal \npegmatites in Nasarawa area, Central Nigeria. Journal of Mining and \nGeology, 43 (1), pp. 15-21. \n\n\n\nBahlburg, H., Dobrzinski, N., 2009. A review of the Chemical Index of \nAlteration (CIA) and its application to the study of Neoproterozoic \nglacial deposits and climate transitions. in: Arnaud, E., Halverson, G.P. & \nShields, G.A., (eds.), The Geological Record of neoproterozoic \nGlaciations. Geological Society, London, Memoir. \n\n\n\nCramer, J.J., Nesbitt, H.W., 1983. Mass-balance relations and trace-element \nmobility during continental weathering of various igneous rocks. \nSciences G\u00e9ologiques M\u00e9moire. Strasbourg, 73, pp. 63 \u2013 73. \n\n\n\nEggleton, R.A., Buseck, P.R., 1980. High resolution of Electron Microscopy \nof feldspar weathering. Clays and Clay Minerals, 28 (3), pp. 173-178. \n\n\n\nFedo, C.M., Nesbitt, H.W., Young, G.M., 1995. Unravelling the effects of \npotassium metasomatism in sedimentary rocks and paleosols, with \nimplications for paleoweathering conditions and provenance. Geology, \n23, 921-924. \n\n\n\nGarba, I., 2003. Geochemical discrimination of newly discovered rare-\nmetal bearing and barren pegmatites in the Pan-African 600 +/- 150 Ma \nbasement of northern Nigeria. Applied Earth Sciences (Trans. Inst. Min. \nMetall. B), pp. 112 287-291. \n\n\n\nHamdan, J., Burnham, C.P., 1996. The contribution of nutrients from parent \nmaterial in three deeply weathering soils of Peninsular Malaysia. \nSedimentary Geology, 55, 319\u2013322.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 59-64 \n\n\n\nCite the Article: Jimoh, M.T., Bolarinwa, A.T, T. O. Kolawole (2020). Mobility And Redistribution Of Major Elements In Weathered Profile Developed On \nPegmatite At Kitibi-Iwoye, Southwestern Nigeria. Malaysian Journal of Geosciences, 4(2): 59-64.\n\n\n\nHarnois, L., 1988. The CIW index: a new chemical index of weathering. \nSedimentary Geology, 55, 319\u2013322. \n\n\n\nIslam, M.R., Peuraniemi, V., Aario R., Rojstaczer, S., 2002. Geochemistry and \nmineralogy of saprolite in Finnish Lapland. Applied Geochemistry, 17, \npp. 885\u2013902. \n\n\n\nJimoh, M.T., Kolawole, T.O., Seweje, T.P., Aransiola, O., 2016. Mineralogical \nstudies of weathered Pegmatite around Kitibi-Iwoye, Southwestern \nNigeria. Ladoke Akintola University of Technology Journal of \nEngineering and Technology, 10 (2), pp. 99-113. \n\n\n\nKabata-Pendias, A., Pendias, H., 2001. Trace elements in soils and plants. \nCRC Press LLC, Boca Raton, Florida. Pp. 413. \n\n\n\nKingsley, A.E., Ekwueme, B.N., 2009. Mineralization of pegmatites in parts \nof the Oban massif, Southwestern Nigeria: A preliminary analysis. \nChinese Journal of Geochemistry, 28, 146-153. \n\n\n\nLi, C., Yang, S.Y., 2010. Is chemical index of alteration a reliable proxy for \nchemical weathering in global drainage basins? American Journal of \nScience, 310, 111\u2013127.  \n\n\n\nMcLennan, S.M., 1993. Weathering and global denudation. Journal of \nGeology, 101, 295\u2013303. \n\n\n\nNesbitt, H.W., 1979. Mobility and fractionation of rare earth elements \nduring weathering of a granodiorite. Nature, 279, 206\u2013210. \n\n\n\nNesbitt, H.W., Markovics, G., 1997. Weathering of granodiorite crust, long-\nterm storage of elements in weathering profiles and petrogenesis of \nsiliciclastic sediments. Geochimica et Cosmochimica Acta, 61, 1653\u2013\n1670. \n\n\n\nNesbitt, H.W., Wilson, R.E., 1992. Recent chemical weathering of basalts. \nAmerican Journal of Science, 292, 740-777.  \n\n\n\nNesbitt, H.W., Young, G.M., 1982. Early Proterozoic climates and plate \nmotions inferred from major element chemistry of lutite. Nature, 299, \n715\u2013717. \n\n\n\nNesbitt, H.W., Young, G.M., 1984. Prediction of Some Weathering Trends of \nPlutonic and Volcanic Rocks Based on Thermodynamic and Kinetic \nConsiderations. Geochim, Cosmochim. Acta, 48, 1523-1534. \n\n\n\nNesbitt, H.W., Young, G.M., 1989. Formation and Diagenesis of Weathering \nProfiles. J. Geol., 97, 129-147. \n\n\n\nParker, A., 1970. An Index of Weathering for Silicate Rocks. Geological \nMagazine, 107, 501\u2013504, \nhttp://dx.doi.org/10.1017/S0016756800058581,  \n\n\n\nPrice, J.R., Velbel, M.A., 2003. Chemical weathering indices applied to \nweathering profiles developed on heterogeneous felsic metamorphic \nparent rocks. Chemical Geology, 202, 397-416. \n\n\n\nRollinson, H., 1993. Using Geochemical Data: Evaluation, Presentation, \nInterpretation. Pearson Prentice Hall, Essex, pp. 352. \n\n\n\nShao, J., Yang, S., Li, C., 2012. Chemical indices (CIA and WIP) as proxies for \nintegrated chemical weathering in China: Inferences from analysis of \nfluvial sediments. Sedimentary Geology, 265-266, pp. 110\u2013120. \n\n\n\nTaylor, G., Eggleton, R.A., 2001. Regolith Geology and Geomorphology. \nJohn Wiley and Sons, NY, Brisbane, Pp. 375. \n\n\n\nTijani, M.N., Okunlola, O.A., Abimbola, A.F., 2006. Lithogenic \nconcentrations of trace metals in soils and saprolites over crystalline \nbasement rocks: A case study from SW Nigeria. Journal of African Earth \nSciences, 46, pp. 427-438. \n\n\n\nVan der Weijden, C.H., Van der Weijden, R.D., 1995. Mobility of major, \nminor and some redox-sensitive trace elements and rare-earth elements \nduring weathering of four granitoids in central Portugal. Chemical \nGeology, 125, pp. 149-167. \n\n\n\nVelde, B., Meunier, A., 2008. The origin of clay minerals in soils and \nweathered Rocks. Springer-Verlag, Berlin, Heidelberg. \n\n\n\nVoicu, G., Bardoux, M., Voicu, D., 1997. Mineralogical Norm Calculations \napplied to tropical weathering profiles. Mineralogical Magazine, 61, pp. \n185-196. \n\n\n\nWhite, A.F., Bullen, T.D., Schulz, M.S., Blum, A.E., Huntington, T.G., Peter, \nN.E., 2000.  Differential rates of feldspar weathering in granitic \nregolith\u2019s. \n\n\n\nYusoff, Z.M., Ngwenya, B.T., Parson, I., 2013. Mobility and Fractionation of \nREEs during deep weathering of geochemically contrasting granites in a \n\n\n\ntropical setting, Malaysia. Chemical Geology, 349-350, pp. 71-86. \n\n\n\n\n\n\n\n\n\n"
"\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 10-12 \n\n\n\nCite the article: LI Guoming\uff0cCHEN Yanmin\uff0cYING Guowei\uff0cWU Xiaoping (2017). Research On Data Management Model Of National Defense Mobilization Potential \n\n\n\nBased On Geo-Spatial Framework. Malaysian Journal Geosciences 1(2) : 10-12.\n\n\n\n  ARTICLE DETAILS \n\n\n\n ARTICLE HISTORY: \n\n\n\nReceived 12 August 2016 \n\n\n\nAccepted 12 December 2016 \n\n\n\nAvailable online 10 January 2017 \n\n\n\nKEYWORDS: \n\n\n\ngeographical spatial framework; \nnational defense mobilization \npotential; spatial data; management \nmode \n\n\n\nABSTRACT\n\n\n\nAt present, the national defense mobilization potential data is mainly unstructured data composed of text, images, \nreport forms, lacking space attribute and location information. Therefore, a large study of national defense \nmobilization potential database has focused on data collection, reporting and information system construction, etc. \nTo solve national defense mobilization potential data application problems in the construction of informatization, \ntaking advantage of the characteristics of geographical spatial framework, this paper discusses national defense \nmobilization potential data management model based on geographical spatial framework. \n\n\n\n1. INTRODUCTION \n\n\n\nNational defense mobilization refers to the emergency measures taken b \ny the state, from peacetime to wartime, and unified mobilization of man \npower, material and financial resources to provide services for the war \n[1]. At present, Although our country has already formed the unique \nmobilization theory, rules and mechanisms, and established the \ncorresponding institutional framework and the methods of \nimplementation, it is still keep the traditional static, passive mobilization \nmode, the way of database storage is given priority to  two-dimensional \ntable, so space utilization rate is not enough to meet the needs of precise \nmobilization, and it\u2019s  bad defects has increasingly visible [2,3].GIS \ntechnology with its powerful spatial data processing analysis ability and \nthe space visual effect, in the field of national defense mobilization \nfunction has been gradually revealed. So, providing technical support to \nestablish a defense mobilization potential database which closely related \nto spatial location of national and strengthening the national defense \nmobilization management information and intelligent level are also \nparticularly important to carry out the national defense mobilization \npotential research. \n\n\n\n2.RESEARCH SIGNIFICANCE\n\n\n\n2.1 New data management model is the necessary basis to \naccelerate the construction of the national defense mobilization \ninformation level \n\n\n\nSince the eighteenth congress, the central military commission \nPresident xi jinping proposed defense mobilization on several \noccasions, and emphasized the statement which said, \"firmly \npromoting national defense mobilization with each direction and \neach field\" [4]. National defensemobilization preparation is the \nimportant measure to condense economic and social resources, stem \nand win the war; is the overall military construction and use, realize \nChina's dream of weaponry  \n\n\n\nfoundation engineering. But due to multiple aspects of reasons such as \nthe policy and financial resources, system and technical, the overall level \nof China's national defense mobilization preparation is still very low, and \nall sorts of both operational and mobilization demand hard software \nconstruction seriously lags the economic development. Especially the \nspatial information that support the national defense mobilization \ncommand communication and the ability of data security, the capacity of \nresisting disturbance with communication equipment in troops and \nability to adapt to the complex battlefield and harsh environment, basic \nit is in a state of absence [5]. \n\n\n\nThe existing national defense mobilization potential information only \nown potential numbers but no ability to evaluate, or focus on human, \nmechanical means without information intelligent matching, can't realize \nthe mobilization preparation work and locate the precise positioning of \nthe construction of national defense, unable to reach a quick, efficient, \naccurate data in national defense mobilization potential. Information is \nthe core and essence of revolution in military affairs with Chinese \ncharacteristics, is also a strategic move in covering defense mobilization \nconstruction and development. Therefore, it is necessary to make the \ndefense mobilization information construction as the goal and carry out \nwith geospatial framework in the relevant technical standard system for \nthe support of the national defense mobilization potential method for \nbuilding a spatial database research. \n\n\n\n2.2 New data management model is the realistic demand of \nimproving the national defense mobilization precision level \n\n\n\nPrecision is the inevitable result of the digital, networked, intelligent and \nintegration, is the basic goal of national defense mobilization information. \nThe national defense mobilization precision is a process which effective \nuse of all kinds of information technology and information means, \naccurate prediction, analysis and grasp the mobilization demand, \nmeticulous investigation, analysis, and grasp the national defense \n\n\n\nContents List available at RAZI Publishing \n\n\n\nMalaysian Journal of Geosciences\nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/ \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (online)\n\n\n\nRESEARCH ON DATA MANAGEMENT MODEL OF NATIONAL \nDEFENSE MOBILIZATION POTENTIAL BASED ON GEO-SPATIAL \nFRAMEWORK \nLI Guoming1* \uff0cCHEN Yanmin2 \uff0cYING Guowei1 \uff0cWU Xiaoping3 \n\n\n\n1The Sixth Landforms Surveying Team of SBSM/Sichuan Third Surveying and Mapping Engineering Institute, Chengdu, China.  \n2.National Defense Mobilization Committee of Sichuan Province. \n3Sichuan Normal University, Chengdu, China. \n*Corresponding author E-mail: li-guoming@foxmail.com, 331293996@qq.com \n\n\n\nhttps://doi.org/10.26480/mjg.02.2017.10.12\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\nmailto:li-guoming@foxmail.com\n\n\nhttps://doi.org/10.26480/mjg.02.2017.10.12\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 10-12 \n\n\n\nCite the article: LI Guoming\uff0cCHEN Yanmin\uff0cYING Guowei\uff0cWU Xiaoping (2017). Research On Data Management Model Of National Defense Mobilization Potential Based On \nGeo-Spatial Framework. Malaysian Journal Geosciences 1(2) : 10-12.\n\n\n\n11\n\n\n\nmobilization potential and precise control, allocate and optimize the \ncombination of various kinds of mobilization resources, thus achieve join \neach other and dynamic balance between mobilization demand and \nsupply.     \n\n\n\nIt is an important task to strengthen the national defense mobilization \npotential database, standardize the technical standards and realize the \nprecision of national defense mobilization work. According to the \nprecision requirement of the construction of national defense \nmobilization, according to universal, standardized, systematic, modular \nand network, gradually unify the national defense mobilization \ninformation construction requirements and software standard, in \nparticular, we should step up efforts to improve the technical standards \nfor information applications. Based on the geospatial framework of \nnational defense mobilization potential database construction, to \nestablish all kinds of mobilized resources of geographical space, and \nother basic information items encoding standards, classification and \ncodes of the army and national defense mobilization related information, \nand gradually realize the harmony and unity in the standard, has an \nimportant role to achieve accurate convergence of military demand and \nsupply mobilization in order to improve the precision level of \nmobilization, and create favorable conditions. \n\n\n\n2.3 The new data management model is a necessary \ncondition to effectively serve the visualization of national \ndefense mobilization potential space \n\n\n\nComputers have been used for scientific computing and data processing \nfor 50 years. However, for a long time, due to the limitations of computer \ntechnology, data can only be processed by batch instead of interactive \nprocessing. The calculation process can not be intervened and guided, but \nonly passively wait for the output of the calculation results. While a large \nnumber of output data can only manually, or use the plotter output \ntwodimensional graphics, the overall concept not only can not be timely \nvisual images and related data, but also caused the loss of large amounts \nof information. In recent years, because of the computer soft hardware \nperformance improvement, continuous improvement of the spatial \ndatabase building method, graphics and image processing algorithms, \nmaking use of spatial data visualization technology, display the data and \ninformation become possible, andinteract. Therefore, the construction of \nthe national defense mobilization potential spatial database can \neffectively get rid of the restrictions of the traditional twodimensional \ngraphics, and solve the problem that the current national defense \nmobilization potential data display effects are not visual, not vivid and the \namount of information is not rich.In summary, geospatial framework is \nthe basis of geographic information resources and the acquisition, \nprocessing, storage, distribution and service management relates to the \npolicies and regulations, standards, technology, facilities, mechanism and \nhuman resources in general, is a spatial information infrastructure, \npublic service oriented, composed of basic geographic information data \nsystem, data directory and exchange system, policies and regulations and \nstandards system, organizational system and public service system etc.. \nThe construction of national defense mobilization potential spatial \ndatabase, requiring geospatial framework of the related technical \nstandard system construction method as a support, realizing the \ninformation, precision and intelligent service of national defense \nmobilization potential data. And it is the basic and important work of civil \nmilitary integration. \n\n\n\n3.CHARACTERISTICS OF GEOGRAPHICAL SPATIAL FRAMEWORK\n\n\n\nThe geo-spatial framework provides a unified spatial positioning \nbenchmark for spatial and non-spatial information, in order to achieve \nintegration of all kinds of information according to standards, and its \nfunction includes the following three aspects [6]: \n\n\n\na) Geospatial framework data constitude the most basic spatial data \nsets in the real world, and it can completely describe the natural, \nsocial and cultural forms of topography, geomorphology, social \neconomy and its basic characteristics. Mainly used for people \nstudy and understand the basic conditions nature to provide \ninformation support; \n\n\n\nb) The development of smart city, digital city, and all kinds of \ninformation system construction, need the most basic spatial data \nset as a basic support, however, these data sets are mainly \nextracted from geospatial framework data; \n\n\n\nc) The qualitative description, quantitative and positioning analysis is \nthe core content of information construction, most of the thematic \ninformation itself does not have the location feature. As a reference \nbenchmark, geo-spatial frames provide special information related \nto spatial location for all types of users to meet the requirements of \nlocation and quantitative processing. \n\n\n\nTo this end, geospatial data framework mainly has the following \ncharacteristics:  \n\n\n\na) Data is rich in information, fast updating, high resolution, large \namount of information, long production cycle, and high cost; \n\n\n\nb) Because of the rapid development and rapid change of information \nsociety, the aging of information is fast and the cost of updating is \nhigh; \n\n\n\nc) The spatial position information and the attribute information are\nmore easily accepted, therefore, \n\n\n\ndigital line drawing DLG, digital elevation model DEM and digital \northophoto map DOM, digital raster graph DRG and   other 5D products are \nthe main forms of information.  \n\n\n\nThrough the study of the geospatial framework, this study can promote the \nintegration and sharing of the potential information of national defense \nmobilization, and provide an important data base for the construction of \nnational defense information.\n\n\n\n4.MANAGEMENT MODEL OF POTENTIAL DATA FOR NATIONAL \nDEFENSE MOBILIZATION    \n\n\n\nThe current national defense mobilization potential data management is \nthe main way to solve any problem, data storage more confusion, \nlack of coordination,multisource data fusion,there is no topological relatio \nn, spatial information expression is not accurate,further it \nis lack of spatial analysis function. Generally speaking, it mainly includes t \nhe following management models: \n\n\n\n4.1 File management model \n\n\n\nGraphical data and attribute data for the potential of national defense \nmobilization are organized in a certain format, and the graphic elements \nare connected with the attribute records through the fields. The \nadvantages of the file management data is flexible, one drawback is that \ndata is huge, the number of data files is even tens of thousands, the \nefficiency of data management efficiency and information utilization is \nlimited, andit is difficult to update, on the other hand, is not \nconducive to collaborative organization, filing data is very difficult to \nachieve record level and entity level data locking operation conflict also, \nthe security of the data mainly depends on the operating system to \nensure that data is not up to the requirements to ensure the \nlegitimate use of. This approach is far from meeting the reliability of \nmobilization, especially for potential data on national defense \nmobilization. \n\n\n\n4.2 Database management mixed mode \n\n\n\nGraphical data for defense mobilization potential are managed by file, \nwhile attribute data is managed through large commercial databases. \nAmong them, spatial data is managed by file, time data is structured, \ndatabase is used to manage, non-spatial attribute data is managed by \ndatabase. This method overcomes the problems of efficiency, security \nand sharing of attribute data management, improves system efficiency, \nand at the same time, it is easy to integrate management with information \nsystem based on text numeric data. But this way is still in the form of files \nor file on the spatial data management and the form of improving \nmanagement through the library, while improving the management \nefficiency, but still can not solve the structural nature of the defect data \nmodel file. \n\n\n\n5.RESEARCH ON POTENTIAL DATA MANAGEMENT OF \nNATIONAL DEFENSE MOBILIZATION BASED ON GEOGRAPHICAL \nSPATIAL DATA FRAMEWORK \n\n\n\nNational defense mobilization potential is based on future informatization \nunder the condition of local war and carry out diversified military tasks co \nmmand and decision. Geospatial framework technology mainly to solve th \ne current situation of China's national defense mobilization potential data \nspace is low, efficiency is not high, poor visual effect, organizational manag \nement and update methods lag, improve the national defense mobilization  \ninformationization level. To this end, the main management model can be \nused in the following two: \n\n\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 10-12 \n\n\n\nCite the article: LI Guoming\uff0cCHEN Yanmin\uff0cYING Guowei\uff0cWU Xiaoping (2017). Research On Data Management Model Of National Defense Mobilization Potential Based On \n\n\n\nGeo-Spatial Framework. Malaysian Journal Geosciences 1(2) : 10-12.\n\n\n\n12\n\n\n\n5.1 File management model \n\n\n\nFull relational spatial database management refers to the management of \ngraphical and attribute data using relational database management \nsystems (RDBMS). In this management method, the spatial coordinate data \nof indefinite length is managed by relational database in the form of binary \ndata block. In other words, coordinate data is integrated into RDBMS to \nform spatial database. RDBMS software vendors do not make any \nexpansion, and are developed by GIS software vendors, which can not only \nmanage structured attribute data, but also manage unstructured graphical \ndata. At present, both in theory and relational database tools have been \nmature, andthey provide a uniform access interface (SQL) with massive \ndata distribution operation, and it can support multiuser concurrent access, \nsecurity control and consistency ch ck. But based on GIS technology, due to \nthe indefinite length of geometric coordinate data, the storage efficiency of \nthe whole relational spatial database management is slightly lower. \n\n\n\n5.2 Object-oriented spatial database management model \n\n\n\nObject oriented spatial database management can extend object data types \nto support spatial data, including point, line, polygon geometry, and allows \nyou to define the basic operation for these geometries, including the \ncalculation of distance, spatial relationship detection, even slightly \ncomplicated operations, such as buffer calculation, superposition model \netc.it can also be seamlessly supported by object database management \nsystems. In this way, it provides a consistent access interface for various \ndata and part of the space model services, by the object database \nmanagement system, it not only has been achieving data sharing and \nservice space model can be sharedalso, the national defense mobilization \nsystem development can focus on the representation of data and complex \nprofessional model. \n\n\n\n5.3 Design principle of potential database for national defense \nmobilization  based on geo-spatial framework \n\n\n\nThe national defense mobilization potential database will include \ngeographic information data (vector data, raster data and thematic data \netc.) and thematic data (military information, such as traffic readiness, \neconomic mobilization, data transmission and exchange, social resources, \ninformation, armed forces, equipment and so on).In order to make all kinds \nof information resources access each other through the interface module, \nthe storage unit should be unified coordinate system, the establishment of \na unified and naming rules, using the data element  \nclassification code and symbolic rules reasonably, ensure data quality, \nrealize the connection between the data,and keep updating the \ndatabase.The data organization of data should follow the combination of \nprogressiveness and practicability, combination of standardization and \ncompatibility, combination of safety and maintainability, combination of \ncentralized management and decentralized management.It should also be \ncompatible with vector data, raster data, multimedia data, text, table data, \nand other data mobilization formats. At the same time, the database should \nhave an effective backup mechanism. \n\n\n\n6.CONCLUSION \n\n\n\nThe current national defense mobilization potential data management \nmainly adopts documents management and file database mixed \nmanagement mode, data redundancy, low availability and low safety of \nmany shortcomings, and it is far from meeting the needs of modern national \ndefense information.  Based on the geo-spatial framework technology, the \ndatabase of national defense mobilization potential is managed, and the \ngeospatial data is linked with the  mobilization potential the attribute \ndata.Thus the potential information can be displayed intuitively on the map, \nand it is directly applied to the national defense mobilization potential \nsystem for interactive inquiry and accurate mobilization command,and it \nplays an important role in enhancing the management and informatization \nof national defense mobilization. \n\n\n\nACKNOWLEDGEMENTS  \n\n\n\nThanks are due to colleagues for assistance with soft science research \nproject support of sichuan province, China (NO. 2017ZR0123, \n2017ZR0122) \n\n\n\nREFERENCE \n\n\n\n[1] Zou, S.M. 2012. China's construction of national defense mobilization \nsystem with characteristics of military and civilian integration. Science and \nTechnology Progress and Policy, 29, 31-36. DOI: 10.3969/j.issn.1001-\n7348.2012.02.008 \n\n\n\n[2] Qu, X.Y. 2014. The system of mobilization ability construction lead \ndefense mobilization innovation and development. National Defense, \n10,19-22 DOI: 10.15969/j.cnki.11-2770/e.2014.10.009 \n\n\n\n[3] Xu, K. 2016. Practice and Exploration in the Building of National Defense \nMobilization System of New China. Contemporary China History Studies, 13 \n,29-36. DOI: 10.3969/j.issn.1005-4952.2006.01.004. \n\n\n\n[4] Zhong, W., and Xie, Q. 2015. Promote the development of our national \ndefense mobilization construction innovation. National Defense, 10, 45-46.\nDOI: 10.3969/j.issn.1002-4484.2015.10.022 \n\n\n\n[5] Rao, N., and Cheng, K.L. 2016. Defense mobilization big data application \nprospect and countermeasures. National Defense, 11,31-33. DOI: \n10.3969/j.issn.1002-4484.2016.11.011 \n\n\n\n[6] Chen, J. 2002. Developing Dynamic and Multi-Dimensional Geo-Spatial \nData Framework. Geo-Information Science, 4,7-13. DOI: \n10.3969/j.issn.1560-8999.2002.01.004\n\n\n\n\n\n\n\n\n\n"
"\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 29-32 \n\n\n\nCite The Article: Isfarita Ismail, Mohd Lokman Husain, Rozaimi Zakaria  (2017). Attenuation Of Waves From Boat Wakes In Mixed Mangrove \nForest Of Rhizophora And Bruguiera Species In Matang, Perak . Malaysian Journal Geosciences, 1(2) : 29-32 \n\n\n\n  ARTICLE DETAILS \n\n\n\nARTICLE HISTORY:\n\n\n\nReceived 12 May2017  \nAccepted 12 July 2017  \nAvailable online 10 September 2017 \n\n\n\nKEYWORDS: \n\n\n\nWave attenuation, boat wake \nwave height, wave reduction, \nmixed mangrove (of \nRhizophora and Bruguiera \nspecies), Matang mangrove \nforest, Perak\n\n\n\nABSTRACT\n\n\n\nIn Malaysia, there are several small rivers and estuaries which are frequented by fishing boats. The wave action due \nto the movement of boats impact the coastal morphology of the area. In this paper, we have studied the wave \nreduction in mixed mangrove forest of Rhizosphere and Bruguiera species based on field observations of waves \nfrom boat wakes in Sg. Sangga Kecil of   Matang forest reserve, west coast of Peninsular Malaysia. The unique \nphysical characteristics of Bruguiera sp. and Rhizophora sp. such as the intricate knee root and numerous \npneumatophores, respectively, impact the wave amplitudes in the mangrove forest. The reduction of wave \namplitudes in a 15 m long transect of mixed mangrove forest at a given study site has been analysed in the present \nstudy. It is found that the wave reduction for each 5-m distance from the vegetation edge ranged from 47.4% to \n9.6%. However, on a cumulative basis the wave reduction inside the mixed mangrove forest ranged between 47.4% \nto 72.8%, with an average of 63%. As far as the vertical trend is concerned the wave reduction in (0-10cm) level \nwas 88.7% while in (10-20cm) level it was found to be 61.2%. \n\n\n\n1. INTRODUCTION \n\n\n\nMangroves are coastal forests that are found in sheltered estuaries and \nalong river banks and lagoons in the tropics and subtropics. Mangroves \nworldwide cover an approximate area of 240 000 km2 of sheltered \ncoastlines in the tropics and subtropics [1]. They are distributed within \nthe tropics and subtropics, reaching their maximum development \nbetween 25o N and 25o S. The first attempt to estimate total mangrove \narea worldwide was undertaken as part of the Food and Agriculture \nOrganization of the United Nations (FAO) and United Nations \nEnvironment Programme (UNEP) Tropical Forest Resource Assessment \nin 1980 [2].  \n\n\n\nMangrove forest ecosystems fulfil many important functions and provide \na wide range of services at the local and national levels. Mangroves \nsupport the conservation of biological diversity by providing habitats, \nspawning grounds, nurseries and nutrients for many animals. Mangrove \necosystem is also used for aquaculture, both as open- water estuarine \nmaricultural and as pond culture. The physical importance of mangrove \nis to act as a wave buffer. The December 2004 tsunami highlighted the \nphysical importance of mangroves. \n\n\n\nThe study about the physical processes in mangrove forest is still poorly \nunderstood due to the lack of extensive research on the subject. As stated \nin a research, it is only a short time since the studies of the physical \nprocesses in mangrove areas have been initiated [3,4]. Some researcher \nhas studied some tidal periodic phenomena in mangrove areas along \nrivers or estuaries protected from the open sea [5-9].  \n\n\n\nIn the present study, we have analysed the reduction in the amplitude of \nwaves produced by the passage of fishing boats in an estuary as these \nwaves travel through the Matang mangrove forest area of  \n\n\n\nPerak. The paper focuses on the mixed mangrove forest of Rhizophora \nand Bruguiera species which was chosen because they are dominant \nspecies in the area. At a selected site, we have chosen a transect which has \nthe longest distance of 15 m inside the mangrove forest. The combination \nof two mangrove species along the transect had the desired effect of the \nwave attenuation. The main objective of this paper is to quantify the \npercentage of wave reduction by the mixed mangrove vegetation of \nRhizophora sp. and Bruguiera sp. in Matang reserve area, Perak.  \n\n\n\nA relation between wave reduction and horizontal distance as well as the \nvertical height (water level) has been analysed in detail. It is found that \nthe wave reduction along the transect, in each 5-m distance from the \nvegetation edge, decreased from 47.4% to 9.6%. However, on a \ncumulative basis the wave reduction increased with increasing distance \ninside the mangrove forest, ranging from 47.4% to 72.8%, with an average \nincrease of 63%. As far as the vertical trend is concerned the wave \nreduction in (0-10cm) level was 88.7% while in (10-20cm) level it was \nfound to be 61.2%, showing that the wave reduction decreases with \nincreasing water level. \n\n\n\n2. METHODOLOGY \n\n\n\n2.1 Study area \n\n\n\nThe Matang forest reserve is a unique ecosystem and covers an area of 40 \n711 ha, along a 52 km stretch of the northern coast of Perak in tropical \nMalaysia which has been systematically managed by the Forestry \nDepartment since 1904 [10]. Matang mangroves are transacted by \nnumerous rivers, large and small. As a result, 70% of its landscape \ncomprises of islands. We have chosen the study site on the bank of Sungai \nSangga Kecil which is located within the Matang mangrove forest area, \nPerak. This region is a forest reserve and based on a survey conducted in \n2000, its area has grown by over 3 %, which is equivalent to 1,250 \nhectares. Most areas in Matang mangrove forest are covered by pure \nstand of bakau forest (Rhizophora). There are also large areas of \n\n\n\nContents List available at RAZI Publishing \n\n\n\nMalaysian Journal of Geosciences \nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/ \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (online)\n\n\n\n1Faculty of Science and Natural Resources, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah. \n2Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu, 21030 Kuala Terengganu, Terengganu. \n3Mathemtaics, Graphics and Visualization Research Group (M-GRAVS), Faculty of Science and Natural Resources, Universiti Malaysia Sabah, \n\n\n\n88400 Kota Kinabalu, Sabah. \n\n\n\n*Corresponding author E-mail: isfarita@yahoo.com1, mlokmn@umt.edu.my2, rozaimi@ums.com.my3 \n\n\n\nIsfarita Ismail1, Mohd Lokman Husain2, Rozaimi Zakaria3 \n\n\n\nATTENUATION OF WAVES FROM BOAT WAKES IN MIXED MANGROVE \nFOREST OF RHIZOPHORA AND BRUGUIERA SPECIES IN MATANG, \nPERAK\n\n\n\nhttps://doi.org/10.26480/mjg.02.2017.29.32\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\nhttps://doi.org/10.26480/mjg.02.2017.29.32\n\n\n\n\n\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 29-32  \n\n\n\nCite The Article: Isfarita Ismail, Mohd Lokman Husain, Rozaimi Zakaria  (2017). Attenuation Of Waves From Boat Wakes In Mixed Mangrove Forest \nOf Rhizophora And Bruguiera Species In Matang, Perak . Malaysian Journal Geosciences, 1(2) : 29-32 \n\n\n\n30\n\n\n\nAvicennia and Nypa, with patches of Berus, Lenggadai, and Tumu forest. \nHowever, Rhizophora makes up about 85% of the total forest area. \n\n\n\nOver 85 per cent of Matang mangroves are tidally influenced, undergoing \na regiment of being flooded by saltwater followed by a drying out period. \nClimate is monsoonal with an average annual rainfall of 3500 \u2013 4800mm \n[11]. Rainfall normally peaks in May and November, coinciding with the \nsouthwest and northeast monsoons. \nFigure 1 shows the study site on the bank of Sg. Sangga Kecil. At this \nlocation, we have chosen a 15 m long transect from the vegetation\u2019s \nedge, which is dominated by a mixed mangrove of Rhizophora and \nBruguiera sp. Rhizophora sp. have numerous pneumatophores while \nBruguiera sp. have intricate knee root system. \n\n\n\nFigure 1: Map of Matang mangrove forest area, Perak. \nFigure 2 shows the mixed mangrove forest for Rhizophora sp. and \nBruguiera sp. meanwhile Figure 3 shows a 15-m idealized transect of \nRhizophora sp. and Bruguiera sp. inside the mangrove forest.  \n\n\n\n2.2 Observation of wave data \nUpstream of the estuary near the study site there is a fishing village \nlocated at Bagan Sangga Kecil. This ensures a steady stream of fishing \nboats which move in and out of the area, creating waves in their wakes. \nThe wave height data were collected using water loggers (Boart Longyear \nInterfels, Germany). The parameters that can be obtained from the water \nloggers are water depth, pressure and temperature. However, we have \nused only the water depth in our study. The time interval for the water \nlogger recording is 1 second.  \nThe wave fluctuations recorded by the water logger due to each passing \nboat is called a wave burst, which ride over the tidal wave and are \ncaptured by the water logger [12]. It may be noted that during the \nobservation the number of wave bursts were about 20 out of which only \n5 were chosen for purpose of analysis of the wave data. \nAlong the transect we chose 4 locations, each 5 m apart. The first location \nat the vegetation\u2019s edge has been designated as plot 0, while plot 1 refers \nto the next location 5 m inside the mangrove, and so on. In this study, the \nwater loggers were set up simultaneously at plots 0, 1, 2 and 3 for one \ntidal cycle during the spring tide. Table 1 shows the sampling variability \ngiving the date, reading and the number of wave bursts. For reading 1 the \nwater loggers were set up at plot 0 and plot 1. For reading 2 they were \nfixed at plot 0 and plot 2, and so on.  \n\n\n\nTable 1: Sampling variability using four water loggers for mixed \n\n\n\nmangrove (Rhizophora sp. and Bruguiera sp.) \n\n\n\nDate Reading Number of \n\n\n\nwave burst \n\n\n\nDistance (m) \n\n\n\n0 5 10 1\n\n\n\n5 \n\n\n\n17/6/2008 1 5 \u221a \u221a \n\n\n\n17/6/2008 2 5 \u221a \u221a \n\n\n\n17/6/2008 3 5 \u221a \u221a \n\n\n\nFigure 4 shows the example of a wave burst and identification of associated \nwaves for mixed mangrove in Matang forest area. Each burst contains a set \nof waves. Wave heights of less than 2 cm are taken as water movement due \nto the wind, and hence not considered as boat induced waves. Thus, only \nwater levels of more than 2 cm are considered in the computation of wave \nheight. \n\n\n\nFigure 4: Example of a wave burst and identification of associated waves. \nAfter identifying the waves 1 to 11 in Figure 4, their mean wave height was \nused to compute the wave reduction [13]. Following , the wave reduction is \ngiven by,  \nwhere, reduction, percentage reduction= rx100  \n\n\n\n = wave height in front of mangrove (plot 0)  \n  = wave height at a location\u2019 L\u2019 inside the mangrove forest \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nFigure 5 shows the wave data collected simultaneously by the four water \nloggers fixed at plot 0, 1, 2 and 3, respectively. For this experiment, three \nsets of data analyses were conducted corresponding to readings 1, 2 and 3. \n\n\n\n Table 2 gives the average percentage of cumulative wave reduction as the \nwaves travel 15 m inside the mixed mangrove forest of Rhizophora and \nBruguiera species. For reading 1, the wave reduction is 47.4% which is \n\n\n\n\n\n\n\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 29-32  \n\n\n\nCite The Article: Isfarita Ismail, Mohd Lokman Husain, Rozaimi Zakaria  (2017). Attenuation Of Waves From Boat Wakes In Mixed Mangrove Forest \nOf Rhizophora And Bruguiera Species In Matang, Perak . Malaysian Journal Geosciences, 1(2) : 29-32 \n\n\n\n31\n\n\n\nthe average reduction in wave heights of the five wave bursts over a 5-m \ndistance. Similarly, over a 10-m distance the average reduction is 68.8%, \nand so on. Thus, it is seen from the table that the wave reduction increases \nwith increasing distance inside the mangrove forest with an overall \naverage reduction of 63%.   \n\n\n\nThe roots/stems of the mangrove trees attenuate the waves more and \nmore as the waves travel inside the mixed mangrove forest. The increase \nin wave reduction shows the mixed mangrove can attenuate the incoming \nwave effectively. This result is similar to the observational study of who \nfound that the root system of the mangrove is one of the primary \ncomponents that help in the reduction of waves [14]. For the relation \nbetween the average wave reduction and the distance inside the \nmangrove forest is it may be seen that average wave reduction increases \nwith increasing distance inside the mangrove. \n\n\n\nTable 2: Cumulative wave reduction inside the mangrove forest \n\n\n\nReading Average Wave Reduction (%) \n\n\n\n  1 (0-5 m) 47.4 \n\n\n\n2 (0-10 m) 68.8 \n\n\n\n3 (0-15m) 72.8 \n\n\n\nOverall Average 63.0 \n\n\n\nOn a more detailed analysis, the data was also used for wave reduction \nover each plot of 5 m distance along the transect as presented in Table 3. \nIt is seen from the table that in the first 5 m the wave was reduced by \n47.4% while for the next 5 m (5-10 m) it reduced further to 43.1%. \nHowever, in the last 5 m (10-15 m) the wave height was reduced to 9.6%. \nThus, we may conclude that the root density is highest in the first 5 m \nresulting in 47.4% reduction in wave height. This is followed by a \nreduction of 43.1% in the next 5 m and suggesting that the root density \nwas less but closer to the root density in the first 5 m.  In the last 5 m, the \nwave reduction was only 9.6% as the root density was substantially lower \nin comparison to the first or the second 5 m distance inside the mangrove \nforest. From this study, it may be inferred that the first 10 m within the \nmangrove forest are most effective in the attenuation of the wave height. \n\n\n\nTable 3: Wave reduction for each 5-m distance inside the mangrove \nforest \n\n\n\nDistance (m) \n\n\n\nAverage Wave Reduction \n\n\n\n(%) \n\n\n\n0 and 5 47.4 \n\n\n\n5 and 10 43.1 \n\n\n\n10 and 15 9.6 \n\n\n\nWe have also analysed the vertical trend of wave reduction in the mixed \nmangrove forest. At the study site, the wave reduction was found in 0 \u2013 20 \ncm level as the tidal amplitude at the time of the experiment was about 20 \ncm. This means that in this case the wave reduction did not exist above 20 \ncm. The percentage of wave reduction is highest in 0 \u2013 10 cm level (88.7%) \nbecause in this level there are many big roots which tend to attenuate the \nwaves more effectively. Table 4 shows the average wave reduction vs. \nvertical height (each 10-cm level) for the mixed mangrove forest. It is \nfound that the wave reduction decreases with increasing water level [12, \n14]. \n\n\n\nTable 4: Wave reduction for vertical layer (each 10 cm) \n\n\n\nLevel (cm) Average Wave Reduction (%) \n\n\n\n0 - 10 88.7 \n\n\n\n10 - 20 61.2 \n\n\n\n4. DISCUSSION AND CONCLUSION\n\n\n\nThis paper focuses on the investigation of wave reduction in \nmixed mangrove species (Rhizophora sp. and Bruguiera sp.) at Matang \nmangrove forest, Perak. These vegetations have their unique \ncharacteristics which help in the reduction of the wave height.  \nWe have chosen a 15 m transect inside the mangrove forest from \nthe vegetation\u2019s edge. For this study, four water loggers were \nsimultaneously used to measure the wave height. The water loggers were \nput at plots 0, 1, 2 and 3 for the three readings. From further analysis, it is \nseen that the wave reduction increases with increasing distance inside \nthe mangrove forest. Also, it is found that the wave reduction decreases \nwith increasing water level.    \nWhen the waves enter the mangrove forest, the wave height was \nreduced by 47.4% in the first 5 m distance from the vegetation edge; in the \nnext 5 m (5-10 m) it has reduced to 43.1% while in the last 5 m (10-15 m) \nthe wave was reduced to 9.6%. In the last 5 m the wave reduction was \nsmall as the root density was substantially lower in comparison to the first \nor the second 5 m distance inside the mangrove forest. It may be \nconcluded that the first 10 m of mixed mangrove are important for wave \nreduction.  \nBased on the measurements of wave height the mixed \nmangrove (Rhizophora sp. and Bruguiera sp.) is found to be good \nspecies for wave attenuation. These mixed species can attenuate the \nwaves with about 63% overall reduction in the wave height over a 15 m \nlong transect.  \n\n\n\nACKNOWLEDGEMENTS \nThis study was supported by an e-Science funding from MOSTI \n(Vot. Number 52012). The authors would like to thank for the facilities \nprovided by the Institute of Oceanography and Environment, Universiti \nMalaysia Terengganu.  \n\n\n\nREFERENCES \n\n\n\n[1] Lugo, A.E., and Brown, S. 1990. Tropical secondary forests. Journal of \nTropical Ecology, 6 (1), 1-32. \n\n\n\n[2] FAO. 2007. The world\u2019s mangroves 1980 \u2013 2005. A thematic study \nprepared in the framework of the Global Forest Resources Assessment \n2005. FAO Forestry Paper 153. Rome. \n\n\n\n[3] Kjerfve, B. 1990. Manual for investigation of hydrological processes in \nmangrove ecosystems. 79. New Delhi, India: UNESCO/UNDP. \n\n\n\n[4] Mazda, Y. 1993. Present situation of physical study in mangrove regions. \nJournal of The Faculty of Marine Science and Technology. Tokai University \n35, 169-184. \n\n\n\n[5] Ridd, P.V., Wolanski, E., and Mazda, Y. 1990. Longitudinal diffusion in \nmangrove \u2013 fringed tidal creeks. Estuarine, Coastal and Shelf Science, 31 (5), \n\n\n\n541-554. \n\n\n\n[6] Wolanski, E., Mazda, Y., King, B., and Gay, S. 1990. Dynamics, flashing, \nand trapping in Hinchinbrook Channel, a giant mangrove swamp, Australia. \nEstuarine, Coastal and Shelf Science 31 (5), 555-579. \n\n\n\n[7] Wolanski, E., Mazda, Y., and Ridd, P.V. 1992. Mangrove hydrodynamics. \nPp. 43-62 in Robertson AI & Alongi DM (Eds.) Tropical Mangrove \nEcosystems. Coastal and Estuarine Studies. American Geophysical Union, \nWashington, DC. \n\n\n\n[8] Mazda, Y., Yokochi, H., and Sato, Y. 1990. Groundwater flow in the \nBashita \u2013 Minato mangrove area, and its influence on water and bottom mud \nproperties. Estuarine, Coastal and Shelf Science, 31 (5), 621-638. \n\n\n\n[9] Mazda, Y., Kanazawa, N., and Wolanski, E. 1995. Tidal asymmetry in \nmangrove creeks. Hydrobiologia, 295 (1-3), 51-58. \n\n\n\n[10] Ahmad, S. 2009. Recreational values of mangrove forest in Larut \nMatang, Perak. Journal of Tropical Forest Science, 21 (2), 81-87. \n\n\n\n[11] Nieuwolt, S. 1981. The climates of continental Southeast Asia. Pp. 1-\n66 in Takahashi K & Arakawa H (Eds.) Climates of Southern and Western \nAsia, world survey of climatology. Elsevier Scientific Publishing Company, \nAmsterdam. \n\n\n\n\n\n\n\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 29-32  \n\n\n\nCite The Article: Isfarita Ismail, Mohd Lokman Husain, Rozaimi Zakaria  (2017). Attenuation Of Waves From Boat Wakes In Mixed Mangrove Forest \nOf Rhizophora And Bruguiera Species In Matang, Perak . Malaysian Journal Geosciences, 1(2) : 29-32 \n\n\n\n32\n\n\n\n[12] Quartel, S., Kroon, A., Augustinus, P.G.E.F., Van Santen, P., and Tri, \nN.H. 2007. Wave attenuation in coastal mangroves in the Red River Delta, \nVietnam. Journal of Asian Earth Sciences, 29 (4), 576\u2013584. \n\n\n\n[13] Mazda, Y., Magi, M., Kogo, M., and Hong, P.N. 1997. Mangroves as a \ncoastal protection from waves in the Tong King Delta, Vietnam.\n\n\n\nMangroves and Salt Marshes, 1 (2), 127\u2013135.  \n\n\n\n[14] Mazda, Y., Magi, M., Ikeda, Y., Kurokawa, T., and Asano, T. 2006. \nWave attenuation in a mangrove forest dominated by Sonneratia sp. \nWetlands Ecology and Management, 14 (4), 365-378. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 45-52 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2022.45.52 \n\n\n\n \nCite The Article: Pukrozo Keyho, Watitemsu Imchen, Meribemo Yanthan, Imomeren Ao, John K. Angami  (2022). Mg-Rich Ultramafics of The Naga Hills Ophiolite, \n\n\n\nNagaland, India:  A Potential Substitute as Basic Flux in Metallurgical Industries. Malaysian Journal of Geosciences, 6(1): 45-52. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2022.45.52 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMG-RICH ULTRAMAFICS OF THE NAGA HILLS OPHIOLITE, NAGALAND, INDIA:  A \nPOTENTIAL SUBSTITUTE AS BASIC FLUX IN METALLURGICAL INDUSTRIES \n\n\n\nPukrozo Keyhoa, Watitemsu Imchena,*, Meribemo Yanthanb, Imomeren Aoa, John K. Angamia \n\n\n\na Geological Survey of India, Dimapur, Nagaland, NE India \u2013 797 112 \nb Geological Survey of India, Shillong, Meghalaya, NE India \u2013 793 006 \n*Corresponding Author Email: watitemsu.ao@gsi.gov.in, watiimchen16@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 21 October 2022 \nRevised 01 November 2022 \nAccepted 08 December 2022 \nAvailable online 12 December 2022 \n\n\n\n\n\n\n\nFlux materials are indispensable in iron ore refining process to effectively segregate gangue minerals and to \nimprove other physicochemical properties. Lately, the demand for metallurgical grade flux materials such as \nlimestone and dolomite for iron and steel manufacturing industries in India has surged manifold and depends \nlargely on imports due to its limited resources. MgO-bearing flux has emerged to be a potential alternative to \nconventional fluxes (limestone and dolomite); though huge resources of Mg-rich ultramafic rocks are \navailable in the Indian subcontinent, their economic utility as metallurgical flux is not fully explored. To \nevaluate the suitability of flux material in iron and steel industries, Mg-rich ultramafics of the Naga Hills \nOphiolite (NHO) have been studied. Major oxides and petro-mineralogical studies have been undertaken to \nunderstand the chemical and mineralogical attributes of NHO Mg-rich ultramafics. Results indicate \nappreciable MgO content (up to 46.7%) barring pyroxenite, with low Al2O3 (< 2 wt%), loss on ignition (< 14 \nwt%), and Cr2O3 (< 1 wt%) conforming to the chemical specifications set for flux/sinter mix by the major steel \nproducers in India. Fouling index further indicates Mg-based flux of NHO as better quality in contrast to \nconventional flux materials, albeit alkali content is relatively high. Finer crystals of NHO Mg-rich rocks are \nadded advantage which would readily assimilate in the melt at lower energy. Mg-based flux in iron and steel \nindustries would aid in augmenting productivity with reduced slag volumes at lower cost, energy and \npollution. \n\n\n\nKEYWORDS \n\n\n\nMg-rich Ultramafics, Flux, Metallurgical Industries, NHO \n\n\n\n1.   INTRODUCTION \n\n\n\nIndia is bestowed with abundant iron ore resources and ranks seventh in \nthe world. Currently, India is the world's second-largest producer of crude \nsteel (2021) and the largest producer of sponge iron. Of late, the demand \nfor iron and steel has increased manifold to meet the ever-growing \ndomestic consumption as well as exports. Manufacturing of high-quality \niron and steel products is the function of the availability of good quality \niron ore resources (high Fe), content of gangue minerals and other \nimpurities (sulphur, alkali, phosphorus, silica, etc). Flux materials are \ncommonly utilised in iron and steel manufacturing processes. Flux is \nadded along with other additives to combine with gangue in metal refining \nprocesses to segregate the impurities from the ores and removes them in \nthe form of slag. Besides, it also prevents base oxidation thereby reducing \nmetal oxide formation at higher temperatures. CaO and MgO are the \nessential chemical components of fluxing agents mainly derived from \nlimestone and dolomite.  \n\n\n\nIn India, the demand for flux materials for use in iron and steel industries \nis met by the available resources of limestone, dolomite, and quartzite of \ndesired quality (Mohanty et al., 2009). India is known for huge resources \nof limestone; however, steel smelting shop (SMS), blast furnace (BF) and \nchemical-grade limestone resources are limited (ibid). As per national \ninventory data based on United Nations Framework Classification system \nas of 01.04.2015, out of the available 16,336 million tonnes reserve, only \n12% are of BF grade. During 2016-17, about 97% of the total production \nof limestones were cement grade, and the remaining constitute iron and \n\n\n\nsteel grade (2%) and chemical grade (1%) respectively as per Indian \nBureau of Mines (IBM, 2018). The limited reserve of metallurgical grade \nlimestone and dolomite as flux materials for blast furnace and steel \nmelting industries results in dependence on imports.  \n\n\n\nFurthermore, rapid depletion of high-grade iron ore resources worldwide \nhas led to the use of low-grade iron ores containing relatively high silica, \nalumina, alkalis, etc. Lately, Mg-rich rocks have emerged to be of great \neconomic significance due to their suitability as flux agents. The use of Mg-\nbased flux in the blast furnaces process is discussed in detail by several \nworkers (Gao et al., 2016; Long et al., 2021; Meraj et al., 2015; Shen et al., \n2014; Shen et al., 2015; Slagnes and Sundqvist, 2008; Somolinos et al., \n2015). Mg-rich flux is preferred over dolomite and limestone fluxes in iron \nand steel industry due to: a) It augments the blast furnace productivity by \n4-5%, b) Stable and better blast furnace operation by improving the \npermeability inside the blast furnace, c) Effective sulphur and alkali \nremoval, d) Less coke consumption by 21 kg/tonne of charge, e) As a sinter \nfeed, it reduces the sintering temperature as much as 1000oC; thus, \nproducing harder sinter which in turn generate fewer fines, f) No preheat \ntreatment is required, g) Less energy consumption, h) Lower CO2 emission \nand lesser slag volume  (IBM, 2014;, Mohanty et al., 2009).  \n\n\n\nOn the contrary, limestone and dolomite require calcination and thereby \nconsumes more energy/fuels and emit CO2 (IBM, 2014). The presence of \nhigher silica in dolomite leads to lower sinter basicity (CaO/SiO2), and it \nrequires higher energy during the sintering process due to its lower \nreactivity relative to MgO fluxes. Moreover, limestone and dolomite are \n\n\n\n\nmailto:watitemsu.ao@gsi.gov.in\n\n\nmailto:watiimchen16@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 45-52 \n\n\n\n\n\n\n\n \nCite The Article: Pukrozo Keyho, Watitemsu Imchen, Meribemo Yanthan, Imomeren Ao, John K. Angami  (2022). Mg-Rich Ultramafics of The Naga Hills Ophiolite, \n\n\n\nNagaland, India:  A Potential Substitute as Basic Flux in Metallurgical Industries. Malaysian Journal of Geosciences, 6(1): 45-52. \n \n\n\n\nknown to contain high alkalis. Pure MgO doesn\u2019t occur in nature but as \nmagnesium silicates (dunite, pyroxenite, and peridotite), magnesite, or \ndolomite. Repositories of good quality Mg-rich rocks i.e. dunite, peridotite, \npyroxenite, and their altered serpentinite are exposed in various parts of \nIndia, viz., Tamil Nadu, Odisha, Jharkhand, Karnataka, Maharashtra, \nAndhra Pradesh, Manipur, Nagaland and Andaman and Nicobar Islands \n(IBM, 2018).  \n\n\n\nUltramafic rocks, occupying the major part of the Naga Hills Ophiolite \n(NHO) are solely utilised for road construction purposes, albeit these \nrocks are of great economic significance owing to their suitability as a \nfluxing material in metallurgical industries. About 4800 tonnes of dunite \nresources have been inferred from NHO (IBM, 2018). Therefore, this study \nis an attempt to evaluate the potentiality of Mg-rich ultramafic rocks of the \nNHO as a substitute for conventional flux in the iron and steel industries.  \n\n\n\n2.   GEOLOGICAL SETTING  \n\n\n\nSeveral detached outcrops of Tethyan ophiolites occur along the Indus-\nYarlung Tsangpo suture zone, which further extends southward along the  \n\n\n\nIndo-Myanmar Ranges (IMR) and the Andaman-Nicobar Islands (Mitchell, \n\n\n\n1993; Searle et al., 2007). Development of such ophiolite complexes is \n\n\n\nascribed to the closure of Tethys ocean during the Late Cretaceous and \n\n\n\nEarly Tertiary, including the NHO along the IMR (Mitchell, 1993; Acharyya, \n\n\n\n2007). IMR is the meeting point of three tectonic plates viz. Indian Plate to \n\n\n\nthe southwest, Myanmar microplate to the east, and Asian Plate to the \n\n\n\nnorth and northeast (Searle et al., 2007). The evolution of IMR \n\n\n\naccretionary wedge is the consequence of eastward subduction of the Neo-\n\n\n\nTethys beneath the Myanmar microplate (Searle et al., 2007). By Mid-\n\n\n\nEocene a stack of ophiolite slices was obducted, as evidenced by age of the \n\n\n\nolistostromal unit and the ophiolite-derived Phokphur sediments \n\n\n\n(Acharyya, 2007). The ophiolites and associated rocks of the Naga Hills are \n\n\n\nbroadly classified into five tectonostratigraphic units, a) Nimi \n\n\n\nFormation/Naga Metamorphics, low to medium-grade metasediments, b) \n\n\n\nNaga Hills Ophiolite Belt, c) Disang Formation, a folded sequence of Late \n\n\n\nCretaceous-Eocene flysch sediments, d) Belt of Schuppen (BoS), and e) \n\n\n\nJopi/Phokphur Formation (Ghose et al., 2010) (Figure 1; Table 1).  \n\n\n\n\n\n\n\nFigure 1: Geological map of Reguri-Thanamir area, NHO, NE India (after GSI, 1986) (BoS, Belt of Schuppen; DF, Dauki Fault; IFB, Palaeogene Inner Fold \nBelt; ITSZ, Indo-Tibetan Suture Zone; MBT, Main Boundary Thrust; MCT, Main Central Thrust; STD, South Tibetan Detachment)\n\n\n\n\n\n\n\nTable 1: Generalised stratigraphic succession of the NHO, Northeast India \n\n\n\nAge Formation Lithology \n\n\n\nMid Eocene Jopi / Phokphur \nOphiolite-derived polymictic conglomerate, tuffaceous \n\n\n\nshale, lithic arenite, and wacke; minor limestone and carbonaceous matter \n\n\n\n --------------------------------------------- Unconformity--------------------------------------------- \n\n\n\nMid Jurassic \u2013                                 Ophiolite       Oceanic pelagic sediments: Shale/phyllite, cherts and  \n\n\n\nEarly Eocene                                Complex       limestones interbedded/intercalated with volcanics. Chert  \n\n\n\n                                                   contains radiolarian and coccoliths \n\n\n\nMid Jurassic: Chert                                                  Late felsic intrusives: Plagiogranite \n\n\n\n(Aitchison et al., 2019)                             Mafic/intermediate volcanics: Basalt, minor andesite and  \n\n\n\nLate Jurassic: Chert                                                                                                             trachyte, volcaniclastics and ignimbrite \n\n\n\n(Baxter et al., 2011)                                                                                                            Meta-basics: Zeolite-prehnite-greenschist facies, high \n\n\n\nEarly Cretaceous:                                                                                                             P/low-T blueschists and barroisite eclogite \n\n\n\nPlagiogranite (116.4\u00b12.2                                               Meta-chert: Containing blue amphibole and magnetite \n\n\n\n& 118.8\u00b11.2 Ma)                                                   Cumulate mafic-ultramafics: Peridotite, pyroxenite, gabbro,                          \n(Singh et al., 2017)                                                   plagiogranite and anorthosite, dolerite, tectonites and spinel                          \nLate Jurassic: Basalt                                                    peridotites, serpentinite and rhodingite \n\n\n\n(148\u00b14 Ma)  \n\n\n\n(Sarkar et al., 1996)                                                                      Mineralisation: Podiform chromitite, magnetite, Cu-Mo  \n\n\n\n                                                                                           sulphides and minor Ni-laterite, native gold and PGE \n\n\n\n------------------------------------------------ Thrust --------------------------------------------------- \n\n\n\nEarly Ordovician                              Naga Metamorphics          Nimi Formation: Feebly metamorphosed limestone,  \n\n\n\n(Aitchison et al., 2019)                                 phyllite, quartzite and quartz-sericite schist  \n\n\n\n                                                                                                                                                       Saramati Formation: \n\n\n\n                       Mica schist, granitoid gneiss, and feldspathic meta-wacke \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 45-52 \n\n\n\n\n\n\n\n \nCite The Article: Pukrozo Keyho, Watitemsu Imchen, Meribemo Yanthan, Imomeren Ao, John K. Angami  (2022). Mg-Rich Ultramafics of The Naga Hills Ophiolite, \n\n\n\nNagaland, India:  A Potential Substitute as Basic Flux in Metallurgical Industries. Malaysian Journal of Geosciences, 6(1): 45-52. \n \n\n\n\n2.1   Naga Metamorphics \n\n\n\nThe low-grade metasediments exposed along the Indo-Myanmar border \nover a stretch of 18x12 km are thrusted over the NHO belt from the east. \nThe dominant members include feldspathic quartzite, phyllite, \ncarbonaceous phyllite, limestone, and quartz-sericite schist. The phyllite \nis light green to deep grey while the associated rocks are dirty white \nfeldspathic quartzite and smoky white quartzite. The preferred alignment \nof sericite or muscovite imparts foliation to the latter. Quartzite invariably \ninterbedded with phyllite locally shows a gritty nature with sub-rounded \nquartz grains. Phyllite and quartzite are associated with limestone which \noccurs both as major bands and minor lenses with thickness varying from \n5-100 m. Locally, whitish grey to grey crystalline limestone and marble are \ngenerally associated with this formation. Presence of mylonitic limestone \nshowing flattened quartz and carbonates suggests its formation under \nextreme pressure as a result of overthrusting (Ghose et al., 2014). The \nrocks are folded with the development of major NNE trending overturned \nanticlines. A group of researchers assigned Early Ordovician age to this \nformation (Aitchison et al., 2019). \n\n\n\n2.2   Naga Hills Ophiolite  \n\n\n\nNHO belt is a linear rootless body covering about 1000 km2 area trending \nNNE-SSW of about 200 km length and has an average width of about 15 \nkm. It is tectonically sandwiched between the Disang sediments (Eocene) \nto the west and Naga Metamorphics (Early Ordovician) to the east. Mg-rich \nultramafics are the predominant rock type of this belt, a host for podiform \nchromite and nickeliferous magnetite. Various lithomembers include \ndismembered and imbricate sheets of serpentinised peridotite \n(predominantly harzburgite with minor lherzolite), gabbro, basalt, spilite, \nplagiogranite, tuffaceous volcanics, and oceanic sediments such as chert \nand limestone (Agrawal and Kacker, 1980; Ghose et al., 2010; Imchen et \nal., 2015). Chert and limestone are inter-bedded with the volcanics. Cherts \nare predominantly of biogenic origin, deposited in an open ocean basin \nand oceanic islands (Thong et al., 2022). A group of researchers assigned \nthe radiolarian assemblages from associated chert as Late Jurassic to Early \nCretaceous age (Duarah et al., 1983). A Late Jurassic (Kimmeridgian-\nLower Tithonian) and the Middle Jurassic (Late Bathonian-Early \nCallovian) affinity has been reported from red-bedded cherts of the NHO \n(Baxter et al., 2011; Aitchison et al., 2019). A group of researchers reported \nU-Pb zircon ages of 116.4 \u00b1 2.2 and 118.8 \u00b1 1.2 Ma for the oceanic \nplagiogranites (Singh et al., 2017). The NHO represents a remnant of \nstacks of tectonic slices of the Tethyan oceanic crust and upper mantle, \nemplaced during Mid-Eocene as evidenced by the olistostromal age and \nophiolite-derived Phokphur sediments due to eastward subduction of the \nIndian plate beneath the Burmese plate (Acharyya, 2007). \n\n\n\n2.3   Disang Group \n\n\n\nThe Disang is the oldest of the Tertiary succession of Nagaland, confined \npredominantly in the Palaeogene Inner Fold Belt (IFB) and lies to the west \nof NHO. The base of this group is not exposed and the thickness is \nestimated to be over 3000 m. The Disang Group is subdivided into two \nformations. The Lower Disang Formation comprises monotonous \nsequences of dark to khaki grey shale with bands of mudstone, \nintercalated with very thin beds of grey, fine-grained sandstone. The \nUpper Disang Formation is made up of well-bedded, fine-grained \nsandstone that alternate with dark grey shale and subordinate mudstone. \nThe volume of sandstones in the lower formation is negligible but \nincreases up the stratigraphic sequence with a gradual reduction in the \nproportion of shale (Imchen et al., 2014). The Disang grades conformably \ngrade upward into the Barail Group of alternate beds of sandstone and \nshale. Cretaceous fauna has been reported from the lower part and \nBartonian-Priabonian age from the upper part (Mishra, 1983; Lokho and \nKumar, 2008). Brecciation, silicification, fault gauge, and intermixing of \nlitho-units are ubiquitous along the contact between Disang sediments \nand ophiolite belt. \n\n\n\n2.4   Belt of Schuppen  \n\n\n\nBoS forms the most prominent morphotectonic unit in the western part of \nthe Naga Hills covering 4500 km2 area. It is a zone of imbricate thrust slices \nbordering Assam valley alluvium extending over a 200 km strike length \nand width of 20-25 km (Evans, 1932). BoS encompass eight or possibly \nmore over-thrusts overriding each other, along which the Naga Hills thrust \nnorth-westward relative to the Precambrian crystalline Foreland Spur \n(Mathur and Evans, 1964). \n\n\n\n2.5   Jopi/Phokphur Formation \n\n\n\nThis formation unconformably overlies the ophiolite at different \ntopographic levels. It consists of thick piles of alternating sequences of \n\n\n\npolymictic conglomerate-grit, pebbly and cobbly sandstone, wacke and \nshale. The basal conglomerate contains angular to sub-rounded boulders, \ncobbles and pebbles derived from the underlying ophiolite suite and \nembedded in reworked tuffaceous/siliceous cement. The succession \ngrades upward into grit, lithic wacke, siltstone, sandstone and shale. The \nsandstones are more arkosic towards the upper sequence. \n\n\n\n3.   MATERIALS AND METHOD \n\n\n\nRepresentative thin sections of NHO ultramafic rock were studied and \nconstituent minerals were identified using Olympus BX51 petrological \nmicroscope. Unweathered ultramafic rocks collected (peridotite \u2500 30 nos, \ndunite  \u2500 3 nos, pyroxenite \u2500 7 nos, serpentinite \u2500 10 nos) were \nthoroughly washed, dried, and homogenized were finely ground to < 120 \nASTM mesh in an agate mortar and analysed for the major oxides by X-ray \nfluorescence spectrometer at Geological Survey of India, Shillong.  Pressed \npowder pellets and fused beads were prepared from the powdered \nsamples and analysed by wavelength dispersive XRF with 4 KW rhodium \nanode tube. The mineral chemistry was carried out with a CAMECA SX-100 \nelectron probe micro analyser (EPMA) with accelerating voltage of 15 kV \nwith beam current of 12 mA using 1-micron beam size for mineral \nidentification.  \n\n\n\nFouling Index (Rf) is computed using the following formula (Khaoash and \nMohanty, 2014). \n\n\n\nRf = (Base/Acid) x Na2O \n\n\n\nWhere, Base = Fe2O3+CaO+MgO+Na2O+K2O, and Acid = SiO2+Al2O3+TiO2 \n\n\n\n4.   RESULTS AND DISCUSSION \n\n\n\n4.1   Petromineralogy \n\n\n\nThe ultramafics of the NHO include peridotite, pyroxenite, dunite, and \nserpentinite. The peridotites are predominantly harzburgitic in \ncomposition and exhibit protogranular texture characterized by medium \nto fined grained granular, tabular, and smoothly curved olivine crystals, \northopyroxene, clinopyroxene, and spinel (Figure 2A). Olivine is forsteritic \n(Fo89.48-90.66). The composition of orthopyroxene is fairly homogeneous and \nenstatitic (En88.53-90.51) while clinopyroxene is diopsidic (En47.49-50.84Fs0.22-\n\n\n\n0.87Wo48.94-52.27; 6.4-7.19 wt% Al2O3, 0.97-1.08 wt% Cr2O3, 0.3-0.54 wt% \nTiO2) (Figure 2B). Spinels are anhedral to subhedral, highly aluminous \n(52.18 wt% Al2O3) and skeletal spinel crystals are ubiquitous along \nfractures and serpentine veins. Magnetite grains are also not uncommon. \nPyroxenite exhibits hypidiomorphic texture characterised by the presence \nof coarse grain dominantly of clinopyroxene (Figure 2C), orthopyroxene \n(enstatite), and serpentinised olivine and opaques filling the fracture and \ncracks along the serpentine veins.  \n\n\n\n\n\n\n\nFigure 2: (A) Peridotite showing protogranular texture, (B) BSE image of \nperidotite (Cpx \u2013 Clinopyroxene, Opx \u2013 orthopyroxene, Sp \u2013 chrome \n\n\n\nspinel, Srp \u2013 serpentinite, Mgt \u2013 Magnetite), (C) Hypidiomorphic texture \nin pyroxenite with coarse grain Cpx (augite/diopside) and altered Opx, \n(D) BSE image showing enstatite (Opx), diopside (Cpx), serpentinised \n\n\n\nolivine along with discrete grains of magnetite \n\n\n\nOlivine occurs as discrete grains. Clinopyroxene is diopsidic in \ncomposition with 3.88 - 4.55 wt% Al2O3, 0.81 - 1.17 wt% Cr203, and 0.14 - \n0.27 wt% of TiO2 (Figure 2D). Dunite contain forsterite olivine \ncharacterised by honeycomb textures with chrome spinel and magnetite \nas accessories (Figure 3A). Forsterite content of olivine in dunite varies \nfrom 86 to 88% (Figure 3B). Serpentinite exhibits varying textures such as \nmesh texture due to metasomatic alteration of olivine (Figure 3C). Besides \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 45-52 \n\n\n\n\n\n\n\n \nCite The Article: Pukrozo Keyho, Watitemsu Imchen, Meribemo Yanthan, Imomeren Ao, John K. Angami  (2022). Mg-Rich Ultramafics of The Naga Hills Ophiolite, \n\n\n\nNagaland, India:  A Potential Substitute as Basic Flux in Metallurgical Industries. Malaysian Journal of Geosciences, 6(1): 45-52. \n \n\n\n\nserpentine, olivine, pyroxene, spinels, apatite, and chlorite constitute the \nminor phases in serpentinite. Serpentinisation is pervasive along the \nolivine grain margin exhibiting flame texture with randomly oriented fan-\nshape antigorite developed due to alteration of amphiboles (Figure 3D). \nAntigorite predominates over lizardite and chrysotile minerals (Ghose et \nal., 1986; Ningthoujam et al., 2012).  \n\n\n\n\n\n\n\nFigure 3: (A) Honeycomb textures in dunite, (B) BSE image showing \nforsterite olivine with varying degrees of serpentinisation, (C) Mesh \ntexture in serpentinite, (D) Flame texture in serpentinite exhibiting \n\n\n\nrandomly oriented fan-shaped antigorite \n\n\n\n4.2   Physicochemical Characterisation  \n\n\n\nFluxes are the key component in the metallurgical process of iron and steel \nmaking. Flux is of three types viz.  acid, basic and neutral.  The basic type \nis the primary flux used in the iron and steel manufacturing industry. The \nprincipal flux minerals used in iron and steel industry include dolomite, \nfluorspar, lime, and olivine, with smaller amounts of bauxite, silica, and \nwollastonite. The surge in iron and steel production in recent years has led \nto an increase in flux consumption. In iron and steel metallurgy, using \nsuitable flux is of paramount importance for the effective removal of \nimpurities by lowering their density, neutralising acidity, preventing \nreactions with atmospheric gases, etc. \n\n\n\nThe chemical properties are indispensable to determine its suitability as \nfluxes in metallurgical industries. It includes concentrations of silica, \nalkalis, loss on ignition, and other oxides like Al2O3 and Cr2O3. The basic \ncriteria for MgO-rich rocks (MgO > SiO2) to be used as flux materials are \nhigh MgO content, low concentrations of Cr2O3 (< 0.5%), Al2O3 (< 1%), \nalkalis, and loss on ignition (Chatterjee and Murty, 1998). Optimum \nbasicity and MgO concentrations in the slag enhance the efficiency of the \nblast-furnace smelting (Nakamoto et al., 2004; Yao et al., 2016). MgO flux \nfrom pyroxenite/olivine increases the melting point of slag, and improves \nthe high temperature properties in pellets (Dwarapudi et al., 2017). \nChemical analytical data of NHO ultramafics (dunite, peridotite, \npyroxenite, and serpentinite) indicate that they are enriched in MgO i.e., > \n35% (Dunite: 37.2-38.8%, av. 38.2%; Peridotite: 39.1-46.6%, av. 42.3%; \nSerpentinite: 36-42%, av. 39%) except for pyroxenite (23.5-31.6%, av. \n26.2%) (Table 2 and 3).  \n\n\n\nMgO/SiO2 ratio of these rocks is around 1 (> 0.6) except for pyroxenite \nwith lower ratio (Dunite: 0.92-1.01%, av. 0.98%; Peridotite: 0.9-1.2%, av. \n1.13%; Pyroxenite: 0.5-0.7%, av. 0.6%; Serpentinite: 0.80-0.90%, av. \n0.81%). MgO/SiO2 ratio with values > 0.6 exhibits improved reducibility \nand softening properties, as the MgO increases the melting point of slag \n(Sugiyama et al., 1983). SiO2 content is less than 48% in these rocks \n(Dunite: av. 38.9%, Peridotite: av. 37.5%; Pyroxenite: av. 46.7%; \nSerpentinite: av. 47.3%), barring few pyroxenite and serpentinite samples. \nSilica, particularly sand or quartzite is normally charged into the blast \nfurnace to obtain low slag basicity (Khaoash and Mohanty, 2014). If no \nadditional silica is added to sinter, the sinter basicity becomes very high \n(CaO/SiO2 \u2248 3.0) which results in high-temperature properties in terms of \npoor softening-melting characteristics and high residuals after meltdown \n(ibid). CaO/SiO2 ratio in NHO ultramafics is relatively low (Peridotite: av. \n0.01; Pyroxenite: av. 0.27; Dunite and Serpentinite: av. 0.0); their ratio \nbeyond 2.0 is not favoured due to the formation of 2CaO\u00b7SiO2 (Dwarapudi \net al., 2011). Low basicity is indispensable in maintaining better fluidity of \nthe melt in the blast furnace (Khaoash and Mohanty, 2014).  \n\n\n\nAl2O3 concentration is < 2% (Dunite: av. 0.08%; Peridotite (av. 0.6%; \nPyroxenite: av. 1.69%; Serpentinite: av. 0.7%) except for few peridotite \n\n\n\nsamples (Table 3). Cr2O3 concentrations in dunite (av. 0.13%), peridotite \n(0.26%), pyroxenite (0.1%), and serpentinite (0.09%). Cr2O3 values are \nrelatively on the higher side (> 0.06) as per the specification set by major \nsteel producers in India (Table 3). However, it conforms to recommended \nchemical specifications prescribed by Chatterjee and Murty (1998) i.e. < \n0.5% and by Tata Steel Ltd and Geological Survey of India i.e. < 1 (IBM, \n2014).  \n\n\n\nHigh alkali (Na2O and K2O) content usually sourced from high ash coke and \nflux are detrimental to blast furnace performance resulting in \ndecrepitation of the ore or coke, damage to refractory lining causing \nhotspots or formation of scaffolds, and hanging of the overburdens \n(Khaoash and Mohanty, 2014). The scaffolds reduce the furnace \npermeability to a great extent and damage the furnace. Low alkali raw \nmaterials are preferred and thus alkali content is one crucial factor in the \nselection of flux material (ibid). Alkali content in NHO ultramafics exceeds \nthe prescribed value (< 0.02%) set by the major steel producers in India \nfor high magnesian rocks for flux/sinter mix (dunite - 0.03%), peridotite - \n0.04%), pyroxenite - 0.29%, and serpentinite 0.07%) (Mohanty et al., \n2009). During beneficiation, bulk of the alkali is removed through slag and \nas gasses, while the remaining alkali circulates in the blast furnace (Yusfin \net al., 1999). Alkali content in the furnace is ideally regulated through \ncontrol of the basicity of the slag and their removal is greatest with slag of \nlow basicity (Sciulli, 1992). Alkalis are essentially removed by addition of \nquartzite, olivine, dunite, and calcium chloride (Ashton et al., 1974; \nKurunov et al., 2009). Furthermore, Corex smelting process effectively \ntreats high-alkali materials as the process is insensitive to the alkali attack \nand thus regulates alkali content (Dastidar et al., 2018). CaO content is also \nlow (< 2%) in peridotite (av. 0.29%), dunite (av. 0.15%), serpentinite (av. \n0.25%); however, their higher concentrations in pyroxenite (av. 12.6%) is \nattributed to diopsidic composition of clinopyroxene evidenced by their \nmineral chemistry.  \n\n\n\nPeridotites exhibit depletion in CaO relative to Al2O3 suggesting the \nremoval of calcium from the peridotite protolith during serpentinisation. \nFe2O3 content is variable (Dunite: av. 8.8%; Peridotite: av. 6.6%; \nPyroxenite: 6.7%; Serpentinite: 5%) but conforms to specifications of flux \ncomposition required in iron and steel industries (Table 3). LOI in \nperidotite and dunite is relatively higher as compared to pyroxenite and \nserpentinite due to a variable degree of alteration. However, LOI of NHO \nultramafics are low (< 14%) compared to conventional carbonate fluxes \nwhich may go up to 40 to 45% (Mohanty et al., 2009). Estimation of \nFouling Index (Rf) is yet an important parameter for determination of apt \nmetallurgical flux. The index estimates the formation of unwanted \nmaterial deposits on heat transfer surfaces during the process of heating \nand cooling. Fouling is not uncommon in all industries leading to heat \ntransfer degradation, flow resistance, and pressure drops. Rf in ultramafic \nrocks of the NHO is low (Dunite: av. 0.03; Peridotite: av. 0.04; Pyroxenite: \n0.19; Serpentinite: 0.05) as compared to limestone (0.75) and dolomite \n(1.09) indicating better quality flux compared to conventional flux \nmaterials such as limestone and dolomite (Khaoash and Mohanty, 2014).  \n\n\n\nBesides, Khaoash and Mohanty emphasised that the grain/crystal size of \nthe flux material should not be ignored (Khaoash and Mohanty, 2014). The \nsmaller the grain size, the lower the energy required and the lesser time it \ntakes to assimilate into the melt in the blast furnace (ibid). Clinopyroxene \ncrystals size varies from 10.51 to 133.57 \u00b5m (av. 48.66 \u00b5m), \northopyroxene (42.06-229.11 \u00b5m, av. 103.57 \u00b5m) and olivine grains from \n26.54-403.32 \u00b5m (av. 155.61 \u00b5m) in peridotite. Olivine crystals in dunite \nare relatively finer than that in peridotite varying from 3.78-27.47 \u00b5m (av. \n11.12 \u00b5m). The crystal grains of NHO ultramafics are predominantly fine \nto medium-grained. In contrast, the average crystal size in dolomite is \nreported to be at least twenty times larger than that of limestone and 4-5 \ntimes larger than that of dunite (ibid).  \n\n\n\nPyroxenite and olivine fluxes on Noamundi iron ores, Jharkhand, \nIndia also indicate substantial lowering of reduction disintegration index \n(RDI) (26% and 23% respectively) and improvement of cold crushing \nstrength (CCS), swelling indices with good reducibility (70%-77%) (Pal et \nal., 2015). MgO-bearing flux effectively improves the swelling index, \nsoftening-melting properties, permeability of iron sinter/pellets, and to \nsome extent the CCS (Iljana et al., 2016; Meraj et al., 2015; Shen et al., 2014; \nZhu et al., 2013). The improved metallurgical properties by using MgO \nbearing flux are attributed to low temperature (< 900\u00b0C) diffusion of \nreduced magnetite phase with magnesioferrite, and with SiO2 phase at \nhigher temperature (~1000\u00b0C) (Semberg et al., 2013). Lately, a study has \nhighlighted the adverse impact of MgO on the oxidation of Fe3O4 into Fe2O3 \n\n\n\naffecting induration and thereby restraining their strength (Gao et al., \n2018). However, these drawbacks can be done away with extended fired \ntime and by adjusting the MgO dosage (ibid).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 45-52 \n\n\n\n\n\n\n\n \nCite The Article: Pukrozo Keyho, Watitemsu Imchen, Meribemo Yanthan, Imomeren Ao, John K. Angami  (2022). Mg-Rich Ultramafics of The Naga Hills Ophiolite, \n\n\n\nNagaland, India:  A Potential Substitute as Basic Flux in Metallurgical Industries. Malaysian Journal of Geosciences, 6(1): 45-52. \n \n\n\n\nTable 2. Major oxides (wt %) of NHO ultramafics, Nagaland \n\n\n\n SiO2 TiO2 Al2O3 Fe2O3t MgO CaO Na2O K2O Cr2O3 LOI Rf Mg/Si Ca/Si Alkalis \n\n\n\nP1 34.50 0.10 3.02 6.27 42.59 1.25 0.01 0.01 0.30 12.16 0.01 1.23 0.04 0.02 \n\n\n\nP2 39.86 0.01 0.70 6.41 41.58 0.14 0.02 0.01 0.40 11.20 0.02 1.04 0.00 0.03 \n\n\n\nP3 39.69 0.01 0.01 6.57 41.60 0.11 0.01 0.01 0.17 11.89 0.01 1.04 0.00 0.02 \n\n\n\nP4 37.62 0.01 0.01 6.02 43.03 0.09 0.01 0.01 0.19 13.10 0.01 1.14 0.00 0.02 \n\n\n\nP5 38.14 0.01 0.03 7.46 41.70 0.12 0.01 0.01 0.25 12.41 0.01 1.09 0.00 0.02 \n\n\n\nP6 38.04 0.01 0.02 5.67 43.6 0.05 0.01 0.01 0.26 12.51 0.01 1.14 0.00 0.02 \n\n\n\nP7 38.60 0.01 0.01 6.05 42.59 0.24 0.01 0.01 0.24 12.37 0.01 1.10 0.01 0.02 \n\n\n\nP8 37.59 0.01 0.03 5.54 44.51 0.10 0.01 0.01 0.28 12.12 0.01 1.18 0.00 0.02 \n\n\n\nP9 37.40 0.01 0.01 6.53 43.29 0.16 0.01 0.01 0.14 12.46 0.01 1.15 0.00 0.02 \n\n\n\nP10 37.28 0.01 0.01 6.56 42.89 0.20 0.02 0.01 0.23 12.92 0.02 1.15 0.01 0.03 \n\n\n\nP11 42.52 0.04 0.87 8.47 42.08 1.57 0.03 0.01 0.33 4.32 0.03 0.99 0.04 0.04 \n\n\n\nP12 34.83 0.01 0.01 5.46 46.66 0.28 0.01 0.01 0.20 12.65 0.01 1.34 0.01 0.02 \n\n\n\nP13 36.10 0.01 0.01 5.52 45.20 0.30 0.01 0.01 0.21 12.76 0.01 1.25 0.01 0.02 \n\n\n\nP14 39.01 0.01 0.62 5.81 41.01 0.14 0.01 0.01 0.31 12.79 0.01 1.05 0.00 0.02 \n\n\n\nP15 35.00 0.01 0.25 6.12 42.08 0.23 0.01 0.01 0.27 15.80 0.01 1.20 0.01 0.02 \n\n\n\nP16 33.00 1.08 3.25 7.12 42.01 0.23 0.01 0.01 0.27 14.80 0.01 1.27 0.01 0.02 \n\n\n\nP17 37.43 0.01 0.01 7.40 41.10 0.16 0.01 0.01 0.19 13.44 0.01 1.09 0.00 0.02 \n\n\n\nP18 37.67 0.01 0.01 7.08 41.65 0.06 0.01 0.01 0.30 13.05 0.01 1.10 0.00 0.02 \n\n\n\nP19 36.56 0.01 0.18 6.66 41.54 0.22 0.01 0.01 0.25 14.31 0.01 1.13 0.01 0.02 \n\n\n\nP20 36.77 0.01 0.01 6.62 42.04 0.32 0.01 0.01 0.30 13.70 0.01 1.14 0.01 0.02 \n\n\n\nP21 36.46 0.01 0.05 6.62 42.30 0.28 0.01 0.01 0.38 13.72 0.01 1.16 0.01 0.02 \n\n\n\nP22 35.37 0.01 2.18 6.54 41.46 0.30 0.01 0.01 0.32 13.60 0.01 1.17 0.01 0.02 \n\n\n\nP23 41.29 0.06 1.45 6.05 39.81 0.04 0.08 0.01 0.37 11.52 0.08 0.96 0.00 0.09 \n\n\n\nP24 40.55 0.04 0.71 6.28 43.77 0.30 0.08 0.01 0.40 8.25 0.09 1.07 0.01 0.09 \n\n\n\nP25 40.79 0.05 0.59 7.05 39.15 0.06 0.06 0.01 0.19 11.59 0.06 0.96 0.00 0.07 \n\n\n\nP26 34.74 0.04 0.07 9.19 40.54 0.80 0.06 0.01 0.09 13.07 0.08 1.16 0.02 0.07 \n\n\n\nP27 36.68 0.03 1.19 7.67 40.09 0.17 0.08 0.01 0.30 9.84 0.10 1.09 0.00 0.09 \n\n\n\nP28 36.00 0.04 0.55 7.12 43.16 0.08 0.11 0.01 0.31 11.83 0.15 1.19 0.00 0.12 \n\n\n\nP29 39.95 0.04 0.50 5.61 42.11 0.55 0.07 0.01 0.19 11.31 0.08 1.05 0.01 0.08 \n\n\n\nP30 36.53 0.03 0.62 5.74 43.61 0.04 0.09 0.01 0.21 13.21 0.12 1.19 0.00 0.10 \n\n\n\nD1 38.65 0.01 0.01 8.67 38.79 0.04 0.01 0.01 0.12 13.72 0.01 1.00 0.00 0.02 \n\n\n\nD2 40.32 0.01 0.01 10.08 37.19 0.04 0.01 0.01 0.13 12.21 0.01 0.92 0.00 0.02 \n\n\n\nD3 37.90 0.04 0.24 7.75 38.63 0.36 0.05 0.01 0.14 12.49 0.06 1.02 0.01 0.06 \n\n\n\nPx1 43.55 0.79 1.97 9.74 26.28 10.33 0.23 0.22 0.04 3.97 0.23 0.60 0.24 0.45 \n\n\n\nPx2 42.89 0.96 1.83 6.84 31.61 10.19 0.19 0.03 0.05 3.90 0.20 0.74 0.24 0.22 \n\n\n\nPx3 44.57 0.51 1.34 9.36 25.58 13.62 0.19 0.01 0.22 4.20 0.20 0.57 0.31 0.20 \n\n\n\nPx4 49.36 0.52 1.62 4.85 23.87 14.94 0.21 0.01 0.10 2.85 0.17 0.48 0.30 0.22 \n\n\n\nPx5 49.31 0.67 1.76 5.79 23.49 13.28 0.18 0.21 0.10 2.87 0.14 0.48 0.27 0.39 \n\n\n\nPx6 48.13 0.83 1.73 5.68 26.28 12.12 0.25 0.05 0.07 3.77 0.21 0.55 0.25 0.30 \n\n\n\nPx7 49.11 0.08 1.61 4.88 26.08 13.47 0.17 0.07 0.10 3.13 0.14 0.53 0.27 0.24 \n\n\n\nSp1 47.08 0.02 0.35 3.86 38.53 0.01 0.05 0.01 0.02 6.50 0.04 0.82 0.00 0.06 \n\n\n\nSp2 47.70 0.03 1.53 4.65 38.45 0.31 0.06 0.01 0.20 5.20 0.05 0.81 0.01 0.07 \n\n\n\nSp3 48.12 0.03 0.47 4.32 37.45 0.08 0.04 0.00 0.06 2.20 0.03 0.78 0.00 0.04 \n\n\n\nSp4 45.01 0.03 0.34 4.02 39.7 0.12 0.06 0.00 0.08 1.50 0.06 0.88 0.00 0.06 \n\n\n\nSp5 48.01 0.04 0.58 5.91 36.54 0.15 0.07 0.01 0.09 5.60 0.06 0.76 0.00 0.08 \n\n\n\nSp6 47.15 0.03 0.52 6.91 39.34 0.95 0.06 0.03 0.08 4.80 0.06 0.83 0.02 0.09 \n\n\n\nSp7 48.51 0.04 0.53 3.21 40.58 0.03 0.05 0.02 0.02 6.10 0.04 0.84 0.00 0.07 \n\n\n\nSp8 47.23 0.03 1.53 6.95 37.81 0.44 0.05 0.01 0.17 3.30 0.05 0.80 0.01 0.06 \n\n\n\nSp9 46.60 0.03 0.51 5.99 41.52 0.09 0.07 0.01 0.09 3.60 0.07 0.89 0.00 0.08 \n\n\n\nSp10 47.20 0.03 0.34 4.80 35.70 0.12 0.06 0.00 0.10 4.10 0.05 0.76 0.00 0.06 \n\n\n\n(P 1-30 \u2500 Pyroxenite; D 1-3 \u2500 Dunite; Px 1-7 \u2500 Pyroxenite; Sp 1-10 \u2500 Serpentinite; Alkalis \u2500 Na + K) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 45-52 \n\n\n\n\n\n\n\n \nCite The Article: Pukrozo Keyho, Watitemsu Imchen, Meribemo Yanthan, Imomeren Ao, John K. Angami  (2022). Mg-Rich Ultramafics of The Naga Hills Ophiolite, \n\n\n\nNagaland, India:  A Potential Substitute as Basic Flux in Metallurgical Industries. Malaysian Journal of Geosciences, 6(1): 45-52. \n \n\n\n\nTable 3:  Comparison of major oxides of NHO ultramafics with the chemical specification of flux for iron and steel industry \n\n\n\nOxides \n\n\n\n(wt%) \n\n\n\nPeridotite        \n(n=30) \n\n\n\nDunite \n\n\n\n(n=3) \n\n\n\nPyroxenite        \n(n=7) \n\n\n\nSerpentinite \n\n\n\n(n=10) \n\n\n\nSpecification set by major \nsteel producers in India* \n\n\n\nMgO \n39.2-46.7 \n\n\n\n(av. 42.3) \n\n\n\n37.19-38.79 \n\n\n\n(av. 38.2) \n\n\n\n23.49-31.61 \n\n\n\n(av. 26.17) \n\n\n\n35.7-41.52 \n\n\n\n(av. 38.56) \n> 35 \n\n\n\nSiO2 \n33.00 - 42.52 \n\n\n\n(av. 37.5) \n\n\n\n37.9 - 40.32 \n\n\n\n(av. 38.9) \n\n\n\n42.89 - 49.36 \n\n\n\n(av. 46.7) \n\n\n\n45.01-48.51 \n\n\n\n(av. 47.28) \n< 48 \n\n\n\nAl2O3 \n0.01 - 3.25 \n\n\n\n(av. 0.57) \n\n\n\n0.01-0.24 \n\n\n\n(av. 0.08) \n\n\n\n1.34-1.97 \n\n\n\n(av. 1.69) \n\n\n\n0.34-1.53 \n\n\n\n(av. 0.70) \n< 2 \n\n\n\nFe2O3t \n5.5 - 9.2 \n\n\n\n(av. 6.6) \n\n\n\n7.75-10.08 \n\n\n\n(av. 8.83) \n\n\n\n4.85 -9.74 \n\n\n\n(av. 6.73) \n\n\n\n3.21-6.95 \n\n\n\n(av. 5.19) \n8-12 \n\n\n\nCr2O3 \n0.09-0.40 \n\n\n\n(av. 0.2) \n\n\n\n0.11-0.13 \n\n\n\n(av. 0.12) \n\n\n\n0.04-0.22 \n\n\n\n(av. 0.09) \n\n\n\n0.02-0.2 \n\n\n\n(av. 0.09) \n\n\n\n< 0.06 / \n\n\n\n< 1** / 0.5%\u00a9 \n\n\n\nCaO \n0.04-1.57 \n\n\n\n(av. 0.3) \n\n\n\n0.04-0.36 \n\n\n\n(av. 0.15) \n\n\n\n10.19-14.94  \n\n\n\n(av. 12.6) \n\n\n\n0.03-0.95 \n\n\n\n(av. 0.25) \n<2 \n\n\n\nAlkalis \n\n\n\n(Na2O + K2O) \n\n\n\n0.02-0.12 \n\n\n\n(av. 0.04) \n\n\n\n0.02-0.06 \n\n\n\n(av. 0.03) \n\n\n\n0.2-0.45 \n\n\n\n(av. 0.28) \n\n\n\n0.04-0.09 \n\n\n\n(av. 0.07) \n\n\n\n< 0.02 / \n\n\n\n< 0.05%$ \n\n\n\nLOI \n4.32-15.80 \n\n\n\n(av. 12.2) \n\n\n\n12.21-13.72 \n\n\n\n(av. 12.80) \n\n\n\n2.85-4.2 \n\n\n\n(av. 3.52) \n\n\n\n1.5-6.1 \n\n\n\n(av. 4.04) \n<14 \n\n\n\nMgO/SiO2 \n0.96- 1.27 \n\n\n\n(av. 1.13) \n\n\n\n0.92-1.01 \n\n\n\n(av. 0.98) \n\n\n\n0.47-0.73 \n\n\n\n(av. 0.56) \n\n\n\n0.76-0.89 \n\n\n\n(av. 0.81) \n- \n\n\n\nCaO/SiO2 \n0.0-0.04 \n\n\n\n(av. 0.006) \n\n\n\n0.0-0.01 \n\n\n\n(av. 0.003) \n\n\n\n0.24-0.31 \n\n\n\n(av. 0.27) \n\n\n\n0.0-0.02 \n\n\n\n(av. 0.004) \n1.2$ \n\n\n\nRf \n0.012-0.152 \n\n\n\n(av. 0.04) \n\n\n\n0.01-0.06 \n\n\n\n(av. 0.03) \n\n\n\n0.14-0.23 \n\n\n\n(av. 0.19) \n\n\n\n0.03-0.07 \n\n\n\n(av. 0.05) \n- \n\n\n\n*Mohanty et al., 2009; **IBM, 2014; \u00a9Chatterjee and Murty, 1998; $Ghose and Chatterjee, 2008 \n\n\n\nA group of researchers observed that commercially produced pyroxenite \nfluxed MgO-rich pellets exhibit better high temperature properties but \ninferior low temperature properties (CCS and RDI) (Dwarapudi et al., \n2017). In contrast, limestone fluxed CaO-rich pellets indicate good RDI and \nlower dust generation, but inferior high temperature metallurgical \nattributes (ibid). Consequently, to improve the bonding phases in pellets, \ndual flux combination technique has been improvised by using carbonate \n(limestone) and silicate (pyroxenite or olivine) minerals which \nsignificantly improved the CCS, RDI and softening temperature, and low \ndust generation at lower coke consumption (Dwarapudi et al., 2017). The \ngeochemical data of NHO ultramafics conforms to the chemical \nspecifications set for flux/sinter mix by the major steel producers in India \nexcept for slightly higher alkali content (Table 3) (Mohanty et al., 2009). It \nis comparable to category \u2018B\u2019 of high magnesian rocks of Orissa except for \nrelatively higher SiO2 content in pyroxenite and serpentinite (Ghosh et al., \n1998). \n\n\n\nSintering agglomeration process is extensively used to produce iron ore \nfines, there is an increasing trend of utilisation of pellets in iron and steel \nindustries in recent years. Utilisation of pellets as feed in the blast furnace \nhas added advantages due to their uniform size, known composition, and \nstrength (IBM, 2020). Major iron producers have incorporated flux into \npellets, replacing lump ore or sinters and the fluxed pellets are \ntransported to iron-producing units. In NHO, a total indicated resource of \n5280 million tonnes (IBM, 2020) of iron ore is available at Phokphur, \nKiphire district, Nagaland. Furthermore, a total of 17, 52, 200 million \ntonnes of limestone resources have also been reported from the same belt \n(ibid). Thus there is ample avenue where MgO-based fluxed pellets can be \ntransported elsewhere in the country and avoiding unwarranted \ntransportation of lump raw materials. \n\n\n\n5.   CONCLUSION \n\n\n\nPhysicochemical parameters indicate that ultramafics of NHO can be a \npotential basic flux substitute in iron and steel industries in lieu of \ndolomite/limestone and quartzite owing to appreciable MgO content (23.4 \nto 46.7 wt%) except for pyroxenite, low concentrations of Al2O3 (< 2 wt%), \nLOI (< 14 wt%), and Cr2O3 (< 1 wt%), though contains relatively higher \nalkalis, it conforms to the chemical specifications set for flux/sinter mix by \nthe major steel producers in India. Alkalis can be removed by adding \nquartzite, olivine, dunite, and calcium chloride. Besides, Corex smelting \ntechnique can effectively treat flux/raw materials with high alkali content. \nThe lower MgO content in pyroxenite can be compensated by appropriate \nblending with other high magnesian rocks. Fouling index indicates NHO \n\n\n\nMg-based flux as better quality flux compared to conventional flux \nmaterials such as limestone and dolomite. Furthermore, Mg-rich rocks as \nflux do not require any heat energy for calcination to expel CO2 and are \neasily fused into the melt. In contrast, limestone, dolomite, and quartzite \nrequire calcination, thereby time-consuming, more energy/fuels, and emit \nCO2. However, further studies on thermal properties of NHO ultramafics \nare warranted.  \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe present work is a contribution of GSI Annual Field Programme \nundertaken during FS: 2012-14 (ME/PM/NER/SMN/2012/028), FS: \n2017-18 (M2AFGBM-MEP/NC/NER/SU-MAN/2018/18235) and FS: \n2020-21 (M2APMM-MP/NC/NER/SU-MAN/2020/30121). The authors \nacknowledge the Addl. DG & HoD, GSI, NER, Shillong, and Dy. DG, State \nUnit: Manipur-Nagaland, Dimapur for encouraging to publish the article. \nThe quality of this manuscript has been enhanced tremendously, thanks \nto the Reviewers.  \n\n\n\nREFERENCES \n\n\n\nAshton, J.D., Gladysz, C.V., Holditch, J.E.R., Walker, G.H., 1974. Alkali control \nat Dofasco. Journal of the Minerals, Metals and Materials Society, 26 \n(40), Pp. 47\u201254. \n\n\n\nAcharyya, S.K., 2007. Collisional emplacement history of the Naga-\nAndaman Ophiolite and the position of the Eastern Indian Suture. \nJournal of the Asian Earth Science, 29, Pp. 229 \u2013 242. \n\n\n\nAgrawal, O.P., Kacker, R.N., 1980. Nagaland Ophiolites, India: A subduction \nzone ophiolite complex in a Tethyan orogenic belt. Intl. Ophiolite \nSym., Cyprus, Pp. 454-461. \n\n\n\nAitchison, J.C., Ao, A., Bhowmik, S., Clarke, G.L., Ireland, T.R., Kachovich, S., \nZhou, R., 2019. Tectonic evolution of the western margin of the \nBurma microplate-based on new fossil and radiometric age \nconstraints. Tectonics, 38, Pp. 1718\u20131741. \nhttps://doi.org/10.1029/2018TC005049. \n\n\n\nBaxter, A.T., Aitchison, J.C., Zyabrev, S.V., Ali, J.R., 2011. Upper Jurassic \nradiolarians from the Naga ophiolite, Nagaland, Northeast India. \nGondwana Research, 20, Pp. 638\u2013644. \n\n\n\nChatterjee, A., Murthy, C.H.V.G.K., 1998. Role of beneficiation in exploiting \nthe new raw materials base at Tata Steel. In: Joshi, Y.G., (Ed.), Proc. \nMega Events, Pp. 169-190. \n\n\n\n\nhttps://ui.adsabs.harvard.edu/search/?q=author%3A%22Ashton%2C+J.+D.%22\n\n\nhttps://ui.adsabs.harvard.edu/search/?q=author%3A%22Gladysz%2C+C.+V.%22\n\n\nhttps://ui.adsabs.harvard.edu/search/?q=author%3A%22Holditch%2C+J.+E.+R.%22\n\n\nhttps://ui.adsabs.harvard.edu/search/?q=author%3A%22Walker%2C+G.+H.%22\n\n\nhttps://doi.org/10.1029/2018TC005049\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 45-52 \n\n\n\n\n\n\n\n \nCite The Article: Pukrozo Keyho, Watitemsu Imchen, Meribemo Yanthan, Imomeren Ao, John K. Angami  (2022). Mg-Rich Ultramafics of The Naga Hills Ophiolite, \n\n\n\nNagaland, India:  A Potential Substitute as Basic Flux in Metallurgical Industries. Malaysian Journal of Geosciences, 6(1): 45-52. \n \n\n\n\nDastidar, M.G., Sarkar, B.K., Mitra, M.K., Dey, R., 2018. Effect of alkali on \ndifferent iron making processes. Material Science and Engineering, \n2 (6), Pp. 304\u2012313. doi: 10.15406/mseij.2018.02.00075. \n\n\n\nDuarah, R., Saikia, M.M., Bhattacharjee, C.C., 1983. Occurrence of \nradiolarian cherts in flysch-ophiolite succession of Indo-Burman \norogen. Journal of the Assam Science Society, 25 (2), Pp. 6\u201313. \n\n\n\nDwarapudi, S., Ghosh, T.K., Shankar, A., Tathavadkar, V., Bhattacharjee, D., \nVenugopal, R., 2011. Effect of pellet basicity and MgO content on the \nquality and microstructure of hematite pellets. International \nJournal of Mineral Processing, 99, Pp. 43-53. \n\n\n\nDwarapudi, S., Sekhar, C., Paul, I., Modi, K., Pal, A.R., Chakraborty, U., Das, \nB.K., 2017. Effect of fluxing agents on the quality and microstructure \nof hematite pellets. International Journal of Metallurgical \nEngineering, 6 (1), Pp. 18-30. \nhttps://doi:10.5923/j.ijmee.20170601.03. \n\n\n\nEvans, P., 1932. Tertiary succession in Assam. Trans. Min. Geol. Inst. Ind., \n27, Pp. 155-260. \n\n\n\nGao, Q., Jiang, X., Zheng, H., Shen, F., 2018. Induration process of MgO flux \npellet. Minerals, 8, Pp. 389. https://doi.org/10.3390/min8090389. \n\n\n\nGao, Q., Shen, Y., Wei, G., Jiang, X., Shen, F., 2016. Diffusion behavior and \ndistribution regulation of MgO in MgO-bearing pellets. \nInternational Journal of Minerals, Metallurgy and Materials, 23, Pp. \n1011\u20131018. \n\n\n\nGeological Survey of India, 1986. Geology of Nagaland ophiolite. In: Ghosh, \nD.B. commemorative volume. Mem. Geol. Surv. India, 119, Pp. 1\u2013\n113. \n\n\n\nGhosh, A., Chatterjee, A., 2008. Ironmaking and steelmaking: Theory and \npractice. PHI Learning Private Limited, New Delhi. \n\n\n\nGhose, N.C., Agrawal, O.P., Singh, R.N., 1986. Geochemistry of the ophiolite \nbelt of Nagaland, N.E. India. In: Ghose, N.C., Varadarajan, S., (Eds.). \nOphiolite and Indian Plate Margin, Pp. 241-293. \n\n\n\nGhosh, S.P., Nanda, J.K., Panda, P.K., 1998. Interim report on the search for \nhigh Mg-igneous rocks in parts of Keonjhar-Deogarh-Sundargarh \ndistrict of Orissa. Pp. 19. \n\n\n\nGhose, N.C., Agrawal, O.P., Chatterjee, N., 2010. Geological and \nmineralogical study of eclogite and glaucophane schists in the Naga \nHills Ophiolite, Northeast India. Island Arc. 19, Pp. 336\u2013356. \n\n\n\nGhose, N., Chatterjee, N., Fareeduddin, 2014. A petrographic atlas of \nophiolite: An example from the eastern India\u2010Asia collision zone. \nSpringer, New Delhi. \n\n\n\nIljana, M., Kemppainen, A., Paananen, T., Mattila, O., Heikkinen, E.P., \nFabritius, T., 2016. Evaluating the reduction-softening behaviour of \nblast furnace burden with an advanced Test. Journal of the Iron and \nSteel Institute of Japan, 56, Pp. 1705\u20131714. \n\n\n\nImchen, W., Thong, G.T., Pongen, T., 2014. Provenance, tectonic setting and \nage of the sediments of the Upper Disang Formation in the Phek \ndistrict, Nagaland. Journal of Asian Earth Sciences, 88, Pp. 11-27. \n\n\n\nImchen, W., Patil, S.K., Rino, V., Thong, G.T., Pongen, T., Rao, B.V., 2015. \nGeochemistry, petrography and rock magnetism of the basalt of \nPhek district, Nagaland. Current Science, 108 (12), Pp. 2240-2249. \n\n\n\nIndian Bureau of Mines, 2014. Dunite and pyroxenite. Indian Mineral \nYearbook 2012 (part-III: Mineral Review) 53rd Edition, Govt. of \nIndia, Ministry of Mines, Nagpur. \n\n\n\nIndian Bureau of Mines, 2018. Limestone and other calcareous materials. \nIndian Mineral Yearbook 2017 (part-III: Mineral Review) 56th \nEdition, Govt. of India, Ministry of Mines, Nagpur. \n\n\n\nIndian Bureau of Mines, 2020. Indian Minerals Yearbook (Part- III: Mineral \nReviews) 59th Ed., Iron Ore (Advance Release), Govt. of India, \nMinistry of Mines, Nagpur. \n\n\n\nKhaoash, S., Mohanty, J.K., 2014. Prospect of magnesian-rich rocks from \nBoula-Nausahi Igneous Complex, Odisha as flux in iron and steel \nindustry. Journal of Geosciences and Geomatics, 2 (5A), Pp. 7-11. \ndoi:10.12691/jgg-2-5A-2. \n\n\n\nKurunov, I.F., Titov, V.N., Emel\u2019yanov, V.L., Lysenko, S.A., Arzamastsev, \nA.N., 2009. Analysis of the behaviour of alkalis in a blast furnace. \nMetallurgist, 53 (9), Pp. 533\u2012542. \n\n\n\nLokho, K., Kumar, K., 2008. Fossil pteropods (thecosomata, holoplanktonic \nmollusca) from the Eocene of Assam-Arakan Basin, Northeastern \nIndia. Current Science, 94, Pp. 647\u2013652. \n\n\n\nLong, F., Shen, F., Jiang, X., Yang, F., Zhou, Y., An, H., 2021. Pelletizing \nanalysis on MgO-fluxed pellets by DEM. Crystals, 11, Pp. 821. \nhttps://doi.org/10.3390/cryst11070821. \n\n\n\nMathur, L.P., Evans, P., 1964. Oil in India. International Geological Congress \n(22nd Session), New Delhi. \n\n\n\nMeraj, M., Pramanik, S., Pal, J., 2015. Role of MgO and its different minerals \non properties of iron ore pellet. Transactions of the Indian Institute \nof Metals, 69, Pp. 1141\u20131153. https://doi.org/10.1007/s12666-\n015-0676-8. \n\n\n\nMitchell, A.H.G.,1993. Cretaceous-Cenozoic tectonic events in the western \nMyanmar (Burma) Assam region. Journal of Geological Society of \nLondon, 150, Pp. 1089-1102. \n\n\n\nMohanty, J.K, Rao, D.S., Paul, A.K., Khaoash, S., 2009. Characterization of \nhigh magnesian rocks for the suitability as flux in iron and steel \nindustry. Journal of Geology and Mining Research, 1 (7), Pp. 149-\n155. \n\n\n\nMishra, U.K., 1983. Palaeontological studies of Disang and Barail \nsedimentaries in a part of Manipur East District. Unpublished \nGeological Survey of India Progress Report. \n\n\n\nNakamoto, M., Tanaka,T., Lee, J., Usui, T., 2004. Evaluation of viscosity of \nmolten SiO2\u2013CaO\u2013MgO\u2013Al2O3 slags in blast furnace operation. Iron \nand Steel Institute of Japan, 44, Pp. 2115\u201319. \n\n\n\nNingthoujam, P.S., Dubey, C.S., Guillot, S., Fagion, A.S., Shukla D.P., 2012. \nOrigin and serpentinization of ultramafic rocks of Manipur \nOphiolite Complex in the Indo-Myanmar subduction zone, \nNortheast India. Journal of Asian Earth Sciences, 50, Pp. 128\u2013140. \n\n\n\nPal, J., Ghorai, S., Nandi, B. Chakraborty, T., Das, G., Venugopalan, T., \n2015. Effect of pyroxenite and olivine minerals as source of MgO in \nhematite pellet on improvement of metallurgical \nproperties. Journal of Central South University, 22, Pp. 3302\u20133310. \nhttps://doi.org/10.1007/s11771-015-2870-6. \n\n\n\nSarkar, A., Datta, A.K., Poddar, B.C., Bhattacharyya, B.K., Kollapuri, V.K., \nSanwal, R., 1996. Geochronological studies of Mesozoic igneous \nrocks from eastern India. Journal of Southeast Asian Earth Sciences, \n13, Pp. 77-81. \n\n\n\nSciulli, C.M., 1992. Blast furnace reactions, blast furnace iron making: \nPrinciples and design. McMaster University, Hamilton, Ontario, 1, \nPp. 3-10. \n\n\n\nSearle, M.P., Noble, S.R., Cottle, J.M., Waters, D.J., Mitchell, A.H.G., Tin Hlaing, \nHorstwood, M.S.A., 2007. Tectonic evolution of the Mogok \nmetamorphic belt, Burma (Myanmar) constrained by U-Th-Pb \ndating of metamorphic and magmatic rocks. Tectonics, 26, TC3014. \nhttps:// doi:10.1029/2006TC002083. \n\n\n\nSemberg, P., Andersson, C., Bj\u00f6rkman, B., 2013. Interaction between iron \noxides and olivine in magnetite pellets during reduction to wustite \nat temperatures of 1000\u20131300\u25e6C. Iron and Steel Institute of Japan, \n53, Pp. 391\u2013398. \n\n\n\nSlagnes, S., Sundqvist, L., 2008. Olivine as slag conditioner in iron making \nand its effect on processing characteristic and CO2 emission. In: 3rd \nInternational meeting on iron making. 2nd Intl. Symposium on iron \nore.  \n\n\n\nSomolinos, Ruisanchez, Escudero, Martinez, 2015. Ironmaking increase of \nthe sinter basicity through the insertion of partially inert \nmagnesium silicate mini lumps with mineralogically stable \ninterphases. In: Conference proceeding of METEC & 2nd ESTAD \n2015. D\u00fcsseldorf. Germany.  \n\n\n\nShen, F., Gao, Q., Jiang, X., Wei, G., Zheng, H., 2014. Effect of magnesia on the \ncompressive strength of pellets. International Journal of Minerals, \nMetallurgy and Materials, 21, Pp. 431\u2013437. \n\n\n\n\nhttps://www.springer.com/journal/12613\n\n\nhttps://ar.booksc.org/author/I.%20F.%20Kurunov\n\n\nhttps://ar.booksc.org/author/V.%20N.%20Titov\n\n\nhttps://ar.booksc.org/author/V.%20L.%20Emel%E2%80%99yanov\n\n\nhttps://ar.booksc.org/author/S.%20A.%20Lysenko\n\n\nhttps://ar.booksc.org/author/A.%20N.%20Arzamastsev\n\n\nhttps://www.springer.com/journal/12666/\n\n\nhttps://www.springer.com/journal/12666/\n\n\nfile:///C:/Users/PC%20Armaggaddon/Desktop/NATASHAAA/PDF%20FORMATTING%20JOURNAL/MJG/Issue%201%20-2022/original/Journal%20of%20Central%20South%20University\n\n\nhttps://www.springer.com/journal/12613\n\n\nhttps://www.springer.com/journal/12613\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 45-52 \n\n\n\n\n\n\n\n \nCite The Article: Pukrozo Keyho, Watitemsu Imchen, Meribemo Yanthan, Imomeren Ao, John K. Angami  (2022). Mg-Rich Ultramafics of The Naga Hills Ophiolite, \n\n\n\nNagaland, India:  A Potential Substitute as Basic Flux in Metallurgical Industries. Malaysian Journal of Geosciences, 6(1): 45-52. \n \n\n\n\nShen, F.M., Wen, Q.L, Jiang, X., Zheng, H.Y., Gao, Q.J., Hu, Y.P., Wei, G., Shen, \nY.S., Wang, L., 2015. Proper methods of adding MgO-bearing flux in \nblast furnace process. In: Conference proceeding of AISTech 2015 - \nThe Iron and Steel Technology Conference and Exposition. 4-7 May \n2015. Cleveland Convention Center. Cleveland. USA.  \n\n\n\nSingh, A.K., Chung, S.L., Bikramaditya, R.K., Lee, H.Y., 2017. New U-Pb \nzircon ages of plagiogranites from the Nagaland-Manipur \nOphiolites, Indo-Myanmar Orogenic Belt, NE India. Journal of the \nGeological Society, 174 (1), Pp. 170\u2013179. \n\n\n\nSugiyama, T., Shirouchi, S., Tsuchi, O., Onoda, M., Fujita, I., 1983. High-\ntemperature reduction and softening properties of pellets with \nmagnesite., 23, Pp. 153\u2013160. \n\n\n\nThong, G.T., Imchen, W., Walling, T., 2022. Geochemical evidences on the \n\n\n\norigin and paleo-oceanic depositional setting of the Naga Hills \nOphiolite cherts, North East India. Geological Journal, 57 (8), Pp. \n3114-3134. \n\n\n\nYao, L., Ren, S., Wang, X., Liu, Q., Dong, L., Yang, J., Liu, 2016. Effect of Al2O3, \nMgO, and CaO/SiO2 on viscosity of high alumina blast furnace slag. \nSteel Research International, 87, Pp. 241-49. \n\n\n\nYusfin, Y.S., Chernousov, P.I., Garten, V.,  Karpov, Y., Petelin A., 1999. The \nrole of alkalis and conserving resources in blast-furnace \nsmelting. Metallurgist, 43, Pp. 54\u201358. \nhttps://doi.org/10.1007/BF02463518. \n\n\n\nZhu, D., Chun, T., Pan, J., Zhang, J., 2013. Influence of basicity and MgO \ncontent on metallurgical performances of Brazilian specularite \npellets, International Journal of Mineral Processing, 125, Pp. 51\u201360.\n\n\n\n \n\n\n\n\nhttp://www.speciation.net/Database/Journals/Transactions-of-the-Iron-and-Steel-Institute-of-Japan-;i1645\n\n\nhttps://www.semanticscholar.org/author/Y.-Karpov/78409081\n\n\nhttps://www.semanticscholar.org/author/A.-Petelin/6749060\n\n\nhttps://doi.org/10.1007/BF02463518\n\n\n\n"
"\n\nMalaysian Journal Geosciences (MJG) 1(1) (2017) 34-37\n\n\n\nContents List available at RAZI Publishing\n\n\n\nMalaysian Journal of Geosciences\nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/\n\n\n\nPAHANG FLOOD DISASTER : THE POTENTIAL FLOOD DRIVERS\n\n\n\nRahmah Elfithri, Syamimi Halimshah, Md Pauzi Abdullah, Mazlin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Maimon Abdullah, \nLee Yook Heng, Khairul Nizam Ahmad Maulud, Syafinaz Salleh, Maizurah Maizan & Nurlina Mohamad Ramzan \nInstitute for Environment and Development (LESTARI) ,Faculty of Science and Technology (FST),Faculty of Social Science and \nHumanities(FSSK),Faculty of Engineering and Built Environment (FKAB),Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia\n\n\n\nARTICLE DETAILS ABSTRACT\nArticle history:\n\n\n\nReceived 22 January 2017  \nAccepted 03 February 2017  \nAvailable online 05 February 2017\n\n\n\nKeywords:\n\n\n\nFlood Potential Analysis (FPAn) \nMulti-Criteria Evaluation (MCE) Sabah, \nMalaysia\n\n\n\nThe northeast monsoon which occurs from November to March carries heavy rainfall which always result in flood \nespecially to the east coast of Peninsular Malaysia. Pahang was one of the state that severely affected by this flood. \nAlthough the heavy rain is the main driver of  the flood but human being cannot  ignore the other flood drivers \nespecially the river and its nearby environment circumstance which regard the flood event. The objective of this \nstudy was to determine the other flood drivers especially the river and its nearby environment circumstance \nwhich regard the flood event. The methodologies used in this study involved data collection through literature \nreviews and flood reports from Drainage and Irrigation Department (DID) and districts and interview to gather \nmore information and verify the issues and other related drivers. The possible drivers of flooding in Pahang \nthat occurred are as follows : 1) High rain intensity (>60 mm/hour, 200 \u2013 450 mm/day) at the upstream that \nincreases the quantity of water in the river and causes it to overflow 2) Water from area that has no drainage \nconnection with the river (lowland, recessed and swamp area) was also flowing out and contributed to the flood 3) \nThe size of the irrigation system is insufficient to bear the water flow rate and the tributary network is unable to \nwithstand the large runoff 4) Increased  reclaim of wetland area for development that causes irrigation system to \nbe narrowed and obstructed for the water to flowing in to the tributaries 5) Prevalent forest clearing and logging \nactivity increased the water non-absorbent area 6) Ground cutting for development purpose decreased the rain \nwater absorption into the ground and increased surface water runoff, thus causes the watershed area decrease \nin its ability to hold water 7) Shallow estuary caused by high sedimentation from various activities leads to slow \nwater conduction flowing from flood area to the sea 8) Most residential area are located at lowland and \nflood plain region coupled with bad irrigation system especially in big residential area, thus \nincreased the flood risk. Each possible driver of flooding in Pahang that occurred in 2014 has to be discussed \nfurther in term of the responsible stakeholders who should involve in the management and maintenance. The \nheavy rainfall from northeast monsoon which was the main flood driver cannot be avoided but some flood drivers \nespecially the river and its nearby environment that may contribute to higher magnitude of flood can be fixed and \ncontrolled by human\n\n\n\nINTRODUCTION\nMalaysia had a hot, wet  humid equatorial climate regime and the most \nobvious attribute is its heavy year-round rainfall ranging from 1, 500 mm to \nmore than 3, 500 mm annually ( Dale, 1974). Peninsular Malaysia is located \nwithin an area which receives the seasonal monsoon winds especially \nthe east coast states of Johor, Pahang, Terengganu and Kelantan which \noccur from November to March (Cheang, 1987 ; Chan 1989). During the \nmonsoon season, the rainfall collected can be reached out to 610 mm in a \nday (Malaysian National Committee, 1976). This Northeast monsoon wind \nwhich comes from the Asian interior bring heavy rain to the East Coast as \nthey are moisture-laden after crossing the South China Sea and the Gulf of \nSiam and often result in flood to the east coast states (Chan, 1995).\nPahang as one of the state located within the East Coast of Peninsular \nMalaysia, experienced this seasonal flood almost every year but the flood \nmagnitude is different for each year. The flood in 1971 was one of the worst \nflood (with recorded information) experienced by the east coast states and \nPahang was severely affected by it with great economic losses and death toll \nof 24 (Chia, 2004). In 2014, the East Coast re-experienced big flood after \nabout 43 years since the 1971 flood. In Pahang, all districts were swept by \nthe flood resulted in almost 68, 000 flood victims had to move out from their \nhouses and about RM 73, 000, 000.00 losses suffered by DID in form of river \nstructure damages. The flood occurred from 22/12/2014 to 15/01/2014 \nin Rompin, Pekan, Kuantan, Maran, Temerloh, Bera, Jerantut, Raub and \nLipis, 05-06/11/2014 and 27/11/2014 in Cameron Highland as well as \n26/10/2014, 05/1/2014, 02/12/2014 and 11/01/2015 in Bentong district \n\n\n\n(DID, 2014).\nAlthough the heavy rainfall brought by the Northeast monsoon wind is \nthe main reason of the flood, but human being cannot  ignore the other \npotential or possible flood factors and drivers that contribute to higher \nflood magnitude. The objective of this study was to determine the other \nflood drivers besides heavy rainfall especially the river and its nearby \nenvironment circumstance which regard the flood event. \nMETHOD\nStudy Area\n\n\n\nThe state of Pahang can be divided into five major river systems, namely, \nPahang, Kuantan, Bebar, Rompin and Endau River. All the five major river \nsystems flow in an easternly direction and ultimately discharge into the \nSouth China Sea (Kajian Sumber Air Negara, 2000). Although the flood \ndrivers discussed in this paper covered the whole Pahang state but focus \nwill be given to the Pahang River Basin as it is the largest basin and covers \n75% of the state. \nThe Pahang River Basin is located in the Peninsular Malaysia between \nlatitude 2\u00b0 48\u201945\u201d - 3\u00b0 40\u2019 24\u201dN and longitude 101\u00b0 16\u2019 31\u201d - 103\u00b0 29\u2019 34\u201dE \nwith total area of 27, 000 km2 and 440 km length. It is a confluence of the \nJelai River and Tembeling River from the upstream which join together at \nKuala Tembeling that situated about 304 km from the river mouth at the \n\n\n\neast coast of Pahang state (Muhammad, 2007). Jelai River is one of the \ntwo main tributaries which drain from the eastern slope of Banjaran and \nTitiwangsa Mountain, the foot of Central Mountain Range. The Central \nMountain Range is the largest mountain in the Peninsular Malaysia and \nseparates the Peninsular into an eastern and western part. Tembeling River \noriginates from the Besar Mountain Range in the Northeast of the basin. \nFor the purpose of fixing its length, however, the Tembeling and Pahang \nare considered as one river (Takeuchi,et al 2007). Other main tributaries \nof the River Pahang are Semantan, Teriang, Bera, Lepar, Gelugor, and Chini \n(Ashenafi, 2010). Flood of 2014 in Pahang involved all districts (Figure 1) \nwhereby Temerloh and Pekan were two districts being affected the most. \n\n\n\nMethodology\nThe methodology used in this study involved data collection through \nliterature reviews from previous studies by researchers around the world \nand flood reports from Drainage and Irrigation Department (DID) of \nprevious years and reports of  Pahang districts offices. Interviews with \ncommunity and stakeholders were also being carried out to gather more \n\n\n\ninformation and verify the issues and other related drivers. \n\n\n\nCite this article as: Fpahang Flood Disaster : The Potential Flood Drivers Rahmah Elfithri, Syamimi Halimshah,, Md Pauzi Abdullah, Mazlin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Maimon \nAbdullah, Lee Yook Heng, Khairul Nizam Ahmad Maulud, Syafinaz Salleh, Maizurah Maizan & Nurlina Mohamad Ramzan/ Mal. J. Geo 1(1)  (2017) 34-37\n\n\n\nISSN: 2521-0920  (Print) \nISSN:  2521-0602 (Online)\n\n\n\nhttps://doi.org/10.26480/mjg.01.2017.34.37\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\n http://www.razipublishing.com/journals/galeri-warisan-sains-gws/ \n\n\nhttp://doi.org/10.26480/mjg.01.2017.34.37\n\n\nhttps://doi.org/10.26480/mjg.01.2017.34.37\n\n\n\n\n\n\nRahmah Elfithri, Syamimi Halimshah,, Md Pauzi Abdullah, Mazlin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Maimon Abdullah, Lee Yook Heng, Khairul Nizam \nAhmad Maulud, Syafinaz Salleh, Maizurah Maizan & Nurlina Mohamad Ramzan / Malaysian Journal of Geosciences 1(1) (2017) 34-37\n\n\n\n35\n\n\n\nCite this article as: Fpahang Flood Disaster : The Potential Flood Drivers Rahmah Elfithri, Syamimi Halimshah,, Md Pauzi Abdullah, Mazlin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Maimon \nAbdullah, Lee Yook Heng, Khairul Nizam Ahmad Maulud, Syafinaz Salleh, Maizurah Maizan & Nurlina Mohamad Ramzan/ Mal. J. Geo 1(1)  (2017) 34-37\n\n\n\n\n\n\n\nC local authority need to generate a new resettlement area at higher region \nto reduce the number of flood victims and reduce the flood  risk. Fifthly, the \ndeveloper need to ensure the possible flood level is taken into consideration \nwhen setting up the floor finish level in any future project. Lastly, to carry \nout the works in flood mitigation plan (Rancangan Tebatan Banjir) which \nincludes bunding of rivers, flood wall and storage ponds of flood attenuation. \nDepartment of Irrigation and Drainage of Temerloh district suggested that \nthe deepening and digging area of Pahang River should starting from Lipis \nto Jerantut to Temerloh (Figure 2). The digging or deepening process should \nnot disrupt the river banks and the mean depth is between 2.0 m to 3.0 m \n(Figure 3). Flood report from Pekan district suggested that the land clearing \narea for agriculture should be limited and reduced since Pahang experienced \nwide land clearing for agriculture especially oil palm plantation.\nBoth flood mitigation measures, structural and non-structural measures \nshould be planned and carried out. Non-structural measures aim is reducing \n\n\n\nthe flood magnitude through the management of catchment conditions as \nwell as reducing the flood damage. Integrated River Basin Management \n(IRBM) is one of the non-structural measures which can be used in \nPahang. IRBM is understood to mean co-ordinated planning, development, \nmanagement and use of land, water and related natural resources within \nhydrologic boundaries (Nigel 2004). In IRBM concept, the whole river \nbasin is planned in an integrated manner and all factors are taken into \nconsideration when a certain development plan is proposed. Factors like \nzoning for river corridors, riparian areas, natural flood plains, conservation \nof wetlands, storage ponds, etc. will be taken into consideration when \npreparing flood management plans (Chia 2004). In IRBM, both government \nand non-government organizations including public people should play \ntheir role in order to preserve the river basin since it is affected by the \nactivities done by human.\nFigure 2. Proposed dredged area for Pahang River Basin\n\n\n\n\n\n\n\n\nRahmah Elfithri, Syamimi Halimshah,, Md Pauzi Abdullah, Mazlin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Maimon Abdullah, Lee Yook Heng, Khairul Nizam \nAhmad Maulud, Syafinaz Salleh, Maizurah Maizan & Nurlina Mohamad Ramzan / Malaysian Journal of Geosciences 1(1) (2017) 34-37\n\n\n\n36\n\n\n\nCite this article as: Fpahang Flood Disaster : The Potential Flood Drivers Rahmah Elfithri, Syamimi Halimshah,, Md Pauzi Abdullah, Mazlin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Maimon \nAbdullah, Lee Yook Heng, Khairul Nizam Ahmad Maulud, Syafinaz Salleh, Maizurah Maizan & Nurlina Mohamad Ramzan/ Mal. J. Geo 1(1)  (2017) 34-37\n\n\n\nREFERENCES\nAshenafi Wondimu Tekolla, 2010. Rainfall and flood frequency analysis for \nPahang River Basin, Malaysia. Master of Science Thesis in Water Resources \nEngineering. Lund University, Sweden. \nChan, N.W. 1989. \u201cMonsoon effects on the rainfall patterns in the east coast \nof Peninsular Malaysia\u201d, Proceedings of the 15th Conference of the New \nZealand Geographical Society, Dunedin, 21-24, pp. 239-45.\n\n\n\nChan, N.W. Chan. 1995. \u201cFlood disaster management in Malaysia: an \nevaluation of the effectiveness of government resettlement schemes\u201d, \nDisaster Prevention and Management: An International Journal, Vol. 4, Issue \n4 pp. 22 - 29\nCheang, B. K.1987. \u201cShort- and long-range monsoon prediction in South-\n\n\n\neast Asia\u201d, in Fein, J.S. and Stephens, P.L. (Eds), Monsoons, John Wiley, New \nYork.\nChia, C.W. 2004. Managing flood problems in Malaysia. Bulletin Ingenieur, \n22 (6\u20138), 38\u201343, Board of Engineers Malaysia, Kuala Lumpur \nDale, W. L. 1974. \u201cThe rainfall of Malaya: Part I\u201d, in Ooi, J.B. and Chia, L.S. \n(Eds), The Climate of Malaysia and Singapore, Longman, Singapore, pp. 132-\n44.\nDepartment of Irrigation and Drainage (DID) Reports 1982 \u2013 2014. \nDr. Nigel Watson. 2004. Integrated river basin management : a case for \ncollaboration. Intl. J. River basin management Vol. 2 No. 4 pp 243-257. \nKajian Sumber Air Negara. 2000. Jilid 16 (Pahang). Kuala Lumpur.\n\n\n\nMalaysian National Committee. 1976. \u201cFlood control in Malaysia\u201d, in Framji, \n\n\n\n\n\n\n\n\nRahmah Elfithri, Syamimi Halimshah,, Md Pauzi Abdullah, Mazlin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Maimon Abdullah, Lee Yook Heng, Khairul Nizam \nAhmad Maulud, Syafinaz Salleh, Maizurah Maizan & Nurlina Mohamad Ramzan / Malaysian Journal of Geosciences 1(1) (2017) 34-37\n\n\n\n37\n\n\n\nCite this article as: Fpahang Flood Disaster : The Potential Flood Drivers Rahmah Elfithri, Syamimi Halimshah,, Md Pauzi Abdullah, Mazlin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Maimon \nAbdullah, Lee Yook Heng, Khairul Nizam Ahmad Maulud, Syafinaz Salleh, Maizurah Maizan & Nurlina Mohamad Ramzan/ Mal. J. Geo 1(1)  (2017) 34-37\n\n\n\nK.K. and Garg, B.C. (Eds), Flood Control in the World: A Global Review, Vol. 2, \nInternational Commission on Irrigation  and Drainage, New Delhi, pp. 561-\n9.\nMohd Hafifi M. N., Suriyani Awang, Ahmad Jamalluddin S., Nasehir Khan E. \nM. Y., Azman Mat J., Mohd Aminur R, Mohd Amiruddin A., Aminuddin Ab \nG. 2015. Sediment transport dynamic in a meandering fluvial system: case \nstudy of sg chini. ARPN Journal of Engineering and Applied Sciences. Asian \nResearch Publishing Network (ARPN).\nMuhammad, G. B. 2007. Hydrology and Water Quality Assessment of the \nLake Chini\u2019s Feeder Rivers, Pahang, Faculty of Science and Technology, \nSchool of Environment and Natural Resource Science, University of \nKebangsaan Malaysia \nNoorazuan Md H., Sulong Muhamad, Kadaruddin A. and Norhayati Yahya. \n2011. Pembangunan tanah hutan dan fenomena banjir kilat: Kes Sungai \nLembing, Pahang. (Land forest development and flash flood phenomenon: A \ncase of Sungai Lembing, Pahang. Journal e-Bangi.Volume 6, Number 2, 155-\n169. \nPan I. L., Muhd. Barzani G., Mohd. Ekhwan T., Sahibin Abd. R. and  Khairul \nA. K. 2011. Hydrological pattern of pahang river basin and their relation to \nflood historical event. Journal e-Bangi.  Volume 6, Number 1, 29-37.\nWahl N.A., W\u00f6llecke B., Bens O., H\u00fcttl R.F. 2005 : Can forest transformation \nhelp reducing floods in forested water\u00acsheds? Certain aspects on soil \nhydraulics and organic matter properties. Journal of Physics and Chemistry \nof Earth, 30: 611\u2013621\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 85-92 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2021.85.92 \n\n\n\n \nCite The Article: Kayode Festus Oyedele, Olawale Babatunde Olatinsu (2021). Geophysical Evaluation of Subsurface Protective Capacity and Groundwater Prospect in \n\n\n\nA Typical Sedimentary Zone, Eastern Dahomey Basin Using Electrical Resistivity Technique . Malaysian Journal of Geosciences, 5(2): 85-92. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nREVIEW ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2021.85.92 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGEOPHYSICAL EVALUATION OF SUBSURFACE PROTECTIVE CAPACITY AND \nGROUNDWATER PROSPECT IN A TYPICAL SEDIMENTARY ZONE, EASTERN \nDAHOMEY BASIN USING ELECTRICAL RESISTIVITY TECHNIQUE \n \nKayode Festus Oyedelea, Olawale Babatunde Olatinsub* \n \na Department of Geoscience, Faculty of Science, University of Lagos, Akoka, Lagos, Nigeria. \nb Department of Physics, Faculty of Science, University of Lagos, Akoka, Lagos, Nigeria. \n*Corresponding Author Email: oolatinsu@unilag.edu.ng \n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 17 September 2021 \nAccepted 20 October 2021 \nAvailable online 01 November 2021 \n\n\n\n\n\n\n\nSubsurface protective capacity evaluation is important in groundwater prospecting. With the aid of Dar-\nZarrouk parameters which show direct relationship with contaminants infiltration time and transmissivity, \njoint interpretation of vertical electrical sounding (VES) and 2-D resistivity imaging were employed to \nevaluate overburden protective capacity and groundwater potentials at Mowe in Obafemi-Owode LGA, \nsouthwest Nigeria.  Total longitudinal conductance S, total transverse resistance T, longitudinal resistivity \u03c1L \nand transverse resistivity \u03c1T were computed. Sand/clayey sand was delineated at 70% of the area either as \nconfined aquifers (78%) or unconfined aquifers (22%). S values in 87% of locations has moderate protective \nrating (0.2071 - 0.5630), one location has good protective rating (0.7736), others have weak protective \nratings (0.1053 - 0.1814). The entire area is characterized by low overburden thickness H (7.9 - 25.6 m), \nwhich agrees with a correlation coefficient of 0.58 between S and H. T values is in the range 235 - 2046 \u03a9m2 \n\n\n\nwith high values indicating high transmissivity zones, suggesting high probability of pollutant contamination \nof aquifer, also agreeing with moderate correlation coefficient of 0.69 between T and H. The study area was \ngrouped into three regions on the cross plot of T versus S: low S and high T \u2013 poor protection and high \ncontaminant transmission; moderate/good S and low T \u2013 good protection with low contaminants \ntransmission; moderate/low S and low T - weak protective capacity and poor transmissivity. Excellent \ncorrelation (0.99) exists between \u03c1L and \u03c1T, with \u03c1T slightly higher than \u03c1L, and low \u03c1L signifying the presence \nof conductive clayey materials in the overburden. \n\n\n\nKEYWORDS \n\n\n\nOverburden, resistivity, Dar-Zarrouk parameters, correlation, evaluation \n\n\n\n1. INTRODUCTION \n\n\n\nA thorough inventory of groundwater and surface water use has revealed \nthe worldwide importance of groundwater in human existence \n(Maduabuchi, 2004; Turner and Wurster, 2017; Walker et al., 2019). \nSeveral reasons account for the advantages of groundwater over surface \nwater: (i) it\u2019s existence relative to where water is demanded; (ii) it's \nsuperior intrinsic state, generally satisfactory for potable supplies for \nmost uses; (iii) where high purity level is required, treatment is usually \nminimal; and (iv) the rather low capital cost for commercial processing \nand production. Furthermore, the development phases, to square up with \nincreasing consumer needs, is usually more readily accomplished for \ngroundwater than for surface water (Cosgrove and Loucks, 2015). \nHowever, most often the importance of groundwater in the water supply \nprogramme is greatly underestimated. In most countries across the globe, \ngroundwater resources are being seriously and rapidly degraded in terms \nof quality and quantity primarily due to human activities, ecosystem and \nlandscape changes, sedimentation, pollution, over-abstraction and climate \nchanges (Vandas et al., 2002; BGS, 2008; Nel et al., 2009; Khatri and Tyagi, \n2015; Zereg et al., 2018). In many countries around the world, population \ngrowth, economic and technological advancement etc., have activated \nunconventional variations in the condition of groundwater systems, which \n\n\n\nhas excited an increasing consciousness in the finiteness and susceptibility \nof this vital natural endowment (Bovolo et al., 2009; D\u00f6ll, 2009). \n\n\n\nThe foremost position of groundwater resources is incontrovertible, and \nby extension, their continual preservation and utilization are, therefore, of \ncrucial importance to human survival and multifarious activities. The \nmedium that hosts the resource (groundwater), the aquifer, has an \nimportant role to play in its protection and conservation. In some \ngeological environments, aquifers that can provide sustained economic \nquantity of freshwater are usually located within formations at depths of \nless than a few hundred meters beneath the ground surface. Also, the \nvariability in the nature and characteristics of the aquifer and the covering \nsedimentary materials above it have a strong impact on the quality and \nquantity of groundwater for the various human needs (Konikow and \nNeuzil, 2007). In sedimentary terrain, a contaminant can enter a network \nof conduits in an aquifer and become a widespread health problem. A large \nnumber of reports of diseases can be traceable to or caused by \ncontaminated groundwater.  \n\n\n\nThe electrical resistivity technique offers a reliable and relatively low-cost \n\n\n\napproach to hydrogeological investigation and subsurface \n\n\n\ncharacterization (Christensen and S\u00f8rensen 1998; Olayinka and \n\n\n\n\nmailto:oolatinsu@unilag.edu.ng\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 85-92 \n\n\n\n\n\n\n\n \nCite The Article: Kayode Festus Oyedele, Olawale Babatunde Olatinsu (2021). Geophysical Evaluation of Subsurface Protective Capacity and Groundwater Prospect in \n\n\n\nA Typical Sedimentary Zone, Eastern Dahomey Basin Using Electrical Resistivity Technique . Malaysian Journal of Geosciences, 5(2): 85-92. \n\n\n\n\n\n\n\nYaramanci, 2000; S\u00f8rensen et al., 2005; Guptal et al., 2015).  It has been \n\n\n\nused extensively in groundwater mapping for investigation of the \n\n\n\nvulnerability of aquifers to possible contamination (Mousatov and Ryjov, \n\n\n\n2005; Worall and Kolpin, 2003; Machiwal et al., 2018; Oroji, 2018; Ayuk, \n\n\n\n2019). This technique is also capable of mapping both low and high \n\n\n\nresistive formations, thereby making it a valuable tool for studies relating \n\n\n\nto groundwater protection (Braga et al., 2006, 2008; Casa et al., 2008; \n\n\n\nAyuk, 2019; Hasan et al., 2019).  \n\n\n\nSeveral authors have established the application of Dar Zarrouk \nparameters, longitudinal unit conductance (S), and transverse unit \nresistance (T), in aquifer protection studies as well as for the evaluation of \nhydrogeologic and hydrogeophysical characteristics of aquifers (Zohdy, \n1965, 1974; Henriet, 1976; Singh et al., 2004; Braga et al., 2006; Batayneh, \n2013). Henriet suggested the possibility of direct proportionality between \nthe protective capacity of a clayey aquifer overburden and its longitudinal \nunit conductance (Henriet, 1976).  Salem employed the correlation \nbetween fluid transmissivity (product of layer thickness and permeability) \nand electric transverse unit resistance (product of layer thickness and \nresistivity) to delineate zones of high potentials in aquifers and \nhydrocarbon reservoirs (Salem, 1999). The protection of aquifers against \nthe infiltration of contaminants, is closely related to the heterogeneous \nproperties of clay cover which acts as a cap or seal. The resistivity of \nformation can be greatly affected by the proportion of clay (clay content). \nWhile permeable formation containing clayey materials manifests low \nresistivities, those with sandy permeable usually have moderately high \nresistivities (Archie, 1942). \n\n\n\nMaillet proposed the term \u201cDar-Zarrouk\u201d (D-Z) parameters into the \ngeophysical literature on electrical prospecting to establish the \nrelationships between subsurface layer resistivity and thickness to define \nthe longitudinal unit conductance (Maillet, 1947). \n\n\n\niii hS \uf072=\n                                                                                                                                  (1) \n\n\n\nand the transverse resistance  \n\n\n\niii hT \uf072=\n                                                                                                                                      (2) \n\n\n\nwhere \u03c1i and hi are the electrical resistivity and thickness of the ith layer, \nrespectively.  \n\n\n\nFrom Equations (1) and (2), for n-1 layers (excluding the aquifer), the total \nlongitudinal conductance and the total transverse resistance respectively \nare: \n\n\n\n\uf0e5\n\u2212\n\n\n\n=\n\n\n\n=\n1\n\n\n\n1\n\n\n\nn\n\n\n\ni\n\n\n\niit hS \uf072\n                                                                                                                               (4) \n\n\n\n\uf0e5\n\u2212\n\n\n\n=\n\n\n\n=\n1\n\n\n\n1\n\n\n\nn\n\n\n\ni\n\n\n\niit hT \uf072\n                                                                                                                                    (5) \n\n\n\nThe longitudinal resistivity in the direction parallel to the subsurface \nlayers and the transverse resistivity in the perpendicular direction to the \nsubsurface layers are: \n\n\n\nt\n\n\n\nL\nS\n\n\n\nH\n=\uf072\n\n\n\n                                                                                                                                       (6) \n\n\n\nH\n\n\n\nTt\n\n\n\nT =\uf072\n                                                                                                                                          (7) \n\n\n\nwhere \n\uf0e5\n\u2212\n\n\n\n=\n\n\n\n=\n1\n\n\n\n1\n\n\n\nn\n\n\n\ni\n\n\n\nihH\n\n\n\n.   \n\n\n\nHenriet established the direct applications of the D-Z parameters in the \n\n\n\ninvestigation of aquifer protection and hydrologic/hydrogeologic \n\n\n\ncharacteristics of aquifers (Henriet, 1976). Other workers have also \n\n\n\nconfirmed the implications of D-Z parameters on the hydrological \n\n\n\nattributes of aquifers in several geologic formations (Niwas and Singhal, \n\n\n\n1981; 1985; Khalil, 2009; Mondal et al., 2013). Several studies aimed at \n\n\n\naquifer protection studies and groundwater prospect evaluation have \n\n\n\nbeen executed in both sedimentary and basement complex regions in \n\n\n\nNigeria. Due to the need to establish certain facilities such as automobile \n\n\n\nmechanic village, abattoir and meat processing factory at Aboru \n\n\n\nresidential estate in Lagos, Nigeria, necessitated a geophysical study aim \n\n\n\nat using longitudinal unit conductance to ascertain the vulnerability of the \n\n\n\nsubsurface aquifers in the study area against the expected long-term \n\n\n\nanthropogenic impacts of the proposed facilities on the groundwater \n\n\n\nsystem (Ayuk, 2019). The total longitudinal unit conductance was found \n\n\n\nto be in the range 0.0164 - 0.1168 mhos indicating a poor/weak protective \n\n\n\ncapacity rating across the study area. As such, the establishment of the \n\n\n\nproposed service facilities in the study area was not recommended due to \n\n\n\nthe possible long-term effects of their operations on aquifer system. Total \n\n\n\nlongitudinal conductance obtained from VES interpretations conducted \n\n\n\nnear forty well bores was used to evaluate the overburden protective \n\n\n\ncapacity and corrosion index in a crystalline basement complex of \n\n\n\nOgbomoso North, southwestern Nigeria (Adabanija and Ajibade, 2020). \n\n\n\nResults obtained from the study revealed a broad range of overburden \n\n\n\nprotective capacity from excellent to good, fair and poor ratings. In \n\n\n\naddition, based on Langelier saturation index (LSI) and aggressive index \n\n\n\n(AI), areas where the groundwater is strongly corrosive and non-corrosive \n\n\n\nwere clearly delineated. Umar and Igwe evaluated the characteristics of \n\n\n\ngranular sandstone aquifers within Lafia, North Central Nigeria for \n\n\n\ngroundwater supply (Umar and Igwe, 2019). It was found that the \n\n\n\ngroundwater resources in about 82% of the area was not fit for human \n\n\n\nconsumption due to high corrosive content. In this present study, \n\n\n\ncomputed D-Z parameters were employed to delineate the overburden \n\n\n\nprotective capacity ratings and possible aquifer units at the study location. \n\n\n\nThe study area and its environs is socially and economically active, and \n\n\n\nfast developing. Thus, there is the necessity to meet up with this vast and \n\n\n\nrapid development in terms of making available fresh and potable water \n\n\n\nsupply for human consumption and other domestic needs. \n\n\n\n2. DESCRIPTION OF THE STUDY AREA \n\n\n\nThe study area is located within Redemption Camp, in Mowe town, Ogun \n\n\n\nState, southwest Nigeria. Ogun State lie within Latitudes 6o 41' N - 7o 9' N \n\n\n\nand Longitudes 3o 16' E - 3o 41' in the humid tropical rain forest region of \n\n\n\nNigeria characterized by two climatic seasons; rainy season (March - \n\n\n\nOctober) and dry season (November - February). The average annual \n\n\n\nrainfall is about 1300 mm; annual evapo-transpiration is about 188 mm \n\n\n\nwith elevation ranging from 40 m in the South to 154 m in the North. Mowe \n\n\n\ntown lies approximately 45 km North-East of Lagos and it is accessible \n\n\n\nthrough the Lagos-Ibadan expressway. It shares a boundary with Ibafo in \n\n\n\nthe west and Shagamu in the east. The geologic units of Ogun state \n\n\n\ncomprise of the crystalline basement rocks and sedimentary rocks of the \n\n\n\nwide-ranging Dahomey Basin. The geographic extent of the Dahomey \n\n\n\nBasin covers the area from southeastern Ghana across Republics of Togo \n\n\n\nand Benin to the western fringes of the Niger Delta towards the East. \n\n\n\n(Jones and Hockey, 1964; Reyment, 1965; Ogbe 1972; Omatsola and \n\n\n\nAdegoke, 1981). The basin is edged on the west by faults and some tectonic \n\n\n\nstructures directed toward the land and stretches into the Romanche \n\n\n\nfracture zone (Adegoke et al., 1981). Similarly, its eastern border is \n\n\n\ninscribed by the Benin Hinge line.  It is also a significant fault structure \n\n\n\nending the western margin of the Niger Delta Basin (Omatsola and \n\n\n\nAdegoke, 1981; Whiteman, 1982). The Tertiary sediments of the Dahomey \n\n\n\nBasin decreases and are particularly separated from the deposits of the \n\n\n\nNiger Delta Basin next to the Okitipupa ridge of the Basement Complex \n\n\n\n(Omatsola and Adegoke, 1981). Stratigraphic units of the eastern \n\n\n\nDahomey Basin consist of the late Cretaceous mainly sandy strata of the \n\n\n\nAbeokuta Group as the oldest. The overlying Tertiary strata encompasses \n\n\n\nlimestone of the Ewekoro Formation, shale of the Akinbo Formation, \n\n\n\nmudstones/shale of the Oshosun Formation, and sandstone of the Ilaro \n\n\n\nFormation (Figure 1). Other younger strata are the Oligocene to Recent \n\n\n\ncontinental sands of the Benin Formation as well as recent alluvial \n\n\n\nsediments (Russ, 1924; Adegoke 1969; Omatsola and Adegoke, 1981). \n\n\n\nSandstone, limestone and sandy sediments are the aquifer units within \n\n\n\nthese geologic formations. The high annual rainfall usually experienced in \n\n\n\nthe state suggests regular recharge of these aquifer units. While major \n\n\n\nsegment of the study area is underlain by the Abeokuta Group, some \n\n\n\nsegments fall within the transition zone (Figure 1). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 85-92 \n\n\n\n\n\n\n\n \nCite The Article: Kayode Festus Oyedele, Olawale Babatunde Olatinsu (2021). Geophysical Evaluation of Subsurface Protective Capacity and Groundwater Prospect in \n\n\n\nA Typical Sedimentary Zone, Eastern Dahomey Basin Using Electrical Resistivity Technique . Malaysian Journal of Geosciences, 5(2): 85-92. \n\n\n\n\n\n\n\n \nFigure 1: Geological map of Ogun State showing the study location falls \n\n\n\nwithin the Oshosun Formation. \n\n\n\n3.  METHODOLOGY \n\n\n\n3.1 Data Acquisition  \n\n\n\nThe geophysical survey carried out at the study site involved the \nintegration of 2-D constant separation traversing (CST) and the vertical \nelectrical resistivity (VES) techniques since both respond favorably to \nmeasurable parameters that can easily assist in distinguishing an aquifer \nfrom other formations. The PASI terrameter was used for the data \nacquisition and the survey was carried out along five traverses with a \nmaximum spread of 200 m (Figure 2). The Wenner array was employed \nfor the 2-D resistivity imaging and the Schlumberger array for the VES. \nMeasurements were taken at an electrode separation of 10 - 60 m for the \n2-D resistivity imaging.  Six (6) VES sounding points were occupied at \ndistances 60, 75, 90, 105, 120, and 140 m along each traverse line, thereby \nmaking a total number of thirty (30) sounding points altogether (Figure \n2).   \n\n\n\n \nFigure 2: Data acquisition map showing the VES data point outlined on \n\n\n\nthe CST traverses. 6 VES arranged on each of the 5 traverses. \n\n\n\n3.2 Data Processing and Interpretation \n\n\n\nThe VES data were interpreted with the aid of both semi-qualitative and \nquantitative procedures. The quantitative interpretation method \nfacilitates the computation of the resistivity and thickness of the \nsubsurface layers through partial curve matching method with master \ncurves designed for stratified and multi-layered Earth models while the \nqualitative interpretation entails the use of software, correlation of \nanomalous resistivity curves, and classification of the different curves \nbased on the resistivity of the subsurface layers. The interpretation was \nenhanced with the use of computer iteration software \"WinResist\u201d. The \nVES results as output from the processing software were used to produce \n\n\n\na geoelectric section for each of the VES data points to represent the \nvariations in the thickness of overburden materials vertically. Samples of \ncurves obtained from iterated VES data are presented in Figure 3. For the \nCST data, apparent resistivity values were calculated manually using Excel \nsoftware and processed using Diprowin software. The software assigns \nthe bulk data into a set of horizontal and vertical rectangular blocks, with \nsome records in each box. The resistivity of each block is then calculated \nto generate an apparent resistivity pseudo-section.  \n\n\n\n \nFigure 3: Samples of VES curves obtained as the output of computer \n\n\n\niteration using WinResist software. \n\n\n\n4. RESULTS AND DISCUSSION \n\n\n\n4.1 Result \n\n\n\nThe summary of computed Dar-Zarrouk parameters (S, T, \u03c1L and \u03c1T) \nobtained from VES analysis is presented in Table 1. Geoelectric sections \ndeveloped from the interpreted VES data and the inverted 2D resistivity \nstructure from CST profiling are presented in pairs in Figures 4 - 8. In each \npair, the first figure (upper) shows the respective sequence of subsurface \nlayer with resistivity and thickness/depth along with a legend depicting \nthe constituting geologic materials. While the second figure (lower) \ndisplays the colour-coded lateral and vertical resistivity distribution of the \nsubsurface layer along with colour scale bar. Good agreement exists \nbetween the two techniques in all the five traverses investigated. Across \nthe traverses, the aquifer unit is delineated in the last subsurface strata \neither as the third or fourth layer with indeterminable thickness since \ncurrent was terminated in this region.  \n\n\n\n\n\n\n\nFigure 4: Geoelectric section and 2D resistivity structure along Traverse \n1 consisting of VES 1 \u2013 6 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 85-92 \n\n\n\n\n\n\n\n \nCite The Article: Kayode Festus Oyedele, Olawale Babatunde Olatinsu (2021). Geophysical Evaluation of Subsurface Protective Capacity and Groundwater Prospect in \n\n\n\nA Typical Sedimentary Zone, Eastern Dahomey Basin Using Electrical Resistivity Technique . Malaysian Journal of Geosciences, 5(2): 85-92. \n\n\n\n\n\n\n\nTable 1: Summary of computed Dar-Zarrouk parameters from VES analysis \n\n\n\nVES Overburden \n\n\n\nLongitudinal \n\n\n\nConductance \n\n\n\nS (mho) \n\n\n\nOverburden \n\n\n\nTransverse \n\n\n\nResistance \n\n\n\nT (\u03a9m2) \n\n\n\nOverburden \n\n\n\nmaterials \nwithout topsoil \n\n\n\nLongitudinal \n\n\n\nResistivity \u03c1L \n(\u03a9m) \n\n\n\nTransverse \n\n\n\nResistivity \u03c1T \n(\u03a9m) \n\n\n\nOverburden \n\n\n\nthickness \n\n\n\nH (m) \n\n\n\nInferred \n\n\n\nAquifer \n\n\n\nType \n\n\n\n1 0.2360 734 Clayey sand 53.81 57.80 12.7 Sand \n\n\n\n2 0.6560 1136 Sandy clay 39.02 44.40 25.6 Sand \n\n\n\n3 0.3952 398 Clayey sand 30.61 32.85 12.1 Clayey sand \n\n\n\n4 0.3460 369 Clayey sand 32.08 33.27 11.1 Clayey sand \n\n\n\n5 0.2272 1508 Clayey sand 80.53 82.40 18.3 Sandy clay \n\n\n\n6 0.5029 862 Sandy clay 41.36 41.43 20.8 Sandy clay \n\n\n\n7 0.2061 366 Sandy clay 41.72 42.56 8.6 nil \n\n\n\n8 0.2693 427 Sandy clay 38.99 40.67 10.5 nil \n\n\n\n9 0.5921 235 Clay 19.76 20.08 11.7 Sandy clay \n\n\n\n10 0.4317 356 Clay 28.49 28.94 12.3 Sandy clay \n\n\n\n11 0.2071 530 Clayey sand 47.79 53.52 9.9 Sand \n\n\n\n12 0.2161 643 Clayey sand 52.74 56.42 11.4 Sand \n\n\n\n13 0.5705 410 Clay 26.64 26.94 15.2 Clayey sand \n\n\n\n14 0.1814 616 Sandy clay 57.87 58.62 10.5 Clayey sand \n\n\n\n15 0.2893 2046 Clayey sand 84.00 84.22 24.3 Sand \n\n\n\n16 0.2489 755 Clayey sand 54.25 55.93 13.5 Sand \n\n\n\n17 0.1486 438 Clayey sand 53.17 55.38 7.9 Sand \n\n\n\n18 0.3383 1150 Clayey sand 57.95 58.65 19.6 Sand \n\n\n\n19 0.3850 679 Sandy clay 41.82 42.18 16.1 nil \n\n\n\n20 0.4016 644 Sandy clay 39.84 40.25 16 nil \n\n\n\n21 0.1248 572 Clayey sand 67.29 68.10 8.4 nil \n\n\n\n22 0.2490 734 Sandy clay 54.21 54.33 13.5 Clayey sand \n\n\n\n23 0.4196 1316 Sandy clay 56.00 56.00 23.5 Sand \n\n\n\n24 0.5630 727 Sandy clay 35.70 36.16 20.1 Sand \n\n\n\n25 0.2725 932 Clayey sand 57.24 59.76 15.6 Sand \n\n\n\n26 0.3718 273 Clay/Clayey sand 26.90 27.30 10 Sand \n\n\n\n27 0.7736 466 Clay 24.43 24.63 18.9 Clayey sand \n\n\n\n28 0.4417 740 Sandy clay 40.75 41.11 18 Clayey sand \n\n\n\n29 0.1053 1143 Clayey sand 103.48 104.84 10.9 Clayey sand \n\n\n\n30 0.4945 811 Sandy clay 40.44 40.55 20 Clayey sand \n\n\n\n\n\n\n\n \nFigure 5: Geoelectric section and 2D resistivity structure along Traverse \n\n\n\n2 consisting of VES 7 - 12. \n\n\n\n\n\n\n\n \nFigure 6: Geoelectric section and 2D resistivity structure along Traverse \n\n\n\n3 consisting of VES 13 - 18. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 85-92 \n\n\n\n\n\n\n\n \nCite The Article: Kayode Festus Oyedele, Olawale Babatunde Olatinsu (2021). Geophysical Evaluation of Subsurface Protective Capacity and Groundwater Prospect in \n\n\n\nA Typical Sedimentary Zone, Eastern Dahomey Basin Using Electrical Resistivity Technique . Malaysian Journal of Geosciences, 5(2): 85-92. \n\n\n\n\n\n\n\n\n\n\n\n \nFigure 7: Geoelectric section and 2D resistivity structure along Traverse \n\n\n\n4 consisting of VES 19 - 24. \n\n\n\n\n\n\n\n \nFigure 8: Geoelectric section along and 2D resistivity structure along \n\n\n\nTraverse 5 consisting of VES 25 - 30 \n\n\n\n5. DISCUSSION \n\n\n\n5.2.1 Aquifer potentials  \n\n\n\nThe characteristics of any subsurface medium that would serve as an \n\n\n\naquifer largely depend on its nature (i.e. constituent geologic material) and \ngeometry. Porous and permeable subsurface materials such as sand give \nthe idea of the water storage capacity of the aquifer as well the capacity to \ntransmit water. But porosity can be greatly reduced with the presence of \nmaterials such as clay, silt, etc. (Konikow et al., 2001). In this study, sand \nand clayey sand were delineated as the last subsurface layer in 12 (40%) \nand 9 (30%) respectively, of the traverses, with the indeterminable \nthickness (invariably due to current electrode spread used) is presumed \nas the aquifer. Tables 2 gives the summary of the description of these \naquifers across the traverses. The remaining 9 (30%) of the traverses are \nunderlain by either sandy clay or clay as the last subsurface medium and \nare not recognized as an aquifer, even though porous, cannot transmit \nwater.  \n\n\n\nWith respect to the overburden materials, the aquifers delineated in the \nstudy location can be categorized into either unconfined or confined \naquifers. An aquifer is said to be confined, if it has layers of aquitard e.g. \nclay, both above and below, causing it to be under pressure (Neuman, \n1972; Parsons, 2004). When a borehole well is drilled into such an aquifer, \nwater is expected to accumulate until it gets to the top of the aquifer. An \nimpermeable formation such as an aquitard possesses very low or zero \npermeability, hence, low hydraulic conductivity, and has the capacity to \nrestrain groundwater flow.  Aquitards are usually composed of \nunconsolidated, very fine-grained sediments like clay, silt or unbroken \nrock fragments such as shale, quartz, or basalt (Parsons, 1995).  They \nscreen against any infiltration from the Earth's surface into groundwater \nnetwork and can protect confined aquifers from surface contamination \n(Neuman, 1972; Nacht, 1983). In contrast, an unconfined aquifer or water \ntable aquifer, usually very close to the land surface is typically overlain by \nthe unsaturated zone and stretches from the top of the water table at \natmospheric pressure, down to an impervious aquitard surface (Wilson, \n1983; Gardner, 1999; Winter, 1999; Parsons, 2004). Since unconfined \naquifers have no impervious barriers above their surface, they are often \nexpose to contamination from the surface.  Table 2 gives a summary of \ndelineated sand/clayey sand aquifers. \n\n\n\nTable 2: Summary of aquifer types with range of resistivity values \nand depth \n\n\n\nTraverses VES Resistivity \nrange \n(\u03a9m) \n\n\n\nDepth \nrange \n(m) \n\n\n\nComment \n\n\n\nSand \n\n\n\n1 1, 2 141-230 12.7-\n25.6 \n\n\n\nVES1 unconfined/ \nVES2 confined \n\n\n\n2 11, 12 170-172 9.9-11.4 Both unconfined \n\n\n\n3 15, 16, \n17, 18 \n\n\n\n125-478 13.5-\n31.9 \n\n\n\nunconfined \n\n\n\n4 23, 24 116-128 20.1-\n23.5 \n\n\n\nBoth confined \n\n\n\n5 25, 26 140-150 15.6-\n20.0 \n\n\n\nVES25 \nunconfined/VES26 \nconfined \n\n\n\nClayey sand \n\n\n\n1 3,4 66-67 18.3-\n20.8 \n\n\n\nBoth unconfined \n\n\n\n2 nil - - - \n\n\n\n3 13, 14 58-80 10.5-\n15.2 \n\n\n\nBoth confined \n\n\n\n4 22 22 13.5 confined \n\n\n\n5 27, 28, \n29, 30 \n\n\n\n64-80 10.9-\n20.0 \n\n\n\nVES27, 28 & 30 \nconfined \n\n\n\nVES29 unconfined \n\n\n\n \n5.2.2 Overburden protective capacity \n\n\n\nThe total longitudinal conductance S has been shown to have a direct \nrelationship with the overburden capacity ratings of geologic materials \n(Henriet, 1976). Based on this fact, overburden protective capacity ratings \ninferred from the computed values of S, indicates that the overburden \nmaterials above the preferred sand/clayey sand aquifers in most locations \nprovide moderate protective rating viz: 0.3460 - 0.6560 along TR1; 0.2071 \n- 0.2161 along TR2; 0.2489 - 0.3383 along TR3; 0.2490 - 0.5630 along TR4 \nand 0.2725 - 0.7736 along TR5.  Only VES27 along TR5 has a fairly good \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 85-92 \n\n\n\n\n\n\n\n \nCite The Article: Kayode Festus Oyedele, Olawale Babatunde Olatinsu (2021). Geophysical Evaluation of Subsurface Protective Capacity and Groundwater Prospect in \n\n\n\nA Typical Sedimentary Zone, Eastern Dahomey Basin Using Electrical Resistivity Technique . Malaysian Journal of Geosciences, 5(2): 85-92. \n\n\n\n\n\n\n\nprotective capacity (0.7736) against infiltration of contaminants from the \nsurface. Weak protective capacity characterizes VES14 (0.1814) and 17 \n(0.1486) along TR3, VES21 (0.1248) along TR4, and VES 29 (0.1053) along \nTR5. Overburden containing clayey materials usually protects the \nunderlying aquifer. The low values of protective capacity may be due to \nthe generally low overburden thickness, 7.9 - 25.6 (Table 1) across all the \ntraverses in the study area. Also, the cross plot of S against H (Figure 9) \nshows a coefficient of correlation of 0.58, implying a fair dependence of S \non H. \n\n\n\nTable 3: Overburden protective capacity rating (Henriet, 1976) \n\n\n\nTotal longitudinal conductance (S) Protective capacity rating \n\n\n\n> 10 Excellent \n\n\n\n5 - 10 Very good \n\n\n\n0.7 \u2013 4.9 Good \n\n\n\n0.2 \u2013 0.69 Moderate \n\n\n\n0.1 \u2013 0.19 Weak \n\n\n\n< 0.1 Poor \n\n\n\n \nThe total transverse resistance, T, which depicts varying thickness of a \nhigh resistivity material and the variation in the hydraulic conductivity \nvalues varies from a minimum of 235 \u03a9m2 at VES9 to a maximum of 2046 \n\u03a9m2 at VES15. Increasing T has a correlation with an increase in the \nthickness of highly resistive materials. High values of T are indicative of \nzones with high transmissivity and thereby suggests a high probability of \nthe underlying aquifer becoming contaminated by infiltrating pollutants \nfrom the surface. The cross plot of T against H (Figure 10) shows a \ncoefficient of correlation of 0.69 indicating a moderate correspondence \nbetween T and H. Also, any location with a combination of low overburden \nprotective capacity rating (low S) and high transmissivity (high T) (oval \nregion I in Figure 11) will probably favour contaminant leaching and \nmigration into the underlying aquifer system. Oval region II of \nmoderate/good S and low T will offer some degree of protection to the \nunderlying aquifer with low transmission of surface contaminants. The \nlocations lying within oval region III with moderate/low S coupled with \nlow T has weak protective capacity and low transmissivity. \n\n\n\nThe variations of \u03c1L and \u03c1T at the VES points are noteworthy. \u03c1L ranges \nfrom 19.76 \u2013 103.48, while \u03c1L ranges from 20.08 \u2013 104.84, showing that \u03c1T \nis slightly higher than \u03c1L at each of the VES points in agreement with \nFlathe, which confirms that current flow and mean hydraulic conduction \nparallel to geologic boundaries (longitudinal) are greater relative to those \nnormal to the boundary plane (Flathe, 1955). A maximum difference of \n5.72 occurs at VES13 and a difference of approximately 0.00 at VES25 \n(Table 1). The cross plot of \u03c1T against \u03c1L (Figure 12) support the linear \nrelationship between the two parameters with a coefficient of correlation \nof 0.99. Furthermore, at 57% (17) of the VES points, the difference is < 1 \nand at 40% (12) of the VES points, it is > 1. The lower longitudinal \nresistivity values are a confirmation of the presence of conductive clayey \nmaterials in the overburden (Keller, 1982). \n\n\n\n \nFigure 9: Cross plot of total longitudinal conductance (S) against \n\n\n\noverburden thickness (H). \n\n\n\n \nFigure 10: Cross plot of total transverse resistance (T) against \n\n\n\noverburden thickness (H). \n\n\n\n \nFigure 11: Cross plot of total transverse resistance (T) total longitudinal \n\n\n\nconductance (H). \n\n\n\nFigure 12: Cross plot of total transverse resistivity ( T\uf072 ) and against \n\n\n\nlongitudinal resistivity(\nL\uf072  ). \n\n\n\n6. CONCLUSION \n\n\n\nGroundwater studies have been carried out within a fast-growing and \ndeveloping community at Mowe, Southwest Nigeria using electrical \nresistivity techniques that involved VES and 2-D profiling techniques. The \nprotective capacity ratings of geological materials overlying the aquifers \nand potential groundwater aquifer for groundwater development have \nbeen evaluated. Sand and clayey sand presumed as the aquifer \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 85-92 \n\n\n\n\n\n\n\n \nCite The Article: Kayode Festus Oyedele, Olawale Babatunde Olatinsu (2021). Geophysical Evaluation of Subsurface Protective Capacity and Groundwater Prospect in \n\n\n\nA Typical Sedimentary Zone, Eastern Dahomey Basin Using Electrical Resistivity Technique . Malaysian Journal of Geosciences, 5(2): 85-92. \n\n\n\n\n\n\n\n(confined/unconfined) have been delineated as the last subsurface layer \nin 12 (40%) and 9 (30%) respectively, of the sampled locations. The \nremaining 9 (30%) of the traverses are underlain by either clay or sandy \nclay as the last subsurface layer. Of these aquifers, 78% are confined, while \n22% are unconfined. \n\n\n\nOverburden protective capacity rating inferred from the computed values \nof total longitudinal conductance S, shows that the overburden materials \nabove the preferred sand/clayey sand aquifers in most locations provides \nmoderate protective rating along all the traverses except for VES27 along \nTR5 which has a fairly good protective capacity (0.7736) and can shield \nagainst the infiltration of contaminants from the surface. Two locations \nalong TR3, one location along TR4 and TR5 respectively are characterized \nby weak protective capacity ratings (0.1053-0.1814). Conventionally, \noverburden with clayey materials is expected to provide a good protective \ncapacity rating, low values of protective capacity defined in the study area \nare possibly due to the generally low overburden thickness, 7.9 - 25.6. The \ncoefficient of correlation of 0.58 between S and H attests to this fact. \n\n\n\nThe total transverse resistance, T, which depicts varying thickness of a \nhigh resistivity material and the variation in the hydraulic conductivity \nvalues varies from a minimum of 235 \u03a9m2 at VES9 to a maximum of 2046 \n\u03a9m2 at VES15. Increasing T has a correlation with an increase in the \nthickness of highly resistive materials. High values of T are indicative of \nzones with high transmissivity, thereby suggesting a high probability of \nthe underlying aquifer becoming contaminated by infiltrating pollutants \nfrom the surface (moderate correlation coefficient of 0.69). Locations \ncharacterized by low overburden protective capacity rating and high \ntransmissivity will support contaminant leaching and migration in the \ngroundwater supply system. \n\n\n\nExcellent correlation (0.99) was established between \u03c1L and \u03c1T and with \n\u03c1T slightly higher than \u03c1L at all the VES points which confirms preferential \nlongitudinal conduction path. The lower longitudinal resistivity is \nevidence of the presence of more conductive clayey materials in the \noverburden. \n\n\n\nCONFLICT OF INTEREST \n\n\n\nThe authors of this article declare that there is no conflict of interest in any \nin relation to the work carried out in the research. \n\n\n\nAUTHOR CONTRIBUTION \n\n\n\nProfessor K.F. Oyedele supervised the project from conceptualization to \nexecution. Dr. O.B. Olatinsu developed the article for publication. \n\n\n\nCOMPLIANCE WITH ETHICAL STANDARDS \n\n\n\nThis article does not contain any studies involving life, whether animal or \nhuman, performed by any of the authors. \n\n\n\nFUNDING \n\n\n\nNo funding was received to accomplish this study. \n\n\n\nETHICAL CONDUCT \n\n\n\nThe authors have conformed with all ethical conducts required in a work \nlike this. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe authors wish to express their gratitude to Mr. Samuel Udofia, GIS Unit, \nGeography Department, University of Lagos, for assisting with the maps. \n\n\n\nREFERENCES \n\n\n\nAdabanija, M.A., Ajibade, R.A., 2020. Investigating groundwater corrosion \nand overburden protective capacity in a low latitude crystalline \nbasement complex of southwestern Nigeria. NRIAG Journal of \nAstronomy and Geophysics, 9 (1), Pp. 245-259. \nhttps://doi.org/10.1080/20909977.2020.1723867. \n\n\n\nAdegoke, O.S., 1969. Eocene Stratigraphy of Southern Nigeria. Collogue sur \nEocene Vol. III Bur. Rech. Geol. Min. Mem., 69, Pp. 23-48. \n\n\n\nAdegoke, O.S., Jeje, L.K., Durotoye, B., Adeleye, D.R., and Ebukanson, E.E., \n1981. The Geomorphology and Aspects of Sedimentology of Coastal \nRegion of Western Nigeria. J. Min. Geol., 17 (2), Pp. 217-223. \n\n\n\n Archie, G.E., 1942. The Electrical Resistivity Log as an Aid in Determining \nSome Reservoir Characteristics. Trans., 146 (01), Pp. 54\u201362. Paper \nNumber: SPE-942054-G https://doi.org/10.2118/942054-G \n\n\n\nAyuk, M.A., 2019. Groundwater Aquifer Vulnerability Assessment using a \nDar-Zarrouk Parameter in a Proposed Aboru Residential Estate, Lagos \nState, Nigeria. J. Appl. Sci. Environ. Management, 23 (12), Pp. 2081-2090. \n\n\n\nBatayneh, A.T., 2013. The estimation and significance of Dar-Zarrouk \nparameters in the exploration of quality affecting the Gulf of Aqaba \ncoastal aquifer systems. Journal of Coastal Conservation, 17, Pp. 623-\n635. https://doi.org/10.1007/s11852-013-0261-4. \n\n\n\nBovolo, C.I., Parkin, G. and Sophocleous, M., 2009. Groundwater resources, \nclimate and vulnerability. Environ. Res. Lett. 4, Pp. 035001. \n\n\n\nBraga, A.C.O., Filho, W.M., Dourado, J.C., 2006. Resistivity (DC) method \napplied to aquifer protection studies. Brazilian Journal of Geophysics, 24, \nPp. 573-581. \n\n\n\nBraga, A.C.O., 2008. Estimation of the natural vulnerability of aquifers: a \ncontribution from the resistivity and longitudinal conductance. \nBrazilian Journal of Geophysics, 26 (1), Pp. 61-68 \n\n\n\nBraga, A.C.O., Francisco, R.F., 2014. Natural vulnerability assessment to \ncontamination of unconfined aquifers by longitudinal conductance \u2013 (S) \nmethod. Journal of Geography and Geology; 6 (4), Pp. 68-79.  \n\n\n\nBritish Geological Survey. 2008. Groundwater Information Sheet: The \nImpact of Industrial Activity, Pp. 6. \n\n\n\nCasas, A., Himi, M., Diaz, Y., Pinto, V., Font, X. and Tapias J.C.A., 2008. \nAssessing aquifer vulnerability to pollutants by electrical resistivity \ntomography (ERT) at a nitrate vulnerable zone in NE Spain. Environ \nGeol., 54, Pp. 515\u2013520. DOI 10.1007/s00254-007-0844-1 \n\n\n\nChristensen, N.B., S\u00f8rensen, K.I., 1998. Surface and Borehole Electric and \nElectromagnetic Methods for Hydro-Geophysical Investigations. \nEuropean Journal of Environmental and Engineering Geophysics, 3, Pp. \n75-90. \n\n\n\nCosgrove, W.J., Loucks, D.P., 2015. Water management: Current and future \nchallenges and research directions, Water Resour. Res., 51, Pp. 4823\u2013\n4839, doi:10.1002/ 2014WR016869. \n\n\n\nD\u00f6ll, P., 2009. Vulnerability to the impact of climate change on renewable \ngroundwater resources: a global-scale assessment. Environ. Res. Lett., 4, \nPp. 035006. \n\n\n\nFlathe, H., 1955. Possibilities and limitations in applying geoelectrical \nmethods to hydrogeological problems in the coastal area of northwest \nGermany. Geophysical Prospecting, 3, Pp. 95-110. \nDOI: https://doi.org/10.1111/j.1365-2478.1955.tb01363.x. \n\n\n\nGardner, K.M., 1999. The importance of surface water/groundwater \ninteractions \u2013 Report EPA-910-R-99-013, Environmental Protection \nAgency, Seattle. \n\n\n\nHasan, M., Yanjun, S., Weijun, J., Gulraiz, A., 2019. Assessment of aquifer \nvulnerability using integrated geophysical approach in weathered \nterrains of south China. Open Geosciences, 11 (1), Pp. 1129-1150. \nhttps://doi.org/10.1515/geo-2019-0087.  \n\n\n\nHenriet, J.P., 1976. Direct application of the Dar-Zarrouk parameters in \ngroundwater survey. Geophysical Prospecting, 24, Pp. 344-353. \nhttps://doi.org/10.1111/j.1365-2478.1976.tb00931.x. \n\n\n\nGupta, G., Patil, S.N., Padmane, S.T., Erram, V.C., Mahajan, S.H., 2015. \nGeoelectric investigation to delineate groundwater potential and \nrecharge zones in Suki River basin, north Maharashtra. J. Earth Syst. Sci., \n124 (7), Pp. 1487\u20131501. DOI: 10.1007/s12040-015-0615-4. \n\n\n\nJones, H.A., Hockey, R.D., 1964. The geology of part of south western \nNigeria. Geol. Survey. Nigeria, Bull., 31, Pp. 1-101. \n\n\n\nKampsax-Kruger and Sshwed Associates, 1977. Underground water \nresources of the Metropolitan Lagos\u201d, Final Report to Lagos State \nMinistry of Works, Pp. 170. \n\n\n\nKeller, G.V., 1982. Electrical properties of rocks and minerals: In: \nHandbook of Physical Properties of Rocks (ed.) Carmichael R.S.., CRC \nPress, Pp. 217-293. \n\n\n\n\njavascript:;\n\n\nhttps://doi.org/10.2118/942054-G\n\n\nhttps://doi.org/10.1007/s11852-013-0261-4\n\n\nhttps://doi.org/10.1111/j.1365-2478.1955.tb01363.x\n\n\nhttps://doi.org/10.1515/geo-2019-0087\n\n\nhttps://doi.org/10.1111/j.1365-2478.1976.tb00931.x\n\n\nhttp://dx.doi.org/10.1007/s12040-015-0615-4\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 85-92 \n\n\n\n\n\n\n\n \nCite The Article: Kayode Festus Oyedele, Olawale Babatunde Olatinsu (2021). Geophysical Evaluation of Subsurface Protective Capacity and Groundwater Prospect in \n\n\n\nA Typical Sedimentary Zone, Eastern Dahomey Basin Using Electrical Resistivity Technique . Malaysian Journal of Geosciences, 5(2): 85-92. \n\n\n\n\n\n\n\nKhalil, M.H., 2009. Hydro geophysical assessment of Wadi El-Sheikh \naquifer, Saint Katherine, South Sinai, Egypt. J. Environ. Eng. Geophys., 14 \n(2), Pp. 77-86. https://doi.org/10.2113/JEEG13.2.77.  \n\n\n\nKhatri, N., Tyagi, S., 2015. Influences of natural and anthropogenic factors \non surface and groundwater quality in rural and urban areas, Frontiers \nin Life Science, 8 (1), Pp. 23-39, DOI: 10.1080/21553769.2014.933716. \n\n\n\nKonikow, L.F., August, L.L., Voss, C.I., 2001. Effects of clay dispersion on \naquifer storage and recovery in coastal aquifers. Transport in Porous \nMedia, 43, Pp. 45-64. https://doi.org/10.1023/A:1010613525547. \n\n\n\nKonikow, L.F., Neuzil, C. E., 2007. Method to estimate groundwater \ndepletion from confining layers. Water Resources Research, 43, Pp. \nW07417. doi:10.1029/2006WR005597. \n\n\n\nMachiwal, D., KumarJha, M., Singh, V.P.,  Mohan, C., 2018. Assessment and \nmapping of groundwater vulnerability to pollution: Current status and \nchallenges. Earth-Science Reviews, 185, Pp. 901-927. \nhttps://doi.org/10.1016/j.earscirev.2018.08.009.  \n\n\n\nMaduabuchi, C.M., 2004. Surface and groundwater resources monitoring \nand management in Nigeria United Nations/Austria/ESA Symposium \non Space Applications for Sustainable Development, Graz, Austria, 13-\n16 September. \n\n\n\nMaillet, R., 1947. The fundamental equations of electrical prospecting. \nGeophysics, 12, Pp. 529-526. https://doi.org/10/1190/1.1437342.                                                                                    \n\n\n\nMondal, N.C., Singh, V.P., Ahmed, S., 2013. Delineating shallow saline \ngroundwater zones from southern India using geophysical indicators. \nEnviron. Monit. Assess., 185, Pp. 4869-4886. \nhttps://doi.org/10.1007/s10661-012-2909-1. \n\n\n\nMousatov, A., Ryjov, A., 2005. Geoelectrical Characterization of Oil \nContaminated Site in Tabasco, Mexico. Geof\u00eds. Int., 44 (3), Pp. 251-263. \nDOI:10.22201/igeof.00167169p.2005.44.3.197 \n\n\n\nNacht, S.J., 1983. Groundwater monitoring system considerations. \nGroundwater Monitoring and Remediation, 3 (2), Pp. 33-\n39.  https://doi.org/10.1111/j.1745-6592.1983.tb01197.x. \n\n\n\nNel, J., Xu, Y., Batelan, O., Brendonck, L., 2009. Benefit and Implementation \nof Groundwater Protection Zoning in South Africa. Water Resources \nManagement, 23, Pp. 2895. https://doi.org/10.1007/s11269-009-\n9415-4. \n\n\n\nNeuman, S.P., 1972. Theory of flow in unconfined aquifers considering \ndelayed response of the water table. Water Resour. Res., 8 (4), Pp. 1031\u2013\n1045. doi:10.1029/WR008i004p01031 \n\n\n\nNiwas, S., Singhal, D.C., 1981. Estimation of aquifer transmissivity from \nDar-Zarrouk parameters in porous media. J. Hydrol., 50, Pp. 393-399. \nhttps://doi.org/10.1016/0022-1694(81)90082-2 \n\n\n\nNiwas, S., Singhal, D.C., 1985. Aquifer transmissivity of porous media from \nresistivity data. J. Hydrol., 82, Pp. 143-153. \nhttps://doi.org/10.1016/0022-1694(85)90050-2 \n\n\n\nOgbe, F.G.A., 1972. Stratigraphy of strata exposed in the Ewekoro quarry, \nWestern Nigeria. In: T. F. J. Dessauvagie and Whiteman (Eds) African \nGeology, University Press, Nigeria, Pp. 305- 322. \n\n\n\nOlayinka, A.I., Yaramanci, U., 2000. Assessment of the reliability of 2D \ninversion of apparent resistivity data. Geophys. Prospec., 48, Pp. 293\u2013\n316. \n\n\n\nOmatsola, M.E., Adegoke, O.S., 1981. Tectonic Evaluation and Cretaceous \nStratigraphy of the Dahomey Basin. Journal of Minning and Geology, 18 \n(1), Pp. 130-137. \n\n\n\nOroji, B., 2018. Assessing groundwater vulnerability by pollution mapping \nin Iran: Case study Hamadan \u2013 Bahar plain. Geof\u00edsica Internacional, 57 \n(3), Pp. 161-174. DOI: 10.22201/igeof.00167169p.2018.57.3.2108 \n\n\n\nParsons, R.P., 1995. A South African aquifer system management \nclassification; WRC report KV 77/95, Water Research Commission, \nPretoria.  \n\n\n\nParsons, R., 2004. Surface Water \u2013 Groundwater Interaction in a Southern \nAfrican Context Prepared for the Water Research Commission WRC \nReport No. TT 218. \n\n\n\nReyment, R.A., 1965. Aspects of the Geology of Nigeria Ibadan University \nPress. Ibadan. Pp. 145. \n\n\n\nRuss, W., 1924. The phosphate deposits of Abeokuta Province. Geol. Surv. \nNigeria, Bull., 7, Pp. 1 \u2013 43. \n\n\n\nSalem, H.S., 1999. Determination of fluid transmissivity and electric \ntransverse resistance for shallow aquifers and deep reservoirs from \nsurface and well-log electric measurements. Hydrology and Earth \nSystem Sciences, 3 (3), Pp. 421-427. DOI: 10.5194/hess-3-421-1999. \n\n\n\nSingh, U.K., Das, R.K., Hodlur, G.K., 2004. Significance of Dar-Zarrouk \nparameters in the exploration of quality affected coastal aquifer \nsystems. Environ Geol., 45, Pp. 696-702. \nhttps://doi.org/10.1007/s00254-003-0925-8.  \n\n\n\nS\u00f8rensen, K.I., Auken, E., Christensen, N.B., Pellerin, L., 2005. An Integrated \nApproach for Hydro-geophysical Investigations: New Technologies and \na Case History. In: Butler, D.K. (Ed.). \n\n\n\nTurner, N., Wurster, F., 2017. Water Resource Inventory and Assessment \nSummary Report \u2013 FY17 Northeast Region, Pp. 91. \n\n\n\nUmar, N.D., Igwe, O., 2019. Geo-electric method applied to groundwater \nprotection of a granular sandstone aquifer. Appl water Sci., 9, Pp. 112. \nhttps://doi.org/10.1007/s13201-019-0980-2. \n\n\n\nVandas, S.J., Winter, T.C., and Battaglin, W.A., 2002. Water and the \nEnvironment.  American Geosciences Institute Environmental \nAwareness Series, Pp. 42-44. \n\n\n\nWalker, D.B., Baumgartner, D.J., Gerba., C.P., Fitzsimmons, K., 2019. \nChapter 16 - Surface Water Pollution, Environmental and Pollution \nScience (Third Edition), Pp. 261-292. https://doi.org/10.1016/B978-0-\n12-814719-1.00016-1. \n\n\n\nWhiteman, A.J., 1982. Nigeria: Its petroleum geology, resources and \npotentials. Graham and Trotman, London, U.K, (1) 176, Pp. 238. \n\n\n\nWilson, L.G., 1983. Monitoring in the vadose zone: Part 3. Groundwater \nMonitoring Review, 3 (1), Pp. 155-166. \n\n\n\nWinter, T.C., Harvey, J.W., Franke, O.L., Alley, W.M., 1999. Ground water \nand surface water \u2013 a single resource; USGS Circular 1139, United States \nGeological Survey, Denver. https://doi.org/10.3133/cir1139 \n\n\n\nWorrall, F., Kolpin, W., 2003. Direct assessment of groundwater \nvulnerability from single observations of multiple contaminants Water \nResources Research, 39 (12), Pp. 1345, doi:10.1029/2002WR001212, \n2003. \n\n\n\nZereg, S., Boudoukha, A., Benaabidate, L., 2018. Impacts of natural \nconditions and anthropogenic activities on groundwater quality in \nTebessa plain, Algeria. Sustainable Environment Research, 28 (6), Pp. \n340-349. https://doi.org/10.1016/j.serj.2018.05.003, \n\n\n\nZohdy, A.R., 1965. The auxiliary point method of electrical sounding \ninterpretation and its relationship to the Dar Zarrouk parameters. \nGeophysics, 30, Pp. 644-660. https://doi.org/10.1190/1.1439636.  \n\n\n\nZohdy, A.R., 1974. Use of Dar Zarrouk curves in the interpretation of \nvertical electrical sounding data. United States Geological Survey \nBulletin 1313-D, 41, Pp. 4.  \nhttps://doi.org/10.3133/b1313D.\n\n\n\n \n\n\n\n\nhttps://doi.org/10.2113/JEEG13.2.77\n\n\nhttps://doi.org/10.1080/21553769.2014.933716\n\n\nhttps://www.sciencedirect.com/science/article/abs/pii/S0012825217304713?via%3Dihub#!\n\n\nhttps://www.sciencedirect.com/science/article/abs/pii/S0012825217304713?via%3Dihub#!\n\n\nhttps://www.sciencedirect.com/science/article/abs/pii/S0012825217304713?via%3Dihub#!\n\n\nhttps://www.sciencedirect.com/science/article/abs/pii/S0012825217304713?via%3Dihub#!\n\n\nhttps://www.sciencedirect.com/science/journal/00128252\n\n\nfile:///C:/Users/DELL/OneDrive/Desktop/Current%20work/185\n\n\nhttps://doi.org/10.1016/j.earscirev.2018.08.009\n\n\nhttps://doi.org/10/1190/1.1437342\n\n\nhttps://doi.org/10.1007/s10661-012-2909-1\n\n\nhttp://dx.doi.org/10.22201/igeof.00167169p.2005.44.3.197\n\n\nhttps://doi.org/10.1111/j.1745-6592.1983.tb01197.x\n\n\nhttps://doi.org/10.1007/s11269-009-9415-4\n\n\nhttps://doi.org/10.1007/s11269-009-9415-4\n\n\nhttps://doi.org/10.1016/0022-1694(81)90082-2\n\n\nhttps://doi.org/10.1016/0022-1694(85)90050-2\n\n\nhttp://dx.doi.org/10.22201/igeof.00167169p.2018.57.3.2108\n\n\nhttp://dx.doi.org/10.5194/hess-3-421-1999\n\n\nhttps://www.sciencedirect.com/science/article/pii/B9780128147191000161#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/B9780128147191000161#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/B9780128147191000161#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/B9780128147191000161#!\n\n\nhttps://www.sciencedirect.com/science/book/9780128147191\n\n\nhttps://www.sciencedirect.com/science/book/9780128147191\n\n\nhttps://doi.org/10.1016/B978-0-12-814719-1.00016-1\n\n\nhttps://doi.org/10.1016/B978-0-12-814719-1.00016-1\n\n\nhttps://doi.org/10.3133/cir1139\n\n\nhttps://www.sciencedirect.com/science/article/pii/S2468203917300638#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S2468203917300638#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S2468203917300638#!\n\n\nhttps://www.sciencedirect.com/science/journal/24682039\n\n\nfile:///E:/DAYANG/DAYANG%20NORAINA/MJG/ISSUE%202/28%20(6\n\n\nhttps://doi.org/10.1016/j.serj.2018.05.003\n\n\nhttps://doi.org/10.1190/1.1439636\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 51-60 \n\n\n\nCite The Article: Cyril C. Okpoli (2019). Delineation Of High-Resolution Aeromagnetic Survey Of Lower Benue Trough For Lineaments And Mineralization: Case Study Of \nAbakikili Sheet 303. Malaysian Journal Of Geosciences, 3(1): 51-60. \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 23 November 2018  \nAccepted 24 December 2018 \nAvailable online 10 January 2019\n\n\n\nABSTRACT\n\n\n\nHigh resolution aeromagnetic dataset of Abakiliki (sheet 303 SW) was used for the characterization of the \nsubsurface lithostructural features in part of the Lower Benue Trough, Nigeria. This study was necessitated for \nmapping and delineating hydrocarbon prospecting zones, in order to boost the Nation\u2019s economy. The aeromagnetic \ndata were subjected to several forms of filtering, reductions, and enhancement techniques for both qualitative and \nquantitative interpretations. The result of the reduction to equator- total magnetic intensity (RTE-TMI) revealed \nthe magnetic intensity of subsurface rocks ranging from 34.14nT to 61.40nT. These range of magnetic intensity \nvalues characterized the rocks in the area as shale and Limestone within the Asu River Group, Awgu shale, Eze-AKu \nshale and Nkporo shale. The upward continued RTE-TMI data to 500m, 1 km, 2 km, 3 km, 7 km and 10 km revealed \nregional trends of these rocks and structure thin \u2013out with measure depth continuation. The second vertical \nderivative (SVD), Tilt-angle derivative (TDR) and Analytical signal (AS) revealed three (3) major faults; F1-F11, F2-\nF12 and F3-F13 in NE-SW, ENE-WSW and NW-SE directions respectively. The depth to top of magnetic source were \nrevealed by the radially averaged power spectrum (RAPS) and Euler deconvolution as 27m and 2.64km for \nshallower and deeper sources respectively. This study has demonstrated the efficiency of aeromagnetic methods, \nwith their improved techniques as tools for regional mapping of lithologies and structures that may host important \nminerals and/or aid hydrocarbon accumulation and their probable depths. \n\n\n\n KEYWORDS \n\n\n\nAeromagnetic, Abakiliki, Lower Benue Trough, Qualitative and Quantitative, Lithostructural, Depths\n\n\n\n1. INTRODUCTION \n\n\n\nGeophysical techniques investigate a unique physical property of the earth \ncrust, which tends to solve a peculiar problem within the earth\u2019s \nsubsurface. While in some cases; a combination of two or more of these \nmethods gives better results [1]. Magnetic method is the oldest \ngeophysical exploration method used in prospecting. Magnetic method \nmeasures variation in the Earth\u2019s magnetic field caused by changes in the \nsubsurface geological structure or the differences in near-surface rocks\u2019 \nmagnetic properties [2]. The aim of a magnetic survey is to investigate \nsubsurface geology on the basis of anomalies in the Earth\u2019s magnetic field \nresulting from the magnetic properties of the underlying rocks. Although \nmost rock-forming minerals are effectively non-magnetic, certain rock \ntypes contain sufficient magnetic minerals to produce significant magnetic \nanomalies. Similarly, man-made ferrous objects also generate magnetic \nanomalies. Magnetic surveying thus has a broad range of applications, \nfrom small scale engineering or archaeological surveys to detect buried \nmetallic objects, to large-scale surveys carried out to investigate regional \ngeological structure [1]. \n\n\n\nThis non-destructive technique has numerous applications in engineering \nand environmental studies, including the location of voids, near-surface \nfaults, igneous dikes, and buried ferromagnetic objects like storage drums, \npipes etc. Magnetic field variations can be interpreted to determine an \nanomaly\u2019s depth, geometry and magnetic susceptibility. Magnetic data \nmeasured in gammas and either collected as total field or gradient \nmeasurements are collected in a grid or along a profile with stations \nspacing. \n\n\n\nAeromagnetic survey is a common type of geophysical survey carried out \nusing a magnetometer aboard or towed behind an aircraft. The principle \nis similar to a magnetic survey carried out with a hand-held magnetometer \nbut allows much larger areas of the Earth's surface to be covered quickly \nfor regional reconnaissance. The aircraft typically flies in a grid-like \npattern with height and line spacing determining the resolution of the data \n[1]. \n\n\n\nThe Benue Trough of Nigeria in which the study area lies, is a major \ntectonic feature in West Africa. It is an elongated rifted depression that \ntrends NE-SW from the south, where it merges with the Niger Delta to the \nnorth, where it sediments are part of the Chad basin succession. The origin \nand evolution of the Benue Trough of Nigeria is now fairly well \ndocumented [3-10]. Generally, the Benue Trough is believed to have been \nformed when the South America separated from Africa. The major \ncomponent units of the Lower Benue Trough include the Anambra Basin, \nthe Abakaliki Anticlinorium and the Afikpo syncline.  \n\n\n\nA researcher produced a detailed report on the geology of Abakaliki \ndomain, likened its development to that which occurs in a complete \nOrogenic cycle including sedimentation, magmatism, metamorphism and \ncompressive tectonism [6,7,11]. The same researcher suggested that the \ncompression responsible for the large-scale folding and cleavage was \ndirected N155\u00baE [11]. The magmatism that occurred resulted in the \ninjection of numerous intrusive bodies into the shale of the EzeAku and \nAsu River Group.  \n\n\n\nMalaysian Journal of Geosciences (MJG) \nDOI : http://doi.org/10.26480/mjg.01.2019.51.60 \n\n\n\nREVIEW ARTICLE \n\n\n\nDELINEATION OF HIGH-RESOLUTION AEROMAGNETIC SURVEY OF LOWER BENUE \nTROUGH FOR LINEAMENTS AND MINERALIZATION: CASE STUDY OF ABAKIKILI \nSHEET 303 \n\n\n\nCyril C. Okpoli* \n\n\n\nDepartment of Earth Sciences, Faculty of Science, Adekunle Ajasin University, PMB 1, Akungba-Akoko, Ondo State, Nigeria. \n\n\n\n*Corresponding author email: cyril.okpoli@aaua.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\n\nmailto:cyril.okpoli@aaua.edu.ng\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 51-60 \n\n\n\nCite The Article: Cyril C. Okpoli (2019). Delineation Of High-Resolution Aeromagnetic Survey Of Lower Benue Trough For Lineaments And Mineralization: Case Study Of \nAbakikili Sheet 303. Malaysian Journal Of Geosciences, 3(1): 51-60. \n\n\n\nAnanaba and Ajakaiye generated a regional magnetic field intensity map \nfrom aeromagnetic data of the Southern Benue Trough and Niger Delta \n[12]. The produced regional map showed prominent features and major \ntectonic trends in the NE-SW direction which when compared with those \nindicated on the tectonic map of Africa, suggested a linear extension of the \nChain and Charcot fracture zone. \n\n\n\nThe study area is characterized by several economic mineral deposit \nwhich have generated a lot of interest on the economic importance of this \nmineral zone. Intense geological investigations have been carried out in \nthese areas at different times in search for different mineral deposits \n[9,13]. \n\n\n\nThis study aim at contributing to our understanding of the geology and \nhydrocarbon potentials of this part of the Lower Benue Trough using high \nresolution aeromagnetic dataset. The aeromagnetic dataset will be used to \ndelineate the subsurface structures which control the anomalous \nmineralization in the area. Forward and inverse modeling techniques \nwere employed for these purposes. This is with a view to characterize the \nsubsurface litho-structural features as well as their lateral and depth \nextents. \n\n\n\n1.1 Location and Geology of the Study Area \n\n\n\nThe study area is located within latitudes 60oo' and 6030'N and longitudes \n80oo' and 8030' E (Figure 1). The Benue Trough was formed as a result of \nseries of tectonics and repetitive sedimentation in the Cretaceous time \nwhen South American continent separated from Africa and the opening of \nthe South Atlantic Ocean. The geology of the Lower Benue Trough has been \ndescribed by several authors [6,7,14]. \n\n\n\nThe Lower Benue Trough is underlain by a thick sedimentary sequence \ndeposited in the cretaceous. The oldest sediments belong to the Asu River \ngroup (Figure 1) which uncomfortably overlies the Precambrain \nBasement Complex that is made of granitic and magmatic rocks. The Asu \nRiver group found in the Abakaliki-Afikpo basins has an estimated \nthickness of 2000 m and is Albian to Ceomanian [10,15]. It comprises of \nargillaceous sandy shale, laminated sandstone, micaceous sandstone and \nminor limestone with an inter fingerings or mafic volcanic deposited on \ntop of the Asu River group [6]. Sediments in the area were the upper \ncretaceous sediments, comprising mostly the Eze-Aku shale. The Turonian \nEze-Aku shale consist of nearly 1000m of calcareous flaggy shale and \nsiltstone, thin sandy and shaly limestone and calcareous sandstone [15]. \nThe Eze-Aku shales at the Afikpo Basin for the Amasiri sandstones. The \nNkporo shale is the youngest unit of the Cretaceous sequence and overlies \nthe Eze-Aku shale unconformably. They are Campanian-Maestrichtian in \nage and are mainly marine in character, with some sandstone \nintercalations. The sediments of the Abakaliki Anticlinorium are exposed \nfrom about 8 km North-East of Okigwe where the folded Eze-Aku shale \nand the Asu River group are unconformably overlain by the Nkporo shale \n[6,7,16]. \n\n\n\nFigure 1: Section of the Map of Nigeria showing the Lower Benue Trough \n[12]. \n\n\n\n1.2 Regional Geology of Nigeria \n\n\n\nThe geology of Nigeria is made up essentially of the Basement Complex, \n\n\n\nthe Younger Granite and the Sedimentary Basins. The Basement Complex, \nwhich is Precambrian in age, is made up of the Migmatite-Gneiss Complex, \nthe Schist Belts, the Older Granites and the Undeformed basic and acidic \ndykes. The Younger Granites comprise several Jurassic magmatic ring \ncomplexes centered on Jos and other parts of North Central Nigeria [17]. \nThe Sedimentary Basins, containing sediment fill of Cretaceous to Tertiary \nages, comprise the Niger Delta, the Anambra Basin, the Benue Trough \n(Lower, Middle and Upper), the Chad Basin, the Sokoto Basin, the Mid-\nNiger (Nupe/Bida) Basin and the Dahomey Embayment (Figure 2). \n\n\n\nThe Benue Trough is a Sedimentary Basin located in Nigeria, extending \nfrom the Gulf of Guinea in the South to the Chad Basin in the North. It is \nbelieved to have originated from a 'pull-apart' basin associated with the \nopening of the Atlantic Ocean which ended in Early Tertiary with the \ndevelopment of the Tertiary Niger Delta. The Benue Trough is \ncharacterized by extensive magmatic activities as evidenced by the \nwidespread occurrence of intrusive and extrusive rocks. These rocks are \nthe result of the tectonic activities within the trough. The Benue Trough \noriginated from Early Cretaceous rifting of the central West African \nbasement uplift. It forms a regional structure which is exposed from the \nnorthern frame of the Niger Delta and runs northeastwards for about l000 \nkm to underneath Lake Chad, where it terminates. Regionally, the Benue \nTrough is part of an Early Cretaceous rift complex known as the West and \nCentral African Rift System. The Trough is subdivided into Lower, Middle \nand Upper Benue Troughs. The Lower Benue Trough is underlain by a \nthick sedimentary sequence deposited during the Cretaceous and made up \nof Albian shales, subordinate siltstones of the Asu River and the presence \nof volcanic. The Benue trough of Nigeria is a major tectonic feature in West \nAfrica. It is an elongated rifted depression that trends NE-SW from the \nsouth, where it merges with the Niger delta to the north, where it \nsediments are part of the Chad basin succession. The origin and evolution \nof the Benue trough of Nigeria is now fairly well documented [3-10].  \n\n\n\nGenerally, the Benue Trough is believed to have been formed when the \nSouth America separated from Africa [8]. The major component unit of the \nLower Benue trough includes the Anambra basin, the Abakaliki \nAnticlinorium and the Afikpo synclinorium. The Benue Trough was \nformed as a result of series of tectonics and repetitive sedimentation in the \nCretaceous time when South American continent separated from Africa \nand the opening of the South Atlantic Ocean. The geology of the Lower \nBenue Trough has been described by several authors [6,7]. \n\n\n\nFigure 2:  Modified geological Map of Nigeria showing the study area \n\n\n\ninset Geological Map of Abakaliki, Sheet 303 SW [17]. \n\n\n\n1.3 Stratigraphic Settings of the Study Area \n\n\n\nSedimentation in the Lower Benue Trough commenced with the marine \nNeocomian \u2013 Albian Asu River Group, although some pyroclastics of \nAptian \u2013 Early Albian age have been scantly reported (Figure 3). The Asu \nRiver Group sediments in the Lower Benue Trough comprises \npredominantly of shales with localized sandstones, siltstones and \nlimestones as well as extrusive and intrusive material of the Abakaliki \nFormation in the Abakaliki area and the Mfamosing Limestone in the \nCalabar. In addition, described the Asu River Group as consisting of arkosic \nsandstones, volcaniclastics, marine shales, siltstones and limestone which \noverly the Precambrian to Lower Paleozoic Crystalline Basement rocks. \nThe arkosic sediments were derived principally from the extensive \nweathering of the basement rocks which were invaded by alkaline basaltic \nrocks prior to the initial rapid marine flooding of the Middle Albian times. \nThe Asu River Group is interpreted as sediments of the first transgressive \ncycle into the Lower Benue Trough [8]. \n\n\n\nThe marine Cenomanian \u2013 Turonian Nkalagu Formation (black shales, \nlimestones and siltsones) and the interfingering regressive sandstones of \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 51-60 \n\n\n\nCite The Article: Cyril C. Okpoli (2019). Delineation Of High-Resolution Aeromagnetic Survey Of Lower Benue Trough For Lineaments And Mineralization: Case Study Of \nAbakikili Sheet 303. Malaysian Journal Of Geosciences, 3(1): 51-60. \n\n\n\nthe Agala and Agbani Formations (Cross River Group) rest on the Asu \nRiver Group.  Although, sequences of sandstones, limestones and shales \nwith calcareous sandstones of Odukpani Formation were deposited \nunconformably on the Basement rocks in the Calabar Flank during the Late \nAlbian and Santonian was a period of non-deposition, folding and faulting. \nThis was followed by uplift and erosion of the sediments [8]. \n\n\n\nThe intensive Middle\u2013Santonian deformation and magmatism in the \nBenue Trough displaced the major depositional axis westward which led \nto the formation of the Anambra Basin [6,7]. Post deformational \nsedimentation in the Lower Benue Trough, therefore, constitutes the \nAnambra Basin. Sedimentation in the Anambra Basin thus commenced \nwith the Early Campanian \u2013 Early Maastrichtian of the Enugu and Nkporo \nFormations (lateral equivalents) which consist of a sequence of bluish to \ndark grey shale and mudstone locally with sandy shales, thin sandstones \nand shelly limestone beds [8]. \n\n\n\nThe shaly facies grade laterally to sandstones of the Owelli and Afikpo \nFormations in the Anambra Basin. The Enugu and Nkporo Formations are \nessentially marine sediments of the third transgressive cycle. These, in \nmost parts of the Anambra Basin is overlain by the Lower Maastrichtian \nsandstones, shales, siltstones and mudstones and the inter-bedded coal \nseams of the deltaic Mamu Formation. The deltaic facies grade laterally \ninto the overlying marginal marine sandstones of the Ajali and Nsukka \nFormations [18]. \n\n\n\nFigure 3: Stratigraphic settings of the Lower Benue Trough [10]. \n\n\n\n1.4 Mineral Resources Associated with Lower Benue Trough \n\n\n\nGeological Survey of Nigeria Agency has played an active role in the \nexploration for mineral deposits in Nigeria. The Lower Benue Trough like \nother Sedimentary Basins in Nigeria is found to be endowed with mineral \nresources. The mineral resources so far reported in the Lower Benue \nTrough of Abakiliki study area by the Geological Survey of Nigeria Agency \nare discussed below [4, 17,19]. \n\n\n\n1.5 Lead-zinc \n\n\n\nDeposits of zinc and lead minerals in the form of their ores of Sphalerite \nand Galena respectively often associated with Barytes mineralization in \nthe Cretaceous sediments of the Lower Benue Trough. The general geology \nof Lower Benue Trough in Abakaliki area is made up of thick sequences \n(500m) of slightly deformed Cretaceous sedimentary rocks made up of \nessentially of Albian shales, subordinate siltstones of the Asu River Group. \nThere is also the presence of volcanic and pyroclastic materials forming \nelongated conical hills in the cores of the Anticlinal structures. The \nAbakaliki lead\u2013zinc is believed to be of hydrothermal origin emplaced at a \nlow temperature of about 140 oC and it is made up of primarily four lodes \nnamely: Ishiagu, Enyigba, Ameri and Ameki in the Lower Benue Trough \nlocated in Ebo [4,20]. \n\n\n\n1.6 Flourspar \n\n\n\nFluorspar occurs in small quantities in the lead-zinc lodes in the Albian \nshales, siltstones and limestones of Asu River Group in Ishiagwu and \nOgoja, but the deposits are generally too small to be of value. Fluorspar or \nFluorite is a mineral composed of calcium fluoride (CaF2), the principal \nfluorine-bearing mineral. The main use of fluorite has been for the \nproduction of hydrofluoric acid, an essential raw material in the \nmanufacture of synthetic cryolite and aluminum fluoride for the aluminum \nindustry, and in many other applications in the chemical industry. It is also \nemployed as a standard flux used in the making of steel, in the smelting of \n\n\n\nlead ores, in ceramic industry and in the production of enamel and opal \nglass, and perfect crystals are used for the manufacture of apochromatic \nlenses [17,19]. \n\n\n\n2. MATERIALS AND METHODS OF STUDY\n\n\n\n2.1 Materials used for study \n\n\n\nThe data set used for this research and the software package are: \n\n\n\n\u2022 Grid and line Aeromagnetic data covering Abakaliki Area, Sheet 303.\n\u2022 Geosoft\u00ae Oasis Montaj\u2122 software version 6.4.2 H.J.\n\u2022 A digitized map of the Study Area.\n\n\n\nThis work used the aeromagnetic data Sheet 303 of Abakaliki area in the \nLower Benue Trough, the aeromagnetic data was acquired from Nigerian \nGeological Survey Agency, which undertook aeromagnetic survey and \ndigitizing of aeromagnetic data in some parts of Nigeria between 2005 and \n2009 (stratigraphy model of interpreting aeromagnetic data) [18].The \ndata was collected at a nominal flight altitude of 80 meters along N-S flight \nlines spaced approximately 1000 meters apart. The aeromagnetic sheet is \non a scale of 1:100,000. Diurnal variation effects on the magnetic field, \nwhich arise due to solar activities, were recorded using additional unit of \nbase station magnetometer (the ScintrexCS3CesiumVapour). Also, \nInternational Geomagnetic Reference Field (IGRF) was subtracted from \nthe total magnetic measurements to get rid of the regional gradient of the \nearth\u2019s magnetic field due to the continual changes in the magnitude and \ndirection of the earth\u2019s magnetic field from one place to another [18]. \n\n\n\n2.2 Methods of Study \n\n\n\nThe data was processed using Geosoft\u00ae Oasis Montaj\u2122 software, other \nsoftware include; Surfer and Microsoft Excel.  \n\n\n\nData reductions such as: removal of near surface noise (NSN) using \nButterworth filter, reduction to magnetic equator, regional field, residual \nfield, automatic gain control (AGC), upward continuation, tilt-angle \nderivative (TDR), second vertical derivative (SVD), analytic signal (AS), \nradial average power spectrum (RAPS) and 3D Euler Deconvolution were \nperformed for better result output. The data reductions and \nenhancements were done using the MAGMAP Step-by-Step filtering \nprocessing. The Magnetic data processing flow chat in Figure 4 shows the \ndata processing stages employed. \n\n\n\nFigure 4: The data processing stages employed. \n\n\n\n2.3 Re-projection of Coordinates and Near Surface Noises (NSN) \nRemoval  \n\n\n\nThe NGSA Total Magnetic Intensity (TMI) data coordinates were \nreprojected from UTM Zone 32N to UTM Zone 31N of the Greenwich \nMercator. This was necessary because the coordinates of the data must \ncorrespond to their actual locations. These re-projected data were gridded \nand adopted as the new TMI data and was filtered to remove Near Surface \nNoises (NSN) caused by metallic materials, fences, cables (both buried and \nsurface), flight height.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 51-60 \n\n\n\nCite The Article: Cyril C. Okpoli (2019). Delineation Of High-Resolution Aeromagnetic Survey Of Lower Benue Trough For Lineaments And Mineralization: Case Study Of \nAbakikili Sheet 303. Malaysian Journal Of Geosciences, 3(1): 51-60. \n\n\n\n2.4 Reduction to magnetic equator \n\n\n\nThe Reduction to Magnetic Equator (RTE) filter was used to produce the \nRTE_TMI image, in order to Centre structures and anomalous bodies over \ntheir exact positions. To produce anomalies, depend on the inclination and \ndeclination of the body\u2019s magnetization, inclination, and declination of the \nlocal earth\u2019s field and orientation of the body with respect to the magnetic \nnorth. As discussed in a study, problems can arise in the reduction to the \npole process at magnetic latitudes less than 15\u00b0, as the Fourier domain \ntransformation process becomes unstable, owing to the need to divide the \nspectrum by a very small term, thereby introducing north\u2013south \nalignment of the anomalies into the data. The RTE_TMI gridded data was \nadopted as our new processed data for subsequent data analyses and \nenhancement [21]. \n\n\n\n2.5 Regional and Residual field \n\n\n\nField associated with deep masses were filtered out to produce the \nresidual data for the area covered. The data were smoothed upward to 4 \nkm to evince the regional field caused by deep basement rocks and high \nwavelength anomalies. The Residual data left after near surface noise and \nthe regional have been removed is produced using equation 1: \n\n\n\nResidual field o observed total field (RTE TMI) \u2013 Regional    (1) \n\n\n\n2.6 The Upward Continuation Filter \n\n\n\nUpward continuation is considered a clean filter because it produces \nalmost no side effects that may require the application of other filters or \nprocesses to correct. Because of this, it is often used to remove or minimize \nthe effects of shallow sources and noise in grids. Also, upward continued \ndata may be interpreted numerically and with modeling programs. This is \nnot the case for many other filter processes. Upward continuation was \ncarried out on the RTE_TMI data to depth of 0.5 km (i.e. 500 m) and 1 km \nrespectively. Equation (2) below can be used for the calculation of the \nupward continuation [22]. \n\n\n\nF(x,y,-h) =   (2) \n\n\n\nWhere, F(x,y,-h) is the total field at the point P(x1, y1,-h) above surface on \nwhich F(x1, y1, 0) is known, h is the elevation above ground surface. \n\n\n\n2.7 The Downward Continuation Filter \n\n\n\nDownward continuation enhances the response of sources at a depth by \neffective bringing the plane of measurement closer to the source. \nDownward continuation highlights the component of higher wave \nnumber, increases the anomaly resolution of the individual sources. Its \nusefulness depends on the elimination of noise, as the computation of \ndownward continuation is unstable and easily distorts the true feature of \npotential field data.  According to a study, when applied to potential field \ndata brings the observation surface closer to the source therefore \nenhancing the responses from sources at depth as shown in equation (3) \n[21,22].  \n\n\n\nT   (3) \n\n\n\n2.8 The Second Vertical Derivative Filter \n\n\n\nThe significance of vertical derivatives is locating the position of the \ndensity or magnetization boundaries were given. The RTE-TMI image \ncontains all the anomalies both shallow and deep sources. Therefore, \nsecond vertical derivatives (SVD) filtering was used to suppress unwanted \nsources that were obscured by broader regional trend. In this case, it \naccentuates short wavelength components to sharpen the edges of the \nanomalies; tends to reduce anomaly complexity and allow clearer contrast \nbetween the geologic unit sand causative structures like lineaments/faults \nand smaller trends [22]. \n\n\n\n2.9 Automatic Gain Control (AGC) \n\n\n\nAutomatic gain control is used to accentuate coherent assemblages and to \nalign anomalies that appear characterless in RTE_TMI image by \naccentuating equal signals with both low and high amplitudes anomalies \nthat mapped out the structures in the areas. Thus, the filter makes smaller \ntrends and fractures to be distinct and clearly seen by unaided eyes [23]. \n\n\n\n2.10 Analytic Signal (AS) \n\n\n\nAnalytic Signal (AS) or total gradient is formed through the combination \nof the horizontal and vertical gradients of the magnetic anomaly and it is \napplied either in space or frequency domain, generating a maximum \ndirectly over discrete bodies as well as their edges. The generated \nmaximum directly over the causative body and depth estimation abilities \nof this filter make it a highly useful technique for magnetic data \ninterpretation. The maximum can be used to detect the structures \nresponsible for the observed magnetic anomalies over an area. Analytic \nSignal images are useful as a type of reduction to the pole, as they are not \nsubjected to the instability that occurs in transformations of magnetic \nfields from low magnetic latitudes; source positions regardless of any \nremanence in the sources. The amplitude A of the Analytic Signal (AS) of \nthe total magnetic field \ud835\udc39 is calculated from the two or three orthogonal \nderivatives of the field for 2D or 3D bodies respectively (equation 4). It is \ntherefore defined as the square root of the squared sum of the vertical and \nhorizontal derivatives of the magnetic field [23]. \n\n\n\nAS = |A (\ud835\udc65, y)| = \u221a (\ud835\udf15\ud835\udc39/\ud835\udf15\ud835\udc65) 2 + (\ud835\udf15\ud835\udc39/\ud835\udf15\ud835\udc66) 2 + (\ud835\udf15\ud835\udc39/\ud835\udf15\ud835\udc67) 2    (4) \n\n\n\n|A (\ud835\udc65, \ud835\udc66)| is the analytic signal amplitude and \ud835\udc39is the observed magnetic \nfield at (x, y). \n\n\n\n2.11 Tilt-angle Derivative (TDR) \n\n\n\nThe Tilt Derivative (TDR) is produced from the tilt derivative filter. It is \nused to determine structures, trends, contacts and edges or boundaries of \nmagnetic sources, as well as to enhance both weak and strong magnetic \nanomalies of the area by placing an anomaly directly over its source, \nespecially at shallow depths by using the theory that the zero contours are \nthe edges of the formation equation (5) \n\n\n\n \ud835\udc3b\ud835\udc37_\ud835\udc47\ud835\udc37\ud835\udc45= \u221a (\ud835\udc51\ud835\udc47/\ud835\udc51\ud835\udc65)+ (\ud835\udc51\ud835\udc47/\ud835\udc51\ud835\udc66)    (5a) \n\n\n\n\ud835\udc47\ud835\udc37\ud835\udc45 = arctan (1\ud835\udc49\ud835\udc37\ud835\udc47/\ud835\udc3b\ud835\udc37_\ud835\udc47\ud835\udc37\ud835\udc45)    (5b) \n\n\n\nWhere; 1VDT is the first vertical derivative in z-direction, dT/dx is the \nderivative in x-direction and dT/dy is the derivative in y- direction.\n\n\n\n2.12 Radial Average Power Spectrum (RAPS) \n\n\n\nAlso known as spectral plot was run for depth to top of magnetic sources \nestimations. The frequency unit is in radians per kilometer (rad/km), the \nmean depth of burial of the ensemble is given by equation 6 (Oasis Montaj \nTM help file) \n\n\n\n\ud835\udc67=\ud835\udc5a/4\ud835\udf0b (6)  \n\n\n\nTherefore, various maps produced through reductions and enhancements \nwere qualitatively and quantitatively interpreted. \n\n\n\n2.13 Euler Deconvolution (ED) \n\n\n\nEuler deconvolution (ED) is a well-known method to determine the shape \nof causative bodies from potential field data. Hood uses this method for \naeromagnetic data interpretation and demonstrated that, the method is \nvalid for point-pole and point-dipole sources [24]. Thomson elaborated \nthe application of the method to 2D sources and derived the structural \nindices for several elementary bodies [25]. A group researcher extended \nthe method to 3D and discusses its applicability to gravity anomalies of \nfinite steps and magnetic anomalies of thin dykes and sloping contacts \n[26]. The Euler deconvolution has emerged as a powerful tool for direct \ndetermination of depth and probable source geometry interpretations. \nThe method can locate or outline the confined sources, dykes and contacts \nwith remarkable accuracy. The Euler deconvolution has been widely used \nin the automatic interpretation model. Usually the structure index (SI) is \nfixed and the locations and depths (x0, y0, z0) of any sources are found \nusing the equation 7. \n\n\n\n(x \u2013 x0) \ud835\udf15T/\ud835\udf15\ud835\udc65 + (y \u2013y0) \ud835\udf15T/\ud835\udf15y (z \u2013z0) \ud835\udf15T/\ud835\udf15z= N(B - T) (7) \n\n\n\nWhere f is the observed field at location (x, y and z) and B is the base level \nof the field (regional value at the points x, y, z and SI is the structural index \nor degree of homogeneity [26]. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nThe total magnetic intensity map was analyzed and interpreted, in order \nto delineate and characterize the lithologies and structures in the study \narea. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 51-60 \n\n\n\nCite The Article: Cyril C. Okpoli (2019). Delineation Of High-Resolution Aeromagnetic Survey Of Lower Benue Trough For Lineaments And Mineralization: Case Study Of \nAbakikili Sheet 303. Malaysian Journal Of Geosciences, 3(1): 51-60. \n\n\n\n3.1 Total Magnetic Intensity (TMI) \n\n\n\nThe Total Magnetic Intensity (TMI) map (Figure 5) shows positive \nmagnetic intensity value as high as 93.76 nT which dominated the \nsouthern with small segment at the western and northwestern part of the \nmap. The high magnetic intensity at the southern part was cut by an \nintermediate magnetic intensity (68.48 nT to 86.67 nT). The high magnetic \nintensity corresponds to the Awgu shale and the intermediate magnetic \nintensity corresponds to the Nkporo shale. \nThe north, north central and northwestern parts of the study area is \ndominated by intermediate magnetic intensity. These areas were \ninterpreted as the Asu River Group which comprises the shale, limestone \nand sandstone. At the central part of the map, fairly low magnetic intensity \nresponse (37.38 nT- 68.48 nT) and low magnetic intensity (34.14 nT to \n61.40 nT) were identified. These features were interpreted as the \ncarbonaceous shale within the Asu River Group. The low magnetic \nintensity response is also observed at the northern region of the map with \na trend of E-W direction and at the northeastern part showing a trend in \nsame direction. At the eastern and northeastern part of the map there exist \nlow magnetic intensity (34.14 nT to 61.40 nT) and intermediate magnetic \nintensity (68.48 nT to 86.67 nT) respectively. Both the low and \nintermediate magnetic intensity correlates with the Eze-aku shale \nFormation. As it can be seen on the map (Figure 5), faults are observed at \nthe southeastern region of the map showing two major trends, NE-SW and \nNW-SE trends. \n\n\n\n Figure 5: Total Magnetic Intensity (TMI) Image of Abakaliki Sheet 303. \n\n\n\n3.2 Reduction to Magnetic Equator (RTE) \n\n\n\nThe Reduction to equator (RTE) of the total magnetic intensity (TMI) map \n(Figure 6) shows positive magnetic intensity value as high as 93.79 nT \nwhich dominated the southeastern and extended towards the southern \nand southwestern part of the study area. Similar feature is noticed at the \nnorthwestern part of the study area striking NE-SW direction. The north, \nnorth central and northwestern parts of the study area is dominated by \nrocks with intermediate to high magnetic intensity. The intermediate to \nhigh magnetic intensity correlate with the Asu River Group within the \nstudy area.  \n\n\n\nThe map (Figure 6) shows variation in the magnetic intensity, possibly \nindicating variations in mineral composition of the rocks in the study area. \nThe central part of the map is dominated by fairly low magnetic intensity \n(34.14 to 61.40 nT) which can be depicted as the limestone within the Asu \nRiver Group (Arls). This feature is also observed at the northern region of \nthe map with a trend of approximately E-W direction and at the \nnortheastern part showing a trend in same direction. Intermediate \nmagnetic intensity (61.40 to 83.63 nT) feature is seen at the southwestern \npart of the map trending almost in E-W direction. This body with \nintermediate magnetic intensity is considered as the Nkporo Shale (Nsh). \nFeatures with high magnetic intensity (86.63 to 93.79 nT) was observed \nat the southwestern and southeastern part of the map showing a trend of \n\n\n\nNE-SW direction. The high magnetic intensity was observed to correlate \nwith the Awgu Shale (Ash). Low magnetic intensity (29.39 to 34.14 nT) \nresponse is seen at the northern part of the area striking almost in E-W \ndirection. It is also identified at the western part of the map trending in the \nsame direction as that of the one at the northern part of the area. This low \nmagnetic intensity corresponds to the carbonaceous shale within the Asu \nRiver Group (Arcs). \n\n\n\nAs it can be seen on the map (Figure 6), three faulting systems, F1-F11\u2019, \nF2-F22\u2019 and F3-F33\u2019were observed with trends of NE-SW, E-W and NW-\nSE direction respectively. According to a study, the variation in trends of \nthe faults was attributed to deeper heterogeneity of the earth crust during \nthe sequence of events at possible opening up of South American and \nAfrican plate [3,27]. \n\n\n\nFigure 6: Reduction to Equator of Total Magnetic Intensity (RTE_TMI) \nImage. \n\n\n\n3.3 Regional and Residual Magnetic Intensity (RMI) \n\n\n\nThe TMI data continued upward to depth of 4 km produced a regional field \nassumed to have resulted from relatively deep-seated structures. On the \nregional map (Figure 7), high magnetic intensity (76.97 nT \u2013 80.99 nT) to \nintermediate magnetic intensity (64.23 nT \u2013 76.97 nT) were observed at \nthe southern part of the map with a trend of approximately N-S direction. \nAt the western, northwestern and northeastern part of the map there \nexists intermediate magnetic intensity (64.23 nT- 76.97 nT) striking in E-\nW direction. Low magnetic intensity was identified at the eastern part of \nthe map trending in E-W direction. Clearer image on the general magnetic \nintensities of the study area is clearly seen on the residual map than what \nwe have on the TMI map because the regional trends have been removed. \n\n\n\nThe RMI map (Figure 7) displays different magnetic intensity, with most \nof them trending in E-W and NE-SW directions. Based on the residual map, \nthe rocks in the study area can generally be classified into four (4) major \nlithologies, (1.) the Asu river Group, (2.) Awgu shale, (3.) Nkporo shale and \n(4.) Ezeaku shale. The magnetic zone division was based on the intensity \nof magnetic signatures.  On the northern, northwestern part of the map, \nthere exist rocks with high magnetic intensity (12.76 nT- 15.59 nT) which \ncan be regarded as Asu River Group. The high magnetic intensity may be \nlinked with galena and associated minerals which may be present within \nthe study are, Low magnetic intensity (-22.62 nT to -5.22) nT) was \nobserved at the northwestern part of the map. This low magnetic can be \nattributed to magnetic response coming from deep basement complex \nwithin the study area. This feature is noticed to correlate with \ncarbonaceous shale within the Asu River Group. High magnetic intensity \n(12.76 nT- 15.59 nT) is noticed at the western region of the map which can \nbe considered as the shale within the Asu River Group. At the southern \npart of the map, high magnetic intensity is observed. This high magnetic \nintensity is seen to be separated by intermediate magnetic intensity (2.05 \nnT- 12.76 nT). The high and intermediate magnetic intensity corresponds \nto the Awgu Shale and Nkporo Shale respectively. Fairly low (-5.22 nT- \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 51-60 \n\n\n\nCite The Article: Cyril C. Okpoli (2019). Delineation Of High-Resolution Aeromagnetic Survey Of Lower Benue Trough For Lineaments And Mineralization: Case Study Of \nAbakikili Sheet 303. Malaysian Journal Of Geosciences, 3(1): 51-60. \n\n\n\n2.05 nT) magnetic intensity was observed at the northeastern part of the \nmap and can be interpreted as the Eze-aku Shale. \n\n\n\nFigure 7: Residual Magnetic Intensity Image of Abakaliki Sheet 303. \n\n\n\n3.4 Automatic Gain Control (AGC) \n\n\n\nIt gives sensible view of well aligned anomalous structures with positive \nintensity signals that ranged from 138.46 nT to 141.58 nT and 135.26 nT \nto 120.44 nT for low anomalous. From the map (Figure 8), high magnetic \nintensity body can be noticed at the southeastern part. This feature strikes \nin approximately NE-SW direction. The high magnetic is seen to have been \ncross cut by fairly magnetic intensity, trending in same direction. The high \nand low magnetic intensity can be interpreted as Awgu Shale (Ash) and \nNkporo Shale (Nsh) respectively. The map is seen to be dominated by \nintermediate magnetic intensity (136.72 nT - 138.46 nT) which shows a \ntrend of approximately NE-SW direction. At the northern, western and \ncentral region of the map, rocks ranging from fairly low (135.74 nT - \n136.72 nT) to Intermediate magnetic intensity (136.72 nT - 138.46 nT) \nwas noticed and it can be interpreted as the Asu River Group (Arsh). Small \nsegment of low magnetic intensity was identified at the southwestern part \nof the map and it correlate with the carbonaceous limestone within the \nAsu River Group (Arls). High magnetic intensity was observed at the \nnorthwestern part of the map. This feature corresponds with the shale \nwithin the Asu River Group (Arsh). \n\n\n\nFigure 8: Automatic Gain Control Image. \n\n\n\n3.5 Upward Continuation and Downward Continuation Maps \n\n\n\nUpward continuation is often used to remove or minimize the effects of \n\n\n\nshallow sources and noise in grids. It is basically used to infer the attitudes \nof structures with depth. Figures 9.10 and 11 show the RTE (grid) upward \ncontinued to 500 m, 1 km, 2 km, 3 km, 7 km and 10 km, respectively; i.e. \nthey show images of the magnetic intensity that would be obtained \nassuming the data were recorded at heights of 500 m, 1 km, 2 km, 3 km, 7 \nkm and 10 km higher than the original datum the data was collected. In \nphysical terms, as the continuation distance is increased, the effects of \nsmaller, narrower and thinner magnetic bodies progressively disappear \nrelative to the effects of larger magnetic bodies of considerable depth \nextent. As a result, upward-continuation maps give the indications of the \nmain tectonic and crustal blocks in an area [28].  \n\n\n\nComparing the Figures, the 500 m upward continuation (Figure 9) shows \nsimilar features as seen in the RTE image (Figure 6). Thus the magnetic \nbody that is producing the anomaly may be at a deeper depth other than \nthe 500 m depth. Moreover, comparing the 1 km, 2 km and 3 km upward \ncontinuation images (Figures 9, 10 and 11) with the RTE image (Figure 6) \nand 500 m upward continuation image (Figure 9), it is seen that linear \nfeature at the southern part of the map striking approximately in NE-SW \ndirection in Figures 6 and 9 are not seen in Figures 9 and 11. The linear \nfeature in Figures 9 and 10 at the southern region of the area diminished \nuntil it is not visible in the 2 km images (Figure 11). This may indicate that \nthe signature is coming from depth shallower than 2 km. \n\n\n\nThe positive anomalies displayed by these images at the northwestern is \nseen to be absent from the 3 km image (Figure 12). On the other hand, on \nFigure 9 and 10 the low magnetic anomalous (21.09 nT \u2013 33.20 nT) \nrounded body noticed at the southwestern part is not obvious in the 3 km \nimage.  \n\n\n\nThe upward continuation images Figures 9, 10, 11, 12, 13 and 14 revealed \nthat the regional trend of the rocks in the study area is in NE-SW and E-W \ndirection. \n\n\n\nFigure 9: Upward Continued RTE_TMI (500m) Image. \n\n\n\nFigure 10: Upward Continued RTE_TMI (1000) Image \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 51-60 \n\n\n\nCite The Article: Cyril C. Okpoli (2019). Delineation Of High-Resolution Aeromagnetic Survey Of Lower Benue Trough For Lineaments And Mineralization: Case Study Of \nAbakikili Sheet 303. Malaysian Journal Of Geosciences, 3(1): 51-60. \n\n\n\nFigure 11: Upward Continued RTE_TMI (2000) Image. \n\n\n\nFigure 12: Upward Continued RTE_TMI (3000) Image. \n\n\n\nFigure 13: Upward Continued RTE_TMI (7000m) Image \n\n\n\nFigure 14: Upward Continued RTE_TMI (10000m) Image. \n\n\n\nThe downward continuation filter enhances responses from shallow \ndepth sources by effectively bringing the plane of measurement closer to \nthe source. However, the data contain short wavelength that appear as \nsignals coming from very shallow sources in the continuation. \nFigures 15 and 16 shows the RTE (grid) continued downward to 20 m and \n50 m respectively. Comparing Figures 5 4.2, 15 4.10 and 16 4.11, the 20 m \ndownward continuation in comparison with the 50 m image shows similar \nfeatures as seen in both images (Figure 15 and 16). Thus, the magnetic \nbody that is producing the anomaly may be at a depth within the range of \n20 m to 50 m depth. Moreover, comparing both the 20 m and 50 m \ndownward continuation images (Figures 15 and 16) with the RTE image \n(Figure 5). It is seen that the high magnetic intensity (86.87 nT- 92.95 nT) \nat the western part of Figures 15 and 16 are observed to appear as an \nintermediate magnetic intensity (64.85 nT- 86.87 nT) on the Figure 5.  \nFrom the downward continuation map, it is seen that the boundaries of \nthe different formations      were not well demarcated compared to the \nupward continuation. The downward continuation map enables the easy \nobservation of short wavelength anomalies not seen in the original RTE \nmap.  \nFrom these maps, structural interpretations were carried out through \nvisual inspection. Two major lineaments/faults systems were identified \non these images by observing the abrupt changes between the positive and \nnegative magnetic anomalies. The lineament/faults systems are: F1-\nF11\u2019and F2-F22\u2019 rending NE-SW and ENE-WSW respectively. \n\n\n\nFigure 15: Downward Continued RTE_TMI (20m) Image. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 51-60 \n\n\n\nCite The Article: Cyril C. Okpoli (2019). Delineation Of High-Resolution Aeromagnetic Survey Of Lower Benue Trough For Lineaments And Mineralization: Case Study Of \nAbakikili Sheet 303. Malaysian Journal Of Geosciences, 3(1): 51-60. \n\n\n\nFigure 16: Downward Continued RTE_TMI (50m) Image \n\n\n\n3.6 Second Vertical Derivative (SVD) \n\n\n\nThe SVD filter decreases broad and more regional anomalies and rather \nenhance local magnetic responses which are interpreted as structures in \nthe area. \n\n\n\nA grey scale is applied to the second vertical derivative of the RTE map. \nThis helped in the identification of features such as lineaments/faults, \ncontacts, edges and trends of various rocks. The grey scale of the second \nvertical derivative (SVD) image of the RTE gridded data (Figure 17) \nenhanced the image by showing major structural and lithological details \nwhich were not obvious in RTE image (Figure 5). \n\n\n\n Prominent lithological contacts observed are: the Awgu shale (marked as \nA) boundaries with other Formation. Points B, C and D, highlight areas \noccupied by Nkporo Shale, the Asu River Group and Eze-aku shale \nrespectively. The Awgu shale boundaries are well defined when compared \nwith other Formations in the area. \n\n\n\n3.7 Analytical Signal (AS) \n\n\n\nTo know the source positions of the magnetic anomaly regardless of \ndirection and remnant magnetization of the sources effects that are mostly \nassociated with the RTE, the analytical signal filter was applied to the RTE \ngrid. The significant characteristic of the analytical signal is that, it is \nindependent of the direction of the magnetization of the source. Moreover, \nthe amplitude of the analytical signal can be related to the amplitude of \nmagnetization. The most significant concentrations of mineral deposits in \nan area are correlated with high analytical signal amplitudes [28]. \n\n\n\nFigure 18 (analytical signal map) shows that, the most prominent features \nare the high analytic signal amplitude that runs in an approximately NE-\nSW direction along the southeastern part of the area and small segment \naround the northwestern border of the area. Four major magnetic \namplitude zones i.e high magnetic amplitude zone (> 0.03 nT/m) which \nare defined as Awgu shale (Ash), intermediate magnetic anomalous zone \n(ranges from 0.02 to 0.03 nT/m) as Asu river Group (Arsh), fairly low \nmagnetic anomalous zone (ranges from 0.01 to 0.02 nT/m) as (Nkporo \nshale) and low magnetic anomalous zone (< 0.01nT/m) as Ezeaku shale \n(Esh),   were delineated (Figure 18). These four different amplitude zones \nare based on the magnetization contrast, produced by varying mineralogy \ncomposition and depth of the magnetic sources. \n\n\n\nFigure 17: Second Vertical Derivative Image. \n\n\n\nFigure 18: Analytical Signal Image. \n\n\n\n3.8 Tilt-angle Derivative (TDR) \n\n\n\nTo determine structures (lineament, fault, joints, etc.), the contacts and \nedges or boundaries of magnetic sources, and to enhance both weak and \nstrong magnetic anomalies of the area, the Tilt-angle Derivative (TDR) \nfilter was applied to the RTE grid. The TDR filter attempts to place an \nanomaly directly over its source [29-31]. Structural deformations such as \nfaults, joints, and arched zones are well pronounced on grey TDR map \n(Figure 19).  \n\n\n\nFrom Figure 19, three major fault systems were noticed on this image by \nobserving the abrupt changes between the positive and negative magnetic \nanomalies. The lineament systems are: F1-F11\u2019, F2-F22\u2019 and F3-F33\u2019 \ntrending NE-SW, ENE-WSW and NW-SE respectively. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 51-60 \n\n\n\nCite The Article: Cyril C. Okpoli (2019). Delineation Of High-Resolution Aeromagnetic Survey Of Lower Benue Trough For Lineaments And Mineralization: Case Study Of \nAbakikili Sheet 303. Malaysian Journal Of Geosciences, 3(1): 51-60. \n\n\n\n3.9 Radially Averaged Power Spectrum (RAPS) \n\n\n\nPower Spectrum is a 2D function of the energy and wave number and can \nbe used to identify average depth of source assemblages [18]. Radially \nAveraged Power Spectrum or Spectral plot (Figure 20) shows the total \ndepth estimate to the top of magnetic sources that produced the observed \nanomalies in the study area using spectral analysis. The gradient of the \nlayers were calculated based on the wavelength of the magnetic sources. \nThe gradient of the shallower and deeper magnetic sources are 3.51 and \n33.15 respectively. Therefore, the total depth estimates to the top of \nmagnetic shallower and deeper sources in the area are 0.279 km (27.9 m) \nand 2.64 km respectively. \n\n\n\nFigure 19: Grey Tilt Derivative Image \n\n\n\nFigure 20: Radially Averaged Power Spectrum/Spectral Plot \n\n\n\n3.10 Euler Deconvolution \n\n\n\nEuler solution was applied in determining the depth to the magnetic \nsources in the survey area by setting different Structural Index, SI 0.0 \n(Figures 21). The gridding interval enables recognition of any anomaly \nthat is up to 100 min wavelength, hence many solution points which sum \nup to 34,595. Result with tightest cluster around recognized sources is \nlikely to give the best solution and therefore accepted [32-34]. Solutions \nfor Abakaliki Southwest regions indicate relatively deep magnetic source, \ngreater than 2000 m in depth as seen underlying Asu River Group at the \nnorthern, western and southwestern to southern part of the map and \nreduced progressively towards southeastern part [35-38].  Euler depths \nresult ranged for regions in the study area such as Abakaliki at the central \npart of the map (>2000 m).  \n\n\n\nFigure 21: Standard Euler solutions of Abakaliki Sheet 303. \n\n\n\n4. CONCLUSION \n\n\n\nThe study considered the use of aeromagnetic dataset, to interpret and \nmap the lithological and structural features of Abakaliki (Sheet 303 SW). \nThe aeromagnetic data was subjected to different forms of filtering \nprocesses in order to enhance the total magnetic intensity (TMI) data. The \nmagnetic image enhancing filters applied to the Total Magnetic Intensity \n(TMI) are Reduction to Equator (RTE), analytic signal, Second Vertical \nDerivative (SVD), Tilt-angle Derivative (TDR), Radially Averaged Power \nSpectrum (RAPS) and Euler Deconvolution using Geosoft Oasis Montaj \npackage. These filters helped define the lithological boundaries and \ngeological structures such as lineaments, faults, joints, etc. The images \nhave revealed different range of magnetic intensity values, suggesting \ndifferent rock types of varying mineralogical compositions, tectonic \nframework and structural features. \n\n\n\nThe results of the interpretation of the aeromagnetic dataset shows that \nthe study area is dominated by four to five lithology as proven by the total \nmagnetic intensity image, reduction to equator, residual magnetic \nintensity map, analytic signal image.The lithologies include shale and \nlimestone belonging to Asu River Group, Awgu shale, Eze-aku shale and \nNkporo shale.  \n\n\n\nFrom the upward continuation maps, it can be concluded that the \nsubsurface geology has regional trend of NE\u2013SW and E-W direction. \n\n\n\nThe interpreted subsurface Faults; FI-F11, F2-F12, F3-F13 elements \ndelineated in the study area were oriented into three major directions \nwhich are NE-SW, ENE-WSW and NW-SE respectively as seen on the tilt \nangle derivative and second vertical derivative. The total depth estimation \nto the top of magnetic sources for the study area as shown on the spectral \nplot ranged from 0.279 km (27.9 m) to 2.64 km for shallower and deeper \nsources respectively. The aeromagnetic dataset proved valuable in the \ndelineation of most of the lithologies and structures in the study area and \nestimating the depth to magnetic source body. It serves a valuable tool for \ncharacterizing the rock formation in the study area. The varied depth \nrange especially the deeper sources makes the study good for \nhydrocarbon accumulation but due to diverse intrusive bodies in the study \narea makes it not viable. \n\n\n\nREFERENCES \n\n\n\n[1] Kearey, P., Brooks, M., Hill, I. 2002. An Introduction to Geophysical \nExploration. 3rded. Blackwell Publishing. 255p. \n\n\n\n[2] Telford, W. M., Geldart, L. P., Sheriff, R.E. 1998. Applied Geophysics, (2 \n148 Nd Ed), Cambridge University Press, USA, 113 \u2013 114. \n\n\n\n[3] Wright, J.B. 1968. South Atlantic continental drift and the Benue\nTrough. Tectonophysics, 6, 301 \u2013 310. \n\n\n\n[4] Wright, J.B., Grant, K. 1985. The Benue Trough. In: Geology of mineral \nresources of West Africa Springer.98p ISBN 0-04-556001-3. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 51-60 \n\n\n\nCite The Article: Cyril C. Okpoli (2019). Delineation Of High-Resolution Aeromagnetic Survey Of Lower Benue Trough For Lineaments And Mineralization: Case Study Of \nAbakikili Sheet 303. Malaysian Journal Of Geosciences, 3(1): 51-60. \n\n\n\n[5] Grant, F.S. 1971. Aeromagnetic, geology and ore environments, In. \nmagnetite, in igneous, sedimentary and metamorphic rocks: an overview. \nGeoexploration, 23, 303 \u2013 333. \n\n\n\n[6] Nwachukwu, S.O. 1972. The tectonic evolution of the southern portion \nof the Benue Trough. Journal of Geology and Mining Research, 11, 45-55. \n\n\n\n[7] Olade, M.A. 1975. Evolution of Nigeria\u2019s Benue Trough (Aulacogen); a \ntectonic model. Geological Magazine, 112, 575 \u2013 583. \n\n\n\n[8] Petters, S.W. 1978. Stratigraphic Evolution of the Benue Trough and its \nimplication for the Upper Cretaceous Paleogeography of West Africa. The \njournal of Geology, 86 (3), 311-322. \n\n\n\n[9] Olade, M.A. 1979. The Abakaliki pyroclastics of southern Benue, \nNigeria: their petrology and tectonic significance. Journal of Mining and \nGeology, 16, 17 \u2013 25. \n\n\n\n[10] Ofoegbu, C.O. 1985. Interpretation of Magnetic anomalies over the \nlover and Middle Benue Trough of Nigeria. Geophysical Journal Royal \nAstronomical Society, 79, 813-823. \n\n\n\n[11] Benkhelil, J. 1988. Structure. Centres Rech., Explor. Prod. Elf and \nGeodynamic Evolution of the Intracontinental Benue Basin (Nigeria) Bull \nAquitaine in 1207, 29-128. \n\n\n\n[12] Ananaba, S.E., Ajakaiye, D.E. 1987. Evidence of tectonic control of \nmineralization of Nigeria from lineament density analysis: a land set study.\nInternational Journal. of Remote Sensing, 1 (10), 1445-1453. \n\n\n\n[13] Uzuakpunwa, A.D.B. 1974. The Abakaliki Pyroclastics, Eastern \nNigeria: New Age and Tectonic Implications. Geological Magazine, 11, 65 \u2013 \n70. \n\n\n\n[14] Ofoegbu, C.O., Onuoha, K.M. 1991. Analysis of Magnetic Data over the \nAbakaliki       Anticlinorium of the Lower Benue Trough. Nigeria Journal of \nMarine Geology, 45 \u2013 51. \n\n\n\n[15] Ukaegbu, V.U., Akpabio, I.O. 2009. Geology and stratigraphy Northeast \nAfikpo Basin Lower Benue Trough Nigeria. Pacific Journal of Science and \nTechnology, 10518-527. \n\n\n\n[16] Ofoegbu, C.O., Onuoha, K.M. 1990. A review of geophysical \ninvestigations in the Benue Trough. In: The Benue Trough, Structure and \nEvolution (Edited by Ofoegbu, C. O.), 171 -201. Vieweg and Sohn,\nBraunschweig, Germany. \n\n\n\n[17] Obaje, N.G. 2009. The Benue Trough Geology and Mineral Resources \nof Nigeria. Springer, Dordrecht Heidelberg, New York, London P.57 ISBN \n3-540-92684-4. \n\n\n\n[18] Spector, A., Grant, F.S. 1970. Statistical models for interpreting\naeromagnetic data. Geophysics, 35, 293-302. \n\n\n\n[19] Nwachukwu, S.O. 1975. Temperature formation of vein minerals in \nthe southern portion of the Benue Trough, Nigeria. J. Mineral. Geology., 11, \n45-54. \n\n\n\n[20] Ezema, P.O., Ezeh, I.D., Ugwu, G.Z., Abudullahi, U.A. 2014. Hydrocarbon \nand mineral exploration in Abakikili southeastern Nigeria. International \njournal of Engineering Sciences, 3(1), 24-30. \n\n\n\n[21] Blakely, R.J. 1995. Potential theory in Gravity and Magnetic \n\n\n\nApplication. Cambridge University Press, Cambridge. 441p. \n[22] Faboye, F.B., Gideon, Y.B. 2003. Improved downward continuation of \npotential field data. Exploration Geophysics, 34, 249-256. \n\n\n\n[23] Nabighian, M.N. 1972. The analytic signal of two-dimensional \nmagnetic bodies with polygonal cross-section: Its properties and use for \nautomated anomaly interpretation. Geophysics, 37, 501\u2013517. \n\n\n\n[24] Hood, P.J. 1965. Aeromagnetic gradiometer program of the \ngeophysical survey of Canada. Geophysics, 54, 1012-1022. \n\n\n\n[25] Thompson, D.T. 1982. EULDPH.  A new technique for making \ncomputer- assisted depth estimate from magnetic data. Geophysics, 47, \n31-37. \n\n\n\n[26] Reid, A.B., Allsop, J.M., Granser, H., Mllettt, A.J., Somerton, I.W. 1990. \nMagnetic interpretation in three dimentions using Euler deconvolution. \nGeophysics, 55, 80-91. \n\n\n\n[27] Ajakaiye, D.E., Hall, D.H., Ashieka, J.A., Udensi, E.E. 1980. Magnetic \nAnomalies in the         Nigerian Continental Mass based on Aeromagnetic \nSurveys. In: Wasilewski, P and Hood, P. (Eds.), Magnetic Anomalies-Land \nand Sea. Tectonophysics, 192, 211-230. \n\n\n\n[28] Reeves, C.V., Macnab, R., Maschenkov. 1998. Compiling all the world\u2019s \nmagnetic anomalies EOS American Geophysical Union, July 14, 338. \n\n\n\n[29] Salem, A., Williams, S., Fairhead, J., Ravat, D., Smith, R. 2007. Tilt-depth \nmethod: a          simple depth estimation method using first-order magnetic \nderivatives. The Leading Edge, 26, 1502-1505. \n\n\n\n[30] Akingboye, A.S. 2017. Derivatives and Analytic Signals: Improved \nTechniques for Lithostructural Classifications.\nhttps://www.researchgate.net/publication/321341519, pp. 1-14. \n\n\n\n[31] Okpoli, C.C., Oladunjoye, M.A. 2017. Precambrian Basement \nArchitecture and Lineaments Mapping of Ado-Ekiti Region Using \nAeromagnetic Dataset. Geosciences Research, 2 (1), 27\u201345. \n\n\n\n[32] Barraclough, D.R., Malin, S.R.C. 1971. Synthesis of International \nGeomagnetic Reference Field Values. Inst. Geol. Sci. Rep., 71 (1), 24-36. \n\n\n\n[33] Kearey, P., Nabighian, M.N., Grauch, J.S., Hansen, R.O., LaFehr, T.R., Li, \nY., Peirce, J.W., Phillips. 2003. The historical development of the magnetic \nmethod in exploration. Geophysics, 70, 33ND-61ND. \n\n\n\n[34] MontajTM Tutorial. 2004. Two \u2013 Dimensional frequency domain \nprocessing of potential field data. \n\n\n\n[35] Milsom, J. 2003. Field Geophysics(3rdedn.): The geological field guide \nseries. John Wiley & Sons Ltd., John Milsom University College, London, 51-\n70. \n\n\n\n[36] Mussett, A.E., Khan, M.A. 2000. 11 Magnetic surveying. Looking into\nthe earth; an introduction to geological geophysics (1. Publ., repr. Ed.) \nCambridge university press. pp. 162-180. ISBN 0-521-78085-3. \n\n\n\n[37] Obaje, N.G., Wehner, H., Abubakar, M.B., Isha, M.T. 2009. Nasara- I will,\nGongola basin Benue Trough Nigeria; source-rock evaluation. Journal of \nPetroleum, 27 (2), 191-206. \n\n\n\n[38] Reilly, W.I. 1972. Gravitational expression of the Dunedin volcano.\nNew Zealand. Journal of Geology and Geophysics, 15 (1), 16-21 \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosc i ences 2(1) (2018) 30-33 \n\n\n\nCite the Article: Sivadass Thiruchelvam, Mohd Fauzi Ismail, Azrul Ghazali, Kamal Nasharuddin Mustapha, Fatin Faiqah Norkhairi, Nora Yahya, Abdul Aziz Mat Isa, Zakaria Che Muda \n(2018). Development of Humanitraian Supply Chain Performance Conceptual Framework in Creating Resilient Logistics Network. Malaysian Journal of Geosciences, 2(1) : 30-33. \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nHumanitarian logistics which is precisely known as humanitarian supply chain (HSC) plays a major role in reducing \n\n\n\nthe impact of disaster on human life and livelihood by providing humanitarian aid in the forms of food, water, \n\n\n\nmedicine, shelter and other supplies. Unfortunately, anecdotal evidences indicated that relief chain tends to be \n\n\n\nunstable, unpredictable and unresponsive to the needs of disaster victims. The 2004 Asian tsunami highlighted the \n\n\n\nlack of coordination between the relief chain linkages that hampered effective supply of aid. This phenomenon was \n\n\n\nfurther evident in our own context during the 2014 flood devastation in Peninsular Malaysia. Floodwaters and \n\n\n\nsubsequent landslides blocked major roads, limiting access to evacuation centres and impeding the delivery of \n\n\n\nemergency relief supplies. Hence, an effective humanitarian supply chain management (HSCM) should be able to be \n\n\n\ndeployed rapidly enabling provision of aid to beneficiaries. Notwithstanding the frequency and impact of disasters, \n\n\n\nhumanitarian organizations today are under continuous pressure of improving their logistics performance. Departing \n\n\n\nfrom this need, this study aims to examine the criteria that influence the humanitarian aid actors in their decision \n\n\n\nmaking while increasing transparency and accountability of relief operations. Therefore, it is imperative for \n\n\n\nhumanitarian sector to quantify the efficiency and effectiveness of a particular relief operation using set of \n\n\n\nperformance metrics. A mixed methods approach comprising qualitative and quantitative survey will be used. The \n\n\n\nstudy intended to identify and define the metrics that would determine successful operational performance of disaster \n\n\n\nrelief. This research will contribute mainly in the development of a HSCM performance model that (i) informs decision \n\n\n\nmakers at the strategic, tactical and operational level in tracking progress, (ii) facilitate a more open and transparent \n\n\n\ncommunication and cooperation between humanitarian actors, and (iii) improve the logistics of disaster management \n\n\n\nboth at the government and at non-governmental level.       \n\n\n\nKEYWORDS \n\n\n\nHumanitarian logistics, supply chain, disaster.\n\n\n\n1. INTRODUCTION \n\n\n\nNatural disaster that usually occur may affect the population area causing \ndestruction to the community which leads to the suffering and deprivation \n[1]. With the increasing number of natural disaster that occur around the \nglobe such as tsunami in Indonesia, earthquake in China and Japan, flood \ndisaster in India and heavy rain in China causing the rapid assistance need \nto be provided to the victims immediately. Providing an assistance for the \nvictims may occur in different ways such as such as salvaging those who \nare wounded and/or stranded, collecting and disposing corpses, resource \nallocation, provision of food aid, shelter and medical care, and restoring \naccess to remote locations [1]. \n\n\n\nPerformance measurement is known to become one of the important \nfactor in order to improve the efficiency and effectiveness of commercial \nsupply chain. However, measuring performance in structured and \nstandardized ways is not common in humanitarian supply chain [2]. \nHumanitarian supply chain need to be focused on as the increasing \nnumber of natural disaster happened. In Malaysia, devastated flood that \noccur in 2014 have become an injection towards a better delivery of \nemergency relief supplies. Therefore, the objectives of this study is to \n\n\n\nestablish four proposed constructs, namely Beneficiary Perspective, \nFinancial Perspective, Internal Process Perspective and Learning and \nGrowth Perspective and establish their definitional dimensions of each of \nthe metric for humanitarian supply chain performance model. \n\n\n\n2. LITERATURE REVIEW \n\n\n\nThe occurrence of both natural and man-made disasters is on the rise \nworldwide. Disaster has been defined as disruption that cripples the \nfunctionality of a community causing major human, material, economic or \nenvironmental losses which surpass the ability of the affected people to \ncope using existing resources [3,4].  The aftermath of these events is \nenormous, not only in the short term which is evident by injuries, loss of \nlife and damaged properties but could also prolong for a great period of \ntime when it comes to social and economic conditions. 2004 Asian tsunami \nand 2005 flooding of New Orleans following hurricane Katrina were \nconsidered to be among the greatest destructions in this century. Both \nevents became an eye-opener of a missing element under the domain of \ndisaster management as surviving inhabitants waited for several days to \nreceive most elementary goods such as water, food and medicine [5,6]. \nHence, an effective and efficient delivery of critical goods is a crucial \nelement of successful disaster relief operation and is closely related to \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : https://doi.org/10.26480/mjg.01.2018.30.33\n\n\n\nDEVELOPMENT OF HUMANITARIAN SUPPLY CHAIN PERFORMANCE \n\n\n\nCONCEPTUAL FRAMEWORK IN CREATING RESILIENT LOGISTICS \n\n\n\nNETWORK \nSivadass Thiruchelvam1, Mohd Fauzi Ismail2, Azrul Ghazali1, Kamal Nasharuddin Mustapha1, Fatin Faiqah Norkhairi1, Nora Yahya1, Abdul Aziz \nMat Isa1, Zakaria Che Muda1 \n\n\n\n1 Institute of Energy Infrastructure, Universiti Tenaga Nasional \n2 Royal Malaysian Police (RMP) \n*Corresponding Author Email: Sivadass@uniten.edu.my; fauzesmail@gmail.com; Kamal@uniten.edu.my; Azrulg@uniten.edu.my; \nFatin.Faiqa@uniten.edu.my; NoraY@uniten.edu.my MZakaria@uniten.edu.my \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \n\n\n\nCODEN : MJGAAN \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 30-33\n\n\n\nCite the Article: Sivadass Thiruchelvam, Mohd Fauzi Ismail, Azrul Ghazali, Kamal Nasharuddin Mustapha, Fatin Faiqah Norkhairi , Nora Yahya, Abdul Aziz Mat Isa, Zakaria Che Muda \n(2018). Development of Humanitraian Supply Chain Performance Conceptual Framework in Creating Resilient Logistics Network. Malaysian Journal of Geosciences, 2(1) : 30-33. \n\n\n\nhumanitarian logistics which is also precisely known as humanitarian \nsupply chain.   \n\n\n\nHumanitarian logistics could be defined as logistical activities that \ncomprise planning, implementing and controlling the efficient, cost-\neffective flow of and storage of goods and materials as well as related \ninformation, from point of origin to point of consumption for the purpose \nof alleviating the suffering of vulnerable people [7]. Goods and materials \nin this context are better known as humanitarian aid in the forms of food, \nwater, medicine, shelter and other supplies. Some researchers indicated \nthat most of the humanitarian supply chain are unstable, unpredictable \nand stiff to respond to the needs of the affected victims [4]. This is further \nworsened by the fact that no single individual or group controls a relief \noperation [8]. More lives could be saved, and great degree of suffering \ncould be reduced by the efficiency and effectiveness of humanitarian aid \ndelivery in response to disasters. A researcher asserted that the only way \nto achieve efficiency and effectiveness is through humanitarian supply \nchain management as 80% of disaster relief efforts are governed by \nlogistics [9]. Katrina and Asian tsunami have proven that the problem of \nfailing links within the logistical chain is quite common at times of disaster. \n\n\n\nHumanitarian supply chain management (HSCM) is about managing the \nprocesses and systems involved in mobilizing people, resources, skills and \nknowledge to help vulnerable people affected by disaster [10]. The HSCM \nand the commercial supply chain management (CSCM) are different in \ntheir motives and operating conditions [11]. HSCM is more towards \nserving the mankind while CSCM focuses on the generation of profit. The \ncustomers in a disaster supply chain include the population at the affected \narea, as well as intermediate customers at local and global storage \nfacilities. Their needs change significantly according to disaster types and \nthe phases in the disaster timeline. The main task is to mobilise the goods, \nfinance and to administer the services to the beneficiaries. For instance, \nthe 2004 Asian tsunami saw the biggest amount of humanitarian aid in the \nhistory, hitting the worth more than USD 13 billion [12]. Disaster relief \nrequires the activities in many dimensions such as rescue efforts, health \nand medical assistance, food, shelter and long-term relief activities. The \nsuccess of any relief activity depends heavily on the logistical operations \nof the supply deliver [13]. Humanitarian supply chain is the central to \ndisaster relief due to its function to serve as a bridge between disaster \npreparedness and response as well as between procurement and \ndistribution [9, 14].  \n\n\n\nAny event of disaster tends to be considered as a test the readiness of \nhuman and relevant systems in place to face it, especially the capacity of \ndifferent actors to work together [15]. Actors in humanitarian supply \nchain refers to various stakeholders who are directly or indirectly \ninvolved in the relief operations such as government, aid donors, other \nNGOs, military, logistic providers, and aid agencies [16,10]. In normal \nsituations, these actors have the least motivation to work together for an \nextended period of time. However, upon the arrival of disaster, they \ncombine their capacity and capability to relieve human suffering [17, 18]. \nAlthough these actors are instrumental to an effective response, they are \ncapable of creating confusion as they compete for with each other for \nfunds, resources, critical infrastructure and decision-makers\u2019 attention \n[7].  \n\n\n\nNotwithstanding the frequency and impact of disasters, humanitarian \norganizations today are under continuous pressure of improving their \nlogistics performance. Departing from this need, this study aims to \nexamine the criteria that influence the humanitarian aid actors in their \ndecision making while increasing transparency and accountability of relief \noperations. Therefore, it is imperative for humanitarian sector to quantify \nthe efficiency and effectiveness of a particular relief operation using set of \nperformance metrics. A mixed methods approach comprising qualitative \nand quantitative survey will be used. The study intended to identify and \ndefine the metrics in the various perspectives such as beneficiary financial, \ninternal process as well as learning and growth that would determine \nsuccessful operational performance of disaster relief. This research \ncontributes mainly in the development of a HSCM performance model as \nillustrated in Figure 1 that informs decision makers at the strategic, \ntactical and operational level in tracking progress as well as to facilitate a \nmore open and transparent communication and cooperation between \nhumanitarian actors whilst improving the logistics of disaster \nmanagement both at the government and non-governmental level. \n\n\n\nFigure 1: Conceptual Framework for Measuring Humanitarian Supply \nChain Performance \n\n\n\n3. THE WAY FORWARD \n\n\n\nWhen a disaster strikes be it natural or man-made, the ultimate aim of \nemergency management stakeholders would be to protect and assist the \ncivilian population in the affected regions. Successful disaster relief \noperations involve various tasks such as rescue efforts, health and medical \naid, food, shelter and long-term relief activities which are heavily reliant \non logistical operations of the supply delivery [13]. However, this is never \nas simple as A, B, C. Effective and efficient humanitarian supply chain is \nimperative in saving lives and reducing suffering for those people affected \nby disaster [19]. Notwithstanding the increasing pressure from the donors \nto prove that aid and goods are really reaching the ones in need during \nemergency relief operations, at present little work has been conducted to \ndetermine the viable metrics and corresponding definitions which could \nassess and further improve humanitarian logistics performance. \n\n\n\nThe purpose of this study is to identify appropriate metrics to quantify \nefficiency and effectiveness which represent two central goals of any \nhumanitarian organization, the weight of importance the metrics are \nconsidered, and how each factor is defined among the actors of supply \nnetwork of humanitarian aid during the recent flood catastrophe. By \nhaving a validated set of metrics and their corresponding definitional \ndimensions, the humanitarian relief operation management key players \ncould apprehend the nature and characteristic of the real-time relief chain \nrelated processes compared to the elements outlined in the current \nStandard Operating Procedures (SOPs). Therefore, formulation of an \nappropriate humanitarian supply chain performance model would help \nhumanitarian aid actors in their decision making to improve the efficiency \nand effectiveness of relief operations which takes into account the issues \nof transparency and accountability. This will further enhance and \nreinforce the disaster planning and preparedness initiatives to be \nundertaken in facing any future calamities. \n\n\n\nThis study is to be carried out using simultaneous qualitative and \nquantitative approaches to identify, define and assign weight of \nimportance to the metrics which quantify efficiency and effectiveness of \nrelief operations during disasters. Through these processes the envisaged \ninformation could be augmented as advisory points by National Disaster \nManagement Agency (Agensi Pengurusan Bencana Negara) as well as by \nother lead agencies during the outbreak of a relevant disaster. Developing \na systematic process and procedure for measuring relief chain \nperformance especially for delivery of aid to the victims can provide the \nkey stakeholders the information required to maximize the effort taken in \nrelief operations during disaster [20]. With a clear perspective of what \nmight transpire during the actual event, a list of proven and tested on the \nground metrics, definitional dimensions, and weight of importance, the \nhumanitarian relief operation management key players could better \n\n\n\n31\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 30-33\n\n\n\nCite the Article: Sivadass Thiruchelvam, Mohd Fauzi Ismail, Azrul Ghazali, Kamal Nasharuddin Mustapha, Fatin Faiqah Norkhairi , Nora Yahya, Abdul Aziz Mat Isa, Zakaria Che Muda \n(2018). Development of Humanitraian Supply Chain Performance Conceptual Framework in Creating Resilient Logistics Network. Malaysian Journal of Geosciences, 2(1) : 30-33. \n\n\n\nanticipate and approach certain scenarios which were not comprehended \nbefore. \n\n\n\nThis study is neither undertaken as a fault-finding exercise nor as a \ncomparison of lead agencies as well as humanitarian organizations \nperformance during the recent flood catastrophe. Instead, it represents an \neffort to provide a list of metrics, corresponding definitional dimensions, \nand weight of importance as identified by actors of supply network of \nhumanitarian aid during the actual disaster relief operations which would \nbe assisting future Disaster Risk Reduction (DRR) strategy in this country. \n\n\n\n4. METHODOLOGY \n\n\n\nThe research process which will be adopted for this study is summarized \nin Figure 2. \n\n\n\nFigure 2: Research Process \n\n\n\n4.1 Literature Review \n\n\n\nA literature review on disaster management, as well as humanitarian \nsupply chain management process will be carried out. \n\n\n\n4.2 Data Collection \n\n\n\nA mixed methods approach comprising qualitative and quantitative \nsurvey will be utilized to examine the stated research questions. \nOperations of lead agencies and humanitarian organizations during the \nrecent flood disaster formed the basis of the study with questionnaire \nsurvey and semi-structured interviews. The participants are targeted \namong key decision-makers. The study intended to identify and define \ndecision making criteria and their corresponding definitional dimensions. \n\n\n\n4.3 Data Analysis \n\n\n\nThe researcher will examine the survey data derived from the multiple-\nchoice questions using the Statistical Package for the Social Sciences \n(SPSS) which facilitates accurate analysis of research data. Data from the \nconducted interviews will be analyzed using NVivo which simplifies the \nprocess for thematic analysis. The quantitative and qualitative findings \nwill be triangulated to provide a deeper understanding of both the \nquantifiable and qualitative drivers of humanitarian supply chain \nperformance model with regards to the Malaysian disaster management \ncontext. Finally, AMOS (Analysis of Moment Structure) will be used to \nestablish confidence in the measurement model which states the \nhypothesized relationships of the observed variables to the underlying \nconstructs. \n\n\n\n4.4 Formulation of Humanitarian Supply Chain Performance \nFramework \n\n\n\nThe triangulated results will form the basis of formulating humanitarian \nsupply chain performance framework. \n\n\n\n4.5 Validation \n\n\n\nA focus group will be conducted to validate the findings of this study. The \nparticipants will be among selected subject matter experts in the domain \nof disaster management in Malaysia. \n\n\n\n5. EXPECTED FINDINGS \n\n\n\nThe current study addressed this gap in the literature review and gave \ndisaster management related officials in the lead agency and humanitarian \norganization an opportunity to contribute their voices to the growing body \nof research about humanitarian supply chain performance tracking for \ndisaster relief operations. The current study provides a greater \nunderstanding of the perceptions that lead agencies and humanitarian \norganizations officials hold regarding delivery of humanitarian aid and the \nextent to which these perceptions are consistent with findings from prior \nstudies. \n\n\n\nThe results of the research can be of value to the current and future \ndirection of disaster management in Malaysia, and the extent to which \ndisaster management related personnel who utilizes it as a source in their \ndecision-making processes. Findings from the current study is envisaged \nto assist in three areas: \n\n\n\ni. To further the research knowledge base regarding humanitarian \nsupply chain for disaster victims in lead agencies and humanitarian \norganizations by focusing on the perceptions of these key \nstakeholders. \n\n\n\nTo inform policy decisions about humanitarian relief decision \nmaking standards. \n\n\n\niii. To aid in the development of a humanitarian supply chain \nperformance model during disaster. \n\n\n\n6. CONCLUSION \n\n\n\nHumanitarian supply chain is one of the way that organization, non-\norganization and stakeholders can practice having better and immediate \nresponse during humanitarian operation. This study is an effort to provide \na list of metrics, corresponding definitional dimensions, and weight of \nimportance as identified by actors of supply network of humanitarian aid \nduring the actual disaster relief operations which would be assisting \nfuture Disaster Risk Reduction (DRR) strategy for Malaysia. One successful \noperation in one humanitarian operation cannot be replicated in other \nhumanitarian operation as the occurrence and nature of disaster will be \nunique from one another. More studies and needs to be done in order to \nimprove the theoretical and the practical aspects.  \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThis work would not have been possible without the unwavering support \nfrom National Disaster Management Agency (NADMA), Tenaga Nasional \nBerhad, Ministry of Higher Education and Malaysia Civil Defense Force. \n\n\n\nREFERENCES \n\n\n\n [1]  Sergio Ricardo Argollo da Costaa, V. B. G. C., Renata Albergaria de \nMello Bandeira. 2012. Supply Chains in Humanitarian Operations: Cases \nand Analysis. Procedia - Social and Behavioral Sciences, 54 (2012), 598 \u2013 \n607. \n\n\n\n[2] Van der Laan, E., De Brito, M., Vergunst, D. 2009. Performance \nmeasurement in humanitarian supply chains. International Journal of Risk \nAssessment and Management, 13 (1), 22-45. \n\n\n\n[3] Kabra, G., Ramesh, A., Arshinder, K. 2015. Identification and \nprioritization of coordination barriers in humanitarian supply chain \nmanagement. International Journal of Disaster Risk Reduction, 13 (1), \n128-138. \n\n\n\n[4] Yadav, D.K., Barve, A. 2015. Analysis of critical success factors of \nhumanitarian supply chain: An application of Interpretive Structural \nModelling. International Journal of Disaster Risk Reduction, 12 (1), 213-\n225. \n\n\n\n[5] Balcik, B., Beamon, B.M., Krejci, C.C., Muramatsu, K.M., Ramirez, M. \n2010. Coordination in humanitarian relief chains: Practices, challenges \nand opportunities. International Journal of Production Economics, 126 (1), \n22-34. \n\n\n\n[6] Th\u00e9venaz, C., Resodihardjo, S.L. 2010. All the best laid \nplans\u2026conditions impeding proper emergency response. International \nJournal of Production Economics, 126 (1), 7-21. \n\n\n\n[7] Thomas, A., Kopczak, L. 2005. From logistics to supply chain \nmanagement: The path forward in the humanitarian sector, white paper, \nFritz Institute, San Francisco, CA. \n\n\n\n32\n\n\n\nii.\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 30-33\n\n\n\nCite the Article: Sivadass Thiruchelvam, Mohd Fauzi Ismail, Azrul Ghazali, Kamal Nasharuddin Mustapha, Fatin Faiqah Norkhairi , Nora Yahya, Abdul Aziz Mat Isa, Zakaria Che Muda \n(2018). Development of Humanitraian Supply Chain Performance Conceptual Framework in Creating Resilient Logistics Network. Malaysian Journal of Geosciences, 2(1) : 30-33. \n\n\n\n[8] Stephenson, M. 2005. Making humanitarian relief networks more \neffective: Operational coordination, trust and sense making. Disasters, 29 \n(4), 337-350. \n\n\n\n[9] Van Wassenhove, L.N. 2006. Humanitarian aid logistics: Supply chain \nmanagement in high gear. Journal of Operational Research Society, 57 (5), \n475-489. \n\n\n\n[10] John, L., Ramesh, A., Sridharan, R. 2012. Humanitarian supply chain \nmanagement: A critical review. International Journal Services and \nOperations Management, 13 (4), 498-524. \n\n\n\n[11] Dubey, R., Ali, S.S., Aital, P., Venkatesh, V.G. 2014. Mechanics of \nhumanitarian supply chain agility and resilience and its empirical \nvalidation. International Journal Services and Operations Management, 17 \n(4), 367- 384. \n\n\n\n[12] Thomas, A., Fritz, L. 2006. Disaster relief, Inc. Harvard Business \nReview, 84 (11), 114-126. \n\n\n\n[13] de la Torre, L.E., Dolinskaya, I.S., Smilowitz, K.R. 2011. Decentralized \napproaches to logistics coordination in humanitarian relief. Proceedings \nof the 2011 Industrial Engineering Conference. \n\n\n\n[14] Thomas, A., Mizushima, M. 2005. Logistics training: Necessity or \nluxury? Forced Migration Review, 22 (22), 60-61. \n\n\n\n[15] Tomasini, R.M., Wassenhove, L.N. 2009. From preparedness to \npartnerships: case study research on humanitarian logistics. International \nTransactions in Operational Research, 16 (1), 549-559. \n\n\n\n[16] Drabek, T.E., McEntire, D.A. 2002. Emergent phenomena and \nmultiorganizational coordination in disasters: Lesson from the research \nliterature. International Journal of Mass Emergencies and Disasters, 20 \n(1), 197-224.   \n\n\n\n[17] Kovacs, G., Spens, K.M. 2007. Humanitarian logistics in disaster relief \noperations. International Journal of Physical Distribution and Logistics \nManagement, 37 (2), 99-114. \n\n\n\n[18] Holguin-Veras, J., Jaller, M., Wassenhove, L.N., Perez, N., Watchtendorf, \nT. 2012. On the unique features of post-disaster humanitarian logistics. \nJournal of Operations Management, 30 (6), 494-506. \n\n\n\n[19] Gizaw, B.T., Gumus, A.T. 2016. Humanitarian relief supply chain \nperformance evaluation: A literature review. International Journal of \nMarketing Studies, 8 (2), 105-120. \n\n\n\n[20] Habib, M.S., Lee, Y.H., Memon, M.S. 2016. Mathematical models in \nhumanitarian supply chain management: A systematic literature review. \nMathematical Problems in Engineering, 2016 (1), 1-20. \n\n\n\n33\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 45-50 \n\n\n\nCite The Article: Shakhawat Hossain (2019).Seismic Geomorphology As A Tool For Reservoir Characterization: A Case Study From Moragot Field Of Pattani Basin, Gulf Of \nThailand. Malaysian Journal of Geosciences, 3(1): 45-50. \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 23 November 2018  \nAccepted 24 December 2018 \nAvailable online 8 January 2019\n\n\n\nABSTRACT\n\n\n\nPattani Basin hosts the greatest number of hydrocarbons producing fields in the Gulf of Thailand. Early to Middle \nMiocene fluvial channel and overbank sands are the main reservoirs in this basin. Due to their nature of very limited \nvertical and horizontal distribution it is not always easy to predict the geometry and distribution of these sands based \non the conventional seismic interpretation. This study aims to study seismic geomorphology at different stratigraphic \nintervals to predict sand distribution by applying advanced imaging techniques such as RMS amplitude analysis, \nspectral decomposition, semblance and dip steered similarity. For this purpose, the study interval is divided into three \nperiods. In period 1, RMS and semblance successfully identified sand bodies and mud filled channels associated with \nchannel belts. On the other hand, deeper stratigraphic levels (period 2 & 3) can be imaged more effectively by using \nspectral decomposition and dip steered similarity volumes. Horizon slices from these attribute volumes show the \ndistribution of sands and mud filled channels at different stratigraphic level. The width of channel belts varies from \n200 m to 3 km. These channel belts are N-S and NW-SE oriented. The findings from seismic geomorphology analysis \nin these three (3) periods were then validated by well log analysis and correlation. Broad channel belts in horizon \nslices in period 3 correspond to stacked channel sands in well log. Whereas narrow channel belts correspond to thin \nsand units in well log in period 2. Widespread occurrence of coals has also been noticed in this interval. Very well-\ndeveloped meander belts in horizon slices are transpired as fining upward succession in well logs in period 1. Mud \nfilled channels identified in the horizon slices might act as a barrier and compartmentalize the reservoir. The proposed \nworkflow of predicting sand distribution in this study might help to reduce exploration risk as well as in planning \ninfill development wells. \n\n\n\n KEYWORDS \n\n\n\nSeismic Geomorphology, Semblance, RMS, Fluvial, Well log.\n\n\n\n1. INTRODUCTION \n\n\n\nThe Pattani Basin in the Gulf of Thailand is an important hydrocarbon \nproducing basin with up to 10km of thick non-marine to marginal marine \nclastic strata of Cenozoic age [1-3]. The study area is located in the western \nflank of south Pattani Basin of the Gulf of Thailand. Fluvial and deltaic \nsands comprise the main reservoirs in the basin and they exhibit complex \nstratigraphic architectures in terms of geometries and continuity of \nreservoirs. The fluvial depositional systems developed as an extensive \nfluvial/delta plain and rapidly avulsing meander belts. These sands are \nmostly thin and laterally limited, occasionally are in the form of thick \npoint-bar accretions. Although many of the reservoirs are laterally \ndiscontinuous, the adjacent occurrence of source rock combined with \nabundant structural and stratigraphic traps make the Pattani Basin a \nprolific gas and condensate producing basin [4]. It is not always possible \nto predict the sand distribution based on conventional seismic data \nbecause of their rapid vertical and horizontal changes. This results in high \nuncertainties in reserve estimation in the basin as well as in planning \ndevelopment wells. Conventional seismic interpretation and well log \nanalysis are not very helpful in delineating the spatial distribution of these \nreservoir sands effectively. Most of the gas sands fall below the tuning \nthickness hence seismic cannot detect them. They can be identified in well \nlogs but then again wells only give a point estimation. Identification of \ntheir lateral extent is not possible as they do not have any equivalent \nseismic reflector. This problem can be overcome by studying seismic \ngeomorphology in horizon slices with small time shifts. The idea is \n\n\n\nidentifying depositional systems in seismic data is easier because of their \nhigher lateral and vertical extent compared to their resultant deposits. \nSince most of the depositional systems have predictable depositional \npattern it will be possible to determine the extent of reservoirs once the \ndepositional setting in the reservoir interval is known. The main endeavor \nof this research work is to better understand the inherent uncertainties in \nreservoir distribution hence the aim of this study is to map the sand \ndistribution by using different seismic attributes and observing seismic \ngeomorphology along stratigraphic horizon slices in the study area.  \nSeismic geomorphology has lately become an essential tool for the analysis \nof depositional settings of a wide range of environments. It has proven to \nserve as an excellent tool for the identification of key architectural \nelements of fluvial deposits. Understanding the fluvial system is essential \nin the hydrocarbon exploration [5]. Seismic attribute analysis is key to the \nidentification of seismic geomorphology [6].  \n\n\n\nIn this study, a 3D seismic dataset has been used for detailed \ndocumentation of the distribution of fluvial systems and related deposits \nin the reservoir interval that ranges from 1200 to 2100 ms. I have used \nsemblance, spectral decomposition both Discrete Fourier Transformation \n(DFT) and Continuous Wavelet Transformation (CWT), dip steered \nsimilarity and RMS amplitude extraction techniques to map fluvial styles \nat different stratigraphic levels in the zone of interest.    \n\n\n\nSpecific objectives of this work are following: \n\u2022 Evaluate the effectiveness of different seismic attributes to image fluvial \nstyles in the study area.  \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : http://doi.org/10.26480/mjg.01.2019.45.50\n\n\n\nRESEARCH ARTICLE \n\n\n\nSEISMIC GEOMORPHOLOGY AS A TOOL FOR RESERVOIR CHARACTERIZATION: A \nCASE STUDY FROM MORAGOT FIELD OF PATTANI BASIN, GULF OF THAILAND \n\n\n\nShakhawat Hossain* \n\n\n\nLecturer, Department of Geology, University of Dhaka, Dhaka 1000, Bangladesh \n*Corresponding Auhtor Email: shakhawat.geo@du.ac.bd \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 45-50 \n\n\n\nCite the Article: Shakhawat Hossain (2019).Seismic Geomorphology As A Tool For Reservoir Characterization: A Case Study From Moragot F ield Of Pattani Basin, Gulf Of \nThailand. Malaysian Journal of Geosciences, 3(1): 45-50. \n\n\n\n\u2022 Define sand bodies and observe changes in channel size and geometries \nof the main reservoirs of the Miocene fluvial system. Identification of the \nreservoir characteristics in different study intervals by integrating seismic \ngeomorphology and well log data. \n\n\n\n1.1 Study area and Data Set \n\n\n\nThe study area is located in the Moragot field which is next to south Pailin \nfield in Pattani basin of the Gulf of Thailand (Figure 1). Water depth is \nabout 31 m. The area is 550 km south of Bangkok and covers \napproximately 463 km2. Chevron Thailand Exploration and Production, \nLtd provided the data set. The seismic 3D volume consists of about 1800 \ncross-lines and 1650 in-lines with 25 and 12.5 meters spacing \nrespectively. \n\n\n\nThe wireline log data consists of Gamma ray, resistivity, and neutron-\ndensity logs from 96 wells. Three of these 96 wells have sonic logs. \nTherefore, these data may be more appropriate to study the seismic \nattributes and spectral decomposition for sand prediction. \n\n\n\nFigure 1: Location map of the study area \n\n\n\n2. METHODOLOGY \n\n\n\nThis section shows the methods applied to detect and map the reservoir \nsand-bodies. These methods consist of well log correlation, amplitude-\nbased attributes and spectral decomposition (frequency attributes). The \nworkflow adopted to image the sand is shown in (Figure 2). \n\n\n\nFigure 2: Adopted methodology for reservoir characterization using \nseismic geomorphology \n\n\n\n3. RESULTS \n\n\n\n3.1 Well to Seismic Tie \n\n\n\nWell to seismic ties were performed by establishing a reasonable \ncorrelation between the seismic and synthetic seismograms by adjusting \nT-D functions through stretch and squeeze. From the synthetic \nseismogram it can be seen that the synthetic trace is matching quite well \nwith the seismic. Lithology wise sand is represented by trough in seismic \nand shale is represented by peak (Figure 3). \n\n\n\nFigure 3: Well to seismic tie shows troughs correspond to sand and peaks \ncorrespond to shale \n\n\n\n3.2 Seismic Geomorphology Analysis \n\n\n\nAn examination of the illustrated eleven horizon slices (Figure 8) reveal a \nlandscape of channels apparently superimposed on each other (Figure 6, \n10). It is a result of the different channel belts being closely spaced in \nvertical space. I tried to map the sands and associated channels by \ncombining different techniques. The sand bodies represented by bright \nnegative amplitudes within the study interval. The study interval is \ndivided into three period based on the interpreted markers. Then seismic \ngeomorphology has been studied in those intervals of interest from the \nhorizon slices (Figure 4). \n\n\n\nPeriod 1 (B to D horizon) \nPeriod 2 (D to K horizon) \nPeriod 3 (K to O horizon) \n\n\n\nFigure 4: Seismic section showing the interpreted and calculated horizons \n\n\n\nWell log data Seismic data\n\n\n\nSynthetic seismogram\n\n\n\nTie well to seismic\n\n\n\nFault and horizon \ninterpretation\n\n\n\nAmplitude attributes & \nseismic coherence\n\n\n\nSpectral decomposition\n(frequency attribute)\n\n\n\nMapping Sand distribution and \nDepositional environment \n\n\n\ninterpretation\n\n\n\n1400\n\n\n\n1700\n\n\n\n1800\n\n\n\n1900\n\n\n\n1600\n\n\n\n1500\n\n\n\nMGWB-20\n\n\n\nHigh\n\n\n\nLow\n\n\n\n500 m\n\n\n\n15001500\n\n\n\n2000\n\n\n\n2500\n\n\n\nB+ 80\n\n\n\nB+ 180\n\n\n\nB+ 250\n\n\n\nB+ 350\n\n\n\nD\n\n\n\nK\n\n\n\nO -100\n\n\n\nO -50\n\n\n\nO\n\n\n\nK -30\n\n\n\nK -150\n\n\n\n200 m\n\n\n\nB\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 45-50 \n\n\n\nCite the Article: Shakhawat Hossain (2019).Seismic Geomorphology As A Tool For Reservoir Characterization: A Case Study From Moragot F ield Of Pattani Basin, Gulf Of \nThailand. Malaysian Journal of Geosciences, 3(1): 45-50. \n\n\n\n3.2.1 Period 1 \n\n\n\nThe horizon slices of this period show the most prominent and well-\ndefined images of fluvial systems as compared to horizon slices of other \nperiods. Horizon B+250 is selected as the representative of period 1. The \nslice of RMS attribute shows very well-developed meander belt running \nacross NW-SE direction. Two major meander belts are easily identifiable \nin this horizon slice. There are plenty of small channel fragments \nsuperimposed on each other in random directions. The width of the \nmeander belt is in the range of 3 km (Figure 5). The dominant channel belt \nwidth is in comparison with the shallow seismic study of high-resolution \nseismic data that indicates the width of meander belts of Gulf Thailand in \nthe range of 5 to 10 km [7]. \n\n\n\nFigure 5: Dip steered similarity (a) and RMS amplitude (b) in Horizon slice \nB+250. The seismic section along A-B shows strong amplitude at the sand \nposition confirmed by log data. Red ellipse indicates well location. \n\n\n\n The associated channel width is around 147m. This is in comparison with \nmodern Chao Phraya River near Bangkok, which width ranges from 150-\n220 m. Very conspicuous scroll bars and overbank splays are easily \nidentifiable. One well drilled in the point bar of one channel belt shows \nblocky pattern on gamma ray log. The thickness of the sand in this \nstratigraphic level is 23 m. Wells drilled in this interval show similar sand \nthickness. From the scroll bars accretion direction, the dominant flow \ndirection is interpreted (Figure 6). It shows the paleo flow direction was \nin NW-SE direction.  \n\n\n\nFigure 6: High RMS value has been interpreted as sands associated with \nmeander belts in Horizon slice B+80 (a). The interpretation shows various \nmeander belts (b). Flow directions from scroll bar accretion (c). \n\n\n\n3.2.2 Period 2 \n\n\n\nThe stratigraphic slices were analyzed between D and K marker for sand \ndistribution. RMS, spectral decomposition 45 Hz slice and RMS overlay \n\n\n\nwith semblance yield similar result. To better image the fluvial system, \nRGB blending of 12Hz, 25Hz and 45Hz were used. The RGB blended image \nreveals some channels (Figure 7) which were not identifiable in other \nattribute maps. Identified channels are running mostly north-south \ndirection. With the help of RGB blended image meander belt width was \ncalculated along with the channels in the horizon slice of spectral \ndecomposition 45 Hz. The width of the meander belts ranges from 300-\n800m and the width of the abandoned channels are in the range of 100-\n270 m. Sands are quite thin compared to the sands in the stratigraphically \nhigher horizon slices. Spectral decomposition CWT 45 Hz slice were used \nto map the sands in this interval as the sands are thinner in this interval. \n\n\n\nFigure 7: RGB blending of 12 Hz, 25Hz and 45Hz (a) and Spectral \ndecomposition 45 slice (b) in Horizon slice D. The interpretation shows \nthe channel pattern identified from RGB blended image. White line \nindicates meander belt and dashed orange lines indicate channels. \n\n\n\nK-30 horizon slice was selected as the representative of this interval. \nSpectral decomposition CWT 45 Hz frequency slice adequately delineate \nthe distribution of sands in this interval (Figure 8). Two distinct channel \nbelts have been identified in the horizon slice. They are running mostly in \nthe N-S direction. One well drilled in the high amplitudes shows the \npresence of an 11 m thick sand (Figure 8). Plenty of thin coal layers have \nbeen identified around this level. High amplitudes are restricted in the \nnorthern part of the study area in this stratigraphic level. The Southern \npart is comprised of low amplitude materials. RGB blending of 12 Hz, 25 \nHz and 45 Hz and dip steered similarity attribute were used to identify \nchannels. The width of the meander belts ranges from 80 to 220 m and the \nwidth of abandoned channels are in the range of 60 to 170 m. In \ncomparison with the channels encountered in the shallow stratigraphic \nlevel i.e. period 1, channels in this period are narrower.  \n\n\n\n2 km\n\n\n\n2 km\n\n\n\nChannelsa\n\n\n\nb\n\n\n\n1\n\n\n\n2\n\n\n\n3\n\n\n\n4\n\n\n\n2 km\n\n\n\n2 km2 km\n\n\n\nFlow  \n\n\n\ndirection\n\n\n\nChannel\n\n\n\nScroll \n\n\n\nbars\n\n\n\nLegend\n\n\n\n2 km\n\n\n\na b\n\n\n\nc\n\n\n\n2 km\n\n\n\nAC\n\n\n\nOS\n\n\n\n2 km\n\n\n\nHigh\n\n\n\nLow\n\n\n\nB+120\n\n\n\nB+250\n\n\n\nB+300\n1600\n\n\n\nTWT\n\n\n\n(ms)\n\n\n\n1500\n\n\n\n500 m\n\n\n\nHigh GR             NPHI\n\n\n\nRHOB\n\n\n\n23m\n\n\n\nRD\n\n\n\nMGWB -15\n\n\n\nMB-15\n\n\n\nMGWB -15\n\n\n\nA B\n\n\n\nA B\n\n\n\na b\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 45-50 \n\n\n\nCite the Article: Shakhawat Hossain (2019).Seismic Geomorphology As A Tool For Reservoir Characterization: A Case Study From Moragot F ield Of Pattani Basin, Gulf Of \nThailand. Malaysian Journal of Geosciences, 3(1): 45-50. \n\n\n\nFigure 8: Dip steered similarity (a) and Spectral decomposition CWT 45 \nHz (b) of horizon K-30. The seismic section along A-B shows strong \namplitude at the sand position confirmed by log data. Red dot indicates \nwell location. \n\n\n\n3.2.3 Period 3 \n\n\n\nSpectral decomposition slice of 20 Hz and RMS overlay on semblance give \nbetter delineation of the sand bodies. Since the sands are thicker in this \ninterval spectral decomposition 20Hz frequency slice has been opted to \nmap sand distribution. \n\n\n\nThis horizon signifies the lowermost stratigraphic level in the zone of \ninterest. Spectral decomposition 20 Hz frequency slice (Figure 9) has been \nused to map the sands in this horizon. Large meander belts have been \nidentified in the middle and eastern part of the horizon slice.  \n\n\n\nFigure 9: Spectral decomposition 20 Hz frequency slice (a).The \ninterpretation shows various meander belts with superimposed pattern \n(b). The seismic section along A-B shows strong amplitude at the sand \nposition confirmed by log data. Red ellipse indicates well location. \n\n\n\nThe width of the meander belt is in the range of 1.5 to 3 km which is \nanalogous to the ones found in period 1. The width of abandoned channels \nidentified from dip steered similarity map is around 120 - 330 m. Another \ninteresting feature is the presence of sand filled channel (Figure 10) in \nperiod 3.  \n\n\n\nFigure 10: Semblance map (a) and RMS overlay on semblance map (b) at \nhorizon O-100. The seismic section along A-B shows that channel shape is \nfilled with high amplitude materials \n\n\n\n3.3 Well log Analysis \n\n\n\n3.3.1 Period 1 (B to D) \n\n\n\nThis is the uppermost period in the zone of interest. The horizon slices in \nthis interval show well developed (Figure 5 & Figure 7) moderate to high \nsinuosity fluvial system. The wireline character of this part is represented \nby fining upward cycles (Figure 11) indicating point bar deposits of the \nfluvial system. Thus, the depositional environment of this period can be \ninterpreted as fluvial.  \n\n\n\nFigure 11: Wire line character of depositional sequences of period 1 \n\n\n\n3.3.2 Period 2 (D to K) \n\n\n\nThe seismic geomorphology in this period is represented by low sinuosity \nbroad channel systems (Figure 8) as well as some narrow meander belts. \nSuch straight channels with little or no evidence of lateral migration \nsuggest a low slope and low accommodation area similar of delta plains \n[8]. The interpretation is well supported by the widespread occurrence of \ncoal (Figure 12). The wireline character of this part is represented by \ncoarsening upward cycles (Figure 12). It implies a deltaic progradation \nsequence in a paralic setting. In the depositional model, this period is \nbroadly interpreted as the marginal marine depositional environment [9-\n14].  \n\n\n\n2 km\n\n\n\na\n\n\n\n2 km\n\n\n\nb\n\n\n\nK\n\n\n\nO\n\n\n\nO -100\n\n\n\n1900\n\n\n\n2200\n\n\n\nTWT\n\n\n\n(ms)\n\n\n\n500m\n\n\n\nHigh\n\n\n\nLow\n\n\n\n18m\n\n\n\nGR             NPHI\n\n\n\nRHOB\n\n\n\nRD\n\n\n\nMGWB - 20\n\n\n\nA B\n\n\n\nA B\n\n\n\nMB-20\n\n\n\n2000\n\n\n\n2200\n\n\n\nTWT\n\n\n\n(ms)\n\n\n\nHigh\n\n\n\nLow\n200m\n\n\n\nA B\n\n\n\nO-120\n\n\n\nO- 50\n\n\n\nO-100\n\n\n\nBA\n\n\n\n2 km 2 km\n\n\n\nHigh\n\n\n\nLow\n\n\n\nHigh\n\n\n\nLow\n\n\n\na b\n\n\n\n0 250\nGR\n\n\n\nNPHI\n\n\n\nRHOB\n0 250\n\n\n\nGR\n\n\n\nNPHI\n\n\n\nRHOB\n\n\n\nMGWA-20 MGWB-20\n\n\n\nO\n\n\n\nD\n\n\n\nK - 30\n\n\n\n1900\n\n\n\n2100\n\n\n\nTWT\n\n\n\n(ms)\n\n\n\nHigh\n\n\n\nLow\n500m\n\n\n\nGR             NPHI\n\n\n\nRHOB\n\n\n\nRD\n\n\n\n11m\n\n\n\nLow\n\n\n\nHigh\n\n\n\nLow\n\n\n\nHigh\n\n\n\nMD-15\n\n\n\nMGWD-15\n\n\n\n2 km 2 km\n\n\n\nA B\nMGWD-15\n\n\n\na b\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 45-50 \n\n\n\nCite the Article: Shakhawat Hossain (2019).Seismic Geomorphology As A Tool For Reservoir Characterization: A Case Study From Moragot F ield Of Pattani Basin, Gulf Of \nThailand. Malaysian Journal of Geosciences, 3(1): 45-50. \n\n\n\nFigure 12: Wire line character of depositional sequences of period 2 \n\n\n\n3.3.3 Period 3 (K to O) \n\n\n\nThis period covers the lower part of the zone of interest. The seismic \nattribute maps show the number of moderate to high sinuosity fluvial \nsystems populating the horizon slices (Figure 9) at that interval in the \nstudy area.  The fluvial systems are mostly concentrated in the central and \neastern part. The wireline character shows the dominance of blocky and \nfining upward cycles (Figure 13). These can be interpreted as channel \nsands and point bar deposits associated with the fluvial systems mapped \nin this interval [9]. The blocky and fining upward log response with high \nnumbers of fluvial systems dominating this interval indicate that the \nsediments are deposited in a fluvial environment. However, the presence \nof sand filled channel in this interval (Figure 10) suggests there might have \nbeen some occasional marine transgression. \n\n\n\nFigure 13: Wire line character of depositional sequences of period 3 \n\n\n\n4. DISCUSSION\n\n\n\nSeismic geomorphology has proved to be very helpful in identifying the \nlateral distribution of reservoir sands. Horizon slices showed sand \ndistribution of various pattern. From their distribution and regional \nstratigraphy, it is interpreted these sand bodies are associated with fluvial \nchannel and channel belts. The observed channel belt widths and channel \nwidths are summarized in Table 1 for comparison. Channel belts width in \nperiod 1 and 3 are larger as compared to channel belt width in period 2.  \n\n\n\nTable 1: Summary of the channel belt and abandoned channel width in the \nzone of interest \n\n\n\nThe horizon slices at O-100, O-50 and O typify the fluvial system in the \ncentral and eastern part of the area. Drilled wells in that region show the \nevidence of stacked channel sands. The fluvial system in period 3 interval \nhas multiple channel sands, which are mostly north-south trending. The \nsands are associated with broad N-S meander belts. Whereas period 2 \nshows narrow meander belts, and there are also some high amplitude \nfeatures in the selected horizon slices without any distinctive pattern. The \nhigh amplitude features are most likely because of the prevalence of coal. \nThe horizon slices B+80, B+180 and B+250 are characterized by paleo flow \ntowards the south-southeast. B+80 and B+250 have sands associated with \nlarge single meander belts, while B+180 shows multiple narrow meander \nbelts. The morphology of meander belts observed in period 1 is similar to \nthe channel belts between period 3. \n\n\n\nFigure 14: Summary of the channel evolution from bottom to top in the \nzone of interest. \n\n\n\nFluvial system size and pattern change rapidly in the area over a short time \nwindow of 15 to 20 ms. The sand distribution model for the entire zone of \ninterest has been prepared (Figure 14). Period 1 shows broad channel \nbelts. Stacking of channels is not very common. Period 2 shows narrow \nchannel belts with broad distributary channels whereas period 3 is \ncharacterized by well-developed meander belts. Multiple stacked channels \nhave been noticed in this interval in the well logs. The only difference \nbetween period 1 and period 3 is that channels are isolated here. Sand \nbodies are laterally continuous both in period 1 and 3 as evidenced by the \nwidespread occurrences of high amplitudes in horizon slices. This has \noccurred mostly due to the fact that rivers migrate laterally. Whereas \nperiod 2 represents the downstream reach of fluvial system grading into \nmarginal marine setting, hence shows patchy high amplitudes in horizon \nslices. Reservoir development in period 2 will require much more \ninvestment and analysis compared to other periods. \n\n\n\nAge Period Key horizon Horizon slices\nChannel belt width\n\n\n\n(m)\n\n\n\nChannel width\n\n\n\n(m)\n\n\n\nB+80 1000-2500 152-470\n\n\n\nB+180 300-500 50-78\n\n\n\nB+250 2000-3200 147-332\n\n\n\n200-800 80-140\n\n\n\nK-150 200-400 90-220\n\n\n\nK-80 250-450 110-200\n\n\n\nK-30 230-420 60-170\n\n\n\n250-800 152-347\n\n\n\nO-100 500-2000 170-300\n\n\n\nO-50 800-2500 130-280\n\n\n\n1500-3000 120-330\n\n\n\nPeriod 2\n\n\n\nPeriod 3\n\n\n\nB\n\n\n\nD\n\n\n\nK\n\n\n\nO\n\n\n\nMiddle \n\n\n\nMiocene\n\n\n\nEarly\n\n\n\nMiocene\n\n\n\nPeriod 1\n\n\n\n0 250\nGR\n\n\n\nNPHI\n\n\n\nRHOB\n\n\n\n0 250\nGR\n\n\n\nNPHI\n\n\n\nRHOB\n\n\n\nMGWA-20\n\n\n\nMGWB-20\n\n\n\nCoal\n\n\n\n0 250\nGR\n\n\n\nNPHI\n\n\n\nRHOB\n\n\n\n0 250\nGR\n\n\n\nNPHI\n\n\n\nRHOB\n\n\n\nMGWC-38\n\n\n\nMGWD-05\n\n\n\nN S\n\n\n\nK\n\n\n\nB\n\n\n\n\u2022 Fluvial channel sand deposit\n\n\n\n\u2022 Broad channel belts\n\u2022 Stacked channel sands\n\u2022 Few coal deposits\n\n\n\n\u2022 NW-SE trending\n\n\n\n\u2022 Narrow channel belts\n\n\n\n\u2022 NW-SE trending\n\u2022 widespread occurrence \nof   coals\n\n\n\n\u2022 Broad meander belts\n\n\n\n\u2022 Point bar deposits\n\u2022 NW-SE trending\n\u2022 few coal deposits\n\n\n\nM\nid\n\n\n\nd\nle\n\n\n\n M\nio\n\n\n\nc\ne\nn\n\n\n\ne\n\n\n\nD\n\n\n\nLegend\n\n\n\nCoal\n\n\n\nFluvial \n\n\n\ndominated\n\n\n\nMarine \n\n\n\ninfluence\n\n\n\nChannel \n\n\n\nbelt\n\n\n\nSand filled \n\n\n\nchannel\n\n\n\nDistributary \n\n\n\nchannel\n\n\n\nP\ne\nri\n\n\n\no\nd\n\n\n\n 2\nP\n\n\n\ne\nri\n\n\n\no\nd\n\n\n\n 3\n\n\n\nO\n\n\n\nE\na\nrl\n\n\n\ny\n \n\n\n\nM\nio\n\n\n\nc\ne\nn\n\n\n\ne\n\n\n\nP\ne\nri\n\n\n\no\nd\n\n\n\n 1\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 45-50 \n\n\n\nCite the Article: Shakhawat Hossain (2019).Seismic Geomorphology As A Tool For Reservoir Characterization: A Case Study From Moragot F ield Of Pattani Basin, Gulf Of \nThailand. Malaysian Journal of Geosciences, 3(1): 45-50. \n\n\n\n5. CONCLUSIONS \n\n\n\nDifferent geophysical techniques were applied to map the reservoir sands. \nKey results and conclusions of the present study are summarized below. \nSpectral decomposition proved to be the most useful technique in \nmapping thin sands in the deeper intervals. Continuous Wavelet \nTransform (CWT) works better than the Discrete Fourier Transform \n(DFT) in identifying sand bodies. The amplitude response of CWT spectral \ndecomposition is different for different thickness of sands. Low \nfrequencies (20Hz) show high amplitudes for thick sands (>15 m), while \nhigher frequencies show bright amplitudes for relatively thinner sand \nbeds. RMS amplitude maps are useful to detect sand distribution \nassociated with meander belts if the sand is sufficiently thick (in this case \nabove 10 m). Dip steered similarity gives more accurate delineation of \nchannel features than semblance calculated from raw seismic volume in \nperiod 2. 20 Hz CWT spectral decomposition along with similarity volume \nsuccessfully mapped sands and mud filled channels in the deeper \nstratigraphic level. These mud-filled channels may act as barrier between \ntwo separate sand bodies and compartmentalize the reservoirs. Meander \nbelts in period 2 are relatively narrow as compared to meander belts \nabove D marker and below K marker. The fluvial channels and channel belt \nwidth change both laterally and vertically. In different periods sand \ndistribution pattern were different. These findings need to incorporate in \nreservoir models for better field development. Exploration in the \nunexplored part of Gulf of Thailand should also take these into \nconsideration.  \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe author would like to thank Chevron Bangladesh and Chulalongkorn \nUniversity for providing all the financial and logistic supports. \n\n\n\nREFERENCES \n\n\n\n[1] Lian, H.M., Bradley, K. 1986. Exploration and Development of Natural \nGas, Pattani Basin, Gulf of Thailand. Fourth Circum- Pacific energy and \nmineral resources conference, Singapore, 4, 171-181. \n\n\n\n[2] Chinbunchorn, N., Pradidtan, S., Sattayarak, N. 1989. Petroleum \npotential of Tertiary Intermontane Basins in Thailand. T. T. Pitak Ed., \nProceedings of International Symposium on Intermontane Basins: \nGeology and Resources, Chiang Mai, Chiang Mai University, 29-41. \n\n\n\n[3] Morley, C.K., Racey, A. 2011. Petroleum geology. In: M.F. Ridd, A.J. \nBarber & M.J. Crow (eds.) The Geology of Thailand, Geol. Soc., London, p. \n223-271.  \n\n\n\n[4] Bustin, R.M., Chonchawalit, A. 1997. Petroleum Source Rock Potential \nand Evolution of Tertiary Strata, Pattani Basin, Gulf of Thailand. AAPG \nBulletin, 81 (12), 2000-2023.  \n\n\n\n[5] Davies, R.J., Posamentier, H.W., Wood, L.J., Cartwright, J.A. 2007. \nSeismic Geomorphology: Applications to Hydrocarbon Exploration and \nProduction. Geological Society of London, Special Publication, 277, 1-14. \n\n\n\n[6] Chopra, S., Marfurt, K.J. 2007. Seismic attributes for prospect \nidentification and reservoir characterization, Geophysical developments \nno. 11, Society of Exploration Geophysicists (SEG), \nhttps://doi.org/10.1190/1.9781560801900 \n\n\n\n[7] Reijenstein, H.M., Posamentier, H.W., Bhattacharya, J.P. 2011. Seismic \ngeomorphology and high-resolution seismic stratigraphy of inner shelf, \nfluvial, estuarine, deltaic and marine sequences, Gulf of Thailand. Aapg \nBulletin, 95 (11), 1959-1990. Doi:10.1306/03151110134 \n\n\n\n[8] Miall, A.D. 2002. Architecture and sequence stratigraphy of Pleistocene \nfluvial systems in the Malay Basin, based on seismic time slice analysis. \nAapg Bulletin, 86 (7), 1201-1216.  \n\n\n\n[9] Rider, M. 1996. The geological interpretation of well logs. \nLatheronwheel, Caithness: Whittles Publishing Roseleigh House.  \n\n\n\n[10] Gang, C., Mingjun, S., Qingzhou, Y., Honglin, G. 2011. Application of \nSpectral Decomposition Technique in Reservoir Exploration in the Junggar \nBasin of West China. Oral presentation at AAPG Annual Convention and \nExhibition, April 10-13. \n\n\n\n[11] Jardine, E. 1997. Dual petroleum systems governing the prolific \nPattani Basin, offshore Thailand. Petroleum systems of S.E. Asia and \nAustralasia Conference, Jakarta, May, 21-23, 351-363. \n\n\n\n[12] Posamentier, H.W., Kolla, V. 2003. Seismic geomorphology and \nstratigraphy of depositional elements in deep-water settings. Journal of \nSedimentary Research, 73, 367\u2013388. \n\n\n\n[13] Racey, A. 2011. Petroleum geology. In: M.F. Ridd, A.J. Barber & M.J. \nCrow (eds.) The Geology of Thailand, Geol. Soc., London, Mem., 351-392.  \n\n\n\n[14] Sinha S. 2005, Spectral decomposition of seismic data with \ncontinuous-wavelet transform. Geophysics, 70 (6), 19\u201325. \n\n\n\n\nhttps://doi.org/10.1190/1.9781560801900\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 45-53 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2022.45.53 \n\n\n\nCite The Article: K.O.Olomo, S. Bayode, O.A. Alagbe, G.M.Olayanju, O.K. Olaleye (2022). Multifaceted Investigation of Porphyry Cu-Au-Mo Deposit in Hydrothermal \nAlteration Zones Within The Gold Field of Ilesha Schist Belt. Malaysian Journal of Geosciences, 6(2): 45-53. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2022.45.53\n\n\n\nMULTIFACETED INVESTIGATION OF PORPHYRY CU-AU-MO DEPOSIT IN \nHYDROTHERMAL ALTERATION ZONES WITHIN THE GOLD FIELD OF ILESHA \nSCHIST BELT \n\n\n\nK.O.Olomo a *, S. Bayode b, O.A. Alagbe b, G.M.Olayanju b, O.K. Olaleye b \n\n\n\na Department of Earth Sciences, Adekunle Ajasin University, Akungba-Akoko, Nigeria \nb Department of Applied Geophysics, Federal University of Technology, Akure, Nigeria \n*Corresponding Author Email: kazeem.olomo@aaua.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 22 February 2022 \nAccepted 28 March 2022 \nAvailable online 01 April 2022\n\n\n\nThis study was carried out to map the distribution of porphyry systems within Ilesha Schist belt \nSouthwestern Nigeria for easy accessibility of commercial gold as well as copper and molybdenum deposits \nto boost exploitation activities within the area. Airborne magnetic and radiometric data of the area were \nutilized. The acquired total magnetic intensity map of the field was enhanced with reduction to equator, \nvertical derivative, analytic signal and spectral analysis. Enhanced total magnetic intensity map of the study \narea revealed magnetic anomalies related to concealed intrusive rock associated with Cu-Au-Mo deposit at \ndepth ranges from 306 - 421 m. Vertical derivative map revealed shallow magnetic bodies in the southern \nand western parts, which are probable zones for Cu-Au-Mo deposit. Analytical signal map, with magnetic \nzones of high intensity value between 0.1 nT/M and 1.7 nT/M highlighted faulted basement blocks \nimpregnated with porphyry the Cu-Au-Mo deposit. Airborne radiometry image shows radioelement \ndistributions for different lithologies. High concentration of potassium signatures observed over some parts \nof the area revealed hydrothermal alteration zones favourable to concentration of Cu-Au-Mo deposits. \nComputed Th/k ratio and composite potassium maps enhanced the signature of potassium enrichment \nassociated with hydrothermal alteration zones. Validation of airborne results using geochemical analyses \nconfirms the presence of copper-gold-molybdenum elements within the mapped hydrothermal alteration \nzones. This research has identified other valuable ore deposit, such as copper and molybdenum, to enhance \nexploitation activities within the area. \n\n\n\nKEYWORDS \n\n\n\nintrusive body, hydrothermal alteration, potassium, Cu-Au-Mo deposit, geochemical \n\n\n\n1. INTRODUCTION \n\n\n\nIt has been evidently proven that porphyry deposits are regarded as the \nmost important source of copper (Cu) and Molybdenum (Mo) in the world \n(Sinclair, 1995). They accounted for about 50-60%, more than 95% of Cu, \nMo production respectively in the world. Porphyry Cu-Au-Mo deposits are \nuniquely characterize by large, low- to medium-grade (Sinclair, 1995). The \nore minerals are dominantly controlled by geological structures \n(stockworks, veins, fractures, and breccias). Genetic relationship between \nthe activities of magmatic process and hydrothermal mineralization in \nporphyry deposits has been noticed by the presence of mineral intrusions \nand breccias that were emplaced during periods of mineralization \n(Kirkham, 1971).  \n\n\n\nThe reliability of geophysical methods and techniques have over the years \nplayed major roles in identifying the fracture system and hydrothermally \naltered zones through processing, visualization as well as interpretation \nof geophysical maps. Such relationship have been established in works \n(Dickson and Scott, 1997; Holden et al., 2000; Verduzco et al., 2004; \nFerreira et al., 2011). These progresses becomes well grounded by \nappropriately considered the chemical and physical properties of geology \nformations in line with important manifestations of different attributes of \n\n\n\ndeposition and relevant highlights of their respective host geology \ndomains (Bierlein et al., 2006, Robert et al., 2007; Austin and Blenkinsop, \n2008; Austin and Blenkinsop, 2009; Core et al., 2009). Detail geophysical \nsignature of gold rich porphyry deposits has been explained (Moslem and \nHooshang, 2008). Magnetic minerals are enriched during potassic \nalteration and destroyed during phyllic alteration, thus a high circular and \nelliptical magnetic anomaly is detected at the potassic intense \nhydrothermal alteration zones and characterize by a surrounding low \nmagnetic low related to phyllic alteration (Moslem and Hooshang, 2008). \nPotassic alteration zone consist of elevated radiometry potassium (K) \nelement facilitating the application of radiometry geophysical method for \ntargeting porphyry deposit zones.  \n\n\n\nAeromagnetic surveys are used to map porphyry Cu-Au-Mo deposits with \nabundant hydrothermal magnetite bearing zones around porphyry \nrelated intrusive rocks. Conversely, some Cu-Au-Mo deposits are \ncharacterized by magnetic lows due to the destruction of magnetite in \nphyllic alteration zones. Likewise, gamma ray spectrometry surveys have \nbeen extensively used to outline potassic alteration zones closely related \nto mineralized zones (Shives et al., 1997). Porphyry deposits tend to have \nlarge geochemical dispersion halos and reconnaissance stream sediment \nand soil geochemical surveys have been an effective exploration tools in \n\n\n\n\nmailto:kazeem.olomo@aaua.edu.ng\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 45-53 \n\n\n\nCite The Article: K.O.Olomo, S. Bayode, O.A. Alagbe, G.M.Olayanju, O.K. Olaleye (2022). Multifaceted Investigation of Porphyry Cu-Au-Mo Deposit in Hydrothermal \nAlteration Zones Within The Gold Field of Ilesha Schist Belt. Malaysian Journal of Geosciences, 6(2): 45-53. \n\n\n\nmany parts of the world (Kelley et al., 2003). A porphyry intrusion is \ncircular or semi-circular features with their rims or boundaries being \nzones of weakness that helps to aid hydrothermal solutions. The intrusion \nitself and the alteration zone are associated with magnetic high anomalies \nand the outer alteration zones are much less magnetic (Macnae, 1995). \nThe application of aeromagnetic data interpretation has its sensitivity \nfrom ore-forming magmatic fluids that are rich in iron, and the level of \nmagnetite deposited leverage on the periodic phases the fluids passes \nthrough as they move into the upper crust (Holden et al., 2000). Deep-\nseated fracture systems provide a conduit when the magmatic fluids are \nascending into the crust, they transform into magnetic properties that are \nhighly anomalous within the host weakly magnetic rocks (Hildenbrand, \n2000). The magnetic variation and distinction between the emerging \nintrusive magnetic body and the host rock pave way for the suitability of \nmagnetic prospecting methods in Cu-Au-Mo porphyry exploration. Most \ntimes, intrusive bodies exhibit magnetic dipoles (Clark, 1996). However, \nthe intrusive body subjected to intense hydrothermal alteration \nconsequently destroyed magnetite resulted into low magnetic feature, \n(Riveros et al., 2014).  \n\n\n\nAirborne magnetic data is one of the most used important non invasive \ngeophysical techniques in mapping magnetic anomalies associated with \nsubsurface geological structural settings in form of lineaments (Andrson \nand Nash, 1997; Holden et al., 2008; Cooper, 2009; Hornby et al., 1999; \nSabin's, 1999; Wemegah et al., 2015; Elkhateeb and Eldosouky, 2016). \nThrough various enhancement techniques, these magnetic anomalies are \nadequate in mapping lithology, hydrothermal alteration system as well as \ndelineating geological structures which allow the transportation of \nmineralising alteration fluids to be deposited in host rocks (Bierlein et al., \n2006; Cooper and Cowan, 2006; Cooper and Cowan, 2008; Henson et al., \n2010; Macnae, 1995; Archibald et al., 1999; Sharma, 1997; Neawsuparp et \nal., 2005). Edge detection enhancement filters such as analytic signal and \nspectral analysis are significant in amplifying magnetic anomaly. This is \nbecause the former is applied on magnetic data to amplify magnetic \ncausative body (Charbonneau et al., 1997; Hsu, 2002; Ansari and Alamdar, \n2009) while the later calculate the depth of magnetic source and \naccentuate local correlation of different basement architectures (Spector \nand Grant 1970, Emberga and Timothy 2014). Elseways, airborne \nradiometry respond to the surface circulation of freely existing of \nradioelements, making it a useful and accurate tool in identifying \nhydrothermal alteration zones related to localization of porphyry Cu-Au-\nMo mineralisation (Roest et al., 1992; Graham, 1993; Jaques et al., 1997; \nPatra et al., 2016). \n\n\n\nGenerally, former studies to recent ones have emphasized the uniqueness \nof aeromagnetic and airborne radiometric surveys in outlining potassic \nalteration zones closely related to mineralized zones. Applied radiometry \nmethod in the Mount Milligan deposit in central British Columbia and the \nCasino deposit in west-central Yukon Territory isolated zone associated \nwith hydrothermalised porphyry deposits (Shives et al., 1997). \nDevelopments in remote sensing have also included the application of \nhyperspectral imaging to map hydrothermal alteration around porphyry \ndeposits (Cudahy et al., 2001; Berger et al., 2003). Recently, localization of \nprobable porphyry deposits in a selected part of the central Eastern Desert \nof Egypt in which lithology mapping, structures and hydrothermal \nalteration zones delineation were executed by using both aeromagnetic \nand airborne radiometric data (Elkhateeb and Abdellatif, 2018). Utilized \nairborne magnetic and radiometric in mapping porphyry Cu-Au-Mo \ndeposits at the Eastern Papuan Peninsula, assessing their economic \npossibility (Mosusu et al., 2021). The results indicate varying degrees of \ncorrelation, with some areas showing a strong correlation between \ngold/copper occurrence and geophysical signatures. Integrated \naeromagnetic, radiometric and satellite imagery data over a region prone \nto Cu-bearing mineralization at Chahargonbad area in Kerman province of \nIran (Riahi et al., 2021). Close relationship of identified geophysical \nanomalies, alteration zones and lineaments with known mineralization \noccurrence emphasized the importance of the integrated method in \nmapping porphyry related deposit from the research.  \n\n\n\nPrevious geological mapping and geochemical studies on western part of \nthe study area have been suggestive of gold and sulfides deposit occur in \ngeologic structure and alluvial settling (Ajayi, 1988; Elueze, 1977; Ariyibi, \n2011; and Oyinloye, 2011). Porphyry Cu-Au-Mo deposit in the study area \nis related to epigenetic deposit. Epigenetic deposits are deposit formed \nlater than the enclosing rocks which relate to changes in rock formation. \nThe mineral composition of the epigenetic deposits differs markedly from \ncomposition of the enclosing rock. Metamorphism, hydrothermal \nalteration and weathering processes of epigenetic mineralisation can \nrestructure the original concentration of radioelement present in rock \nformation. Also, geophysical studies in the investigated area were focused \non magnetic and electrical methods to investigate geologic setting and \n\n\n\ntheir implication on gold mineralization (Ako et al., 1979; Ako, 1980; \nAkinlalu et al., 2018; Olomo et al., 2018). However, the porphyry Cu-Au-\nMo deposits which are associated with gold mineralisation in the study \narea have not been assessed. It is for these reasons that airborne datasets \ncomprising of magnetic and radiometry were utilized for the study to \nmapping magnetic anomalies associated with Porphyry Cu-Au-Mo deposit \nand its related hydrothermal alteration zones.  \n\n\n\nOne of the most fundamental problems in exploration for porphyry \ndeposits is to identify appropriate geophysical methods that can isolate \nporphyry deposits and general agreement that their formation involves \nthe separation of metalliferous fluids from ore-related hydrothermal \nalteration, the processes that form porphyry deposits are diverse and \nsignificant knowledge gaps remain. This paper utilized aeromagnetic and \nairborne radiometric data to map subsurface geologic features and \nhydrothermally altered zones with a view of studying Porphyry Cu-Au-Mo \ndeposit in the area of study. The study is anticipated to providing evidence \nof Porphyry Cu-Au-Mo deposit within the studied area and its viability to \nbe of economic interest. Validations of airborne data interpretation are \ncarried out with geochemical analysis. \n\n\n\n2. GEOLOGIC SETTING\n\n\n\nThe research location is at Ilesha schist belt southwestern, Nigeria. It lies \nwithin longitude 4\u00b0 37' 30\u201d E to 40 55\u2019 00\u201d E and latitude 7\u00b0 30' 20.82\" N to \n7o 41\u201930\u201d N. The geology of Ilesha has been described in detail by several \nauthors (Ajibade, 1979; Odeyemi, 1981, Odeyemi, 1993; Ajayi and \nOgedegbe, 2003; Anifowose and Borode, 2007; Kayode et el., 2011, \nAdemeso et al., 2013). It is associated with Precambrain rocks which led \nto the formation of basement complex of south-western Nigeria \n(Rahaman, 1988; Dada, 2006). The rocks in the area of study have a strong \nconnection with proterozoic schist belt fond in Nigeria. The major \nlithologies are shown in Fig.1. These include amphibolites, granite gneiss \nand migmatite gneiss complex. It has over the time experienced intense \nshearing and fracturing (Odeyemi, 1981; Odeyemi, 1993; Ademeso et al., \n2013). They\u2019re strongly foliated, banded and conspicuously exhibit \ngeology structures which implies poly deformation (Hubbard, 1975; \nAdeoti and Okonkwo, 2017). \n\n\n\n3. MATERIALS AND METHOD\n\n\n\n3.1   Airborne magnetic data  \n\n\n\nAeromagnetic data (Sheet 243) utilized was conducted and processed by \nFugro Airborne Surveys in 2009. It was done under the supervision of \nNigeria Geological Survey Agency (NGSA). The aircraft is equipped with 3x \nScintrex C53 Cesium Vapour magnetometer. The flight line survey was \nimplemented with spacing of 0.5 km and spacing along the tie line of 5 km \nwas observed. It follows parallel flight lines along Northeast Southwest \ndirection of 45\u00b0 azimuth and 135\u00b0 from the true north and terrain \nclearance of 0.08 km. cultural noise orchestrated by metallic objects such \nas gates, electrical cables were completely suppressed from distorting \ntotal magnetic intensity map (TMI) interpretation. This was done by \napplying upward continuation filter on TMI to a height of about 100 m. \nThis therefore enhances magnetic anomalies over the study area (Fig.2). \nResulting map after cultural noises have been removed was subjected to \nthe reduction to the equator (RTE) filter. This is to enable easy \ninterpretation and placed the magnetic anomalies over the source bodies. \nOther enhancement techniques were subsequently applied processed on \nRTE map. \n\n\n\n3.1.1 Vertical derivative \n\n\n\nThis method was initiated, by applying 3-D Hilbert transforms in the x and \ny coordinate (Nabighian, 1984). The filter accentuates shallow geologic \nfeatures and outline magnetic lineaments in a specific direction. It \nenhances short-wavelengths anomalies by suppressing long-wavelengths. \nConsequently, shallower causative bodies are effectively exposed.  \n\n\n\n3.1.2 Analytic signal \n\n\n\nAnalytic signal (AS) enhancement technique is derived in frequency \ndomain and generates a maximum over discrete bodies by enhancing \nmagnetic bodies edges (Ndousa-Mbarga et al., 2012). The direction of its \nmagnetisation is independent of amplitude (Ansari and Alamdar, 2009). \nIts functions is not only limited to enhancing the anomaly texture but also \nhighlight anomaly discontinuities (Roest et al., 1992). As demonstrated, \n\n\n\nthe absolute value of AS, |A(xx, yy)| of a given anomaly field,F(xx, yy), can \n\n\n\nbe calculated from its horizontal and vertical derivatives (Roest et al., \n1992; Sharma, 1997): \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 45-53 \n\n\n\nCite The Article: K.O.Olomo, S. Bayode, O.A. Alagbe, G.M.Olayanju, O.K. Olaleye (2022). Multifaceted Investigation of Porphyry Cu-Au-Mo Deposit in Hydrothermal \nAlteration Zones Within The Gold Field of Ilesha Schist Belt. Malaysian Journal of Geosciences, 6(2): 45-53. \n\n\n\n|A(xx, yy)| = [(\n\u2202\n\n\n\n\u2202x\nF(xx, yx))\n\n\n\n2\n\n\n\n+ (\n\u2202\n\n\n\n\u2202y\nF(xy, yy))\n\n\n\n2\n\n\n\n+ (\n\u2202\n\n\n\n\u2202z\nF(xx, yy))\n\n\n\n2\n\n\n\n]\n\n\n\n1 2\u2044\n\n\n\n  (1) \n\n\n\nFigure 1: Map showing Geology of the study area (modified after) \n(Elueze, 1986, Odeyemi, 1993). \n\n\n\nFigure 2: Upward Continued to 100 meter Map \n\n\n\n3.1.3 Spectral Analysis of depth estimation of magnetic source \n\n\n\nDepth analysis of magnetic bodies which could be fracture, fault zones and \ncontacts between two rocks were estimated with the aid of spectral \nanalysis frequency. It is based on the application of the periodic functions \nwhich can be expressed as the sum of an infinite series of sines and cosine \nknown as Fast Fourier Transform (FFT). Frequency domain allows the \ndistribution of long and short wavelength in all measured high and low \nfrequency which can be prepared and analyzed. The power is split into \nseries of linear segments. Cumulative response of each linear segment is \nrepresented by ensemble magnetic bodies in depth. The slope of each \nlinear segment is equivalent to depth of magnetic sources (Spector and \nGrant, 1970; Kivior and Boyd, 1998). The slope (M) of the fitting line to the \nsemi-log of power versus wave number = -2Z. Assumed that, the inverse \nof period (frequency unit) is measured in inverse of a second per \nkilometre, the relation can however be written as \u22124\u03c0z where z is the \nmean depth of ensemble, therefore z = \u2212M/4\u03c0 (Kivior and Boyd, 1998). \n\n\n\nFor unhindered coverage, the entire area of study was divided (gridded) \n\n\n\ninto fifteen (15) overlaying blocks (Fig.3). Every block was 100 kilometer \nsquare metre; this is to allow approximately 1 km depth to be captured.  \n\n\n\n3.2   Airborne radiometric data \n\n\n\n Airborne radiometric technique is deployed to identify zones that are \nhydrothermally altered. The aero radiometric data of the study area was \nconducted by the Nigerian Geological Survey Agency (NGSA) on a scale of \n1:50,000 with mean terrain clearance of 80 m and flight line distance of \n400 m along the NE\u2013SE direction. The entire area is covered at a maximum \nairspeed of 250 kilometer per hour. A potassium and thorium map was \nproduced to display the surface distribution of these elements and \nlineaments.  \n\n\n\nThorium potassium ratio (Th/K) and potassium composite map (PC) were \nalso produced to adequately map and effectively identify alteration zone \n(Portnov, 1987, Gandhi et al., 1996). Th/K and PC remove the devastating \neffect of vegetation and soil moisture which mimic and distort radiometric \nresponse and signature.  \n\n\n\n3.3   Processing Software  \n\n\n\nIn processing airborne dataset, Geosoft Oasis Montaj\u2122 version 6.4.2 (HJ) \nsoftware (Geosoft Inc., Toronto, Canada) was used. The software is \nequipped with different filters and applications which offer wide range of \nusage for easy analyzing and interpreting airborne geophysical data.The \nsoftware has capabilities to enhance short wavelength features, thereby \naccentuate information from shallow geologic features, and enhance long \nwavelength features in data by attenuating short wavelength features \nthereby removing the effect of shallowest geologic features and transform \ndata from one form to another ( e.g. reduction to equator). Software \napplication such as Surfer \u2122 Version 12 and ArcGIS version 10.2 were also \nused \n\n\n\n3.4   Geochemical Analysis \n\n\n\nThe soils were collected at 0.50\u20131.0 meter from selected artisanal mining \npits established by artisanal miners at various depths. The soils were \nexcavated with the use of stainless steel hand auger and collected directly \ninto a polythene bag. The preparation method involved the air dry of the \nsamples and crushing with jaw crusher. The soil samples were sieved \nusing mesh size of 53 macrons to make it powdered. 40 g each of the \nsamples were packaged in sample bags and taken to the laboratory \ngeochemical analysis using Energy Dispersive X-Ray Fluorescence (ED-\nXRF) machine ((EDX3600B model) to find the element which are \ncommonly associated with porphyry deposit. The concentration factors of \nthe composition element were calculated by dividing elemental \nconcentration by their respective crustal abundance. \n\n\n\nFigure 3: Fifteen (15) overlying block for spectral analysis \n\n\n\n4. DISCUSSION OF RESULTS \n\n\n\nThe total magnetic intensity (Fig. 4) of magnetic field detected in specific \nvicinity is as a result of the Earth\u2019s magnetic field produced by magnetic \nsources in the subsurface as a form of magnetic anomaly map. This \nmagnetic anomaly map was interpreted with a view to derive important \ninformation about various subsurface geologic units in relation to their \nmagnetic intensities for an infill resulting from weathering, depth of \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 45-53 \n\n\n\nCite The Article: K.O.Olomo, S. Bayode, O.A. Alagbe, G.M.Olayanju, O.K. Olaleye (2022). Multifaceted Investigation of Porphyry Cu-Au-Mo Deposit in Hydrothermal \nAlteration Zones Within The Gold Field of Ilesha Schist Belt. Malaysian Journal of Geosciences, 6(2): 45-53. \n\n\n\nmagnetic anomaly, anomaly direction and closures as it applies to \nPorphyry Cu-Au-Mo deposit in the study area. Total magnetic intensity \nmap reduced to equator (TMI_RTE) (Fig. 5), emphasizes intensity and \nwavelengths of local anomalies shows magnetic intensity ranges from  \n\n\n\nFigure 4: Total Magnetic Intensity (TMI) Map of the Study area \n\n\n\n-322nT to 500nT implies different lithology unit in the bedrock of the \nstudy area and depict several areas which might be interest of intrusive \nbody. It is characterized remarkably by magnetic high ranging from 1.9 nT \nto 500 nT correlating with amphibolite schist and amphibolite in the study \narea. These high might be as a result of intrusion of high magnetic minerals \nand high concentrations of Porphyry Cu-Au-Mo deposit. The anomaly \ntrends NE-SW direction was observed implying the uniqueness of the Pan \nAfrican Orogeny. The relative low magnetic intensity values (-1.9 nT and -\n322 nT) which are prominent on amphibiolite, granite gneiss, migmatite \ngneiss complex, quartz schist and quartzite. This low magnetic anomaly \nsuggests that geological structures (fracture/fault) provide a pathway for \nhydrothermal mineralising fluid and responsible for structural controlled \nPorphyry Cu-Au-Mo deposits impregnated in faults fractures and shears \nzones (Parasnis, 1986). Contrasting magnetic high between rock bodies \nalong the fault zone was also observed, it is an indication of potential fluid \nmigration and mineralisation pathway. Lineament that runs regionally \nfrom NE-SW was observed on TMI, it correlated with regional fault system \nidentified as Ifewara fault on the surface geology map.  \n\n\n\nDerivative map (Fig. 6) enhances shallow geologic features such as faults \nand fracture represented by blue and green colors. Derivative based \nmethods provide fast means for processing magnetic grids and through \ninterpretation provides accurate information about structural settings, \ntectonic trends, and depths of anomalous bodies in the study area. It \nenhances short-wave\u00aclength components of the anomalies responsible for \nshallow bodies while de-emphasizing long-wavelength components. It is \nobserved that the southern and western part of the map is characterized \nby short-wavelength anomalous bodies implying shallow depth of the \ncausative magnetic bodies. Correlation of the vertical derivative map (Fig. \n6) with TMI_RTE (Fig 5) displays a significant improvement and \ndistinctness of structural geologic settings such as faults, fractures and \nfolds. The elongated features seen on Fig. 6 are significantly enhanced in \nthe vertical derivative map as a linear feature with NW-SE trending \ndirection.  \n\n\n\nFigure 5: Total Magnetic Intensity Reduced to Equator (TMI_RTE) Map \nof the Study area \n\n\n\nStructural features correlate with granite gneiss on surface geologic map \nmay probably act as a pathway and conduit for hydrothermal alteration in \nthe study area. It was also noted that the researched area feature few \ncrosscutting faults trending NE-SW and E-W directions describing the \nvarious tectonic activities associated with the area. This zone of major \nfracture system is regional and it is understood to have contributed \nporphyry Cu-Au-Mo deposit in the study area. Analytical signal map (Fig. \n7) highlights discontinuities and anomaly texture thereby enhances \nmagnetic anomalies, therefore showing the evidence that the lineaments \nare responsible for shallow magnetic bodies. \n\n\n\nFigure 6: Vertical derivative map of the Study area \n\n\n\nFigure 7 displays the magnetic value between 0.1 nT/M and 1.7 nT/M \n(pink) which are dominant in the west and southern part of the map \ninterpreted as faulted basement blocks having high magnetization. The \nhigh magnetic anomalies zones indicate the presence porphyry Cu-Au-Mo \nrich bearing rocks. The high magnetic signatures represented by H1-H5 on \nthe analytical map are related to quartz schist and migmatite gneiss \ncomplex. Low magnetic value between 0.00065 and 0.02860 nT/M \nrepresented by blue colour is observed in the northern part of the study \narea. Also, low values on granite gneiss could be as a result of acidic nature \nof granitic rock with low magnetic minerals such as quartz compared to \nmigmatite rocks.  \n\n\n\nFigure 7: Analytical Signal Map of the Study Area \n\n\n\nRadial spectral analysis was performed on fifteen (15) overlaying blocks. \nThe method proposes by Spector and Grant, 1970 was utilized in \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 45-53 \n\n\n\nCite The Article: K.O.Olomo, S. Bayode, O.A. Alagbe, G.M.Olayanju, O.K. Olaleye (2022). Multifaceted Investigation of Porphyry Cu-Au-Mo Deposit in Hydrothermal \nAlteration Zones Within The Gold Field of Ilesha Schist Belt. Malaysian Journal of Geosciences, 6(2): 45-53. \n\n\n\ncomputing the depth of magnetic body which reflects the thickness of the \noverburden. The application of spectral analysis is well established by \n(Mishra and Naidu, 1974; Emberga and Timothy, 2014) as a means of \ndepth estimation of magnetised source body. Table 1 is a summary of \nestimated depth of magnetic bodies with the depth ranges from 306 to 421 \nm. Fig. 8a, 8b and 8c represent depth estimate by power spectrum. Figure \n9 is a 3-D view of basement map showing the rippling character of the \nbasement. Deeper depth synonymous to high wavelength component is \ncharacterized by the sharp gradient on the map vice versa.  \n\n\n\nPotassium (K) finds its way in rocks by mineralising hydrothermal \nsolutions. K is therefore the most reliable signal and key factor in \nidentifying hydrothermal related porphyry Cu-Au-Mo deposit, because of \nits immense presence in altered rock surrounding the deposits (Hoover \nand Pierce, 1990). Minerals such as potassic feldspars (orthoclase and \nmicrocline with 13% K) and micas (biotite and muscovite with 8% K) are \nthe source of K in rocks that are hydrothermally altered, and it can be \ndetected by strong K activity. Rocks without these minerals have very low \ncount. A number of potassium anomalies occur in the radiometric survey \nimage (Fig.10). A strong K signature occurs in East and South-East, \ncorrelate with amphibolites, granite gneiss and quartzite. High K \nanomalies are represented by H1-H5 on K. The K signature is generally low \nin quartzite schist, amphibolites schist and migmatite gneiss complex. The \nhigh K activities on potassium concentration map are perfectly correlated \nwith analytical signal map (Fig 7). This implies that geological structures \nin the study area provide a pathway for hydrothermal alteration \nresponsible for porphyry Cu-M mineralisation.  \n\n\n\nFigure 11 is the thorium (Th) concentration map of the area. It shows some \nregions with high concentration of thorium (Th) that coincides with high \nconcentration of potassium. Thorium high concentrations are closely \nassociated with granite gneiss, qurtzite schist and qurtz and on other hand \nthorium low concentrations tallies with amphiboliote and amphiolite \nschist (mafic rocks). The strong thorium concentration at eastern parts of \nthe study area is related to intense weathered colluvial deposits. High Th \ncount rates are commonly associated with minerals such as pegmatites, \nzircon, monazite, allanite, sphene apatite and xenotime. These minerals \nare concentrated in igneous rocks (Keary et al., 2002). Thorium is very \ninactive and consequently considered to be immobile (Silva et al., 2003); \nlow thorium concentrations indicate that Th was significantly mobilized \nduring hydrothermal process. Patterns associated with low Th signals \ndifferent alteration patterns in various lithology including lithological \nboundaries and within these zones geological feature such faults and shear \nzones playing host to hydrothermal fluid. \n\n\n\nTable 1: Depth estimates from spectral analysis.  \n\n\n\nBLOCKS XMIN XMAX YMIN YMAX DEPTH(M) \n\n\n\nA1 680000 690000 840000 850000 306 \n\n\n\nA2 685000 695000 840000 850000 421 \n\n\n\nA3 690000 700000 840000 850000 358 \n\n\n\nA4 695000 705000 840000 8500000 325 \n\n\n\nA5 700000 710000 840000 850000 337 \n\n\n\nB1 680000 690000 835000 845000 380 \n\n\n\nB2 685000 695000 835000 845000 337 \n\n\n\nB3 690000 700000 835000 845000 346 \n\n\n\nB4 695000 705000 835000 845000 365 \n\n\n\nB5 700000 710000 835000 845000 318 \n\n\n\nC1 680000 690000 830000 840000 311 \n\n\n\nC2 685000 695000 830000 840000 346 \n\n\n\nC3 690000 700000 830000 840000 346 \n\n\n\nC4 695000 705000 830000 840000 362 \n\n\n\nC5 700000 710000 830000 840000 318 \n\n\n\nFigure 8a: Radially averaged analysis and depth estimate Blocks. (A1 \u2013 \nA5) \n\n\n\nFigure 8b: Radially averaged analysis and depth estimate Blocks. (B1 \u2013 \nB5) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 45-53 \n\n\n\nCite The Article: K.O.Olomo, S. Bayode, O.A. Alagbe, G.M.Olayanju, O.K. Olaleye (2022). Multifaceted Investigation of Porphyry Cu-Au-Mo Deposit in Hydrothermal \nAlteration Zones Within The Gold Field of Ilesha Schist Belt. Malaysian Journal of Geosciences, 6(2): 45-53. \n\n\n\nFigure 8c: Radially averaged analysis and depth estimate. Blocks (C1 \u2013 \nC5) \n\n\n\nFigure 9: 3-D view of depth to basement map of the study area \n\n\n\nFigure 10: Potassium concentration map of the Study Area \n\n\n\nFigure 11: Thorium Concentration Map of the Study Area \n\n\n\nThorium-potassium ratio (Th/K) perfectly separate potassium \nenrichment related to hydrothermal system from lithology. Th/K map \nshow some variation in lithology and remarkably accentuate alteration \nsignatures. Gnojek and Prichystal (1985) emphasize the important of \nTh/K ratio as an indicator for hydrothermal alteration. Also Shives et al., \n(1997), positioned that low Th/K ratios are reliable indicator for chemical \nalteration. This is based on the fact that thorium enrichment does not \nmove along with potassium during hydrothermal system. Thorium is \ngenerally immobile and geochemically inactive (Galbraith and Saunders, \n1983). Thorium-potassium ratio (Th/K) concentrations map (Fig. 12) \nsignifacantly enhances alteration signatures. Mafic rocks generally lack K-\nbearing minerals but exhibit intense alteration as observed in study area. \nHowever, K and other porphyry Cu-Au-Mo constituents are added to some \nrocks other than felsic rock by hydrothermal fluids, and it can be identified \non amphibolite rock or along geologic contacts in which alteration process \nrelated to silification is intense. Therefore increase in potassium \nconcentration and subsequent decrease in potassium thorium ratio as \nseen on the felsic rocks in the study area is an indication of hydrothermal \nsystem. Low Th/K values represented by L1-L4 (Fig.12) are porphyry Co-\nMo mineralisation related to hydrothermal processes. It shows a good \ncorrelation with Figures 6. 7 and 10. The alteration zones are observed on \ngranite gneiss, amphibolite and quartzite units and this is undoubtedly \nhydrothermal alterations zones.  \n\n\n\nThe potassium (K) composite image map (Fig.13) involves the \ncombination of K (in red) along side with K/Th (in green) and K/U (in \nblue). It displays spatial distribution of relative potassium concentrations \nshowing as anomalous bright zones (high values) with respect to Th and \nU. These anomalous bright zones play host to porphyry Cu-Au-Mo deposits \nand are well correlated with the granite gneiss and amphibolite. \nWeathering effect on radiometric response and signature is less evident \non potassium composite map.  \n\n\n\nFigure 12: Ratio Map of Th and K (Th / K) of the study area \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 45-53 \n\n\n\nCite The Article: K.O.Olomo, S. Bayode, O.A. Alagbe, G.M.Olayanju, O.K. Olaleye (2022). Multifaceted Investigation of Porphyry Cu-Au-Mo Deposit in Hydrothermal \nAlteration Zones Within The Gold Field of Ilesha Schist Belt. Malaysian Journal of Geosciences, 6(2): 45-53. \n\n\n\nFigure 13: Potassium (K) Composite Image Map of the Study Area \n\n\n\n4. VALIDATION OF AIRBORNE RESULTS\n\n\n\nValidation is required to reinforce the preliminary airborne investigation \nof potential and valuable porphyry Cu-Au-Mo mineralisation within the \n\n\n\nhydrothermal alteration zones. This was done by carrying out geochemical \nanalysis of some selected soil and stream sediment samples of \nhydrothermal alteration zones. Hydrothermal alteration zones identified \nas H1, H2, H4 and H5 in Figure 10 were targeted for further geochemical \nstudies. Geochemical studies, particularly stream sediment have been \nsuccessfully used in locating undiscovered mineral target (Cheng, 2007, \nZuo et al., 2009) in different part of world. However the presence of \nelevated gold occurrences within the investigated area serve as a guide to \nidentify porphyry Cu-Au-Mo systems. The concentration factor of each \nelement was calculated and compare with crustal abundance of the \nelement. This is to determine whether Cu-Au-Mo is significant enough to \nbe an ore for exploitation (Evans, 1999). Table 2 is the concentration factor \nestimation of Cu-Au- Mo deposit. From the result of twenty seven (27) \nelements analyzed, copper (Cu), Gold (Au) and molybdenum (Mo) \nelements were detected in almost all parts of the study area. Figure 14 is \nthe bar chart of elemental composition of Cu-Au-Mo and crustal \nabundance. The bar chart shows that Au and Mo exceeded the crustal \nabundance value implying that the mineral are probable significant \nenough to be mined except alteration zone H1 where there is no presence \nof gold. On the other hand, Cu concentration factor is less than crustal \nabundance indicating that the mineral is not enough to be mineable. \nHowever the geochemical analysis confirms the presence of Cu as a trace \nelement in the study area. Geochemical results confirm the airborne \ngeophysical signature of porphyry Cu-Au-Mo within the investigated area \n\n\n\nTable 2: Analysis of Concentration Factor within the study area \n\n\n\nArea Location \nRecoverable Grade (ppm)  \n\n\n\nCrustal Abundance \n(Taylor, 1964) \n\n\n\nConc. Factor (ppm) \n\n\n\nCu Au Mo Cu Au Mo Cu Au Mo \n\n\n\nH1 \n\n\n\nL1 334 0 235.7 \n\n\n\n55 0.004 0.15 \n\n\n\n6.1 0 1571.3 \n\n\n\nL2 396 0 207.9 7.2 0 1386.0 \n\n\n\nL3 421 0 210.5 7.7 0 1403.3 \n\n\n\nH2 L1 601 943 165.8 55 0.004 0.15 10.9 235750 1105.3 \n\n\n\nH4 \nL1 320 268 223.9 \n\n\n\n55 0.004 0.15 \n5.8 67000 1492.7 \n\n\n\nL2 190 220 222.3 3.5 55000 1482.0 \n\n\n\nH5 L1 437 746 223.4 55 0.004 0.15 7.9 186500 1489.3 \n\n\n\nFigure 14: elemental composition of Cu-Au-Mo and crustal abundance \nbar chart of the Study Area. \n\n\n\n5. CONCLUSIONS \n\n\n\nThe assessment of possible types of mineralisation target apart from gold, \nto improve the confidence in mineral prospecting within the studied area, \nwas achieved. Possibility of porphyry Cu-Au-Mo deposit was investigated \nusing general geophysical signature of porphyry deposit in other region of \nthe world as a template. Multiple datasets utilized to identify possible \nporphyry system target(s) within the study area are airborne geophysics \ndatasets (magnetic and radiometry) and geochemistry data. Airborne \nmagnetic and radiometry data were processed and interpreted in order to \nestablish relationship between hydrothermal alteration system and faults \nzones. Filtering algorithm such as the vertical derivative, analytical signal \nand spectral analysis enhanced the interpretation of aeromagnetic data. \nThe enhanced geologic structures (shear zone, faults, and fracture \nsystems) serve as a conduit for hydrothermal fluid migration, the \ninterpreted structures trend predominantly NE-SW direction amplifying \nthe appreciable implication of the Pan African Orogeny. Depth estimation \n\n\n\nobtained from spectral analysis reveals that magnetic sources range from \n306 to 421 m. The airborne radiometric interpretations help in \nunderstanding the radioactivity elements spatial distribution within \ndifferent rock units and locating hydrothermally altered zones. Potassium \nenrichment from potassium concentration map is also characterized by \nanomalously low value of Th/K ratio normal lithological signatures and \npotassium composite map to give a strong indicator of hydrothermal \nalteration zones thus providing significant exploration clue for porphyry \nCu-Au-Mo deposit. These results showed that structures and \nhydrothermally altered zones are the areas of promising porphyry Cu-Au-\nMo mineralisation in the area. The mapped alteration zone was validated \nwith geochemical analysis of some selected soil and stream sediment \nsamples which reveal the presence of porphyry Cu-Au-Mo mineral as \npossible revolutionary trends in mineralisation targets in the study area. \nThis research will serve as a guide for future investigation of porphyry \ndeposit within other parts of Ilesha Schist Belt and other schist belt region \nacross the country. However, further studied is hereby recommended to \nlook at other types of porphyry deposits in the study area. Also borehole \ncore data of the investigated zones is encouraged. \n\n\n\nREFERENCES \n\n\n\nAdeoti, B., Okonkwo, C.T., 2017. Structural evolution of Iwaraja shear zone, \nSouthwestern Nigeria. Journal of Africa Earth Science 131, 115-124.  \n\n\n\nAdemeso, O.A., Adekoya, J.A., Adetunji, A., 2013. Further evidence of \ncataclasis in the ife-ilesha schist belt, Southwestern Nigeria. J. Nat. Sci. \nRes. 3 (11), 51-56. \n\n\n\nAiro, M. L. 2002. Aeromagnetic and aeroradiometric response to \nhydrothermal alteration. Surveys in Geophysics 23, 277\u2013298. \n\n\n\nAjayi, T. R. 1988. Integrated exploration and statistical studies of \ngeochemical data in Ife \u2013 Ilesha Goldfield: Ph. D Thesis Obafemi \nAwolowo University, Ile - Ife, Nigeria. 459-503. \n\n\n\nAjayi, T.R., Ogedengbe, O., 2003. Opportunity for the exploitation of \nprecious and rare metals in Nigeria. In: Elueze, A.A. (Ed.), Prospects \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 45-53 \n\n\n\nCite The Article: K.O.Olomo, S. Bayode, O.A. Alagbe, G.M.Olayanju, O.K. Olaleye (2022). Multifaceted Investigation of Porphyry Cu-Au-Mo Deposit in Hydrothermal \nAlteration Zones Within The Gold Field of Ilesha Schist Belt. Malaysian Journal of Geosciences, 6(2): 45-53. \n\n\n\nfor Investment in Mineral Resources of Southwestern Nigeria. \nNigerian Mining and Geosciences Society (NMGS), 1476.  \n\n\n\nAjibade, A.C., 1979. The Nigerian Precambrian and the Pan-African \norogeny. In: Oluyide, P.O. (Ed.), Precambrian Geology of Nigeria. \nProceedings, Geological Survey of Nigeria, 47-52. \n\n\n\nAkinlalu A.A., Adelusi A.O., Olayanju G.M., Adiat K.A.N., Omosuyi G.O., \nAnifowose A.Y.B., Akeredolu B.E., 2018. Aeromagnetic mapping of \nbasement structures and mineralization characterisation of Ilesa \nSchist Belt, Southwestern Nigeria. Journal of African Earth Sciences \n138 (2018) 383-391 \n\n\n\nAko, B. D. 1979. Geophysical prospecting for groundwater in part of \nSouthwestern Nigeria: PhD Thesis, University of Ife, Ile-Ife. 371-372. \n\n\n\nAko, B. D. 1980. A contribution to mineral exploration in the Precambrian \nbelt of part of Southwestern Nigeria: Journal of Mining and Geology 17 \n(2) 130-1141.  \n\n\n\nAndrson, H., Nash, C., 1997. Integrated lithostructural mapping of the \nRossing area, Namibia, using high-resolution aeromagnetic, \naeroradiometric, landsat data and aerial photographs. Exploration \nGeophysics 28, 187\u2013190. \n\n\n\nAnifowose, A.Y.B., Borode, A.M., 2007. A Photogeological study of the fold \nstructure in Okemesi area. Niger. J. Min. Geol. 43 (2), 121-126. \n\n\n\nAnsari, A.H., Alamdar, K., 2009. Reduction to the pole of magnetic \nanomalies using analytic signal. World Appl. Sci. J. 7, 407\u2013408. \n\n\n\nArchibald, N., Gow, P., and Boschetti, F., 1999. Multiscale edge analysis of \npotential field data: Exploration Geophysics, 30, 38\u201344. doi: 10.1071/ \nEG999038. \n\n\n\nAriyibi, E. A. 2011. Integrated Geochemical and Geophysical Approach to \nMineral Prospecting A Case Study on the Basement Complex of Ilesha \nArea, Nigeria, Advances in Data, Methods, Models and Their \nApplications in Geoscience, Dr. DongMei Chen (Ed.), ISBN: 978-953-\n307-737-6, InTech  76-89. \n\n\n\nAustin, J. R., and Blenkinsop, T. G., 2008. The Cloncurry Lineament: \ngeophysical and geological evidence for a deep crustal structure in the \nEastern Succession of the Mount Isa. Precambrian Research, 163 52\u2013\n67. doi:10.1016/j.precamres.2007.08.012 \n\n\n\nBerger, B.R., King, T.V.V., Morath, L.C., and Phillips, J.D., 2003. Utility of \nhigh-altitude infrared spectral data in mineral exploration: \napplication to northern Patagonia Mountains, Arizona: Economic \nGeology, v. 98, p. 1003-1018. \n\n\n\nBhattacharyya, B.K., 1966. Continuous spectrum of the total magnetic field \nanomaly due to a rectangle prismatic body. Geophysics 31, 99-119. \n\n\n\nBierlein, F. P., Murphy, F. C., Weinberg, R. F., and Lees, T., 2006. Distribution \nof Orogenic gold deposits in relation to fault zones and gravity \ngradients: targeting tools applied to the Eastern Goldfields, Yilgarn \nCraton, Western Australia: Mineralium Deposita, 41, 109\u2013116. doi: \n10.1007/s00126-005-0044-4 \n\n\n\nCharbonneau, B.W., Holman, P.B., Hetu, R.J., 1997. Airborne gamma \nspectrometer magnetic-VLF survey of northeastern Alberta. In \nExploring for minerals in Alberta: Geological Survey of Canada \nGeoscience contributions, edited by MacQueen, Canada-Alberta \nagreement on mineral development. Geological Survey of Canada \nBulletin 500, 109-131. \n\n\n\nCheng, Q. 2007. Mapping singularities with stream sediment geochemical \ndata for prediction of undiscovered mineral deposits in Gejiu, Yunnan \nProvince, China. Ore Geol. Rev., 32, 314\u2013324. [CrossRef] \n\n\n\nClark, D.A. 1999. Magnetic petrology of igneous intrusions: Implications \nfor exploration and magnetic interpretation. Explor. Geophys., 30, 5\u2013\n26. [CrossRef]\n\n\n\nCooper, G. R. J., and Cowan, D. R., 2006. Enhancing potential field data using \nfilters based on the local phase: Computers & Geosciences, 32, 1589\u2013\n1590. doi:10.1016/j.cageo.2006.02.016  \n\n\n\nCooper, G.R.J., 2009. Balancing images of potential field data. Geophysics \n74, 18-21. \n\n\n\nCore, D., Buckingham, A., Belfield, S., 2009. Detailed structural analysis of \n\n\n\nmagnetic data \u2014 done quickly and objectively, SGEG Newsletter. \n\n\n\nCudahy, T.J., Wilson, J., Hewson, R., Linton, P., Harris, P., Sears, M., Okada, \nK., and Hackwell, J.A., 2001. Mapping porphyry-skarn alter ation at \nYerington, Nevada, using hyperspectral VNIR-SWIR-TIR imaging data: \nGeoscience and Remote Sensing Symposium, 2001, p. 631-633. \n\n\n\nDada, S.S., 2006. Proterozoic evolution of Nigeria. In: Oshin, O. (Ed.), the \nBasement Complex of Nigeria and its Mineral Resources (A Tribute to \nProf. M.A. O. Rahaman). Akin Jinad & Co. Ibadan, 30-42. \n\n\n\nDickson, B.L., and Scott, K.M., 1997. Interpretation of aerial gamma ray \nsurveys adding the geochemical factors; AGSO Journal of Australian \nGeology and Geophysics, v. 17, number 2, 189-198. \n\n\n\nElkhateeb, S. O., and Eldosouky, A. M., 2016. Detection of Porphyry \nIntrusions Using Analytic Signal (AS), Euler Deconvolution, and Center \nfor Exploration Targeting (CET) Technique at Wadi Allaqi Area, South \nEastern Desert, Egypt. International Journal of Scientific & \nEngineering Research, Volume 7, Issue 6, June 16, ISSN 2229-5518, \n473- 475  \n\n\n\nElkhateeb S.O; Abdellatif M.A.G.2018. Delineation potential gold \nmineralization zones in a part of Central Eastern Desert, Egypt using \nAirborne Magnetic and Radiometric data. NRIAG Journal of \nAstronomy and Geophysics 361\u2013376 \n\n\n\nElueze, A. A. 1977. Geological and geochemical studies in the Ife-Ilesa \nSchist belt in relation to gold mineralization. Unpubl. M. Phil. Thesis, \nUniv. of Ibadan, Nigeria \n\n\n\nElueze, A. A. 1986. Geology of the Precambranian Nigeria: Geological \nsurvey.. 80-87. \n\n\n\nEmberga, T.T., Timothy, C., 2014. Spectral Re-evaluation of the magnetic \nbasement depth over yola arm of upper benue trough Nigeria using \naeromagnetic data. Stand. Sci. Res. Essays 12 (9), 364-370. \n\n\n\nFerreira, F., de Castro, L., Bongiolo, A., de Souza, J., and Romeiro, M., 2011. \nEnhancement of the total horizontal gradient of magnetic anomalies \nusing tilt derivatives: part II \u2014 application to real data: SEG Technical \nProgram Expanded Abstracts, 889\u2013890. \n\n\n\nGalbraith, J.H., and Saunders, D.F., 1983. Rock classification by \ncharacteristics of aerial gamma ray measurements: Journal of \nGeochemical Exploration volume 18, 52-69. \n\n\n\nGandhi, S.S., Prasad, N., and Charbonneau, B.W., 1996. Geological and \ngeophysical signatures of a large polymetallic exploration target at \nLou Lake, southern great Bear magmatic zone, Northwest Territories; \nIn: Current Research 1996-E; Geological Survey of Canada, 152-155. \nGnojek, I., and Prichystal A., 1985. A new zinc mineralization detected \nby airborne gamma-ray spectrometry in Northern Moravia \n(Czechoslovakia): Geoexploration, v. 23, no. 4, p. 497\u2013 501. \n\n\n\nGraham, D.F., 1993. Airborne radiometric data a tool for reconnaissance \ngeological mapping using a GIS Thematic Conference. In: Proceedings, \n9th, Geologic Remote Sensing, volume 1, 45-53 \n\n\n\nHenson, P. A., Blewett, R. S., Roy, I. G., Miller, J. McL., and Czarnota, K., 2010. \n4D architecture and tectonic evolution of the Laverton region, eastern \nYilgarn Craton, Western Australia: Precambrian Research, 183, 342\u2013\n349. doi:10.1016/j.precamres.2010.08.003 \n\n\n\nHildenbrand, T.G. (2000). Regional Crustal Structures and Their \nRelationship to the Distribution of Ore Deposits in the Western United \nStates, Based on Magnetic and Gravity Data. Econ. Geol., 95, 1583\u2013\n1603. [CrossRef] \n\n\n\nHolden, E.J., Dentith, M., Kovesi, P., 2008. Towards the automatic analysis \nof regional aeromagnetic data to identify regions prospective for gold \ndeposits. Computer. Geoscience. 34, 1507\u20131511 \n\n\n\nHoover, D. B., and Pierce, H. A., 1990 Annotated bibliography of gamma-\nray methods applied to gold exploration: U.S. Geol. Survey Open-File \nRept. 90\u2013203, 23 p. \n\n\n\nHornby, P., Boschetti, F., and Horowitz, F. G., 1999. Analysis of potential \nfield data in the wavelet domain: Geophysical Journal International, \n137, 175\u2013196. doi:10.1046/j.1365-246x.1999.00788.x \n\n\n\nHsu, S. K., 2002. Imaging magnetic sources using Euler\u2018s equation. \nGeophysical prospecting, 50,.17-23. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 45-53 \n\n\n\nCite The Article: K.O.Olomo, S. Bayode, O.A. Alagbe, G.M.Olayanju, O.K. Olaleye (2022). Multifaceted Investigation of Porphyry Cu-Au-Mo Deposit in Hydrothermal \nAlteration Zones Within The Gold Field of Ilesha Schist Belt. Malaysian Journal of Geosciences, 6(2): 45-53. \n\n\n\nHubbard, F.H., 1975. Precambrian crustal development in western Nigeria: \nindications from the Iwo region. Gcol. Soc. Amer Bull. 20 (1), 119-135 \n\n\n\nJaques, A.I.,Wellman, P., Whitaker,A.&Wyborn,D., 1997. High resolution \ngeophysics in modern geological mapping. AGSO Journal Australia \nGeology Geophysics, 17, 154\u2013173. \n\n\n\nKayode, J.S., Adelusi, A. O., and Nyabeze, P. K., 2011. Horizontal Derivatives \nof the Ground Magnetic Interpretation in Part of Ilesha Area, \nSouthwestern Nigeria. Scientific Research and Essays. Volume 6(20), \n4161-4169. \n\n\n\nKelley, D.L., Hall, G.E.M., Closs, G.L., Hamilton, I.C., and McEwen, R.M., 2003, \nThe use of partial extraction geochemistry of copper exploration in \nnorthern Chile: Geochemistry - Exploration, Environment, Analysis, v. \n3, p. 85-104. \n\n\n\nKirkham, R.V., 1971, Inter mineral intrusions and their bearing on the \norigin of porphyry copper and molybdenum deposits: Economic \nGeology, v. 66, p. 1244-1250 \n\n\n\nKivior, I., and Boyd, 1998. Interpretation of the aeromagnetic experimental \nsurvey in Eromanga/Cooper Basin, Journal Canadian exploration \nGeophysics. 34:. 52-64. \n\n\n\nLindgren, W., 1933. Mineral deposits, 4th ed.: New York, McGraw-Hill, 930 \np. \n\n\n\nMacnae, J., 1995. Applications of geophysics for the detection and \nexploration of kimberlitic and lamproites. In: Griffin, W.L. (Ed.), \nDiamond Exploration into the 21st Century. Journal of Geochemical \nExploration, 217\u2013233. \n\n\n\nMishra, D.C., Naidu, P.S., 1974. Two dimensional power spectral analysis of \naeromagnetic fields. Geophysics Prospecting 22, 347-521. \n\n\n\nMoslem F. and Hooshang A.H 2008, geophysical signature of the gold rich \nporphyry copper deposits- A case study at the Dally Cu-Au porphyry \ndeposit. Journal of economic Geology. Vol. 10. no. 2, 639-675 \n\n\n\nMosusu, N.; Bokuik, A.; Petterson, M.; Holm, R. 2021, Stream Sediment \nDatasets and Geophysical Anomalies: A Recipe for Porphyry Copper \nSystems Identification\u2014The Eastern Papuan Peninsula Experience. \nGeosciences, 11, 299. https:// \ndoi.org/10.3390/geosciences11070299  \n\n\n\nNabighian, M.N., 1984. Toward a three-dimensional automatic \ninterpretation of potential field data via generalized Hilbert \ntransforms: fundamental relations. Geophysics 49 (6), 782\u2013785. \n\n\n\nNdousa-Mbarga, T., Fenmoue, A.N.S., Manguelle-Dicoum, E., Fairhead, J.D., \n2012. Aeromagnetic data interpretation to locate buried faults in \nsouth-East Cameroon. Geophysical 48 (1-2), 52-61. \n\n\n\nNeawsuparp, K., Charusiri, P., and Meyers, J., 2005. New processing of \nairborne magnetic and electromagnetic data and interpretation for \nsubsurface structures in the Loei area, Northeastern Thailand: Science \nAsia, 31, 288\u2013297. doi:10.2306/scienceasia1513-1874.2005.31.283 \n\n\n\nNGSA consultancy service limited 2012. Ifewara gold project field \ncampaign drilling.  \n\n\n\nNigerian Geological Survey Agency, 2009. Airborne Geophysical Survey \nResidual Magnetic Intensity Map of Ilesha (Sheet) Area \n\n\n\nObaje, N.G., 2009. Geology and Mineral Resources of Nigeria. Springer \nDordrecht, London. \n\n\n\nOdeyemi, I.B., 1981. A review of the orogenic events in the basement \ncomplex of Nigeria. West Africa. Geol. Rundsch. 70 (3), 893-907. \n\n\n\nOdeyemi, I.B., 1993. A comparative study of remote sensing images of the \nstructure of okemesi fold belt, Nigeria. ITC J. 1, 78-80 \n\n\n\nOlomo, K.O., Olayanju, G.O., Adiat, K.A.N. and Akinlalu, A.A. 2018. \nIntegrated Approach Involving Aeromagnetic and LandSat for \nDelineating Structures and its Implication on Mineralisation. \nInternational Journal of Scientific and Technology Research Volume 7, \nIssue 2. 208- 217. \n\n\n\nOyinloye A. O., 2011. Geology and Geotectonic Setting of the Basement \nComplex Rocks in South Western Nigeria: Implications on Provenance \nand Evolution. Earth and Environmental Sciences, 5-118 \n\n\n\nParasnis D.S., 1986. Principles of Applied Geophysics, Chapman Hall, \nLondon, 43-44. \n\n\n\nPatra, I., Srinivas, D., Tripathi, S., Patel, A.K., 2016. High resolution Airborne \nGamma ray Spectrometric data in Geological mapping-A case study \nfrom parts of Shillong Basin, Meghalaya. J. Geophys. V 3, 174\u2013177. \n\n\n\nPortnov, A.M., 1987. Specialization of rocks toward potassium and \nthorium in relation to mineralization. Int. Geol. Rev. 29, 325\u2013341. \n\n\n\nRahaman, M.A., 1988. Recent advances in the study of the basement \ncomplex of Nigeria. In: precambrian geology of Nigeria. Geol. Surv. \nNiger. Kad.. 11-43. \n\n\n\nRiahi S; Abbas B; Maysam A; Soheila A; and Gholam-R. E. 2021, Integration \nof airborne geophysics and satellite imagery data for exploration \ntargeting in porphyry Cu systems: Chahargonbad district, Iran. \nEuropean Association of Geoscientists & Engineers, Geophysical \nProspecting, 1\u201322 \n\n\n\nRiveros, K.; E Veloso, E.; Campos, E.C.; Menzies, A.; Veliz, W. 2014.Magnetic \nproperties related to hydrothermal alteration processes at the \nEscondida porphyry copper deposit, northern Chile. Miner. Deposita, \n49, 693\u2013707. [CrossRef] \n\n\n\nRobert, F., Brommecker, R., Bourne, B. T., Dobak, P. J., McEwan, C. J., Rowe, \nR. R., and Zhou, X., 2007. Models and exploration methods for major \ngold deposit types, in B. Milkereit, ed., Proceedings of Exploration 07: \nFifth Decennial International Conference on Mineral Exploration, \n691\u2013711. \n\n\n\nRoest, W.R., Verhoef, J., Pilkington, M., 1992. Magnetic interpretation using \n3-D analytic signal. Geophysics 57, 117\u2013123. \n\n\n\nSabin's, F.F., 1999. Remote sensing for mineral exploration. Ore Geology \nReviews. 14, 159\u2013180. \n\n\n\nSharma, P. V., 1997. Environmental and Engineering Geophysics, \nCambridge University Press \n\n\n\nSinclair, W.D., 1995. Molybdenum, tungsten and tin deposits and \nassociated granitoid intrusions in the northern Canadian Cordillera \nand adjacent parts of Alaska, in Schroeter, T.G., ed., Porphyry deposits \nof the Northwestern Cordillera of North America: The Canadian \nInstitute of Mining and Metallurgy, Special Volume 46, p. 58-76. \n\n\n\nShives, R.B.K., Charbonneau, B.K., Ford, K.L., 1997. The detection of \npotassic alteration by gamma ray recognition of alteration related to \nmineralization, in: Exploration 97, Fourth Decennial Intern. Conf. \nMineral Exploration (Toronto, Canada), p. 345\u2013353. \n\n\n\nSpector, A., Grant, F.S., 1970. Statistical Models for interpreting \naeromagnetic data. Geophysics 35 (2), 295-301. \n\n\n\nTaylor S. R. (1964): Abundance of chemical elements in the continental \ncrust: a new table. Geochimicuet CosmochimicnA cta, Vol. 28, 1273-\n1285. Pwgsmon Press Ltd. Priiited in Xortheru Ireland \n\n\n\nVerduzco, B., Fairhead, J. D., Green, C. M., and MacKenzie, C., 2004. New \ninsights into magnetic derivatives for structural mapping: The \nLeading Edge, 23, 116\u2013119. doi:10.1190/1.1651454 \n\n\n\nWemegah, D.D., Preko, K., Noye, R.M., Boadi, B., Menyeh, A., Danuor, S.K., \nAmenyoh, T., 2015. Geophysical interpretation of possible gold \nmineralization zones in Kyerano, South-Western Ghana using \naeromagnetic and radiometric datasets. J. Geosci. Environ. Prot. 2015 \n(3), 67\u201382. \n\n\n\nZuo, R.; Cheng, Q. 2009. Agterberg, F.; Xia, Q. Application of singularity \nmapping technique to identify local anomalies using stream sediment \ngeochemical data, a case study from Gangdese, Tibet, western China. J. \nGeochem. Explor. 101, 225\u2013235. [CrossRef]. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 01-05 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2021.01.05 \n\n\n\nCite the Article: Muhammad Imran Hafeez Abbasi (2021). Is Makran A Separate Microplate? A Short Review.  \nMalaysian Journal of Geosciences, 5(1): 01-05. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nREVIEW ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2021.01.05 \n\n\n\nIS MAKRAN A SEPARATE MICROPLATE? A SHORT REVIEW \n\n\n\nMuhammad Imran Hafeez Abbasi \n\n\n\nCOMSATS Institute of Information Technology - Abbottabad Campus, Abbottabad, Khyber Pakhtunkhwa PAKISTAN. \n*Corresponding Author Email: emraan@hotmail.co.uk \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 18 September 2020 \nAccepted 20 October 2020 \nAvailable online 19 November 2020\n\n\n\nMakran Subduction Zone (MZS) is important as this region lies on both sides of the border of Iran and Pakistan \nalong the coastline. Makran Subduction complex has pervasive seismicity and diverse focal mechanism \nsolutions and being in the vicinity of Triple Junction where three major Tectonic plates; Arabian, Eurasian and \nIndian plates are connecting. Both of Chabahar and Gwadar ports are located in this vicinity, on which China \nis investing for CPEC, Belt and Road Initiative. The whole world is looking at these projects of Makran, as this \nmay define and transform the future of trade. Hence Geoscience point of view is notable as well in \nconsideration for the successful execution of these projects. Several Microplates/blocks have been proposed \naround the vicinity MSZ and Indian-Eurasian Plate boundary including the Ormara microplate, Lut Block, \nHelmand Block, and Pakistan-Iran Makran microplate (PIMM). The purpose of this review is to shed light on \nPIMM. Despite previous researches related to Makran, still many researchers are working to solve puzzles \nrelated to the complexity of MSZ. It is divided into Eastern and Western Makran due to seismicity and North \nto South into four parts based on stratigraphy, thrusts and folds. This review aims to give suggestions for the \nhypothesis on PIMM which was inferred as a separate microplate.  \n\n\n\nKEYWORDS \n\n\n\nPIMM, Earthquake Seismology, MSZ, Gwadar and Chabahar ports.\n\n\n\n1. INTRODUCTION\n\n\n\nMSZ is known to have complex tectonic settings being near to the Triple \n\n\n\nJunction, where three tectonic plates (i.e. Eurasian, Indian and Arabian) \n\n\n\nare interacting (Curray et al., 1982; Mokhtari et al., 2019). Through \n\n\n\nprevious studies of tectonic reconstruction, it was inferred that the \n\n\n\nsubduction was originated in the Late Cretaceous (McCall and Kidd, 1982; \n\n\n\nMcCall, 2002). Since Early Eocene, a massive accretion is being build-up \n\n\n\ntowards the southwestern part of the Eurasian plate (McCall et al., 1982; \n\n\n\nEllouz-Zimmermann et al., 2007). This plate is categorized as a typical fold \n\n\n\nand thrust belt, where landward extension of thrust followed by a very \n\n\n\nhigh accumulation of sediments at the frontal wedge, with uplifting and \n\n\n\nthickening of accretionary complex,  then dip angle of MSZ is very low \n\n\n\n(almost 2-3\u2070) (Schl\u00fcter et al., 2002; Ellouz-Zimmermann et al., 2007; \n\n\n\nHeidarzadeh et al., 2008; Harms et al., 1984). The collision between the \n\n\n\nArabian oceanic plate and the Eurasian continental plate at MSZ caused \n\n\n\nthe growth of long spread Thrust and fold belt of  Zagros ranges, which is \n\n\n\nabout 1500 km widespread in Northwest of Makran (Kashfi, 1976; Jenkins \n\n\n\net al., 2013).  \n\n\n\nMSZ area lies at the west (left side) of the Left lateral Chaman strike-slip \n\n\n\nfault, Ornach-Nal fault (C-OFS), and Sonne pas fault (SPF), which is \n\n\n\nintersecting the wedge and extending in an abyssal plane (Kukowski et al., \n\n\n\n2000). In Iran, MSZ is at South East of Right lateral Zendan strike-slip fault, \n\n\n\nbounded towards North by Jiroft Fault (ophiolites) and towards the south \n\n\n\nby Oman trench of this subduction zone in the offshore (Figure) (Minshull \n\n\n\net al., 1992; Siddiqui and Jadoon, 2012).  \n\n\n\nFigure 1: Tectonic setting along proposed Pakistan-Iran Makran \nMicroplate \n\n\n\nChaman fault is originally a transform fault due to left lateral movement, \n\n\n\nwith almost North-South orientation (Khan et al., 1991). Hoshab Fault has \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 01-05 \n\n\n\nCite the Article: Muhammad Imran Hafeez Abbasi (2021). Is Makran A Separate Microplate? A Short Review.  \nMalaysian Journal of Geosciences, 5(1): 01-05. \n\n\n\nthe same sense of motion as C-OFS and is located at its southern section, \n\n\n\nalthough in a curved form (Avouac et al., 2014). The surface of the \n\n\n\naccretionary prism of Makran is marked by steep and asymmetrical folds \n\n\n\nwith imbricate thrust wedges. These folds  are mainly striking East to West \n\n\n\nand almost parallel to these fold axes, there are reverse faults that are \n\n\n\nroughly dipping from North to West (Ahmed, 1969). Normal faults in the \n\n\n\nChabahar region (Figure 1) are dipping towards the headland (Normand \n\n\n\net al., 2019).  \n\n\n\nFigure 2: Location Map for Chabahar and Gwadar ports \n\n\n\nThe paleo-stress inversion is suggesting an N-S extension along the \n\n\n\nsubduction zone rather than compression. MSZ is produced by the \n\n\n\nsubduction of the Arabian oceanic Plate under the Eurasian continental \n\n\n\nplate with an average speed of 40 mm/yr (Siddiqui and Jadoon, 2012; \n\n\n\nSmith et al., 2012). MSZ can be distributed into Eastern and Western \n\n\n\nMakran because of its different seismicity patterns at both sections \n\n\n\nseparated by a sinistral fault (SPS) (Figure) (Mokhtari et al., 2019). PIMM \n\n\n\nhas been designated as a separate microplate after interpreting Tectonics \n\n\n\nof Indian and Eurasian plates (Siddiqui and Jadoon, 2012). So, in this \n\n\n\nreview paper, we are trying to see aspects behind this hypothesis (Siddiqui \n\n\n\nand Jadoon, 2012). And we have come out with some recommendations to \n\n\n\nsupport this hypothesis by scientific evidence.  \n\n\n\n2. SEISMICITY\n\n\n\nMSZ is complex and active subduction zone, situated near a triple junction \n\n\n\nbut still exhibits comparatively low seismicity than other subduction \n\n\n\nzones of the World (Mokhtari et al., 2008). The Eastern side of Makran \n\n\n\nseems activate in recent historical times, while the other side which is \n\n\n\ntermed as Western Makran (WM) tends to be aseismic (Figure 3) (Jacob \n\n\n\nand Quittmeyer, 1979; Musson, 2009; Rajendran et al., 2013). By \n\n\n\nobserving previous work, we can infer three things about the WM:  \n\n\n\n1. Along with the WM,  the subduction tends to be locked and pending the\n\n\n\npotential of a great magnitude earthquake.  \n\n\n\n2. WM/Zargos is subducting virtually aseismically having anticlockwise \n\n\n\nmotion with respect to its Northwards drift and low seismicity.  \n\n\n\n3. Subduction seems to be diminished in the WM (Masson et al., 2005; Paul \n\n\n\net al., 2006). \n\n\n\nAround Zagros ranges in Southern Iran, the seismicity seems to be \n\n\n\nrestricted to upper Crust only  (depth of < 20 KMS) (Maggi et al., 2000).  \n\n\n\nFigure 3: Regional Tectonic map, with area selected to be study for \n\n\n\nseismicity of Western Makran. \n\n\n\nA group researchers used satellite imagery, from Interferometric \n\n\n\nSynthetic Aperture Radar (InSAR)  for investigation of active time series of \n\n\n\nconvergence between slip on the megathrust and internal deformation \n\n\n\n(Lin, 2015; Lin et al., 2015). He inferred that elastic strain is accumulating \n\n\n\nin the region which can trigger a high magnitude earthquake as in the past.  \n\n\n\nFigure 4: Earthquake events (1990-2019) recorded in the \n\n\n\nMakran region. \n\n\n\n3. EXTENSION \n\n\n\nMakran accretionary wedge ( Eastern and Western Makran)  extends \n\n\n\nalmost 1000\u202fkm between the Zendan/Minab dextral fault at WM in Iran \n\n\n\nand the Chaman sinistral strike-slip fault system in Pakistan (Figure 3). \n\n\n\nThis wedge trending N-S and is originally a transform fault with Left lateral \n\n\n\nmovement (Byrne et al., 1992; Kopp et al., 2000; Siddiqui and Jadoon, \n\n\n\n2012; Khan et al., 1991). The 3D models of the density variation and the \n\n\n\nmagnetic susceptibility along a few cross segments opposite to the EW \n\n\n\nstrike of the MSZ demonstrate that the Arabian oceanic plate is subducting \n\n\n\nwith a very trivial plunge slant beneath the Eurasian plate (Abedi and \n\n\n\nBahroudi, 2016). From a thorough examination of seafloor spreading in \n\n\n\noffshore Makran,  the pattern of oceanic ridges has been observed (Okal \n\n\n\nand Synolakis, 2008).  And by fault mechanism studies, these tectonic \n\n\n\nplates reported to be have convergence rates ranging from 35.50 to \n\n\n\n36.50\u202fmm/a towards WM and 40 to 42\u202fmm/a towards the Eastern Makran \n\n\n\n(Haghipour et al., 2012; Burg, 2018). However, stated that the \n\n\n\nconvergence rate ranges between 19.5\u201327 mm/a through his GPS \n\n\n\nemployed work (Reilinger et al., 2006; Vernant, 2014; Vernant et al., \n\n\n\n2004). Moho depth beneath the Oman Trench is around 20-25 km \n\n\n\nobserved with variation in acoustic impedance in high-resolution maps \n\n\n\n(Abdetedal et al., 2014).  \n\n\n\nObservation of the Morphotectonics of the MSZ by the aid of Swath \n\n\n\nMapping and Parasound echo sounding of 3.5 kHz, exhibits approximately \n\n\n\n7 km of sediment deposited in the Oman trench and the main rivers are \n\n\n\nbringing a large number of sediments to the offshore area in the east \n\n\n\n(Kukowski et al., 2001).  The growth of such a large Accretionary \n\n\n\nwedge/prism is believed to be because of the deposition and deformation \n\n\n\nof the marine flysch deposit (Burg, 2011). A thick sediment accumulation \n\n\n\naccreted during the Cenozoic (Farhoudi and Karig, 1977). Based on \n\n\n\nTectono-stratigraphy, Makran has been subdivided into four parts from \n\n\n\nNorth to South, 600 km from offshore Oman trench to Volcanic arc, and \n\n\n\nophiolites (Kopp et al., 2000). These parts are known as Northern Makran \n\n\n\nand are mainly surrounded by the Quaternary deposits (Glennie et al., \n\n\n\n1990; Burg et al., 2013). Next to North Makran, is Inner Makran, which is \n\n\n\nmainly composed of marine deposits and is followed by Outer Makran \n\n\n\nwhich lies between Ghasr Ghand Thrust GGT and Chah Khan thrust CKT \n\n\n\nhaving Marls, Calcareous sandstones, shales and deep marine turbiditic \n\n\n\nsequence (McCall and Eftekhar-Nezad, 1993; Harms et al., 1982; Dolati, \n\n\n\n2010). And at the end is Coastal Makran where transitional facies, marl, \n\n\n\ncalcareous sandstones are dominated for several kilometers, which is \n\n\n\nbounded by Oman Trench (Haghipour et al., 2015; Mohammadi et al., \n\n\n\n2016). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 01-05 \n\n\n\nCite the Article: Muhammad Imran Hafeez Abbasi (2021). Is Makran A Separate Microplate? A Short Review.  \nMalaysian Journal of Geosciences, 5(1): 01-05. \n\n\n\n4. MICROPLATES \n\n\n\nRecent research work using the latest technology for plate tectonics can \n\n\n\ngive clues about the microplates in this region where the Arabian oceanic \n\n\n\nplate is undergoing beneath the Eurasian continental plate. The Lut block \n\n\n\nis at North of the Jiroft fault, near Zagros ranges in SW of Iran. It covers \n\n\n\naround 900 KMs in NS direction from 28\u2070 to 35\u2070 North latitude and along \n\n\n\nEW direction 200 KMs wide from 57\u2070 to 61\u2070 East longitude (Figure 1) \n\n\n\n(Stocklin, 1968; St\u00f6cklin et al., 1972a; Hussain et al., 2002; Saadat et al., \n\n\n\n2010). High seismicity can be observed only adjacent to the junction of the \n\n\n\nLut block, where the flysch zone in the East is prominent (St\u00f6cklin et al., \n\n\n\n1972b).  \n\n\n\nAfghan/Helmand Block is located towards the northern boundary of \n\n\n\nPIMM, in Afghanistan. Ormara Microplate (OM) was separated from the \n\n\n\neastern segment of the Arabian oceanic plate along with SPF and \n\n\n\npossessing separate attributes (Pang et al., 2014; Penney et al., 2017). The \n\n\n\nNortheast edge of the Arabian plate is an isolated block and seismicity can \n\n\n\nbe visible around OM as resulting due to SPF and its splays. PIMM bounded \n\n\n\nfrom all sides by Faults, already described above (Kukowski et al., 2000; \n\n\n\nSiddiqui and Jadoon, 2012). \n\n\n\n5. FOCAL MECHANISM SOLUTION \n\n\n\nEarthquake mechanism or commonly known as Focal Mechanism Solution \n\n\n\n(FMS) tells us how an earthquake originated or the behavior of a particular \n\n\n\nevent of an earthquake. Events related to fault\u2019s FMS has been integrated \n\n\n\nas a fault plane solution. Seismologists plot FMS as Beach ball diagrams so \n\n\n\nthat it will be easier for Geologists to interpret. Its famous method \n\n\n\nnowadays because of its significance in the evaluation of seafloor \n\n\n\nspreading, plate motions, fault behaviors. The seismological waves encode \n\n\n\ninformation among them, that decoding seismic waves unlock secrets \n\n\n\nabout many processes including source rupture, Subduction zones, plate \n\n\n\ntectonics, etc (Banghar and Sykes, 1969; Shimizu et al., 2019). \n\n\n\nA study performed by Penney et al., 2017, for FMS of MSZ on limited data, \n\n\n\nshowed us subsurface attributes of this area. Earthquake events used for \n\n\n\nthat research were between 1945 and 2013 with a magnitude of 4 or \n\n\n\ngreater, have been selected for FMS study for MZS. Recently a study \n\n\n\nperformed geophysical research on Iranian Makran and its vicinity by \n\n\n\nusing virtual Seismograms data acquired by local stations equipped by the \n\n\n\nIranian Seismological Center (Shirzad, 2019; Shirzad et al., 2019). Around \n\n\n\n630 Earthquake events recorded from January 2006 till May 2019, with a \n\n\n\nmagnitude of 4 or greater, were processed to generate precise \n\n\n\nTomographic Maps.  Along C-OFS deep focus events recorded in past, \n\n\n\nbeyond crustal thickness, suggesting that the Eastern edge of PIMM is \n\n\n\ncutting beyond crustal thickness at some points. Generally, events are \n\n\n\nabove 50Km depth but some deep events recorded along Normal fault, \n\n\n\npinched crustal wedge and 80Km is maximum depth recorded in MSZ. \n\n\n\n6. CONCLUSION \n\n\n\nDespite previous research work related to MSZ, critics arise on PIMM. \n\n\n\nAlthough FMS data is available for this region, a proper study is missing \n\n\n\nthat deals with microplates inferred for this region. Our recommendation \n\n\n\nis to plot Focal depths and Mechanism Solutions for MZS, by drawing \n\n\n\ncross-sections along MZS to see the crustal thickness in this area and depth \n\n\n\nof hypocentres. (Figure 5), map for Earthquake events initially taken for \n\n\n\nFMS study showing data cluster of Earthquake events in this region. \n\n\n\n(Figure), has been plotted by using Generic Mapping Tool (GMT),  where \n\n\n\nFMS plotted, after filtering out noisy data The numbers mentioned above \n\n\n\nBeach Balls are Earthquake Event number as used in the processing of \n\n\n\ndata.  \n\n\n\nIt is suggested to take the velocity model with 5-6 subsurface layers. That \n\n\n\ncan give us a better view of PIMM, as we will easily see either the faults \n\n\n\nsurrounding PIMM are of the crustal thickness or not. That will also let us \n\n\n\nvisualize the shallow angle of the subduction, which is a unique attribute \n\n\n\nof MSZ.  \n\n\n\nFigure 5: FMS of Makran derived from filtered data of events that \n\n\n\noccurred from 1990 to 2019 \n\n\n\nREFERENCES \n\n\n\nAbdetedal, M., Shomali, Z.H., Gheitanchi, M.R., 2014. Crust and upper \n\n\n\nmantle structures of the Makran subduction zone in south-east Iran by \n\n\n\nseismic ambient noise tomography: Solid Earth Discussions, 6, Pp. 1\u201334. \n\n\n\nAbedi, M., Bahroudi, A., 2016. A geophysical potential field study to image \n\n\n\nthe Makran subduction zone in SE of Iran: Tectonophysics, 688, Pp. \n\n\n\n119\u2013134. \n\n\n\nAhmed, S.S., 1969, Tertiary geology of part of south Makran, Baluchistan, \n\n\n\nWest Pakistan: AAPG Bulletin, 53, Pp. 1480\u20131499. \n\n\n\nAvouac, J.P., Ayoub, F., Wei, S., Ampuero, J.P., Meng, L., Leprince, S., Jolivet, \n\n\n\nR., Duputel, Z., Helmberger, D., 2014, The 2013, Mw 7.7 Balochistan \n\n\n\nearthquake, energetic strike-slip reactivation of a thrust fault: Earth and \n\n\n\nPlanetary Science Letters, 391, Pp. 128\u2013134. \n\n\n\nBanghar, A.R., Sykes, L.R., 1969, Focal mechanisms of earthquakes in the \n\n\n\nIndian Ocean and adjacent regions: Journal of Geophysical Research, 74, \n\n\n\nPp. 632\u2013649, doi:10.1029/jb074i002p00632. \n\n\n\nBurg, J.P., 2011. The Asia\u2013Kohistan\u2013India collision: review and discussion, \n\n\n\nin Arc-continent collision, Springer, Pp. 279\u2013309. \n\n\n\nBurg, J.P., 2018. Geology of the onshore Makran accretionary wedge: \n\n\n\nSynthesis and tectonic interpretation: Earth-Science Reviews,. \n\n\n\nBurg, J.P., Dolati, A., Bernoulli, D., and Smit, J., 2013, Structural style of the \n\n\n\nMakran Tertiary accretionary complex in SE-Iran, in Lithosphere \n\n\n\ndynamics and sedimentary basins: The Arabian Plate and analogues, \n\n\n\nSpringer, Pp. 239\u2013259. \n\n\n\nByrne, D.E., Sykes, L.R., and Davis, D.M., 1992, Great thrust earthquakes \n\n\n\nand aseismic slip along the plate boundary of the Makran subduction \n\n\n\nzone: Journal of Geophysical Research: Solid Earth, 97, Pp. 449\u2013478. \n\n\n\nCurray, J.R., Emmel, F.J., Moore, D.G., and Raitt, R.W., 1982, The Ocean \n\n\n\nBasins and Margins: Structure, tectonics, and geological history of the \n\n\n\nnortheastern Indian Ocean AEM Nairn, FG Stehli, Plenum, New York. \n\n\n\nDolati, A., 2010, Stratigraphy, structural geology and low-temperature \n\n\n\nthermochronology across the Makran accretionary wedge in Iran: \n\n\n\nEllouz-Zimmermann, N., Deville, E., M\u00fcller, C., Lallemant, S., Subhani, A.B., \n\n\n\nand Tabreez, A.R., 2007. Impact of sedimentation on convergent margin \n\n\n\ntectonics: Example of the Makran accretionary prism (Pakistan), in \n\n\n\nThrust Belts and Foreland Basins, Springer, Pp. 327\u2013350. \n\n\n\nFarhoudi, G., Karig, D.E., 1977, Makran of Iran and Pakistan as an active arc \n\n\n\nsystem: Geology, 5, Pp. 664\u2013668. \n\n\n\nGlennie, K.W., Clarke, M.W.H., Boeuf, M.G.A., Pilaar, W.F.H., and Reinhardt, \n\n\n\nB.M., 1990. Inter-relationship of Makran-Oman Mountains belts of \n\n\n\nconvergence: Geological Society, London, Special Publications, 49, Pp. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 01-05 \n\n\n\n\n\n\n\n \nCite the Article: Muhammad Imran Hafeez Abbasi (2021). Is Makran A Separate Microplate? A Short Review.  \n\n\n\nMalaysian Journal of Geosciences, 5(1): 01-05. \n \n\n\n\n\n\n\n\n773\u2013786. \n\n\n\nHaghipour, N., Burg, J.P., Ivy-Ochs, S., Hajdas, I., Kubik, P., Christl, M., 2015. \n\n\n\nCorrelation of fluvial terraces and temporal steady-state incision on the \n\n\n\nonshore Makran accretionary wedge in southeastern Iran: Insight from \n\n\n\nchannel profiles and 10Be exposure dating of strath terraces: Bulletin, \n\n\n\n127, Pp. 560\u2013583. \n\n\n\nHaghipour, N., Burg, J.P., Kober, F., Zeilinger, G., Ivy-Ochs, S., Kubik, P.W., \n\n\n\nFaridi, M., 2012. Rate of crustal shortening and non-Coulomb behaviour \n\n\n\nof an active accretionary wedge: The folded fluvial terraces in Makran \n\n\n\n(SE, Iran): Earth and Planetary Science Letters, 355, Pp. 187\u2013198. \n\n\n\nHarms, J.C., Cappel, H.N., and Francis, D.C., 1984. The Makran coast of \n\n\n\nPakistan: its stratigraphy and hydrocarbon potential: Marine geology \n\n\n\nand oceanography of Arabian Sea and coastal Pakistan, 3, Pp. 27. \n\n\n\nHarms, J.C., Cappel, H.N., Francis, D.C., 1982, Geology and petroleum \n\n\n\npotential of the Makran Coast, Pakistan: Society of Petroleum Engineers. \n\n\n\nHeidarzadeh, M., Pirooz, M.D., Zaker, N.H., Yalciner, A.C., Mokhtari, M., and \n\n\n\nEsmaeily, A., 2008. Historical tsunami in the Makran Subduction Zone \n\n\n\noff the southern coasts of Iran and Pakistan and results of numerical \n\n\n\nmodeling: Ocean Engineering, 35, Pp. 774\u2013786. \n\n\n\nHussain, J., Butt, A., Pervaiz, K., 2002. Makran Coast: A potential seismic \n\n\n\nrisk belt: Geological Bulletin Univ. Peshawar, 35, Pp. 43\u201356. \n\n\n\nJacob, K.H., Quittmeyer, R.L., 1979. Geodynamics of Pakistan: Geological \n\n\n\nSurvey of Pakistan, Pp. 305\u2013317. \n\n\n\nJenkins, J., Turner, B., Turner, R., Hayes, G.P., Davies, S., Dart, R.L., Tarr, A.C., \n\n\n\nVillase\u00f1or, A., and Benz, H.M., 2013. Seismicity of the Earth 1900\u20132010 \n\n\n\nMiddle East and vicinity: US Geological Survey. \n\n\n\nKashfi, M.S., 1976, Plate tectonics and structural evolution of the Zagros \n\n\n\ngeosyncline, southwestern Iran: Geological Society of America Bulletin, \n\n\n\n87, Pp. 1486\u20131490. \n\n\n\nKhan, M.A., Raza, H.A., and Alam, S., 1991. Petroleum geology of the Makran \n\n\n\nregion: implications for hydrocarbon occurrence in cool basins: Journal \n\n\n\nof Petroleum Geology, 14, Pp. 5\u201318. \n\n\n\nKopp, C., Fruehn, J., Flueh, E.R., Reichert, C., Kukowski, N., Bialas, J., and \n\n\n\nKlaeschen, D., 2000. Structure of the Makran subduction zone from \n\n\n\nwide-angle and reflection seismic data: Tectonophysics, 329, Pp. 171\u2013\n\n\n\n191. \n\n\n\nKukowski, N., Schillhorn, T., Flueh, E.R., and Huhn, K., 2000. Newly \n\n\n\nidentified strike-slip plate boundary in the northeastern Arabian Sea: \n\n\n\nGeology, 28, Pp. 355\u2013358. \n\n\n\nKukowski, N., Schillhorn, T., Huhn, K., von Rad, U., Husen, S., and Flueh, E.R., \n\n\n\n2001. Morphotectonics and mechanics of the central Makran \n\n\n\naccretionary wedge off Pakistan: Marine Geology, 173, Pp. 1\u201319. \n\n\n\nLin, Y.N., Jolivet, R., Simons, M., Agram, P.S., Martens, H.R., Li, Z., and Lodi, \n\n\n\nS.H., 2015. High interseismic coupling in the Eastern Makran (Pakistan) \n\n\n\nsubduction zone: Earth and Planetary Science Letters, 420, Pp. 116\u2013\n\n\n\n126. \n\n\n\nMaggi, A., Jackson, J.A., Priestley, K., and Baker, C., 2000. A re\u2010assessment \n\n\n\nof focal depth distributions in southern Iran, the Tien Shan and \n\n\n\nnorthern India: Do earthquakes really occur in the continental mantle? \n\n\n\nGeophysical Journal International, 143, Pp. 629\u2013661. \n\n\n\nMasson, F., Ch\u00e9ry, J., Hatzfeld, D., Martinod, J., Vernant, P., Tavakoli, F., and \n\n\n\nGhafory-Ashtiani, M., 2005, Seismic versus aseismic deformation in Iran \n\n\n\ninferred from earthquakes and geodetic data: Geophysical Journal \n\n\n\nInternational, 160, Pp. 217\u2013226. \n\n\n\nMcCall, G.J.H., 2002. A summary of the geology of the Iranian Makran: \n\n\n\nGeological Society, London, Special Publications, 195, Pp. 147\u2013204. \n\n\n\nMcCall, G.J.H., and Eftekhar-Nezad, J., 1993. Explanatory Text of the \n\n\n\nNikhshahr Quadrangle Map, 1: 250 000, L14: Geological Survey of Iran, \n\n\n\nTehran. \n\n\n\nMcCall, G.J.H., and Kidd, R.G.W., 1982. The Makran, Southeastern Iran: the \n\n\n\nanatomy of a convergent plate margin active from Cretaceous to \n\n\n\nPresent: Geological Society, London, Special Publications, 10, Pp. 387\u2013\n\n\n\n397. \n\n\n\nMcCall, G.J.H., Kidd, R.G.W., and Leggett, J.K., 1982. Trench-Forearc \n\n\n\nGeology: Sedimentation and Tectonics on Modern and Ancient Active \n\n\n\nPlate Margins. \n\n\n\nMinshull, T.A., White, R.S., Barton, P.J., and Collier, J.S., 1992. Deformation \n\n\n\nat plate boundaries around the Gulf of Oman: Marine Geology, 104, Pp. \n\n\n\n265\u2013277. \n\n\n\nMohammadi, A., Burg, J.P., Winkler, W., Ruh, J., and von Quadt, A., 2016. \n\n\n\nDetrital zircon and provenance analysis of Late Cretaceous\u2013Miocene \n\n\n\nonshore Iranian Makran strata: Implications for the tectonic setting: \n\n\n\nBulletin, 128, Pp. 1481\u20131499. \n\n\n\nMokhtari, M., Amjadi, A.A., Mahshadnia, L., and Rafizadeh, M., 2019. A \n\n\n\nreview of the seismotectonics of the Makran Subduction Zone as a \n\n\n\nbaseline for Tsunami Hazard Assessments: Geoscience Letters, 6, Pp. 1\u2013\n\n\n\n9. \n\n\n\nMokhtari, M., Fard, I.A., and Hessami, K., 2008, Structural elements of the \n\n\n\nMakran region, Oman sea and their potential relevance to \n\n\n\ntsunamigenisis: Natural hazards, 47, Pp. 185\u2013199. \n\n\n\nMusson, R.M.W., 2009. Subduction in the Western Makran: the historian\u2019s \n\n\n\ncontribution: Journal of the Geological Society, 166, Pp. 387\u2013391. \n\n\n\nNormand, R., Simpson, G., and Bahroudi, A., 2019. Extension at the coast of \n\n\n\nthe Makran subduction zone (Iran): Terra Nova, 31, Pp. 503\u2013510. \n\n\n\nOkal, E.A., and Synolakis, C.E., 2008. Far-field tsunami hazard from mega-\n\n\n\nthrust earthquakes in the Indian Ocean. Geophysical journal \n\n\n\ninternational, 172, Pp. 995\u20131015. \n\n\n\nPang, K., Chung, S., Zarrinkoub, M.H., Chiu, H., and Li, X., 2014. On the \n\n\n\nmagmatic record of the Makran arc, southeastern Iran: Insights from \n\n\n\nzircon U\u2010Pb geochronology and bulk\u2010rock geochemistry. Geochemistry, \n\n\n\nGeophysics, Geosystems, 15, Pp. 2151\u20132169. \n\n\n\nPaul, A., Kaviani, A., Hatzfeld, D., Vergne, J., and Mokhtari, M., 2006. \n\n\n\nSeismological evidence for crustal-scale thrusting in the Zagros \n\n\n\nmountain belt (Iran). Geophysical Journal International, 166, Pp. 227\u2013\n\n\n\n237. \n\n\n\nPenney, C., Tavakoli, F., Saadat, A., Nankali, H.R., Sedighi, M., Khorrami, F., \n\n\n\nSobouti, F., Rafi, Z., Copley, A., and Jackson, J., 2017. Megathrust and \n\n\n\naccretionary wedge properties and behaviour in the Makran \n\n\n\nsubduction zone. Geophysical Journal International, 209, Pp. 1800\u2013\n\n\n\n1830. \n\n\n\nRajendran, C.P., Rajendran, K., Shah-Hosseini, M., Beni, A.N., Nautiyal, C.M., \n\n\n\nand Andrews, R., 2013. The hazard potential of the western segment of \n\n\n\nthe Makran subduction zone, northern Arabian Sea: Natural hazards, \n\n\n\n65, Pp. 219\u2013239. \n\n\n\nReilinger, R., McClusky, S., Vernant, P., Lawrence, S., Ergintav, S., Cakmak, \n\n\n\nR., Ozener, H., Kadirov, F., Guliev, I., and Stepanyan, R., 2006. GPS \n\n\n\nconstraints on continental deformation in the Africa\u2010Arabia\u2010Eurasia \n\n\n\ncontinental collision zone and implications for the dynamics of plate \n\n\n\ninteractions: Journal of Geophysical Research: Solid Earth, 111. \n\n\n\nSaadat, S., Karimpour, M.H., and Stern, C., 2010, Petrochemical \n\n\n\ncharacteristics of Neogene and Quaternary alkali olivine basalts from \n\n\n\nthe Western Margin of the Lut Block, Eastern Iran: Iranian Journal of \n\n\n\nEarth Sciences, 2, Pp. 87\u2013106. \n\n\n\nSchl\u00fcter, H.U., Prexl, A., Gaedicke, C., Roeser, H., Reichert, C., Meyer, H., and \n\n\n\nVon Daniels, C., 2002. The Makran accretionary wedge: sediment \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 01-05 \n\n\n\n\n\n\n\n \nCite the Article: Muhammad Imran Hafeez Abbasi (2021). Is Makran A Separate Microplate? A Short Review.  \n\n\n\nMalaysian Journal of Geosciences, 5(1): 01-05. \n \n\n\n\n\n\n\n\nthicknesses and ages and the origin of mud volcanoes: Marine Geology, \n\n\n\n185, Pp. 219\u2013232. \n\n\n\nShimizu, K., Yagi, Y., Okuwaki, R., Fukahata, Y., 2019. Development of an \n\n\n\ninversion method to extract information on fault geometry from \n\n\n\nteleseismic data. \n\n\n\nShirzad, T., Riahi, M.A., and Assump\u00e7\u00e3o, M.S., 2019. Crustal structure of the \n\n\n\nCollision-subduction Zone in south of Iran Using Virtual seismometers. \n\n\n\nScientific reports, 9, Pp. 1\u201311. \n\n\n\nSiddiqui, N.K., and Jadoon, I.A.K., 2012. Indo-Eurasian Plate collision and \n\n\n\nthe evolution of Pak-Iran Makran Microplate, Pishin-Katawaz Fault \n\n\n\nBlock and the Porali Trough, in Search and Discovery Article# 30265 \n\n\n\n(2013), PAPG/SPE Annual Technical Conference, Pp. 3\u20135. \n\n\n\nSmith, G., McNeill, L., Henstock, T.J., and Bull, J., 2012. The structure and  \n\n\n\n\n\n\n\nfault activity of the Makran accretionary prism: Journal of Geophysical \n\n\n\nResearch: Solid Earth, 117. \n\n\n\nStocklin, J., 1968. Structural history and tectonics of Iran: a review: AAPG \n\n\n\nbulletin, 52, Pp. 1229\u20131258. \n\n\n\nSt\u00f6cklin, J., Eftekhar-Nezhad, J., and Hushmand-Zadeh, A., 1972a, Central \n\n\n\nLut reconnaissance, East Iran: Geological Survey of Iran. \n\n\n\nSt\u00f6cklin, J., Eftekharnezhad, J., and Hushmandzadeh, A., 1972b. Central Lut \n\n\n\nreconnaissance East Iran, Geological Survey of Iran: Report. \n\n\n\n\n\n\n\nVernant, P., Nilforoushan, F., Hatzfeld, D., Abbassi, M.R., Vigny, C., Masson, \n\n\n\nF., Nankali, H., Martinod, J., Ashtiani, A., and Bayer, R., 2004. Present-day \n\n\n\ncrustal deformation and plate kinematics in the Middle East \n\n\n\nconstrained by GPS measurements in Iran and northern Oman. \n\n\n\nGeophysical Journal International, 157, Pp. 381\u2013398. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 01-11 \n\n\n\nCite The Article: Md. Yousuf Gazi, Md. Ashraful Islam, Shakhawat Hossain(2019). Flood-Hazard Mapping In A Regional Scale \u2013 Way Forward To The Future Hazard Atlas In \nBangladesh. Malaysian Journal of Geosciences, 3(1): 01-11. \n\n\n\n\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN : MJGAAN \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 15 November 2018  \nAccepted 17 December 2018 \nAvailable online 2 January 2019 \n\n\n\nABSTRACT\n\n\n\nFlood causes substantial economic loss and hindrance to development activities in many developing countries of \n\n\n\nthe world. Bangladesh, a developing country in South-east Asia is ranked as the world\u2019s ninth-most disaster-prone \n\n\n\ncountry by the World Risk Report, 2018 because of its high exposure to multiple hazards and less coping and \n\n\n\nadaptive capacities.   The country is recurrently hit by flood hazard almost every year. Being a densely populated \n\n\n\ncountry with the fragile economic condition, Bangladesh urgently needs to focus on future flood-risk reduction with \n\n\n\nmore effective measures in order to sustain the development milestone achieved till now. Flood hazard mapping, \n\n\n\nan initial phase of risk understanding (i.e., perception and knowledge), is often considered to be an indispensable \n\n\n\ncomponent of flood-risk reduction strategies. In line with the contention, the present study aimed towards flood \n\n\n\nhazard mapping in Bangladesh where flood prone northeastern part of the country is taken as a case area. Multi-\n\n\n\ncriteria evaluation technique (MCE) for hazard mapping has been employed where elevation, slope, distance from \n\n\n\nriver, land use and landcover (LULC), precipitation, flow length, and population density were taken as the causative \n\n\n\nfactors. Each factor, as well as their subclasses, were assigned with pertinent weight values based on expert \n\n\n\nknowledge by analytical hierarchy process (AHP)and subsequently integrated into geographic information system \n\n\n\n(GIS) platform. According to the final flood-susceptibility map, ~4241 km2 (~ 20% of the total area) area is \n\n\n\ncategorized as the highest flood potential zone which encompasses mostly the southern part of the study area, \n\n\n\nincluding Gazipur, Narsingdi, and Brahmanbaria districts. In contrast, low flood potential zone covers ~9362 Km2 \n\n\n\n(~43% of the total area) area covering the northwestern and southwestern parts (e.g., Mymensing and Tangail \n\n\n\ndistricts) of the study region. Besides, a considerable portion of the study region, mostly in the western part (e.g., \n\n\n\nSunamganj and Kishoreganj districts) is categorized as moderate flood potential zone encompassing ~7823 km2 (~ \n\n\n\n35% of the study area) area. Population density, distance to river and topographic characteristics are found as the \n\n\n\nmost influencing factors for the mapping of flood-risk zones in the current study. This type of assessment in a \n\n\n\nregional scale may serve as a guide to the relevant stakeholders to formulate flood hazard atlas and minimize the \n\n\n\nadverse impact of the future flood in Bangladesh. \n\n\n\nKEYWORDS \n\n\n\nFlood, Northeastern Bangladesh, AHP, GIS, and Hazard Map atlas \n\n\n\n1. INTRODUCTION \n\n\n\nFlooding, one of the most common hydro-meteorological phenomena, \n\n\n\ninflicts harmful impacts on society from the dawn of civilization [1]. Flood \n\n\n\nmay occur in various way. The most prevalent ones are an overflow of \n\n\n\nrivers/streams, excessive rain, breach in flood-protection structures and \n\n\n\nrapid melting of ice in the mountains. Except for flash flooding, which is \n\n\n\nrestricted to foothills, most floods take hours to days to develop. In the \n\n\n\npast, highly destructive flooding events have taken place once in a century, \n\n\n\nhowever, global climate change, those high-magnitude hundred-year \n\n\n\nfloods have been occurring worldwide with alarming regularity over the \n\n\n\nlast few decades [2].  \n\n\n\nGlobally, flood is regarded as one of the most destructive hazards due to \n\n\n\nits negative impact on human life, surrounding environments and \n\n\n\neconomy [3]. For instance, the Yellow River valley in China experienced  \n\n\n\nsome of the world\u2019s worst floods during the last century; millions of people  \n\n\n\nhave perished in or been impoverished by floods [4]. Economic loss due to  \n\n\n\nflood is common in many developed countries in the world, even in the \n\n\n\nUnited States, despite advanced flood mitigation and prediction, floods \n\n\n\ncause ~ US$6 billion worth of damage every year. A study by the \n\n\n\nOrganization for Economic Cooperation and Development found that \n\n\n\ncoastal flooding results in some US$3 trillion worth damage worldwide \n\n\n\n[5]. In contrast to the experience of developed nation in flood, the impact \n\n\n\nscenarios (e.g., flood causalities and damage) are more alarming in the \n\n\n\ndeveloping nations due to their inadequate risk reduction measures \n\n\n\nagainst flood disaster [6].  \n\n\n\nBangladesh, a developing nation in the southeast Asian region, situated in \n\n\n\nthe confluence of mighty Ganges-Brahmaputra-Meghna river system, \n\n\n\nexperiences flooding every year during the monsoon season from June to \n\n\n\nSeptember. Excessive rainfall and upstream water discharge during rainy \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : http://doi.org/10.26480/mjg.01.2019.01.11 \n\n\n\n   RESEARCH ARTICLE \n\n\n\nFLOOD-HAZARD MAPPING IN A REGIONAL SCALE \u2013 WAY FORWARD TO THE \nFUTURE HAZARD ATLAS IN BANGLADESH   \n\n\n\nMd. Yousuf Gazi*, Md. Ashraful Islam, Shakhawat Hossain \n\n\n\nDepartment of Geology, University of Dhaka, Dhaka-1000 \n\n\n\n*Corresponding Author Email: yousuf.geo@du.ac.bd \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\nmailto:yousuf.geo@du.ac.bd\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 01-11 \n\n\n\nCite The Article Md. Yousuf Gazi, Md. Ashraful Islam, Shakhawat Hossain(2019). Flood-Hazard Mapping In A Regional Scale \u2013 Way Forward To The Future Hazard Atlas In \nBangladesh. Malaysian Journal of Geosciences, 3(1): 01-11. \n\n\n\nseason eventually causes overflow of the river systems in Bengal basin and \n\n\n\nseverely affects the cropland, settlements and transportation system of \n\n\n\nBangladesh [7].  Physiographically, landmass of Bangladesh exhibits flood \n\n\n\nplains that renders the nation at risk of periodic flooding. Three mighty \n\n\n\nrivers, the Brahmaputra, Ganges and Meghna and their tributaries and \n\n\n\ndistributaries contribute to the genesis of flood plain in their respective \n\n\n\ncatchment areas and floods of varying magnitude occur on a regular basis \n\n\n\ndue to low elevated flood plains within these catchments [8-11]. In the last \n\n\n\n100 years, floods resulted over 50,000 people deaths, left nearly 32 million \n\n\n\nhomeless and affected more than 300 million people in Bangladesh [12]. \n\n\n\nEach year ~ 26,000 km2 (around 20% of the country) is flooded and during \n\n\n\nthe severe floods, the affected area may exceed 68% of the country [13]. \n\n\n\nFor example, the 1998 flood alone killed more than 3,500 people and \n\n\n\ndestroyed crops and infrastructure worth more than US$2 billion [14].  \n\n\n\nFloods cannot be controlled entirely but increasing attention to flood \n\n\n\nregulation by the identification of risky areas can be an effective approach \n\n\n\nto minimize losses [15]. Permanent protection from flooding by building \n\n\n\nreinforced-concrete defenses, raised houses and roads above flood level \n\n\n\nhave been the traditional practice for quite a while [16]. Although this has \n\n\n\nproven effective, flood protection by structural means alone may not be \n\n\n\nsufficient or economically feasible. Along with flood-prevention \n\n\n\nstructures, non-structural flood control is also very useful in managing \n\n\n\nfloods and minimizing flood damage [17]. For example, flood hazard \n\n\n\nmapping for the identification of risky zones is extremely useful in the \n\n\n\ndevelopment of automated methods for quantifying the spatial variation \n\n\n\nin flood susceptibility and has been widely used in supporting surface-\n\n\n\nwater modelling and flood-hazard exposure [18-24]. \n\n\n\nFlood hazard  map  in the form of a flood atlas is popular in various part of \n\n\n\nthe world [25].  Hazard Atlas generally provide information on the current \n\n\n\nsituation of a particular hazard for a country in terms of vulnerability and \n\n\n\nrisk. Though new, it is worth mentioning that Bangladesh attempted an \n\n\n\natlas on the seismic hazards. The prime focus of the atlas was to \n\n\n\ndisseminate earthquake history of the country, vulnerability and risk with \n\n\n\nrespect to papulation, infrastructure, building stock, and emergency \n\n\n\nfacilities in six major cities, as well as potential damage and loss \n\n\n\nassessment [26]. Compare to earthquake, flooding is more recurring \n\n\n\nphenomena in Bangladesh, however, no such attempt was observed in the \n\n\n\nexisting  disaster management policy in Bangladesh to develop a flood \n\n\n\nhazard atlas. High frequency of floods in Bangladesh over the last years \n\n\n\nurge an indispensable need to provide accurate and extensive information \n\n\n\nto the people at threat to minimize future damages. Hence, a flood zonation \n\n\n\nmap can be a useful tool for identification of risky areas and will provide \n\n\n\nvaluable information to local community through hazard atlas [27].    \n\n\n\nCurrently, spatial technique, exclusively in GIS platform has grasp the \n\n\n\nattention among hazard mapping personals [28]. In a GIS environment, \n\n\n\nquantitative approaches, including the idea of ranking and weighting \n\n\n\nmethods, are frequently employed MCE \u2013purpose of the decision-making \n\n\n\ntool, eventually compare and rank alternatives and to evaluate their \n\n\n\nconsequences according to given criteria. For example, the analytic \n\n\n\nhierarchy process (AHP) has widely been employed in many decision-\n\n\n\nmaking process in disaster managed domain particularly hazard and \n\n\n\nvulnerability mapping where GIS integrate the data and execute final \n\n\n\nresult [29-32]. The use of GIS and MCE has been successful in the analysis \n\n\n\nof natural hazards [33,34]. For instances, some researchers used an \n\n\n\nintegrated approach of MCE with GIS for urban flood mapping [35]. Other \n\n\n\nresearchers determined the risk zone for flooding in Terengganu, Malaysia \n\n\n\n[36]. In other hand, the researchers used a multicriteria approach for \n\n\n\nflood-risk mapping of the Mulde River, Germany [37]. Additionally \n\n\n\nresearchers developed a GIS-based spatial multicriteria method for flood-\n\n\n\nrisk assessment in the Dongting Lake Region, Hunan, central China [38]. \n\n\n\nIn recent times, remote sensing and GIS tools have been used for the \n\n\n\ncreation of national-level flood-hazard maps of Bangladesh [39,40]. \n\n\n\nHydrologic information has been integrated with population density and \n\n\n\nother socio-economic data to identify priority zones for instigating flood-\n\n\n\nprevention measures [41]. Several studies on flood hazard zonation using \n\n\n\nMCE in Bangladesh and adjacent areas have also been undertaken from \n\n\n\ndifferent perspectives [42,43]. Other researchers evaluated flood hazard \n\n\n\nfor land-use planning in greater Dhaka, Bangladesh using remote-sensing \n\n\n\nand GIS techniques [44]. Akiko determined flood-vulnerable areas in \n\n\n\nBangladesh using a spatial MCE [45]. Rahman and Saha selected the Bogra \n\n\n\ndistrict of Bangladesh for determining flood zonation in a GIS environment \n\n\n\nusing AHP [42]. Despite a number of GIS based research on flood hazard \n\n\n\nin Bangladesh, majority are conducted in local scale with diverse \n\n\n\nmotivation and scope, however, none of these researches acknowledge \n\n\n\nand/or comprehend the urgency of a regional flood hazard mapping for a \n\n\n\ncomprehensive flood disaster management in Bangladesh. \n\n\n\nThus, this study, though deployed a common MCE technique in GIS \n\n\n\nenvironment, is particularly intended to focus on a regional scale flood \n\n\n\nhazard mapping (up to upazila level) in northeastern part of Bangladesh. \n\n\n\nThe prime objective of this study, evading the complex modeling \n\n\n\ntechniques, is to use the widely accepted causative attributes of flood \n\n\n\nhazard mapping and to acknowledge the implication of flood hazard atlas \n\n\n\ncreation in Bangladesh. This particular approach of hazard mapping, if \n\n\n\npublicize through an atlas, will ease the decision-making process  in future \n\n\n\nrisk reduciton in the northeasterm part of bangladesh. \n\n\n\n2. STUDY AREA \n\n\n\nThe northeastern part of Bangladesh was selected for the study; it covers \n\n\n\neight districts, Brahmanbaria, Gazipur, Narsingdi, Netrokona, Kishoreganj, \n\n\n\nMymensingh, Tangail and Sunamganj (Figure 1). The approximate surface \n\n\n\narea of the study region is ~12,298 km2 and the total population ~12 \n\n\n\nmillion, with the average household size 5.3 [46]. Nearly half the \n\n\n\npopulation is involved in rice production and fishing. Due to the frequency \n\n\n\nof natural disasters and adverse weather due to climate change, they are \n\n\n\ntherefore highly vulnerable to flooding. Less industrial activities present \n\n\n\nin the area however, infrastructural development is increasing now a day, \n\n\n\nwith changing patterns of land use across the area [47]. \n\n\n\nThe region has characterized by a diverse geomorphological setting, with \n\n\n\nelevated topography of Plio-Miocene hills along the border [48]. At the \n\n\n\ncenter, there is a vast low-lying flood basin, locally called known as Haor \n\n\n\nBasin. The basin covers an area of ~ 4505 km2 and goes underwater for \n\n\n\nseveral months each year due to episodic flooding.  \n\n\n\nThe northeastern part of Bangladesh falls under monsoon climatic zone, \n\n\n\nwith an annual average maximum temperature of 23\u00b0C (Aug\u2013Oct) and an \n\n\n\naverage minimum temperature of 7\u00b0C (Jan) [49]. Flash flooding is common \n\n\n\nin this region, which occurs frequently from month May to the middle of \n\n\n\nOctober. The network of rivers, streams and channels overflows and fills \n\n\n\nthe haors in the early part of the rainy season. Floodwaters in the study \n\n\n\narea, mainly in the Sunamganj and Netrakona districts, recently created \n\n\n\nhuge shortages in the local economy. Large-scale floods frequently occur \n\n\n\nand cause huge economic loss in this region, as is evident from the \n\n\n\nhistorical flood records of 1988, 1992 and 1998 [50]. \n\n\n\nFigure 1: The study area, covering the northeastern part of Bangladesh. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 01-11 \n\n\n\nCite The Article Md. Yousuf Gazi, Md. Ashraful Islam, Shakhawat Hossain(2019). Flood-Hazard Mapping In A Regional Scale \u2013 Way Forward To The Future Hazard Atlas In \nBangladesh. Malaysian Journal of Geosciences, 3(1): 01-11. \n\n\n\n3. MATERIALS AND METHODS \n\n\n\n3.1 Data \n\n\n\nAfter reviewing existing literatures on flood hazard mapping, the spatial \n\n\n\ndata used in this study were obtained from open-source spatial databases \n\n\n\n(Table 1). A simplified methodological flowchart (Figure 2) shows the \n\n\n\nspatial operations in a GIS environment. The topographic attributes such \n\n\n\nas slope and flow accumulation were created using SRTM DEM \n\n\n\n(earthexplorer.usgs.gov/). For a detailed land-use/land-cover (LULC) \n\n\n\nmap, a recent multispectral satellite image (Landsat 8), dated January \n\n\n\n2018, was obtained from the same source as the DEM data. Yearly average \n\n\n\nrainfall data from the available rainfall stations in the study area were \n\n\n\nsourced from the Bangladesh Meteorological Department (BMD). Upazila \n\n\n\npopulation data were extracted from the latest population census [46]. \n\n\n\nFigure 2: Methodological framework for the flood-hazard zonation. \n\n\n\nTable 1: Spatial data used in the study \n\n\n\nParameter Spatial Technique Data Source Justification  \n\n\n\nElevation Layer tinting of elevation raster \n\n\n\nShuttle Radar Topography Mission \n\n\n\n(SRTM) 30m digital elevation \n\n\n\nmodel (DEM) from \n\n\n\nEarth Explorer \n\n\n\nearthexplorer.usgs.gov/ \n\n\n\n[51, 52] \n\n\n\nFlowlength Spatial hydrologic analysis and flow-direction \n\n\n\nfunction. Spatial hydrologic analysis and \n\n\n\nflowlength \n\n\n\nDrainage distance Spatial hydrologic analysis and Euclidean \n\n\n\ndistance function \n\n\n\n[43, 53] \n\n\n\nLand-use/land-cover \n\n\n\n(LULC) Unsupervised-classification techniques in \n\n\n\nERDAS Imagine (version 14) \n\n\n\nLandsat 8 OLI & TIRS (18 \n\n\n\nNovember 2014) \n\n\n\nEarth Explorer \n\n\n\nearthexplorer.usgs.gov/ \n\n\n\n[54, 55] \n\n\n\nRainfall (precipitation) Spatial statistics and Inverse Distance \n\n\n\nWeighted (IDW) method \n\n\n\nBangladesh Meteorological \n\n\n\nDepartment (BMD) \n\n\n\n[56]  \n\n\n\nPopulation density Conversion of upazila polygon (containing \n\n\n\npopulation density field) to raster using \n\n\n\nconversion tool \n\n\n\nCensus Report [46] [42, 54]  \n\n\n\n3.2 Data Preparation \n\n\n\nThe creation of spatial data involves multiple steps and needs expertise in \n\n\n\ngeospatial data handling.  In the present analysis, Digital Elevation Model \n\n\n\n(DEM) was the key to produce elevation, flow length and drainage distance \n\n\n\nraster. An elevation map was created from DEM using the natural break \n\n\n\nclassification techniques in ArcGIS version 10.3. Hydrology tools in the \n\n\n\nsame software were used for flow accumulation. Prior to extraction of all \n\n\n\nthese DEM-derived data, a median filter function was run over the entire \n\n\n\nDEM to minimize/remove artifacts. For LULC mapping, supervised \n\n\n\nclassification technique was employed in this study. Reasonable accuracy \n\n\n\nwas achieved by using 100 random ground control points (GCP) from \n\n\n\nGoogle Earth. The accuracy assessment was satisfied, with ~75% of the \n\n\n\nGCP matched exactly on the classified map. Two image-enhancement \n\n\n\ntechniques (histogram equalization and contrast stretching) were applied \n\n\n\nprior to the final image classification. The final LULC map has six distinct \n\n\n\nclasses: Water; Vegetation; Agriculture; Barren Land; Swamp; and \n\n\n\nSettlement. For the rainfall-distribution mapping, the inverse-distance-\n\n\n\nweighted (IDW) interpolation technique was used. Rainfall was divided \n\n\n\ninto five classes by means of natural break classification techniques. A new \n\n\n\nfield in the vector file rainfall point map was created using spatial \n\n\n\ninterpolation in the GIS environment from the location coordinates of the \n\n\n\nrainfall stations. Each thematic layer was put into one of five classes on the \n\n\n\nbasis of its effect on flooding. For the population-density mapping, an \n\n\n\n\nhttps://earthexplorer.usgs.gov/\n\n\nhttps://earthexplorer.usgs.gov/\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 01-11 \n\n\n\nCite The Article Md. Yousuf Gazi, Md. Ashraful Islam, Shakhawat Hossain(2019). Flood-Hazard Mapping In A Regional Scale \u2013 Way Forward To The Future Hazard Atlas In \nBangladesh. Malaysian Journal of Geosciences, 3(1): 01-11. \n\n\n\nadministrative vector file was created from the LGED hardcopy upazila \n\n\n\nmaps. The feature class containing the input field of population density \n\n\n\nconverted to a raster dataset using conversion tool in GIS. \n\n\n\n3.3 Analytical Model  \n\n\n\nMCE was used to determine the vulnerable areas in the present study. AHP \n\n\n\ntechnique was used for the determination of weights of the individual \n\n\n\nparameters (Table 2) [30]. The AHP empowers decision makers to find out \n\n\n\na solution that best outfits their wide-ranges of goals [57]. This \n\n\n\nmathematical decision-making method lessens the complexity of the \n\n\n\ndecision problem into a series of pairwise comparisons among competing \n\n\n\nattributes [58]. This is very helpful for decision makers when they find it \n\n\n\nhard to determine relative importance of weights for complex multi-\n\n\n\nattribute problems [59,60]. Here, the Pairwise Comparison Method was \n\n\n\nused in first defining the weights for the criteria. This method allows \n\n\n\nassessment of two criteria at a time. \n\n\n\nTable 2: Scale of relative importance [29,30]. \n\n\n\nIntensity of Importance Definition Intensity of Importance Definition \n\n\n\n1 Equal importance 6 Strong plus \n\n\n\n2 Weak importance 7 Demonstrated importance \n\n\n\n3 Moderate importance 8 Very strong \n\n\n\n4 Moderate plus 9 Extreme importance \n\n\n\n5 Strong importance \n\n\n\nAHP uses several equations to ascertain the weight of individual criteria. \n\n\n\nprincipal eigenvalue (PEV) is calculated by the following equation: \n\n\n\nPEV= 11/\ud835\udc5b\u2211 \ud835\udc4b\ud835\udc56/\ud835\udc36\ud835\udc56\ud835\udc5b\n\ud835\udc56=1  \n\n\n\nHere, n= number of criteria; Xi= consistency vector and Ci= consistency of \n\n\n\nthe weight values \n\n\n\nThen, the consistency index (CI) can be calculated from the PEV value by \n\n\n\nthe equation; \n\n\n\n\ud835\udc36\ud835\udc3c = (\ud835\udc43\ud835\udc38\ud835\udc49 \u2212 \ud835\udc5b)/(\ud835\udc5b \u2212 1) \n\n\n\nFinally, to ensure the consistency of the pairwise comparison matrix, the \n\n\n\nconsistency decision must be cross-checked for the suitable value of n by \n\n\n\nCR [61].  \n\n\n\n\ud835\udc36\ud835\udc45 = \ud835\udc36\ud835\udc3c/\ud835\udc45\ud835\udc3c \n\n\n\nwhere RI is the random consistency index. A composite map of flooding \n\n\n\nrisk was prepared using the raster calculator from the equation (weights \n\n\n\nin Table 3 and Table 4):  \n\n\n\nFlood Hazard Index (FHI) = WP \u00d7 Precipitation raster + WLULC \u00d7 LULC raster \n\n\n\n+ WD\u00d7 Drainage distance raster + WF \u00d7 Flow length raster + WE \u00d7 Elevation \n\n\n\nraster + WPD \u00d7 Population density raster. Here, W= AHP weight for \n\n\n\nindividual parameter (values were inputted from Table 4). \n\n\n\n4. RESULTS AND DISCUSSION\n\n\n\nIn this study, different parameters were considered for their individual \n\n\n\nimpacts on flood risk. The relative importance of individual layers and \n\n\n\ntheir sub-classes used in this study are discussed in the following sections. \n\n\n\n4.1 Elevation \n\n\n\nThere is considerable variation in elevation over the study area. In general, \n\n\n\nthe eastern part has lower elevation than the western part. On the basis of \n\n\n\nflood hazard due to elevation, the study area was divided into five \n\n\n\ncategories. The lowest areas (eastern part of the study area) have the \n\n\n\nhighest susceptibility to flooding and are in Category 1. The five categories \n\n\n\nare (with total areas): 1 very high susceptibility (299 km2); 2 high \n\n\n\nsusceptibility (1610 km2); 3 moderate susceptibility (5250 km2); 4 low \n\n\n\nsusceptibility (8147 km2); and 5 very low susceptibility (6138 km2).  \n\n\n\n4.2 Rainfall (precipitation) \n\n\n\nRainfall is one of the most important factors influencing flood severity \n\n\n\n[62]. Areas with low annual precipitation, less than 1800 mm, are in \n\n\n\nCategory 5 and cover approximately 43% of the study area (9090 km2), \n\n\n\nwhile the northwestern part of the study area (1818 km2), with very high \n\n\n\nannual precipitation (3200\u20133700 mm), is in Category 1.  \n\n\n\n4.3 Population density \n\n\n\nPopulation density has a significant impact on flooding. Population density \n\n\n\nranges from 0-437 per km2 (Category 5) to 2550\u20135276 per km2 (Category \n\n\n\n1). Population density is highest in the south and southeastern part of the \n\n\n\nstudy area (the Gazipur Sadar Upazila of the Gazipur district) whereas the \n\n\n\nnortheastern part (the Khaliajuri Upazila of Netrokona) has the lowest \n\n\n\npopulation density. Places with high population density are more prone to \n\n\n\nflooding.  \n\n\n\n4.4 Flowlength \n\n\n\nFlowlength is the upstream or downstream distance, or weighted distance, \n\n\n\nalong the flow path for each cell of the raster. Regions with long \n\n\n\nflowlengths had lower flood depths, and so were less susceptible to \n\n\n\nflooding. In contrast, areas with shorter flowlengths had higher flood \n\n\n\ndepths, and were more often flooded. The shortest flow lengths (<4419 m) \n\n\n\nwere recorded in the northeastern part of the study area, corresponding \n\n\n\nto a high risk of flooding. The remaining area (~97% of the study region, \n\n\n\n20,904 km2) had longer flowlengths (>50,675 m), and hence are less likely \n\n\n\nto flood. \n\n\n\n4.5 Drainage distance \n\n\n\nIn a flood-susceptibility study, the distance of an area from major rivers is \n\n\n\nvery significant. In general, areas near a river are more often flooded than \n\n\n\nareas far away from a river. Places adjacent to a river are inundated once \n\n\n\nthe flow in the river overtops its banks. Drainage distances ranged from 0 \n\n\n\nm to 11,008 m. The minimum average distance (<1036 m) was recorded \n\n\n\nin the eastern part of the study area, indicating a greater likelihood of \n\n\n\nflooding; this area lies between major rivers and the sea. The maximum \n\n\n\naverage drainage distance was recorded in the western part of the study \n\n\n\narea, indicating this region is less prone to flooding.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 01-11 \n\n\n\nCite The Article Md. Yousuf Gazi, Md. Ashraful Islam, Shakhawat Hossain(2019). Flood-Hazard Mapping In A Regional Scale \u2013 Way Forward To The Future Hazard Atlas In \nBangladesh. Malaysian Journal of Geosciences, 3(1): 01-11. \n\n\n\nFigure 3: Reclassified maps: (A) Elevation (m); (B) Precipitation (mm); (C) Flowlength (m); (D) Drainage distance (m); (E) Land-use/Land-cover (LULC); \n\n\n\n(F) Population density (/km2)\n\n\n\n4.6 Land use and land cover (LULC) \n\n\n\nLand-use types were assigned to five different categories depending on the \n\n\n\nflood susceptibility (see Table 3). The risk of flooding is highest near water \n\n\n\nbodies (rivers, lakes and haors); hence these areas were categorized as \n\n\n\nvery high or high susceptibility. Built-up areas and forests were \n\n\n\ncategorized as low or very low susceptibility. High-risk zones comprise \n\n\n\nabout 450 km2 in the eastern part of the study area (Figure 3).  \n\n\n\nThe flood hazard index (FHI) map assigns different levels of flood hazard \n\n\n\nto the different upazilas in the study area after values for the relative \n\n\n\nimportance of each of the factors discussed above (Table 4) are assigned. \n\n\n\nThe pairwise comparison matrix of the flood-hazard parameters was \n\n\n\ncalculated using AHP after reclassification of all parameters to compute \n\n\n\nthe weights. All the parameters used in this research are thus combined to \n\n\n\nproduce the flood-hazard map, with the FHI ranging from 0.05 to 0.31. We \n\n\n\nhave shown five hazard classes on the final hazard map: very low (0.05 \u2013 \n\n\n\n0.10); low (0.10 \u2013 0.13); moderate (0.13 \u2013 0.17); high (0.17 \u2013 0.22); and \n\n\n\nvery high (0.22 \u2013 0.31). \n\n\n\nAbout 20% of the total study area (~4241 km2), mostly in the north-\n\n\n\neastern and south-eastern parts, is in the high hazard category (Table 6), \n\n\n\nwith some small scattered patches also evident in the east-central part \n\n\n\n(Fig. 4). These high flood-hazard zones cover parts of the Gazipur sadar, \n\n\n\nNarsingdi sadar, Tahirpur upazila of Sunamgonj and Tarail upazila of \n\n\n\nKishoreganj district (Table 5). The Gazipur Sadar and Narsingdi sadar \n\n\n\nupazila are high hazard zones due to their high population density. The \n\n\n\nTahirpur and Tarail Upazila are the most flood-prone areas.  \n\n\n\nAn area of 7823 km2, approximately 36% of the total study area, has a \n\n\n\nmoderate flood-hazard, so that the high and moderate flood hazard zones \n\n\n\ntogether cover about 56% of the total area. The moderate flood-hazard \n\n\n\nzones are in a little away from the rivers located in the eastern part of the \n\n\n\nstudy area and include depressed areas (haors) such as those in the \n\n\n\nSunamganj Kishoreganj and Netrakona districts. All the upazila in the \n\n\n\nKishoreganj district, except Bajitpur and Katiadi (are in high hazard zone), \n\n\n\nare in the moderate hazard zone. The Kaliganj upazila of Gazipur (~81 \n\n\n\nkm2), Nabinagar, Kasnba and Akhaura upazila of Brahmanbaria (~360 \n\n\n\nkm2), Tahirpur and Dowarabazar Upazila of the Sunamganj district \n\n\n\n(~1500 km2) and Delduar, Basail upazila of Tangail district (~189 km2) \n\n\n\nare recognized to be in the moderate flood hazard zone (Figure 4).  \n\n\n\nThe low flood hazard zones are about 43% of the study area \n\n\n\ncovering~9362 Km2. The northwestern and southwestern parts of the \n\n\n\nstudy area come under these categories. These areas are away from the \n\n\n\nrivers, their elevation is higher than other areas, and so they are the least \n\n\n\nvulnerable to flood. All the upazila of the Mymensing and Tangail districts, \n\n\n\nexcept Trishal and Mymensing sadar in Mymensing and Nagarpur, \n\n\n\nDelduar and Basail in Tangail, are in one of these categories. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 01-11 \n\n\n\nCite The Article Md. Yousuf Gazi, Md. Ashraful Islam, Shakhawat Hossain(2019). Flood-Hazard Mapping In A Regional Scale \u2013 Way Forward To The Future Hazard Atlas In \nBangladesh. Malaysian Journal of Geosciences, 3(1): 01-11. \n\n\n\nTable 3: AHP values for individual parameters. The numbers in the left-hand column are category numbers. Category 1 corresponds to 'very high \n\n\n\nsusceptibility', Category 5 to 'very low susceptibility'. The relevant range of the parameter (type for LULC) for each category is given at the end of each \n\n\n\nsection in the table.  Priority: numbers associated with the nodes of an AHP hierarchy; PEV: principal eigenvalue; CR: consistency ratio. \n\n\n\nElevation (m) \n\n\n\nPairwise Comparison Matrix \n\n\n\nWeight PEV CR \n\n\n\nArea \n\n\n\n[1] [2] [3] [4] [5] Priority (%) km2 % \n\n\n\n[1] 1 2 2 3 4 41.9 0.4185 \n\n\n\n5.068 0.015 \n\n\n\n299 1.4 \n\n\n\n[2] 1/2 1 1 2 3 26.3 0.2625 1610 7.5 \n\n\n\n[3] 1/3 1/2 1/2 1 2 16.0 0.1599 5250 24.5 \n\n\n\n[4] 1/4 1/3 1/3 1/2 1 9.7 0.0972 8147 38.0 \n\n\n\n[5] 1/5 1/4 1/4 1/3 1/2 6.2 0.0617 6138 28.6 \n\n\n\n  Category: [1] 3\u20138m; [2] 8\u201312m; [3] 12\u201317m; [4] 17\u201324m; [5] 24\u2013114m \n\n\n\nPopulation \n\n\n\ndensity (/km2) \n\n\n\nPairwise Comparison Matrix \n\n\n\nWeight PEV CR \n\n\n\nArea \n\n\n\n[1] [2] [3] [4] [5] Priority (%) km2 % \n\n\n\n[1] 1 1/2 1/3 1/4 1/5 5.9 0.0592 \n\n\n\n5.132 0.029 \n\n\n\n1591 7.4 \n\n\n\n[2] 2 1 1/2 1/3 1/4 8.8 0.0877 7681 35.8 \n\n\n\n[3] 3 2 1 1/2 1/3 14.2 0.1423 6113 28.5 \n\n\n\n[4] 4 3 2 1 1/2 22.9 0.2288 5507 25.7 \n\n\n\n[5] 5 5 4 3 1 48.2 0.4818 552 2.6 \n\n\n\nCategory: [5] 0\u2013437/km2; [4] 437\u2013793/km2; [3] 793\u20131243/km2; [2] 1243\u20132550/km2; [1] 2550\u20135276/km2 \n\n\n\n  Flowlength  \n\n\n\n(m) \n\n\n\nPairwise Comparison Matrix \n\n\n\nWeight PEV CR \n\n\n\nArea \n\n\n\n[1] [2] [3] [4] [5] Priority (%)  km2 (%) \n\n\n\n[1] 1 3 4 5 6 49.2 0.0590 \n\n\n\n5.089 0.020 \n\n\n\n45 0.2 \n\n\n\n[2] 1/3 1 2 3 4 22.7 0.0777 44 0.2 \n\n\n\n[3] 1/4 1/2 1 2 3 14.2 0.1523 156 0.8 \n\n\n\n[4] 1/5 1/3 1/2 1 1 7.6 0.2188 295 1.4 \n\n\n\n[5] 1/6 1/4 1/3 1/2 1 6.4 0.4828 20904 97.5 \n\n\n\n Category: [1] 0\u20134419m; [2] 4419\u201315025m; [3] 15025\u201330641m; [4] 30641\u201350675m; [5] 50675\u201375129m \n\n\n\nRainfall \n\n\n\n (mm) \n\n\n\nPairwise Comparison Matrix \n\n\n\nWeight PEV CR \n\n\n\nArea \n\n\n\n[1] [2] [3] [4] [5] Priority (%) km2 (%) \n\n\n\n[1] 1 1/2 1/3 1/3 1/4 7.1 0.0714 \n\n\n\n5.222 0.049 \n\n\n\n9090 42.4 \n\n\n\n[2] 2 1 2 1/3 1/3 14.7 0.1468 3544 16.5 \n\n\n\n[3] 3 1/2 1 1/2 1/3 13 0.1299 4531 21.1 \n\n\n\n[4] 3 3 2 1 1/2 26.3 0.2627 2461 11.5 \n\n\n\n[5] 4 3 3 2 1 38.9 0.3889 1818 8.5 \n\n\n\nCategory: [5] 1800\u20132150; [4] 2150\u20132500; [3] 2500\u20132800; [2] 2800\u20133200; [1] 3200\u20133700 \n\n\n\nDrainage \n\n\n\ndistance (m)  \n\n\n\nPairwise Comparison Matrix \n\n\n\nWeight PEV CR \n\n\n\nArea \n\n\n\n[1] [2] [3] [4] [5] Priority (%) km2 (%) \n\n\n\n[1] 1 2 3 4 5 41.9 0.4185 \n\n\n\n5.068 0.015 \n\n\n\n965 4.5 \n\n\n\n[2] 1/2 1 2 3 4 26.3 0.2625 2570 12.0 \n\n\n\n[3] 1/3 1/2 1 2 3 16 0.1599 4380 20.4 \n\n\n\n[4] 1/4 1/3 1/2 1 1 9.7 0.0972 6171 28.8 \n\n\n\n[5] 1/5 1/4 1/3 1/2 1 6.2 0.0617 7358 34.3 \n\n\n\nCategory: [1] 0\u20131036m; [2] 1036\u20132288m; [3] 2288\u20133755m; [4] 3755\u20135655m; [5] 5655\u201311008m \n\n\n\nLULC  \n\n\n\nPairwise Comparison Matrix \n\n\n\nWeight PEV CR \n\n\n\nArea \n\n\n\n[1] [2] [3] [4] [5] Priority (%) km2 % \n\n\n\n[1] 1 2 3 4 5 57.5 0.5747 \n\n\n\n10.62 0.041 \n\n\n\n488 3.0 \n\n\n\n[2] 1/2 1 2 3 4 15.6 0.1564 2266 10.0 \n\n\n\n[3] 1/3 1/2 1 2 3 15 0.1496 18095 83.7 \n\n\n\n[4] 1/4 1/3 1/2 1 1 8.3 0.0834 124 0.7 \n\n\n\n[5] 1/5 1/4 1/3 1/2 1 3.5 0.0355 471 2.7 \n\n\n\nCategory: [1] Water body; [2] Haor; [3] Swamp forest or agriculture; [4] Bare land or built-up area; [5] Forest. \n\n\n\nTable 4: AHP parameter values used in this study. Priority: numbers associated with the nodes of an AHP hierarchy; PEV: principal eigenvalue; CR: \nconsistency ratio. Number of comparisons: pairwise comparisons with the other parameters and with itself. \n\n\n\nParameter* \n\n\n\nPairwise Comparison Matrix \n\n\n\nWeight PEV CR \n\n\n\nNumber  \n\n\n\nof comparisons [1] [2] [3] [4] [5 [6] Priority \n\n\n\n(%) \n\n\n\nRank \n\n\n\n[1] 1 1/3 1/2 2 3 4 17.2 3 0.1715 \n\n\n\n6.156 0.025 15 \n[2] 3 1 2 3 4 5 36.1 1 0.3607 \n\n\n\n[3] 2 1/2 1 2 3 4 22.8 2 0.2276 \n\n\n\n[4] 1/2 1/3 1/2 1 2 3 11.9 4 0.1185 \n\n\n\n[5] 1/3 1/4 1/3 1/2 1 2 7.3 5 0.0734 \n\n\n\n[6] 1/4 1/5 1/4 1/3 1/2 1 4.8 6 0.0481 \n\n\n\n* [1] Population density; [2] Elevation; [3] LULC; [4] Rainfall; [5] Drainage distance; [6] Flowlength.\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 01-11 \n\n\n\nCite The Article: Md. Yousuf Gazi, Md. Ashraful Islam, Shakhawat Hossain(2019). Flood-Hazard Mapping In A Regional Scale \u2013 Way Forward To The Future Hazard Atlas In \nBangladesh. Malaysian Journal of Geosciences, 3(1): 01-11. \n\n\n\nTable 5: Area Statistics of Flood Hazard Zonation in the study area \n\n\n\nHazard Class Area (Km2) Area (%) \n\n\n\nLow 9362.16 43.68 \n\n\n\nModerate 7823.08 36.51 \n\n\n\nHigh 4241.29 19.98 \n\n\n\nThe flood hazard index map (Figure 4) shows the flood-risk zones classified into three categories. The statistics of the flood hazard index map from AHP \n\n\n\nare given in Table 4.\n\n\n\nFigure 4: Flood Hazard Index (FHI) map showing flood-risk zones in the study area. \n\n\n\nTable 6: Flood hazard zones by upazila. \n\n\n\nDistrict Upazila \n\n\n\nLow Moderate High \n\n\n\nArea (km2) (%) Area (km2) (%) Area (km2) (%) \n\n\n\nTangail Basail 50 31.4 109 68.5 0 0 \n\n\n\nBhuapur 160 64.5 50 20.1 38 15.3 \n\n\n\nDelduar 80 44.6 80 44.6 19 10.6 \n\n\n\nGhatail 440 97.1 10 2.2 3 0.6 \n\n\n\nGopalpur 189 84.7 30 13.4 4 1.7 \n\n\n\nKalihati 240 79.4 40 13.2 22 7.2 \n\n\n\nMadhupur 512 99.0 5 0.9 0 0 \n\n\n\nMirzapur 160 43.7 206 56.2 0 0 \n\n\n\nNagarpur 95 37.5 150 59.2 8 3.1 \n\n\n\nSakhipur 432 97.7 10 2.2 0 0 \n\n\n\nTangail S. 270 85.7 30 9.5 15 4.7 \n\n\n\nGazipur Gazipur S. 5 1.4 160 46.3 175 50.7 \n\n\n\nKaliakair 150 48.7 150 48.7 8 2.5 \n\n\n\nKaliganj 60 29.8 81 40.2 60 29.8 \n\n\n\nKapasia 210 58.4 147 40.9 2 0.5 \n\n\n\nSreepur 300 64.3 165 35.4 1 0.2 \n\n\n\nNarshingdi Belabo 55 47.8 55 47.8 5 4.3 \n\n\n\nManohardi 45 23.3 100 51.8 47 24.3 \n\n\n\nNarsingdi S. 1 0.4 22 10.3 190 89.2 \n\n\n\nPalash 45 48.9 46 50.0 1 1.0 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 01-11 \n\n\n\nCite The Article Md. Yousuf Gazi, Md. Ashraful Islam, Shakhawat Hossain(2019). Flood-Hazard Mapping In A Regional Scale \u2013 Way Forward To The Future Hazard Atlas In \nBangladesh. Malaysian Journal of Geosciences, 3(1): 01-11. \n\n\n\nRaipur 45 13.8 260 80.0 20 6.1 \n\n\n\nShibpur 80 35.3 80 35.3 66 29.2 \n\n\n\nBrahmanbaria Akhaura 20 22.9 50 57.4 17 19.5 \n\n\n\nBancharampur 25 12.2 150 73.5 29 14.2 \n\n\n\nBrahmanbaria S. 130 23.9 280 51.5 133 24.4 \n\n\n\nKasba 110 50.2 100 45.6 9 4.1 \n\n\n\nNabinagar 36 10.4 210 60.6 100 28.9 \n\n\n\nNasirnagar 29 9.3 195 63.1 85 27.5 \n\n\n\nSarail 30 14.4 95 45.8 80 38.6 \n\n\n\nKishoreganj Astagram 30 10.0 178 59.7 90 30.2 \n\n\n\nBajitpur 10 5.4 50 27.1 124 67.3 \n\n\n\nBhairab 10 8.5 70 59.8 37 31.6 \n\n\n\nHossainpur 45 38.4 47 40.1 25 21.3 \n\n\n\nItna 20 5.2 260 68.4 100 26.3 \n\n\n\nKarimganj 58 29.2 72 36.3 68 34.3 \n\n\n\nKatiadi 45 20.4 45 20.4 130 59.0 \n\n\n\nKishoreganj S. 40 22.4 95 53.3 43 24.1 \n\n\n\nKuliarchar 40 40.0 40 40.0 20 20.0 \n\n\n\nMithamain 5 2.3 140 65.1 70 32.5 \n\n\n\nNikli 5 2.5 115 58.9 75 38.4 \n\n\n\nPakundia 75 43.5 79 45.4 30 17.2 \n\n\n\nTarail 28 20.0 85 60.7 27 19.2 \n\n\n\nMymensing Bhaluka 305 70.2 99 22.8 30 6.9 \n\n\n\nDhobaura 240 87.5 34 12.4 0 0 \n\n\n\nGaffargaon 190 47.7 198 49.7 10 2.5 \n\n\n\nGauripur 185 82.9 28 12.5 10 4.4 \n\n\n\nHaluaghat 280 93.0 21 6.9 0 0 \n\n\n\nPhulpur 270 88.5 30 9.8 5 1.6 \n\n\n\nMuktagachha 197 63.1 105 33.6 10 3.2 \n\n\n\nMymensingh S. 240 63.1 100 26.3 40 10.5 \n\n\n\nNandail 150 46.2 150 46.2 24 7.4 \n\n\n\nIshwarganj 105 53.8 80 41.0 10 5.1 \n\n\n\nPhulbari 284 70.4 103 25.5 16 3.9 \n\n\n\nTrishal 200 5.9 105 31.1 27 8.0 \n\n\n\nSunamganj Chhatak 26 6.3 185 45.0 200 48.6 \n\n\n\nDerai 20 4.8 80 19.4 312 75.7 \n\n\n\nDharampasha 40 8.8 260 57.7 150 33.3 \n\n\n\nDowarabazar 30 8.5 219 62.7 100 28.6 \n\n\n\nJagannathpur 72 19.3 160 43.0 140 37.6 \n\n\n\nJamalganj 46 10.3 100 22.4 300 67.2 \n\n\n\nSullah 20 7.6 120 46.1 125 48.0 \n\n\n\nSunamganj S. 30 5.9 70 13.9 401 80.0 \n\n\n\nTahirpur 18 5.8 150 48.7 140 45.4 \n\n\n\nBishwamvarpur 14 7.1 85 43.5 96 49.2 \n\n\n\nNetrakona Madan 30 12.6 187 78.9 20 8.4 \n\n\n\nMohanganj 103 40.7 100 39.5 50 19.7 \n\n\n\nKendua 165 19.7 150 44.7 20 5.9 \n\n\n\nKhaliajuri 45 15.6 117 40.7 125 43.5 \n\n\n\nKalmakanda 107 27.8 193 50.2 84 21.8 \n\n\n\nDurgapur 197 68.4 91 31.5 0 0 \n\n\n\nBarhatta 175 79.9 25 11.4 19 8.6 \n\n\n\nAtpara 100 51.2 90 46.1 5 2.5 \n\n\n\nPurbadhala 210 60.0 90 25.7 50 14.2 \n\n\n\nNetrokona S. 150 47.7 150 47.7 14 4.4 \n\n\n\n5. IMPLICATIONS AND LIMITATIONS OF THE STUDY \n\n\n\nNatural disasters are posing a threat to economic development \n\n\n\ncontinuously in recent times [63]. Clear concepts on the geographic \n\n\n\npatterns, causes, and effects of local hazards are crucial for serving peoples \n\n\n\nin future responding to the risk [64-66]. Regrettably, it is often hard to find \n\n\n\ncomprehensive sources of data about local hazards. Several countries in \n\n\n\nthe world, as for example India have come up a long way in plummeting \n\n\n\nthe disaster risk. Understanding disaster risk and its potential impact on \n\n\n\nhuman lives and livelihoods including social, economic, and \n\n\n\nenvironmental assets made it easier to reduce the losses [27]. Timely, \n\n\n\naccurate, and comprehensible information on disaster risk and losses \n\n\n\nought to be integral to both public and private investment planning \n\n\n\ndecisions. \u201cWorld Atlas of Natural Disasters Risk\u201d is now a blessing to \n\n\n\nenhance understanding of hazard, vulnerability, risk, and exposure. Atlas \n\n\n\ncontains the spatial distribution of disaster risk in many parts of the world. \n\n\n\nOnline hazards atlas is a essential tool for awareness buildup, education \n\n\n\nand important decision-making process [67,68].  \n\n\n\nAssessment of flood hazard maps and web mapping services as \n\n\n\ninformation tools in flood risk management is a significant approach for \n\n\n\nthe preparation online and print hazard atlas. In the present study, a \n\n\n\ncomprehensive flood hazard index map has been prepared up to a upazila \n\n\n\nlevel where hazard potential could be assessed for local hazard mitigation \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 01-11 \n\n\n\nCite The Article Md. Yousuf Gazi, Md. Ashraful Islam, Shakhawat Hossain(2019). Flood-Hazard Mapping In A Regional Scale \u2013 Way Forward To The Future Hazard Atlas In \nBangladesh. Malaysian Journal of Geosciences, 3(1): 01-11. \n\n\n\nand prevention. The information and results outlined in this research will \n\n\n\nserve enormous wealthy of information for the creation of regional online \n\n\n\nand print flood hazard atlas in Bangladesh [69]. We believe outcomes of \n\n\n\nthe present study along with the other existing flood hazard maps in \n\n\n\nBangladesh will contribute significantly in flood atlas preparation in a \n\n\n\nregional scale.  \n\n\n\nApart from the advantage and implication of this study, we want to \n\n\n\nmention some of the limitations related regarding mapping to the theme. \n\n\n\nDistinct LULC classes could be identified and feed into the flood hazard \n\n\n\nmodeling. Since the area is relatively flat, high resolution DEM may \n\n\n\nprovide exact topographic characteristics of the study region. Mouza wise \n\n\n\nvillage data, if utilized could have been a realistic depiction of exiting \n\n\n\npopulation. To overcome these limiation, future work could be \n\n\n\nimplemented through the usage of high-resolution satellite imagery as \n\n\n\nwell as DEM. In addition, ground truth data may be added for LULC \n\n\n\naccuracy assessment. Flood vulnerability assessment on human \n\n\n\nproperties (settlements and other infrastructure) may lifted the advantage \n\n\n\nto the future planning in this region.  \n\n\n\n6. CONCLUSIONS \n\n\n\nMCE technique proved to be an effective tool for the creation of hazard \n\n\n\nindex in the study area. The flood risk potential in different parts of the \n\n\n\nstudy area and their underlying causes might be discernable from the \n\n\n\nresulted map. MCE aided flood susceptibility analysis has revealed that ~ \n\n\n\n55% (~ 12,064 Km2) of the study area falls under moderate to high risk \n\n\n\nzone. Northeastern part of the study found as more susceptible to flooding \n\n\n\nwhilst western part has low risk potential. Population density seems to be \n\n\n\nthe most significant contributor to flooding hazard, as indicated by the \n\n\n\nhigh flood susceptibility in places with high population density. Several \n\n\n\nother parameters viz., LULC, elevation, and precipitation, also have \n\n\n\nsignificant impacts on final hazard map. This study should provide a more \n\n\n\ninteractive, meaningful and detailed flood-risk assessment for the relevant \n\n\n\ndecision makers and flood managers at all levels to understand the factors \n\n\n\ntriggering flood inundation.  We expect that this study will be able to serve \n\n\n\nas a prototype to develop a nation-wide flood hazards atlas. This work was \n\n\n\ndone solely in a GIS environment, with very little input from field data. \n\n\n\nSupplementary information on and an analysis of the field conditions, \n\n\n\nhydrological status and characteristics of flood-prevention structures are \n\n\n\nnecessary to substantiate the findings yielded from this study, as well as \n\n\n\nfor a comprehensive flood-risk assessment. In order to determine the \n\n\n\nextent and severity of flood impact in any specific part of the study area in \n\n\n\na more quantitative manner, a comprehensive study needs to gather all \n\n\n\nrelevant information from all available sources. \n\n\n\nACKNOWLEDGMENTS \n\n\n\nAuthors are grateful to the University of Dhaka for supporting this work \n\n\n\nthrough the provision of laboratory facilities. We are grateful to Dr Peter \n\n\n\nMcIntyre, University of New South Wales, Canberra for his enthusiastic \n\n\n\nediting efforts and other relevant comments. \n\n\n\nREFERENCES \n\n\n\n[1] Kundzewicz, Z.W., Takeuchi, K. 1999. Flood protection and \n\n\n\nmanagement: quo vadimus? Hydrological Sciences Journal, 44 (3), 417-\n\n\n\n432. \n\n\n\n[2] O'Connor, J.E., Costa, J.E. 2004. The world's largest floods, past and \n\n\n\npresent: their causes and magnitudes. US Geological Survey, 1254. \n\n\n\n[3] Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., \n\n\n\nWatanabe, S., Kanae, S. 2013. Global flood risk under climate change. \n\n\n\nNature Climate Change, 3 (9), 816.  \n\n\n\n[4] Taylor, J. 2009. The Generalissimo. Harvard University Press. \n\n\n\n[5] Organization for Economic Cooperation and Development. 2007. \n\n\n\nDepartment of Environment & Energy, Australian Government, 7. \n\n\n\n[6] Alcantara-Ayala, I. 2002. Geomorphology, natural hazards, \n\n\n\nvulnerability and prevention of natural disasters in developing countries. \n\n\n\nGeomorphology, 47 (2-4), 107-124.  \n\n\n\n[7] Ali, A.M.S. 2007. September 2004 flood event in southwestern \n\n\n\nBangladesh: a study of its nature, causes, and human perception and \n\n\n\nadjustments to a new hazard. Natural Hazards, 40 (1), 89-111.  \n\n\n\n[8] Bhattacharya, A. 1997. On the origin of non\u2010tidal flaser bedding in point \n\n\n\nbar deposits of the river Ajay, Bihar and West Bengal, NE India. \n\n\n\nSedimentology, 44 (6), 973-975. \n\n\n\n[9] Kale, V.S., Hire, P., Baker, V.R. 1997. Flood hydrology and \n\n\n\ngeomorphology of monsoon-dominated rivers: the Indian \n\n\n\nPeninsula. Water International, 22 (4), 259-265. \n\n\n\n[10] Muramoto, Y. 1988. Investigation of the flood disaster caused by \n\n\n\nheavy rainfall in Bangladesh during the 1987 monsoon season. Report of \n\n\n\nScientific Research for Natural Disaster, No. B-62-5, Ministry of Education \n\n\n\n(Tokyo). \n\n\n\n[11] Rahman, A. 1996. Peoples' perception and response to flooding: The \n\n\n\nBangladesh experience. Journal of Contingencies and Crisis \n\n\n\nManagement, 4 (4), 198-207. \n\n\n\n[12] BWDB. 2012. Annual Flood Report 2008, Bangladesh Water \n\n\n\nDevelopment Board, Government of the People\u2019s Republic of Bangladesh, \n\n\n\nDhaka. \n\n\n\n[13] Del Ninno, C. (Ed.). 2001. The 1998 floods in Bangladesh: disaster \n\n\n\nimpacts, household coping strategies, and response. Intl Food Policy Res \n\n\n\nInst, 122.  \n\n\n\n[14] BWDB. 2008. Annual Flood Report, Bangladesh Water Development \n\n\n\nBoard, Government of the People\u2019s Republic of Bangladesh, Dhaka. \n\n\n\n[15] Burby, R.J. 2001. Flood insurance and floodplain management: the US \n\n\n\nexperience. Global Environmental Change Part B: Environmental Hazards, \n\n\n\n3 (3), 111-122.  \n\n\n\n[16] Thampapillai, D.J., Musgrave, W.F. 1985. Flood damage mitigation: A \n\n\n\nreview of structural and nonstructural measures and alternative decision \n\n\n\nframeworks. Water Resources Research, 21 (4), 411-424. \n\n\n\n[17] Kundzewicz, Z.W., Kanae, S., Seneviratne, S.I., Handmer, J., Nicholls, N., \n\n\n\nPeduzzi, P., Muir-Wood, R. 2014. Flood risk and climate change: global and \n\n\n\nregional perspectives. Hydrological Sciences Journal, 59 (1), 1-28. \n\n\n\n[18] Gao, W., Shen, Q., Zhou, Y., Li, X. 2018. Analysis of flood inundation in \n\n\n\nungauged basins based on multi-source remote sensing \n\n\n\ndata. Environmental monitoring and assessment, 190 (3), 129. \n\n\n\n[19] Liu, R., Chen, Y., Wu, J., Gao, L., Barrett, D., Xu, T., Yu, J. 2017. Integrating \n\n\n\nEntropy\u2010Based Na\u00efve Bayes and GIS for Spatial Evaluation of Flood \n\n\n\nHazard. Risk analysis, 37 (4), 756-773. \n\n\n\n[20] Lyu, H.M., Sun, W.J., Shen, S.L., Arulrajah, A. 2018. Flood risk \n\n\n\nassessment in metro systems of mega-cities using a GIS-based modeling \n\n\n\napproach. Science of the Total Environment, 626, 1012-1025. \n\n\n\n[21] Tehrany, Shafapour, M., Pradhan, Biswajeet, Jebur, Neamah, M. 2014. \n\n\n\nFlood susceptibility mapping using a novel ensemble weights-of-evidence \n\n\n\nand support vector machine models in GIS. J. Hydrol., 512, 332-343.  \n\n\n\n[22] Kourgialas, N.N., Karatzas, G.P. 2017. A national scale flood hazard \n\n\n\nmapping methodology: The case of Greece\u2013Protection and adaptation \n\n\n\npolicy approaches. Science of the Total Environment, 601, 441-452. \n\n\n\n[23] Boyle, S.J., Tsanis, I.K., Kanaroglou, P.S. 1998. Developing geographic \n\n\n\ninformation systems for land use impact assessment in flooding condition. \n\n\n\nJournal of Water Resources Planning and Management, ASCE, 124 (2), 89-\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 01-11 \n\n\n\nCite The Article Md. Yousuf Gazi, Md. Ashraful Islam, Shakhawat Hossain(2019). Flood-Hazard Mapping In A Regional Scale \u2013 Way Forward To The Future Hazard Atlas In \nBangladesh. Malaysian Journal of Geosciences, 3(1): 01-11. \n\n\n\n98. \n\n\n\n[24] Paudyal, G.N. 1996. An integrated GIS-numerical modelling system for \n\n\n\nadvanced flood management. In Proc. Int. Conf. on Water Resources & \n\n\n\nEnvironment Research: Towards the 21st Century, Water Resources \n\n\n\nResearch Center, Kyoto Univ., Kyoto, Japan, 555-562. \n\n\n\n[25] Moel, H.D., Alphen, J.V., Aerts, J.C.J.H. 2009. Flood maps in Europe-\n\n\n\nmethods, availability and use.  \n\n\n\n[26] Ministry of Disaster Management and Relief (MoMDR). 2015. Seismic \n\n\n\nRisk Assessment in Bangladesh: Atlas for Bogra, Dinajpur, Mymensingh, \n\n\n\nRajshahi, Rangpur and Tangail City Corporation/ Paurashava Areas, \n\n\n\nBangladesh.  \n\n\n\n[27] Hagemeier-Klose, M., Wagner, K. 2009. Evaluation of flood hazard \n\n\n\nmaps in print and web mapping services as information tools in flood risk \n\n\n\ncommunication. Natural hazards and earth system sciences, 9 (2), 563-\n\n\n\n574.  \n\n\n\n[28] Rahmati, O., Zeinivand, H., Besharat, M. 2016. Flood hazard zoning in \n\n\n\nYasooj region, Iran, using GIS and multi-criteria decision analysis. \n\n\n\nGeomatics, Natural Hazards and Risk, 7 (3), 1000-1017. \n\n\n\n[29] Saaty, T.L. 1977. A scaling method for priorities in hierarchical \n\n\n\nstructures. Journal of Mathematical Psychology, 15 (3), 234-281. \n\n\n\n[30] Saaty, T.L. 1980. The analytic hierarchy process: planning, priority \n\n\n\nsetting, resources allocation. New York: McGraw-Hill, 281.   \n\n\n\n[31] Zhan, X.G., Zhu, G.R., Wen, Y.Y. 2003. Risk evaluation for flood \n\n\n\ndisasters in the plain area based on GIS. Resour. Environ. Yangtze Basin, \n\n\n\n12 (4), 388-392.  \n\n\n\n[32] Whalen, T.M., Savage, G.T., Jeong, G.D. 2002. The method of self-\n\n\n\ndetermined probability weighted moments revisited. J. Hydrol., 268, 177-\n\n\n\n191. \n\n\n\n[33] Rashed, T., Weeks, J. 2003. Assessing vulnerability to earthquake \n\n\n\nhazards through spatial multicriteria analysis of urban \n\n\n\nareas. International Journal of Geographical Information Science, 17 (6), \n\n\n\n547-576. \n\n\n\n[34] Gamper-Rabindran, S. 2006. Did the EPA's voluntary industrial toxics \n\n\n\nprogram reduce emissions? A GIS analysis of distributional impacts and \n\n\n\nby-media analysis of substitution. Journal of Environmental Economics \n\n\n\nand Management, 52 (1), 391-410. \n\n\n\n[35] Gigovi\u0107, L., Pamu\u010dar, D., Bo\u017eani\u0107, D., & Ljubojevi\u0107, S. 2017. Application \n\n\n\nof the GIS-DANP-MABAC multi-criteria model for selecting the location of \n\n\n\nwind farms: A case study of Vojvodina, Serbia. Renewable Energy, 103, \n\n\n\n501-521. \n\n\n\n[36] Elsheikh, R.F.A., Ouerghi, S., Elhag, A.R. 2015. Flood risk map based on \n\n\n\nGIS, and multi criteria techniques (case study Terengganu \n\n\n\nMalaysia). Journal of Geographic Information Systems, 7 (04), 348. \n\n\n\n[37] Meyer, V., Scheuer, S., Haase, D. 2009. A multicriteria approach for \n\n\n\nflood risk mapping exemplified at the Mulde River, Germany. Natural \n\n\n\nHazards, 48 (1), 17-39. \n\n\n\n[38] Wang, J.J., Jing, Y.Y., Zhang, C.F., Zhao, J.H. 2009. Review on multi-\n\n\n\ncriteria decision analysis aid in sustainable energy decision-\n\n\n\nmaking. Renewable and Sustainable Energy Reviews, 13(9), 2263-2278. \n\n\n\n[39] Tareq, S.M., Rahman, M.T.U., Islam, A.Z., Baddruzzaman, A.B.M., Ali, \n\n\n\nM.A. 2018. Evaluation of climate-induced waterlogging hazards in the \n\n\n\nsouth-west coast of Bangladesh using Geoinformatics. Environmental \n\n\n\nmonitoring and assessment, 190 (4), 230. \n\n\n\n[40] Ghosh, M.K., Kumar, L., Langat, P.K. 2018. Mapping tidal channel \n\n\n\ndynamics in the Sundarbans, Bangladesh, between 1974 and 2017, and \n\n\n\nimplications for the sustainability of the Sundarbans mangrove \n\n\n\nforest. Environmental monitoring and assessment, 190 (10), 582. \n\n\n\n[41] Islam, M.M., Sado, K. 2002. Development priority map for flood \n\n\n\ncountermeasures by remote sensing data with geographic information \n\n\n\nsystem. Journal of Hydrologic Engineering, 7 (5), 346-355. \n\n\n\n[42] Rahman, M.R., Saha, S.K. 2007. Flood hazard zonation \u2013 A GIS aided \n\n\n\nmulti criteria evaluation (MCE) approach with remotely sensed data. Int. \n\n\n\nJ. Geoinf., 3 (3), 25-35. \n\n\n\n[43] Zehra, S., Afsar, S. 2016. Flood hazard mapping of lower Indus basin \n\n\n\nusing Multi-Criteria Analysis. Journal of Geoscience and Environment \n\n\n\nProtection, 4 (4), 54. \n\n\n\n[44] Dewan, A.M., Kumamoto, T., Nishigaki, M. 2006. Flood hazard \n\n\n\ndelineation in greater Dhaka, Bangladesh using an integrated GIS and \n\n\n\nremote sensing approach. Geocarto International, 21 (2), 33-38. \n\n\n\n[45] Masuya, A. 2014. Flood vulnerability and risk assessment with spatial \n\n\n\nmulti-criteria evaluation. In Dhaka Megacity, pp. 177-202. Springer \n\n\n\nNetherlands. \n\n\n\n[46] BBS. 2011. Population and Housing Census, Bangladesh Bureau of \n\n\n\nStatistics (BBS). Ministry of Planning, Government of the People\u2019s Republic \n\n\n\nof Bangladesh, Dhaka. \n\n\n\n[47] Rai, R., Zhang, Y., Paudel, B., Li, S., Khanal, N.R. 2017. A synthesis of \n\n\n\nstudies on land use and land cover dynamics during 1930\u20132015 in \n\n\n\nBangladesh. Sustainability, 9 (10), 1866. \n\n\n\n[48] Alam, M. 1989. Geology and depositional history of Cenozoic \n\n\n\nsediments of the Bengal Basin of Bangladesh. Palaeogeography, \n\n\n\nPalaeoclimatology, Palaeoecology, 69, 125-139. \n\n\n\n[49] Mahmood, R. 1998. Air temperature variations and rice productivity \n\n\n\nin Bangladesh: a comparative study of the performance of the YIELD and \n\n\n\nthe CERES-Rice models. Ecological modelling, 106 (2-3), 201-212.  \n\n\n\n[50] Paul, B.K. 1997. Flood research in Bangladesh in retrospect and \n\n\n\nprospect: a review. Geoforum, 28 (2), 121-131.  \n\n\n\n[51] Ouma, Y.O., Tateishi, R. 2014. Urban flood vulnerability and risk \n\n\n\nmapping using integrated multi-parametric AHP and GIS: methodological \n\n\n\noverview and case study assessment. Water, 6 (6), 1515-1545.  \n\n\n\n[52] Fern\u00e1ndez, D.S., Lutz, M.A. 2010. Urban flood hazard zoning in \n\n\n\nTucum\u00e1n Province, Argentina, using GIS and multicriteria decision \n\n\n\nanalysis. Engineering Geology, 111 (1-4), 90-98.  \n\n\n\n[53] Yahaya, S., Ahmad, N., Abdalla, R.F. 2010. Multicriteria analysis for \n\n\n\nflood vulnerable areas in Hadejia-Jama'are River basin, Nigeria. European \n\n\n\nJournal of Scientific Research, 42 (1), 71-83. \n\n\n\n[54] Holtrop, J. F., Keizer, J. 1969. Some aspects of stratigraphy and \n\n\n\ncorrelations of the Surma Basin wells, East Pakistan, Stratigraphic \n\n\n\ncorrelation between sedimentary basins of ESCAP region (2nd \n\n\n\nvol.). ESCAFE Mineral Resource Development Series, 36. \n\n\n\n[55] Lin, H., Wan, Q., Li, X., Chen, J., Kong, Y. 1997. GIS-based multicriteria \n\n\n\nevaluation for investment environment. Environment and Planning B: \n\n\n\nPlanning and Design, 24 (3), 403-414. \n\n\n\n[56] Merz, B., Thieken, A.H., Gocht, M. 2007. Flood risk mapping at the local \n\n\n\nscale: concepts and challenges. In Flood risk management in Europe, pp. \n\n\n\n231-251. Springer, Dordrecht.  \n\n\n\n[57] Shahin, A., Mahbod, M.A. 2007. Prioritization of key performance \n\n\n\nindicators: An integration of analytical hierarchy process and goal setting. \n\n\n\nInternational Journal of Productivity and Performance Management, 56 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 01-11 \n\n\n\nCite The Article Md. Yousuf Gazi, Md. Ashraful Islam, Shakhawat Hossain(2019). Flood-Hazard Mapping In A Regional Scale \u2013 Way Forward To The Future Hazard Atlas In \nBangladesh. Malaysian Journal of Geosciences, 3(1): 01-11. \n\n\n\n(3), 226-240.  \n\n\n\n[58] Saaty, T.L. 2008. Relative measurement and its generalization in \n\n\n\ndecision making why pairwise comparisons are central in mathematics for \n\n\n\nthe measurement of intangible factors the analytic hierarchy/network \n\n\n\nprocess. RACSAM-Revista de la Real Academia de Ciencias Exactas, Fisicas \n\n\n\ny Naturales. Serie A. Matematicas, 102 (2), 251-318.  \n\n\n\n[59] Greco, S., Figueira, J., Ehrgott, M. 2016. Multiple criteria decision \n\n\n\nanalysis. New York: Springer.  \n\n\n\n[60] Dong, Y., Zhang, G., Hong, W.C., Xu, Y. 2010. Consensus models for AHP \n\n\n\ngroup decision making under row geometric mean prioritization method. \n\n\n\nDecision Support Systems, 49 (3), 281-289.  \n\n\n\n[61] Zou, X., Li, D. 2008. A multidisciplinary GIS-based approach for the \n\n\n\npotential evaluation of land consolidation projects: a model and its \n\n\n\napplication. In: 7th WSEAS International conference on applied computer \n\n\n\nand applied computational science, China, 551\u2013556.  \n\n\n\n[62] Adiat, K.A.N., Nawawi, M.N.M., Abdullah, K. 2012. Assessing the \n\n\n\naccuracy of GIS-based elementary Multi Criteria Decision Analysis as a \n\n\n\nspatial prediction tool \u2014 A case of predicting potential zones of \n\n\n\nsustainable groundwater resources. Journal of Hydrology, 440-441, 75-89.  \n\n\n\n[63] Benson, C., Clay, E. 2004. Understanding the economic and financial \n\n\n\nimpacts of natural disasters. The World Bank.  \n\n\n\n[64] Battersby, S.E., Mitchell, J.T., Cutter, S.L. 2011. Development of an \n\n\n\nonline hazards atlas to improve disaster awareness. International \n\n\n\nResearch in Geographical and Environmental Education, 20 (4), 297-308.  \n\n\n\n[65] Izadkhah, Y., Hosseini, M. 2005. Towards resilient communities in \n\n\n\ndeveloping countries through education of children in disaster \n\n\n\npreparedness. International Journal of Emergency Management, 2 (3), 1\u2013\n\n\n\n11.  \n\n\n\n[66] Cross, J.A. 1988. Hazards maps in the classroom. Journal of \n\n\n\nGeography, 87 (6), 202\u2013211.  \n\n\n\n[67] Thomas, D.S.K., Mitchell, J.T., Scott, M.S., Cutter, S.L. 1999. Developing \n\n\n\na digital atlas of environmental risks and hazards. Journal of Geography, \n\n\n\n98, 201\u2013207.  \n\n\n\n[68] Trainor, T. 1995. A forward to electronic atlases: National and \n\n\n\nregional applications. Cartographic Perspectives, 20, 3\u20134. \n\n\n\n[69] Wang, Y., Li, Z., Tang, Z., Zeng, G. 2011. A GIS-based spatial multi-\n\n\n\ncriteria approach for flood risk assessment in the Dongting Lake Region, \n\n\n\nHunan, Central China. Water resources management, 25 (13), 3465-3484.  \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 90-95 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2020.90.95 \n\n\n\nCite the Article: Ibrahim Sufiyan, Magaji J.I, A.T. Ogah (2020). Hydrologic Assessment Of Food Using Swat As Geospatial Techniques In The Catchment Area Of \nTerengganu Malaysia. Malaysian Journal of Geosciences, 4(2): 90-95. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2020.90.95\n\n\n\nHYDROLOGIC ASSESSMENT OF FOOD USING SWAT AS GEOSPATIAL TECHNIQUES \n\n\n\nIN THE CATCHMENT AREA OF TERENGGANU MALAYSIA \n\n\n\nIbrahim Sufiyana, Magaji J.Ib, A.T. Ogahb \n\n\n\na Federal Polytechnic Nasarawa1, Nasarawa State, Nigeria.  \nb Department of Geography, Nasarawa State University Keffi, Nigeria. \n\n\n\n*Corresponding Author Email: ibrahimsufiyan0@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 25 June 2020 \nAccepted 28 July 2020 \nAvailable online 13 August 2020\n\n\n\nRisks and hazards are two important issues currently threatening humanity and the environment. Flood has \nclaimed many lives and destroyed properties in Malaysia and Africa and Nigeria. It is global catastrophe.  The \napplication of geospatial science is, therefore, very important advantages that it offers solutions to flood. This \nstud uses of Advanced Space-borne Thermal Emission and Reflection Radiometer Digital Elevation Model \n(ASTER-DEM), and the Soil Water Assessment Tool (SWAT) in visualizing floods disaster risk. The whole \ncatchment area of Terengganu has been delineated. The 25 sub-basins have been identified and the flood risk \nzones have been modeled. The complete watersheds are characterized by different sub-basins and Hydrologic \nRespond Units (HRUs) which can be viewed in 3D environment.  \n\n\n\nKEYWORDS \n\n\n\nGeospatial technique, Floods, Watershed, HRU, Assessment.\n\n\n\n1. INTRODUCTION \n\n\n\nFlood is a hazardous natural phenomenon occurring in most of the world. \nRainfall-triggered flow accumulation upstream to downstream overflow \nthe river banks causing damages on the surface of the earth including \nhuman lives (Bronstert, 2003). Flood has been affecting human habitats \ncreating anunsustainable environment. Among the various natural \nhazards, the flood is considered to be a devastating disaster with an \nextensive account of damages (Youssef et al., 2011). The current study \nabout hazard events in many cities, towns, or villages cannot be \noveremphasizedbecause climatic conditions are not static. The presence \nof land covers as well as the geographical settings as great effects on the \nflood. There is a need to highlight the removal of vegetation cover or the \ntypes of land cover which may influence flooding, the soil, and water \nassessment tool will monitor the Hydrologic Response Units (HRUs) and \nthe subdivision of the watershed sub-basins within the drainage basin of \nriver Terengganu.  \n\n\n\nThe continuous precipitation due to climate change is effectively \nconsidered in susceptible flood-prone areas inmost coastal regions \n(Bubeck et al., 2012). According to a study, there is an issue of excessive \nuse of land cover by an anthropogenic factor for agriculture, urbanization \nas well as other benefits that change the topography (Dawson et al., 2006). \nWe intend to develop a sustainable land cover system to reduce the \nrampant overuse of natural land cover through mitigating and create \nawareness to avoid a flood in the flood-prone areas within the scope of this \nstudy. The Geographic Information System (GIS) is the acquiring of spatial \ndata. The river flow is high during the monsoon, and the water level \noverflow it banks. We need to get informed or be informed about the \naftermath of flood event as quickly as possible, to assess the magnitude of \nthe flood. Flood is a most severe hazard in Malaysia Watershed is also \nknown as a basin or catchment, or simply an area delineated with a \n\n\n\nspecified outlet point that emptied in a large body of water (Liu et al., \n2003). \n\n\n\nThe 3D view can help in determining the water levels an extent to which \nthe flood event will be analyzed. It also helps in displaying a real-time \nanimation of flood events for proper visualization.The study can be useful \nin the assessment of impacts of HRUs developed from SWAT through \nArcScene simulation. In another study, researchers also used GIS to \ndetermine flood management (Arnold et al., 2010). He further explained \nthat flood eventscould be the resultant effects of the complex interaction \nof the natural system as a result of excessive rainfall flowing downstream. \nThe component of the system involved rainfall event, topography, soil and \nchannel characteristics. A flood event is as a result of such complex \ninteraction within the watershed system.  \n\n\n\nThe flood modeling are used for designing a flood risk map. However, \nthere are various management practices in mitigating flood; this includes \nsuitability of land cover practice, vegetation and forest conservation and \nother structural management. The investigation of multi-dimensional \nflood estimation was done using the artificial neural network with the \ndesign of modeling techniques. Various researchers have been analyzing \nflood mapping assessment in GIS gathered information concerning \nexisting methods their views was the transformation of input factors into \na single model using a differentapproach, weighing, computing, and \ninterpolation techniques (Chau and Chan 2005; Mukerji et al., 2009). It \nmeans techniques acquired thorough knowledge base, quantitative \ntechniques and data mining techniques are essential in map scaling \n(Meyer et al., 2009). \n\n\n\nThe objectives of this study are to evaluate flood by delineating the \nwatershed and to categorize them into sub-basins so visualize the affected \nflooded areas. During the High flow period of monsoon, this can be \n\n\n\n\nmailto:ibrahimsufiyan0@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 90-95 \n\n\n\nCite the Article: Ibrahim Sufiyan, Magaji J.I, A.T. Ogah (2020). Hydrologic Assessment Of Food Using Swat As Geospatial Techniques In The Catchment Area Of \nTerengganu Malaysia. Malaysian Journal of Geosciences, 4(2): 90-95. \n\n\n\nachieved by setting the reference points or benchmarks of different land \nuses/ covers and the role played by the HRUs in the Terengganu \ncatchment The 3D visualization can provide essential models for decision \nmaking in Terengganu as well as the entire catchment in Malaysia. The \nissue of flood disaster is a global phenomenon that requires attention in \nother to control life and properties. This study will be crucial in \nhighlighting flood warnings and to people who are vulnerable to flood \ndisasters.  \n\n\n\nThe primary significance of this study is to alert people of the coming of \nthe flood. Our communities are subjected to more flood vulnerability. \nAlthough flood is natural phenomena, human activities and human \ninterventions have been affecting the ecosystem as well as wetland and \nthe watershed in the drainage basins.  The urbanization process, \nagricultural practices, and deforestation have considerably changed the \nsituation in whole river basins.At the same time, exposition to risk and \nvulnerability in flood-prone areas has constantly being growing. Flash \nfloods occurred quite rapidly (Toriman et al., 2009). \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Study Area \n\n\n\nThe study area is located at the upper left corner of 500 30I.40II N, 1020 23I \n15II E and the lower right corner is 400 39I 25II N, 1030 11I 62II E \nrespectively.The study focuses on flood mitigation in one of the flood-\nproneregions in the Eastern part of Malaysia Peninsula called Kuala \nTerengganu River Catchment. The Terengganu catchment has a total area \nof stream definition of 5,730.1452 (Ha) about 14,159.497acres. However, \nthis study focuses on the tropical environment (rainforest zone) where \nthere is excessive rainfall and the wet season is higher than the dry season. \nThe terrain is undulating with tropical equitorial climate, rainfall is heavy \n(above 2500mm) tempersture almost high throughout the year. Relative \nhumidity is high 80-90%. Intensive agriculture is employed with \ncultivation of  rice and palm kernel (Marghany et al., 2002).   \n\n\n\nFigure 1: Map of the study area; Terengganu River Catchment Malaysia \n(2017) \n\n\n\nThe study flow chart in figure 2, explain the data collected including the \nground survey, aquaring the satellite data and the inventory such as the \ndigitised soil map. The Hydrolgic Response Units (HRUs) arease on the \nland cover, soil and slope of the study area. The simulation of the water or \nflood was done in ArcScene. The 3D was desined from the output in \nArcScene  \n\n\n\nFigure 2: Flow chart of the study \n\n\n\nDigital elevation model (DEM) is used to simulate the flow direction at a \nregular interval in ArcScene.  \n\n\n\n2.2 SWAT Data Sources \n\n\n\n\u2192 Department of Irrigation and Drainage (DID) \n\n\n\n\u2192 Data of flood event in the study area (previously) \n\n\n\n\u2192 The stream flows data These are obtainable base on a different \nlocation of the stations \n\n\n\n\u2192 Climate data from the Malaysian Meteorological Department \n(MET Malaysia) from 2000-2015 \n\n\n\n\u2192 Land cover images from the Malaysian Remote Sensing Agency \n(MRSA) \n\n\n\n\u2192 Malaysian soil map was obtainable from online source European \nDigital Archives of soil maps (EuDASM) named Reconnaissance \nsoil map Peninsular Malaysia 1968. \n\n\n\nThe input data for ArcSWAT includes the following: Required spatial \ndatasets and Optional spatial datasets as show in Table 1. \n\n\n\nTable 1: Data acquisition \nReqiured spatial Dataset Optional spatial datasets \n1. Digital elevation Model \n(DEM) \n\n\n\nWeahther parameters \n\n\n\n2. Land Cover Daily rainfall data \n3. Soil map/data Daily streamflow \n\n\n\nDaily suspended sediment \n\n\n\n2.3 SWAT Analysis  \n\n\n\n1.  The Digital Elevation Model DEM was set up and loaded from the stored \nlocation in C drive from the computer \n\n\n\n2. The DEM coordinate was transformed and setup\n\n\n\n3. The Masked of River Terengganu was superimposed and loaded from \nthe C drive \n\n\n\n4. The Burn-In was also defined and loaded \n\n\n\n5. The River Flow direction and accumulation were calculated based on \nthe DEM  \n\n\n\n6. The result of the stream definition was obtained from the total area in \nhectares and the calculated raster cells of the catchment. \n\n\n\n7. Stream network and outlets were created \n\n\n\n8. The whole watershed outlets from the Terengganu River mouth was \nformed \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 90-95 \n\n\n\nCite the Article: Ibrahim Sufiyan, Magaji J.I, A.T. Ogah (2020). Hydrologic Assessment Of Food Using Swat As Geospatial Techniques In The Catchment Area Of \nTerengganu Malaysia. Malaysian Journal of Geosciences, 4(2): 90-95. \n\n\n\n9. All the watershed in the River Terengganu Catchment has been \ndelineated  \n\n\n\n10. The Sub-basins parameters within the catchment area under study \nwere also been calculated \n\n\n\n11. The selection of appropriate Reservoir was also done in the River\nTerengganu catchment.  \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nThe result from the SWAT was obtained on 13th May 2017 at 05:29 pm \nwith the total area of the watershed having 286,507.3500 hectares or \n707,973.9872 acres. The total numbers of sub-basins obtained in \nTerengganu River catchment are 25 characterized by 305 numbers of \nHydrologic Response Units (HRUs). The threshold from the SWAT output \nfor this study was based on 10/10/10 percent as required by the software \nto get the model fit. There is a need to monitor the activities of the flood by \napplying the modern technology of Geographic Information System (Chan, \n2015). The system will assist in mitigation and controlling flood. Many \npeople died and lost their properties as a result of flooding. \n\n\n\n3.1 SWAT Watershed Delineation Result \n\n\n\nThe figure below represents the delineated watershed of Kuala \nTerengganu Catchment. The boundary with brown color in figure 4 is the \ndemarcation of the delineated watershed of the study area. The blue color \nis the main Rivers that flow toward the South China Sea. The green color \nis the minor streams in the sub-basins. \n\n\n\nFigure 4: Delineation of the watershed and the main Rivers of \nTerengganu catchment \n\n\n\n3.2 Stream Network and Reservoirs  \n\n\n\nThe stream links are developed through the stream network. 10 stream \nlinks (small maron dots) are obtained from connectivity in SWAT by the \njunction where the watershed was delineated. Each stream link had been \nconnected with the defined sub-basin. The 3 major reservoirs were \nidentified within the watershed  (the redish dots) aas shown in figure 5. A \nreservior is depression in the catchment where all the water drained \ntoward it and empty into the South China Sea through the last dot that \ntouches the watershed delineated boundary in the eastern direction. Refer \nto figure 4 the stream are emptied all through the Terengganu river Mouth. \n\n\n\nFigure 5: Stream Links and Reservoirs emptied into the South China Sea \n\n\n\n3.3 Sub-Basins Parameter \n\n\n\nThere are about 25 different sub-basins in the study area created by the \nSWAT. Each of the sub-basins was characterized by a distinct parameter \nfor easy classification and hydrologic analyses. Figure 6 shows the \nclassified sub-basins in Kuala Terengganu catchment. \n\n\n\nFigure 6: Sub-basins Parameters of Terengganu Catchment \n\n\n\nThe major Terengganu Rivers as shown in figure 7 are added connecting \nby the stream links to the watershed and the main rivers were appended \nto the whole catchment as in figure 4. Each of the sub-basins was defined \nby the water input and all the hydrologic response units. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 90-95 \n\n\n\nCite the Article: Ibrahim Sufiyan, Magaji J.I, A.T. Ogah (2020). Hydrologic Assessment Of Food Using Swat As Geospatial Techniques In The Catchment Area Of \nTerengganu Malaysia. Malaysian Journal of Geosciences, 4(2): 90-95. \n\n\n\nFigure 7: Terengganu Catchment Sub-basins Stream-links and the main \nRivers \n\n\n\n3.4 Hydrologic Response Units (HRUs) \n\n\n\nThe hydrologic response units (HRUs) results in consist of the land use, \nsoil types, and the catchment slope. They are characterized by unique \nperformance and distributions of the individual report within the \ncatchment area. In this study, we can find out the following results as \nshown in tables 2, 3, 4 and 5 below. \n\n\n\n3.5 Land Use/Cover Results \n\n\n\nTable 2 below presents the SWAT output from one of the HRU results. The \nland cover plays an important role in controlling the climate as well as the \nwater flow that causes a flood. The forest land cover for instance in the \nstudy area is the major predominant land cover. If some portion of the \nforest is removed the flood will inundate other areas occupying the lower \nelevations. This has been mentioned in the study method flow in figure 2. \n\n\n\nTable 2: Land Use Result \n\n\n\nFigure 8 depicts the land cover map of the Kuala Terengganu catchment. \nThe legend below explains the different pattern of the land cover which \nincludes forest, water, urban land use, rubber, paddy, orchard, oil palmand \n\n\n\ngrassland. The Terengganu catchment was fully occupied by forest \nevergreen where most of the forest products are found. The map in figure \n8 has illustrated that the forest evergreen as the predominant land cover \nin the whole of the study area. \n\n\n\nFigure 8: Land Use Classification ofTerengganu River catchment \n\n\n\n3.6 Soil Types Classification Results \n\n\n\nThe soil classification was base on the USGS with default SWAT and can \nupdate the local soil database. The local soils in the study area are edited \nbase on the SWAT update from the existing soils of the world. Table3 \nshows the result of the soil classification with total areas in hectares, acres \nas well as the total percent obtained during the analysis. All the local \nMalaysia soil name can be obtain in world soil classification book \n(European Digital Archives of soil maps (EuDASM). \n\n\n\nTable 3: Local Soil types result \n\n\n\nSoils Area [ha] Area[acres] % wat. \nArea  \n\n\n\nKuala Brang 35,605 87,981 12.43 \n\n\n\nMarang 26,763 66,132 9.34 \n\n\n\nPeat 47,32 11,694 1.65 \n\n\n\nRudua 1,358 3,355 0.47 \n\n\n\nSteepland Soil 200,118 494,501 69.85 \n\n\n\nTelemong 10,250 25,328 3.58 \n\n\n\nTok Yong 7,682 18,983 2.68 \n\n\n\nTotal  286,508 707,974 100% \n\n\n\nFigure 9 presents the digitized soil map of the Kuala Terengganu \ncatchment. The Soil can absorb moisture and get cooler and hotter quickly. \nDepending on the temperature, the water retention capacity varies from \nequatorial wet climate to monsoon as well as arid and semi-arid \nenvironments. The predominant local soil in the Terengganu River \ncatchment is steep and with the highest elevation; most of these areas \naround the steel and are flood risk-free zones.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 90-95 \n\n\n\nCite the Article: Ibrahim Sufiyan, Magaji J.I, A.T. Ogah (2020). Hydrologic Assessment Of Food Using Swat As Geospatial Techniques In The Catchment Area Of \nTerengganu Malaysia. Malaysian Journal of Geosciences, 4(2): 90-95. \n\n\n\nFigure 9: Soil map of Kuala Terengganu Catchment Area \n\n\n\n3.7 Slope Analysis  \n\n\n\nThe slope data derived from the SWAT database was an inbuilt developed \nfrom the chosen threshold of 10/10/10 percent from the HRU. Table 4 \nshows the result of total area from each category of slope in hectares and \nacres while taking cognizance of slope percent from 0-10 up to 40 meters \nabove. From table 4 below, we can also conclude that areas occupied by \nthe steepland land cover having the highest elevation and \nlargestpercentage of 66,130.4348 hectares about 23.08%. This justifies \nthe result obtained from the soil classification model in figure 9 with \nsteepland representing the largest space in the Terengganu River \ncatchment. \n\n\n\nTable 4: Slope Results from Terengganu Catchment \n\n\n\nFigure 10 below presents the elevation or slope map of the Kuala \nTerengganu catchment. The dark grey color depicts the lowest elevation \nthat is 0-10 meters. The green color pattern is 10-20 meter slope, the blue \ncolor is between 20 -30 and lastly, the light grey color in the map \nrepresents the highest slope. Most of the flood event occurs near the open \nsea toward the outlet because of the low elevation. \n\n\n\nFigure 10: Slope model of KualaTerengganu River Catchment \n\n\n\n3.8 Flow Direction  \n\n\n\nThe water flow pattern and direction are from the highland to lowland. \nThe flow was directed toward the river (Sungai) Terengganu outlet and \nempty into the South China Sea as shown in figure 10. The flow direction \nhas vital relevance in terms of water flow and movement within the \nTerengganu catchment. Each flow direction has some counts. For example, \nflow direction number 1 has 604140 flow counts. However, the flow \ndirections also follow the slope gradient into different outlets. The hydro-\ncounts determine how big the sub-flow generates and the location of each \nflow as indicated in Table 5 below. According to this study the flow are in \nthe same dirction. \n\n\n\nTable 5: Flow Direction and Locations \n\n\n\n3.9 Geospatial Model of flood in the Catchment Area \n\n\n\nThe flood risk model shown in figure 11 below. The yardstick is to measure \nthe magnitude of the flood risk in the catchment area of Kuala Terengganu. \nHere we arrived at the categories of flood risk from the highest risk to \nmoderate and to no risk zones within the watershed. The flood risk map \nrepresents the risk zones that can be used for mitigation, planning and a \nwarning to the public. The slopes to the lower course of the Terengganu \nRiver entered into the South China Sea through the major outlet at the \nlower DEM. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 90-95 \n\n\n\nCite the Article: Ibrahim Sufiyan, Magaji J.I, A.T. Ogah (2020). Hydrologic Assessment Of Food Using Swat As Geospatial Techniques In The Catchment Area Of \nTerengganu Malaysia. Malaysian Journal of Geosciences, 4(2): 90-95. \n\n\n\nFigure 11: High and Low Flood Risk Map of TerengganuRiver catchment \n\n\n\n3.10 Impact of Sub-basin Flood Risk Model \n\n\n\nFigure 12 was developed from both the ArcSWAT-HRU and the 3D \nsimulated model appended to it and shows which zone is severely affected \nby the flood. There are about 25 sub-basins in the study area. Each of \nwhich can stand at different GIS analysis to depict the magnitude of the \nflood risk. As illustrated in figure 11 above, the water flow follows the \nslope. The slope and the flow direction with volume increase have \ndetermined the flood risk in the Terengganu river catchment. For \nmitigation action, we can select and predict which sub-basins in the \ncatchment are highly substitutable and liable to flood at a point in time, \ndepending on the intensity and duration of the rainfall.  \n\n\n\nFigure 12: Impact of Sub-basins Flood Risk analysis of Terengganu River \nCatchment \n\n\n\n4. CONCLUSION \n\n\n\nThe catchment area of Terengganu River was delineated with a total of 28, \n6507.3500 (Ha) with 305 number of HRUs and 25 sub-basins refer to \nTable 1. There are about 3 major reservoirs right at the center of the \ncatchment refers to figure 5 with 10 stream links connecting to the other \nstreams. The land cover sustainability depends on the highest land use \nidentified within the Terengganu catchment. In Table2, we arrived at the \nsummary of land cover categories with forest land cover having 73.93%. \nThe study finds out that every HRU has different characteristics especially \nthe sizes of the sub-basins parameters which are used to determine the \nwater flow for simulations of flood risk zones. \n\n\n\nGenerally, the local soils in the catchment area are summarized in table 3 \nwith Steepland having 69.85 %, indicating that the soil formation is the \nhighland type found in most of the tropical rainforest. There is a \ncorrelation between slope and the soil type found in Kuala Terengganu. \nThe elevation from 40 meters and above is having the highest percent \n23.08%, which interprets the steepness of the slope with characterized \nsoil type of steepland in the catchment. The flow directions of almost all \nthe streams are towards the lower slope as shown in figure 10. The slope \nvalues as indicated in the same model were drained from the Lake Kenyir \nand flows toward the North-East direction and enter into the South China \nSea. \n\n\n\nREFERENCES \n\n\n\nArnold, J.G., Allen, P.M., Volk, M., Williams, J.R., Bosch, D.D., 2010. \nAssessment of different representations of spatial variability on SWAT \nmodel performance. Transactions of the ASABE, 53 (5), Pp. 1433\u20131443. \n\n\n\nBronstert, A., 2003. Floods and climate change: interactions and impacts. \nRisk Analysis, 23 (3), Pp. 545\u2013557. \n\n\n\nBubeck, P., Botzen, W.J.W., Aerts, J.C.J.H., 2012. A review of risk perceptions \nand other factors that influence flood mitigation behavior. Risk \nAnalysis, 32 (9), Pp. 1481\u20131495. \n\n\n\nChan, N.W., 2015. Impacts of disasters and disaster risk management in \nMalaysia: The case of floods. In Resilience and Recovery in Asian \nDisasters. Springer, Pp. 239\u2013265. \n\n\n\nChau, K.T., Chan, J.E., 2005. The regional bias of landslide data in \ngenerating susceptibility maps using logistic regression: a case of Hong \nKong Island. Landslides, 2 (4), Pp. 280\u2013290. \n\n\n\nDawson, C.W., Abrahart, R.J., Shamseldin, A.Y., Wilby, R.L., 2006. Flood \nestimation at ungauged sites using artificial neural networks. Journal of \nHydrology, 319 (1), Pp. 391\u2013409. \n\n\n\nLau, C.L., Smythe, L.D., Craig, S.B., Weinstein, P., 2010. Climate change, \nflooding, urbanization and leptospirosis: fuelling the fire? Transactions \nof the Royal Society of Tropical Medicine and Hygiene, 104 (10), Pp. \n631\u2013638. \n\n\n\nLiu, Y.B., Gebremeskel, S., De Smedt, F., Hoffmann, L., Pfister, L., 2003. A \ndiffusive transport approach for flow routing in GIS-based flood \nmodeling. Journal of Hydrology, 283 (1), Pp. 91\u2013106. \n\n\n\nMarghany, M., Ibrahim, Z., Van Genderen, J., 2002. Azimuth cut-off model \nfor significant wave height investigation along coastal water of Kuala \nTerengganu, Malaysia. International Journal of Applied Earth \nObservation and Geoinformation, 4 (2), Pp. 147\u2013160. \n\n\n\nMeyer, V., Scheuer, S., Haase, D., 2009. A multicriteria approach for flood \nrisk mapping exemplified at the Mulde River, Germany. Natural \nHazards, 48 (1), Pp. 17\u201339. \n\n\n\nMukerji, A., Chatterjee, C., Raghuwanshi, N.S., 2009. Flood forecasting \nusing ANN, neuro-fuzzy, and neuro-GA models. Journal of Hydrologic \nEngineering, 14 (6), Pp. 647\u2013652. \n\n\n\nToriman, M.E., Hassan, A.J., Gazim, M.B., Mokhtar, M., Mastura, S.A.S., Jaafar, \nO., Karim, O., Aziz, N.A.A., 2009. Integration of 1-d hydrodynamic model \nand GIS approach in flood management study in Malaysia. Research \nJournal of Earth Sciences, 1 (1), Pp. 22\u201327. \n\n\n\nYoussef, A.M., Pradhan, B., Hassan, A.M., 2011. Flash flood risk estimation \nalong the St. Katherine road, southern Sinai, Egypt using GIS-based \nmorphometry and satellite imagery. Environmental Earth Sciences, 62 \n(3), Pp. 611\u2013623.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 70-78 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2020.70.78 \n\n\n\nCite the Article: Khanchoul K., Balla F., and Othmani O. (2020). Assessment Of Soil Erosion By Rusle Model Using Gis: A Case Study Of Chemorah Basin, Algeria . \nMalaysian Journal of Geosciences, 4(2): 70-78.\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2020.70.78\n\n\n\nASSESSMENT OF SOIL EROSION BY RUSLE MODEL USING GIS: A CASE STUDY OF \nCHEMORAH BASIN, ALGERIA \n\n\n\nKhanchoul K.a*, Balla F.b, and Othmani O.c \n\n\n\naDepartment of Geology, Laboratory Soils and Sustainable Development, Badji Mokhtar University-Annaba, P.O.Box 12, Annaba, Algeria \nbDepartment of Hydraulics, Badji Mokhtar University-Annaba, P.O.Box 12, Annaba, Algeria \ncDepartment of Biology, Laboratory Soils and Sustainable Development, Badji Mokhtar University-Annaba, P.O.Box 12, Annaba, Algeria \n*Corresponding Author Email: kamel.khanchoul@univ-annaba.dz; kam.khanchoul@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 02 April 2020 \nAccepted 05 May 2020 \nAvailable online 18 May 2020\n\n\n\nSoil erosion by water is one of the major sources of land degradation. Erosion contributes to the temporary or \npermanent lowering of the productive capacity of agricultural land and sedimentation of dams. The purpose \nof this study is to assess soil loss rate using a GIS/RUSLE approach at the Chemorah basin by focusing on two \ncatchments, namely, Reboa and Soultez.  The assessment of soil erosion aims thus to identify the lands more \nprone to erosion which are vital for erosion management process. RUSLE model supported by GIS software is \nto predict the spatial variability of erosion occurring in the Chemorah basin and its sub-basins. Five inputs \nsuch as rainfall erosivity, soil erodibility, slope and length of slope, plant cover and anti-erosion practices, are \nused in the model to compute the erosion loss rates. The mean annual soil loss in Chemorah river basin is \nestimated at 7.52 T/ha/year, and varying between 3.78 T/ha/year in Soultez catchment and 6.06 T/ha/year \nin Reboa sub-basin. The study shows that low erosion (\u2264 7 T/ha/year) covers 52% and high to very high \nerosion (> 7 T/ha/year) which does not exceed 23% of the Chemorah basin area. The results indicate that \nReboa catchment faces the greatest risk of soil erosion compared to Soultez one, with contributions of 44 % \nand 32 % of their basin areas respectively. Use of the erosion factors\u2019 information coupled with GIS/RUSLE \nprogram can help to design the appropriate land management to minimize soil erosion in the basin.  \n\n\n\nKEYWORDS \n\n\n\nChemorah basin, soil erosion, RUSLE, GIS, mapping. \n\n\n\n1. INTRODUCTION \n\n\n\nSoil erosion by water is a serious global problem that threatens land \n\n\n\nproductivity and environmental quality (Montanarella et al., 2016; \n\n\n\nMontgomery, 2007). Meanwhile, concerns and problems related to \n\n\n\nerosion by water are reported worldwide; especially countries in the \n\n\n\nMaghreb appear to be under severe threat. This is largely attributable to \n\n\n\nthe huge pressure on the land, often in combination with a lack of suitable \n\n\n\nland management practices, raising awareness among farmers, and \n\n\n\napplication of proper policies to mitigate soil erosion (Haregeweyn et al., \n\n\n\n2017; Fenta et al., 2020). Obviously, in developing nations, soil erosion is \n\n\n\nmost threatening because of its multitude adverse \n\n\n\ntransformation/dissection of the landscape, such as decreased soil fertility \n\n\n\nand crop production, muddy floods and siltation of reservoirs (Bastida et \n\n\n\nal., 2018; Haregeweyn et al., 2017). Being a work function, soil induced by \n\n\n\nerosion action is subject to several processes, which include detachment \n\n\n\nof particles from aggregates or soil mass, entrainment of detached \n\n\n\nparticles, redistribution of soil over the landscape, and deposition of soil \n\n\n\nin depressional sites. \n\n\n\nA study indicated that 33% of the soils around the planet were affected by \n\n\n\nprocesses of degradation (Lal, 2015). In India, about 53% of the total land \n\n\n\narea is prone to erosion and were estimated about 5,334 metric tonnes of \n\n\n\nsoil being detached annually. In Mexico, 76% of the territory is affected to \n\n\n\nsome degree by water erosion, 26.37% to moderate, and 37.06% to low \n\n\n\nerosion (Bola\u00f1os et al., 2016) and the main causes are urbanization, \n\n\n\nagricultural and livestock activities (Aguirre et al., 2017). In Ethiopia, the \n\n\n\nproblems of soil degradation and low agricultural productivity are severe \n\n\n\nin the rural highlands, mainly caused by water erosion due to rugged \n\n\n\ntopography, mismanagement of land resources, and loss of vegetation \n\n\n\ncover.  Recent study estimated the rates of soil erosion as 20 Mgha-1 year-\n\n\n\n1 on currently cultivated lands and 33 Mgha-1 year-1 on formerly cultivated \n\n\n\ndegraded lands (Adugna et al., 2015; Dagnachew et al., 2020). \n\n\n\nMediterranean soils are particularly prone to erosion because of the \n\n\n\nmarked topography (45% of the region has slopes greater than 8%), the \n\n\n\nhigh rainfall concentration in autumn and winter on arable cropping lands, \n\n\n\nthe presence of poor, shallow and skeletal soils, and sparse natural \n\n\n\nvegetation. Overgrazing and deforestation can greatly accelerate soil \n\n\n\nerosion. (Garc\u00eda-Ruiz et al., 2013; Raclot et al., 2016). \n\n\n\nIn most developing countries such as Algeria, there is no consensus on the \n\n\n\nextent and severity of land degradation by soil erosion as well as its \n\n\n\nimpacts (Haregeweyn et al., 2015). In Algeria, erosion is a major problem; \n\n\n\nits intensity varies from a zone to another zone. It is recognized by \n\n\n\ncolonists and agronomists that soil erosion in this country is an \n\n\n\nenvironmental problem since the year 1930. More than 180 million tonnes \n\n\n\nof sediments are evacuated into the sea each year reducing thereby the \n\n\n\ndams\u2019 lifetime (Remini et al., 2015). The deposition level increased these \n\n\n\n\nmailto:kamel.khanchoul@univ-annaba.dz\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 70-78 \n\n\n\nCite the Article: Khanchoul K., Balla F., and Othmani O. (2020). Assessment Of Soil Erosion By Rusle Model Using Gis: A Case Study Of Chemorah Basin, Algeria. \nMalaysian Journal of Geosciences, 4(2): 70-78. \n\n\n\nyears due to the fact of the highly-catchment basins erosion, especially at \n\n\n\nthe East of the country where the erosion affected almost 40% of all lands \n\n\n\n(Khanchoul and Khanchoul, 2019). \n\n\n\nStudies erosion in northeastern Algeria were carried out on determining \n\n\n\nsome overall sediment transport rate on an annual basis. The studies by \n\n\n\nKhanchoul and Khanchoul (2019) have treated the phenomenon of \n\n\n\nerosion in a watershed during a number of years. Their work was based \n\n\n\non measured data of concentration and water discharge in order to \n\n\n\nevaluate mean annual sediment yield. The importance of the sediment \n\n\n\ndischarge in Bouhamdane catchment has been underlined in a study of \n\n\n\nerosion by Khanchoul et al. (2010) and Louamri et al. (2013) and they used \n\n\n\nmeasured water discharge and sediment concentration to quantify the \n\n\n\nsediment yield in the basin and estimate the siltation of the Debagh Dam, \n\n\n\nlocated at its outlet. A study of the hydro-sedimentary flow using \n\n\n\nmodelling in Soultez and Reboa semi-arid catchments were undertaken by \n\n\n\nBalla et al. (2017). The approach adopted for the quantification of \n\n\n\nsediment transport has consisted on researching the best regressive \n\n\n\nmodels to represent the statistical relation between the sediment \n\n\n\ndischarge and the water discharge. \n\n\n\nIn recent years, the use of GIS and remote sensing for erosion assessment \n\n\n\nhas proved to be a reliable tool (Ganasri and Ramesh, 2016), when \n\n\n\ncombined with empirical/semiempirical models (Pal and Shit, 2017). The \n\n\n\nUSLE model of Wischmeier and Smith is the most commonly used \n\n\n\nempirical model, The Revised Universal Soil Loss Equation (RUSLE) with \n\n\n\nits integration to geographic information system (GIS) became a widely \n\n\n\napplied empirical model used for the assessment of the annual loss of soil \n\n\n\ndue to water erosion (Ali and Hagos, 2016). In Algeria, there is a number \n\n\n\nof research studies pertaining to the peril of soil erosion at various spatial \n\n\n\nand temporal scales that have used the RUSLE models (Bouguerra et al., \n\n\n\n2017; Hallouz et al., 2018; Koussa and Bouziane, 2019; Khanchoul et al., \n\n\n\n2020).  \n\n\n\nThe objective of this study is to estimate the soil erosion by applying the \n\n\n\nRUSLE along with the Geographic Information Systems (GIS) in the locality \n\n\n\nof Chemorah catchment with its Soultez and Reboa sub-basins. The \n\n\n\nongoing lack of sufficiently detailed information on soil erosion risks in the \n\n\n\nChemorah catchment has posed a major challenge towards reducing soil \n\n\n\nerosion, where land degradation in forest areas, a loss of biodiversity and \n\n\n\na decrease in soil nutrients available for crops are detected. There is \n\n\n\ntherefore a need for the identification of critical areas most susceptible to \n\n\n\nwater erosion to guide effective conservation planning.    \n\n\n\n2. STUDY AREA \n\n\n\nThe study two catchments, Reboa and Soultez wadis, belong to the \n\n\n\nChemorah basin (755 km\u00b2), which are located in the Aur\u00e8s region, \n\n\n\nnortheast of Algeria (Figure 1). There areas are 327 km\u00b2 and 207 km\u00b2 \n\n\n\nrespectively. The two wadis flow into the Koudiet Medouar Dam, having \n\n\n\nwater capacity of 20 Million m3. \n\n\n\nFigure 1: Location map of the Chemorah basin \n\n\n\nThe Chemorah basin belongs to the semi-arid Mediterranean climate with \n\n\n\nwet and cold winter, hot and dry in summer. The precipitations are very \n\n\n\nirregular and are characterized by intense rainstorms. The mean annual \n\n\n\nrainfall is equal to 330 mm in Soultez sub-catchment and 458 mm in Reboa \n\n\n\nbasin for the period from 1985 to 2016. The rainy months are observed \n\n\n\nfrom September to February. The autumn (October and November) and \n\n\n\nwinter seasons are characterized by high storm events with rains \n\n\n\nexceeding 30 mm an hour. The mean monthly temperatures vary between \n\n\n\n6\u00b0C and 24\u00b0C with a mean annual value of 16\u00b0C. \n\n\n\nIn the Aur\u00e8s region of Algeria, a series of mountains are widespread over \n\n\n\nthe area where elevations may reach 2328 m as in Mahmel (2320 m) and \n\n\n\nRas er Rih (1920 m) mountains. Meanwhile, it is interesting to notice that \n\n\n\nthe Reboa catchment is more mountainous than Soultez sub-basin. The \n\n\n\nlithology of the study catchments was done using geological maps of \n\n\n\n1:50,000 in scale. The lithological analysis of the two basins has revealed \n\n\n\nthe existence of several rocks whose surface formations are distinguished \n\n\n\nfirstly by quaternary formations, dissected by gullies at different places. \n\n\n\nThese formations occupy 54% and 34% of the basins of Soultez and Reboa \n\n\n\nrespectively. \n\n\n\nThe sandstone and clay outcrops of Miocene age are mainly observed at \n\n\n\nthe center of Chemorah basin; they occupy 22% and 18% of the of Soultez \n\n\n\nand Reboa catchments. These rocks include the reliefs of Amrane, \n\n\n\nTimagoult, Koudiat Safia which are home to important landslides. The \n\n\n\nmarly limestone occupies mainly the Reboa basin from east to west, with \n\n\n\nan area of 35% (114 km\u00b2). The gully erosion is dominant at the weak marly \n\n\n\nformations. Other less resistant lithologic formations are present in the \n\n\n\ntwo catchments such as clay (Cretaceous); series of marl, conglomerate \n\n\n\nand limestone (Miocene). The resistant rocks such as limestone are less \n\n\n\nspread, found only in the Reboa catchment (with 17 km\u00b2 or 5% of its basin \n\n\n\narea). \n\n\n\nIt is obvious that the pedogenetic processes and the formation of soils can \n\n\n\nbe compromised by the rapidity of sedimentation phenomena, so it \n\n\n\nimportant to present its terrestrial skeleton regarding the two basins \n\n\n\noperated by the different erosive processes. Four soil types are \n\n\n\ndistinguished: (1) poorly developed mineral soils (Lithosol, Regosol): they \n\n\n\nare recent and distinguished by a slight degree of weathering of the \n\n\n\nminerals and a low organic matter content (Figure 2). These soils \n\n\n\nconstitute the finest materials and the organic matter quickly disappears \n\n\n\nfrom regosols when these soils settle on soft materials (clays, marls, sands, \n\n\n\netc.) and from lithosols when these soils meet on hard materials; (2) \n\n\n\ncalcimagnesic soils and brown soils: these soils are rich in Rendzine but \n\n\n\npoor in humus. They are soils recently rejuvenated by erosion. These are \n\n\n\ndeep soils and rich in clay. This richness in clay influences the \n\n\n\ndecarbonation processes; (3) calcimagnetic soils with calcareous crusts: \n\n\n\nthese are relatively thin and hard formations, extended on surface formed \n\n\n\ngenerally by the progressive accumulation of limestone due to leaching; \n\n\n\n(4) poorly developed alluvial soils: they are considered as alluvial soils \n\n\n\nwhere their deposition in the alluvial plain is made by rivers and streams. \n\n\n\nThese soils are fertile, rich in silt and well fed in water; (5) poorly \n\n\n\ndeveloped mineral soils: they are found on sloping areas where the rate of \n\n\n\nerosion prevents the in situ development of a fully formed soil profile.   \n\n\n\nFigure 2: Soil type map of the study catchments \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 70-78 \n\n\n\nCite the Article: Khanchoul K., Balla F., and Othmani O. (2020). Assessment Of Soil Erosion By Rusle Model Using Gis: A Case Study Of Chemorah Basin, Algeria. \nMalaysian Journal of Geosciences, 4(2): 70-78. \n\n\n\nSlope as a topography factor also plays an important role on soil erosion \n\n\n\n(Smith and Wischmeier, 1958). It is therefore necessary to understand the \n\n\n\nrelationship between slope gradient and soil erosion for rational planning \n\n\n\nof land use in a catchment, particularly in hilly areas (Zhang et al., 2015). \n\n\n\nThe slope distribution map of the two study catchments was created using \n\n\n\nDigital Terrain Model (DTM) and Arcgis 10.4. Soultez catchment has \n\n\n\nshown a significant distribution of slopes from 3 to 10%, which is equal to \n\n\n\n41% of its area against 27% in the Reboa basin; while the steep slopes \n\n\n\n(>15%) are less dominant in the first basin (with 19%) but more extended \n\n\n\nin the second one (with 49.50%). Slopes less than 15% occupy the same \n\n\n\nbasin areas with 15% of their surface extension. It seems from the slope \n\n\n\nmap that the Reboa catchment has the steeper slopes, which may favour \n\n\n\nsoil erosion. \n\n\n\nLand use is one of the most important factors influencing soil erosion \n\n\n\nbecause of its effects on variations on some parameters such as surface \n\n\n\nroughness, organic content of soil, soil structure and infiltration rate. All \n\n\n\nof these factors make important contributions to the spatial and temporal \n\n\n\ndynamics of hillslope hydrology and sediment production, transport and \n\n\n\ndelivery to rivers (Fiener et al., 2011). The predominant land use in both \n\n\n\ncatchments are cultures (wheat and barley), exceeding 60% of area. The \n\n\n\ndense forest is less dominant, with only 10% in Soultez and 11% in Reboa; \n\n\n\nbut the most permanent vegetation of forest and shrubs (25%) is sparse \n\n\n\nand occupying the hilly areas. In addition, it is mainly found on poorly \n\n\n\ndeveloped soils of sandstone and marly limestone on slopes greater than \n\n\n\n15%. Both catchments have been during years damaged by livestock, fires \n\n\n\nduring summer season, and overgrazing, leaving thus the soil exposed to \n\n\n\ndifferent erosive processes. \n\n\n\n3. MATERIAL AND METHODS \n\n\n\n3.1 Data Sources \n\n\n\nThe major factors available that determine soil erosion rate are rainfall, \n\n\n\nthe nature of the soil and vegetation cover. Critical data concerning these \n\n\n\nfactors in the study area were obtained as follows: the work required the \n\n\n\nprocurement of six 1:50,000 topographic maps with a 20-m contour \n\n\n\ninterval, for the extraction of the digital terrain model (DTM) of the study \n\n\n\ncatchment. Monthly rainfall data were obtained from 15 meteorological \n\n\n\nstations in Chemorah basin for 32-year period (1985-2016), provided by \n\n\n\nthe National Meteorological Office and the National Agency of Hydraulic \n\n\n\nResources. The rainfall datasets were used to develop the rainfall erosivity \n\n\n\nmap. Information on lithology was extracted from four 1:50,000 geological \n\n\n\nmaps that partially cover the study area of Chemorah catchment.  \n\n\n\nIn addition, the used soil data are relatively scarce and result largely from \n\n\n\na soil map of Algeria at a scale of 1:500,000 developed by Durand (1954) \n\n\n\nand Digital Soil Map of the World (2007) produced by FAO-UNESCO \n\n\n\n(1:5,000,000 scale), found at the following link: \n\n\n\nwww.fao.org/geonetwork/srv/en/metadata.show?id=14116&currTab=d\n\n\n\nistribution. Both maps were prepared to derive the erodibility K factor. \n\n\n\nThe land use map was realized using satellite imageries of Landsat-8 from \n\n\n\n2014 and Google Earth Professional images at high resolution. The \n\n\n\nancillary data used in the present study included the use of agricultural \n\n\n\nand forest map of Chemoura basin at 1:50,000 conducted in 1986 (BNEF \n\n\n\n1986) and substantial amount of field data collected to support image \n\n\n\nclassification and validation. \n\n\n\n3.2 RUSLE Parameters Modelling \n\n\n\nVarious types of soil erosion models ranging from simple empirical models \n\n\n\n(e.g., Revised Universal Soil Loss Equation) to complex process-based \n\n\n\nmodels have been developed to assess soil erosion by water. Process-\n\n\n\nbased models require large amounts of input data and calibration routines. \n\n\n\nFurthermore, the poor data availability related to soil controlling factors \n\n\n\nconstrain the application of these complex models at larger spatial \n\n\n\ndomains (Haregeweyn et al., 2017). RUSLE-type models reduce the \n\n\n\ncomplex process-based models to a simple one while maintaining the main \n\n\n\nfactors that influence the soil erosion process.  \n\n\n\nIn the present study, the Revised Universal Soil Loss Equation (RUSLE) \n\n\n\nmodel is applied to estimate soil erosion in Chemorah catchments \n\n\n\nconcluding its Soultez and Reboa sub-basins. This model is revised version \n\n\n\nof the USLE model (Renard et al., 1991; Wischmeier and Smith, 1978). In \n\n\n\nprevious studies, this model has shown remarkable flexibility for the \n\n\n\navailable data and efficiency of the cartographic method in semi-arid \n\n\n\nzones in the Maghreb and Mediterranean countries (Khanchoul and Selmi, \n\n\n\n2020). \n\n\n\nRevised universal soil loss equation is an empirically based model that has \n\n\n\nthe ability to predict the long-term average annual rate of soil erosion in a \n\n\n\nfield slope as result of rainfall pattern, soil type, topography, crop coverage \n\n\n\nand management practices (Atoma et al., 2020). The estimates of the five \n\n\n\nparameters of soil loss are expressed as follows:  \n\n\n\nloss rainfall erosivity factor (R), soil erodibility factor (K), slope length and \n\n\n\nsteepness factor (LS), cover management factor (C) and conservation \n\n\n\npractice factor (P) were used in the RUSLE model. The relationship is \n\n\n\nexpressed as: \n\n\n\n  A = R x K x LS x C x P    (1) \n\n\n\nwhere (R) is soil loss rainfall erosivity factor, (K) is soil erodibility factor, \n\n\n\n(LS) is slope length and steepness factor, (C) is cover management factor \n\n\n\nand (P) is conservation practice factor. In order to identify the spatial \n\n\n\npattern of potential soil erosion in the study areas, the considered erosion \n\n\n\nfactors were surveyed and computed. Individual GIS files, relevant for the \n\n\n\nRUSLE, were built for each and combined on a cell by cell-grid modeling \n\n\n\nprocedure in ArcGIS 10.4 (resolution of 30 m) in order to predict soil loss \n\n\n\nin a spatial approach (Atoma et al., 2020). All layers were projected with \n\n\n\nUTM Zone 31N using the WGS 1984 datum. The schematic representation \n\n\n\nof the methodology is shown in Figure 3. \n\n\n\nFigure 3: Flow chart methodology for soil loss assessment (Source: \n\n\n\nKhanchoul and Selmi, 2020) \n\n\n\n3.2.1 Rainfall Erosivity Factor (R) \n\n\n\nThe rainfall erosivity factor (R) describes the erosivity of rainfall at a \n\n\n\nparticular location based on the rainfall amount and intensity and reflects \n\n\n\nthe effect of rainfall intensity on soil erosion (Koirala et al., 2019). The \n\n\n\nrainfall erosivity used in the RUSLE quantifies the result of rainfall impact \n\n\n\nand reproduces the quantity and rate of runoff factor (Gelagay and Minale, \n\n\n\n2016). Its unit is expressed in MJ mm ha-1 h-1 year-1. In this study, the \n\n\n\ncreated rainfall map was used to generate (R) factor. The rainfall map \n\n\n\nrepresents mean annual precipitation over the study area, produced from \n\n\n\nthe meteorological stations around and in the study basins. The equation \n\n\n\nintegrated to generate the R-factor is given by the simplified equation (2) \n\n\n\nused by Rango et Arnoldus (Gao et al., 2012) as follows:  \n\n\n\n Log R =1.74 x log(Pi2/P)+1.29    (2) \n\n\n\nWhere R is the rainfall erosivity factor; P is the mean annual precipitation \n\n\n\n(mm); Pi is the mean monthly rainfall of the considered years (mm).  \n\n\n\nThe spatial distribution of average annual precipitation (P) in the study \n\n\n\narea was estimated using the kriging method of interpolation (Figure 4). \n\n\n\nIn the process of interpolation, 28 year-rainfall for 15 rain-gaging stations \n\n\n\nare considered. It is observed that the highest rainfalls have occurred in \n\n\n\nChelia and Foum el Toub regions and the lowest rainfalls have happened \n\n\n\nin Ali ben Tenoun and Tazoult regions. \n\n\n\n\nhttp://www.fao.org/geonetwork/srv/en/metadata.show?id=14116&currTab=distribution\n\n\nhttp://www.fao.org/geonetwork/srv/en/metadata.show?id=14116&currTab=distribution\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 70-78 \n\n\n\nCite the Article: Khanchoul K., Balla F., and Othmani O. (2020). Assessment Of Soil Erosion By Rusle Model Using Gis: A Case Study Of Chemorah Basin, Algeria. \nMalaysian Journal of Geosciences, 4(2): 70-78. \n\n\n\nFigure 4: Rainfall distribution map of the study catchments \n\n\n\n3.2.2 Soil Erodibility Factor (K) \n\n\n\nSoil erodibility factor (K) indicates the sensitivity of soil or surface \n\n\n\ncomponents to erosion, transportability of the silt, and the amount of \n\n\n\nrunoff assumed an individual rainfall contribution as calculated in a \n\n\n\nstandard condition (Narayan et al., 2018). \n\n\n\nThe main soil properties influencing the K factor are soil texture, organic \n\n\n\nmatter, soil structure and permeability of the soil profile. Each uploaded \n\n\n\nsatellite image was accompanied by a \"DSMW\" file offering all possible \n\n\n\ninformation on each layer in the form of a shapefile and Excel, giving thus \n\n\n\nthe possibility to show the taken period of these coordinates' projection \n\n\n\nsystem, image quality and other information dealing with calibration. The \n\n\n\nused equation for K determination is that of Wiliams as follows: \n\n\n\n  K = fcsand x fcl-si xf orgC x f hisand    (3) \n\n\n\nWith: \n\n\n\nfcsand: is a factor that lowers the K indicator in soils with high coarse-sand \n\n\n\ncontent and higher for soils with little sand; fcl-si: gives low soil erodibility \n\n\n\nfactor for soils with high clay-silt ratios; f orgC: reduces K values in soils with \n\n\n\nhigh organic carbon content; while f hisand lowers K values for soils with \n\n\n\nextremely high sand content. \n\n\n\nWhere:  \n\n\n\nfcsand = [0.2 + 0.3 x exp[\u22120.256 x ms x [1 \u2212 msilt /100]]]    (4) \n\n\n\nfcl-si = [ msilt /(mc+msilt)]    (5) \n\n\n\nf orgC = [(1 \u2212 0.25 x OrgC)/ OrgC+exp[3.72\u22122.95 x OrgC] ]    (6) \n\n\n\nf hisand = [1 \u2212 0.7 x [1\u2212msilt/100 ]/[1\u2212msilt 100 ]+exp[\u22125.51+22.9 x [1\u2212ms \n\n\n\n/100]]   (7) \n\n\n\nwith: ms \u2013 the sand fraction content (0.05-2.00 mm diameter) [%]; msilt \u2013 \n\n\n\nthe silt fraction content (0.002-0.05 mm diameter) [%]; mc \u2013 the clay \n\n\n\nfraction content (<0.002 mm diameter) [%]; orgC \u2013 the organic carbon \n\n\n\n(SOC) content [%]. The above formulas have allowed us to determine the \n\n\n\nK factor, presented in table 1. \n\n\n\nTable 1: Distribution of K factor values in the study catchments \n\n\n\nSoil unit symbol Zo type Yh type Bk type \n\n\n\nsand % arable layer 43.20 54.80 81.60 \n\n\n\nSilt % arable layer 24.60 20.60 6.80 \n\n\n\nClay % arable layer 32.40 24.90 11.70 \n\n\n\nOrganic carbon \narable layer \n\n\n\n0.40 0.53 0.44 \n\n\n\nfcsand 0.33 0.30 0.24 \n\n\n\nfcl-si 0.78 0.79 0.74 \n\n\n\nf orgC 0.99 0.99 0.99 \n\n\n\nf hisand 1.00 1.00 1.00 \n\n\n\nK 0.26 0.23 0.18 \n\n\n\nK usle 0.034 0.30 0.023 \n\n\n\nSoil sensitivity Very low \nerodable \n\n\n\nLow \nerodable \n\n\n\nModerately \nerodable \n\n\n\nZo: Orthic Solonchaks; Yh: Haplic Yermosols; Bk: Calcic Cambisols. \n\n\n\n3.2.3 Topographic Factor (LS) \n\n\n\nThe topographic factor or Slope Length and Steepness Factors (LS) were \n\n\n\ncreated from two sub-factors: a slope gradient factor (S) and a slope-\n\n\n\nlength factor (L); both of which are determined from the Digital Elevation \n\n\n\nModel (DEM). Slope-length and gradient is the important parameter in the \n\n\n\nsoil erosion modeling (Koirala et al., 2019), in calculating the transport \n\n\n\ncapacity of overland flow (Surface runoff). The L represents the effect of \n\n\n\nslope length on erosion and the S represents the effect of slope steepness \n\n\n\non erosion (Ganasri and Ramesh, 2016). Therefore, the soil loss per unit \n\n\n\narea increases as the slope length increases but this loss increases more \n\n\n\nrapidly with slope steepness than it does with slope length. The slopes \n\n\n\ngradient and slope length factors were calculated from the DEM and \n\n\n\ncombined to the result in the topographical factor grid, using the following \n\n\n\nequations (Gao et al. 2012): \n\n\n\nL= (\u03bb/22.13)m   (8) \n\n\n\nwhere, L = slope length factor, \u03bb = slope length (m), m = slope-length \n\n\n\nexponent \n\n\n\nm = F/ (1+F')   (9) \n\n\n\nF = sin\uf062 0.0896 /3(sin\uf062)0.8 + 0.56   (10)  \n\n\n\nwhere, F = Ratio of rill erosion to interrill erosion, \u03b2 = slope angle (\u25e6) \n\n\n\nIn ArcGIS, L is calculated as: \n\n\n\nL = (flowacc+625)(m+1) \u2212 flowacc(m+1) / 25(m+2) x 22.13m    (11) \n\n\n\nFor slope gradient factor, the calculation is as:  \n\n\n\nS =Con((Tan(slope x 0.01745) < 0.09), (10.8 x Sin(slope x 0.01745) + \n\n\n\n0.03), (16.8 x Sin(slope x 0.01745)\u2212 0.5))         (12) \n\n\n\nFinal computation with LS = L\u2217 S \n\n\n\n3.2.4 Cover Management Factor (C) \n\n\n\nThe cover management factor (C) was used to indicate the effect of \n\n\n\ncropping and management practices on erosion rates in agricultural lands. \n\n\n\nThe role of vegetation canopy and ground covers on reducing soil erosion \n\n\n\nin forested regions varies with season and crop production system \n\n\n\n(Ganasri and Ramesh, 2016). The seasonal variation of C-factor depends \n\n\n\non many factors such as rainfall, agricultural practice, type of crops etc. \n\n\n\nThe crop management factor map (Figure 5) was prepared on the basis of \n\n\n\nland use cover map of the study areas. The land use is classified with five \n\n\n\nmain classes, namely, water body, forest area, built-up land, agriculture \n\n\n\nland, shrubs and grassland based on the ground information.  \n\n\n\nFigure 5: Land use and land cover map of the study catchments \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 70-78 \n\n\n\nCite the Article: Khanchoul K., Balla F., and Othmani O. (2020). Assessment Of Soil Erosion By Rusle Model Using Gis: A Case Study Of Chemorah Basin, Algeria. \nMalaysian Journal of Geosciences, 4(2): 70-78. \n\n\n\nThese major land use features found in Chemorah basin were extracted \n\n\n\nfrom Landsat 8 image (Landsat ETM+ and ASTER image) using supervised \n\n\n\nclassification method. The supervised classification method requires \n\n\n\nground truth information for each land cover category that is collected \n\n\n\nusing global position system (GPS) and trained the algorithm to extract the \n\n\n\noverall land cover. The overall accuracy of the supervised classification \n\n\n\nmethod is about 83%. The normalized vegetation index (NDVI) is the \n\n\n\nsource of C factor values. The cover management factor is a dimensionless \n\n\n\nfor each grid cell ranging from 0 to 1 under standard fallow conditions. \n\n\n\nThe ratio between index C and NDVI is given by the following formula \n\n\n\n(Bouderbala et al., 2018): \n\n\n\n  C = exp \n\uf0fa\n\uf0fb\n\n\n\n\uf0f9\n\uf0ea\n\uf0eb\n\n\n\n\uf0e9\n\n\n\n\u2212\n\uf0b4\u2212\n\n\n\nNDVI1\n\n\n\nNDVI\n2\n\n\n\n   (13)                 \n\n\n\nThe area associated with each land use classes were calculated and C-\n\n\n\nfactor values were assigned. The land use-land cover map is reclassified \n\n\n\nbased on C-factor value for the creation of the C-factor map. \n\n\n\n3.2.5 Conservation Practice (P) Factor \n\n\n\nThe P-factor (dimensionless) represents the ratio of soil loss after \n\n\n\nimplementation of a structural conservation measure to that from \n\n\n\nstraight-row cultivation running up and down a slope (Fenta et al., 2020). \n\n\n\nP-factor values can be derived from satellite image classifications and \n\n\n\nreports of previous studies. The P-factor values are allocated over the land \n\n\n\nuse/land cover map, according to the management practice (Ali and \n\n\n\nHagos, 2016). It varies between 1 on bare ground without any anti-erosion \n\n\n\nprotection at about 0.1, when on a low slope, ridging is practiced. The high \n\n\n\npercentage of low slopes in the Soultez catchment and high sleepness of \n\n\n\nthe Reboa basin proves the scarcity of anti-erosion practices raised during \n\n\n\nfield visits. However, due to the fact that there are no anti-erosion \n\n\n\npractices adopted throughout the study area, this factor was considered as \n\n\n\na unit value equal to 1. \n\n\n\n4. RESULTS \n\n\n\nThis study has used a modelling approach called the RUSLE based method \n\n\n\nto develop a detailed spatial assessment of the distribution of erosion risk \n\n\n\nacross the Soultez and Reboa catchments using remotely-sensed data and \n\n\n\nGIS software. \n\n\n\n4.1 USLE factor mapping \n\n\n\n4.1.1 Rainfall erosivity factor (R) \n\n\n\nThe rainfall erosivity map based on annual data over a 28-year period \n\n\n\nclearly shows a decline from north to southeast (Figure 6). The values of R \n\n\n\nrange from 25 to 82 MJ.mm.h-1.y-1. The mean annual value of R is equal to \n\n\n\n48.4 MJ.mm.h-1.y-1. Figure 6 shows that the high degree of aggressiveness \n\n\n\nis observed mainly in the Reboa basin where R varies between 25 \u00e0 82 \n\n\n\nMJ.mm.h-1.y-1, while the Soultez basin has R-factor that varies between 25 \n\n\n\nand 66 MJ.mm.h-1.y-1. Timgad and Reboa, which belong to Reboa basin, are \n\n\n\nshowing the least affected areas by rainstorms. \n\n\n\nFigure 6: Spatial Rain erosivity (R) map of the study catchments \n\n\n\nThe lowest R values presented by the class 25 to 35 MJ.mm.h-1.y-1 occupy \n\n\n\nalmost 13% of the Chemorah basin and are mainly found on 70% of the \n\n\n\nflood plain towards the catchment outlet; while the highest values, more \n\n\n\nthan 50 MJ.mm.h-1.y-1   (45%) are observed at areas of the highest \n\n\n\nmountains of the basin, at Reboa catchment (Table 2). These results allow \n\n\n\nus to conclude that Chemorah basin and particularly Reboa one are \n\n\n\ngenerally subject to a significant erosive power. \n\n\n\nTable 2: R-factor distribution in the Chemorah basin and its sub-\nbasins \n\n\n\nR-factor > 35 35-45 45-50 50-66 > 66 \n\n\n\nBasin area \n\n\n\n(km\u00b2) \n\n\n\n96.50 64.00 257.5. 236.50 100.50 \n\n\n\nBasin area \n\n\n\n(%) \n\n\n\n12.80 8.50 34.10 31.30 13.30 \n\n\n\nIn general, the variation in rainfall intensity is probably due to variation in \n\n\n\nelevation and exposure, where the maximum elevations are 2320 m in the \n\n\n\nsouthern part (Reboa catchment), 1849 to 1920 m in the western part \n\n\n\n(Soultez catchment). \n\n\n\n4.1.2 Soil erodibility K-factor \n\n\n\nAfter assigning K factors for the different soil types in the area, the \n\n\n\nresulting map was converted to a grid map of 30 m cell size taking K \n\n\n\nfactors as values for the cells (Figure 7). The calculated K-factor in the \n\n\n\nChemorah catchment varies from less than 0.0235 to 0.034 \n\n\n\nt.ha.h/ha.MJ.mm  and has a mean value equal to 0.029 t.ha.h/ha.MJ.mm, \n\n\n\nwhich is relatively high (Figure 7).  \n\n\n\nThe low values are mainly located in the extreme southern (represented \n\n\n\nby Reboa catchment) and northern parts of the Chemorah basin where \n\n\n\nsoils are more marly and calcareous types with an increase in the amount \n\n\n\nof organic matter, providing high penetration amounts and abridging \n\n\n\nrunoff (Khanchoul et al., 2020). Also, this class of low erodibility, covering \n\n\n\n29% of the basin area, is more protected by vegetation where shrubs and \n\n\n\nforest are the dominated vegetation cover.  \n\n\n\nThe high values are located in clayey soils, covering 45% of the basin area \n\n\n\nand are touching both study catchments with almost the same percentage. \n\n\n\nThe modest to fairly high K values occupy most of the Soultez catchment \n\n\n\nand an important part of Reboa basin. These areas are mainly dominated \n\n\n\nby weak quaternary formations. The highest to moderate erodibility \n\n\n\nvalues indicate that the soils are highly vulnerable to erosion because they \n\n\n\nhave low stability and low infiltration rate, which may lead to high runoff \n\n\n\nand soil loss. \n\n\n\nFigure 7: Soil erodibility K-factor map in the study catchments \n\n\n\n4.1.3 Topographic factor (LS) \n\n\n\nThe topographic factor,  which represents the influence of slope length and \n\n\n\nslope steepness on erosion process, was calculated by considering the flow \n\n\n\naccumulation and slope in percentage as an input. From the analysis, it is \n\n\n\nobserved that LS-factor values vary between 0.03 and 328.55, with a mean \n\n\n\nof 5.18. The class 0 - 5 occupies the most dominant Chemorah basin area \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 70-78 \n\n\n\nCite the Article: Khanchoul K., Balla F., and Othmani O. (2020). Assessment Of Soil Erosion By Rusle Model Using Gis: A Case Study Of Chemorah Basin, Algeria. \nMalaysian Journal of Geosciences, 4(2): 70-78. \n\n\n\nwith 46%. The high LS values are present in only 5% of the basin area and \n\n\n\nhighly represented by Reboa catchment (Figure 8).  \n\n\n\nThese results are proportional to the soultez catchment topography, \n\n\n\ndominated by relatively flat surfaces where the low slopes are observed in \n\n\n\n66% (\uf0a3 10\u00b0 of slope) against only 35% for Reboa catchment. The high LS \n\n\n\nvalues are attributed to the upstream Chemorah basin, particularly at \n\n\n\nReboa upstreams, where the relief is high. These areas should be the most \n\n\n\nsensitive to sheetwash, rills and bank erosion processes. \n\n\n\nFigure 8: Map of the LS-factor in the study catchments \n\n\n\n4.1.4 Crop management factor (C) \n\n\n\nInformation on land use permits a better understanding of the land \n\n\n\nutilization aspects of cropping pattern, fallow land, forest, waste landand \n\n\n\nsurface water bodies,which are vital for developmental planning/erosion \n\n\n\nstudies (Ganasri and Ramesh, 2016). In the present study, remote sensing \n\n\n\nand GIS technique have generated a thematic layer of land use and land \n\n\n\ncover of the Chemorah basin. The values of C-factor were introduced as a \n\n\n\nraster image corresponding to each land use that is determined from the \n\n\n\ntables of Wischmeier and Smith (1978) and Cormary and Masson (1963). \n\n\n\nFigure 9: Crop management factor (C) in the study catchments \n\n\n\nThe C-factor values range between 0.05 and 0.99, with a mean equal to \n\n\n\n0.61 (Figure 9). The map shows that the lowest values are located along \n\n\n\nmost of the divide borders of the Reboa and Soultez catchments, which \n\n\n\ncontain mainly forest and shrubs. The Chemorah basin has values that \n\n\n\nvary between 0.26 and 0.99, covering almost 49% of its area, and contains \n\n\n\nagricultural lands, shrubs and steppes. The moderate C values (0.08-0.26) \n\n\n\noccupy 30% of the basin and are somehow more distributed in the Reboa \n\n\n\ncatchment. The spatial distribution of the C-factor confirms that the study \n\n\n\narea has been for a long time undergoing human activities land use and \n\n\n\nclimatic changes which have led to the degradation of the forests and the \n\n\n\ntransformation of these areas into cropland, induced grassland and \n\n\n\nsteppes (Khanchoul et al., 2020). \n\n\n\n5. DISCUSSION \n\n\n\nThe evaluation of land losses by water erosion, obtained by the \n\n\n\nmultiplication of layers with a resolution presented by the RUSLE \n\n\n\nparameters and thematic maps, was carried out by the empirical formula \n\n\n\nof Wischmeier and Smith (RUSLE) with their databases using Arcgis 10.4 \n\n\n\nto produce soil loss rates. Each pixel of the resulting map had a unique \n\n\n\nvalue which corresponded to its possible erosive potential. Figure 10 \n\n\n\nindicates each pixel of soil loss gives a very reliable description of the \n\n\n\npotential erosion in the study basin. Thus, the estimates of the soil losses \n\n\n\nusing RUSLE are theoretical and relatively reflect the reality of the \n\n\n\nChemorah basin. \n\n\n\nFigure 10 shows that the areas with a high soil erosion are found mainly \n\n\n\nin the south part of Chemorah basin, essentially at Reboa catchment. In \n\n\n\norder to be able to analyze and estimate the soil erosion rates, soil erosion \n\n\n\nvalues are grouped into four classes. The value of 7.00 T ha-1 yr-1 is used as \n\n\n\nthe average tolerance limit for soil erosion (Sadiki et al., 2007).  \n\n\n\nFigure 10: Soil loss rates in the Chemorah basin \n\n\n\nIt is known that soil losses are spatially related to vegetation, slope, soil \n\n\n\ntypes and rainfall maps, in a way where erosion bu water is dependent \n\n\n\nmostly on topography, permanent land cover such as forest and shrubs, \n\n\n\nrunoff of the study region. Yet underlying geology may influence the \n\n\n\nefficacy of soil production and sediment transport on hillslopes. Usually, \n\n\n\nsoil losses may be higher on moderate slopes. Mountain soils, often \n\n\n\nsuperficial, are mixed with various rock fragments, which will increase \n\n\n\ntheir resistance to the beat of raindrops and to the shear of runoff (Roose \n\n\n\net al., 1993). In the dense forest region, the soil is permanently covered (C \n\n\n\nclose to zero) and the rates of water erosion become practically zero. \n\n\n\nThe soil losses at the Chemorah basin vary from 0.0014 to 191.71 \n\n\n\nT/ha/year with a mean soil loss of 7.52 T/ha/year. The map in figure 10 \n\n\n\nshows that very low erosion potential belongs to soil loss of less or equal \n\n\n\nto 7 T/ha/year and which represents more than half (77%) of the total \n\n\n\nbasin area. This is explained by the low values of LS factor and by the \n\n\n\nprotective effect of the vegetation in the sloping areas of Soultez and \n\n\n\ndownstream Chemorah basin rivers. Soil losses greater than 7 T/ha/year \n\n\n\noccupy only 23% of the total area and are located mainly in areas of steep \n\n\n\nslopes where the soil has fragile skin composed of marly and clayey \n\n\n\nformations. The erosion potential of this area can be distinguished by an \n\n\n\nintensity of high to very high. \n\n\n\nTable 3 shows the results of the used factors related to RUSLE model and \n\n\n\nthe computed soil loss in the Reboa and Soultez catchments. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 70-78 \n\n\n\nCite the Article: Khanchoul K., Balla F., and Othmani O. (2020). Assessment Of Soil Erosion By Rusle Model Using Gis: A Case Study Of Chemorah Basin, Algeria. \nMalaysian Journal of Geosciences, 4(2): 70-78. \n\n\n\nTable 3: Results of the RUSLE parameters and the mean soil loss in the \n\n\n\nstudy catchments \n\n\n\nCatchment Values R-\n\n\n\nfactor \n\n\n\nK-\n\n\n\nfactor \n\n\n\nLS-\n\n\n\nfactor \n\n\n\nC-\n\n\n\nfactor \n\n\n\nP-\n\n\n\nfactor \n\n\n\nMean soil loss \n\n\n\n(T/ha/year) \n\n\n\nMin 25.00 0.0235 0.03 0.05 1 0.0014 \n\n\n\nSoultez Max 81.62 0.0336 185.44 0.99 1 94.05 \n\n\n\nMean 58.75 0.029 6.29 0.61 1 3.78 \n\n\n\nMin 27.75 0.0235 0.03 0.05 1 0.002 \n\n\n\nReboa Max 56.23 0.0336 83.75 0.99 1 191.71 \n\n\n\nMean 46.63 0.029 4.46 0.62 1 6.06 \n\n\n\nThe minimum soil loss (< 3 T/ha/year) is observed at Soultez catchment \n\n\n\n(Figure 11), occupying 57% (118 km\u00b2) of its area. It can partly be \n\n\n\nexplained by the slight slopes of this sub-basin comprising a larger alluvial \n\n\n\nplain (slopes < 3%). On the contrary, the maximum soil loss (> 12 \n\n\n\nT/ha/year) is seen at the Reboa sub-basin with 14% (45.92 km\u00b2) (Figure \n\n\n\n11). It is high because of the steep slopes and the marly-clayey soils of the \n\n\n\nhills delimiting the sub-basin. However, the downstream alluvial plain \n\n\n\npresents a much lower erosion (< 7 T/ha/year). Moreover, the comparison \n\n\n\nof the soil loss greater than 7 T/ha/year shows that there is a significant \n\n\n\ndifference in area of occupation between Reboa and Soultez sub-basins, \n\n\n\nthey are 44% and 32% respectively. This implies that Reboa catchment is \n\n\n\nhighly erodible; the mean soil loss in the Reboa sub-basin reflects this \n\n\n\ndifference where the mean is equal to 6.06 T/ha/year, much higher than \n\n\n\nSoultez sub-basin (3.78 T/ha/year) (Table 3). However, this difference is \n\n\n\nless observed for soil loss less than 7 T/ha/year, where Reboa and Soultez \n\n\n\nsub-basins occupy 66% and 68% of their areas respectively. \n\n\n\nFigure 11: Soil loss rates in the Reboa and Soultez catchments \n\n\n\nThe total soil loss in lower Soultez region is found to occur near the 1st \n\n\n\nand 2nd order streams where the presence of weathered material is more \n\n\n\n(drainage density  = 2.84 km-1). It is also seen that the soil loss is more in \n\n\n\nthe upper Reboa area where the drainage density and the thickness of the \n\n\n\nsoil are more (drainage density = 2.73 km-1). It is also observed that most \n\n\n\npart of the study area (Reboa and Soultez sub-basins) comes under \n\n\n\nmoderate to high erosion category, which could be found in almost all \n\n\n\nareas, very high erosion occurs only in a few regions where the steep slope \n\n\n\nwith barren land exists. High erosion occurs in the foothills where \n\n\n\nagricultural area and sparse shrubs and forest with moderate to high \n\n\n\nslopes exist. \n\n\n\nThe accuracy of the model may be further increased if statistical data (on \n\n\n\ncrop composition, tillage practices, cover crops and plant residues) are \n\n\n\navailable (Panagos et al., 2015). Taking into account its uncertainties, the \n\n\n\nRUSLE model can be used by policy makers at the Algerian and local level \n\n\n\nto run scenarios on crop rotation, land use and conservation practices. \n\n\n\n6. CONCLUSION \n\n\n\nLand degradation by soil erosion is an important problem in the semiarid-\n\n\n\nprone Chemorah region. Lack of good quality data and adoptable methods, \n\n\n\ncombined with heterogeneity of environmental factors has pushed us to \n\n\n\nadopt RUSLE model with remote sensing and GIS to assess and map the \n\n\n\nspatial distribution of soil loss by erosion in the Chemorah basin with a \n\n\n\ncase study in two catchments, namely Reboa and Soultez. The RUSLE \n\n\n\nmodel is found to be the most suitable modelling approach for estimating \n\n\n\nsoil loss in the two catchments. The present study has revealed the role \n\n\n\nand change of land cover on accelerating the process of soil erosion. Such \n\n\n\nbasic information is given to understand the land cover/land use \n\n\n\nrelationship and to enhance the value of a land cover mapping for the \n\n\n\nplanning and catchment management.  \n\n\n\nThe mean soil loss using RUSLE is found to be equal to 7.52 T/ha/year; \n\n\n\nwhich leads to point that the Chemorah basin presents a moderate soil \n\n\n\nerosion loss. In fact, the erosion potential is tolerable in both study \n\n\n\ncatchments, Reboa and Soultez, with means of 3.78 and 6.06 T/ha/year. In \n\n\n\nthe Reboa sub-basin, the soil erosion is intense and localized in its upper \n\n\n\nzone at hills and piedmonts. On the other hand, this erosion is high and \n\n\n\ngeneralized over the northern sub-basin of the study area (downstream \n\n\n\nChemorah sub-basin). With regard to the risk of erosion depending on \n\n\n\nland use during autumn and winter seasons, the majority of soil losse rates \n\n\n\nare found in sparsely vegetated areas and barren soils. In fact, the \n\n\n\nagricultural lands are also contributing in the soil erosion and thus cannot \n\n\n\nbe neglected, concentrated in the central part of the Chemorah basin. \n\n\n\nThe RUSLE model can provide valuable assistance to be followed in \n\n\n\ndecision making regarding the prioritization of the vulnerable zones that \n\n\n\nrequire protection and erosion control. Vegetal cover has to be highly \n\n\n\ntaken in consideration because it is a parameter on which land use \n\n\n\nplanning actions could be based to limit sensitivity to land erosion. \n\n\n\nSustainable land management practices are urgently needed to reduce the \n\n\n\nrates of soil erosion located in the Chemorah basin to improve land \n\n\n\nproductivity, farm program practices and to reduce Koudiat el Medouar \n\n\n\nreservoir siltation, located downstream from Reboa and Soultez wadis. \n\n\n\nThis approach might not have produced predicted maps with highest \n\n\n\naccuracy, but it has provided a quick, realistic and simple method for \n\n\n\ncombining physical variables for mapping and monitoring the erosion \n\n\n\npotential of soil. . It is also strongly recommended that more developed \n\n\n\ntechnical approaches and solutions should be applied to solve similar \n\n\n\nsedimentation problems in other existing areas. \n\n\n\nREFERENCES  \n\n\n\nAdugna, A., Abegaz, A., Cerd\u00e0, A., 2015. Soil erosion assessment and control \n\n\n\nin Northeast Wollega, Ethiopia. Solid Earth Discussions, 7, 3511-3540. \n\n\n\nAguirre Salado, C.A., Miranda Arag\u00f3n, L., Pompa Garc\u00eda, M., Reyes \n\n\n\nHern\u00e1ndez, H., Soubervielle-Montalvo, C., Flores Cano, J.A., M\u00e9ndez \n\n\n\nCort\u00e9s, H., 2017. Improving identification of areas for ecological \n\n\n\nrestoration for conservation by integrating USLE and MCDA in a GIS-\n\n\n\nenvironment: A pilot study in a priority region Northern Mexico. ISPRS \n\n\n\nInternational Journal of Geo-Information, 6, 262. \n\n\n\nAli, S.A., Hagos, H., 2016. Estimation of soil erosion using USLE and GIS in \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 70-78 \n\n\n\nCite the Article: Khanchoul K., Balla F., and Othmani O. (2020). Assessment Of Soil Erosion By Rusle Model Using Gis: A Case Study Of Chemorah Basin, Algeria. \nMalaysian Journal of Geosciences, 4(2): 70-78. \n\n\n\nAwassa Catchment, Rift Valley, Central Ethiopia. Geoderma Regional, 7, \n\n\n\n159-166. \n\n\n\nAtoma, H., Suryabhagavan, K.V., Balakrishnan, M., 2020. Soil erosion \n\n\n\nassessment using RUSLE model and GIS in Huluka watershed, Central \n\n\n\nEthiopia. Sustainable Water Resources Management, 6, 12. \n\n\n\nBalla, F., Kabouche, N., Khanchoul, K., Bouguerra, H., 2017. Hydro-\n\n\n\nsedimentary flow modelling in some catchments Constantine highlands, \n\n\n\ncase of Wadis Soultez and Reboa (Algeria). Journal of Water and Land \n\n\n\nDevelopment, 34, (1), 21-32. \n\n\n\nBastida, F., Hern\u00e1ndez, T., Garc\u00eda, C., 2018. Soil Erosion and C Losses: \n\n\n\nStrategies for Building Soil Carbon. In: Garcia, C., Nannipieri, P., \n\n\n\nHernandez, T. (Eds.). The future of soil carbon. Academic Press, 215-238. \n\n\n\nBNEF, 1986. Le plan de mobilisation agricole et foresti\u00e8re du bassin \n\n\n\nversant de l'Oued Chemoura. Bureau National d'Etudes foresti\u00e8res. Une \n\n\n\ncarte au 1/50.000. \n\n\n\nBola\u00f1os Gonz\u00e1lez, M.A., Paz Pellat, F., Gaistardo, C., Carlos, O., Argumedo \n\n\n\nEspinoza, J.A., Romero Ben\u00edtez, V.M., de la Cruz Cabrera, J.C., 2016. \n\n\n\nErosion map of Mexico soils and its posible implications for soil carb\u00f3n \n\n\n\npool. Terra Latinoamericana, 34, 271-288. \n\n\n\nBouderbala, D., Souidi, Z., Donze, F., Chikhaoui, M., Nehal, L., 2018. Mapping \n\n\n\nand monitoring soil erosion in a watershed in western Algeria. Arabian \n\n\n\nJournal of Geosciences, 11, 744. \n\n\n\nBouguerra, H., Bouanani, A., Khanchoul, K., Derdous, O., Tachi, S.E., 2017. \n\n\n\nMapping erosion prone areas in the Bouhamdane watershed (Algeria) \n\n\n\nusing the Revised Universal Soil Loss Equation through GIS. Journal of \n\n\n\nWater and Land Development, 32, 13-23. \n\n\n\nCormary, Y., Masson, J., 1963. Application \u00e0 un projet type de la formule de \n\n\n\nperte de sols de Wischmeier. Etude de conservation des eaux et du sol \n\n\n\nau Centre de recherches du g\u00e9nie rural de Tunisie, 5-26. \n\n\n\nDagnachew, M., Moges, A., Kebede, A., Abebe, A., 2020. Effects of Soil and \n\n\n\nWater Conservation Measures on Soil Quality Indicators: The Case of \n\n\n\nGeshy Subcatchment, Gojeb River Catchment, Ethiopia. Applied and \n\n\n\nEnvironmental Soil Science, 2020, 1-16.  \n\n\n\nDurand, J.H., 1954. Carte des sols de l'Alg\u00e9rie au 1/500 000, 1\u00e8re \u00e9d. \n\n\n\nInspection G\u00e9n\u00e9rale de l'Agriculture, Alger. Une carte en 8 coupures, en \n\n\n\ncouleurs et Notice, 11 p. \n\n\n\nEaton D., 1996. The Economics of Soil Erosion: A Model of Farm Decision-\n\n\n\nMaking. Environmental Economics Programme, Discussion Paper, \n\n\n\nInternational Institute for Environment and Development, 1, 48. \n\n\n\nFenta, A.A., Tsunekawa, A., Haregeweyn, N., Poesen, J., Tsubo, M., Borrelli, \n\n\n\nP., Panagos, P., Vanmaercke, M., Broeckx, J., Yasuda, H., Kawai, T., \n\n\n\nKurosaki, Y., 2020. Land susceptibility to water and wind erosion risks \n\n\n\nin the East Africa region. Science of the Total Environment, 703, 135016.  \n\n\n\nFiener, P., Auerswald, K., van Oost, K., 2011. Spatio-temporal patterns in \n\n\n\nland use and management affecting streamflow response of agricultural \n\n\n\ncatchments-a review. Earth Science Reviews, 106, 92-104. \n\n\n\nGao, G.Y., Fu, B.J., Liu, Y., Wang, S., Zhou, J., 2012. Coupling the modified SCS-\n\n\n\nCN and RUSLE models to simulate hydrological effects of restoring \n\n\n\nvegetation in the Loess Plateau of China. Hydrology and Earth System \n\n\n\nScience, 16, 2347-2364. \n\n\n\nGanasri, B.P., Ramesh, H., 2016. Assessment of soil erosion by RUSLE \n\n\n\nmodel using remote sensing and GIS - A case study of Nethravathi basin. \n\n\n\nGeoscience Frontiers, 7, 953-961. \n\n\n\nGarc\u00eda-Ruiz, J.M., Nadal-Romero, E., Lana-Renault, N., Beguer\u00eda, S., 2013. \n\n\n\nErosion in Mediterranean landscapes: changes and future challenges. \n\n\n\nGeomorphology, 198, 20-36. \n\n\n\nGelagay, H.S., Minale, A.S., 2016. Soil loss estimation using GIS and remote \n\n\n\nsensing techniques: A case of Koga watershed, northwestern Ethiopia. \n\n\n\nInternational Soil and Water Conservation Research, 4(2), 126-136. \n\n\n\nHallouz, F., Meddi, M., Mah\u00e9, G., Alirahmani, S., Keddar, A., 2018. Modeling \n\n\n\nof discharge and sediment transport through the SWAT model in the \n\n\n\nbasin of Harraza (Northwest of Algeria). Water Science, 32, 79-88. \n\n\n\nHaregeweyn, N., Tsunekawa, A., Nyssen, J., Poesen, J., Tsubo, M., Tsegaye \n\n\n\nMeshesha, D., Sch\u00fctt, B., Adgo, E., Tegegne, F., 2015. Soil erosion and \n\n\n\nconservation in Ethiopia: a review. Progress in Physical Geography, 39 \n\n\n\n(6), 750-774. \n\n\n\nHaregeweyn, N., Tsunekawa, A., Poesen, J., Tsubo, M., Meshesha, D.T., \n\n\n\nFenta, A.A., Nyssen, J., Adgo, E., 2017. Comprehensive assessment of soil \n\n\n\nerosion risk for better land use planning in river basins: Case study of \n\n\n\nthe Upper Blue Nile River. Science of the Total Environment, 574, 95-\n\n\n\n108. \n\n\n\nKhanchoul, K., Benslama, M., Remini, B., 2010. Regressions on monthly \n\n\n\nstream discharge to predict sediment inflow to a reservoir in Algeria. \n\n\n\nJournal of Geography and Geology, 2(1), 36-47. \n\n\n\nKhanchoul, K., Khanchoul, S., 2019. Estimation of suspended sediment load \n\n\n\nin the Ressoul watershed, Algeria. International Journal of Hydrology \n\n\n\nResearch, 4(1), 1-12. \n\n\n\nKhanchoul, K., Selmi, K., Benmarce, K., 2020. Assessment of soil erosion by \n\n\n\nRUSLE model in the Mellegue watershed, northeast of Algeria. \n\n\n\nEnvironment and Ecosystem Science, 4(1), 15-22. \n\n\n\nKoirala, P., Thakuri, S., Joshi, S., Chauhan, R., 2019. Estimation of Soil \n\n\n\nErosion in Nepal Using a RUSLE Modeling and Geospatial Tool. \n\n\n\nGeosciences, 9, 1-19. \n\n\n\nKoussa, M., Bouziane, M.T., 2019. Estimation des param\u00e8tres de l'\u00e9rosion \n\n\n\nhydrique par Approche SIG/USLE : cas du bassin versant de l'Oued Arab \n\n\n\n(r\u00e9gion de Khenchela, Nord-Est de l\u2019Alg\u00e9rie). Agriculture and Forestry \n\n\n\nJournal, 3(1), 36-45. \n\n\n\nLal, R., 2015. Restoring soil quality to mitigate soil degradation. \n\n\n\nSustainability, 7, 5875-5895. \n\n\n\nL\u00f3pez-Garc\u00eda, E.M., Torres-Trejo, E., L\u00f3pez-Reyes, L., Flores-Dom\u00ednguez, \n\n\n\n\u00c1.D., Pe\u00f1a-Moreno, R.D., L\u00f3pez-Olgu\u00edn, J.F., 2020. Estimation of soil \n\n\n\nerosion using USLE and GIS in the locality of Tzicatlacoyan, Puebla, \n\n\n\nM\u00e9xico. Soil and Water Research, 15, 9-17. \n\n\n\nLouamri, A., Mebarki, A., Laignel, B., 2013. Variabilit\u00e9 interannuelle et \n\n\n\nintra-annuelle des transports solides de l\u2019Oued Bouhamdane, \u00e0 l\u2019amont \n\n\n\ndu barrage Hammam Debagh (Alg\u00e9rie orientale). Hydrological Sciences \n\n\n\nJournal, 58(7), 1559-1572. \n\n\n\nMontanarella, L., Jon Pennock, D., McKenzie, N., Badraoui, M., Chude, V., \n\n\n\nBaptista, I., Mamo, T., Yemefack, M., Aulakh, M., Yagi, K., Hong, S., \n\n\n\nVijarnsorn, P., Zhang, G., Arrouays, D., Black, H., Krasilnikov, P., Sobock\u00e1, \n\n\n\nJ., Alegre, J., Henriquez, C., Mendon\u00e7a-Santos, M., Taboada, M., Espinosa-\n\n\n\nVictoria, D., Al Shankiti, A., Alavi Panah, S., Elsheikh, E., Hempel, J., Camps \n\n\n\nArbestain, M., Nachtergaele, F., Vargas, R., 2016. World\u2019s soils are under \n\n\n\nthreat. Soil, 2, 79-82.  \n\n\n\nMontgomery, D.R., 2007. Soil erosion and agricultural sustainability. \n\n\n\nProceedings of the National Academy of Sciences, 104, 13268-13272. \n\n\n\nNarayan K., Khanindra P., Abhisek C., Satiprasad S. 2018. Evaluation of soil \n\n\n\nloss estimation using the RUSLE model and SCS-CN method in hillslope \n\n\n\nmining areas. International Soil and Water Conservation Research 6, 31-\n\n\n\n42. \n\n\n\nPanagos, P., Borrelli, P., Poesen, J., Ballabio, C., Lugato, E., Meusburger, K., \n\n\n\nMontanarella, L., Alewell, C., 2015. The new assessment of soil loss by \n\n\n\nwater erosion in Europe. Environmental Science and Policy, 54, 438-\n\n\n\n447. \n\n\n\nRaclot, D., Le Bissonnais, Y., Annabi, M., Sabir, M., 2016.  Challenges for \n\n\n\nmitigating Mediterranean soil erosion under global change. The \n\n\n\nMediterranean region under climate change: a scientific update, 311-\n\n\n\n318. \n\n\n\nRemini, B., Bensafia, D., Nasroun, T., 2015. Impact of sediment transport of \n\n\n\nthe Chellif River on silting of the Boughezoul reservoir (Algeria). Journal \n\n\n\nof Water and Land Development, 24, 35-40. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 70-78 \n\n\n\nCite the Article: Khanchoul K., Balla F., and Othmani O. (2020). Assessment Of Soil Erosion By Rusle Model Using Gis: A Case Study Of Chemorah Basin, Algeria. \nMalaysian Journal of Geosciences, 4(2): 70-78. \n\n\n\nRenard, K.G., Foster, G.R., Weesies, G.A., Porter, J.P., 1991. RUSLE: revised \n\n\n\nuniversal soil loss equation. Journal of Soil and Water Conservation, 46 \n\n\n\n(1), 30-33. \n\n\n\nRoose, E., Arabi, M., Brahamia, K., Chebbani, R., Mazour, M., Morsli, B., 1993. \n\n\n\n\u00c9rosion en nappe et ruissellement en montagne m\u00e9diterran\u00e9enne \n\n\n\nalg\u00e9rienne. Cahiers Orstom, s\u00e9rie p\u00e9dologie, 28(2), 289-308. \n\n\n\nSadiki, A., Faleh, A., Navas, A., Bouhlassa, S., 2007. Assessing soil erosion \n\n\n\nand control factors by the radiometric technique in the Boussouab \n\n\n\ncatchment, Eastern Rif, Morocco. Catena, 71(1), 13-20. \n\n\n\nSmith, D.D., Wischmeier, W.H., 1958. Factors affecting sheet and rill \n\n\n\nerosion. Transactions of the American Geophysical Union, 38, 889-896. \n\n\n\nWischmeier, W.H., Smith, D.D., 1978. Predicting rainfall erosion. A guide to \n\n\n\nconservation planning. USDA-ARS. Agriculture Handbook, 537, 58. \n\n\n\n Zhang, Z., Sheng, L., Yang, J., Chen, X.A., Kong, L., Wagan, B., 2015. Effects of \nLand Use and Slope Gradient on Soil Erosion in a Red Soil Hilly \n\n\n\nWatershed of Southern China. Sustainability, 7, 14309-14325. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 54-58 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2020.54.58 \n\n\n\nCite the Article: Atat, J. G., Akankpo, A. O., Umoren, E. B., Horsfall, O. I., Ekpo, S. S (2020). The Effect Of Density-Velocity Relation Parameters On Density Curves In Tau (\u03c4) Field, \nNiger Delta Basin. Malaysian Journal of Geosciences, 4(2):  54-58. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2020.54.58\n\n\n\nTHE EFFECT OF DENSITY-VELOCITY RELATION PARAMETERS ON DENSITY \n\n\n\nCURVES IN TAU (\u03c4) FIELD, NIGER DELTA BASIN  \n\n\n\nAtat, J. G.a, Akankpo, A. O.a, Umoren, E. B.a, Horsfall, O. I.b, Ekpo, S. Sa \n\n\n\na Department of Physics, University of Uyo, Uyo, Nigeria \nb Department of Physics, Rivers State University, Port Harcourt, Nigeria \n*Corresponding Author Email: josephatat@uniuyo.edu.ng\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 11 February 2020 \nAccepted 13 March 2020 \nAvailable online 03 April 2020\n\n\n\nWe considered the constants obtained for tau (\ud835\udf0f)Field in the Niger Delta basin from well-log data of three \nwells (A,B,C) to investigate the effect of inclusion of these constants on density-velocity relation using \nHampson Russell Software to generate density curve in tau field. The curves were compared to those \ngenerated from Gardner and Lindseth constants and in-situ density curves. Many researchers have worked on \nconstants for density-velocity equations for different Fields; their results always differ from Gardner and \nLindseth constants including the constants of Atat et al., 2020 which are considered in this investigation as \nTau Field local fit constants. Our findings support the results of these researchers. Results indicate over \nestimation of density curves when using Gardner and Lindseth constants. The challenge is that in-situ density \ncurves are not accurate due to sand-shale overlap of density values. The most improved and accurate result is \ngiven by the density curves obtained using the constants for specific sand and shale lithologies (local fits). This \nverifies the need for the determination of constants for local fit of the oil field under investigation. The pink \ncurves truly indicate the density estimation for the tau field which is very reliable in the characterisation of \nreservoir.  \n\n\n\nKEYWORDS \n\n\n\nDensity, Velocity, Curves, Effect, Field, Parameters and Well-log data.\n\n\n\n1. INTRODUCTION \n\n\n\nA density-velocity constant depend on geology when result from a good fit \nusing sonic and density well-log information and is very common in the \npetroleum study (Gardner et al., 1974). Gardner\u2019s (Nwozor, et al., 2017) \nand Lindseth\u2019s constants as confirmed by Quijada and Stewart (2007) and \nother researches are frequently a source of inaccuracy in some empirical \nstudies that required working with lithology-specific functions. Gardner\u2019s \nconstants were provided for density-velocity relation to solve the problem \nof sand-shale overlap. Density is required in the identification of \nlithologies, pore fluids, porosity and overburden stress estimations and \npore pressure prediction; it is one of the major desires in exploration, \nreservoir characterization and well planning. These constants are non-\nunique for most datasets from many sedimentary basins across the world \nas density-velocity data would not obey the original Gardner (Krasovsky, \n1981) and Lindseth curves. Overlap in sands and shales could show \nvariation in petrophysical properties (porosity of fluid saturation) making \ndensity not a good indicator of lithology (Atat et al, 2020b). Modelling is \nnecessary to evaluate the effect of these properties on density. Bulk \ndensity is an important acoustic indicator of the presence of shale; \naccuracy in density estimation enables the accurate location of shales in \nthe reservoir. \n\n\n\nLocal examinations are compulsory to improve the precision of rock \nproperties that were not captured by Gardner or Lindseth during their \ncalibrations. The difference between the local variations and the original \n\n\n\nGardner or Lindseth relationship may be attributed to the differences in \ncomposition of the original rock types; perhaps Niger Delta is not naturally \nconnected to the sedimentary basins assessed by them. Constants could be \nachieved by modifying the coefficients for visual best fitting curves \nthrough the dataset.  \n\n\n\nDensity is the property of a matter which specifies an interface among \nfluids or solids; it is expressed in kgm-3. It can also be expressed in g/cm3 \nor g/cc. Density distinguish hydrocarbon from other fluid types (Koughnet \net al., 2003). This makes precise density evaluations important for \ndescription of reservoir. Analysing petrophysically, bulk density is a better \nacoustic indicator of shale presence. In order to achieve the aim of \nmodelling of different density-velocity equations and their constraints, \ncompressional wave velocity \ud835\udc49\ud835\udc5d and shear wave velocity \ud835\udc49\ud835\udc60 data and \n\n\n\nGamma Ray GR and density data from a well are required (Quijada & \nStewart, 2007). \n\n\n\nThe formation density logging tool offers a radioactivity measurement \nthat produces in-situ formation densities. The density log measures the \nbulk density of a formation by bombarding it with a radioactive material; \nthe resultant gamma ray count after the effects of compton scattering and \nphotoelectric absorption are recorded. This helps to derive a value for the \ntotal porosity of the formation which could be used in the detection of gas-\nbearing formations and recognition of evaporates. The bulk density tools \nare induced radiation tools. A formation with a high bulk density has high \nnumber density of electrons; it attenuates the gamma rays significantly; \nlow gamma ray count rate is logged at the sensors. A formation with a low \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 54-58 \n\n\n\nCite the Article: Atat, J. G., Akankpo, A. O., Umoren, E. B., Horsfall, O. I., Ekpo, S. S (2020). The Effect Of Density-Velocity Relation Parameters On Density Curves In Tau (\u03c4) Field, \nNiger Delta Basin. Malaysian Journal of Geosciences, 4(2):  54-58. \n\n\n\nbulk density has a low density of electrons and it attenuates the gamma \nrays less than a high density formation; thus, rate of higher gamma ray \ncount is logged at the sensors. \n\n\n\nRate of Gamma ray count depends on the electron number density which \nis related to the bulk density of a substance (Equation 1).  \n\n\n\n\ud835\udc5b =  \n\ud835\udc41 \ud835\udc4d\n\n\n\n\ud835\udc34\n\ud835\udf0c                 (1) \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 \ud835\udc5b is the electron density number in electron cm-3; \ud835\udc41 is the \nAvogadro\u2019s number. Avogadro\u2019s constant allows us to connect density to \nthe number of nuclei /cm3 and the number of electrons (Tamunobereton-\nari et al., 2013). Without the density information, Petrophysicists would \nbe unable to estimate the reserves of hydrocarbon that contained in the \nreservoir. Density descriptions can aid the determination of the locations \nand orientations of the formation limits and fault planes; it also helps the \noperator to know the size and shape of the reservoir and to place the well \nwithin it. Bulk density of a rock depends on the solid minerals of which it \nis composed, its porosity and the density of the fluids occupying that \nporosity. It also aids in lithological identification. \n\n\n\n1.1 Log presentation \n\n\n\nThe formation density log is documented in tracks 2 and 3 of the standard \nAPI log arrangement on a linear scale. The scale is in g/cm3 and usually \nwithin 1.95 to 2.95 g/cm3 as this is the common range for rocks. \n\n\n\n1.2 Identification of Lithology \n\n\n\nWhen used alone, density log is not a good tool for identifying most \nlithologies because most rocks have an extensive range of densities \nresulting from their different mineralogical compositions and their \nvariable porosities. Shales have bulk densities within the range of 1.8 to \n2.8 g/cm3 with variable clay minerals densities. Sandstones, limestones \nand dolomites all have bulk density ranges that overlay each other and that \nof shales (Kearey et al., 2002; Bosch et al., 2002; Reynolds, 1997; \nTamunobereton-ari et al., 2013). The existence of organic matter can also \ndiminish the density of shales by up to 0.5 g/cm3. It is easier to compute \nthe total organic carbon (TOC) content of a source rock from the variation \nin bulk density. \n\n\n\n1.3 Density of rocks \n\n\n\nDensity of rocks (Table 1) is affected by a lot of factors such as \nconcentration of atoms, change in volume due to temperature, change in \nvolume due to pressure and rock type and time. Concentration of Atoms: \nThe mass of a material is necessary for the density determination. Atoms \nin some solids are closely packed. They could have a low atomic numbers \nbut are heavier than those with high atomic numbers for a given volume. \nThis is the case of Tin (with 50; weight - 118.7 amu) and Manganese \n(having atomic number of 25 with 54.9 amu, almost half of Tin value). Tin \nhas a lower density of 7.31gcm-3; density of Manganese is 7.43gcm-3. \n\n\n\nChange in Volume due to Temperature: High temperature caused the \nrocks to become ductile and flow; if temperature is low, rocks become \nbrittle and fracture. Change in Volume due to Pressure: Buried rocks are \nunder the influence of a force due to confining pressure. The Earth\u2019s crust \nmaterials are squeezed; the rocks deeply buried are held together and \ntend to flow. Rock type and Time: When forces are applied to rocks like \nshale, rock salt, limestone and schist over a long time, they behave as \nductile. \n\n\n\nAccording to Atat et al., 2020, the estimated constants for local fit for tau \nreservoir is given in Table 2. This table also contains information on \nGardner and Lindseth default parameters. If there is no accuracy in the \nestimation of fluid density, the porosity will not be appreciable. The mud \nfiltrate fluid is noted by the formation density device. The density of fresh \nwater is 1.0 gcm-3 and that of salt water is 1.1gcm-3. Mud fluid density is \nvery useful in the porosity calculations and can be corrected directly for \npressure and temperature in some softwares (Glover, 2013; Etu-Efeotor, \n1997; Telford et al., 1978). \n\n\n\nTable 1: Rock types with their corresponding range of densities. \n\n\n\nS/N Rock/Mineral types Density (x 10-2gcm-3) \n\n\n\n1 Alluvium (wet) 196 \u2013 200 \n\n\n\n2 Shale 206 \u2013 266 \n\n\n\n3 Sandstone 205 \u2013 255 \n\n\n\n4 Limestone 260 \u2013 280 \n\n\n\n5 Chalk 194 \u2013 223 \n\n\n\n6 Dolomite 228 \u2013 290 \n\n\n\n7 Anorthosite 261 \u2013 275 \n\n\n\n8 Halite 210 \u2013 240 \n\n\n\n9 Granite 252 \u2013 275 \n\n\n\n10 Granodiorite 267 \u2013 279 \n\n\n\n11 Basalt 270 \u2013 320 \n\n\n\n12 Gabbro 285 \u2013 312 \n\n\n\n13 Gneiss 261 \u2013 299 \n\n\n\n14 Cassiterite 680 \u2013 710 \n\n\n\n15 Quartzite 260 \u2013 270 \n\n\n\n16 Amphibolite 279 \u2013 314 \n\n\n\n17 Chromite 430 \u2013 460 \n\n\n\n18 Pyrrhotite 450 \u2013 480 \n\n\n\n19 Magnetite 490 \u2013 520 \n\n\n\n20 Galena 740 \u2013 760 \n\n\n\nSource: Glover (2013); Milsom (1996). \n\n\n\nTable 2: Estimated constants for density-velocity relation in tau \nreservoir \n\n\n\nConstants Lithology \nSand from \n\n\n\nVp \nSand from \n\n\n\nVs \nShale from \n\n\n\nVp \nShale from \n\n\n\nVs \nb 0.23 0.12 0.52 0.23 \nn 0.27 0.24 0.45 0.30 \n\n\n\nGardner\u2019s default: b = 0.31 and n = 0.25 \ne 0.320 0.340 0.350 0.350 \n\n\n\nf 3481 1640 1595 838 \nLindseth default: e = 0.308 and f = 1054 \n\n\n\nSource: Atat et al. (2020). \n\n\n\n2. LOCATION AND GEOLOGY OF THE STUDY AREA \n\n\n\nThe Niger Delta is located (Klett et al., (1997) between latitudes 30N and \n60N; longitudes 50E and 80E (Reijers et al., 1996). The area of study is Tau \nField (Figure 1). The sediment capacity is 0.5 x 106km3 (Hospers, 1965) \nwith deposit width of about 10km (Kaplan et al., 1994). The Akata \nformation is made up of mostly marine shales; about predictable thickness \nof up to 0.7 x 104m (Doust & Omatsola, 1990). The Agbada formation is the \ncore oil reservoir in the Niger Delta. The region experiences both wet and \ndry seasons. \n\n\n\nFigure 1: Map of Africa showing the Tau Field in Niger Delta region \n\n\n\n(Source: Sam et al., 2017; Atat et al., 2018). \n\n\n\n3. METHODOLOGY \n\n\n\nWell-log data for three oil wells in the tau field were acquired. The log was \nconditioned, analyzed and carried out necessary cross plots using \nHampson Russell Software (HRS). The HRS crossplot space figures the \nconstants of linear regression lines to march the data. The density versus \nvelocity curves were plotted on a cross-plot interface to determine \ncoefficients using gamma ray as colour key. The regression line to make \nappropriate march with shales and sand with gamma cuff of greater than \n75 API for shale was adjusted.  This result was considered to obtain \ndifferent density curves: first, using Gardner and Lindseth default \nparameters; second, by replacing Gardner and Lindseth constants with the \nconstants of Atat et al., 2020 for specific rock types in the tau field. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 54-58 \n\n\n\nCite the Article: Atat, J. G., Akankpo, A. O., Umoren, E. B., Horsfall, O. I., Ekpo, S. S (2020). The Effect Of Density-Velocity Relation Parameters On Density Curves In Tau (\u03c4) Field, \nNiger Delta Basin. Malaysian Journal of Geosciences, 4(2):  54-58. \n\n\n\nConsidering the Gardner and Lindseth default curves (Figures 2 and 3 \nrespectively) for the sands and shales, the coefficient determined falls \nwithin 0.31 and 0.35; the sands should be within 0.30 and 0.27.  Gardner \ncurve requires density to vary as (2.0 <  \ud835\udf0c\ud835\udc4f  < 2.5) gcm-3 corresponding to \n(1.5 \u2264  \ud835\udc49\ud835\udc5d  \u2264 4.5) kms-1 for shales and (2.0 <  \ud835\udf0c\ud835\udc4f  < 2.5) gcm-3 \n\n\n\ncorresponding to (2.0 \u2264  \ud835\udc49\ud835\udc5d  \u2264 5.0) kms-1 for sandstones. Our expected \n\n\n\noutcome of density curve estimate for the two curves, show for shales, the \nred curve is fit through purple line; for sands, the green curve is the fit \nthrough purple line (Figures 4 and 5).  \n\n\n\nFigure 2: Gardner's empirical relationship of density versus p-wave \n\n\n\nvelocity. (Source: Sheriff and Geldart, 1995). \n\n\n\nFigure 3: Lindseth's empirical relationship of acoustic impedance versus \n\n\n\nrock velocity (Source: Potter and Stewart, 1998) \n\n\n\nFigure 4: Log Density-Log Velocity relationships plot established for \n\n\n\nsingle-fit, sand and shale coefficients determination for the study using \n\n\n\nGardner approach. \n\n\n\nFigure 5: Impedance-Velocity relationships plot established for single-fit, \n\n\n\nsand and shale coefficients determination for the study using Lindseth\u2019s \n\n\n\napproach. \n\n\n\n4. RESULTS \n\n\n\nThree wells of tau Field were analyzed in this research and the results are \npresented in Figures 6 to 11. Density estimated from the well log data is \ncompared to that obtained from local fit constants and Gardner and \nLindseth default parameters.  \n\n\n\nFigure 6: Density estimates for well A using measured density (black \n\n\n\ncurve), using Gardner default values (blue curve) and using specific \n\n\n\nvalues for sand and shale (Pink curves) of \ud835\udc49\ud835\udc5d and \ud835\udc49\ud835\udc60. \n\n\n\nFigure 7: Density estimates for well B using measured density (black \n\n\n\ncurve), using Gardner default values (blue curve) and using specific \n\n\n\nvalues for sand and shale (Pink curves) of \ud835\udc49\ud835\udc5d and \ud835\udc49\ud835\udc60. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 54-58 \n\n\n\nCite the Article: Atat, J. G., Akankpo, A. O., Umoren, E. B., Horsfall, O. I., Ekpo, S. S (2020). The Effect Of Density-Velocity Relation Parameters On Density Curves In Tau (\u03c4) Field, \nNiger Delta Basin. Malaysian Journal of Geosciences, 4(2):  54-58. \n\n\n\nFigure 8: Density estimates for well C using measured density (black \n\n\n\ncurve), using Gardner default values (blue curve) and using specific \n\n\n\nvalues for sand and shale (Pink curves) of \ud835\udc49\ud835\udc5d and \ud835\udc49\ud835\udc60. \n\n\n\nFigure 9: Density estimates for well A using measured density (black \n\n\n\ncurve), using Lindseth default values (blue curve) and using specific \n\n\n\nvalues for sand and shale (Pink curves) of \ud835\udc49\ud835\udc5d and \ud835\udc49\ud835\udc60. \n\n\n\nFigure 10: Density estimates for well B using measured density (black \n\n\n\ncurve), using Lindseth default values (blue curve) and using specific \n\n\n\nvalues for sand and shale (Pink curves) of \ud835\udc49\ud835\udc5d and \ud835\udc49\ud835\udc60. \n\n\n\nFigure 11: Density estimates for well B using measured density (black \n\n\n\ncurve), using Lindseth default values (blue curve) and using specific \n\n\n\nvalues for sand and shale (Pink curves) of \ud835\udc49\ud835\udc5d and \ud835\udc49\ud835\udc60. \n\n\n\n5. DISCUSSION\n\n\n\nDensity estimated from the well log data is compared to that obtained \nfrom local fit constants and Gardner and Lindseth default parameters. The \ndiscrepancies above are investigated using a density curves, \nsuperimposed with the original density curves (Figures 6 to 8). The plots \nusing Gardner\u2019s relation shows that density varies with lithologies. This \nsuggests the correlation of rock property is better when precise rock \ncategories are investigated. Figures 6 to 8 indicate the established density-\nvelocity curves for shales and sands are not as the unique Gardner curve \n(Density-G). The suitable coefficient decays toward the sand (Density \nsand-pink) and increases towards the shales (Density shale-pink); for \nGardner\u2019s curve (Density G-blue), there is a sharp increase in density at \nthe top. Considering shales from (Vs), the appropriate coefficients b and n \nare 0.23 and 0.30 respectively (Table 2); in the sands (Vs) coefficients are \n0.23 and 0.12 respectively (Table 2). This results in the residual standards \nof 0.05 and 0.0195 for shale and sand correspondingly. \n\n\n\nConsidering Lindseth\u2019s relation, it is observed that the resulting curves \n(Figures 9 to 11) for shales and sands are changed from the default \nLindseth\u2019s curve (Density-L). The fitting coefficient slightly drops near \nsand (Density sand-pink); sharply moves near the shales (Density shale-\npink) and Lindseth (Density L- blue) at the top. In shales (Vs), the \nappropriate constants of e and f are 0.35 and 838 respectively (Table 2); \nin the sands (Vs), are 0.34 and 1640 respectively (Table 2). This results in \nthe residual standards of 0.2562 and 0.176 for shale and sand \ncorrespondingly.  \n\n\n\nA density-velocity constant depend on geology when result from a good fit \nusing sonic and density well-log information. Density is required in the \nidentification of lithologies, pore fluids and others; it is one of the major \ndesires in exploration, reservoir characterization and well planning. \nOverlap in sands and shales could show variation in petrophysical \nproperties (porosity of fluid saturation) making density not a good \nindicator of lithology. These constants are non-unique for most datasets \nfrom many sedimentary basins across the world as density-velocity data \nwould not obey the original Gardner and Lindseth curves. Modelling is \nnecessary to evaluate the effect of these properties on density. Local \nexaminations are compulsory to improve the accuracy of rock properties \nthat were not captured by Gardner or Lindseth during their calibration. \nMoreso, the difference between the local variations and the original \nGardner or Lindseth relationship may be attributed to the differences in \ncomposition of the original rock types; perhaps Niger Delta is not naturally \nconnected to the sedimentary basins assessed by them. Constants could be \nachieved by modifying the coefficients for visual best fitting curves \nthrough the dataset. Well log data information shows high resistivity, \nindicating the hydrocarbon reservoir with less water saturation. This \nencourages the reason for considering these wells. \n\n\n\n6. CONCLUSION \n\n\n\nWe have considered the coefficients for specific rock type (sand and shale) \nfor \ud835\udf0f Field in the Niger Delta basin using both P-wave and S-wave \ninformation from well log data. The density curves generated from default \nparameters differ from those of sand and shale lithologies for the field \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 54-58 \n\n\n\nCite the Article: Atat, J. G., Akankpo, A. O., Umoren, E. B., Horsfall, O. I., Ekpo, S. S (2020). The Effect Of Density-Velocity Relation Parameters On Density Curves In Tau (\u03c4) Field, \nNiger Delta Basin. Malaysian Journal of Geosciences, 4(2):  54-58. \n\n\n\nunder investigation. Gardner and Lindseth curves were strongly away \nfrom the in-situ; the constants for specific rock type improve the curves. \nGeology may upset these coefficients meaningfully and could result in loss \nof evidence if constants for local fits are not estimated before the inclusion \nin the density-velocity relation for reservoir characterization. \n\n\n\nREFERENCES \n\n\n\nAtat, J.G., Uko, E. D., Tamunobereton-ari, I., Eze, C.L., 2018. Major Lithology \n\n\n\nof Tau Field as defined by Density in the Niger Delta Basin. World Journal \n\n\n\nof Applied Science and Technology, 10(2), Pp. 259 \u2013 264. \n\n\n\nAtat, J.G., Uko, E.D., Tamunobereton-ari, I., Eze, C.L., 2020. The Constants of \n\n\n\nDensity-Velocity Relation for Density Estimation in Tau Field, Niger \n\n\n\nDelta Basin. IOSR Journal of Applied Physics, 12(1), 19 \u2013 26. DOI: \n\n\n\n10.9790/4861-1201011926. \n\n\n\nAtat, J.G., Uko, E.D., Tamunobereton-ari, I., Eze, C.L., 2020b. Site-Dependent \n\n\n\nGeological Model for Density Estimation in the Niger Delta Basin, \n\n\n\nNigeria. Malaysian Journal of Geosciences (MJG), 4(1), 1 \u2013 6. DOI: \n\n\n\nOrg/10.26480/mjg.01.2020.01.06. \n\n\n\nBosch, M., Zamora, M., Utama, W., 2002. Lithology Discrimination from \n\n\n\nPhysical Rock Properties. Geophysics, 67, Pp. 573 \u2013 581. DOI: \n\n\n\n10.1190/1.1468618 \n\n\n\nDoust, H., Omatsola, E., 1990. Niger Delta. In: Edwards, J. D. & Santogrossi, \n\n\n\nP. A. Editions. Divergent/Passive Margin Basins. American Association \n\n\n\nof Petroleum Geologists Memoir 48. Tulsa: American Association of \n\n\n\nPetroleum Geologists. Pp. 239 \u2013 248. \n\n\n\nEtu-Efeotor, J.O., 1997. Fundamentals of Petroleum Geology. Port \n\n\n\nHarcourt: Paragraphics. \n\n\n\nGardner, G.H.F., Gardner, L.W., Gregory, A.R., 1974. Formation Velocity and \n\n\n\nDensity \u2013 the Diagnostic Basics for Stratigraphic Traps. Geophysics, 39, \n\n\n\nPp. 770 \u2013 780. \n\n\n\nGlover, P., 2013. Petrophysics MSc Course Notes on Formation Density \n\n\n\nLog, Pp. 121\u2013138. \n\n\n\nHospers, J., 1965. Gravity Field and Structure of the Niger Delta, Nigeria, \n\n\n\nWest Africa. Geological Society of American Bulletin, 76, Pp. 407 \u2013 422. \n\n\n\nDOI: org/10.1130/0016-7606 \n\n\n\nKaplan, A., Lusser, C.U., Norton, I.O., 1994. Tectonic Map of the World. \n\n\n\nTulsa: American Association of Petroleum Geologists. \n\n\n\nKearey, P., Brooks, M., Hill, I., 2002. An Introduction to Geophysical \n\n\n\nExploration. Oxford: Blackwell Science. \n\n\n\nKlett, T.R., Ahlbrandt, T.S., Schmoker, J.W., and Dolton, J. L., 1997. Ranking \n\n\n\nof the World\u2019s Oil and Gas Provinces by Known Petroleum Volumes. \n\n\n\nUnited State Geological Survey Open-File Report, 97, Pp. 463. \n\n\n\nKoughnet, V.R.W., Skidmore, C.M., Kelly, M.C., Lindsay, R., 2003. \n\n\n\nProspecting with Density Cube. The Leading Edge, 22, Pp. 1038 \u2013 1045. \n\n\n\nKrasovsky, S.S., 1981. Reflection of Continental-Type Crustal Dynamics in \n\n\n\nthe Gravity Field. Naukova Dumka, Kiev, Pp. 264 \n\n\n\nMilsom, J., 1996. Field Geophysics. 2nd Edition. Chichester, England: John \n\n\n\nWiley & Sons Limited. \n\n\n\nNwozor, K.K., Onuorah, L.O., Onyekuru, S.O., Egbuachor, C.J., 2017. \n\n\n\nCalibration of Gardner Coefficient for Density-Velocity Relationships of \n\n\n\nTertiary Sediments in Niger Delta Basin. Journal of Petroleum \n\n\n\nExploration and Production Technology, 7, Pp. 627 \u2013 635. \n\n\n\nPotter, C.C., Stewart, R.R., 1998. Density Predictions using Vp and Vs Sonic \n\n\n\nLogs. CREWES Research Report, 10, Pp. 1 \u2013 10. \n\n\n\nQuijada, M.F., Stewart, R.R., 2007. Density Estimations using Density-\n\n\n\nVelocity Relations and Seismic Inversion. CREWES Research Report, 19, \n\n\n\nPp. 1 \u2013 20. \n\n\n\nReijers, T.J.A., Petter, S.W., Nwajide, C.S., 1996. The Niger Delta basin: \n\n\n\nReijers, T.J.A., ed., Selected Chapter on Geology: SPDC Wa.: LP1O3-118. \n\n\n\nReynolds, J.M., 1997. An Introduction to Applied Environmental \n\n\n\nGeophysics. New York: Wiley Press. \n\n\n\nSam, K., Coulon, F., Prpich, G., 2017. Management of Petroleum \n\n\n\nHydrocarbon Contaminated Sites in Nigeria: Current Challenges and \n\n\n\nFuture Direction. Land Use Policy, 64, Pp. 133 \u2013 144. DOI: \n\n\n\n10.1016/j.landusepol.2017.01.051. \n\n\n\nSheriff, R.E., Geldart, L.P., 1995. Exploration Seismology, 2nd Edition. \n\n\n\nEdinburgh, UK: Cambridge University Press. \n\n\n\nTamunobereton-ari, I., Uko, E.D., Omubo-Pepple, V.B., 2013. Estimation of \n\n\n\nLithological and Mineralogical Contents of Rocks from Matrix Density in \n\n\n\npart of Niger Delta Basin Nigeria using Well-log Data. Journal of \n\n\n\nEmerging Trends in Engineering and Applied Sciences, 4(6), Pp. 828 \u2013 \n\n\n\n836. \n\n\n\nTelford, W.M., Geldart, L.P., Sheriff, P.E., Keys, D.A., 1978. Applied \nGeophysics. London: Cambridge University Press. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal Geosciences (MJG) (2017) 07-12 \n\n\n\nContents List available at RAZI Publishing\n\n\n\nMalaysian Journal of Geosciences\nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/\n\n\n\nAssociation of Evacuation Dimensions towards Risk Perception of the Malaysian students \nwho studied at Jakarta, Medan, and Acheh in Indonesia \nAhmad Azan Ridzuan, Ungku Azly Ungku Zahar, Noor Akmar Mohd Noor \nNational Defence University of Malaysia, Sungai Besi Camp, 57000 Kuala Lumpur, Corresponding Author: azan6142@yahoo.com, Tel: 0192726202\n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle history:\n\n\n\nReceived 22 January 2017  \nAccepted 03 February 2017  \nAvailable online 05 February 2017\n\n\n\nKeywords:\n\n\n\nEvacuation dimensions, risk perception, \ndisaster, SmartPLS\n\n\n\nDisasters can strike anywhere at any time that may result in injuries or loss of life for those individuals who are \nill-prepared for disaster situations. Evacuation is a vital part of disaster management. Successful evacuation \nrequires involvement of the community by understanding evacuation orders, knowing evacuation routes and \ntimely decision making to evacuate. This study was conducted to measure the relationship between evacuation \ndimensions (behavioral, organizational, physical hazard, response, social, and warning) and risk perception using \nquestionnaires gathered from the Malaysian students who studied at Jakarta, Medan, and Acheh in Indonesia. The \noutcomes of SmartPLS path model showed six important findings: firstly, behavioral not significantly correlated \nwith risk perception. Second, organizational significantly correlated with risk perception. Third, physical hazard \nsignificantly correlated with risk perception. Fourth, response significantly correlated with risk perception. Fifth, \nsocial significantly correlated with risk perception. Sixth, warning significantly correlated with risk perception. \nStatistically, this result confirms that the implementation of organizational, physical hazard, response, social, and \nwarning have been important determinant of risk perception. Conversely, the implementation of behavioral had \nnot enhanced the risk perception in the organizational sample. In addition, discussion, implications and conclusion \nare elaborated.\n\n\n\nINTRODUCTION\nDisasters can strike anywhere at any time, vary in severity, and have devastating \nconsequences that may result in injuries or loss of life for those individuals who \nare ill-prepared for disaster situations. According to Perry and Lindell (1997), \ndisasters are a significant cause of death and disability around the world \nand also have tremendous social, economic, and political effects on society. \nEvacuation is a vital part of disaster management (Cova & Johnson, 2003) \nthat is described as moving people at risk to safety (Na et al., 2012). Effective \nevacuation depends on several factors such as warning time, response time, \ninformation and instructions dissemination procedure, evacuation routes, \ntraffic flow conditions, dynamic traffic control measures, and others (Pel et al., \n2012) to mention a few.\nEvacuation is largely a function of people defining themselves as being in danger \nand believing that leaving the area in question is beneficial (Fitzpatrick & Mileti, \n1991). Successful evacuation requires involvement from both the community \nby issuing evacuation orders, providing marked exit routes and the individual \ndecision making (Riad & Norris, 1998). Although community involvement is \nimportant in evacuation, external social influence can only go so far because \nultimately the individual is responsible for the decision. Evacuation decisions \nare influenced by societal norms, different population subgroups, with different \nnorms, may have different rates of evacuation (Moore, 1963). Case studies \ninvolving toxic spills, near nuclear meltdowns, and varied natural disasters such \nas hurricanes, volcanoes and floods have assumed that disaster behaviors such \nas evacuations or preparedness are prompted primarily by risk perceptions of \nan impending disaster (Sjoberg, 2000).\n\n\n\nRisk concerns both the probability for and the consequences of the happening \nof an event (Adams, 1995). People are expected to vary in whether they focus \nupon probability or consequence (Drottz-Sj\u00f6berg, 1991). Risk is all about \nthoughts, beliefs and constructs (Sj\u00f6berg, 1979). According to Adams (1995: 69) \n\u201crisk, according to the definitions most commonly found in the safety literature, \nis the probability of an adverse future event multiplied by its magnitude\u201d. \nThe perceived risk concerns how an individual understands and experiences \nthe phenomenon. Many factors may influence perceptions of risk, such as \nfamiliarity with the source of danger (Ittelson, 1978), control over the situation \n(Rachman, 1990), and the dramatic character of the events \u2013 rare, striking \nevents tend to be overestimated, while frequency of common events tend to \nbe underestimated (Lichtenstein, et al., 1978).\n\n\n\nRisk perception is associated by demands for risk mitigation (Oltedal, 2004). \nRisk perception as defined by Slovic (1987) refers to people\u2019s intuitive and \nsubjective evaluation of the riskiness of an activity, technology or event. In \nthe past decades, substantial studies have been conducted to understand the \nriskiness of various technologies, activities, and events as judged by people \nand factors affecting such a judgment from different disciplines. People\u2019s \nrisk perception is believed be affected by their prior experience, their socio-\ndemographic characteristics, social, cultural and institutional environment, and \n\n\n\nthe characteristics of risks (Taylor-Gooby & Zinn, 2006). Risk perception, among \nother factors, is believed to affect people\u2019s preparedness for, responses to and \nrecovery from natural disasters (Grothmann & Reusswig, 2006; Bradford et al., \n2012). Risk perception is defined as the subjective assessment of risk, not actual \nrisk. Risk perception determines how people respond to hazards (Pennings & \nSmidts 2000; Pennings & Smidts 2003).\n\n\n\nRisk perception, people\u2019s subjective judgment of the riskiness of activities, \ntechnologies, and events, is a prerequisite of their risky behavior. In coping \nwith natural disasters, it is important to know how people perceive disasters \nrisks and what factors affect their perceived level of riskiness, to predict their \nself-protective behavior and their response to public measures (Xu, 2014). Lin \net al. (2008) studied general public and victims\u2019 risk perception and mitigation \nbehavior towards floods and landslides in Taiwan. They found that risk \nperception, social trust and social economic factors (income and education) are \npositive predictors of mitigation measures, while sense of powerlessness and \nhelplessness are negative predictors of mitigation measures. As stated by Slovic \n(1987), one challenge when dealing with risk perception at the individual level \nis that decision are often based on what is deemed as acceptable risk. Often \ncitizens remain under-prepared because they may view disasters as an anomaly \nand remain dependent on governmental agencies to ensure their safety (Chen, \n2012). \n\n\n\nEmergency conditions change behavior and norms (Fritz, 1957; Perry, 1979). \nWhen a warning is received, people engage in what evacuation researchers \nhave historically called the warning confirmation process. The aim of early \nwarning is to raise awareness and encourage preparedness. The efforts for \nearly warning would be more effective if risk perception had been taken \ninto account (Xu, 2014). Waugh, (2008), the goals of warning systems are to \ncommunicate either directly or indirectly with all persons at risk and to elicit \nan appropriate protective response to reduce or eliminate that risk. Ideally, \nall at risk should hear (or see or feel), understand, believe, personalize, and \nrespond to the warning by taking protective action (e.g., evacuating, sheltering \nin place, etc.). The issue of access should also include whether those at risk \nactually hear or see warnings, not just be able to hear or see them, including \nwhether the warning system is reasonably effective in reaching those at risk. No \nsystem reaches everyone. Evacuation decisions, for example, typically are made \nby families and other social groupings. People consult with relatives, friends, \nand colleagues before deciding to evacuate.\nObjectives of the Study\nThis study is conducted to measure six relationships: first, relationship between \nbehavioral on risk perception. Second, relationship between organizational on \nrisk perception. Third, relationship between physical hazard on risk perception. \nFourth, relationship between response on risk perception. Fifth, relationship \nbetween social on risk perception. Lastly, relationship between warning on risk \nperception\n\n\n\nCite this article as: Association of Evacuation Dimensions towards Risk Perception of the Malaysian students who studied at Jakarta, Medan, and Acheh in \nIndonesia. Ahmad Azan Ridzuan, Ungku Azly Ungku Zahar, Noor Akmar Mohd Noor / Mal. J. Geo 1(1) (2017) 06-11\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online)\n\n\n\nhttps://doi.org/10.26480/mjg.01.2017.07.12\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\n http://www.razipublishing.com/journals/galeri-warisan-sains-gws/ \n\n\nhttp://doi.org/10.26480/mjg.01.2017.07.12\n\n\nhttps://doi.org/10.26480/mjg.01.2017.07.12\n\n\n\n\n\n\nAhmad Azan Ridzuan, Ungku Azly Ungku Zahar, Noor Akmar Mohd Noor / Malaysian Journal of Geosciences 1(1) (2017) 07\u201312 8\n\n\n\nCite this article as: Association of Evacuation Dimensions towards Risk Perception of the Malaysian students who studied at Jakarta, Medan, and Acheh in \nIndonesia. Ahmad Azan Ridzuan, Ungku Azly Ungku Zahar, Noor Akmar Mohd Noor / Mal. J. Geo 1(1) (2017) 06-11\n\n\n\n                                                 LITERATURE REVIEW\nRelationship between evacuation dimensions and risk perception\nExtant studies about  evacuation dimensions were conducted using different \nsamples, such as a sample of 1,000 adults from Savannah, GA., Charleston, \nS.C, Charlotte, NC and Greenville, SC that experienced Hurricanes Hugo and \nAndrew evacuation decisions and risk perception (Riad & Norris, 1998), \nsamples from Cedar Rapids, Iowa a mail-out survey technique to 1000 \nhouseholds located in the mandatory evacuation zone however 196 were \ncompleted and returned evacuees\u2019 perception of risk associated with the \nevacuation (Siebeneck & Cova, 2008), national  urban household sample \n(814) in Israel, behavior preparedness actions due to risk perceptions \n(Kirschenbaum, 2005), and Seigler (2014) studied samples of 15,608 \nrandomly selected addresses 3,272 surveys were returned from residents of \nthe coastal regions South Carolina were surveyed on previous and potential \nevacuation behaviors in regards to a hurricane strike to the conglomerates \nof Northern (Horry and Georgetown); Central (Charleston, Berkeley, and \nDorchester) and Southern (Beaufort, Colleton, and Jasper) citizens that be \nsubject to a mandated evacuation with the necessary information in order \nmake personal planning and preparing a top priority, which will in turn \nlead to an effective evacuation. These studies found that the people\u2019s risk \nperception is believed be affected by their disaster evacuation dimensions \n(i.e., behavioral, organizational, physical hazard, response, social, and \nwarning) in respective disaster affected areas (Riad & Norris, 1998; \nSiebeneck & Cova, 2008; Kirschenbaum, 2005; Seigler, 2014).\nThis finding is consistent with the notion of Dash and Gladwin (2005) provide \na comprehensive framework .for evacuation as a function of individual \nfactors, event characteristics and risk perception. Dash illustrates that \nrisk perception is made up of a variety of elements that vary by individual \nand community, including; socioeconomic factors, experience factors, \ntrust of authorities, storm knowledge, home characteristics, and message \ndissemination. These variables interact with each other to either enhance \nor reduce risk perception. Dash and Gladwin\u2019s approach is to organize the \nfactors involved in decision-making, understand how information is used, \nand determine what influences individual action. Another theory, according \nto Douglas (1978) and Thompson et al. (1990), cultural theory is a general \nsociological theory. Cultural theory aims at explaining how people perceive \nand act upon the world around them. More specifically the theory claims \nthat this is largely determined by social aspects and cultural adherence.  \nThis theory claims that this is largely determined by social aspects and \ncultural adherence. Both theories related to the literature that has been \nused to develop the conceptual framework for this study as shown in Figure \n1.\nConceptual Framework and Research Hypothesis\nThe literature has been used as foundation to develop a conceptual \nframework for this study as shown in Figure 1.\n Independent variables                                  Dependent variable\n\n\n\nFigure 1 Relationship between evacuation dimensions and risk perception.\nBased on the framework, it can be hypothesized that:\nH1:  There is a relationship between behavioral on risk perception. \nH2:  There is a relationship between organizational on risk \nperception. \nH3:  There is a relationship between physical hazard on risk \nperception.\nH4:  There is a relationship between response on risk perception.\nH5:  There is a relationship between social on risk perception. \nH6:  There is a relationship between warning on risk perception.\n                                                METHODOLOGY\nResearch Design\nThis study used a quantitative based case study which allowed the \nresearchers to integrate risk perception literature, a pilot study and the \nactual survey as the main procedure to gather data for this study. Using this \n\n\n\ndata collection procedure may gather accurate, less bias and high quality \ndata (Davis, 1996; Cresswell, 1998; Sekaran, 2000). This study gathered data \nfrom the students who studied at Jakarta, Medan, and Acheh in Indonesia \nunder the Ministry of Education Malaysia. This organizational responsible \nto the Malaysian Embassy in Indonesia where its role and function are to \nprovide the administration for the students that studied in Indonesia. \nAt the initial stage, a pilot study was conducted by discussing survey \nquestionnaires with five experienced officers comprising of two \nrepresentatives from Malaysian Embassy in Indonesia (Jakarta and Medan), \nand two representatives of Ministry of Education Malaysia in Jakarta and \nMedan who have backgrounds in emergency evacuation in case of any \ndisaster. The information gathered from such officers helped the researcher \nto develop the content and format of survey questionnaires for an actual \nresearch. Hence, a back translation technique was used to translate the \ncontent of questionnaires in Malay and English languages in order to \nincrease the validity and reliability of the instrument (Sekaran & Bougie, \n2010; Creswell, 2012).\n                                         Measures\nThe survey questionnaire had three sections. First, behavioral had 4 items \nthat were modified from related behaviour characteristics (Sorensen & \nSorensen, 2006). Second, organizational had 3 items, physical hazard had 3 \nitems, response had 3 items, social had 3 items and warning had 3 items that \nwere modified from Sorensen, Vogt & Mileti (1987). Third, risk perception \nhad 3 items that were modified from related risk perception characteristics \n(Dash & Gladwin, 2005). All these items were measured using a 7-item \nscale ranging from \u201cvery strongly disagree\u201d (1) to \u201cvery strongly agree\u201d (7). \nDemographic variables were used as the controlling variables because this \nstudy focused on students perceptions towards emergency evacuation.\nUnit of Analysis and Sample\nThe researchers had obtained an official approval to conduct the study from \nthe Ministry of Education Malaysia and also received advice from Malaysian \nEmbassy in Indonesia about the procedures of conducting surveys in \nJakarta, Medan, and Acheh. The targeted population for this study was \n1,500 students who studied in Jakarta, Medan, and Acheh in Indonesia. This \norganizational allowed the researchers to conduct this study, but the list of \nstudents was not provided to the researchers. Considering this situation, \na convenient sampling technique was used to distribute the survey \nquestionnaires to students who studied in respective locations in Indonesia. \nA total of 200 questionnaires were distributed. From the total number, 162 \nusable questionnaires were returned to the researchers, yielding 81 percent \nresponse rate. The survey questionnaires were answered by participants \nbased on their consents and a voluntarily basis. The number of sample \nexceeds the minimum sample of 30 participants as required by probability \nsampling technique, showing that it may be analyzed using inferential \nstatistics (Chua, 2006; Sekaran & Bougie, 2010).\n Data Analysis\nThe research statistical analysis was conducted using structural equation \nmodel generated by SmartPLS version 2.0 (Ringle et al., 2005).  The \nprocedure of analyzing data is: first, validity test was performed by the \nconvergent and discriminant validity. Second, the reliability analysis was \nperformed by Cronbach alpha and composite reliability. Third, the structural \nmodel is assessed by examining the path coefficients using standardized \nbetas (\u03b2) and t statistics. In addition, R2 is used as an indicator of the overall \npredictive strength of the model. The value of R2 are considered as follows; \n0.19 (weak), 0.33 (moderate) and 0.67 (substantial) (Chin, 1998; Henseler \net al., 2009).  \n                                                                           RESULTS\nSamples profile \nIn relation to sample profile, Table 1 shows that the majority respondent \ncharacteristics were females (50.6%), ages between 21 to 25 years old \n(61.1%), bachelor status (96.6%), location of study - Acheh (53.7%), field of \nstudy \u2013 Islamic studies (55.6%), education level \u2013 bachelor (77.8%), years \nof study  between 4 to 5 years (53.7%), frequency of attending exposure on \nevacuation within studied periods \u2013 once (77.8%), and real experience in \nevacuation - no (69.8%).\n\n\n\n\n\n\n\n\nAhmad Azan Ridzuan, Ungku Azly Ungku Zahar, Noor Akmar Mohd Noor / Malaysian Journal of Geosciences 1(1) (2017) 07\u201312 9\n\n\n\nCite this article as: Association of Evacuation Dimensions towards Risk Perception of the Malaysian students who studied at Jakarta, Medan, and Acheh in \nIndonesia. Ahmad Azan Ridzuan, Ungku Azly Ungku Zahar, Noor Akmar Mohd Noor / Mal. J. Geo 1(1) (2017) 06-11\n\n\n\nConfirmatory Factor Analysis\nTable 2 shows the results of convergent and discriminant validity tests. All \nconstructs had the values of AVE larger than 0.5 indicating that the met the \nacceptable standard of convergent validity (Barclay et al., 1995; Fornell & \nLarcker, 1981; Henseler et al., 2009). Besides that, all constructs had the \nvalues of \u221a AVE in diagonal were greater than the squared correlation \nwith other constructs in off diagonal, showing that all constructs met the \nacceptable standard of discriminant validity (Henseler et al., 2009). \n                                   \n\n\n\nNote: \u221a AVE is shown in a diagonal\n\n\n\nTable 3 shows the factor loadings and cross loadings for different constructs. \nThe correlation between items and factors had higher loadings than other \nitems in the different constructs. The loadings of variables more strongly on \ntheir own constructs in the model, greater than 0.7 are considered adequate \n(Chin, 1998; Fornell & Larcker, 1981; Gefen & Straub, 2005; Henseler et al., \n2009).  In sum, the validity of measurement model meets the criteria. \n\n\n\n\n\n\n\n\nAhmad Azan Ridzuan, Ungku Azly Ungku Zahar, Noor Akmar Mohd Noor / Malaysian Journal of Geosciences 1(1) (2017) 07\u201312 10\n\n\n\nCite this article as: Association of Evacuation Dimensions towards Risk Perception of the Malaysian students who studied at Jakarta, Medan, and Acheh in \nIndonesia. Ahmad Azan Ridzuan, Ungku Azly Ungku Zahar, Noor Akmar Mohd Noor / Mal. J. Geo 1(1) (2017) 06-11\n\n\n\nTable 4 shows the results of reliability analysis for the instrument. The \ncomposite reliability and Cronbach\u2019s Alpha had values of greater than 0.8, \nindicating that the measurement scale used in this study had high internal \nconsistency (Chua, 2006; Henseler et al., 2009; Nunally & Benstein, 1994; \nSekaran & Bougie, 2010).\n\n\n\n                       Table 4  Composite Reliability and Cronbach\u2019s Alpha\n\n\n\nTable 3 Factor Loading and Cross Loading\n\n\n\n\n\n\n\n\nAhmad Azan Ridzuan, Ungku Azly Ungku Zahar, Noor Akmar Mohd Noor / Malaysian Journal of Geosciences 1(1) (2017) 07\u201312 11\n\n\n\nCite this article as: Association of Evacuation Dimensions towards Risk Perception of the Malaysian students who studied at Jakarta, Medan, and Acheh in \nIndonesia. Ahmad Azan Ridzuan, Ungku Azly Ungku Zahar, Noor Akmar Mohd Noor / Mal. J. Geo 1(1) (2017) 06-11\n\n\n\nOutcomes of Testing Direct Effects Model\nTable 5 shows the outcomes of direct effect model consist of H1, H2, \nH3, H4 and H5.  First, behavioral not significantly correlated with risk \nperception (\u03b2 = 0.227; t =1.319), therefore H1 was not supported. \nSecond, the hypothesis of organizational significantly correlated with \nrisk perception (\u03b2 = 0.486; t =6.263), therefore H2 was supported. \nThird, the hypothesis of physical hazard significantly correlated with \nrisk perception (\u03b2 = 0.395; t =5.100), therefore H3 was supported. \nFourth, the hypothesis of response significantly correlated with risk \nperception (\u03b2 = 0.422; t =5.578), therefore H4 was supported. Fifth, \nThe hypothesis of social significantly correlated with risk perception \n\n\n\n(\u03b2 = 0.372; t =4.178), therefore H6 was supported. In terms of \nexplanatory power, quality of model predictions in the analysis can \nbe demonstrated by the score of R2. The inclusion of risk perception \nhad explained the variance in the behavioral 5.1 percent (R2 = 0.051 \n(weak)), organizational 23.5 percent (R2 = 0.235 (weak)), physical \nhazard 15.6 percent (R2 = 0.156 (weak)), response 17.8 percent (R2 \n= 0.178 (weak)), social 13.8 percent (R2 = 0.138 (weak)) and warning \nhad explained 18.6 percent of the variance in the risk perception (R2 \n= 0.186 (moderate).\n\n\n\nOutcomes of Testing Direct Effects Model\nTable 5 shows the outcomes of direct effect model consist of H1, H2, H3, H4 \nand H5.  First, behavioral not significantly correlated with risk perception \n(\u03b2 = 0.227; t =1.319), therefore H1 was not supported. Second, the \nhypothesis of organizational significantly correlated with risk perception \n(\u03b2 = 0.486; t =6.263), therefore H2 was supported. Third, the hypothesis \nof physical hazard significantly correlated with risk perception (\u03b2 = 0.395; \nt =5.100), therefore H3 was supported. Fourth, the hypothesis of response \nsignificantly correlated with risk perception (\u03b2 = 0.422; t =5.578), therefore \nH4 was supported. Fifth, \nThe hypothesis of social significantly correlated with risk perception (\u03b2 \n= 0.372; t =4.178), therefore H6 was supported. In terms of explanatory \npower, quality of model predictions in the analysis can be demonstrated \nby the score of R2. The inclusion of risk perception had explained the \nvariance in the behavioral 5.1 percent (R2 = 0.051 (weak)), organizational \n23.5 percent (R2 = 0.235 (weak)), physical hazard 15.6 percent (R2 = 0.156 \n(weak)), response 17.8 percent (R2 = 0.178 (weak)), social 13.8 percent (R2 \n= 0.138 (weak)) and warning had explained 18.6 percent of the variance in \nthe risk perception (R2 = 0.186 (moderate).\n                                        DISCUSSION AND IMPLICATIONS\nThe findings of this research show that evacuation dimensions such as \norganizational, physical hazard, response, social, and warning does act \nas an important determinant of risk perception in the organizationalal \nsample. In the context of this study, the Malaysian Embassy in Indonesia \nhas taken a proactive action to plan, maintain, and monitor its service to \nstudents who studied in Jakarta, Medan, and Acheh in Indonesia. According \nto the interviewed respondents, organizational, physical hazard, response, \nsocial, and warning been properly exposed to the students will contribute \nto risk perception outcomes. Thus, the ability of the Malaysian Embassy in \nIndonesia to properly implement such evacuation dimensions has enhanced \ndisaster risk perception in the studied areas. \nThis study provides significant impacts on three major aspects: theoretical \ncontribution, robustness of research methodology, and practical \ncontribution. In terms of theoretical contribution, this study reveals that \nevacuation dimensions\n\n\n\nsuch as organizational, physical hazard, response, social, and warning act as\nimportant determinants of risk perception. This finding also has supported \nand broadened studies by Riad & Norris (1998); Siebeneck & Cova (2008); \nKirschenbaum (2005); and Seigler (2014). \nRegarding the robustness of research methodology, the survey \nquestionnaires used in this study have exceeded the minimum standards of \nvalidity and reliability analyses; this can lead to the production of accurate \nand reliable findings. \nWith respect to practical contribution, the findings of this study can be \nused as a guideline by management of Malaysian Embassy in Indonesia to \nimprove the risk perception of the students who studied at Jakarta, Medan, \nand Acheh in Indonesia. The possible suggestions are: firstly, disaster \nevacuation training program needs to be properly provided to students who \nstudied in Indonesia in order to increase their awareness and readiness for \ndisaster response. Secondly, to consider better exposure to individual staff \nwho are committed to improve disaster evacuation planning for students \nwho studied in Indonesia. Thirdly, selection of staff who served at Malaysian \n\n\n\nEmbassy in Indonesia needs to have knowledge of disaster risk management \nso that they will be better efficiency in providing information to smoothly \nimplement disaster response. If these suggestions are heavily considered \nthis may motivate the students who studied in Indonesia to perform better \ndisaster evacuation in case the disaster occur\n                                                   CONCLUSION\nThis study proposed the conceptual framework based on the disaster risk \nperception research literature. The measurement scales used in this study \nmet the acceptable standards of validity and reliability analyses. The findings \nof SmartPLS path model confirmed that evacuation dimensions such as \norganizational, physical hazard, response, social, and warning did act as an \nimportant determinant of risk perception. This result has supported and \nbroadened risk perception research literature published in most Western \ncountries. Therefore, current research and practice within risk perception \nmodels needs to consider the evacuation dimensions as crucial elements of \ndisaster response. The findings of this study further suggest that the ability \nof evacuation dimensions such as organizational, physical hazard, response, \nsocial, and warning contribute to risk perception outcomes. Hence, these \npositive outcomes may lead to sustained and achieved the goals of the \nMalaysian Embassy in Indonesia where its role and function are to provide \nthe administration for the students that studied in Indonesia\n                                                      REFERENCES\nAdams, J. (1995). Risk. London: UCL Press.\n\n\n\nBarclay, D. Higgins, C. & Thompson, R. (1995). The Partial Least Squares \n(PLS) Approach to Causal Modeling: Personal Computer Adoption and Use \nas an Illustration. Technology Study, 2 (2), 285-309.\n\n\n\nBradford, R. A., O\u2019Sullivan, J. J., Van der Craats, I. M., Krywkow, J., Rotko, \nP.,Aaltonen, J., Bonaiuto, M., DeDominicis, S., Waylen, K. and Schelfaut, K. \n(2012). Risk Perception\u2013Issues for Flood Management in Europe. Natural \nHazards and Earth System Sciences, 12, 2299\u20132309.\n\n\n\nChin, W.W. (1998). The Partial Least Squares Approach to Structural \nEquation Modelling, in Hoyle, R.H. Statistical Strategies for Small Sample \nResearch, Sage Publication, Inc., Thousand Oaks \u2013 California, 307-341.\n\n\n\nChen, V., Banerjee, D., & Liu, L. (2012). Do People Become Better Prepared in \nthe Aftermath of a Natural Disaster? The Hurricane Ike Experience. Journal \nof  Public Health Management and Practice. 18(3): 241-249. \n\n\n\nChua, Y.P. (2006). Kaedah Penyelidikan. Kuala Lumpur: McGraw Hill.\n\n\n\nCreswell, J.W. (2012). Educational Research: Planning, Conduct, and \nEvaluating Quantitative and Qualitative Research (Fourth Edition). Boston: \nPearson.\n\n\n\nCova, T., Johnson, J. (2003) A Network Flow Model for Lane-based \nEvacuation Routing. Transportation Research Part A: Policy and Practice, \n37(7), 579\u2013604.\nDash, N., & Gladwin, H. (2005). Evacuation Decision Making and Behavioral \nResponses: Individual and Household. Prepare for Hurricane Focus Social \nEconomic Workshop, February, 16-18.\n\n\n\n\n\n\n\n\nAhmad Azan Ridzuan, Ungku Azly Ungku Zahar, Noor Akmar Mohd Noor / Malaysian Journal of Geosciences 1(1) (2017) 07\u201312 12\n\n\n\nCite this article as: Association of Evacuation Dimensions towards Risk Perception of the Malaysian students who studied at Jakarta, Medan, and Acheh in \nIndonesia. Ahmad Azan Ridzuan, Ungku Azly Ungku Zahar, Noor Akmar Mohd Noor / Mal. J. Geo 1(1) (2017) 06-11\n\n\n\nDavis, D. (1996). Business Research for Decision Making. (4th ed.). Belmont: \nDuxbury Press.\n\n\n\nDouglas, M. (1978). Cultural Bias. Occasional Paper no. 35, Royal \nAnthropological Institute of Great Britain and Ireland.\n\n\n\nDrottz-Sj\u00f6berg, B.-M. (1991). Non-experts Definitions of Risk and Risk \nPerception (RHIZIKON: Risk Research Report No.3). Stockholm: Center for \nRisk research.\n\n\n\nFitzpatrick, C., & Mileti, D. (1991). Motivating Public Evacuation. Mass \nEmergencies and Disasters, 9, 137-152. \n\n\n\nFornell,C. & Larcker, D.F. (1981). Evaluating Structural Equation Models \nwith Unobservable Variables and Measurement Error. Journal of Marketing \nResearch, vol XVIII, no. February, 39-50.\n\n\n\nFritz, C.B., & Williams, H.B. (1957). The Human Being in Disasters: A \nResearch Perspective. The Annals of the American Academy of Political and \nSocial Science, 309, 42-51. \n\n\n\nGefen, D. & Straub, D. (2005). A Practical Guide to Factorial Validity Using \nPLS-Graph: Tutorial and Annotated Example. Communication of the \nAssociation for Information Systems, 16, 91 \u2013 109.\nGrothmann, T. & Reusswig, F. (2006). People at Risk of Flooding: Why Some \nResidents Take Precautionary Action While Others Do Not. Natural Hazards. \n38, 101-120.\n\n\n\nHenseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The Use of Partial Least \nSquares Path Modeling in International Marketing. In: Sinkovics, R. R., \nGhauri, P. N. (Eds.), Advances in International Marketing. Bingley: Emerald, \n277-320.\n\n\n\nIttelson, W.H.(1978). Environmental Perception and Urban Experience. \nEnvironment and Behaviour, 10, 193-213.\n\n\n\nKirschenbaum, A. (2005) Preparing for The Inevitable: Environmental \nRisk Perceptions and Disaster Preparedness. International Journal of Mass \nEmergencies and Disasters. August, Vol. 23, No. 2, pp. 97-127.\n\n\n\nLichtenstein, S., Slovic, P., Fiscoff, B., Layman M. & Combs B. (1978). Judged \nFrequently at Lethal Events. Journal of Experimental Psychology, 4, 551-\n578.\n\n\n\nLin, S., Shaw, D., & Ho, M. C. (2008). Why are Flood and Landslide Victims \nLess Willing to Take Mitigation Measures than the Public? Natural Hazards, \n44, 305\u2013314.\n\n\n\nMoore, H., Bates, F., Layman, M., & Parenton, V. (1963). Before the Wind: \nA Study of Response to Hurricane Carla. National Academy of Sciences/ \nNational Research Council on Disasters, Study #19.  Washington, D.C.: \nNational Academy of Sciences. \n\n\n\nNa, L., Xueyan,S., & Mingliang, Q. (2012). A Bi-objective Evacuation Routing \nEngineering Model with Secondary Evacuation Expected Costs. Systems \nEngineering Procedia, 5(0),1\u20137.\n\n\n\nNunally, J.C. & Bernstein, I.H. (1994). Psychometric Theory. New York: \nMcGraw-Hill.\n\n\n\nOltedal, S., Moen, B-E. Klempe, H., Rundmo, T. (2004). Explaining Risk \nPerception. An Evaluation of Cultural Theory. Rotunde. Norway.\n\n\n\nPel, A., Bliemer, M., Hoogendoorn, S., (2012) A Review on Travel Behaviour \nModeling in Dynamic Traffic Simulation Models for Evacuations. \nTransportation, 39, 97\u2013123.\n\n\n\nPennings, J.M.E. & Smidts, A. (2000). Assessing the Construct Validity of Risk \nBehavioral. Management Science, 46(10), 1337\u20131348. \n\n\n\nPennings, J.M.E. & Smidts, A. (2003). The Shape of Utility Functions and \nOrganizational Behavior. Management Science, 49(9), 1251\u20131263.\n\n\n\nPerry, R. W. (1979). Evacuation Decision-making in Natural Disasters. Mass \nEmergencies, 4, 25-38. \n\n\n\nPerry, R.W., & Lindell, M.K. (1997). Aged Citizens in the Warning Phase of \nDisasters: Re-Examining the Evidence. International Journal of Aging and \nHuman Development, 44(4), 257-267.\n\n\n\nRachman, S.J. (1990). Fear and Courage. San Francisco: W F Freeman.\n\n\n\nRiad, J.K., & Norris, F.H. (1998). Hurricane Threat and Evacuation Intentions: \nAn Analysis of Risk Perception, Preparedness, Social Influence, and \nResources. University of Delaware Disaster Research Center Preliminary \nPaper #271. \n\n\n\nRingle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 (M3) Beta, Hamburg: \nhttp://www.smartpls.de.\n\n\n\nSekaran, U. (2000). Research Methods for Business: A Skill Building \nApproach. New York: John Wiley and Sins, Inc.\n\n\n\nSekaran, U. & Bougie, R. (2010). Research Methods for Business: A Skill \nBuilding Approach, New York: John Wiley & Sons, Inc.\n\n\n\nSiebeneck, L.K., & Cova, T.J. (2008). Risk Perception Associated with the \nEvacuation and Return-entry Process of the Cedar Rapids, Iowa Flood. A \nQuick Response Research Report for the Natural Hazards Center, Boulder, \nColorado\n\n\n\nSeigler, W.C. (2014). Analysis of Hurricane Preparedness Levels and \nEvacuation Intent for South Carolina Coastal Residents. Thesis Degree \nof Master of Earth and Environmental Resources Management in Earth \nand Environmental Resources Management College of Arts and Sciences \nUniversity of South Carolina. \n\n\n\nSjoberg, L. (2000). Factors in Risk Perception. Risk Analysis 20:1-11.\n\n\n\nSj\u00f6berg, L. (1979). Strength of Belief and Risk. Policy Sciences, 11, 39-57.\n\n\n\nSlovic, P. (1987). Perception of Risk. Science. 236(4799), 280-285.\n\n\n\nSlovic, P. (1987). Perception of Risk Science. 236(4799): 280-285.\n\n\n\nSorensen, J.H, Vogt, B.M., & Mileti, D.S. (1987). Evacuation: An Assessment \nof Planning and Research. ORNL-6376, Oak Ridge National Laboratory: U.S. \nDepartment of Energy.\nSorensen, J.H., & Sorensen, B.V. (2006). Community Processes: Warning and \nEvacuation. In: Rodriguez H, Quarantelli EL, Dynes, RR (eds) Handbook of \nDisaster Research. Springer, New York, 183-199.\n\n\n\nTaylor-Gooby, P. & Zinn, J. O. (2006). Current Directions in Risk Research: \nNew Developments in Psychology and Sociology. Risk Analysis. 26(2), 397-\n411.\n\n\n\nThompson, M., Ellis, R. & Wildavsky, A. (1990). Cultural Theory. Boulder: \nWestview Press.\n\n\n\nWaugh, Jr., W.L. (2008). Access to Warnings by People with Sensory \nDisabilities: A Review of the Social Science Warning Literature. Report for \nthe WGBH Working Group on Access to Warnings, November 2008. Georgia \nState University. \n\n\n\nXu, J., Zhang, Y., Liu, B. & Xue, L. (2014). Risk Perception in Natural \nDisaster Management. http://www.efdinitiative.org/sites/default/files/\npublications/paper_tech4dev_2014_xu_jianhua_0.pdf\n\n\n\n\n\n\n\n\n\n"
"\n\n\n\n\n\nARTICLE DETAILS \n\n\n\nArticle History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nWater quality degradation through different kinds of pollutants is a major concern globally. Hence, this study becomes \nimperative in order to determine the effects of groundwater pollution due to septic tank effluent. Integrated electrical \nresistivity imaging, physicochemical and microbiological analyses of water samples from ten hand-dug wells (HdW) \nand three boreholes (BH) were carried out around residential areas surrounded by septic tank systems using \nstandard field and laboratory procedures. Results of the 2-D resistivity imaging of the subsurface along nine traverses \naround the study area using Wenner configuration with constant electrode separation ranging from 2 to 12 m showed \nanomalously low resistivity zones, less than 20 \u03a9m suspected to be septic plume accumulation. The accumulation was \nmore pronounced in the northern and southern part of the study area to a depth of about 10 m. The direction of \ngroundwater flow suggests that the water in the wells is flowing towards the direction of the septic plume. \nPhysicochemical analyses showed the groundwater samples to be within the specified limits of WHO/NSDWQ for \ndrinking purpose. However, higher values of concentrations of most analyzed parameters were noticed in HdW 6 and \nHdW 7 due to their nearness to septic tank system. The microbiological analysis indicates excessive amount of \nmicrobes in the samples, an indication of groundwater contamination by effluent from the septic tanks. The study \nrecommends treatment and regular monitoring of groundwater sources, adequate distance from water sources to \nseptic systems and adoption of clean technology to prevent contamination of groundwater flow. \n\n\n\n KEYWORDS\n\n\n\nResistivity, Microbiological, Septic-tank effluent, Groundwater, Hydrogeochemical, Contamination.\n\n\n\n1. INTRODUCTION\n\n\n\nGroundwater constitutes a viable option for potable water provisions all \n\n\n\nyear round in the study area [1]. Groundwater pollution is one of the major \n\n\n\nproblems threatening human and environmental health globally. \n\n\n\nGroundwater pollution occurs when unsuitable substances released by \n\n\n\nhuman activities and natural processes find their means into groundwater \n\n\n\nbodies or insoluble liquid droplets that become suspended in it \n\n\n\ndeteriorate/degrade the natural quality of the water. Septic systems also \n\n\n\nreferred to as \u201con-site wastewater treatment systems\u201d are designed for the \n\n\n\npurpose of treating human waste. A septic system consists of two main \n\n\n\nparts, a septic tank and field lines installed into a drain field or soil \n\n\n\nabsorption field (Fig. 1). The septic tank is a watertight box, usually made \n\n\n\nof concrete or fiberglass, with an inlet and outlet pipe (Fig. 2). Wastewater \n\n\n\nflows from the home to the septic tank through the sewer pipe. The septic \n\n\n\ntank treats the wastewater naturally by holding it in the tank. If the tank is \n\n\n\nproperly constructed, only wastewater will flow through the field lines. \n\n\n\nField lines generally are installed in trenches dug into soils having a \n\n\n\nminimum thickness of 3 feet, where the bottom of the trench is underlain \n\n\n\nby at least 2 feet of soil above the water table [2].  \n\n\n\nMuch of the population increase is occurring in rural areas that are \n\n\n\ntypically not served by municipal waste facilities, resulting in the \n\n\n\nexpanded use of on-site waste water disposal systems. The most common \n\n\n\ntype of residential on-site waste water disposal system is the conventional \n\n\n\ngravity flow septic tank system that consists of a septic tank, which \n\n\n\npromotes removal of solids by settling or liquefaction (Fig. 2), and the \n\n\n\nsubsurface soil absorption system, or drain field, that treats waste water \n\n\n\nby soil filtration, chemical and biological processes (Fig. 3) [3]. Unlike a \n\n\n\nsewer system, which discharges treated wastewater into a body of water, \n\n\n\nthe septic system depends on the soil around the home to treat and \n\n\n\ndispose of sewage effluent (Fig. 3). For this reason, a septic system can be \n\n\n\nused only on soils that will adequately absorb and purify the effluent. If a \n\n\n\nseptic system is installed in soil that cannot do so, the effluent will seep out \n\n\n\nonto the soil surface overlying the drainfield. In addition to causing an \n\n\n\nunpleasant smell, this untreated effluent can pose health problems. In \n\n\n\nsome cases where the soils do not adequately absorb the wastewater, the \n\n\n\ntoilets and sinks might not drain freely. If the soil can absorb the effluent \n\n\n\nbut cannot treat it, the sewage may contaminate the groundwater.  \n\n\n\nWhen operating properly, septic systems remove many pollutants and \nprovide some measure of protection for human and environmental health. \nSubsurface sewage disposal systems are the largest sources of wastewater \nto the ground, and are the most frequently reported causes of \ngroundwater contamination [4]. The likelihood of groundwater \ncontamination by these systems is greatest where septic systems are \nclosely spaced as in subdivided tracts in sub-urban areas and in areas \nwhere the bedrock is covered by little or no soil [2].  \n\n\n\nFigure 1: A typical septic system [5] \n\n\n\nELECTRICAL RESISTIVITY AND HYDROGEOCHEMICAL EVALUATION OF SEPTIC-\nTANKS EFFLUENT MIGRATION TO GROUNDWATER \n\n\n\nDepartment of Earth Sciences, Adekunle Ajasin University, Akungba-Akoko, Nigeria. \nCorrespondence E-mail: omowumi.ademila@aaua.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited \n\n\n\nCite the article: Ademila Omowumi  (2018). Electrical Resistivity and Hydrogeochemical Evaluation of Septic-Tanks Effluent Migration to Groundwater. \nMalaysian Journal of Geosciences, 2(2) : 01-10.\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 01-10 \n\n\n\nMalaysian Journal of Geosciences (MJG)\nDOI: https://doi.org/10.26480/mjg.02.2018.01.10\n\n\n\nPrint ISSN : 2521-0920\nOnline ISSN : 2521-0602 \n\n\n\nCODEN: MJGAAN\n\n\n\nAdemila Omowumi\n\n\n\n\nmailto:omowumi.ademila@aaua.edu.ng\n\n\n\n\n\n\nFigure 2: A septic tank [6] \n\n\n\nThe disposal of domestic wastewater in the study area is not performed \nby sewer systems, except by using septic tanks and seepage fields. The \nwaste is hereby processed in septic tanks and the resulting effluent leaches \ninto the subsoil from more drains. Leakage of effluents and solid wastes \nfrom septic tanks may find its way into groundwater and contaminate it \nwith phosphorus, nitrates, chloride, sulphate, household chemicals, oil, \ndetergents, bacteria, viruses and other pollutants resulting to health \nenvironmental problems. Elevated concentrations of chloride, ammonia \nand sulfate have been interpreted to be the result of septic tank effluent \n[7].  Septic plume develops and moves with groundwater flow.  \n\n\n\nApproximate times for septic effluent to pass through the unsaturated \nzone to groundwater range from a few hours to fifty days, depending on \nthe volume of effluent and the distance to groundwater [8]. In urban areas, \nthe infiltration of wastewater from septic tanks into groundwater \nincreases the pollution problems. A septic plume in groundwater moves at \na rate similar to the groundwater velocity. Contaminant removal \nefficiencies by septic tank systems are dependent on design and \nmaintenance, properties of waste water, and site hydrologic, soil and \nclimatic characteristics [3].  \n\n\n\nReliance on groundwater for drinking, agriculture and industrial activities \nin some areas has made the rate of abstraction surpass recharge from \nrainfall and snow. Septic tanks are not highly efficient at nutrient removal; \nestimates of removal are on the order of 5% to 8% for nitrogen and 20% \nto 30% for phosphorus [9]. While not very effective in waste water \nnutrient removal, anaerobic processes within septic tanks result in the \nconversion of organic forms of phosphorus and nitrogen to inorganic \nforms. Soluble orthophosphates and ammonium comprise approximately \n75% to 80% of the total phosphorus and nitrogen within septic tank \neffluent [10; 11]. There is need for surface and groundwater sustainability \nand integrity, as groundwater resources have been under increasing stress \nin some parts of the world due to pollution. Therefore, the availability of \nquality and quantity groundwater resources is important to improve the \nstate of well-being of the people and socioeconomic development of an \narea. The design life of many septic tank systems is in the order of 10-15 \nyears. Due to the rapid rate of placement of septic tank systems in the \n1960\u2019s, the usable life of many of the systems is being exceeded, and \ngroundwater contamination is beginning to occur [12]. The use of \nelectrical resistivity method for groundwater resource mapping and water \nquality evaluations has increased over the years because it is versatile, \nfast, cost effective and a non-destructive geophysical technique. Electrical \nresistivity imaging has no adverse impacts to the environment and serves \nas a good method for delineating the relatively rapid variations in the \nsubsurface during environmental remediation [13].  \n\n\n\nWater quality is determined by assessing three classes of attributes which \nare biological, chemical, and physical and there are standards of water \nquality set for each of these three classes of attributes. The biological and \nchemical characteristics of wastewater from individual households can \nhave a profound impact on the performance of freshwater system [12]. \nThis study is conducted by integrating electrical resistivity method and \nhydro-physicochemical analysis with the aim of assessing the impact of \nseptic systems density and nearness to water sources on groundwater \nquality. Also, determine the suitability of the water in the study area for \nhuman consumption from WHO and NSDWQ established standards, \nclarifying the risks associated with health implication on existing hand-\ndug wells and boreholes around the septic systems and suggest options for \nmitigation. \n\n\n\nFigure 3: Schematic cross-section through a conventional septic tank soil \ndisposal system for on-site disposal and treatment of domestic liquid \n\n\n\nwaste [14]  \n\n\n\n2. DESCRIPTION OF THE STUDY AREA (GEOLOGY \nAND HYDROGEOLOGY) \n\n\n\nPioneer Girls\u2019 Hostel of Adekunle Ajasin University, Akungba Akoko is \nlocated within the North Senatorial district of Ondo State, Nigeria (Fig. 4). \nIt lies between latitudes 7\u00b0 26\u2019 to 7\u00b0 29\u2019 North of the equator and \nlongitudes 5\u00b0 43\u2019 to 5\u00b0 45\u2019 East of the Greenwich meridian. The study area \nfalls within the humid and sub-humid tropical climate of southwestern \nNigeria, characterized by alternating wet and dry seasons with a mean \nannual rainfall of over 1500 mm. The area is also characterized with a \nfairly uniform temperature and high relative humidity of about 75% - 95% \n[15]. The inhabitants of this town are mainly indigenous, students and \ngovernment workers. This restricts their occupation mainly to farming, \nschooling and civil service. The area is drained by river Odowara and river \nOse with many tributaries. The rivers dominate the drainage system of the \nstudy area and they are mainly dendritic. The area is moderately to highly \nundulating with an average surface elevation of about 359 m.    \n\n\n\nThe study area falls within the Precambrian basement complex terrain of \n\n\n\nsouthwestern Nigeria. The basement complex rocks consist of crystalline \n\n\n\nigneous and metamorphic rocks, which form part of the African Crystalline \n\n\n\nshield with the rocks belonging to the youngest of the three major \n\n\n\nprovinces of the West African Craton [16]. These rocks occur either \n\n\n\nexposed or covered by shallow mantle of superficial deposits. They are \n\n\n\nloosely categorized into three main subdivisions namely the migmatite-\n\n\n\ngneiss complex; the schist belt and Pan-African (Ca 600 Ma) Older granite \n\n\n\nseries [17].  \n\n\n\nBased on field observation of the study area, the identified lithological \n\n\n\nunits comprise of predominantly migmatite gneiss, charnockitic rocks and \n\n\n\nfine to medium grained biotite granite. Granite gneiss is the dominant rock \n\n\n\ntype in the study area (Fig. 5) and they occur as ridges and hills which \n\n\n\ncontain quartz veins, dykes, quartzo-feldspathic intrusion and pegmatitic \n\n\n\nveins usually very extensive. The structural elements in the study area \n\n\n\ninclude faults, joints, xenoliths, minor folds and foliations. The older \n\n\n\ngranite of the study area is grey with a speckled appearance caused by the \n\n\n\ndarker crystals. Granite gneiss in the study area is of two types; the biotite \n\n\n\nrich gneiss and the banded gneiss. The biotite rich gneiss is fine to medium \n\n\n\ngrained that show strong foliation trending westwards and is usually dark \n\n\n\nin colour. The banded gneiss show parallel alignment and alteration.  \n\n\n\nThe aquifer system around the study area is typical of the basement \n\n\n\ncomplex terrain with the aquifer types consisting of weathered layer \n\n\n\naquifer and weathered/fractured (unconfined) aquifer. Various rock types \n\n\n\nof both igneous and metamorphic generally occur and they are \n\n\n\nimpermeable except in areas where they are cleaved, sheared, jointed and \n\n\n\nfissured [18]. The hydrogeological setting of the area is such that the \n\n\n\nfractured basement of granite gneiss, grey gneiss and charnockite acts as \n\n\n\naquifers for the boreholes and the wells in the area draw their water \n\n\n\nfrom the clay overburden.   The unconfined nature and the near- surface \noccurrence of the aquifer system makes it vulnerable to surface/near \nsurface pollutants from septic tank effluent in the area. \n\n\n\nCite the article: Ademila Omowumi  (2018). Electrical Resistivity and Hydrogeochemical Evaluation of Septic-Tanks Effluent Migration to Groundwater. \nMalaysian Journal of Geosciences, 2(2) : 01-10.\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 01-10\n\n\n\n\n\n\n\n\nFigure 4: Geological map of Ondo State showing the study area [19] \n\n\n\nFigure 5: Geological map of the study area and adjoining areas \n\n\n\n 3.MATERIALS AND METHODS\n\n\n\n3.1 Two-Dimensional (2-D) Electrical Resistivity Survey\n\n\n\nThe study area was selected due to the closely spaced septic-tank systems \nin the area and determine if the occurrence or existence of the septic tank \nsystems have influence on groundwater system. Two-dimensional (2-D) \nresistivity survey was carried out within the study area using a digital \nread-out Abem Terrameter SAS 1000C. Eight traverses were mapped out  \n\n\n\nwithin the hostel and a control traverse at about 500 m away from the \nlocation of septic tank systems using Wenner array configuration. A \nmaximum of eight levels were attained for each of the traverses with an \nelectrode spacing ranged from 2 \u2013 16 m and a station interval of 2 m. The \ntraverse length ranged from 25 to 80 m. The orientation of four of the \ntraverses were in N-S direction, while, the remaining four were in W-E \ndirection (Figure 6). The orientation of control traverse was in N-S \ndirection. \n\n\n\nCite the article: Ademila Omowumi  (2018). Electrical Resistivity and Hydrogeochemical Evaluation of Septic-Tanks Effluent Migration to Groundwater. \nMalaysian Journal of Geosciences, 2(2) : 01-10.\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 01-10\n\n\n\n\n\n\n\n\nMeasurements were made at sequences of increasing offset distances at \nelectrode spacing of 2 m along each traverse oriented in the North-South \nand East-West directions. Electrical resistivity imaging involves \nmeasuring a series of constant separation traverses along the same line \nbut with the electrode spacing being increased with each successive \ntraverse. In Wenner array, a fixed electrode spacing is chosen. The Wenner \nelectrode array consists of four electrodes (C1, P1, P2 and C2), one current \nelectrode (C1) followed by two potential electrodes, (P1, P2) and ends with \nsecond current electrode (C2).  These electrodes are equally spaced and \nthe whole arrangement is moved progressively along the traverse after \neach measurement is taken. The geometric factor K, for the Wenner spread \nis KW = 2\uf070a. Measurements commenced at one end of the traverse line with \nelectrode spacing a = 2 m at electrode positions 0, 2, 4 and 6. Next, each \nelectrode (C1, P1, P2 and C2) was shifted a distance of 2 m, the active \nelectrode positions being 2, 4, 6 and 8. The procedure was continued to \nthe end of the traverse line. At each measurement, the resistivity meter \ndisplayed field resistance value and the corresponding root mean square \n(rms) error of the reading. The apparent resistivity of the subsurface can \nbe computed using the formula \u03c1a = 2\u03c0aR, where \u201ca\u201d is the electrode \nspacing distance and R is the field resistance value.   \n\n\n\nTo be able to give a two-dimensional picture of the resistivity distribution \nwithin the subsurface as well as show a qualitative picture of the resistivity \ndata obtained, the resistivity measurements obtained are used to \nconstruct a pseudosection and then contoured. The 2-D inverse resistivity \nmodels of the subsurface were obtained from the input resistivity data \nusing [20] software. This 2-D inversion program automatically determines \nthe 2-D resistivity models of the subsurface from the input resistivity data \nusing iterative smoothness constrained least squares method [21]. It is a \n2-D inversion program that needs no previous knowledge of the \nsubsurface as the initial guess model is constructed directly from field \nmeasurements. The resistivity measurement is based on the difference in \nresistivity values of the model blocks directing towards minimizing the \ndifference between the calculated and the measured apparent resistivity\nvalues from the field. The accuracy of fit is expressed in terms of the RMS \nerror [22]. \n\n\n\nFigure 6: Data acquisition map for electrical resistivity survey \n\n\n\n3.2 Sampling and Sample Preparation\n\n\n\nQuality of water is mainly characterized by physicochemical indicators, \nsuch as pH, electrical conductivity (EC) and heavy metals, especially \ncadmium and lead concentrations in the water environment. Sampling \nsites were chosen with the aim of collecting water samples that truly \nrepresents the entire locations. A global positioning system (GPS) was \nused at each sample station to measure coordinates of the station and \nheights above sea level. Duplicate water samples were collected using \npre-cleaned bottles to which a rope was attached in the case of hand-dug \nwells. At the point of collection, the bottles were rinsed for about three \ntimes with the water samples. Each bottle was labeled according to the \nsampling location.   \n\n\n\nThirteen representative water samples (ten from different hand-dug \nwells, HdW 1 \u2013 HdW 10, and three from boreholes, BH 1, BH2 and BH 3) \nwere collected randomly from thirteen different locations. The depth of \nthe wells and static water level of the wells were noted. The groundwater \nflow direction in the area was determined based on the information \nobtained from the static water levels of the wells. Samples for anion\n\n\n\nanalyses were unfiltered and unacidified. However, samples for the \nanalysis of dissolved trace elements were filtered through 0.45 \u00b5m \ncellulose acetate membrane filter (Whatman, Schleicher and Schull FP \n30) and acidified with 30 \u00b5l of HNO3 (conc. Suprapure) [23]. Samples \nwere transported in ice-box to the laboratory and later stored in the \nrefrigerator at a temperature of about 4 \u1d3cC prior to the analysis \n(American Public Health Association, [24]. Parameters like pH, electrical \nconductivity (EC), turbidity and temperature were determined in the \nfield using calibrated Hannah pH meter, EC meter, turbidity meter and \nmercury-bulb thermometer respectively.\n\n\n\n3.3 Chemical Analysis\n\n\n\nThe physicochemical parameters observed in this study were determined \nusing the standard methods [24]. The chloride and sulphate were \ndetermined using Mohr\u2019s and turbidimetric methods respectively. For the \nnitrate and phosphate, the cadmium reduction method and ascorbic acid \nmethods were employed respectively. Others like total dissolved solids \n(TDS), total hardness (TH) and all the mineral elements were determined \nusing titration methods. The concentrations of trace metals in water \nsamples were determined after digestion with acid mixture (HCl/HNO3) \nwith flame atomic absorption spectrophotometer (Alpha 4AAS). The \ncalibration standards were prepared using the metal stock solutions. The \nbacteriological quality analysis included the determination of total \nheterotrophic bacteria (THB), total heterotrophic fungi (THF), total \ncoliform bacteria (TCB) and faecal coliform (FC). The total bacterial count \nwas determined by pour plate technique and most probable number \n(MPN) index techniques following standard method. The analysed data \ncan be used for the classification of water for various purposes and their \npercentage compliance with WHO and Nigerian Standard for Drinking \nWater Quality permissible limits [25; 26]. All the data obtained were \nsubjected to statistical analysis; the mean, range, standard deviation were \ncalculated. \n\n\n\n4. RESULTS AND DISCUSSION\n\n\n\n4.1 Interpretation of 2D resistivity sections\n\n\n\nThe inverted sections of the subsurface and resistivity distribution \nderived from measured 2-D resistivity data taken along the nine traverses \nare presented in Figures 7 - 15. The results indicate the lateral variation of \nthe subsurface lithology with depth. Figure 7 shows inverse model section \nof traverse 1, where low resistivity anomalies below 10 \u03a9m, an indication \nof septic plume migration occur at horizontal distances 51 \u2013 58 m along \nthe traverse. The septic plume has accumulated up to 5 m, have reached \nthe water table due to weathered basement before the dome shaped septic \nplume. The weathered zone with resistivity values ranging from 27 \u2013 102 \n\u03a9m shows that the layers are prone to groundwater contamination as the \nseptic system ages. There is an indication that the deeper layers are prone \nto groundwater contamination towards the northern side and vertical \nmigration up to 5 m depth below the surface and up to 2 m depth along \nhorizontal distances 20 \u2013 48 m and 52 to 76 m. Along the horizontal \ndistances 18 \u2013 48 m and 64 \u2013 74 m, high resistivity layer indicating fresh \nbasement is seen to an extent that the basement outcrop the surface \ntowards the southern part of the traverse. As shown in Figure 8, the 2D \nsection showed a low resistivity oval shaped anomaly which is an \nindication of septic plume accumulation with resistivity values between \n14 and 20 \u03a9m towards the southern end of the traverse 2 up to the depth \nof 10 m below the surface. The rest of the traverse towards the northern \nand southern end of the traverse at horizontal distance of 4 \u2013 13 m and 25 \n\u2013 36 m showed resistive anomaly between 45 \u2013 100 \u03a9m suggesting \npresence of clay materials while an isolated high resistive region of \nresistivity values between 224 and 500 \u03a9m, an indication of sandy \nmaterial was observed mainly at position 12 \u2013 24 m along the traverse. \n\n\n\nIn Figure 9, the resistivity section for traverse 3 shows low resistivity \nanomaly zone at position 12 \u2013 16 m with resistivity values between 10 and \n20 \u03a9m occurring at approximately 4 \u2013 10 m depth below the surface. This \nis an indication of septic plume accumulation. Weathered basement rock \nwith resistivity values between 20 \u2013 77 \u03a9m were noticed at depth. High \nresistive region which tends to separate the weathered basement along \nthe traverse were seen protruding to the surface which is an indication of \nbedrock at position 19 m \u2013 27 m at the central part of traverse at depth \nup to 10 m with resistivity values between 305 and 1000 \u03a9m.  \n\n\n\nFigure 10 shows 2-D inverted section of traverse 4 in which at position \n10 \u2013 24 m towards the northern part of the traverse, there is presence of \nlow resistivity anomaly with resistivity values below 10 \u03a9m, an \nindication of septic plume accumulation that formed a dome shape at the \nbase of the traverse at approximate depth of 4 \u2013 10 m. there is an \nindication that the top soil within the position 6 \u2013 28 m compose high \nresistive materials with resistivity values ranging between 405 and 1500 \n\n\n\nCite the article: Ademila Omowumi  (2018). Electrical Resistivity and Hydrogeochemical Evaluation of Septic-Tanks Effluent Migration to Groundwater. \nMalaysian Journal of Geosciences, 2(2) : 01-10.\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 01-10\n\n\n\n\n\n\n\n\n\u03a9m at approximate depth of 0 \u2013 6 m. Following the resistive top soil is an \nunderlying layer of low resistivity values ranging between 30 and 110 \n\u03a9m indicating weathered materials protruding to the surface towards the \nsouthern end of the traverse at depth. This weathered layer is prone to \ngroundwater contamination as the plume has reached the depth of 10 m \nalong the traverse.  \n\n\n\nThe 2-D inverted section of traverse 5 is shown in Figure 11. Generally for \nthe traverse, the resistivity distributions are mostly greater than 50 \u03a9m. \nHigh resistivity anomaly with resistivity values between 370 and 1000 \u03a9m \nwas noticed at horizontal position 8 \u2013 14 m and 18 \u2013 20 m indicating clayey \nsand and laterite protruding to the surface along the traverse. Low \nresistive region of resistivity values between 20 \u2013 160 \u03a9m and 160 \u2013 370 \n\u03a9m characterized the whole traverse which suggests weathered basement \nrock. This is an indication of no evidence of septic accumulation on the \ntraverse as the near surface resistivity values were higher than 20 \u03a9m. \n\n\n\nThe 2-D inverted section of traverse 6 shows an upper layer of high \nresistive material with resistivity values between 510 and 1600 \u03a9m to the \ndepth of about 2 m below the surface towards the western part and \ncontinues to the eastern part up to the depth of 10 m. Weathered basement \nrock with resistivity values between 59 and 173 \u03a9m were noticed at \napproximate depth 2 \u2013 10 m at horizontal position 4 \u2013 12 m towards the \nwestern side of the traverse. There is no evidence of septic plume across \nthe traverse (Figure 12).  \n\n\n\nGenerally for traverse 7, the resistivity distributions are greater than 50 \n\u03a9m, an indication of no septic plume across the traverse (Figure 13). The \nresistivity section shows an upper layer of high resistive material with \nresistivity values between 376 and 1000 \u03a9m to the depth of about 2 m \nbelow the surface towards the western part and continues to the eastern \npart up to the depth of 10 m. Weathered basement rock with resistivity \nrange values 51 - 141 \u03a9m and 141 \u2013 376 \u03a9m were noticed at approximate \ndepth 2 \u2013 10 m at horizontal position 4 \u2013 12 m towards the western side \nof the traverse.  \n\n\n\nFigure 14 shows 2-D inverted section of traverse 8 in which the upper part \nof the section at position 4 \u2013 28 m, 32 \u2013 44 m, 48 \u2013 54 m and 58 \u2013 64 m \nacross the traverse, there is presence of high resistive anomaly from \nwestern to eastern side. The resistivity distribution section shows the top \n2 m of the high resistive region having resistivity values between 224 and \n2500 \u03a9m, an indication that the top soil within this position range is clayey \nsand, laterite and even basement outcropping/protruding the surface. \nFollowing the resistive zone is an underlying layer of low resistivity values \nranging between 39 and 67 \u03a9m indicating closures of clay materials across \nthe traverse. The eastern end at horizontal position 64 \u2013 76 m showed low \nresistive anomaly at depth from the surface to 10 m with resistivity values \nranging from 20 \u2013 67 \u03a9m and 67 \u2013 224 \u03a9m, suggesting presence of clayey \nmaterials (clay and sandy clay/clayey sand). This is an indication of no \nevidence of septic plume on the traverse as near surface resistivity values \nwere higher than 20 \u03a9m. The lower part of the section shows undulating \nbedrock at position 6 m in the western part of the traverse to 48 m towards \nthe eastern part of the traverse with resistivity values between 628 and \n2500 \u03a9m. \n\n\n\nThe 2-D inverted section of control traverse is shown in Figure 15. High \nresistivity anomaly with resistivity values between 255 and 1300 \u03a9m \nmajorly characterized the entire traverse from the surface to the depth of \n10 m. It was noticed at northern part of the traverse at horizontal position \n14 \u2013 24 m that an isolated low resistive region of resistivity values \nbetween 50 and 113 \u03a9m was observed, an indication of clay material. This \nis an evidence of no septic plume accumulation on the control traverse as \nnear surface resistivity values were higher than 20 \u03a9m. Weathered \nbasement rock with resistivity values between 113 and 255 \u03a9m was also \nobserved at approximate depth of 3 m to 10 m at the same position 14 \u2013 \n24 towards the northern part of the traverse. It forms closures of resistive \nanomaly with resistivity value of 255 \u03a9m towards the southern part of \nthe traverse. The measurement encounters the bedrock at horizontal \nposition 62 \u2013 96 m at depth greater than 5 m with resistivity values \nbetween 576 and 1300 \u03a9m.\n\n\n\nGenerally, the results obtained from the geophysical method suggest that \nthe septic plume occurs in traverses oriented in N-S direction at depth \nranging between 1 \u2013 5 m in traverse 1 and 3 \u2013 10 m in traverses 2, 3 and \n4. \n\n\n\nFigure 7: 2-D subsurface resistivity images along traverse 1 \n\n\n\nFigure 8: 2-D subsurface resistivity images along traverse 2 \n\n\n\nFigure 9: 2-D subsurface resistivity images along traverse 3 \n\n\n\nFigure 10: 2-D subsurface resistivity images along traverse 4 \n\n\n\nFigure 11: 2-D subsurface resistivity images along traverse 5 \n\n\n\nFigure 12: 2-D subsurface resistivity images along traverse 6 \n\n\n\nCite the article: Ademila Omowumi  (2018). Electrical Resistivity and Hydrogeochemical Evaluation of Septic-Tanks Effluent Migration to Groundwater. \nMalaysian Journal of Geosciences, 2(2) : 01-10.\n\n\n\nCite the article: Ademila Omowumi  (2018). Electrical Resistivity and Hydrogeochemical Evaluation of Septic-Tanks Effluent Migration to Groundwater. \nMalaysian Journal of Geosciences, 2(2) : 01-10.\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 01-10\n\n\n\n\n\n\n\n\nFigure 13: 2-D subsurface resistivity images along traverse 7 \n\n\n\nFigure 14: 2-D subsurface resistivity images along traverse 8 \n\n\n\nFigure 15: 2-D subsurface resistivity images along traverse 9 \n\n\n\n4.2 Static water level (SWL)\n\n\n\nThe static water level is the distance from ground level down to the water \nin a well, also known as the resting level ground of water when the well is \nnot being pumped. The static water level obtained from the wells (HdWs) \nin the area varies between 2.23 \u2013 5.4 m as seen on the static water level \ncontour map (Figure 16). From the analysis of hydrogeological data, the \ntotal depth of wells varies from 5.33 m to 8.92 m and the static water \nelevation across the study area ranges from 345 m to 359 m (Table 1). The \nstatic water level helps in determining the groundwater flow direction in \nthe area which is flowing in the N \u2013 S and NE \u2013 SW directions. The direction \nof groundwater flow suggests that the water in the wells around the study \narea is flowing towards the direction of the septic plume as the results \nobtained from the geophysical method suggest that septic plume occurs in \ntraverses oriented in N \u2013 S directions. \n\n\n\nTable 1: Sampling well characteristics \n\n\n\nWell Elevation (m) Depth to water table (m) Depth to bottom (m) \nHdW 1 345 3.4 5.3 \n\n\n\n4.6 7.1 \n4.8 7.3 \n5.6 7.7 \n5.5 7.9 \n2.4 6.0 \n4.4 8.7 \n3.6 5.9 \n3.8 6.1 \n\n\n\nHdW 2 351 \nHdW 3 354 \nHdW 4 353 \nHdW 5 352 \nHdW 6 350 \nHdW 7 352 \nHdW 8 353 \nHdW 9 359 \nHdW 10 352 4.5 8.9 \n\n\n\nFigure 16: Static water level contour map showing groundwater flow \ndirection \n\n\n\n4.3 Groundwater Physicochemical parameters\n\n\n\nThe results of the groundwater physicochemical parameters are \npresented in Table 2. However, the distribution pattern across the \nvarious sampling locations compared with WHO standard is presented in \nform of line graph and bar chart as shown in Figures 17a and 17b \nrespectively. 77 % of the water sampled are colourless and odourless \nwith a temperature ranged between 26.2 \u1d3cC and 27.3 \u1d3cC. The presence of \ncolour is an indication of pollution and confirmed septic effluent \ninfiltration into the well bodies of HdW 6, HdW 7 and BH 2. The \ntemperatures were found outside the range of the WHO standard of 5 \u00b0C \nfor domestic water, hence suggesting the presence of foreign bodies.  \n\n\n\nContamination from the closely spaced septic tanks and well bodies may \nalso be responsible for the high values recorded for temperature in the \nwater samples analyzed. The pH of the HdWs vary from 4.7 to 8.0 with the \nmean values of 6.89\u00b10.9, while for the BHs, it ranged between 4.5 and 6.8 \nwith the mean values of 6.0\u00b11.3 indicating that the groundwater is \ngenerally neutral-alkaline with the exception of the pH parameter values \nin the HdW 6 and BH 2. The pH values of HdW 6 and BH 2 are 4.7 and 4.5 \nrespectively, which is slightly acidic as against the WHO standards of 6.5 \nto 8.5 pH value (Table 2).  \n\n\n\nThe acidic nature of HdW 6 and BH 2 suspected that septic tank effluent or \ndisinfectants have found their ways to the well water levels through \nseepage. The electrical conductivity (EC) is an indicator of the amount of \nmaterial dissolved in water. The EC of the HdWs ranged between 147 and \n956 \u00b5S/cm with the mean values of 367.4\u00b1239 \u00b5S/cm, while for the BHs, \nit ranged between 462 and 603 \u00b5S/cm with the mean value of 522.3\u00b173 \n\u00b5S/cm. Chloride is potentially a good indicator of a septic plume. The range \nin chloride concentration was very wide (4.1 \u2013 31.1 mg/l) for the HdWs. \nHowever, for the BHs, there were significant differences (SD > 1.0) in \nchloride limits. The low concentrations of electrical conductivity and \nchlorides indicate that there is absence of salt water intrusion in the study \narea. \n\n\n\nFrom the result of the analysis, low levels of turbidity ranging from 1.6 to \n4.3 NTU were obtained. The WHO recommends a limit of 5 NTU. The \ngroundwater obtained from the study area is therefore suitable for \ndrinking. Total hardness is normally expressed as the total concentration \nof Ca2+ and Mg2+ in mg/l. Total hardness of water sampled ranged between \n52.1 and 246.7 mg/l. Values above 200 mg/l for total hardness (TH) do not \nhave any associated health implications on humans but it\u2019s an indication \nof deposits of Ca and /or Mg ions. Their presence will disallow water from \nforming lathe with soap thereby preventing economic management of \nwater resources. The bicarbonates concentration of groundwater samples \nfrom the study area is however low (23.5 \u2013 94.3 mg/l) with a mean value \nof 52.0\u00b124.4 mg/l while for the BHs, it ranged between 24.8 and 29.1 mg/l \nwith the mean value of 26.7\u00b12.2 mg/l compared to WHO limit of 600 mg/l. \nCarbondioxide is the main source from the atmosphere. \n\n\n\nThe total dissolved solids (TDS) of the HdWs ranged between 83.5 and \n647.3 mg/l with the mean value of 398.9\u00b1189.9 mg/l while for the BHs, it \nranged between 286.4 and 311.2 mg/l with the mean value of 296.9\u00b112.8 \nmg/l. Based on WHO limit (1000 mg/l), the water is fresh. The range falls \nwithin the stipulated 1000 mg/l recommended by WHO and NSDWQ. \nThough, the concentration of TDS in the groundwater samples is low and \nfound within the specified WHO and NSDWQ standards for drinking \nwater quality (Table 2), it still indicates pollution as a result of \nsuspension that were shown during analysis.   \n\n\n\nCite the article: Ademila Omowumi  (2018). Electrical Resistivity and Hydrogeochemical Evaluation of Septic-Tanks Effluent Migration to Groundwater. \nMalaysian Journal of Geosciences, 2(2) : 01-10.\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 01-10\n\n\n\n\n\n\n\n\nConcentrations of sulphate, phosphate and nitrate in groundwater \nsamples were low and found within the specified WHO and NSDWQ \nstandards for drinking water quality (Table 2). Sulphate ion concentration \nfrom the analysis ranged from 3.1 to 26.3 mg/l and 6.8 to 19.4 mg/l for \nHdWs and BHs respectively. The observed values of sulphate ions were \nlow and within the permissible limit of 250 mg/l specified by [25; 26]. \nBased on the results, the water is not likely to pose health risk to those \nconsuming it. Thus, the groundwater obtained from the study area is \nsuitable for drinking. High concentrations of sulphate in water could cause \nbitter taste and lead to dehydration and diarrhea in children because \nchildren are more sensitive to it than adults. Phosphate levels are in the \nrange 0.07 to 0.19 mg/l in all locations with average value of 0.13\u00b10.04 \nmg/l for HdWs and 0.08\u00b10.01 mg/l for BHs, far below the WHO stipulated \ntolerance level of 5.0 mg/l for potable water. Traces of PO42- as small as 0.1 \nmg/L in water has harmful effect on water quality by promoting growth of \nalgal and the development of an unpleasant thick liquid substance known \nas slimes [27].   \n\n\n\nThe concentration of nitrate in the groundwater samples ranged from 1.8 \nto 12.7 mg/l (Table 2). Studies by US Geological Survey (USGS) defined \nconcentrations of nitrate in water above 2 mg/l as the level indicating \nhuman impact on water quality [2]. All obtained values are below the WHO \npermissible limit of 10 mg/l for potable water except HdW 6 recording the \nhighest level in the analysis, but the level of nitrate in HdW 6 is far below \nthe NSDWQ maximum permissible level of 50 mg/l. The natural levels of \nnitrate in groundwater may be increased by the leaching of waste waters \nfrom waste disposal sites, cesspools or cesspits, agricultural chemicals and \nsanitary landfills. The concentration of nitrate in water samples depends \non the nitrification activities of micro-organisms. The higher \nconcentration of chloride over sulphate, nitrate and phosphate may be as \na result of rocky nature of the study area suspected to contain some \nchloride mineral salts.  \n\n\n\nTotal suspension solid (TSS) concentration ranged from 0.1 to 1.4 mg/l. \n\n\n\nThe values are below the WHO standard of 30 mg/l. This is an indication \n\n\n\nof absence of suspended particles in the groundwater. The effect of \n\n\n\npresence of total suspended solids is the turbidity due to silt and organic \n\n\n\nmatter. The dissolved oxygen is an important factor used for water control \n\n\n\nquality as a result of the capacity of water to hold oxygen. The dissolved \n\n\n\noxygen concentration in groundwater samples ranged from 3.9 to 4.8 mg/l \n\n\n\nwith a mean concentration of 4.1\u00b10.8 mg/l and 4.4\u00b10.4 mg/l for HdWs and \n\n\n\nBHs respectively. These values indicate a fairly high level of dissolved \n\n\n\noxygen in groundwater samples. The value of 2.1 mg/l in the HdW 6 \n\n\n\nsample is an indication of oxygen depletion which infers the presence of \n\n\n\npollutants that use up the oxygen in water. Total/heavy usage of the \n\n\n\ndissolved oxygen by the pollutants were observed and indicated that the \n\n\n\nwell is unsuitable for drinking consumption.  \n\n\n\nAll the mineral elements (Ca, Mg, Na and K) were obtained at levels below \n\n\n\nWHO tolerable limits. The metals averaged of the HdWs and BHs are \n\n\n\nbetween 55.1\u00b126.6 mg/l and 57.1\u00b121.7 mg/l for calcium, 24.9\u00b18.6 mg/l \n\n\n\nand 28.1\u00b13.9 mg/l for magnesium, 9.7\u00b13.4 mg/l and 12.1\u00b12.2 mg/l for \n\n\n\nsodium and 17.9\u00b16.7 mg/l and 17.9\u00b18.9 mg/l for potassium (Table 2 and \n\n\n\nFigures 18a and 18b). They are all below the WHO and NSDWQ standard \n\n\n\nlimits (Figures 18a and 18b). Presence of calcium ion in groundwater is as \n\n\n\na result of dissolution from silicate and feldspar minerals percolating the \n\n\n\nwater. The primary source of sodium in the groundwater is from the \n\n\n\nrelease of soluble products during the weathering of plagioclase feldspars \n\n\n\n[28]. \n\n\n\nTable 2: Statistical Description of Chemical Characteristics of water samples in the study \narea and their comparisons with the WHO and NSDWQ standards \n\n\n\nSD = Standard deviation, Min. = minimum, Max. = maximum, NSDWQ = Nigerian \nStandard for Drinking Water Quality; where a = n1 or n2: n1 = Number of \nsamples for HdW = 10 and  n2 = Number of samples for BH = 3, NS = not specified \n\n\n\nParameters Min. Max. Mean\u00b1SDa WHO(2011)   NSDWQ(2007) \npH HdW 4.7 8.0 6.9\u00b10.9 6.5 \u2013 8.5 6.5 \u2013 8.5 \n\n\n\n BH 4.5 6.8 6.0\u00b11.3 \n\n\n\nEC (\u00b5S/cm)         1000 HdW 147 956 367\u00b1239 \n   BH 462 603 522\u00b173 \n\n\n\nCl- (mg/l)     HdW       4.1 31.1 11.6\u00b18.2 250 250 \nBH 5.4 8.0 6.8\u00b11.3 \n\n\n\nTurbidity (NTU) HdW 1.6 4.3 2.7\u00b10.9 5 \nBH 2.2 4.1 3.3\u00b11.0 \n\n\n\nTH (mg/l)    200         HdW 52.1 246.7 144.5\u00b155.4 500 \nBH 60.6 225.3 142.5\u00b182.4 \n\n\n\nCo32- (mg/l)         HdW 23.5 94.3 52.0\u00b124.4 600 \nBH 24.8 29.1 26.7\u00b12.2 \n\n\n\nTDS (mg/l) 500  HdW 83.5 647.3 398.9\u00b1189.9 1000 \n            BH 286.4 311.2 296.9\u00b112.8 \n\n\n\nSO42- (mg/l)        250 250 HdW 3.1 26.3 17.1\u00b17.1 \nBH 6.8 19.4 12.5\u00b16.4 \n\n\n\nPO43- (mg/l)        HdW 0.09 0.19 0.13\u00b10.04 5.0 \nBH 0.07 0.09 0.08\u00b10.01 \n\n\n\nNO3- (mg/l)         HdW 2.5 12.7 6.1\u00b12.9 10 50 \nBH 2.2 6.9 3.9\u00b12.6 \n\n\n\nTSS (mg/l)          HdW 0.1 1.4 0.87\u00b10.4 30 \nBH 0.1 0.6 0.33\u00b10.3 \n\n\n\nDO (mg/l)           HdW 2.1 4.8 4.1\u00b10.8 NS NS \nBH 3.9 4.7 4.4\u00b10.4 \n\n\n\nCa (mg/l)          200  HdW 9.3 55.2 33.1\u00b112.7 200 \nBH 23.5 44.5 34.0\u00b114.8 \n\n\n\nMg (mg/l)          150  HdW 12.4 46.5 27.6\u00b19.1 \nBH 34.5 34.5 34.5\u00b10 \n\n\n\nNa (mg/l)/l)         200 dW 15.3 87.5 51.9\u00b123.7 200 \nBH 18.5 73.4 45.9\u00b138.9 \n\n\n\nK (mg/l)       HdW 1.2 26.7 6.0\u00b17.2 50 50 \n      BH 1.2 4.6 2.9\u00b12.4 \n\n\n\nCite the article: Ademila Omowumi  (2018). Electrical Resistivity and Hydrogeochemical Evaluation of Septic-Tanks Effluent Migration to Groundwater. \nMalaysian Journal of Geosciences, 2(2) : 01-10.\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 01-10\n\n\n\n\n\n\n\n\nseptic tanks. Consequently, adverse effects such as blue baby syndrome, \ngoiter in adults, objectionable tastes and precipitation problems, \nneurological problems and corrosion of intestinal tracts due to of Fe, Mn, \nPb, Zn and Cr respectively may occur [25; 29]. The usually high level of \nthese chemical parameters in HdW 6 and HdW 7 may be attributed to the \ncloseness to the septic tank system and contamination of groundwater by \nseptic tank effluent. This suggests that wells must be properly lined and \nshould not be sited very close to septic tank in order to avoid heavy \ncontaminations.  \n\n\n\nTable 3: Statistical Description of Heavy metals in the water samples\n\n\n\nParameters \n(mg/l) \n\n\n\nMin. Max. Mean\u00b1SDa WHO \n(2011) \n\n\n\nNSDWQ \n(2007) \n\n\n\nFe    HdW 0.06 2.1 0.43\u00b10.73 0.3 0.3 \n\n\n\n  BH 0.07 0.2 0.12\u00b10.07 \n\n\n\nMn    HdW 0.03 1.1 0.27\u00b10.37 0.3 0.2 \n  BH 0.07 0.1 0.09\u00b10.02 \n\n\n\nPb    HdW 0.001 0.8 0.14\u00b10.30 0.01 0.01 \n  BH 0.003 0.008 0.01\u00b10.003 \n\n\n\nZn    HdW 1.8 3.6 2.36\u00b10.59 3 - 5 3.0 \n  BH 1.6 2.2 1.83\u00b10.32 \n\n\n\nCr    HdW 0.01 1.1 0.21\u00b10.40 0.05 0.05 \n  BH 0.01 0.03 0.02\u00b10.01 \n\n\n\nCd    HdW nd nd nd 0.003 0.003 \n  BH nd nd nd \n\n\n\nCu    HdW nd nd nd 2.0 1.0 \n  BH nd nd nd \n\n\n\nNi    HdW nd nd nd 0.07 0 (< \n0.01) \n\n\n\n  BH nd nd nd \n\n\n\nSD = Standard deviation, Min. = minimum, Max. = maximum, WHO = World \nHealth Organization, NSDWQ = Nigerian Standard for Drinking Water \nQuality, nd = Not detectable, where a = n1 or n2: n1 = Number of samples \nfor HdW = 10 and n2 = Number of samples for BH = 3 \n\n\n\nFigure 19a: Plots of heavy metal distribution across sampling \nlocations compared with [25]. \n\n\n\nFigure 17a: Plots of distribution pattern of some physicochemical \nparameters across sampling locations compared with [25] standard. \n\n\n\nFigure 17b: Distribution pattern of some physicochemical \nparameters across sampling locations compared with [25] standard.\n\n\n\nFigure 18b: Distribution pattern of the mineral elements across \nsampling locations compared with [25; 26] standards.\n\n\n\n4.4 Heavy Metals Distribution\n\n\n\nThe total metal concentrations in groundwater from the study area are \nsummarized statistically in Table 3 while Figures 19a and 19b display the \ndistribution of each heavy metal (Fe, Mn, Pb, Zn and Cr) across the \nvarious sampling locations. Apart from Fe, Mn, Pb, Zn and Cr, other \nmetals like Cd, Cu and Ni were below the instrument detection limits. \nAcross all the locations, the concentrations (in mg/l) of Fe, Mn, Pb, Zn and \nCr in HdWs and BHs were found to be below the recommended WHO \nlimit and NSDWQ maximum concentration for drinking water [25; 26]. \nThe exception of HdWs 6 and 7 are indications of water contact with the \n\n\n\nFigure 19b: Heavy metal distribution across sampling locations \ncompared with [25]. \n\n\n\nCite the article: Ademila Omowumi  (2018). Electrical Resistivity and Hydrogeochemical Evaluation of Septic-Tanks Effluent Migration to Groundwater. \nMalaysian Journal of Geosciences, 2(2) : 01-10.\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 01-10\n\n\n\n\n\n\n\n\n4.5 Microbiological Characteristics\n\n\n\nTable 4 summarizes the microbiological studies of the water from the \n\n\n\nstudy area. Water samples analyzed from the HdWs showed average \n\n\n\nconcentrations of total heterotrophic bacteria (THB) count and total \n\n\n\nheterotrophic fungi (THF) count of 2.3\u00d7102\u00b10.7\u00d7102 cfu/100 ml and \n\n\n\n2.1\u00d7102\u00b11.0\u00d7102 cfu/100 ml while that of the BHs showed average \n\n\n\nconcentrations of 1.8\u00d7102\u00b10.4\u00d7102 cfu/100 ml and 2.3\u00d7102\u00b11.0\u00d7102 \n\n\n\ncfu/100 ml. The temperature of any water body affects the rate of \n\n\n\nproliferation of micro-organisms [30]. The temperature range of 26.2 \u1d3cC \n\n\n\nand 27.3 \u1d3cC could be responsible for the growth of heterotrophic bacteria \n\n\n\nspecies present in the samples. The total heterotrophic bacteria count of \n\n\n\nthe water samples do not conform to the limit of 100 cfu/ml allowed for \n\n\n\npotable water [26]. The bacteriological constituents of the water samples \n\n\n\nindicate bacterial contamination and sources may be due to water contact \n\n\n\nwith the septic tanks.   \n\n\n\nThe average concentrations of total coliform bacteria and faecal coliform \n\n\n\nbacteria of the water samples from the HdWs are 3.0\u00d7102\u00b11.5\u00d7102 ml and \n\n\n\n2.1\u00d7102\u00b10.4\u00d7102 ml while that of the BHs are 2.9\u00d7102\u00b12.1\u00d7102 ml and \n\n\n\n1.7\u00d7102\u00b10.6\u00d7102 ml in 100 ml of the original water sample respectively  \n(Table 3). The total coliform bacteria exceeded the acceptable level of no \nbacteria. According to [25], potable drinking water should be devoid of  \ntotal coliform and faecal coliform in any given sample. All the HdWs and \nBHs are contaminated with faecal coliform. The presence of faecal \ncoliform in water indicates faecal contamination and may indicate \npossible presence of diseases causing pathogens such as bacteria, viruses \nand parasites.  \n\n\n\nThe total heterotrophic bacteria, total heterotrophic fungi, coliform \nbacteria and faecal coliform are high and greater than one in all the \nsamples analyzed, which is an indication of faecal pollution of human \nwastes. The results of this study show that the samples do not conform to \nthe WHO and NSDWQ requirements for microbiological characteristics \n\n\n\nfor human consumption. The presence of these microbes in the samples \n\n\n\nof the study area is also an indication of possible groundwater \n\n\n\ncontamination by effluent from the septic tanks. The concentration of the \n\n\n\ncontaminants corresponds to the density of the septic systems in the \n\n\n\narea. It is clear that the water need to be treated for domestic \n\n\n\nconsumption. \n\n\n\nTable 4: Statistical description of microbial analysis of the water samples \n\n\n\nSD = Standard deviation, Min. = minimum, Max. = maximum, WHO = World Health Organization, NSDWQ \n= Nigerian Standard for Drinking Water Quality; where a = n1 or n2: n1 = Number of samples for HdW = 10 \nand n2 = Number of samples for BH = 3 \n\n\n\n5. CONCLUSION\n\n\n\nIntegrated electrical resistivity imaging and hydrogeochemical \n\n\n\ninvestigation were employed to assess groundwater contamination \n\n\n\naround a girls\u2019 hostel with closely spaced septic-tanks. These have led to \n\n\n\na detailed understanding of the area than could have been achieved with \n\n\n\nthe use of a single investigative method. The interpretation of 2-D \n\n\n\nresistivity structures obtained from the electrical resistivity imaging \n\n\n\nshow low resistive zones with resistivity values less than 20 \u03a9m as \n\n\n\nindicative of septic plume which has migrated to  depths 10 m below the \n\n\n\naquifer in the study area.  Traverses oriented in North\u2013South direction \n\n\n\nshow strong evidence of septic plume at varying depth.  \n\n\n\nThe static water level map reveals that direction of flow of groundwater in \n\n\n\nthe area is towards the wells in the N-S and NE - SW directions. This \n\n\n\nsuggests that the water in the wells is flowing towards the direction of the \n\n\n\nseptic plume. This is buttressed by elevated concentrations of heavy \n\n\n\nmetals parameters observed from HdW 6 and HdW 7 above the maximum \n\n\n\npermissible limit for quality water by the WHO. The level of metals \n\n\n\nobserved in this study has shown that some of the water used for drinking \n\n\n\nand domestic purposes are polluted. This has posed a major threat to \n\n\n\nhuman and environmental health. The result of other physicochemical \n\n\n\nparameters of analyzed groundwater samples lie within WHO and NSDWQ \n\n\n\nspecification limits for drinking purpose. \n\n\n\nEvidence of groundwater contamination is also showed by the low pH \n\n\n\nvalues in HdW 6 and BH 2 and excessive amount of micro-organisms \n\n\n\n(Bacteria, fungi and coliform) in all the analyzed groundwater samples. \n\n\n\nThe low pH value is an indication that the groundwater is slightly acidic. \n\n\n\nThe presence of these microbes in the water samples of the study area is \n\n\n\nan indication of effluent/contaminated run-off from leaky septic tanks into \n\n\n\nthe water sources.  \n\n\n\nThis study has shown that contaminations of the shallow wells in the study \n\n\n\narea can be linked up to large number of people residing in the area. This \n\n\n\nis a major threat to human population consuming the water for drinking \n\n\n\npurpose. Hence, groundwater contamination from septic systems can only \n\n\n\nbe minimized by limiting the density of septic systems within a given area. \n\n\n\n It is therefore recommended that hand-dug wells and boreholes are \n\n\n\ndrilled far away from septic tanks. Septic tank must be properly designed, \n\n\n\nsited and monitored to prevent leaking of effluent to nearby water \n\n\n\nsources because leaks in concrete septic tanks can occur most readily \n\n\n\naround the inlet and outlet holes. Distance from water sources to septic \nsystems in a particular location must be widely spaced. Regular \ninvestigation using integrated electrical resistivity method and chemical \nanalyses of groundwater samples from nearby hand-dug wells and \nboreholes around septic tank should be carried out to determine position \nof septic plume and quality for drinking and consumption purposes as \nthe septic tank ages. Finally, ideal technologies for wastewater \nmanagement must be operative to protect the environment and prevent \ncontamination of drinking water sources.  \n\n\n\n Parameters (cfu/100 ml) Min. Max.    Mean\u00b1SDa \nWHO \n(2011) \n\n\n\nNSDWQ \n(2007) \n\n\n\nTHB count        HdW 1.8\u00d7102 3.8\u00d7102 \nBH 1.4\u00d7102 3.2\u00d7102 \n\n\n\n2.3\u00d7102\u00b10.7\u00d7102 100cfu/ml 100cfu/ml \n2.1\u00d7102\u00b11.0\u00d7102  \n\n\n\nTHF count        HdW 1.1\u00d7102 2.3\u00d7102 \nBH 1.1\u00d7102 3.0\u00d7102 \n\n\n\n1.8\u00d7102\u00b10.4\u00d7102 \n2.3\u00d7102\u00b11.0\u00d7102 \n\n\n\nTotal Coliform  HdW 1.1\u00d7102 4.9\u00d7102 \n    BH 1.1\u00d7102 5.2\u00d7102 \n\n\n\n3.0\u00d7102\u00b11.5\u00d7102 0/100 ml 0/100 ml \n2.9\u00d7102\u00b12.1\u00d7102  \n\n\n\nFaecal Coliform HdW 1.7\u00d7102 3.0\u00d7102 \nBH 1.1\u00d7102 2.2\u00d7102 \n\n\n\n2.1\u00d7102\u00b10.4\u00d7102 0/100 ml 0/100 ml \n1.7\u00d7102\u00b10.6\u00d7102  \n\n\n\nREFERENCES \n\n\n\n[1] Oluwafemi, O. and Oladunjoye, M. A. 2013. Integration of surface \nelectrical and electromagnetic prospecting methods for mapping \noverburden structures in Akungba Akoko, Southwestern Nigeria. \nInternational Journal of Science and Technology, 2(1):122-147. \n\n\n\n[2] Hanchar, D. W. 1991. Effects of septic-tank effluent on groundwater \nquality in Northern Williamson County and Southern Davidson County, \nTennessee. Tennessee Department of Environment and Conservation, \nDivision of water pollution control Tennessee. U.S. Geological Survey \nWater Resources Investigations Report 91-4011. \n\n\n\nCite the article: Ademila Omowumi  (2018). Electrical Resistivity and Hydrogeochemical Evaluation of Septic-Tanks Effluent Migration to Groundwater. \nMalaysian Journal of Geosciences, 2(2) : 01-10.\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 01-10\n\n\n\n\n\n\n\n\n[3] Reay, W. G. 2004. Septic tank impacts on groundwater quality and near\nshore sediment nutrient flux. Groundwater, 42(7):1079-1089. \n\n\n\n[4] Miller, D. W. 1980. Waste disposal effects on groundwater. Berkley, \n\n\n\nCalif., Premier Press, 512. \n\n\n\n[5] https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRsCxAjm8\n8ZUni4X2VeTcVDezSZ8E-6Dgc42ftfnoxSsWIQ03QHsQ \n\n\n\n[6] https://encryptedtbn0.gstatic.com/images?q=tbn:ANd9GcTN3nWXFf\n2gmoB5ZPwnYH9oV29_3XO6nO8qP7xvkxiK-kM7ObKr \n\n\n\n[7] Waller, B. G., Howie, B. and Causaras, C. R. 1987. Effluent migration \nfrom septic tank systems in two different lithologies, Broward County, \nFlorida: U.S. Geological Water Resources Investigations Report 87-\n4075:22. \n\n\n\n[8] Robertson, W. D., Cherry, J. A. and Sudicky, E. A. 1991. Groundwater \ncontamination from two small septic systems on sand aquifers.\nGroundwater, 29:82-92. \n\n\n\n[9] Valiela, I., Collins, G., Kremer, J. Lajtha, K., Geist, M., Seely, B., Brawley, J. \nand Sham, C. H. 1997. Nitrogen loading from coastal watersheds to \nreceiving estuaries. New method and application. Ecological Applications,\n7(2):358-380. \n\n\n\n[10] Weiskel, P. K. and Howes, B. L. 1992. Differential transport of sewage-\nderived nitrogen and phosphorus through a coastal watershed. \nEnvironmental Science and Technology, 26(2):352-360. \n\n\n\n[11] Wilhelm, S. R., Schiff, S. L. and Robertson, W. D. 1994. Chemical fate \nand transport in a domestic septic system: Unsaturated and saturated \nzone geochemistry. Environmental Toxicology and Chemistry, 13(2):193-\n203. \n\n\n\n[12] Bicki, T. J., Brown, R. B., Collins, M. E., Mansell, R. S. and Rothwell, D. F.\n1984. Impact of on-site sewage disposal systems on surface and \ngroundwater quality. Soil Science Department, G-159 McCarty Hall,\nInstitute of Food and Agricultural Sciences, University of Florida, \nGainesville, Florida 32611. Report to Florida Department of Health and \nRehabilitative Services under contact number LC170. \n\n\n\n[13] Kumar, D. 2012. Efficacy of electrical resistivity tomography technique\n\n\n\nin mapping shallow subsurface anomaly. Journal of Geological Society, \n\n\n\nIndia, 80:304-307. \n\n\n\n[14] https://encryptedtbn0.gstatic.com/images?q=tbn:ANd9GcSfyPhZ_K\n1Ra9y_Gshf1AGK18WTZOWt9_Cb5n_a-rLkSEGgcO8 \n\n\n\n[15] Nigeria Meteorological Agency, (NIMET). 2007. Daily weather forecast\n\n\n\non the Nigerian Television Authority. Nigerian Metrological Agency,\n\n\n\nOshodi, Lagos, Nigeria. \n\n\n\n[16] Jones, H. A. and Hockey, R. D. 1964. The geology of Southwestern\n\n\n\nNigeria. Geological Survey of Nigeria Bulletin. 31:89. \n\n\n\n[17] Elueze, A. A. 2000. Compositional appraisal and petrotectonic\n\n\n\nsignificance of the Imelu banded ferruginous rock in Ilesha Schist Belt,\n\n\n\nSouthwestern Nigeria. Journal of Mining and Geology, 36(1):9-18. \n\n\n\n[18] Olayinka, A. I. 1972. Bulletin of the International Association of\nHydrogeological Sciences, XVII, 14/1972. \n\n\n\n[19] Obaje, N. G. 2009. Geology and mineral resources of Nigeria. Lecture\nnotes in Earth Sciences 120. Springer-Verlag Berlin Heidelberg. 221. \n\n\n\n[20] Dippro for Windows 2000. DipproTM Version 4.0 Processing and\n\n\n\nInterpretation software for Dipole \u2013 Dipole electrical resistivity data.\n\n\n\nKIGAM, Daejon, South Korea. \n\n\n\n[21] Sasaki, Y. 1992. Resolution of resistivity tomography inferred from\nnumerical simulation. Geophysics Prospect, 40:453-464. \n\n\n\n[22] Loke, M. H. and Barker, R. D. 1996. Rapid least squares inversion of \n\n\n\napparent resistivity pseudosections by a quasi-Newton method.\n\n\n\nGeophysics Prospect, 44:131-152. \n\n\n\n[23] Nickson, R. T., McArthur, I. M., Shrestha, B., Kyaw-Myint, I. O. and Lowly,\n\n\n\nD. 2005. Arsenic and other drinking water uses, Muzaffargarh District, \n\n\n\nPakistan. Applied Geochemistry, 20:55 \u2013 68. \n\n\n\n[24] American Public Health Association (APHA) 2005. Standard Methods \nfor the Examination of Water and Waste water, twenty-first edition. \nAmerican Public Health Association,  Washington, DC. \n\n\n\n[25] World Health Organization. 2011. Guidelines for Drinking Water \nQuality 4th Edition. ISBN 978 9241548151. \n\n\n\n[26] Nigerian Standard for Drinking Water Quality (NSDWQ). 2007. \nNigerian Industrial Standard (NIS), 554:30. \n\n\n\n[27] Adekunle, I. M., Adetunji, M. T., Gbadebo, A. M. and Banjoko, O. B. 2007. \nAssessment of  Groundwater Quality in a Typical Rural Settlement in \nSouthwest, Nigeria. International Journal of Environmental Research and \nPublic Health, 4(4):307 \u2013 318. \n\n\n\n[28] Davis, S. N. and De Wiest, R. J. M. 1996. Hydrogeology. New York: John\nWiley and sons. \n\n\n\n[29] Longe, E. O. and Balogun, M. R. 2010. Groundwater Quality \n\n\n\nAssessment near a Municipal Landfill, Lagos, Nigeria. Research Journal of \n\n\n\nApplied Sciences, Engineering and Technology, 2(1):39 \u2013 44. \n\n\n\n[30] Pelczar, M. J., Chan, E. C. S. and Noel, R. K. 2005. Microbiology, 5th\n\n\n\nEdition, Tata Mc Graw Hill, New Delhi, pp. 571. \n\n\n\nCite the article: Ademila Omowumi  (2018). Electrical Resistivity and Hydrogeochemical Evaluation of Septic-Tanks Effluent Migration to Groundwater. \nMalaysian Journal of Geosciences, 2(2) : 01-10.\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 01-10\n\n\n\n\nhttps://encryptedtbn0.gstatic.com/images?q=tbn:ANd9GcTN3nWXFf2gmoB5ZPwnYH9oV29_3XO6nO8qP7xvkxiK-kM7ObKr\n\n\nhttps://encryptedtbn0.gstatic.com/images?q=tbn:ANd9GcTN3nWXFf2gmoB5ZPwnYH9oV29_3XO6nO8qP7xvkxiK-kM7ObKr\n\n\nhttps://encryptedtbn0.gstatic.com/images?q=tbn:ANd9GcSfyPhZ_K1Ra9y_Gshf1AGK18WTZOWt9_Cb5n_a-rLkSEGgcO8\n\n\nhttps://encryptedtbn0.gstatic.com/images?q=tbn:ANd9GcSfyPhZ_K1Ra9y_Gshf1AGK18WTZOWt9_Cb5n_a-rLkSEGgcO8\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 69-75 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2021.69.75 \n\n\n\n \nCite The Article: Raja Asim Zeb, Muhammad Haziq Khan, Intikhab Alam, Ahtisham Khalid, Muhammad Faisal Younas (2021). Well Logs Analysis To Estimate The \n\n\n\nParameters of Sawan-2 and Sawan-3 Gas Field. Malaysian Journal of Geosciences, 5(2): 69-75. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nREVIEW ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2021.69.75 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWELL LOGS ANALYSIS TO ESTIMATE THE PARAMETERS OF SAWAN-2 AND \nSAWAN-3 GAS FIELD \n \nRaja Asim Zeba, Muhammad Haziq Khanb, Intikhab Alamc, Ahtisham Khalida, Muhammad Faisal Younasc \n \n  a Institute of Earth Sciences, University of Poonch Rawalakot AJK \nb Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu China \nc The Institute of Geology, University of Azad Jammu and Kashmir AJK \n*Corresponding Author Email: haziqkhan912@gmail.com \n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 14 September 2021 \nAccepted 17 October 2021 \nAvailable online 22 October 2021 \n\n\n\n\n\n\n\nThe lower Indus basin is leading hydrocarbon carriage sedimentary basin in Pakistan. Evaluation of two sorts \nout wells namely Sawan-2 and Sawan-3 has been assumed in this work for estimation and dispensation of \npetro physical framework using well log data. The systematic formation assessment by using petro physical \nstudies and neutron density cross plots reveal that lithofacies mainly composed of sandstone. The \nhydrocarbon capability of the formation zone have been mark through several isometric maps such as water \nsaturation, picket plots, cross plots, log analysis Phie vs depth and composite log analysis. The estimated petro \nphysical properties shows that reservoir have volume of shale 6.1% and 14.0%, total porosity is observed \nbetween 14.6% and 18.2%, effective porosity ranges 12.5-16.5%, water saturation exhibits between 14.05% \nand 31.58%, hydrocarbon saturation ranges 68.42% -86.9%, The lithology of lower goru formation is \ndominated by very fine to fine and silty sandstone. The study method can be use within the vicinity of central \nIndus basin and similar basin elsewhere in the globe to quantify petro physical properties of oil and gas wells \nand comprehend the reservoir potential. \n\n\n\nKEYWORDS \n\n\n\nPetrophysical Evaluation, Central Indus basin, Lower Goru formation, Reservoir Potential \n\n\n\n1. INTRODUCTION \n\n\n\nThe petro physical constant appraisal from well logs is a key part of the \nhydrocarbon exploration and production which comprehend the \nsubsurface reservoir properties like porosity, permeability, water and \nhydrocarbon saturation etc. In 1927, Schlumberger brothers initially \ninstigate wire line logging in Alsace, France. It can be executed by visual \nexamination of samples acquired to the surface from subsurface (e.g., \ncuttings logs, core logging or petro physical logging) or by lowering the \nequipment\u2019s into the borehole (Ofwona, 2010). Basically, there are three \ntypes of logging that included open hole logging, cased hole logging and \nproduction logging. Frequently used logs include Gamma ray (GR), density \n(RHOB), sonic (DT), neutron (NPHI), resistivity, caliper log and cement \nbond log (Rider, 1996). The present research work is supervised by make \nuse of conventional logs run in the Cretaceous Lower Goru Formation \nperforate in Sawan-2 and Sawan-3 wells of the Sawan Gas Field, Central \nIndus Basin, Pakistan. Sawan Gas Field is located at latitude \n(27\u00b002'22.7''N) and longitude (68\u00b058'19.5''E) in Central Indus Basin, \nKhairpur, Pakistan. The Central Indus Basin comprised of Sulaiman Fold \nBelt, Sulaiman Fore deep and Punjab Platform shown in (Figure 1).  It is \nadjoining by Sargodha High and Pezu uplift; Indian Shield; Indian Plate \nmarginal zone and Sukkur Rift in the North, East, West and South \nrespectively (Kadri, 1995). Accordant with lithology, the Goru Formation \nis subdivided into Lower Goru and Upper Goru (Kadri, 1995). The Lower \nGoru Formation on the whole is made up of basal, middle and upper sands \nunstrung by lower and upper shales. Main hydrocarbon producing zones \nbelongs to the upper sands. This zone consists of sub zones i.e. A, B, C and \n\n\n\nD sands divided by Turk, Badin and Jhol shale respectively (Quadri and \nShuaib, 1986). \n\n\n\n \nFigure 1: Regional tectonic map showing the location of Sawan Gas Field \n\n\n\n(Krois et al., 1998). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 69-75 \n\n\n\n\n\n\n\n \nCite The Article: Raja Asim Zeb, Muhammad Haziq Khan, Intikhab Alam, Ahtisham Khalid, Muhammad Faisal Younas (2021). Well Logs Analysis To Estimate The \n\n\n\nParameters of Sawan-2 and Sawan-3 Gas Field. Malaysian Journal of Geosciences, 5(2): 69-75. \n\n\n\n\n\n\n\nIn the subsurface of the Southern Sindh Monocline (Lower Indus Basin, \nPakistan), a studied environments of deposition of Upper sands (B-sand) \nof Early Cretaceous Lower Goru Formation (Solangi et al., 2016). They \nconcluded that the Lower Goru Formation (Bsand) is a reservoir facies and \ndeposited as barrier bar and transgressive facies in deltaic to shallow \nmarine conditions. In Sanghar Block of the Lower Indus Basin, some \nresearchers evaluated the petrophysical properties of the Lower Goru \nFormation penetrated in Fateh-01 and Panairi-01 wells (Nisar et al., \n2016). According to them, the Lower Goru Formation is water-saturated \nin the drilled wells. A group researchers conducted the petrophysical \ncharacterization of the Lower Goru Formation in Sawan-03 and Sawan-07 \nwells and determined that the subject formation has good effective \nporosity but low water saturation (Abbas et al., 2015). A group \nresearchers worked out the sedimentological investigation of the Lower \nGoru Formation (upper sands) using ditch cuttings and geophysical logs \npenetrated in the Sindh Monocline, Southern Indus Basin, Pakistan (Sahito \net al., 2013). According to them, the sands are moderate to well sorted,sub-\nangular to well-rounded with mean sand grain size varying from fine to \nmedium grained. \n\n\n\n1.1 Geology and tectonic setting  \n\n\n\nA geologically depressed area with thick sediment in the center and \nthinner towards edges called a sedimentary basin. This formation is \nprecisely fit for Baluchistan and Indus basin where stratigraphic record is \nnot much influenced by tectonic activities (Shah, 2009). Due to intense \nphase of quartz overgrowth the initial porosity was lost. In addition, 5 to \n10% porosity was found due to secondary alteration and finally it was \nconcluded that lower goru is tight sand reservoir and will produce in \nfuture with proper fracturing job (Mohsin, et al 2010). A comparison of \ncore and well log permeability of sawan tight sand of lower goru has done \ncore-log and well test permeability are not usually the same because core-\nlog gives absolute while well test data provide effective permeability \nprofile of reservoir rock. The noticeable difference has been detected in \ncore-log permeability and well test permeability was also compared with \nother industry reported case (Ahmed et al., 2010). The use of spectroscopy \nlog provides accurate petrophysical evaluation by characterizing the rock \nmatrix in terms of lithology and matrix properties which in turn is very \nimportant for accurate calculations of porosity and permeability (Aziz et \nal., 2011). Pakistan is unique because it\u2019s located at the junction of these \ntwo diverse domains the southern part of it belong to gondwanian domain \nand it is sustained by indo-pak crustal plate. Second is tethyan domain \nwhich is consisting of complex geology of northern and western part of \nPakistan (Kazmi and jan, 1997). The subduction of northern edge finally \nclosed the Neo-Tethys Ocean and Indian Ocean and collision structured \nthe Himalayan and other mountain ranges. This gives rise to thrust belts \ndipping in north-south direction (Shami and Baig, 2002). Out crops of \nsedimentary rocks in Punjab platform are not exposed on surface and are \ncovered with thick alluvium deposit of clay silt and sand layers. \nTectonically it is broad monoclinic dipping gently toward the sulamain \ndepression (Raza et al., 1989). Seismic evidences of this area are buried \nanticlines that might form at the expense of flow of Eocene shale (Kadri, \n1995).  \n\n\n\n1.2 Stratigraphic correlation  \n\n\n\nThe stratigraphy of study area is comprised from the rock ranging from \nsember formation to alluvium (Wandery et al., 2004). Effective sealing \nmechanism provided by transgression shale of the upper and lower goru \nfor the entrapment of hydrocarbon in the lower goru sand reservoir (Nisa, \n1986). The maturation of the source rock started in cretaceous time and \nreached its peak during Eocene-Miocene time (Kadri, 1995). The lower \ngoru formation proposing great to brilliant source potential have \nfavorable porosity and permeability to store and transmit fluid at the same \ntime. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nLog based petro physical analysis were conducted on the data from two \n\n\n\nwells located in the central Indus basin namely Sawan-2 and Sawan-3. The \n\n\n\nwell log data comprises of conventional logs such as neutron, density, \n\n\n\nsonic, gamma ray and resistivity (shallow and deep). These data sets were \n\n\n\nprovided by DGPC Pakistan. Kingdom SMT 8.2 was used for developing the \n\n\n\nburial history model while the interactive petrophysics was used to \n\n\n\nconduct qualitative and quantitative reservoir evaluation. Lithological \n\n\n\ncomposition was identified based on Schlumberger charts such as (NPHI \n\n\n\nvs. RHOB) neutron vs. density. Moreover, neutron density initial model \n\n\n\nwas used to derive the total and effective porosities. A 10% porosity cut-\n\n\n\noff was applied to identify more porous and promising zones within the \n\n\n\nreservoir (EL-Din et al., 2013). The density porosity was calculated by \n\n\n\nusing Wyllie\u2019s equation while the neutron porosity was estimated based \n\n\n\non response given by neutron tool (Wyllie\u2019s 1963). \n\n\n\n\u2205\ud835\udc37 = (\ud835\udf0c\ud835\udc5a\ud835\udc4e \u2212 \ud835\udf0c\ud835\udc4f)/(\ud835\udf0c\ud835\udc5a\ud835\udc4e \u2212 \ud835\udf0c\ud835\udc53) \n\n\n\nWhere \u2205\ud835\udc37density porosity, \ud835\udf0c\ud835\udc5a\ud835\udc4e is the matrix density, \ud835\udf0c\ud835\udc4f is bulk density \n\n\n\nand \ud835\udf0c\ud835\udc53 is the mud filtrate density. Moreover, it is essential to determine \n\n\n\nthe type of shale distribution witin the reservoir. Relation between SP and \n\n\n\nGamma ray logs was used to determine the shale habbit in the reservoir. \n\n\n\nIn the present study Sawan-2 and Sawan-3 were subjected to dual water \n\n\n\nsaturation model (Clavier et al., 1984). In addition, 50% cut-off was \n\n\n\napplied to differentiate between sandy and shaly zones its mean that rocks \n\n\n\ndisplaying more than 50% of shale content were considered as non-\n\n\n\nreservoir rocks while the rocks with equal or less than 50% of shale were \n\n\n\nconsidered as promising reservoir. \n\n\n\n \nFigure 2: Shows relationship b/w water saturation and depth. \n\n\n\n \nFigure 3: Shows relationship b/w Gamma ray log and SP log indicating \n\n\n\nthe volume of shale \n\n\n\nThe water saturation of uninvaded zone (Sw) in the lower goru \n\n\n\nformation was estimated by using dual equation while the hydrocarbon \n\n\n\nsaturation (Sh) was estimated by the equation given below (Rider 1996).  \n\n\n\nSh=1-Sw \n\n\n\nIn addition, residual hand moveable hydrocarbon saturation was also \n\n\n\ncalculated by following equations (Asquith and Krygowski, 2004).  \n\n\n\nShr = 1-Sxo \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 69-75 \n\n\n\n\n\n\n\n \nCite The Article: Raja Asim Zeb, Muhammad Haziq Khan, Intikhab Alam, Ahtisham Khalid, Muhammad Faisal Younas (2021). Well Logs Analysis To Estimate The \n\n\n\nParameters of Sawan-2 and Sawan-3 Gas Field. Malaysian Journal of Geosciences, 5(2): 69-75. \n\n\n\n\n\n\n\nShm = Sxo-Sw \n\n\n\nMoreover bulk volume of water in uninvaded and flushed zone was \n\n\n\ncalculated by multiplying Sw and Sxo with the effective porosity \u2205\ud835\udc52 using \n\n\n\nthe equations below (Asquith and Krygowski, 2004). \n\n\n\nBVW =Sw\u00d7 \ud835\udf03\ud835\udc50 \n\n\n\nBVSXO = Sxo\u00d7  \ud835\udf03\ud835\udc50 \n\n\n\nAfter 50% water saturation cut-off was applied to distinguish between \n\n\n\nwater wet and hydrocarbon bearing zones. \n\n\n\n3. RESULT AND DISCUSSION \n\n\n\n3.1 Neutron (NPHI) versus Density (RHOB) cross-plot \n\n\n\nThe neutron (NPHI) versus density cross-plot shows that the Lower Goru \n\n\n\nFormation within the selected wells appears to be dominantly composed \n\n\n\nof sandstone with carbonates (Limestone and Dolomite) as shown in \n\n\n\nfigure 4 the NPHI vs. RHOB cross-plot also indicates the presence of \n\n\n\nsubordinate shale\u2019s. This cross-plot also helps in estimating a relationship \n\n\n\nbetween different lithology and porosity types (Hakimi et al., 2017; Al-\n\n\n\nQayim and Rashid, 2012). Since the cross plot indicate the presence of both \n\n\n\nsandstone and carbonates which also indicate the presence of both inter \n\n\n\ngranular and secondary porosities with an equal proportion.  \n\n\n\n\n\n\n\n \nFigure 4: Picket plots shows resistivity of water/ water saturation \n\n\n\n3.2 Gross and net pay thickness \n\n\n\nNet pay is important parameters when it comes to evaluate the reservoir \n\n\n\npotential as it identifies the zones having enough hydrocarbon volume and \n\n\n\nacting as producing intervals (Worthington, 2010). Net pay is a sub \n\n\n\ninterval within the gross thickness and is quantified by applying petro \n\n\n\nphysical cut-offs to well log data Phi< 10%, Vsh<30%, Sw>50%. In the \n\n\n\npresent study a total twenty-three net pay sun intervals have been \n\n\n\nidentified in Sawan-2 (8 pay zones), Sawan-3, and the highest number of \n\n\n\npay zones identified in Sawan-2. The results indicate the total net pay \n\n\n\nzones for studied well range between 36.76 and 140.06 m. \n\n\n\n \nFigure 5: Cross plots shows the lithology / matrix index \n\n\n\n\n\n\n\nFigure 6: Shows Petrophysical results of the derived well logs (Sawan-2) \n\n\n\n \nFigure 7: Shows relationship b/w PHIE and Depth. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 69-75 \n\n\n\n\n\n\n\n \nCite The Article: Raja Asim Zeb, Muhammad Haziq Khan, Intikhab Alam, Ahtisham Khalid, Muhammad Faisal Younas (2021). Well Logs Analysis To Estimate The \n\n\n\nParameters of Sawan-2 and Sawan-3 Gas Field. Malaysian Journal of Geosciences, 5(2): 69-75. \n\n\n\n\n\n\n\n3.3 Volume of Shale\u2019s \n\n\n\nIn figure 8, curve is giving response with respect to shale volume. Density \nof the shales is higher than that of limestone or sandstone. From depth \n3365 to 3410m in Figure 8, curve is in the right side of the red line which \nmean density of shale is higher because of lower volume of shale, \nrepresenting sand lithology. The again from 3350 to 3600 m, curve is \nmoving to right side of the red line giving higher values of density of shale \nrepresenting high volume of shales.  \n\n\n\nTable 1: Result shows petrophysical parameters of Sawan-2 and \nSawan-3 wells \n\n\n\nParameters \n\n\n\nAverage \n\n\n\nRange Average Range \n\n\n\n                                 Sawan-2                                  Sawan-3 \n\n\n\nRHOB 2.1-3 0.6 2.1-3.3 3.75 \n\n\n\nNPHI 0-0.3 0.15 0-0.4 0.2 \n\n\n\nBVW 30-75 67.5 40-70 75 \n\n\n\nGR Clean 3110-3350 80-85 1300-3600 50-55 \n\n\n\nSw 25-40 32.5 25-40 45 \n\n\n\nHc 60-75 97.5 60-75 25-40 \n\n\n\nTotal \nporosity \n\n\n\n15-25 27.5 18-26 31 \n\n\n\nEffective \nporosity \n\n\n\n30-35 47.5 30-40 50 \n\n\n\nDepth 910-3100 - 0-3600 - \n\n\n\nPhie 0.2-0.5 0.45 0-0.3 0.15 \n\n\n\nRt LLD 25-50 50 15-30 30 \n\n\n\n \nFigure 8: Composite log shows the stratigraphic columns. \n\n\n\nSawan-3 Figures \n\n\n\n \nFigure 9: Shows relationship b/w water saturation and depth. \n\n\n\n3.4 Log derived porosity estimation (Total and Effective) \n\n\n\nThe total and effective porosities range from 14.6 to 19.03 respectively. \nThe type of porosity within the reservoir formation has been estimated by \nNeutron density log and depth vs. PHIE log (Figure 2).   \n\n\n\n3.5 Fluid saturation Estimation \n\n\n\nLowe goru formation act as a reservoir because of 55% sandstone with 9% \neffective porosity. Percentage of water saturation in this unit is 25 to 35% \nwhich mean hydrocarbon are present (Figure 10). While percentage of \nwater saturation in the pores is 1 %. \n\n\n\n \nFigure 10: Shows relationship b/w Gamma ray log and SP log indicating \n\n\n\nthe volume of shale \n\n\n\n \nFigure 11: Picket plots shows the resistivity of water \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 69-75 \n\n\n\n\n\n\n\n \nCite The Article: Raja Asim Zeb, Muhammad Haziq Khan, Intikhab Alam, Ahtisham Khalid, Muhammad Faisal Younas (2021). Well Logs Analysis To Estimate The \n\n\n\nParameters of Sawan-2 and Sawan-3 Gas Field. Malaysian Journal of Geosciences, 5(2): 69-75. \n\n\n\n\n\n\n\n \nFigure 12: Shows relationship b/w PHIE and Depth. \n\n\n\n \nFigure 13: Shows Petrophysical results of the derived well logs (Sawan-\n\n\n\n3) \n\n\n\n \nFigure 14: Cross plots shows the lithology / matrix index \n\n\n\n \nFigure 15: Composite log shows the stratigraphic columns (Sawan-3). \n\n\n\n4. CONCLUSION \n\n\n\nPresent study reveals the reservoir character tics of Sawan-2 and Sawan-\n3 wells of central Indus basin of Pakistan. A total twenty-three net pay sum \nintervals have been identified in Sawan-2 (8 pay zones), Sawan-3,(15) pay \nzone  and the highest number of pay zones identified in Sawan-2. The \nresults indicate that the total net pay zones for studied wells range \nbetween 36.76 and 140.06 likewise cross plots reveal that the formation \nof interest is mainly composed of sandstone, shale and carbonates. The \nvolume of shale has been estimated between 6.1% and 14.0% from the \nstudied wells. Similarly, the total porosity is observed between 14.6% and \n18.2% while the effective porosity ranges 12.5-16.5%. The water \nsaturation exhibits between 14.05% and 31.58%. Moreover, the \nhydrocarbon saturation ranges 68.42% -86.9% in the studied wells. 23 \npay zones with variable thickness and significant hydrocarbon presence \nhave been identified within the studied wells, thus proving the Lower Goru \nFormation as a promising reservoir. Lowe goru formation act as a \nreservoir because of 55% sandstone with 9% effective porosity. \nPercentage of water saturation in this unit is 25 to 35% and hydrocarbon \nsaturation is 70-75% which means that hydrocarbon is present while only \n1% pores are saturated with water. Results reveal that wells are favorable \nfor hydrocarbon extraction. \n\n\n\nREFERENCES \n\n\n\nAdabanija, M.A., Ajibade, R.A., 2020. Investigating groundwater corrosion \nand overburden protective capacity in a low latitude crystalline \nbasement complex of southwestern Nigeria. NRIAG Journal of Astronomy \nand Geophysics, 9 (1), Pp. 245-259. \nhttps://doi.org/10.1080/20909977.2020.1723867. \n\n\n\nAdegoke, O.S., 1969. Eocene Stratigraphy of Southern Nigeria. Collogue sur \nEocene Vol. III Bur. Rech. Geol. Min. Mem., 69, Pp. 23-48. \n\n\n\nAdegoke, O.S., Jeje, L.K., Durotoye, B., Adeleye, D.R., and Ebukanson, E.E., \n1981. The Geomorphology and Aspects of Sedimentology of Coastal \nRegion of Western Nigeria. J. Min. Geol., 17 (2), Pp. 217-223. \n\n\n\n Archie, G.E., 1942. The Electrical Resistivity Log as an Aid in Determining \nSome Reservoir Characteristics. Trans., 146 (01), Pp. 54\u201362. Paper \nNumber: SPE-942054-G https://doi.org/10.2118/942054-G \n\n\n\nAyuk, M.A., 2019. Groundwater Aquifer Vulnerability Assessment using a \nDar-Zarrouk Parameter in a Proposed Aboru Residential Estate, Lagos \nState, Nigeria. J. Appl. Sci. Environ. Management, 23 (12), Pp. 2081-2090. \n\n\n\nBatayneh, A.T., 2013. The estimation and significance of Dar-Zarrouk \nparameters in the exploration of quality affecting the Gulf of Aqaba \ncoastal aquifer systems. Journal of Coastal Conservation, 17, Pp. 623-635. \nhttps://doi.org/10.1007/s11852-013-0261-4. \n\n\n\nBovolo, C.I., Parkin, G., Sophocleous, M., 2009. Groundwater resources, \nclimate and vulnerability. Environ. Res. Lett., 4, Pp. 035001. \n\n\n\nBraga, A.C.O., Filho, W.M. and Dourado, J.C., 2006. Resistivity (DC) method \napplied to aquifer protection studies. Brazilian Journal of Geophysics, 24, \nPp. 573-581. \n\n\n\n\njavascript:;\n\n\nhttps://doi.org/10.2118/942054-G\n\n\nhttps://doi.org/10.1007/s11852-013-0261-4\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 69-75 \n\n\n\n\n\n\n\n \nCite The Article: Raja Asim Zeb, Muhammad Haziq Khan, Intikhab Alam, Ahtisham Khalid, Muhammad Faisal Younas (2021). Well Logs Analysis To Estimate The \n\n\n\nParameters of Sawan-2 and Sawan-3 Gas Field. Malaysian Journal of Geosciences, 5(2): 69-75. \n\n\n\n\n\n\n\nBraga, A.C.O., 2008. Estimation of the natural vulnerability of aquifers: a \ncontribution from the resistivity and longitudinal conductance. Brazilian \nJournal of Geophysics, 26 (1), Pp. 61-68. \n\n\n\nBraga, A.C.O., Francisco, R.F., 2014. Natural vulnerability assessment to \ncontamination of unconfined aquifers by longitudinal conductance \u2013 (S) \nmethod. Journal of Geography and Geology, 6 (4), Pp. 68-79.  \n\n\n\nBritish Geological Survey, 2008. Groundwater Information Sheet: The \nImpact of Industrial Activity, Pp. 6. \n\n\n\nCasas, A., Himi, M., Diaz, Y., Pinto, V., Font, X., Tapias J.C.A., 2008. Assessing \naquifer vulnerability to pollutants by electrical resistivity tomography \n(ERT) at a nitrate vulnerable zone in NE Spain. Environ Geol., 54, Pp. 515\u2013\n520. DOI 10.1007/s00254-007-0844-1 \n\n\n\nChristensen, N.B., S\u00f8rensen, K.I., 1998. Surface and Borehole Electric and \nElectromagnetic Methods for Hydro-Geophysical Investigations. \nEuropean Journal of Environmental and Engineering Geophysics, 3, Pp. \n75-90. \n\n\n\nCosgrove, W.J. and Loucks, D.P., 2015. Water management: Current and \nfuture challenges and research directions, Water Resour. Res., 51, Pp. \n4823\u20134839, doi:10.1002/ 2014WR016869. \n\n\n\nD\u00f6ll, P., 2009. Vulnerability to the impact of climate change on renewable \ngroundwater resources: a global-scale assessment. Environ. Res. Lett., 4, \nPp. 035006. \n\n\n\nFlathe, H., 1955. Possibilities and limitations in applying geoelectrical \nmethods to hydrogeological problems in the coastal area of northwest \nGermany. Geophysical Prospecting, 3, Pp. 95-110. \nDOI: https://doi.org/10.1111/j.1365-2478.1955.tb01363.x. \n\n\n\nGardner, K.M., 1999. The importance of surface water/groundwater \ninteractions \u2013 Report EPA-910-R-99-013, Environmental Protection \nAgency, Seattle. \n\n\n\nHasan, M., Yanjun, S., Weijun, J., Gulraiz, A., 2019. Assessment of aquifer \nvulnerability using integrated geophysical approach in weathered \nterrains of south China. Open Geosciences, 11 (1), Pp. 1129-1150. \nhttps://doi.org/10.1515/geo-2019-0087.  \n\n\n\nHenriet, J.P., 1976. Direct application of the Dar-Zarrouk parameters in \ngroundwater survey. Geophysical Prospecting, 24, Pp. 344-353. \nhttps://doi.org/10.1111/j.1365-2478.1976.tb00931.x. \n\n\n\nGupta, G., Patil, S.N., Padmane, S.T., Erram, V.C. and Mahajan, S.H., 2015. \nGeoelectric investigation to delineate groundwater potential and \nrecharge zones in Suki River basin, north Maharashtra. J. Earth Syst. Sci., \n124 (7), Pp. 1487\u20131501. DOI: 10.1007/s12040-015-0615-4. \n\n\n\nJones, H.A., Hockey R.D., 1964. The geology of part of south western \nNigeria. Geol. Survey. Nigeria, Bull., 31, Pp. 1-101. \n\n\n\nKampsax-Kruger and Sshwed Associates, 1977. Underground water \nresources of the Metropolitan Lagos. Final Report to Lagos State Ministry \nof Works, Pp. 170. \n\n\n\nKeller, G.V., 1982. Electrical properties of rocks and minerals: In: \nHandbook of Physical Properties of Rocks (ed.) Carmichael R.S.., CRC \nPress, Pp. 217-293. \n\n\n\nKhalil, M.H., 2009. Hydro geophysical assessment of Wadi El-Sheikh \naquifer, Saint Katherine, South Sinai, Egypt. J. Environ. Eng. Geophys., 14 \n(2), Pp. 77-86. https://doi.org/10.2113/JEEG13.2.77.  \n\n\n\nKhatri, N., Tyagi, S., 2015. Influences of natural and anthropogenic factors \non surface and groundwater quality in rural and urban areas, Frontiers \nin Life Science, 8 (1), Pp. 23-39, DOI: 10.1080/21553769.2014.933716. \n\n\n\nKonikow, L.F., August, L.L., Voss, C.I., 2001. Effects of clay dispersion on \naquifer storage and recovery in coastal aquifers. Transport in Porous \nMedia, 43, Pp. 45-64. https://doi.org/10.1023/A:1010613525547. \n\n\n\nKonikow, L.F. and Neuzil, C.E., 2007. Method to estimate groundwater \ndepletion from confining layers. Water Resources Research, 43, W07417, \ndoi:10.1029/2006WR005597. \n\n\n\nMachiwal, D., KumarJha, M., Singh, V.P. and  Mohan, C., 2018. Assessment \nand mapping of groundwater vulnerability to pollution: Current status \nand challenges. Earth-Science Reviews, 185, Pp. 901-927. \nhttps://doi.org/10.1016/j.earscirev.2018.08.009.  \n\n\n\nMaduabuchi, C.M., 2004. Surface and groundwater resources monitoring \nand management in Nigeria United Nations/Austria/ESA Symposium on \nSpace Applications for Sustainable Development, Graz, Austria, 13-16 \nSeptember, 2004.  \n\n\n\nMaillet, R., 1947. The fundamental equations of electrical prospecting. \nGeophysics, 12, Pp. 529-526. https://doi.org/10/1190/1.1437342.                                                                                        \n\n\n\nMondal, N.C., Singh, V.P. and Ahmed, S., 2013. Delineating shallow saline \ngroundwater zones from southern India using geophysical indicators. \nEnviron. Monit. Assess., 185, Pp. 4869-4886. \nhttps://doi.org/10.1007/s10661-012-2909-1. \n\n\n\nMousatov, A., and Ryjov, A., 2005. Geoelectrical Characterization of Oil \nContaminated Site in Tabasco, Mexico. Geof\u00eds. Int., 44 (3), Pp. 251-263. \nDOI:10.22201/igeof.00167169p.2005.44.3.197 \n\n\n\nNacht, S.J., 1983. Groundwater monitoring system considerations. \nGroundwater Monitoring and Remediation, 3 (2), Pp. 33-\n39.  https://doi.org/10.1111/j.1745-6592.1983.tb01197.x. \n\n\n\nNel, J., Xu, Y., Batelan, O., and Brendonck, L., 2009. Benefit and \nImplementation of Groundwater Protection Zoning in South Africa. \nWater Resources Management, 23, Pp. 2895. \nhttps://doi.org/10.1007/s11269-009-9415-4. \n\n\n\nNeuman, S.P., 1972. Theory of flow in unconfined aquifers considering \ndelayed response of the water table. Water Resour. Res., 8 (4), Pp. 1031\u2013\n1045. doi:10.1029/WR008i004p01031 \n\n\n\nNiwas, S., Singhal, D.C., 1981. Estimation of aquifer transmissivity from \nDar-Zarrouk parameters in porous media. J. Hydrol., 50, Pp. 393-399. \nhttps://doi.org/10.1016/0022-1694(81)90082-2 \n\n\n\nNiwas, S., Singhal, D.C., 1985. Aquifer transmissivity of porous media from \nresistivity data. J. Hydrol., 82, Pp. 143-153. \nhttps://doi.org/10.1016/0022-1694(85)90050-2 \n\n\n\nOgbe, F.G.A., 1972. Stratigraphy of strata exposed in the Ewekoro quarry, \nWestern Nigeria. In: T. F. J. Dessauvagie and Whiteman (Eds) African \nGeology, University Press, Nigeria, Pp. 305- 322. \n\n\n\nOlayinka, A.I., Yaramanci, U., 2000. Assessment of the reliability of 2D \ninversion of apparent resistivity data. Geophys. Prospec., 48, Pp. 293\u2013\n316. \n\n\n\nOmatsola, M.E., Adegoke, O.S., 1981. Tectonic Evaluation and Cretaceous \nStratigraphy of the Dahomey Basin. Journal of Minning and Geology, 18 \n(1), Pp. 130-137. \n\n\n\nOroji, B., 2018. Assessing groundwater vulnerability by pollution mapping \nin Iran: Case study Hamadan \u2013 Bahar plain. Geof\u00edsica Internacional, 57 \n(3), Pp. 161-174. DOI: 10.22201/igeof.00167169p.2018.57.3.2108 \n\n\n\nParsons, R.P., 1995. A South African aquifer system management \nclassification; WRC report KV 77/95, Water Research Commission, \nPretoria.  \n\n\n\nParsons, R., 2004. Surface Water \u2013 Groundwater Interaction in a Southern \nAfrican Context Prepared for the Water Research Commission WRC \nReport No. TT 218. \n\n\n\nReyment, R.A., 1965. Aspects of the Geology of Nigeria Ibadan University \nPress. Ibadan. Pp. 145. \n\n\n\nRuss, W., 1924. The phosphate deposits of Abeokuta Province. Geol. Surv. \nNigeria, Bull., 7, Pp. 1 \u2013 43. \n\n\n\nSalem, H.S., 1999. Determination of fluid transmissivity and electric \ntransverse resistance for shallow aquifers and deep reservoirs from \nsurface and well-log electric measurements. Hydrology and Earth System \nSciences, 3 (3), Pp. 421-427. DOI: 10.5194/hess-3-421-1999. \n\n\n\nSingh, U.K., Das, R.K. and Hodlur, G.K., 2004. Significance of Dar-Zarrouk \nparameters in the exploration of quality affected coastal aquifer systems. \nEnviron Geol., 45, Pp. 696-702. https://doi.org/10.1007/s00254-003-\n0925-8.  \n\n\n\nS\u00f8rensen, K.I., Auken, E., Christensen, N.B., and Pellerin, L., 2005. An \nIntegrated Approach for Hydro-geophysical Investigations: New \nTechnologies and a Case History. In: Butler, D.K. (Ed.). \n\n\n\nTurner, N., and Wurster, F., 2017. Water Resource Inventory and \n\n\n\n\nhttps://doi.org/10.1111/j.1365-2478.1955.tb01363.x\n\n\nhttps://doi.org/10.1515/geo-2019-0087\n\n\nhttps://doi.org/10.1111/j.1365-2478.1976.tb00931.x\n\n\nhttp://dx.doi.org/10.1007/s12040-015-0615-4\n\n\nhttps://doi.org/10.2113/JEEG13.2.77\n\n\nhttps://doi.org/10.1080/21553769.2014.933716\n\n\nhttps://www.sciencedirect.com/science/article/abs/pii/S0012825217304713?via%3Dihub#!\n\n\nhttps://www.sciencedirect.com/science/article/abs/pii/S0012825217304713?via%3Dihub#!\n\n\nhttps://www.sciencedirect.com/science/article/abs/pii/S0012825217304713?via%3Dihub#!\n\n\nhttps://www.sciencedirect.com/science/article/abs/pii/S0012825217304713?via%3Dihub#!\n\n\nhttps://www.sciencedirect.com/science/journal/00128252\n\n\nfile:///C:/Users/DELL/OneDrive/Desktop/Current%20work/185\n\n\nhttps://doi.org/10.1016/j.earscirev.2018.08.009\n\n\nhttps://doi.org/10/1190/1.1437342\n\n\nhttps://doi.org/10.1007/s10661-012-2909-1\n\n\nhttp://dx.doi.org/10.22201/igeof.00167169p.2005.44.3.197\n\n\nhttps://doi.org/10.1111/j.1745-6592.1983.tb01197.x\n\n\nhttps://doi.org/10.1007/s11269-009-9415-4\n\n\nhttps://doi.org/10.1016/0022-1694(81)90082-2\n\n\nhttps://doi.org/10.1016/0022-1694(85)90050-2\n\n\nhttp://dx.doi.org/10.22201/igeof.00167169p.2018.57.3.2108\n\n\nhttp://dx.doi.org/10.5194/hess-3-421-1999\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 69-75 \n\n\n\n\n\n\n\n \nCite The Article: Raja Asim Zeb, Muhammad Haziq Khan, Intikhab Alam, Ahtisham Khalid, Muhammad Faisal Younas (2021). Well Logs Analysis To Estimate The \n\n\n\nParameters of Sawan-2 and Sawan-3 Gas Field. Malaysian Journal of Geosciences, 5(2): 69-75. \n\n\n\n\n\n\n\nAssessment Summary Report \u2013 FY17 Northeast Region, 91p. \n\n\n\nUmar, N.D., and Igwe, O., 2019. Geo-electric method applied to \ngroundwater protection of a granular sandstone aquifer. Appl water Sci., \n9, Pp. 112. https://doi.org/10.1007/s13201-019-0980-2. \n\n\n\nVandas, S.J., Winter, T.C., and Battaglin, W.A., 2002. Water and the \nEnvironment.  American Geosciences Institute Environmental \nAwareness Series, Pp. 42-44. \n\n\n\nWalker, D.B., Baumgartner, D.J. and Gerba., C.P. and Fitzsimmons, K., 2019. \nChapter 16 - Surface Water Pollution, Environmental and Pollution \nScience (Third Edition), Pp. 261-292. https://doi.org/10.1016/B978-0-\n12-814719-1.00016-1. \n\n\n\nWhiteman, A.J., 1982. Nigeria: Its petroleum geology, resources and \npotentials, (1) 176, (2) Pp. 238.Graham and Trotman, London, U.K. \n\n\n\nWilson, L.G., 1983. Monitoring in the vadose zone: Part 3. Groundwater \nMonitoring Review, 3 (1), Pp. 155-166. \n\n\n\nWinter, T.C., Harvey, J.W., Franke, O.L. and Alley, W.M., 1999. Ground water \n\n\n\nand surface water \u2013 a single resource; USGS Circular 1139, United States \nGeological Survey, Denver. https://doi.org/10.3133/cir1139 \n\n\n\nWorrall, F., Kolpin, W., 2003. Direct assessment of groundwater \nvulnerability from single observations of multiple contaminants Water \nResources Research, 39 (12), Pp. 1345, doi:10.1029/2002WR001212, \n2003. \n\n\n\nZereg, S., Boudoukha, A., and Benaabidate, L., 2018. Impacts of natural \nconditions and anthropogenic activities on groundwater quality in \nTebessa plain, Algeria. Sustainable Environment Research, 28 (6), Pp. \n340-349. https://doi.org/10.1016/j.serj.2018.05.003, \n\n\n\nZohdy, A.R., 1965. The auxiliary point method of electrical sounding \ninterpretation and its relationship to the Dar Zarrouk parameters. \nGeophysics, 30, Pp. 644-660. https://doi.org/10.1190/1.1439636.  \n\n\n\nZohdy, A.R., 1974. Use of Dar Zarrouk curves in the interpretation of \nvertical electrical sounding data. United States Geological Survey Bulletin \n1313-D, 41 p4.  \nhttps://doi.org/10.3133/b1313D.\n\n\n\n \n\n\n\n\nhttps://www.sciencedirect.com/science/article/pii/B9780128147191000161#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/B9780128147191000161#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/B9780128147191000161#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/B9780128147191000161#!\n\n\nhttps://www.sciencedirect.com/science/book/9780128147191\n\n\nhttps://www.sciencedirect.com/science/book/9780128147191\n\n\nhttps://doi.org/10.1016/B978-0-12-814719-1.00016-1\n\n\nhttps://doi.org/10.1016/B978-0-12-814719-1.00016-1\n\n\nhttps://doi.org/10.3133/cir1139\n\n\nhttps://www.sciencedirect.com/science/article/pii/S2468203917300638#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S2468203917300638#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S2468203917300638#!\n\n\nhttps://www.sciencedirect.com/science/journal/24682039\n\n\nfile:///C:/Users/User/Desktop/Paper%20Formating/2021/ISSUE%202/MJG/28%20(6\n\n\nhttps://doi.org/10.1016/j.serj.2018.05.003\n\n\nhttps://doi.org/10.1190/1.1439636\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 29-35 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2022.29.35 \n\n\n\n \nCite The Article: Olabode Olabanji Olofinyo, Temitayo Olamide Ale, Oluremi Success Odebode, David Shola Esan (2022). Effect of Compaction at Different \n\n\n\nEnergy Levels on The Geotechnical Properties of Stabilized Soils. Malaysian Journal of Geosciences, 6(1): 29-35. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2022.29.35 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\ns \n\n\n\nEFFECT OF COMPACTION AT DIFFERENT ENERGY LEVELS ON THE \nGEOTECHNICAL PROPERTIES OF STABILIZED SOILS \n \nOlabode Olabanji Olofinyoa, Temitayo Olamide Aleb*, Oluremi Success Odebodec, David Shola Esand \n\n\n\na Department of Geology, Faculty of Science, University of Ibadan, Ibadan.  \nb Department of Earth Sciences, Faculty of Science, Adekunle Ajasin University Akungba Akoko. \nc Department of Geology, Delhi University Near 34, Chhatra Marg, Faculty of Science, Delhi, India. \nd Ozone Geoscience and Mining Solutions Utako FCT, Abuja. \n*Corresponding Author Email: ale.temitayo@aaua.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 02 March 2022 \nAccepted 04 April 2022 \nAvailable online 12 April 2022 \n\n\n\n\n\n\n\nThis study is aimed at evaluating the influence of compaction (at different energy levels) on the geotechnical \nproperties of stabilized soils. To achieve this, four bulk soil samples (BDL1, BDL2 BDL3 & BDT) consisting of \ntermite reworked soils and residual lateritic soils were collected at New Stadium Road, Awo hall, University \nof Ibadan, Nigeria at a depth of 2m for strength tests and at depths of 1m, 1.5m & 2m for index tests. \nGeotechnical analysis (index tests and strength tests) and geochemical analysis (X-ray Diffraction) were \ncarried out on the sampled soils. The study revealed that the mineral constituents of the sampled soils are \nquartz, kaolinite and hematite; with the termite-reworked soil richer in kaolinite content than the quartz \nschist derived soil with about 125% increase. The values of index tests for both soils met the required Nigeria \nspecification for good soil with termite reworked soil performing better. The values of specific gravity for \nboth soils are close suggesting similar origin. AASHTO classification put termite-reworked soil within the \nrange of A-2-7 (good rating) while most of the residual soil samples fall within A-7-5 to A-7-6 range (fair to \npoor rating).  The stabilisation of residual lateritic soil using termite-reworked soil as stabiliser brought about \nincrease in the values of maximum dry density, uncured unconfined compressive strength as well as the sun-\ncured unconfined compressive strength of the studied soil. The influence of stabilisation using termite-\nreworked soil was strongest at the highest level of compaction ((30%) of termite-reworked soil with the \nweight of residual soil). There also exist a fairly strong positive correlation between the amount of termite \nreworked soil and energy of compaction and between the uncured and sun-cured unconfined compressive \nstrength was plotted against the number of blows for BDL1, BDL2 and BDL3 respectively. In sum, these \nstabilised soils are suitable for foundation and landfills materials. \n\n\n\nKEYWORDS \n\n\n\ncompaction; index test; X-ray Diffraction; stabilization; strength test. \n\n\n\n1. INTRODUCTION \n\n\n\nLateritic soils abound in most parts of the tropical world including Nigeria \nand it is predominant in most of the sub-grade soils in Nigeria. These \nlateritic soils have over the years found a wide range of applications as \nfoundations for structures and more importantly as construction \nmaterials for structures such as building, roads, highways, dams and \nembankments (to show its acceptance). Incessant occurrences of road \npavement failure and building collapse have caused more accidents in \nrecent times than before and these place limitations on the growth of a \nnation (Ale, 2021; Ale et al., 2022). This has informed the decision to \nproperly understand the geotechnical properties of residual lateritic soils. \nEach of the ubiquitous soils often exhibits unique set of physical, chemical \nand mineralogical properties (which in turn define the engineering \nproperties).  \n\n\n\nThese properties make lateritic soils fair to good engineering soils. In some \ncases, the properties of soils in the immediate vicinity of the construction \nsite may not meet the required standards. In such cases it may not be \n\n\n\neconomically justifiable to import materials that meet such standards to \nthe construction sites.  For proper utilizations of any engineering purpose, \nthere is need to carry out a thorough geotechnical evaluation. This is to be \ndone through the process of stabilization in order to improve the \nproperties of the available soils (Adeyemi, 2003). Over the years, the \nprocess of compaction has aided the stabilization of lateritic soils. As \ndesired by the engineer, the dry density and moisture content of lateritic \nsoils can be economically managed within limits during construction to \nproduces oils that would nearly exhibit the properties like shrinkage, \ncalifornia bearing ratio, unconfined compressive strength, shear strength \nparameters, consolidation, etc. (Gidigasu, 1976).  \n\n\n\nSeveral researchers who have compacted some Nigerian lateritic soils, \nconcluded that soil compaction cannot be underestimated in the \nconstruction of sub-grades and stabilized bases (Madu, 1977; Malomo et \nal., 1983; Meshida, 1985; Ogunsanwo, 1989a&b; Adeyemi, 1992; Adedeji, \n2001). Majorly, the level of soil compaction depends on pedogenic factors \nof parent rock, topography, climate, drainage condition, vegetation, \netc. These researchers have investigated the influence of reworking by \ntermites on some engineering properties of lateritic soils, with little efforts \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 29-35 \n\n\n\n\n\n\n\n \nCite The Article: Olabode Olabanji Olofinyo, Temitayo Olamide Ale, Oluremi Success Odebode, David Shola Esan (2022). Effect of Compaction at Different \n\n\n\nEnergy Levels on The Geotechnical Properties of Stabilized Soils. Malaysian Journal of Geosciences, 6(1): 29-35. \n\n\n\n\n\n\n\nmade at investigating the influence of termite-reworked soil on the \nengineering properties of such soils. This has led to an attempt to study \nthe variation of strength characteristics of soils with respect to the \namounts of termite-reworked soil and energy of compaction. In order to \ndetermine which combination of the two variables will yield the best \nstabilized soil. \n\n\n\n2.  STUDY AREA \n\n\n\nThe study area is located in the northern part of Ibadan, and it is between \nGongola and Barth Road within the University of Ibadan, Ibadan, Nigeria \n(Figure 1). The study area lies between Longitudes 3053\u201910\u2019\u2019 and 3053\u201925\u2019\u2019 \nEast of Greenwich Meridian and Latitudes 7026\u201910\u2019\u2019 and 7026\u201920\u2019\u2019 North of \nthe Equator. The elevation ranges from 254m to265m above the mean sea \nlevel. The sampling location (longitude 3053\u201920\u2019\u2019, latitude 7026\u201912\u2019\u2019) is \nalong Barth Road, within the University of Ibadan, close to the new sport \nComplex. This area is characterized by two distinct seasons: the rainy \nseason (April\u2013October); and dry season (November\u2013March). The \nsampling location is generally accessible through network of roads, \nfootpaths etc. The fieldwork was carried out in October when the \nvegetation in the area has not been thick, thus making this area more \naccessible. Extreme temperature of about 290C and 340C occur at the peak \nof the wet season and onset of the wet season respectively while the lowest \ntemperature being about 210C. The mean annual rainfall is between \n788mm and 1884mm (Nigeria Meteorological Agency). These high rainfall \nand temperatures are likely to enhance the chemical weathering of rocks. \n\n\n\n\n\n\n\nFigure 1: Map showing the accessibility and road network of the study \narea \n\n\n\n3.  GEOLOGY OF THE STUDY AREA  \n\n\n\nThe Ibadan study area falls within the Basement Complex of Southwestern \nNigeria (Figure 2). The area is dominated by the Migmatite Complex and \nquartzite of metasedimentary sands. These rocks are intruded by quartz \nveins, aplite, dolerite, pegmatite, and quartzo-feldspatic intrusions. The \nQuartz-Schist outcrops occur as long ridges with relatively high elevation \nmaking them conspicuous. The sampling points are underlain dominantly \nby quartz schist with coarse grained texture and the occurrences of \nquartzo-feldsparthic veins in them. \n\n\n\n\n\n\n\nFigure 2: Geological map of study area showing the sampling spot (NGSA \n1966) \n\n\n\n4.  MATERIALS AND METHODS \n\n\n\nThe project was carried out in two major stages, namely: field \ninvestigation, which included sample collection, description and \npreparation; and the laboratory analyses, that involved pretest \npreparation of the samples, classification and strength tests. The four bulk \nsoil samples consisting of 1 termite-reworked soil (TRS; Figure 2) and 3 \nresidual lateritic soils were collected at New Stadium Road, Awo hall, \nUniversity of Ibadan, at a depth of 2m for strength analysis. For index test, \nsamples were taken at depth 1m, 1.5m and 2m respectively. The Ibadan \nresidual lateritic soils used for the study were collected from test pits \nestablished at different locations around a termitarium confirmed to be \nunderlain by Quartz Schist. Bulk samples were strictly taken from the \nlateritic horizon of complete and well pronounced profile within the \nvicinity of the termitarium. This is to ensure that they are not transported \nsoils. The laboratory analyses carried out on the samples are the \ngeotechnical analysis (index tests and strength tests) and geochemical \nanalysis (X-ray Diffraction). The basic index properties were determined \nby following the procedures stipulated by the British Standard 1377 of \n1975. However, modifications were introduced whenever necessary.   In \norder to effect adequate segregation of grains of the soil into appropriate \nsize grades, each soil sample was soaked in a dispersing or deflocculating \nagent known as weak calgon solution (sodium hexametaphosphate \nsolution) i.e. 10 grams in 4 litres of distilled water for 24 hours, during \nwhich it was regularly agitated and squeezed before being wet sieved. The \nsoils are generally mottled reddish brown stiff sandy silty clay. \n\n\n\n5. RESULTS AND DISCUSSION \n\n\n\n5.1  Geochemical properties \n\n\n\nThe mineralogy of the soil showed that they contain no undesirable \nmineral constituent as they contain mainly quartz, kaolinite and hematite \n(Table 1and Figure 3). BDL1 and BDT have quartz and kaaolinite as their \nmajor minerals. However, termite-reworked soil is richer in kaolinite \ncontent than the quartz schist derived soil with about 125% increase. \nBDL2 and BDL3 compose mainly of quartz, kaolinite and hematite. The \nhematite content could have been due to the excess iron and aluminium \nduring lateralization (Osinubi and Katte, 1997). Good drainage and slightly \nacidic conditions favour the formation of laterites containing stable clay \nminerals such as kaolinite exist in all the locations where the samples were \ntaken (Gididgasu, 1976; Duane and Robert, 1997). \n\n\n\nTable 1: Quantitative mineralogical composition of studied soils \n\n\n\nSamples Major Minerals Minor Minerals \n\n\n\nBDL1 Quartz-88.47% Kaolinite-11.53% \n\n\n\nBDL2 Quartz-88.51% \nKaolinite-8.39%, \n\n\n\nHematite-3.09% \n\n\n\nBDL3 Quartz-78.06% \nKaolinite-16.16% \n\n\n\nHematite-5.78% \n\n\n\nBDT Quartz-65.06% Kaolinite-34.94% \n\n\n\n \nFigure 3: Diffractogram showing the mineralogy of BDT \n\n\n\n5.2 Index properties \n\n\n\nTable 2 shows the summary of the results of index tests on the soil \nsamples. The liquid limit of the termite-reworked soil samples (BDT10-\nBDT12) range from 41.29% to 44.77% while those of the residual lateritic \nsoil samples (BDL1-BDL9) range from 41.31% to 56.41%. This shows that \nthe liquid limits of the termite-reworked soil samples are lower than those \nof the residual lateritic soil samples. The plasticity indexes of the termite-\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 29-35 \n\n\n\n\n\n\n\n \nCite The Article: Olabode Olabanji Olofinyo, Temitayo Olamide Ale, Oluremi Success Odebode, David Shola Esan (2022). Effect of Compaction at Different \n\n\n\nEnergy Levels on The Geotechnical Properties of Stabilized Soils. Malaysian Journal of Geosciences, 6(1): 29-35. \n\n\n\n\n\n\n\nreworked soil samples (BDT10-BDT12) ranges from 16.62%-17.39% \nwhile those of the residual lateritic soils ranges from 16.76%-22.12%. The \nplasticity values were generally lower than 25, the maximum value \nrecommended for sub-grade tropical Africa soils (Medina 1963; Simon et \nal., 1973). Both the termite-reworked soil samples and the residual \nlateritic soil samples meet this standard specification. According to \nFederal Ministry of Works and Housing, subgrade/fill material should \nhave liquid limit \u226450% and plasticity index \u226430% while for sub-base, \nliquid limit should be \u2264 30% and plasticity index \u2264 12% (Federal Ministry \nof Works and Housing, 1997). Also, according to the liquid limit values of \n40% and above are assumed high in pavement construction (Wright, \n1985). \n\n\n\nThey pointed out further that plasticity index value of 10% and above are \nalso assumed high in pavement design. All the soils meet the requirement \nfor use as subgrade/fill materials. The lower values of the termite-\nreworked soils make them better road construction materials than the \nQuartz schist-derived lateritic soil samples. The Casagrande chart \nclassification (Figure 4) places virtually all the soil samples in the medium \nplasticity/ compressibility region (except BDL1, BDL4 and BDL6), hence \nthese soils would be expected to exhibit medium swelling potential (Ola, \n1983). The values of the linear shrinkage of the studied soil range from 6% \nto 16% (Table 2). The average linear shrinkage of the termite-reworked \nsoil (7%) was lower than the maximum value of 8% and 10% \nrecommended by for highway sub-base and sub-grade soils respectively \n(Madedor, 1983). While the average linear shrinkage values of the nearby \nresidual lateritic soil was higher than the maximum value of 8% and 10% \nrecommended for highway sub-base and sub-grade soils respectively \n(Madedor, 1983). Therefore, the residual lateritic soil would pose a field \ncompaction problem unless it is stabilized.  \n\n\n\nThe lower values exhibited by termite-reworked soil samples agrees with \nthat of the plasticity index. The specific gravity values of the sampled soils \nrange from 2.60 to 2.66 (Table 2). Alexander and Cady gave values of 2.50 \nto 3.60 for specific gravity of lateritic soils while De-Graft Johnson \nrecommended a range of value between 2.60 and 3.40 for specific gravity \nof lateritic soils (Alexander and Cady, 1962; De-Graft Johnson, 1969). Thus, \n\n\n\nthe specific gravity value of the derived soil samples falls within the \nstipulated range. Again, there is a very close range in the values of the \nspecific gravity of the grains of the studied soils which is an indication that \nthe soils are of the same genetic origin and similar weathering \nenvironment. It is worthy of note that the termite-reworked samples gave \nthe highest values while the soil samples derived from Quatrz Schist \nexhibit the lowest values. The difference in the value of specific gravity can \nonly be due to reworking by termites (in termite-reworked soils). The \ngrading curves of the studied soils are presented in Figures 5. The \nsummary of the grain size distribution characteristics of the studied soils \nare shown in the Table 2 below.  \n\n\n\nThe coarse contents of the soils range from 33.88% to 63.05% while the \nfine contents of the soils range from 33.95% to 66.12%.  It can be seen that \ntermite-reworked soils have a much lower percentage of fines (clay and \nsilt-sized particles) (with an average of 34.92) than the nearby residual \nlateritic soil indicating better geotechnical characteristics. The reworking \nby termites on the termite-reworked soil has some remarkable influence \non the grain size distribution. Daniel recommended number of fines of at \nleast 20% for landfill seals i.e. for soil that can be good for base of landfill \n(Daniel, 1993b). Therefore, the studied soils meet this standard \nspecification and can be used for base of landfill. Using the American \nAssociation of State Highway and Transportation Official (AASHTO) \nclassification in table 2, termite-reworked soils (BDT10, BDT11 and \nBDT12) fall within the A-2-7.  \n\n\n\nThis implies that the soil samples are rated between excellent to good sub-\ngrade materials while the residual lateritic soils (BDL1, BDL4, BDL6) fall \nwithin A-7-5, BDL2, BDL3 fall with A-7-6, BDL5, BDL7, BDL8, and BDL9 fall \nwithin A-2-7. This implies that most of the residual lateritic soils are rated \nunder the range of fair to poor, hence needs to be improved (stabilized).  \nFrom the results obtained the values of the soils activities range between \n0.41 and 0.63 (Table 2). This implies that the clay minerals present in all \nthe soils is kaolinite. Soils rich in kaolinite are less hydrophilic and plastic \nin nature. These are characterized by low moisture affinity because of \ntheir small surface area and inter layer spacing of 7\u00c5. Hence, they are good \nmaterials for engineering construction. \n\n\n\n\n\n\n\nTable 2: Summary of all the engineering index tests on the soil samples \n\n\n\nSample LL PL PI LS SG \nFines \n(%) \n\n\n\nPercent \nof Course \n\n\n\n(%) \nActivity GI AASHTO USCS Clay type \n\n\n\nBDL1 52.38 32.13 20.25 16 2.60 66.12 33.88 0.45 13.5 A-7-5 CH kaolinite \n\n\n\nBDL2 47.11 28.73 18.38 12 2.60 51.06 48.94 0.46 6.8 A-7-6 CI kaolinite \n\n\n\nBDL3 44.14 26.06 18.08 12 2.60 51.72 48.28 0.49 6.8 A-7-6 CI kaolinite \n\n\n\nBDL4 56.41 34.29 22.12 17 2.65 35.69 64.31 0.46 6.8 A-7-5 CH kaolinite \n\n\n\nBDL5 41.31 21.73 19.58 16 2.65 41.53 58.47 0.63 3.9 A-2-7 CI kaolinite \n\n\n\nBDL6 50.77 32.01 18.76 12 2.60 51.48 48.52 0.43 7.4 A-7-5 CH kaolinite \n\n\n\nBDL7 48.02 30.82 17.20 11 2.65 41.12 58.88 0.41 3.4 A-2-7 CI kaolinite \n\n\n\nBDL8 43.24 26.48 16.76 7 2.65 35.87 64.13 0.45 1.6 A-2-7 CI kaolinite \n\n\n\nBDL9 46.10 29.07 17.03 7 2.65 41.90 58.10 0.43 3.6 A-2-7 CI Kaolinite \n\n\n\nBDT10 43.56 26.46 17.10 8 2.65 33.95 66.05 0.46 1.8 A-2-7 CI Kaolinite \n\n\n\nBDT11 41.29 24.67 16.62 6 2.66 34.30 65.70 0.48 1.2 A-2-7 CI Kaolinite \n\n\n\nBDT12 44.77 27.38 17.39 7 2.65 36.52 63.48 0.46 2.0 A-2-7 CI kaolinite \n\n\n\nLL liquid limit, PL plasticity limit, PI plasticity index, LS linear shrinkage, SG specific gravity, GI group index, \n\n\n\nAASHTO American Association of State Highway Transportation Office. \n\n\n\n\n\n\n\nFigure 4: Casagrande Chart Classification of the Studied Soil Samples \n\n\n\n\n\n\n\nFigure 5: Grading curves of all soil samples (residual lateritic and \ntermite-reworked soils) \n\n\n\n0\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n100\n\n\n\n0.001 0.01 0.1 1 10\n\n\n\nP\nER\n\n\n\nC\nEN\n\n\n\nTA\nG\n\n\n\nE \nP\n\n\n\nA\nSS\n\n\n\nIN\nG\n\n\n\n %\n\n\n\nPARTICLE SIZE (MM)\n\n\n\nBDL1\n\n\n\nBDL2\n\n\n\nBDL3\n\n\n\nBDL4\n\n\n\nBDL5\n\n\n\nBDL6\n\n\n\nBDL7\n\n\n\nBDL8\n\n\n\nBDL9\n\n\n\nBDT10\n\n\n\nBDT11\n\n\n\nBDT12\n\n\n\nLEGEND\n\n\n\nCLAY SILT SAND GRAVEL\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 29-35 \n\n\n\n\n\n\n\n \nCite The Article: Olabode Olabanji Olofinyo, Temitayo Olamide Ale, Oluremi Success Odebode, David Shola Esan (2022). Effect of Compaction at Different \n\n\n\nEnergy Levels on The Geotechnical Properties of Stabilized Soils. Malaysian Journal of Geosciences, 6(1): 29-35. \n\n\n\n\n\n\n\n5.3 Soil Strength Properties \n\n\n\nThe maximum dry density (MDD) values of the studied soil range from \n1761.10 Kg/m3 to 1911.15 kg/m3 while the optimum moisture content \n(OMC) values of the studied soil range from 16.40% to 20.41% at all the \nlevels of compaction (Table 3). The termite-reworked soils had better \ncompaction parameters than those of residual lateritic soils when \ncompacted and also, the optimum moisture content of the studied soils \nreduce drastically. The influence of level of compaction is stronger at 30% \nby volume of stabilizer than what was obtained at 10% and 20% (table 4, \nfigure 6). Regression method was employed to establish relationships \nbetween some of the geotechnical parameters and the quantity of the \napplied stabilizer. This was done to understand the degree of influences of \nthe parameters on each other and on the overall behaviour of the soils. \nThis is confirmed by the strongest positive correlation of 0.96 established \nat 55 blows (figure 7 & 8).  For any soil to be suitable for general filling and \nconstruction of sub-grade and sub-base courses of roads, the maximum \n\n\n\ndry density (MDD) must exceed 1700 Kg/m3 (Nigerian General Standard \nAcceptable Limits FMW,1997). All of these sampled soils exceed the \nrequired standard. \n\n\n\nTable 3: Summary of compaction test result \n\n\n\nSample \n\n\n\nLevel Of Compaction \n\n\n\nWest African Level \n\n\n\nMDD OMC \n\n\n\nTermite \u2013reworked soil (BDT) 1911.15 16.40 \n\n\n\nResidual lateritic soil  (BDL1) 1761.10 20.41 \n\n\n\nResidual lateritic soil  (BDL2) 1817.88 17.22 \n\n\n\nResidual lateritic soil  (BDL3) 1872.03 18.61 \n\n\n\n\n\n\n\nTable 4: Variation of the Maximum Dry Density (MDD) and Optimum Moisture Content (OMC) with termite-reworked soil content for sample BDL1-\nBDL3 compacted at different levels \n\n\n\nSample Stabilizer (%) \n\n\n\nLevel Of Compaction \n\n\n\n25 \n\n\n\nBLOWS \n\n\n\n35 \n\n\n\nBLOWS \n\n\n\n45 \n\n\n\nBLOWS \n\n\n\n55 \n\n\n\nBLOWS \n\n\n\nMDD \n\n\n\n(Kg/m3) \nOMC (%) \n\n\n\nMDD \n\n\n\n(Kg/m3) \nOMC (%) \n\n\n\nMDD \n\n\n\n(Kg/m3) \nOMC (%) \n\n\n\nMDD \n\n\n\n(Kg/m3) \nOMC (%) \n\n\n\nBDL1 \n\n\n\n10 1781.47 19.61 1811.32 20.01 1832.16 19.62 1862.34 20.06 \n\n\n\n20 1791.47 20.00 1830.24 21.21 1849.75 21.61 1892.24 20.82 \n\n\n\n30 1806.50 20.09 1842.14 20.81 1861.34 22.02 1930.01 21.61 \n\n\n\nBDL2 \n\n\n\n10 1871.95 17.60 1890.39 18.03 1912.81 19.20 1925.30 19.60 \n\n\n\n20 1886.54 18.02 1901.84 18.41 1921.82 18.82 1939.92 18.99 \n\n\n\n30 1891.66 18.37 1911.26 18.61 1940.05 19.01 1961.24 19.62 \n\n\n\nBDL3 \n\n\n\n10 1881.03 18.40 1890.04 18.80 1912.14 17.62 1929.88 19.61 \n\n\n\n20 1870.14 18.20 1891.23 18.40 1921.95 19.00 1901.99 18.62 \n\n\n\n30 1892.65 18.81 1912.08 18.61 1926.49 18.43 1940.16 18.00 \n\n\n\n\n\n\n\nFigure 6: A compaction curve of a typical studied soil (BDL1 at 55blows \nwith 30% termite rework soils) \n\n\n\n\n\n\n\nFigure 7: The regression line of the relationship between MDD (kg/m3) \nand Termite-reworked soil content (%) of sample BDL1 at 55 blows \n\n\n\n\n\n\n\nFigure 8: The regression line of the relationship between OMC (%) and \nTermite-reworked soil content (%) of sample BDL1 at 55 blows \n\n\n\nThe obtained results of uncured strength of the soils range from \n240KN/m2 to 450KN/m2. The sun-cured strengths of the studied soil \nrange from 1080KN/m2 to 2800KN/m2 (Table 5, Figure 9). Table 5 present \na summary of the strength characteristics (uncured and sun-cured) of the \nstabilized soil samples compacted at West Africa level, 25 blows, 35 blows, \n45 blows, and 55 blows. Soils compacted at 55 blows have higher uncured \nand sun-cured strength than those compacted at other levels. The Central \nRoad Research Institute of India recommended 1034KN/m2 as the \nminimum value for the cured strength of road soils (De-Graft-Johnson and \nBhatia, 1969). On the other hand, the minimum acceptable value for \nuncured strength of soils is 103KN/m2 (Ola, 1977). Both the uncured and \nsun-cured strength of the soil samples supersede this standard except \nBDT1 when compacted at West Africa level with 0% stabilizer as seen in \ntable 5.  From values obtained, the strength of the soil increased as a result \nof curing of the sample.  \n\n\n\nFurthermore, addition of 30% by volume of termite-reworked soil has \ngreater positive influence on both the cured and uncured unconfined \ncompressive strength of Quartz-schist-derived soil. Table 6 shows the \nsummary of the Regression Equation and Correlation Coefficient of \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 29-35 \n\n\n\n\n\n\n\n \nCite The Article: Olabode Olabanji Olofinyo, Temitayo Olamide Ale, Oluremi Success Odebode, David Shola Esan (2022). Effect of Compaction at Different \n\n\n\nEnergy Levels on The Geotechnical Properties of Stabilized Soils. Malaysian Journal of Geosciences, 6(1): 29-35. \n\n\n\n\n\n\n\nsamples BDL1, BDL2 and BDL3 with variation of % stabilizer of TRS. A \nvery strong positive correlation coefficient range of 0.77-0.99, 0.77-0.99 \nand 0.45-0.95 was obtained when the uncured and sun-cured unconfined \ncompressive strength was plotted against the number of blows for BDL1, \nBDL2 and BDL3 respectively (Figures 10 and 11). This also confirms the \n\n\n\npositive influence of compaction on the strength of soil and substantiates \nthe fact that the tropical soils are better compacted at Modified AASHTO \nlevel (55 blows). The summary of the regression equation and correlation \ncoefficient of samples BDL1, BDL2 and BDL3 at varying energy levels of \ncompaction are presented in table 7. \n\n\n\nTable 5: Influence of levels of compaction on the unconfined compressive strength (Co) of stabilized samples BDL1-BDL3 \n\n\n\nSAMPLE \n% \n\n\n\nSTABILIZER \n\n\n\nLEVEL OF COMPACTION (WEST AFRICAN) \n\n\n\n25 BLOWS 35 BLOWS 45 BLOWS 55 BLOWS \n\n\n\nUNCURED \n(KN/m2) \n\n\n\nCURED \n(KN/m2) \n\n\n\nUNCURED \n\n\n\n(KN/m2) \n\n\n\nCURED \n\n\n\n(KN/m2) \n\n\n\nUNCURED \n\n\n\n(KN/m2) \n\n\n\nCURED \n\n\n\n(KN/m2) \n\n\n\nUNCURED \n\n\n\n(KN/m2) \n\n\n\nCURED \n\n\n\n(KN/m2) \n\n\n\nBDL1 \n\n\n\n10 200 1080 380 1200 400 1540 420 1800 \n\n\n\n20 280 1240 400 1600 400 1680 400 1800 \n\n\n\n30 320 1240 360 1640 400 1640 400 1900 \n\n\n\nBDL2 \n\n\n\n10 240 1680 360 1800 400 2000 400 1900 \n\n\n\n20 360 1880 400 1950 400 2100 400 2200 \n\n\n\n30 400 2200 400 2500 400 2550 450 2800 \n\n\n\nBDL3 \n\n\n\n10 380 1680 400 1800 380 1880 400 2200 \n\n\n\n20 400 1800 400 1800 400 1950 400 2000 \n\n\n\n30 400 2500 400 2400 400 2700 450 2750 \n\n\n\n\n\n\n\nFigure 9: Representation of uncured and sun-cured strength for all \nsamples at West African level \n\n\n\n\n\n\n\nFigure 10: Regression line of the relationship between uncured UCS and \nthe level of compaction (No of blows) at 30% TRS for BDL3 \n\n\n\ny = 1.5x + 352.5\nR\u00b2 = 0.6\n\n\n\n380\n\n\n\n390\n\n\n\n400\n\n\n\n410\n\n\n\n420\n\n\n\n430\n\n\n\n440\n\n\n\n450\n\n\n\n460\n\n\n\n0 20 40 60\n\n\n\nU\nN\n\n\n\nC\nU\n\n\n\nR\nED\n\n\n\n U\nN\n\n\n\nC\nO\n\n\n\nN\nFI\n\n\n\nN\nED\n\n\n\n C\nO\n\n\n\nM\nP\n\n\n\nR\nES\n\n\n\nSI\nV\n\n\n\nE \nST\n\n\n\nR\nEN\n\n\n\nG\nTH\n\n\n\n (\nK\n\n\n\nN\n/m\n\n\n\n2\n)\n\n\n\nNO. OF BLOWS\n\n\n\nTable 6: Summary of the Regression Equation and Correlation Coefficient of samples BDL1, BDL2 and BDL3 with variation of % stabilizer of TRS \n\n\n\nSample code/no of \nblows \n\n\n\nRegression equation Correlation coefficient (r) Regression equation Correlation coefficient (r) \n\n\n\nBDL1 AT 25BLOWS Y= 1.453X + 1763.4 0.991 YO = 0.024X + 19.42 0.941 \n\n\n\nBDL1 AT 35BLOWS Y = 2.6024X + 1771.9 0.946 YO = 0.04X + 19.87 0.650 \n\n\n\nBDL1 AT 45BLOWS Y= 3.183X + 1778.3 0.914 YO = 0.12X + 18.88 0.930 \n\n\n\nBDL1 AT 55BLOWS Y = 5.3663 + 1780.9 0.957 YO = 0.0775 + 19.28 0.990 \n\n\n\nSample code/no of \nblows \n\n\n\nRegression equation Correlation coefficient (r) Regression equation Correlation coefficient (r) \n\n\n\nBDL2 AT 25BLOWS Y = 2.359X + 1831.6 0.901 YO = 0.0385X + 17.227 0.998 \n\n\n\nBDL2 AT 35BLOWS Y = 2.9159X + 1836.6 0.885 YO = 0.029 + 17.770 0.984 \n\n\n\nBDL2 AT 45BLOWS Y = 3.755X + 1841.8 0.886 YO = 0.0095X + 19.20 0.500 \n\n\n\nBDL2 AT 55BLOWS Y = 4.447 + 1844.4 0.898 YO = 0.001X + 19.383 0.028 \n\n\n\nSample code/no of \nblows \n\n\n\nRegression equation Correlation coefficient (r) Regression equation Correlation coefficient (r) \n\n\n\nBDL3 AT 25BLOWS Y = 0.504X + 1871.5 0.630 YO = 0.0205X + 18.06 0.659 \n\n\n\nBDL3 AT 35BLOWS Y= 1.206X + 1873.4 0.950 YO = 0.0095X + 18.793 0.470 \n\n\n\nBDL3 AT 45BLOWS Y = 1.7319X + 1882.2 0.900 YO = 0.0405X + 17.54 0.580 \n\n\n\nBDL3 AT 55BLOWS Y = 1.765X + 1884.5 0.745 YO = 0.0805X + 17.133 0.990 \n\n\n\nWHERE Y = Maximum Dry Density (MDD), X = % of TRS Stabilizer  WHERE YO = Optimum Moisture Content (OMC) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 29-35 \n\n\n\n\n\n\n\n \nCite The Article: Olabode Olabanji Olofinyo, Temitayo Olamide Ale, Oluremi Success Odebode, David Shola Esan (2022). Effect of Compaction at Different \n\n\n\nEnergy Levels on The Geotechnical Properties of Stabilized Soils. Malaysian Journal of Geosciences, 6(1): 29-35. \n\n\n\n\n\n\n\nTable 7: The summary of the regression equation and correlation coefficient of samples BDL1, BDL2 and BDL3 at varying energy levels of compaction \n\n\n\nUcs/ % Stabilizer Of Trs (Bdl1) Regression Equation Correlation Coefficient (R) \n\n\n\nUNCURED /MIX WITH 10% TRS Y = 6.8X + 78 0.87 \n\n\n\nSUN-CURED/ MIX WITH 10% TRS Y = 26X + 405 0.99 \n\n\n\nUNCURED /MIX WITH 20% TRS Y = 3.6X + 226 0.77 \n\n\n\nSUN-CURED/ MIX WITH 20% TRS Y = 17.6X + 876 0.94 \n\n\n\nUNCURED /MIX WITH 30% TRS Y = 2.8X + 258 0.94 \n\n\n\nSUN-CURED/ MIX WITH 30% TRS Y = 19.8X + 813 0.94 \n\n\n\nUcs/ % Stabilizer Of Trs (Bdl2) Regression Equation Correlation Coefficient (R) \n\n\n\nUNCURED /MIX WITH 10% TRS Y = 5.2X + 142 0.89 \n\n\n\nSUN-CURED/ MIX WITH 10% TRS Y = 8.6X + 1501 0.81 \n\n\n\nUNCURED /MIX WITH 20% TRS Y= 1.2X + 342 0.77 \n\n\n\nSUN-CURED/ MIX WITH 20% TRS Y = 1101X + 1588.5 0.99 \n\n\n\nUNCURED /MIX WITH 30% TRS Y = 1.5X + 352.5 0.77 \n\n\n\nSUN-CURED/ MIX WITH 30% TRS Y = 1.5X + 352.5 0.97 \n\n\n\nUcs/ % Stabilizer Of Trs (Bdl3) Regression Equation Correlation Coefficient (R) \n\n\n\nUNCURED /MIX WITH 10% TRS Y = 0.4X + 374 0.45 \n\n\n\nSUN-CURED/ MIX WITH 10% TRS Y = 16.4X + 1234 0.95 \n\n\n\nUNCURED /MIX WITH 20% TRS Y = 1.5X + 352.5 0.77 \n\n\n\nSUN-CURED/ MIX WITH 20% TRS Y = 7.5X + 1587.5 0.94 \n\n\n\nUNCURED /MIX WITH 30% TRS Y = 1.5X + 352.5 0.77 \n\n\n\nSUN-CURED/ MIX WITH 30% TRS Y = 10.5X + 2167.5 0.82 \n\n\n\nWhere Y = Uncured/ Sun-Cured Unconfined Compressive Strength (Ucs), X = No. Of Blows \n\n\n\n\n\n\n\n\n\n\n\nFigure 11: Regression line of the relationship between Sun-cured UCS and the level of compaction (No of blows) at 30% TRS for BDL3 \n\n\n\nThe relationship between shear strength and sun-cured strength of \nsamples compacted at West Africa level show that the studied soils \npossess good shear strengths (table 8). However, result confirms that the \ntermite-reworked soils have higher compressive strength as well as shear \n\n\n\nstrength than the residual lateritic soils at both cured and uncured \nconditions. Hence, the strength of the termite-reworked soil (BDT) is twice \nthat of the lateritic residual soils (BDL1, BDL2, BDL3) derived from quartz-\nschist. \n\n\n\nTable 8: Relationship between shear strength and sun-cured strength of samples compacted at West Africa level \n\n\n\nSample Sun-Cured Stress (KN/m2) Uncured Stress (KN/m2) \nShear Strength (KN/m2 = K pa) \n\n\n\nSun-Cured Uncured \n\n\n\nBDL1 840 300 420 150 \n\n\n\nBDL2 1640 240 820 120 \n\n\n\nBDL3 1760 400 880 440 \n\n\n\nBDT 1900 400 950 475 \n\n\n\n5. CONCLUSIONS \n\n\n\nThe mineralogy of the soil showed that they contain no undesirable \nmineral constituent as they contain mainly quartz, kaolinite and hematite; \nwith the termite-reworked soil richer in kaolinite content than the quartz \nschist derived soil with about 125% increase. Both the residual lateritic \n\n\n\nsoil samples and termite-reworked soil samples are well graded and they \nexhibit medium to high plasticity. Values of Liquid limit of termite-\nreworked soil samples are lower than those of the residual lateritic soil \nsamples. The plasticity indices of the samples taken from termitarium \nmeet the specifications of the Federal Ministry of Works for roads and \nbridges. They also have lower plasticity indices than the residual lateritic \n\n\n\nr  = 0.82 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 29-35 \n\n\n\n\n\n\n\n \nCite The Article: Olabode Olabanji Olofinyo, Temitayo Olamide Ale, Oluremi Success Odebode, David Shola Esan (2022). Effect of Compaction at Different \n\n\n\nEnergy Levels on The Geotechnical Properties of Stabilized Soils. Malaysian Journal of Geosciences, 6(1): 29-35. \n\n\n\n\n\n\n\nsoils therefore they can be used to stabilize the weaker residual lateritic \nsoils. Conclusively, the stabilization of residual lateritic soil using termite-\nreworked soil as stabilizer brought about increase in the values of \nmaximum dry density, uncured unconfined compressive strength as well \nas the sun-cured unconfined compressive strength of the studied soil. The \ninfluence of stabilization using termite-reworked soil was strongest at the \nhighest level of compaction ((30%) of termite-reworked soil and \nsubjected to the highest compactive energy). Both the amount of termite-\nreworked soil and energy of compaction show fairly strong positive \ncorrelations with the strength of the studied soil with values ranging from \n0.63-0.99. \n\n\n\nDECLARATIONS \n\n\n\nFunding: This research was personally sponsored  \n\n\n\nConflicts of interest/Competing interests: There is no conflict of interest \nsince I am the sole author. \n\n\n\nAvailability of data and material (data transparency): All the analysis was \ncarried out in accordance with the code of investigation and practice. \n\n\n\nCode availability (software application or custom code): Surfer and Excel \nwere used \n\n\n\nAuthors' contributions (optional: please review the submission guidelines \nfrom the journal whether statements are mandatory) very good but can be \nmade faster. \n\n\n\nREFERENCES \n\n\n\nAASHTO. 1993. Standard specification for transportation materials and \nmethods of sampling and testing, 14th edn. American Association of \nState Highway and Transportation Officials, Washington, DC. \n\n\n\nAdedeji, B.G., 2001. Mechanical stabilization of a lateritic soil in Ago-Iwoye, \nsouthwestern Nigeria. Unpublished B.Sc. (Geology) Project, Olabisi \nOnabanjo University, Ago-Iwoye, Nigeria. \n\n\n\nAdeyemi, G.O., 1992. Highway geotechnical properties of laterised residual \nsoils in the Ajebo-Ishara geological transition zone of southwestern \nNigeria. Unpublished Ph.D. Thesis, Obafemi Awolowo University, Ile-\nIfe, Nigeria. \n\n\n\nAdeyemi, G.O., 2003. The influence of compaction on some geotechnical \nproperties of a migmatite-gneiss-derived lateritic soil from soil from \nsouthwestern Nigeria. Journal of Geotechnology, Mineral Wealth, 128, \nPp. 7-12 \n\n\n\nAle, T.O., 2021. Engineering properties of sub-soils along Akungba-Ikare \nroad, Southwestern Nigeria: Appraising the effect on road \nconstruction. Journal of mining and Geology, 57 (2), Pp. 513-520. \n\n\n\nAle, T.O., Ogunribido, T.H.T., Olatunji, Y.I., Faseki, O.E., Olomo, K.O., \n\n\n\nAjidahun, J., Olofinyo, O.O., Johnson, T.D., Asubiojo, T.M., 2022. \nEngineering properties of soil samples from stable and failed sections: \nAn example of Akure-Idanre road, Southwestern Nigeria. Journal of \nmining and Geology \n\n\n\nBritish Standard (BS) 1377. 1975. Method of testing of soils for Civil \nengineering purposes. British standard Institute. \n\n\n\nDe-Graft-Johnson, J.W.S., and Bhatia, H.S., 1969. Engineering properties of \nlateritic soils. General report of specialty session on engineering \nproperties of lateritic soils. In: 7th International conference on soil \nmechanics and foundation engineering, Mexico, 1, Pp. 17-128. \n\n\n\nFederal Ministry of Works and Housing (FMWH). 1997. General \nspecification for roads and bridges, vol II. Federal Highway \nDepartment Lagos, Abuja, Pp. 317. \n\n\n\nGidigasu, M.D., 1976. Laterite soil engineering Elsevier, Amsterdam, Pp. \n554. \n\n\n\nMadedor, A.O., 1983. Pavement design guidelines and practice for \ngeological areas in Nigeria. In Ola, S. A. (ed) \u201cTropical soils of Nigeria \nin Engineering Practice\u201d A. A. Balkema (publisher) Rotterdam, Pp. \n291-297. \n\n\n\nMadu, R.M., 1977. An investigation into the geotechnical and engineering \nproperties of some laterites of Eastern Nigeria \n\n\n\nMeshida, E.A., 1985. The Influence of Geological factors on the engineering \nsome Western Nigerian residual lateritic soils as highway \nconstruction materials. Unpublished Ph.D. (Geology) thesis, \nUniversity of Ife, Nigeria \n\n\n\nOgunsanwo, O., 1989a. Some geotechnical properties of two laterite soils \ncompacted at two different energies. Technical note. Engineering \ngeology, Amsterdam, 26, Pp. 261-269. \n\n\n\nOgunsanwo, O., 1989b. CBR and shear strength of compacted laterite soil \nfrom southwestern Nigeria. Quarterly Journal of Engineering geology, \nLondon, 22, Pp. 317-328. \n\n\n\nOla, S.A., 1977. Potentials of lime stabilization of lateritic soils. Engineering \nGeology, 11, Pp. 305-317. \n\n\n\nOla, S.A., 1983. Geotechnical Properties and Behavior of Some Nigerian \nLateritic Soils In S.A Ola Ed. Tropical Soils of Nigeria In Engineering \nPractice. A.A. Balkama Netherlands, Pp. 61-84. \n\n\n\nSimon, A.B., Giesecke, J., Bidlo, G., 1973. Use of lateritic soils for road \nconstruction in North Dahomey, Engineering Geology, Amsterdam, 7, \nPp. 197-128. \n\n\n\nWright, J.B., 1985. Geology and mineral resources of West Africa. George \nAllen & Unwin, London, Pp. 187. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 18-21 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2023.18.21 \n\n\n\n \nCite The Article: Suleman K.O., Ogunmola O.L., Adesina R.O., Adeoye T.O., Sunmonu L.A(2023). Groundwater Exploration for  \n\n\n\nWater Well Sites in Abata Asunkere Community, Ilorin, Nigeria. Malaysian Journal of Geosciences, 7(1): 18-21. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2023.18.21 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGROUNDWATER EXPLORATION FOR WATER WELL SITES IN ABATA ASUNKERE \nCOMMUNITY, ILORIN, NIGERIA \n\n\n\nSuleman K.Oa., Ogunmola O.Lb., Adesina R.Ob., Adeoye T.Oc., Sunmonu L.Ab \n\n\n\na Department of Physics, Nigeria Maritime University Okerenkoko, Warri, Delta State, Nigeria  \nb Department of Pure and Applied Physics, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria.  \nc Department of Geophysics, University of Ilorin, Ilorin, Kwara State, Nigeria. \n*Corresponding Author Email: kamaldeen.suleman@nmu.edu.ng.  \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 15 January 2022 \nRevised 01 February 2022 \nAccepted 02 March 2023 \nAvailable online 06 March 2023 \n\n\n\n\n\n\n\nIn this study, Electrical resistivity investigation was carried out at different sections of Abata Asunkere \nCommunity, Ilorin, Nigeria with the aim of evaluating groundwater potential and properties of the aquifers. \nGeophysical technique adopted in this work were the Vertical Electrical Sounding (VES) and 2-D Electrical \nResistivity Tomography (ERT) using Schlumberger array configuration. ERT was established on two profiles \nat the study area with total distance of 80 m apart with 5 m electrode spacing. Eight (8) VES points were \nsounded along the established profiles. The results revealed four geo-electric layers which correspond to the \ntopsoil, clayey layer, sandy clay and the last layer to the bedrock in all the VES points sounded. The topsoil \nhas apparent resistivity values ranging from 5 to 437 \u03a9-m and 0.5 m to 1.7 m   while the last layer which is \nthe fractured/fresh basement rock has resistivity ranges of 27 \u03a9-m to 9800 \u03a9-m and is characterized by \ninfinite thickness. The curve types obtained are H, QH and HK curve type signatures with the H curve type \ncovering about 60 % of the study area. High potential for groundwater was observed at the northeastern and \nsouthern part of the study area. Aquifers in most part of the study areas are relatively shielded from \ncontamination because of high protective capacity of the overburden material. It was hence concluded that, \nthe study area has a promising groundwater potential. Based on these results, groundwater exploration at \noptimum yield was recommended. Depending on the volume of water available during drilling, the maximum \ndepth of drilling may reach up to 60 m. \n\n\n\nKEYWORDS \n\n\n\nGroundwater, Survey, drilling, profile, Psuedo-section, Geosections. \n\n\n\n\n\n\n\n1.   INTRODUCTION \n\n\n\nWater is commonly referred to as an essential food and basic component \nupon which life depends (Asry et al., 2014). Water has many diversified \nfunctions which include, those of drinking and developmental purpose \n(Akinola et al., 2014; Al-Garni, 2009). In recent time, the use and by \nextension, the sustainability of water is becoming complex as a result of \npopulation growth, urbanization and industrialization (Mishra et al., 2021; \nSchwarzenbach et al., 2010). There is a either a direct or indirect \ncorrelation between development and water utilization (Chinyem, 2013; \nEke et al., 2010; Hazell et al., 1988). For most developmental activities, the \nquality and availability of both surface and groundwater are the major \ncomponents. For areas with poor feasibility of surface water for desired \nactivities, groundwater is the most reliable alternative, in as much as the \nanticipated yield and quality are good enough. As a result, site \ninvestigation/exploration, which is also referred to as pre-construction \nevaluation, is usually performed for to ensure effective and sustainable \nutilization of groundwater resources (Arefayne and Abdi, 2015; \nTzanakakis et al., 2020; Paranychianakis et al., 2015). \n\n\n\nGroundwater has countless advantages over surface water. These include, \nbut not limited to its portability, all year availability, as well as its presence \nin almost all spots beneath the earth, ready to be exploited. As a result of \nthese, groundwater is the most sort out for. Determining the depth at \nwhich groundwater exists is usually not a straightforward task, several \n\n\n\nscientific techniques are usually employed to extract information \nregarding its depth of occurrence in the earth\u2019s subsurface. Hydro-\ngeophysical methods are the most usually employed method since they \nprovide us with necessary information regarding the subsurface geology \nof the area while acting as tools for groundwater resource mapping and \nhelp in water quality evaluations (Hazell et al., 1988; Onimisi et al., 2014). \nOther commonly used methods employed in the exploration of \ngroundwater include electrical resistivity tomography, magnetometry, \ngravity, ground penetrating radar, seismic, and remote sensing. These \ntechniques have been employed in groundwater exploration with varying \ndegree of success. More importantly, gravity and magnetics methods have \nbeen used successfully to map regional aquifers and large scale basin \nfeatures reported the use of seismic methods in the delineation of \nsubsurface aquifers and fractured rock systems (Aizebeokhai et al., 2018; \nOyeyemi et al., 2020; Reynolds, 1997). \n\n\n\nElectrical resistivity tomography is the most commonly used method in \ngroundwater exploration owing to its effectiveness in the correlation of \nmany geological formation properties such as porosity and permeability \nwhich are critical to hydrogeology with electrical conductivity signatures \n(Eke et al., 2011; Osinowo et al., 2020; Hung et al., 2019). This method is \nalso widely appreciated because of its portability in equipment, ease of \noperation, usefulness and efficiency in drilling operations (Chinyem, \n2013). Decline in borehole yield and groundwater quality is a common \nproblem within Ilorin metropolis. Most borehole failures have been \nattributed to poor pre-drill survey, data misinterpretation due to handlers\u2019 \n\n\n\n\nmailto:kamaldeen.suleman@nmu.edu.ng\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 18-21 \n\n\n\n\n\n\n\n \nCite The Article: Suleman K.O., Ogunmola O.L., Adesina R.O., Adeoye T.O., Sunmonu L.A(2023). Groundwater Exploration for  \n\n\n\nWater Well Sites in Abata Asunkere Community, Ilorin, Nigeria. Malaysian Journal of Geosciences, 7(1): 18-21. \n \n\n\n\ninexperience and poor communication between geophysicists and the \ndrillers. As a result, this study used the electrical resistivity method in \nsearch of potential well locations for groundwater supply zones in Ansaru- \n\n\n\nIslam Secondary School along Alore Okekere Ilorin, Kwara State, with a \nview to improving yields of future boreholes. \n\n\n\n2.   MATERIALS AND METHODS \n\n\n\n2.1   Site Description and Geological Setting \n\n\n\n\n\n\n\nFigure 1: Geological map of Nigeria showing the study area (Akinola et al., 2014). \n\n\n\nThe study was conducted in Southeastern Nigeria sedimentary basin \nwithin Ansarul Islam Secondary School, along Abata Asunkere, Alore area \nof Ilorin, Kwara State. The area is situated on the latitude N 8\u00b0 30\u201916.76 N \nand longitude 4\u00b031\u201955.72\u2019\u2019 East, with elevation of 303.91 m area cover by \n400 m by 200 m. Figure 1 is the geological map of Nigeria showing the \nstudy location. \n\n\n\n2.2   Field Survey \n\n\n\nA 3-day assessment was carried out in October 2021 employing the \nelectrical resistivity method. The assessment was conducted with Herojat \nresistivity meter. A levelled terrain in the study area was located. Eight (8) \nVES points along two profiles were marked and sounded as shown in \nFigure 2. The Terrameter was used to measure and record the resistance \nof the subsurface with a set maximum (AB/2) of 80 m. The required \napparent resistivity obtained is the product of the values of the resistance \nobtained in the field with their respective Geometric factor (k). The \nobtained data was plotted on a bi-log graph and the resultant curve was \nquantitatively and qualitatively read. \n\n\n\n\n\n\n\nFigure 2: Base map of the study area \n\n\n\n3.   RESULTS AND DISCUSSION \n\n\n\n3.1   Curve Matching And Interpretations \n\n\n\nThe apparent resistivity and thickness obtained were modelled using the \nIPI2WIN software. The outcomes of these models are displayed as series \nof curves in Figures 3 (a-h). It was revealed that most of the modelled \ncurves show four (4) subsurface geo-electrical layers in VES 2, 3, 4, 5, and \n7 while VES 1, 6 and 8 revealed three (3) subsurface geo-electrical layers. \nThis is suggestive of stratified sedimentary structure, which comprises of \na minimum of three Geo-electrical strata, which consists majorly of diverse \nclay and sandy soil. A maximum of four Geo-electrical strata beneath the \nearth surface was mapped out showing geo-electric signature involving \nKH, QH and H curve types. The first layer which is the topsoil consists of \nClayey sand to sandy clay material with resistivity and thickness range of \n5 - 437 \u03a9-m and 0.5 - 1.7 m, respectively. This layer also occurs in multi-\nlayer in some parts of the study area serving as the second layer. Beneath \nthis, is a weathered basement rock with resistivity and thickness range of \n1 - 16 \u03a9-m and 5 - 15 m, respectively. This layer overlaid an infinitely \ncontinuous layer suspected to be fractured/fresh basement rock which \nhas resistivity range of 27 - 9800 \u03a9-m, characterized by infinite thickness. \nThe bedrock layer (weathered/fractured basement layer) has revealed \nlower resistivity value which is an indicator of good water zone. \n\n\n\n\n\n\n\nFigure 2a: Sounding curve for VES 1 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 18-21 \n\n\n\n\n\n\n\n \nCite The Article: Suleman K.O., Ogunmola O.L., Adesina R.O., Adeoye T.O., Sunmonu L.A(2023). Groundwater Exploration for  \n\n\n\nWater Well Sites in Abata Asunkere Community, Ilorin, Nigeria. Malaysian Journal of Geosciences, 7(1): 18-21. \n \n\n\n\n\n\n\n\nFigure 2b: Sounding curve for VES 2 \n\n\n\n\n\n\n\nFigure 2c: Sounding curve for VES 3 \n\n\n\n\n\n\n\nFigure 2d: Sounding curve for VES 4 \n\n\n\n\n\n\n\nFigure 2e: Sounding curve for VES 5 \n\n\n\n\n\n\n\nFigure 2f: Sounding curve for VES 6 \n\n\n\n\n\n\n\nFigure 2g: Sounding curve for VES 7 \n\n\n\n\n\n\n\nFigure 2h: Sounding curve for VES 8 \n\n\n\n3.2   Geoelectric Sectioning \n\n\n\nIn Figure three (3) and four (4) which represent the geo-electric sections \nacross profiles one and two, shows different lithology, ranging from sandy \nclay topsoil, weathered rock and the fractured to fresh rock with respect \nto their depths and thickness with resistivity values of each layer been \ndisplayed respectively. \n\n\n\n\n\n\n\nFigure 3: Profile One 2D Geo-electric Section \n\n\n\n\n\n\n\nFigure 4: Profile Two 2D Geo-electric Section \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 18-21 \n\n\n\n\n\n\n\n \nCite The Article: Suleman K.O., Ogunmola O.L., Adesina R.O., Adeoye T.O., Sunmonu L.A(2023). Groundwater Exploration for  \n\n\n\nWater Well Sites in Abata Asunkere Community, Ilorin, Nigeria. Malaysian Journal of Geosciences, 7(1): 18-21. \n \n\n\n\n\n\n\n\nFigure 5: 3D Bedrock Topography Map \n\n\n\n\n\n\n\nFigure 6: Overburden thickness Contour Map \n\n\n\nFrom the data obtained from the models and curves, bedrock and \noverburden thickness maps, Figure 5 and Figure 6, were produced by \npicking all depths and thickness values corresponding to the respective \nresistivity for all VES points using Golden Surfer Geophysical software \npackage. \n\n\n\nIn Figure 5, it was observed that the bedrock has an irregular topography. \nThe areas with shallow bedrock depth correspond to VES 5, 7 and 8. The \narea with intermediate bedrock depth corresponds to VES 2 and 4. Also, \nthe area which corresponded to VES 1, 3 and 6 has the deepest depth to \nthe hard rock. It was inferred that, the bedrock slopes from the East to the \nWest. That is, where we have the deepest, intermediate and the shallowest \nbedrock depth corresponded to thickest, intermediate and the thinnest \noverburden thickness respectively. Figure 6, which is the aquifer thickness \nmap of the study area with thickness range of 5 to 15 m, shows high aquifer \nthickness (12 - 15 m) along profile one which covers VES 1 to 4 points, \nwith intermediate aquifer thickness (8 - 10 m) showing up only at VES \npoint 7. Also, the areas which is covered by the remaining VES points along \nprofile 2 (VES 5, 6 and 8) indicated the presence of thin aquiferous \nformation (< 8 m). This means that the aquifer is thicker towards the \nsouth-west with intermediate and thinner thickness towards North-\nEastern part. \n\n\n\n4.   CONCLUSION \n\n\n\nElectrical resistivity investigations have been successfully carried out at \ndifferent sections of Abata Asunkere community, Alore area of Ilorin \nmetropoly. High potential for groundwater was observed at the \nnortheastern and southern part of the study area. Considering the result \nobtained, it is an indication that the study area has a good potential for \nboreholes due to the features that enhance groundwater permeability and \nstorage. \n\n\n\nREFERENCES \n\n\n\nAizebeokhai, A.P., Ogungbade, O., and Oyeyemi, K.D., 2018. Geoelectrical \nResistivity Data Set for Characterising Crystalline Basement \n\n\n\nAquifers in Basiri, Ado-Ekiti, southwestern Nigeria. Data in Brief, 19, \nPp. 810 -816. doi: 10.1016/j.dib.2018.05.091. \n\n\n\nAkinola, O.O., Bolarinwa, A.T., and Ademilua, O.L., 2014. Lithologic \nFeatures and Petrochemical Characteristics of Metasedimentary \nRocks of Igbetti Area, Southwestern Nigeria, 2 (4), Pp. 82 - 89. \n\n\n\nAl-Garni, M.A., 2009. Geophysical Investigations for Groundwater in a \nComplex Subsurface Terrain, Wadi Fatima, KSA: A Case History.  \nJordan Journal of Civil Engineering, 3 (2), Pp. 118 - 136. \n\n\n\nArefayne, S.H., and Abdi, S., 2015. Groundwater Exploration for Water Well \nSite Locations Using Geophysical Survey Methods. Hydrology \nCurrent Research, 7 (1), Pp. 1 - 7. doi: 10.4172/2157-\n7587.1000226. \n\n\n\nAsry, Z., Samsudin, A.R., Yaacob, W.Z., and Yaakub, J., 2014. Groundwater \nExploration Using 2-D Geoelectrical Resistivity Imaging Technique \nat Sungai. Udang, Melaka. Journal of Earth Science and Engineering, \n2, Pp. 624-630. \n\n\n\nChinyem, F.I., 2013.  Hydrogeophysical Investigation of Asaba Area, Delta \nState, Nigeria. Indian Journal of Science and Technology, 6 (5), Pp. \n4453 - 4458.  \n\n\n\nEke, K., Igboekwe, M., and O.M., 2011. Geoelectric Investigation of \nGroundwater in Some Villages in Ohafia Locality, Abia State, Nigeria. \nBritish Journal of Applied Science and Technology. 1 (4). doi: \n10.9734/bjast/2011/600. \n\n\n\nHazell, J.R.T., Cratchley, C.R., and Preston, A.M., 1988. The Location of \nAquifers in Crystalline Rocks and Alluvium in Northern Nigeria \nUsing Combined Electromagnetic and Resistivity Techniques. \nQuarterly Journal of Engineering Geology and Hydrogeology, 21 (2), \nPp. 159 - 175. https://doi.org/10.1144/GSL.QJEG.1988.021.02.05 \n\n\n\nHung, Y.C., Lin, C.V., Lee, C.T., and Weng, K.W., 2019. 33D and Boundary \nEffects on 2D Electrical Resistivity Tomography. Applied Science, 9 \n(15), Pp. 1 - 19. doi:10.3390/app9152963. \n\n\n\nMishra, P., Kumar, B.K., Saraswat, S., Chakraborty, C., and Gautam, A., 2021. \nWater Security in a Changing Environment\u202f: Concept. Water., 13 \n(490), Pp. 1 - 21. https://doi.org/10.3390/w13040490. \n\n\n\nOnimisi, M., Ayuba, R., and Daniel, A., 2014. Electromagnetic Geophysical \nProspecting For Groundwater in Basement Complex Terrain: A Case \nStudy of Ola-Oluwa Area of Osun State, Southwestern Nigeria.  IOSR \nJournal of Applied Geological Geophysics, 2 (1), Pp. 26 - 30. doi: \n10.9790/0990-02112630. \n\n\n\nOsinowo, O.O., Agbaje, M.A., and Ariyo, S.O., 2020. Integrated Geophysical \nInvestigation Techniques for Mapping Cassava Effluent Leachate \nContamination Plume, at a Dumpsite in Ilero, Southwestern Nigeria. \nScientific African, 8, Pp. 1 - 12. doi:10.1016/j.sciaf.2020.e00374. \n\n\n\nOyeyemi, K.D., Olofinnade, O.M., Aizebeokhai, A.P., Sanuade, O.A., \nOladunjoye, M.A., Ede, A.N., Adagunodo, T..A., Ayara, W.A., 2020. \nGeoengineering Site Characterization for Foundation Integrity \nAssessment. Cogent Engineering, 7 (1), Pp. 1 - 15. \ndoi:10.1080/23311916.2020.1711684. \n\n\n\nParanychianakis, N.V., Salgot, M., Snyder, S.A., and Angelakis, A.N., 2015. \nWater Reuse in EU States: Necessity for Uniform Criteria to Mitigate \nHuman and Environmental Risks. Critical Reviews in \nEnvironmental Science and Technology, 45 (13), Pp. 1409 - 1468. \ndoi: 10.1080/10643389.2014.955629. \n\n\n\nReynolds, J.M., 1997.  An introduction to applied and environmental \ngeophysics.  \n\n\n\nSchwarzenbach, R.P., Egli, T., Hofstetter, T.B., Von Gunten, U., and Wehrli, \nB., 2010. Global water pollution and human health. Annual Review \nof Environment and Resources. 35, Pp. 109 - 136. \ndoi:10.1146/annurev-environ-100809-125342. \n\n\n\nTzanakakis, V.A., Paranychianakis, N.V., and Angelakis, A.N., 2020. Water \nsupply and water scarcity. Water (Switzerland), 12 (9), Pp. 1 - 16. \ndoi: 10.3390/w12092347. \n\n\n\n \n\n\n\n\nhttps://doi.org/10.1144/GSL.QJEG.1988.021.02.05\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 06-11 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2021.06.11 \n\n\n\nCite the Article: Mohd Sahrul Syukri Narimah Samat, Mohd Hasmadi Ismail (2021). The Integration Of Gis, Ahp, And Remote Sensing Methods For Potential Areas \nGroundwater: Case Study For Pontian District, Johor, Malaysia. Malaysian Journal of Geosciences, 5(1): 06-11. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2021.06.11 \n\n\n\nTHE INTEGRATION OF GIS, AHP, AND REMOTE SENSING METHODS FOR \nPOTENTIAL AREAS GROUNDWATER: CASE STUDY FOR PONTIAN DISTRICT, \nJOHOR, MALAYSIA \n\n\n\nMohd Sahrul Syukria* Narimah Samatb, Mohd Hasmadi Ismailc \n\n\n\na Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia (UTHM), 86400 Parit Raja, Batu Pahat, Johor \nb Section of Geography, School of Humanities, Universiti Sains Malaysia (USM), 11800 Minden, Penang \nc Faculty of Forestry and Environment, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor. \n*Corresponding Author Email: shahrulm016@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 12 September 2020 \nAccepted 15 October 2020 \nAvailable online 19 November 2020\n\n\n\nIn Malaysia, production and conservation of groundwater are essential to the ecosystem\u2019s climate and \nsustainability. The decline of groundwater level data is a related problem for managing water supplies in the \nPontian District, Johor, particularly in rural areas. With demand for household water, agriculture and \nindustrial use is still increasing. Studies-based Remote Sensing (RS) and Geographic Information System \n(GIS) have gained more advantages in groundwater exploration as it is rapid knowledge about the research \nand development tool. Therefore, the present study has conducted an example of mapping potential \ngroundwater zones in the Pontian District, Johor, and assessing the factors leading to explore future \ngroundwater opportunities. To identify possible groundwater areas, RS data and GIS are being used, and the \ndata collected by the Department of Mineral and Geoscience Malaysia (JMG). The present study utilized \nintegration between GIS through analytical hierarchy process techniques (AHP). Five different maps were \nprepared and studied for the potential groundwater area, such as Roughness, Topographic Wetness Index \n(TWI), Elevation, Curvature, and Slope. Weights in all the thematic maps assigned to each class using the AHP \nmethod on their characteristics and potential water capability. The production accuracy has checked using \ngroundwater prospects information, and the process is approximately 87.5 percent accurate. The resulting \nmap of groundwater capacity was graded into five groups-very good, good, moderate, low, and very low. The \nanalysis shows that about 57.3 percent of the area occupies the low potential groundwater area. The potential \nzones of good and moderate groundwater are observed in 1.28 percent and 18.94 percent, respectively. Only \nin minimal areas is the area under perfect potential areas registered. The results from this study can be useful \nin the preparation and growth planning of related agencies in Malaysia, for possible groundwater exploration \nto provide a fast system and cost reduction and a shorter period.  \n\n\n\nKEYWORDS \n\n\n\nGeographic Information System, Groundwater Potential, Remote Sensing, Analytical Hierarchical Process, \nPrediction, Mapping.\n\n\n\n1. INTRODUCTION \n\n\n\n1.1 Overview \n\n\n\nThe subsurface geological systems of the earth's crust provide \ngroundwater, as one of the essential natural resources (Fitts, 2002; Al- \nRuzouq, 2015; Arulbalaji et al., 2019). Groundwater is an essential role in \nMalaysia used for agriculture, domestic consumption, and industrial uses \n(Andualem and Demeke, 2019). Groundwater is an alternative resource to \nmeet a demand for various purposes (Saaty, 2004). Precipitation and flow \ndischarge into rivers and lakes, springs, pumping, and evaporation are the \nprimary sources of recharge for groundwater.  (Arivalagan et al., 2014). \nThe issue of groundwater in developing countries were recorded to \nidentify the potential development of extraction water resources in future.  \nIn the previous study, the conventional methods are based on ground \nsurveys and field observation using a geophysical technique such as \n\n\n\nresistivity, ground penetrating-radar, geological and hydrogeological \ntechnique (Rao and Jugran, 2003; Adeyeye et al., 2019). The current study, \ngeospatial technologies by GIS, and remote sensing are a helpful technique \nfor the mapping of potential groundwater areas (Chowdhury et al., 2009). \nGIS and remote sensing applications are more comfortable, less expensive, \nand provides for efficiently handling spatial data for the planning of \nnatural resources (Machiwal et al., 2011).  \n\n\n\nGroundwater potential zones are detected on controlling variables, \nnamely slope, topographic wetness index (TWI), lineament density, \ndrainage, rainfall, elevation, TPI, normalized difference vegetation index \n(NDVI), curvature and roughness. For groundwater management plans, \nthe detection area of potential groundwater areas is preserved. For higher \ncognitive processes in groundwater fields, integration of the GIS and \nAnalytical Hierarchy Process (AHP) methods were used in 1980 by \nThomas Saaty. Consequently, the present study aims to map the future \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 06-11 \n\n\n\nCite the Article: Mohd Sahrul Syukri Narimah Samat, Mohd Hasmadi Ismail (2021). The Integration Of Gis, Ahp, And Remote Sensing Methods For Potential Areas \nGroundwater: Case Study For Pontian District, Johor, Malaysia. Malaysian Journal of Geosciences, 5(1): 06-11. \n\n\n\ngroundwater areas of the Pontian District, Johor, with efficient and \neffective using remote sensing and GIS applications (Badamasi et al., \n2016). Hence, the results of groundwater potential areas were checked \nand approved by field identification data to accurate results. \n\n\n\n1.2 Research background \n\n\n\nMany researchers used remote sensing GIS to locate the possible area of \ngroundwater. A group researcher investigated the delineation \ngroundwater potential in Nekor Basin, Central Rif of Morocco using GIS, \nremote sensing, and AHP (Bourjila et al., 2020). The various thematic \nlayers for variables potential groundwater included elevation, rainfall, \nland use, geology, drainage density, slope, lineament density, curvature, \nroughness, TWI, and TPI. The generated map shows that only 20.48 \npercent of the basin has a good and very good potential groundwater. \n\n\n\nA studied groundwater potential with GIS and AHP in a drought \nsusceptible semi-arid region in eastern India (Mukherjee and Singh 2020). \nThis assessment includes the 12 variables, including fault and lineament \ndensity, soil, roughness, land use, rainfall, slope, TPI, TWI, geology, \ndrainage density geomorphology, and curvature. The outcome of this \nanalysis was 80.49 percent, close to the tube well data observation. \n\n\n\nExploring the potential groundwater area for southern Banjarnegara, \nCentral Java, Indonesia using GIS, AHP, and remote sensing techniques \n(Atmaja et al., 2019). Five factors are used to evaluate groundwater \npotential involving slope, rainfall, drainage, lithology, and lineament \ndensity. The result validation obtained via 52 springs and two bore wells \ndata. The map generated and divided into five groups: very low, low, \nmoderate, high, and very high. This study area has high potential \ngroundwater, and it covered only 15.51 km2 equally to 17.98 percent. This \nGroundwater potential area map can provide guidance and information on \nthe desirable area in the prospective groundwater exploration for local \nauthorities and planners. \n\n\n\nThe groundwater productivity spatial prediction model in the Langat \nBasin Area, Selangor using twelve groundwater variables includes stream \npower index (SPI), land use, lithology, elevation, slope, curvature, TPI, \ndrainage capacity line density, NDVI, rainfall and soil (Nampak et al., \n2014). Potential groundwater map findings from the map of beliefs have \nbeen tested using test data. \n\n\n\nA study reported a statistical model and GIS for groundwater spring \npotential maps on the Taleghan Watershed, Alborz Province (Moghaddam \net al., 2013). Factors such as slope, aspect, elevation, TPI, SPI, roads, \nfailures, soil, lithology, land uses, and drainage density in the groundwater \nsource. The statistical method has calculated, and ArcGIS software has \nreported the results. \n\n\n\nThe combination of numerical model and GIS was developed by a \nresearcher in Banganga River, India (Gaur et al., 2011). In this study, the \nGIS was used to show different thematic from various variables like \ngeology, slope, land use, geomorphology, soil, drainage, to identify the \npotential groundwater areas. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Study aim and objectives \n\n\n\nThis study aims to classify possible areas of groundwater in the Pontian, \nJohor with GIS and remote sensing applications. The specific aims are to: \n\n\n\n\u2022 Create thematic maps of variables that affect the capacity of \n\n\n\ngroundwater in the region. \n\n\n\n\u2022 Weighting by hierarchical analytical method (AHP) of each of \n\n\n\nthematic map. \n\n\n\n\u2022 Map the possible areas of groundwater by overlaying the index \n\n\n\nweighted in GIS. \n\n\n\n2.2 Study area \n\n\n\nThe study area falls in Pontian District, Johor, in Malaysia, located between \n1.4869\u00b0 N latitudes and 103.3890\u00b0 E longitudes. The area covered by the \nresearch encompasses a total area of 1,017.2 km2, with a population of \napproximately 164,400 peoples in 2016 (DOSM, 2017). Pontian consists of \n11 Mukim\u2019s, namely Benut, Pontian, Ayer Masin, Serkat, Jeram Batu, \nPengkalan Raja, Ayer Baloi, Rimba Terjun, Sungai Pinggan, Api-Api, and \nSungai Karang. Pontian District, Johor has South China Sea monsoon rain \nfrom November to March. The annual average rainfall is 2355 mm, and the \nrange of temperatures is from 25.5\u00b0C - 27.8\u00b0C. The area of study is elevated \n\n\n\nfrom 0 m to 177 m. Figure 1 shows the Johor state map and the Pontian \nmap location of the study area shown in Figure 2. \n\n\n\nFigure 1: Johor State map \n\n\n\nFigure 2: Site map of the area of study \n\n\n\n2.3 Application technology and data used \n\n\n\nThis paper used geospatial methods to detect the Pontian District; Johor is \npossible for groundwater areas using five layers that monitor variables. \nThe application of GIS and remote sensing, namely Erdas Imagine 9.1 and \nArcGIS 10.5, is a technique by combining spatial data and attributes from \ndifferent sources. The different thematic maps, including boundary, \ndrainage, contour, and road, were obtained. Satellite data were collected \nand mosaicked from ASTER image to determine the digital elevation \nmodel (DEM), curvature, elevation, TWI, roughness, and slope. Tube well \nwas obtained from the Department of Mineral and Geoscience Malaysia \nJohor to overlay and locate with the GW map. \n\n\n\n2.4 Stage of data processing \n\n\n\n2.4.1 Stage 1 - Identify the Variables \n\n\n\nThe processing of data starts with the digitization, merging, and \nconverting all thematic maps into grid maps. The factors of groundwater \npotential were developed from satellite images using QGIS and ArcGIS. The \ndrainage and slope maps are obtained using the spatial analyst tool in the \nArcGIS application. All data have been overlaid and geographically \nreferenced by the World Geodetic System (WGS 84). AHP model was \nconducted to determine the thematic maps' weighting based on the \nconsiderable influence on groundwater accumulation within a scale of 1 \nto 9. The weighted index overlay technique did the mapping of \ngroundwater potentials in ArcGIS 10.5 after the reclassification process. \nFigure 3 summarizes the methodology charts for this analysis. \n\n\n\nFigure 3: Methodology chart used for mapping potential of groundwater. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 06-11 \n\n\n\nCite the Article: Mohd Sahrul Syukri Narimah Samat, Mohd Hasmadi Ismail (2021). The Integration Of Gis, Ahp, And Remote Sensing Methods For Potential Areas \nGroundwater: Case Study For Pontian District, Johor, Malaysia. Malaysian Journal of Geosciences, 5(1): 06-11. \n\n\n\n2.4.2 Stage 2 - Analysis of Multi criteria decision Approach (MCDA) \n\n\n\nMCDA derived as the weighting of each controlling variable based on the \nSaaty AHP technique. The result shows that the highest possible value is \ngiven to the indicator with the highest ground-water potential and the \nlowest possible minimum value. Based on a study, scale 1\u20139, where scale \n1 is the same score between the two controls and scale nine, implies that \none controlling variable is highly significant relative to the other (Saaty, \n1980; 1992). The following equation (1) is referenced as a consistency \nmeasure by Saaty called Consistency Index (CI): \n\n\n\n\ud835\udc36\ud835\udc3c =\n\ud835\udf06\ud835\udc5a\ud835\udc4e\ud835\udc65\u2212\ud835\udc5b\n\n\n\n\ud835\udc5b\u22121\n\n\n\n\ud835\udc36\ud835\udc3c =\n5\u22125\n\n\n\n5\u22121\n= 0    (Equ.1) \n\n\n\n\ud835\udeccmax is the peer-wise matrix\u2019s largest self-value, and n is the number of \ngroups or characteristics.  \n\n\n\nConsistency Ratio (CR) is a pairwise comparison matrix consistency \nmeasure given by equation (2):  \n\n\n\n\ud835\udc36\ud835\udc45 =\n\ud835\udc36\ud835\udc3c\n\n\n\n\ud835\udc45\ud835\udc3c\n                                                                                                               (Equ.2) \n\n\n\nThe index of the ratio is RI and CR is also defined as CR = CI / RCJ = Random \nConsistency Index Value (RCIV) derived from the norm of Saaty (Table 1). \n\n\n\n\ud835\udc36\ud835\udc45 =\n0\n\n\n\n1.12\n= 0 \n\n\n\n2.4.3 Stage 3 \u2013 Weights assigned for thematic maps and overlay \n\n\n\nanalysis \n\n\n\nThe weights of the various themes are qualitatively assigned by scale 1 to \n9 based on their groundwater effect. Each topic's different characteristics \nwere given weights on a scale from 1 to 9 according to its relative impact \non groundwater production. The different thematic characteristics were \nevaluated accordingly in a qualitative manner: poor (1-2); moderately \npoor (2-4); fair-moderate (4-6); fair (6-8); excellent (8-9) (Table 2). \n\n\n\nTable 2: Qualitative weightage of different themes \nTheme Weight \n\n\n\nElevation 9 \nSlope 7 \n\n\n\nRoughness 3 \nTWI 5 \n\n\n\nCurvature 1 \n\n\n\nAt the last procedure, the weightage and scores were obtained from a \nweighted linear combination in a raster format using the spatial analyst in \nArcGIS 10.5 software. Finally, all thematic layers are incorporated using \noverlay analysis and created groundwater areas Polygons weighted to \nachieve a reasonable amount of groundwater in the following equation (3) \n(Rao and Briz-Kishore, 1991): \n\n\n\n2.4.4 Stage 4 \u2013 Calculation of GWPAI \n\n\n\n\ud835\udc3a\ud835\udc4a\ud835\udc43\ud835\udc34\ud835\udc3c = {(\ud835\udc46\ud835\udc3f\ud835\udc64)(\ud835\udc46\ud835\udc3f\ud835\udc64\ud835\udc56) + (CTw)(CTwi) + (RNw)(RNwi) +\n(\ud835\udc47\ud835\udc4a\ud835\udc3c\ud835\udc64)(\ud835\udc47\ud835\udc4a\ud835\udc3c\ud835\udc64\ud835\udc56) + (\ud835\udc38\ud835\udc49\ud835\udc64)(\ud835\udc38\ud835\udc49\ud835\udc64\ud835\udc56)             (Equ.3) \n\n\n\nGWPAI = Groundwater Potential area Study area indices SL=slope, \nCT=curvature, RN=roughness, TWI=topographic wetness index. \nMoreover, subscription 'w' and 'wi' indicates the normalized and \nnormalized weight of each issue. \nOther methods of generating Pontian District groundwater potential area \nmap using equation (4). \n\n\n\n\ud835\udc3a\ud835\udc4a\ud835\udc43\ud835\udc34\ud835\udc3c = \u2211 (\ud835\udc4b\u2090 \u00d7 \ud835\udc4c\u1d66)    (Equ. 4)\ud835\udc5b\n\ud835\udc56   \n\n\n\nGWPAI = Groundwater Potential Area Index, X- represents the thematic \nlayer weight; Y-represent the thematic layers of the sub-class category. \nThe thematic maps are represented by the \u2018a\u2019 term (a=1, 2, 3 ......, X). \nFurthermore, a set of thematic maps defined by \u2018\u03b2\u2019 (\u03b2 = 1, 2, 3 ..., Y). \n\n\n\n3. RESULTS AND DISCUSSIONS\n\n\n\nAll different thematic maps of slope, elevation, curvature, roughness, and \nTWI in the study area are generated by GIS and RS data in the following \nsection to present the groundwater potential map. \n\n\n\n3.1 Slope \n\n\n\nThe slope is an indication of the ground surface's steepness. The more \nextensive slopes produce fewer recharges because the water received \nfrom precipitation flows down rapidly during rainfall. These variables can \nbe viewed as one possible accessibility measure for groundwater (Al Saud, \n2018; Machireddy, 2019). Therefore, this condition does not provide \nenough time to penetrate and regenerate (Reu et al., 2013; Arulbalaji et al., \n2019). Figure 4 shows the Pontian District slope diagram. The study area \nslope ranges were carried from 0o to 64o. The slope values have been \ncategorized and reclassified into five groups: flat (0-3.27), gentle (3.27-\n6.55), medium (6.55-12.09), steep (12.09-24.44), and very steep (24.44-\n64.27). \n\n\n\nFigure 4: Slope map \n\n\n\n3.2 Elevation \n\n\n\nAn area elevation has a pronounced impact on an area's GWP. Water \nappears to be collected at lower topography, rather than at higher \ntopographies. Higher elevation reduces groundwater potential and vice \nversa (Ramu et al., 2015; Hamid et al., 2018). The elevation of the study \narea varies from 0 meters to 177 meters from the meantime sea level. The \nresearch field is fragmented into five classes by height (Figure 5): 0-11, 11-\n19, 19-35, 35-71, and 71-177. \n\n\n\nFigure 5: Elevation map \n\n\n\n3.3 TWI \n\n\n\nCommonly, the Topographic Wetness Index (TWI) is used to measure the \ntopographical influence of hydrological processes and represent \ngroundwater's possible intrusion due to the topographical impact \n(Mokarram, 2015). The TWI was developed using 'TOPMODEL' \u2013 a model \nthat stimulates hydrological flows in the watershed. For the estimation of \nTWI, the equation (5) given below was used. \n\n\n\n\ud835\udc47\ud835\udc4a\ud835\udc3c = In\n\u0251\n\n\n\ntan \ud835\udefd\n    (5) \n\n\n\n\u0251 =  \ud835\udc48\ud835\udc60\ud835\udc59\ud835\udc5c\ud835\udc5d\ud835\udc52 \ud835\udc50\ud835\udc5c\ud835\udc5b\ud835\udc61\ud835\udc5f\ud835\udc56\ud835\udc4f\ud835\udc62\ud835\udc61\ud835\udc56\ud835\udc5b\ud835\udc54 \ud835\udc4e\ud835\udc5f\ud835\udc52\ud835\udc4e; \ud835\udefd = \ud835\udc47\ud835\udc5c\ud835\udc5d\ud835\udc5c\ud835\udc54\ud835\udc5f\ud835\udc4e\ud835\udc5d\u210e\ud835\udc56\ud835\udc50 \ud835\udc3a\ud835\udc5f\ud835\udc4e\ud835\udc51\ud835\udc56\ud835\udc52\ud835\udc5b\ud835\udc61 (\ud835\udc60\ud835\udc59\ud835\udc5c\ud835\udc5d\ud835\udc52) \n\n\n\nThe study area of the TWI ranged from 2.71 to 15.43. Five ranges, such as \n2.71-6.00, 6.00-6.95, 6.95-8.05, 8.05-9.54, and 9.54-15.43, reclassified the \n\n\n\nTable 1: Index Saaty ratio for N various values. \nThe consistency of mutual matrices randomly generated \nMatrix series \nN 1 2 3 4 5 \nRCI value 0.00 0.00 0.58 0.90 1.12 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 06-11 \n\n\n\nCite the Article: Mohd Sahrul Syukri Narimah Samat, Mohd Hasmadi Ismail (2021). The Integration Of Gis, Ahp, And Remote Sensing Methods For Potential Areas \nGroundwater: Case Study For Pontian District, Johor, Malaysia. Malaysian Journal of Geosciences, 5(1): 06-11. \n\n\n\nvalues. For lower TWI, the lower weights were allocated, and vice versa. \nFigure 6 displays The Pontian Johor's TWI map. \n\n\n\nFigure 6: Topographic Wetness Index map \n\n\n\n3.4 Curvature \n\n\n\nCurvature is used to see concave or convex upward profiles as a \nquantitative representation of the surface profile character (Nair et al., \n2017). Water continues to slow down and is susceptible to accumulation \nby profile. Curvature variables in the study area ranged from 22.05 to-\n22.76. The ranges are divided into five groups such as -22.76 to -1.49, -1.49 \nto -0.44, -0.44 to 0.43, 0.43 to 1.66 and 1.66 to 22.05. For optimum \ncurvature value, assigned high weight, and minimal curvature value, \nassigned low weight. Figure 7 shows the Pontian Johor's curvature map. \n\n\n\nFigure 7: Curvature map \n\n\n\n3.5 Roughness \n\n\n\nThe roughness index indicates the difference in height between the \nneighbouring DEM cells (Riley, 1999). The index of roughness typically \nreflects the undulation of the topography. The roughness increased, the \nundulation increased and the roughness decreased, and the undulation \ndecreased. Undulated topography is typical of a mountainous area where \nweathering and erosion processes constantly alter a rough and smooth \nsurface landscape on a long-term basis (Nair, 2017). Figure 8 shows the \nPontian roughness map and the values ranged from 0.02 to 0.88. \nReclassified the values into five classes: 0.02-0.32, 0.32-0.39, 0.39-0.45, \n0.45-0.52 and 0.52-0.88. For low roughness value, the high weights are \ngiven, and vice versa. \n\n\n\nFigure 8: Roughness map \n\n\n\n3.6 Groundwater potential area \n\n\n\nThen, a matrix of comparison in pairs was built using Saaty's AHP to \ndetermine all parameters (Das et al., 2019). AHP scale: Figure 9 indicates \nthat the CR is 7.8 percent equal to 0.078, suggesting that this value was \nagreed and reasonably consistent. Saaty assumed that a CR of 0.10 or less \nwould be sufficient for the study to proceed. When the consistency value \nreaches 0.10, then the decision must be updated to find and correct the \ncauses of the inconsistency. When the CR value is 0, it implies that the pair-\nwise relation has a perfect consistency. The essential factor is elevation, \nfollowed by slope, TWI, roughness, and curvature. \n\n\n\nFigure 9: 10 Pairwise using AHP Priorities scale 1 to 9 for all criteria \n\n\n\nFigure 10 shows that the criteria resulting weights are based on pair \ncomparison and decision matrix to get a fundamental value of their own \n(eigenvalue). The elevation is the highest percentage for all variables. The \nvalue of its eigenvalue is 5.353. \n\n\n\nFigure 10: The priorities and decision matrix for the different criteria \n\n\n\nAfter combining all previous layers, the groundwater area map was \ngraded, as shown in Figure 11, beginning from very poor to very strong. \nThe suitability map with the different spectra of colors may identify the \ngroundwater level is. \n\n\n\nFigure 11: Groundwater Potential map \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 06-11 \n\n\n\nCite the Article: Mohd Sahrul Syukri Narimah Samat, Mohd Hasmadi Ismail (2021). The Integration Of Gis, Ahp, And Remote Sensing Methods For Potential Areas \nGroundwater: Case Study For Pontian District, Johor, Malaysia. Malaysian Journal of Geosciences, 5(1): 06-11. \n\n\n\nTable 3: Potential groundwater areas with coverage area and \npercentage \n\n\n\nGroundwater Areas Area Covered \n(KM2) \n\n\n\nPercentage \nCovered (%) \n\n\n\nVery Low 226.3818 22.26 \nLow 583.2978 57.34 \nModerate 192.7016 18.94 \nGood 13.00787 1.28 \nVery Good 1.815258 0.18 \n\n\n\nThe weighted overlay technique showed that 57.34 percent of the study \narea was occupied by the low zone, equivalent to 583.29 km2. The \nfollowing are very low, moderate, good, and very good areas with 22.26 \npercent, 18.94 percent, 1.28 percent and 0.18 percent equivalent to \n226.38km2, 192.70km2, 13.00km2 and 1.81km2 respectively (Table 3). It \nindicates that only 1.46 percent of the study area has a secure capacity to \nsupply water. In the research area, elevation plays the most vital position \nin groundwater developments, followed by slope, TWI, curvature, and \nroughness. For validation, on the groundwater potential map, the position \nfor groundwater or tube well was overlaid from JMG data. From the JMG \ntube well database, eight tube well exploration is valid, and the seven only \nare located in the right zone. The only one tube well-considered as a \nmoderate and low category.  \n\n\n\n4. CONCLUSION \n\n\n\nThe conclusion of this analysis carried out with the five possible \ngroundwater areas includes very good, good, moderate, low, and very low. \nFive different thematic layers, namely, slope, elevation, curvature, \nroughness, and TWI, are prepared using satellite images. These layers are \nimplemented in GIS with a weighted overlay to create possible maps of \ngroundwater. This study's finding was validated using some existing tube \nwell from the JMG database in Pontian Johor. Hence, potential \ngroundwater mapping area using RS and GIS methods are inexpensive, \nfast, accurate, and covers a large area. This method will reduce \nunnecessary work labor, save time and cost (Okoli, & Marcellinus, 2019). \nThis data finding from this study using the AHP technique from GIS device \nis an excellent tool to make a policy, evaluating, and decision making for \nsustainable water resources plan (Zeinolabedini & Esmaiely, 2015; Patle, \n2019). The groundwater potential map shows that about 57.3 percent of \nthe area occupies the low potential groundwater area, good and moderate \ngroundwater are observed in 1.28 percent and 18.94 percent, respectively. \nFuture research must concentrate on identifying more complex factors \nthat may lead to shifting potential groundwater to ensure the final map's \naccuracy and validity. \n\n\n\nRECOMMENDATIONS \n\n\n\nFuture research will concentrate on checking and enhancing the findings \nby implementing more checked weight values and exploring other factors \nthat can lead to possible groundwater areas changes. To ensure the end \nmap's accuracy and authenticity, the digital maps' reliability and the full \nrange needs to examine. It should be noted that the maps produced need \nto be verified and improved before they are adopted for future research. \n\n\n\nREFERENCES \n\n\n\nAdeyeye, O.A., Ikpokonte, E.A., Arabi, S.A., 2019. GIS-based groundwater \npotential mapping within Dengi area, North Central Nigeria. The \nEgyptian Journal of Remote Sensing and Space Science, 22, Pp. 175-181. \n\n\n\nAl-Ruzouq, R., Shanableh, A., Merabtene, T., 2015. Geomatics for Mapping \nof Groundwater Potential Zones in Northern Part of the United Arab \nEmiratis - Sharjah City. ISPRS - International Archives of the \nPhotogrammetry, Remote Sensing and Spatial Information Sciences, Pp. \n581-586. \n\n\n\nAl Saud, M., 2018. Mapping potential areas for groundwater storage in \nWadi Aurnah Basin, western Arabian Peninsula, using remote sensing \nand geographic information system techniques. Hydrogeology Journal, \n18 (6), Pp. 1481\u20131495. \n\n\n\nAndualem, T.G., Demeke, G.G., 2019. Groundwater potential assessment \nusing GIS and remote sensing: A case study of Guna tana landscape, \nupper Blue Nile Basin, Ethiopia. Journal of Hydrology: Regional Studies, \n24.  \n\n\n\nArivalagan, S., Sureshbabu, S., 2014. Delineation of Groundwater Potential \nZones Using RS and GIS Techniques: A Case Study for Eastern Part of \n\n\n\nKrishnagiri District, Tamilnadu. \n\n\n\nArulbalaji, P., Padmalal, D., Sreelash, K., 2019. GIS and AHP Techniques \nBased Delineation of Groundwater Potential Zones: a case study from \nSouthern Western Ghats, India. Scientific Reports, 9. \n\n\n\nAtmaja, R.R., Putra, D.P., Setijadji, L.D., 2019. Delineation of groundwater \npotential zones using remote sensing, GIS, and AHP techniques in \nsouthern region of Banjarnegara, Central Java, Indonesia. Other \nConferences. \n\n\n\nBadamasi, S., Sawa, B.A., Garba, M.L., 2016. Groundwater Potential Zones \nMapping Using Remote Sensing and Geographic Information System \nTechniques (GIS) in Zaria, Kaduna State, Nigeria. American Scientific \nResearch Journal for Engineering, Technology, and Sciences. 24, Pp. 51-\n62. \n\n\n\nBourjila, A., Dimane, F., Nouayti, N., Taher, M., Ouarghi, H.E., 2020. Use of \nGIS, Remote Sensing and AHP Techniques to Delineate Groundwater \nPotential Zones in the Nekor Basin, Central Rif of Morocco. Proceedings \nof the 4th Edition of International Conference on Geo-IT and Water \nResources 2020, Geo-IT and Water Resources. \n\n\n\nChowdury, A., Jha, M.K., Mal, B.C., Chowdary, V.M., 2009. Delineation of \nGroundwater Prospects Zones using Remote Sensing and GIS \ntechniques: A Case Study. Proceedings of the International Conference \nW E E S, New Delhi. \n\n\n\nDas, B., Pal, S.C., Malik, S., Chakrabortty, R., 2019. Modeling groundwater \npotential zones of Puruliya district, West Bengal, India using remote \nsensing and GIS techniques. Geology, Ecology, and Landscapes, 3 (3), Pp. \n223\u2013237. \n\n\n\nDOSM. 2017. Population Distribution and Basic Demographic 2016. \nDepartment of Statistics Malaysia. \n\n\n\nFitts, C.R., 2002. Groundwater Science. Elsevier. \n\n\n\nGanapuram, S., Kumar, G.T., Krishna, I.V., Kahya, E., Demirel, M.C., 2009. \nMapping of groundwater potential zones in the Musi basin using remote \nsensing data and GIS. Adv. Eng. Softw., 40, Pp. 506-518. \n\n\n\nGaur, S., Chahar, B.R., Graillot, D., 2011. Combined use of groundwater \nmodeling and potential zone analysis for management of \ngroundwater. Int. J. Appl. Earth Obs. Geoinformation., 13, Pp. 127-139. \n\n\n\nHamid, E.N., Khan, D.J., Malik, D.M., 2018. Multi-criteria evaluation-based \ngroundwater potential zonation of Pampore watershed using geo-\nspatial tools. International Journal of Research and Analytical Reviews, \n5 (2), Pp. 352-358. \n\n\n\nKumar, P., Herath, S., Avtar, R., Takeuchi, K., 2016. Mapping of \ngroundwater potential zones in Killinochi area, Sri Lanka, using GIS and \nremote sensing techniques. Sustainable Water Resources Management, \n2, Pp. 419-430. \n\n\n\nMachiwal, D., Jha, M.K., Mal, B.C., 2011. Assessment of Groundwater \nPotential in a Semi-Arid Region of India Using Remote Sensing, GIS and \nMCDM Techniques. Water Resources Management, 25, Pp. 1359-1386. \n\n\n\nMachireddy, S.R., 2019. Delineation of groundwater potential zones in \nSouth East part of Anantapur District using remote sensing and GIS \napplications. Sustainable Water Resources Management, 5 (4), Pp. \n1695\u20131709. \n\n\n\nMoghaddam, D.D., Rezaei, M., Pourghasemi, H.R., Pourtaghie, Z.S., Pradhan, \nB., 2013. Groundwater spring potential mapping using bivariate \nstatistical model and GIS in the Taleghan Watershed, Iran. Arabian \nJournal of Geosciences, 8, Pp. 913-929. \n\n\n\nMokarram, M., Roshan, G., Negahban, S., 2015. Landform classification \nusing topography position index (case study: salt dome of Korsia-Darab \nplain, Iran). Modeling Earth Systems and Environment, 1, Pp. 1-7. \n\n\n\nMukherjee, I., Singh, U.K., 2020. Delineation of groundwater potential \nzones in a drought-prone semi-arid region of east India using GIS and \nanalytical hierarchical process techniques. Catena, 194, Pp. 104681. \n\n\n\nNair, H.C., Padmalal, D., Joseph, A., Vinod, P.G., 2017. Delineation of \nGroundwater Potential Zones in River Basins Using Geospatial Tools\u2014\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 06-11 \n\n\n\nCite the Article: Mohd Sahrul Syukri Narimah Samat, Mohd Hasmadi Ismail (2021). The Integration Of Gis, Ahp, And Remote Sensing Methods For Potential Areas \nGroundwater: Case Study For Pontian District, Johor, Malaysia. Malaysian Journal of Geosciences, 5(1): 06-11. \n\n\n\nan Example from Southern Western Ghats, Kerala, India. Journal of \nGeovisualization and Spatial Analysis, 1, Pp. 1-16. \n\n\n\nNampak, H., Pradhan, B., Manap, M.A., 2014. Application of GIS based data \ndriven evidential belief function model to predict groundwater \npotential zonation. Journal of Hydrology, 513, Pp. 283-300. \n\n\n\nOkoli, Marcellinus. 2019. Groundwater Potential Mapping in Ado Ekiti, \nNigeria Using GIS and Remote Sensing Techniques. \n\n\n\nPatle, D., 2019. Groundwater Potential Zoning in Tikamgarh District of \nBundelkhand Using Remote Sensing and GIS. International Journal of \nAgriculture Environment and Biotechnology, 12 (4).  \n\n\n\nRamu, Mahalingam, B., Vinay, M., 2015. Identification of ground water \npotential zones using GIS and Remote Sensing Techniques: A case study \nof Mysore taluk-Karnataka. International journal of Geomatics and \nGeosciences, 5, Pp. 393-403. \n\n\n\nRao, Y.S., Jugran, D.K., 2003. Delineation of groundwater potential zones \nand zones of groundwater quality suitable for domestic purposes using \nremote sensing and GIS. Hydrological Sciences Journal, 48, Pp. 821 - \n833. \n\n\n\nReu, J.D., Bourgeois, J., Bats, M., Zwertvaegher, A., Gelorini, V., Smedt, P.D., \nChu, W., Antrop, M., Maeyer, P.D., Finke, P.O., Meirvenne, M.V., Verniers,  \n\n\n\nJ., Cromb\u00e9, P., 2013. Application of the topographic position index to \nheterogeneous landscapes. Geomorphology, 186, Pp. 39-49. \n\n\n\nRiley, S.J., 1999. Index that quantifiers topographic heterogeneity. Intermt. \nJ. Sci., 5, Pp. 23-27. \n\n\n\nSaaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York, \nNY. \n\n\n\nSaaty, T.L., 1992. Decision Making for Leaders. RWS Publications. \nPittsburgh. \n\n\n\nSaaty, T.L., 1999. Fundamentals of the analytic process, International \nSymposium of the Analytic Hierarchy Process (ISAHP), Kobe, Japan. \n\n\n\nSaaty, T.L., 2004. Fundamentals of the analytic network process \u2014 \nmultiple networks with benefits, costs, opportunities and risks. Journal \nof Systems Science and Systems Engineering, 13, Pp. 348-379. \n\n\n\nZeinolabedini, M., Esmaeily, A., 2015. Groundwater Potential Assessment \nUsing Geographic Information Systems and AHP Method (Case Study: \nBaft City, Kerman, Iran). ISPRS - International Archives of the \nPhotogrammetry, Remote Sensing and Spatial Information Sciences, Pp. \n769-774. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 17-21  \n\n\n\nCite The Article: Sivadass Thiruchelvam, Rahsidi Sabri Muda, Azrul Ghazali, Fatin Faiqah Norkhairi, Kamal Nasharuddin Mustapha, Nora Yahya, Rosnafisah \nSulaiman, Zakaria Che Muda (2018). Inception Of 3es In Promoting Disaster Resilient Communities Living Near Hydropower Dams Of Peninsular Malaysia.  \n\n\n\nMalaysian Journal of Geosciences, 2(2) : 17-21. \n\n\n\n\n\n\n\n    ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 26 June 2018  \nAccepted 2 July 2018  \nAvailable online 1 August  2018 \n\n\n\nABSTRACT\n\n\n\nABSTRACT \n\n\n\nExcessive rain pattern has been the major cause contributing to flooding of low land due to excess water release \nfrom affected dams. This deliberate measure has to be taken to prevent the catastrophic effect of a dam break \nscenario. Therefore, this kind of disaster is considered as a local phenomenon. The local communities are the  \nvulnerable population to face the immediate impact of such disaster. Needless to mention that they are also first \nemergency responders which is crucial for saving lives. It is therefore imperative for the involved stakeholders to \nimprove local communities\u2019 resilience to dam related disasters. This resonates well with the Hyogo Framework for \nAction, which identify local communities as integral cornerstone for saving lives and livelihoods. In the case of \ncommunities living near main hydropower dams owned by Tenaga Nasional Berhad, an initiative known as \nIntegrated Community Based Disaster Management (ICBDM) has been launched in May 2015. This initiative adopts \nthe concept of 3Es; embrace, educate and empower. The priority is to ensure the vulnerable communities embrace \nthe reality, being educated to face any upcoming situation as well as being empowered to take charge of immediate \nlive saving efforts in the future. The initiative involves five key scopes encompassing technical and non-technical \nareas and promotes the strategic partnerships between dam owner, authority and the community. It is anticipated \nthat this initiative will build the resilience of communities to dam related disaster.  \n\n\n\n KEYWORDS \n\n\n\nDam, disaster management, embrace, educate, empower.\n\n\n\n1. INTRODUCTION\n\n\n\nDisaster risk reduction is one of the important aspects that need to be \n\n\n\nseriously concerned by everyone. With the increasing number of disaster \n\n\n\nhappened around the world, exposing the community of the affected area \n\n\n\nto the high risk and vulnerabilities [1]. Post disaster reconstruction and \n\n\n\nrehabilition is a complex issue with several dimensions [2]. Government, \n\n\n\nnongovernment, stakeholders and international organization have their \n\n\n\nown outlines in disaster recovery program and the unity must be establish \n\n\n\namong them as well as the community. In order to minimize the damage \n\n\n\ncause by the disaster, various effort were taken care. Participation from \n\n\n\nthe community is an important aspects to ensure that the effort in disaster \n\n\n\nmanagement sustainability will last longer. Without the sustainability \n\n\n\ndisaster management will not preserve. The most common elements of \n\n\n\ncommunity involvements are partnership, participation, empowerment \n\n\n\nand ownership by the local people [3].  \n\n\n\nOn 2013, Malaysia was shocked by the catastrophic flood that hit Bertam \n\n\n\nValley in Cameron Highlands. Due to the heavy rainfall, the mud flood have \n\n\n\noccur causing the release of water from Sultan Abu Bakar dam as it \n\n\n\nreached the danger level. The flood cause many destructive damage to the \n\n\n\narea in term of loss of human life, damage to the property, destruction of \n\n\n\nstocks and many more. Taking into the action through this disaster, \n\n\n\nTenaga Nasional Berhad (TNB) took an initiative known as Integrated \n\n\n\nCommunity Based Disaster Management (ICBDM) for their hydroelectric \n\n\n\ndam scheme. The disaster that occur in Cameron Highlands gives a \n\n\n\nwakeup call as it leads to the development of better mechanism for \n\n\n\ndisaster rescue operations could be performed in such efficient and \n\n\n\neffective manners [4]. Although proper notification mechanism to the \n\n\n\nauthority (relevant district, state and federal agencies) has been clearly \n\n\n\noutlined in the ERP, the inclusion of the members of public (community) \n\n\n\nis still at minimal stage.  \n\n\n\nIntegrated Community Based Disaster Management or ICBDM is a \n\n\n\nconceptual framework which aims to synergy between three major \n\n\n\nstakeholders; the community, relevant authorities and related agencies, to \n\n\n\nminimize loss life and property damages in the event of flood related \n\n\n\ndisaster. Therefore consistent consequence estimation approaches must \n\n\n\nbe achieve to identify those assets within this critical infrastructure sector \n\n\n\nwhose failure or disruption could potentially lead to the most severe \n\n\n\nimpacts. The initiative looked into five main aspect comprises of technical \n\n\n\nand non-technical areas such as a 2-D modelling, life safety models, \n\n\n\ncommunity based early warning system, community based training and \n\n\n\nawareness and lastly, stakeholder\u2019s engagement programs. \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: https://doi.org/10.26480/mjg.02.2018.17.21\n\n\n\nINCEPTION OF 3Es IN PROMOTING DISASTER RESILIENT COMMUNITIES LIVING \nNEAR HYDROPOWER DAMS OF PENINSULAR MALAYSIA \n\n\n\nSivadass Thiruchelvam1*, Rahsidi Sabri Muda2, Azrul Ghazali1, Fatin Faiqah Norkhairi1, Kamal Nasharuddin Mustapha1, Nora Yahya1, Rosnafisah \nSulaiman3, Zakaria Che Muda1 \n\n\n\n1Institute of Energy Infrastructure, Universiti Tenaga Nasional \n2TNB Research Sdn. Bhd. \n3Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional \n*Corresponding Author Email: Sivadass@uniten.edu.my\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\n\n\n\n\nISSN : 2521-0920 (Print)\nISSN : 2521-0602 (Online) \nCODEN: MJGAAN\n\n\n\n\nhttps://doi.org/10.26480/mjg.02.2018.11.16\n\n\nmailto:Sivadass@uniten.edu.my\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 17-21 \n\n\n\nFigure 1: Integrated Community Based Disaster Management Initiative \n(ICBDM) \n\n\n\nA 2D model for flood can be able to grasp the movement and areas affected \n\n\n\nby flood waters in an incident of dam failure as it was done previously at \n\n\n\nthe Kelantan River Basin [5]. This is important later on to develop a life \n\n\n\nsafety model that acts as a guideline for evacuation procedures [6]. Based \n\n\n\non both models, researcher believe it will help with the process of \n\n\n\nidentifying location to establish early warning system and will be a great \n\n\n\nform of help to the community as well as authorities to be well informed \n\n\n\nof an impending danger [7,8]. Additionally, importance is also placed in \n\n\n\ngiving training and spreading awareness among the community on dam \n\n\n\nrelated disaster, as it is believe that infrastructure without knowledge is \n\n\n\nmeaningless [9]. This also acts as an initiative to conducting real life \n\n\n\nevacuation drills to prepare and educate in case of a real emergency. \n\n\n\nThe recent incident at Sultan Abu Bakar (SAB) dam has proven that loss of \n\n\n\nlife could also be contributed by dam associated flash floods which are \n\n\n\nobviously not rare compared to dam failure flooding. Dam failure and \n\n\n\nassociated flash floods can result in high fatality rates, when flooding \n\n\n\noverwhelms an unsuspecting group of people. However, dam failures that \n\n\n\nproduce slowly rising floods tend to result in lower fatality rates. \n\n\n\n2. LITERATURE REVIEW\n\n\n\nDams have huge importance in the modern day living, which includes \n\n\n\nproviding energy that helps in the development as well as improving living \n\n\n\nstandards. Besides being an energy production structure, dams also acts \n\n\n\nas a reservoirs for large quantities of water. Dams contributes significantly \n\n\n\nto the economic growth of the nation but are not immune to risk of failure \n\n\n\ndue to its diminishing lifespan and natural hazards [10]. Dam failures are \n\n\n\nhard to predict and can come unexpectedly even with great effort placed \n\n\n\ninto design, construction, operation and maintenance. Dams also have to \n\n\n\nbe safe as it is usually constructed within close proximity to populated \n\n\n\nareas [6]. Dam failures can cause catastrophic damages to nature, \n\n\n\ninfrastructure and even loss of lives. Because of that, an emergency \n\n\n\nresponse system is essential to minimize the negative effects and optimize \n\n\n\nresources [11]. \n\n\n\nOne example of a catastrophic dam disaster that occurred was the 1975 \n\n\n\nBanqiao Dam failure in China. It was one of the worst recorded dam failure \n\n\n\nthat took an estimate of 171,000 lives and displaced 11 million others [12]. \n\n\n\nThe dam failure was believed to be caused by the collision of Typhoon Nina \n\n\n\n[13]. The consequences of a catastrophic dam failure are enormous with \n\n\n\nlives, properties, communities, economy well-bring and nature at stake. \n\n\n\nMalaysia has more than 50 dams under different ownerships where 60% \n\n\n\nof these dams are earth fill dams [14]. Dams have periodically failed \n\n\n\nthroughout history, as they are not build to last forever. Dam failures are \n\n\n\nrare but causes large consequences. Fortunately, there have not been \n\n\n\nmany dam disasters in Malaysian history. However in October 2013, \n\n\n\nBertam Valley, Cameron Highlands have been woken up by a tragedy \n\n\n\naccompanied with the loss of life of four individuals that came into the way \n\n\n\nof discharge water from the Sultan Abu Bakar Dam [15]. \n\n\n\nThis disaster event have raised the question for the need for mitigation of \n\n\n\nhuman risk in dam related disaster that very well affects the communities \n\n\n\nliving below dams that are high risk of experiencing flooding. Dam owners \n\n\n\nare asked to make life saving decision during a crisis as well as to provide \n\n\n\nearly warning and assist in evacuation procedures in case of a dam \n\n\n\ndisaster [6]. The time available for evacuation was one of the important \n\n\n\nfactors to consider in any operation. Additionally, many believe that \n\n\n\neducating and warning the community about dam safety is equally \n\n\n\nimportant and have to be addressed [16]. \n\n\n\nAs dams in Malaysia continue to age, more attention is being places upon \n\n\n\nthe dam safety and the emergency responses during disaster to minimize \n\n\n\nloss of life and damages to the community. To ensure humans are at less \n\n\n\nrisk of a disaster event, effective collaboration and shared understanding \n\n\n\non the dam risk among stakeholders, authorities and community have to \n\n\n\nbe optimized in order to come out with the best plan for evacuation \n\n\n\nprocedures and logistics [17]. \n\n\n\n3. METHODOLOGY\n\n\n\n3.1 2D Flood Modelling \n\n\n\nIn order to prepare emergency response plans, revise dam operation \n\n\n\nstrategies, priorities dam rehabilitation, etc., it is important to assess the \n\n\n\nconsequences of possible dam break in terms of the affected areas, the \n\n\n\ntime available to evacuate people and the damage which the flood wave \n\n\n\nwill cause. This can be most effectively assessed through model studies \n\n\n\nand flood mapping. \n\n\n\nCritical aspects for the flood risk analysis of a dam break event are: \n\n\n\ni. Failure moment: at a specific time or related to certain \n\n\n\nhydraulic conditions. \n\n\n\nii. Failure mode: breach development, piping failure leading \n\n\n\nto erosion, or erosion through overtopping. The failure \n\n\n\nmode has a significant impact on the outflow hydrograph \n\n\n\n(flood wave). The worst case is instant removal of the dam, \n\n\n\nwhile the peak discharge would be considerably lower if \n\n\n\nthe breach develops gradually. \n\n\n\niii. Hydraulic conditions in the river and floodplain\n\n\n\ndownstream. \n\n\n\n3.2 Life Safety Model \n\n\n\nFloodwater released when a dam fails can be a devastating force.  Dam \n\n\n\nfailures have historically taken many lives and have destroyed much \n\n\n\nproperty. By the same token, a number of dams fail each year in the United \n\n\n\nStates without a single life lost. Physical and human factors both \n\n\n\ncontribute to potential life loss, as does a certain amount of chance [18].  \n\n\n\nCritical factors that affect potential life loss in dam failure scenarios:  \n\n\n\ni. Number of people occupying the area inundated by a dam-\n\n\n\nbreak flood. \n\n\n\nii. Amount of warning provided in relation to the time \n\n\n\nrequired to move to a safe location. \n\n\n\niii. Intensity of the flow to which people are exposed.\n\n\n\niv. Timing of the dam failure (e.g. day or night).  Timing can\n\n\n\naffect both the number of people downstream and the\n\n\n\namount of warning time available. \n\n\n\n3.2.1 Determination of Predicted Life Loss \n\n\n\nOnce the dam breach and flood characteristics have been assigned and the \n\n\n\npopulation at risk identified, there are two basic types of analysis to \n\n\n\nestimate life loss: one is notional, empirically based on a small number of \n\n\n\npast instances, while the other employs simulation that attempts to model \n\n\n\npeople\u2019s response to the situation.  Fatality rates for both the notional and \n\n\n\nsimulation methods depend on flood severity, which can be tied to flood \n\n\n\ndepths and velocities. The recommended fatality rates from any method \n\n\n\ncan be adjusted when justified by extenuating circumstances. If a \n\n\n\nparticularly devastating earthquake is responsible for dam failure, it is \n\n\n\nquite likely the earthquake has also devastated infrastructure and \n\n\n\ncommunications in population centers in the vicinity. Every aspect of \n\n\n\nwarning (i.e. detection, decision, notification, and dissemination) may be \n\n\n\naffected, and evacuation routes may be compromised. Emergency \n\n\n\nmanagement personnel would be responding to several situations and will \n\n\n\nnot be able to devote their entire attention on a developing situation at a \n\n\n\ndam. It may be reasonable to increase the fatality rates for this case. \n\n\n\nCite The Article: Sivadass Thiruchelvam, Rahsidi Sabri Muda, Azrul Ghazali, Fatin Faiqah Norkhairi, Kamal Nasharuddin Mustapha, Nora Yahya, Rosnafisah \nSulaiman, Zakaria Che Muda (2018). Inception Of 3es In Promoting Disaster Resilient Communities Living Near Hydropower Dams Of Peninsular Malaysia.  \n\n\n\nMalaysian Journal of Geosciences, 2(2) : 17-21. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 17-21 \n\n\n\n3.2.2 Simulation Modelling  \n\n\n\nSimulation method utilizes computers to combine models of dam break, \n\n\n\nflood routing, warning dissemination, evacuation, and life loss. Safe areas \n\n\n\nare designated, populated zones are assigned warning times and choices \n\n\n\nregarding means of travel (e.g. by car or walking), road and intersection \n\n\n\ncapacities are assigned, and the computer model determines how many \n\n\n\npeople are likely to successfully evacuate. Whereas fatality rates for \n\n\n\nReclamation's notional method are multiplied by the initial population at \n\n\n\nrisk, the simulation methods multiply the fatality rate by an estimated \n\n\n\nfraction of the initial population at risk who might be remaining within \n\n\n\ninundation boundaries at the time the flood wave arrives. These fatality \n\n\n\nrates depend on flood severity and upon an assigned shelter survivability \n\n\n\ncategory. \n\n\n\n3.3 Community Based Early Warning System (CBEWS) \n\n\n\nCBEWS is a system operated and maintained by the communities \n\n\n\nthemselves. While establishing the system, the community will explore \n\n\n\nexternal support from different individuals, communities, organizations \n\n\n\nand institutions. It is essential that the community develops and maintains \n\n\n\nclose coordination and links with these stakeholders. The performance of \n\n\n\nearly warning systems can be assessed via performance parameters such \n\n\n\nas timeliness, accuracy, reliability, user friendliness, flexibility, and costs \n\n\n\n& benefits, as shown below in figure 2: \n\n\n\nFigure 2: Four Steps in EWS Feasibility Study \n\n\n\nThe typical components of an EWS are: \n\n\n\n\u2022 Monitoring involves the collection of meteorological data \n\n\n\nand hydrological data, e.g. real-time and historic \n\n\n\nmeasurements. \n\n\n\n\u2022 Forecasting entails utilising monitored data to model \n\n\n\nfuture situations and thus give a forecast, e.g. where and \n\n\n\nwhen will certain water levels occur. \n\n\n\n\u2022 Warning incorporates receiving flood forecasts, \n\n\n\ninterpretation of the data and subsequent issuing of \n\n\n\nwarnings based on preset trigger criteria. \n\n\n\n\u2022 Response involves informing the public, coordination of \n\n\n\nemergency response activities. \n\n\n\n\u2022 Evaluation assesses the overall performance of the \n\n\n\naforementioned components individually as well as \n\n\n\ncombined (e.g. carry out flood emergency exercises) and \n\n\n\nresults in feedback regarding the Improvement of the EWS. \n\n\n\nAs such Evaluation and Improvement are often considered \n\n\n\nseparately. \n\n\n\nFigure 3:  Main Components of an Early Warning System \n\n\n\n3.4 Community based Training and Awareness Program (CBTAP) \n\n\n\nEffective community engagement has real and considerable benefits for \n\n\n\nboth the governing bodies and agencies as well as the community alike. On \n\n\n\nthe other hand, there is a significant challenge in being able to provide \n\n\n\navenues for involvement that are inclusive, productive and cost-effective, \n\n\n\nwithin the parameters of our system of government and the diversity of \n\n\n\nour society. Whilst feedback is often positive, it also sometimes indicates \n\n\n\nthat approaches to communication and consultation have not been as good \n\n\n\nas might be expected. A key task for public agencies and officials in \n\n\n\nplanning community engagement is to assess which engagement \n\n\n\ntechniques are most appropriate in particular circumstances. Involving \n\n\n\nstakeholders in the planning stage will help create a sense of ownership of \n\n\n\nthe issue and enable clients, citizens, communities and governments to \n\n\n\nwork together to determine the most appropriate approach to \n\n\n\nengagement. CBTAP proposes three distinct phases for effective planning \n\n\n\nand implementation of the Integrated Community-Based Disaster \n\n\n\nManagement (ICBDM). \n\n\n\nTable 1: Three different phases for CBTAP \n\n\n\nPhase I: Inception And Informative Phase II: Community Engagement Phase III: Active Participation \n\n\n\nInformation provision is a one-way \n\n\n\nrelationship in which authority \n\n\n\ndisseminates information to community. It \n\n\n\ncovers both passive and active access such \n\n\n\nas mass media, web sites, education and \n\n\n\nawareness activities \n\n\n\nEngagement is a two-way relationship in \n\n\n\nwhich agency, authority and key figures \n\n\n\nseek and receive the views of \n\n\n\ncommunities on programs that affect \n\n\n\nthem directly or in which they may have \n\n\n\na significant interest. \n\n\n\nActive participation recognizes and \n\n\n\nacknowledges a role for community in \n\n\n\nproposing and/or shaping the desired \n\n\n\nprogram. Deliberative processes often \n\n\n\ntake time and numerous resources since \n\n\n\nparticipants need to build their \n\n\n\nawareness and knowledge about the \n\n\n\nissues in order to contribute effectively. \n\n\n\n3.5 Multi-Stakeholders Engagement Program (MSEP) \n\n\n\nStakeholder engagement is crucial to risk, adaptation, and vulnerability \n\n\n\nassessments. Stakeholders can be characterised as individuals or groups \n\n\n\nwho have anything of value that can be affected by natural disaster \n\n\n\nphenomenon or by the actions taken to manage disaster risks. Individual \n\n\n\nand institutional knowledge and expertise are the principal resources for \n\n\n\nadapting to disaster. The adaptive capacity can be developed if \n\n\n\nSTEP 1\n\n\n\n\u2022 Identification of Types of\nAssessment\n\n\n\nSTEP 2\n\u2022 Fieldwork\n\n\n\nSTEP 3\n\u2022 Desktop analysis\n\n\n\nSTEP 4\n\u2022 Review & Recommendations\n\n\n\nCite The Article: Sivadass Thiruchelvam, Rahsidi Sabri Muda, Azrul Ghazali, Fatin Faiqah Norkhairi, Kamal Nasharuddin Mustapha, Nora Yahya, Rosnafisah \nSulaiman, Zakaria Che Muda (2018). Inception Of 3es In Promoting Disaster Resilient Communities Living Near Hydropower Dams Of Peninsular Malaysia.  \n\n\n\nMalaysian Journal of Geosciences, 2(2) : 17-21. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 17-21 \n\n\n\nstakeholders have time to strengthen networks, knowledge, and \n\n\n\nresources, and of course if they have the willingness to find effective \n\n\n\nsolutions. Approaches to stakeholder engagement vary in a large area \n\n\n\nfrom passive interactions (the stakeholders only provide information) to \n\n\n\nthe level where the stakeholders themselves initiate and design the \n\n\n\nprocess. Stakeholder involvement represents an integral part of a \n\n\n\nstepwise process of decision-making by sharing information, consulting, \n\n\n\ndialoguing, or deliberating on decisions. MSEP proposes two phases for \n\n\n\neffective planning and implementation of the Integrated Community-\n\n\n\nBased Disaster Management (ICBDM). \n\n\n\nTable 2: Two different phases for MSEP \n\n\n\nPhase I: Data Collection  Phase II: Engagement and \n\n\n\nDevelopment \n\n\n\nData collection is important and \n\n\n\nhelpful in determining the \n\n\n\nappropriate level of stakeholder\u2019s \n\n\n\nengagement level. This is done \n\n\n\nthrough preliminary discussion, \n\n\n\nthe contact with or the \n\n\n\nobservation of target \n\n\n\nstakeholders groups and also the \n\n\n\nreview of statutory requirements \n\n\n\nEngagement is a multi-way \n\n\n\nrelationship in which agencies, \n\n\n\nauthorities and key figures sits \n\n\n\ntogether and manage emergency \n\n\n\nsituation when disaster occurs \n\n\n\n4. FINDINGS\n\n\n\nFrom the methodology above, it is clearly stated that the ICBDM research \n\n\n\nframework stands upon five pillar that support the studies. A 2D flood \n\n\n\nmodelling is conducted in the initial phase of this study to see the impact \n\n\n\nof dam release on flooding. The modelling can provide analysis of timing \n\n\n\nand quantity release and be used to test whether different operating \n\n\n\nprocedure could yield different consequences. Detailed knowledge both in \n\n\n\nspatialy and temporally such flow downstream, depth of floodwaters, and \n\n\n\nflow velocity can be valuable information that will enhance the \n\n\n\npreparedness planning. \n\n\n\nThe results obtained from 2D flood modelling will be used to carry out the \n\n\n\nnumerical loss of life using Life Safety Model (LSM). LSM will exhibit the \n\n\n\neffect from road closure, warning dissemination, safe havens and \n\n\n\nawareness. LSM also can model the fatalities, injuries, vehicle being swept \n\n\n\naway and building destruction. The dynamic interaction between people, \n\n\n\nvehicles and building could be the elements to estimate the flood risk to \n\n\n\npeople in terms of loss of life and injuries, evacuation time and \n\n\n\nimprovements in emergency planning. \n\n\n\nOnce the computed results have produce, the preliminary study is develop \n\n\n\nfor identification of appropriate early warning system to minimize the \n\n\n\ncasuallity and property damages. The early warning system is us as a \n\n\n\nmeasure for community to be both alerted and notified of the pending \n\n\n\nemergencies. The key factor for effective warning system is that the \n\n\n\ncommunity are alerted,that will bring them to an understanding of the \n\n\n\nemergency situations which are guided to appropriate actions in the \n\n\n\nappropriate time frame. \n\n\n\nFrom the informations of three pillar of ICBDM could designated the \n\n\n\ncommunity awareness program which aim to explained the nature of the \n\n\n\nemergency and community shall be empowered by the information given \n\n\n\nto act according to nesessity to maximize the personal safety and protect \n\n\n\ntheir belonging. It is necessary that the community engagement program \n\n\n\ncould deliver the message or information about the potential \n\n\n\nconsequences from dam related disasters.  \n\n\n\nThe involvement of multiple parties engagement could be carried out in \n\n\n\nterm of tabletop exercise to measure the readiness of emergency plans and \n\n\n\nresponse capabilities during emergency crisis. A shared risk \n\n\n\nresponsibiities shold be developed between dam owners, authorities and \n\n\n\npublic.  \n\n\n\n5. CONCLUSIONS\n\n\n\nFrom the studies above, the ICBDM framework aims to the synergy \n\n\n\nbetween three major stakeholders; loca authorities, agencies and \n\n\n\ncommunty. The key important aspect in this framework is participation \n\n\n\nand support from the communities. The communities is the one that facing \n\n\n\nthe situation when the disaster happen. It is important for the profesionals \n\n\n\nin disaster management area to share informations on disaster \n\n\n\npreparedness and increase the awareness to minimize the loss of life.  In \n\n\n\nthe end, our hope is that each individual can take responsibility and \n\n\n\ninitiative for save lives when a disaster occurs \n\n\n\nREFERENCES \n\n\n\n[1] Aditi Madan, J.K.R. 2015. Institutional framework for preparedness and \n\n\n\nresponse of disaster management institutions from national to local level \n\n\n\nin India with focus on Delhi. International Journal of Disaster Risk \n\n\n\nReduction, 14, 545-555. \n\n\n\n[2] Arain, F. 2015. Knowledge-based Approach for Sustainable Disaster \n\n\n\nManagement: Empowering emergency response management team. \n\n\n\nProcedia Engineering, 118, 232-239. \n\n\n\n[3] Pandey, B.K.O. 2008. Community Based Disaster Management: \n\n\n\nEmpowering Communities To Cope With Disaster Risks. Research Report \n\n\n\nUNCRD Disaster Management Planning Hyogo Office, Japan. \n\n\n\n[4] Erdogan, N. 2006a. United Nations Earthquake Field Coordination \n\n\n\nSystem: Through the perspective of the contingency approach. Turkish \n\n\n\nJournal of Disaster, 56-62. \n\n\n\n[5] Azad, W.H., Sidek, L.M., Basri, H., Fai, C.M., Saidin, S., Hassan, A.J. 2017. \n\n\n\n2 Dimensional Hydrodynamic Flood Routing Analysis on Flood \n\n\n\nForecasting Modelling for Kelantan River Basin. in MATEC Web of \n\n\n\nConferences, 87,7. EDP Sciences. \n\n\n\n[6] Tagg, A., Murphy, A., Davison, M., Goff, C. 2016. The use of smart \n\n\n\ninfrastructure in dams to protect communities from flooding. CDA 2015 \n\n\n\nAnnual Conference, 5-8 October 2015, Mississauga, Canada.  \n\n\n\n[7] Cools, J., Innocenti, D., O\u2019Brien, S. 2016. Lessons from flood early \n\n\n\nwarning systems. Environmental Science and Policy, 58, 117-122. \n\n\n\n[8] Smith, P.J., Brown, S., Dugar, S. 2017. Community-based early warning \n\n\n\nsystems for flood risk mitigation in Nepal. Natural Hazards and Earth \n\n\n\nSystem Sciences, 17 (3), 423. \n\n\n\n[9] Illyani, I., Aiman, G.A., Samsuddin, J., Hakim, M.L. 2017. Awareness and \n\n\n\nInvolvement of Downstream Residents Toward the Mitigation Plan of Dam \n\n\n\nFailure: A Case Study of Klang Gate Dam. Advanced Science Letters, 23 (7), \n\n\n\n6091-6094. \n\n\n\n[10] Toromanovi\u0107, J., Mattsson, H., Knutsson, S. 2016. Effects on an earth \n\n\n\nand rockfill dam undergoing dam safety measures. in Nordic Geotechnical \n\n\n\nMeeting: Challanges in Nordic Geotechnics. \n\n\n\n[11] Wolshon, B., Renne, J., Mitchell, B. 2016. Planning, Modeling, and \n\n\n\nEvaluating Transportation Systems for Emergency Evacuations. Journal of \n\n\n\nemergency management, 13 (2): 85-86. \n\n\n\n[12] Jonkman, S.N., Maaskant, B., Kolen, B., Needham, J.T. 2016. Loss of life \n\n\n\nestimation\u2013Review, developments and challenges. in E3S Web of \n\n\n\nConferences. EDP Sciences, 7,7. \n\n\n\n[13] Yang, L., Liu, M., Smith, J.A., Tian, F. 2017. Typhoon Nina and the \n\n\n\nAugust 1975 Flood over Central China. Journal of Hydrometeorology, 18 \n\n\n\n(2), 451-472. \n\n\n\n[14] Abidin, Z., Othman, I. 2016. Overview of dam safety in Malaysia. \n\n\n\n[15] Md Said, N.F., Sidek, L.M., Basri, H., Muda, R.S., Abdul Razad, A.Z. 2016. \nIntroduction of an Emergency Response Plan for flood loading of Sultan \nAbu Bakar Dam in Malaysia. in IOP Conference Series: Earth and \nEnvironmental Science. IOP Publishing, 32. \n\n\n\n[16] Nifa, F.A.A., Abbas, S.R., Lin, C.K., Othman, S.N. 2017. Developing a \n\n\n\ndisaster education program for community safety and resilience: The \n\n\n\npreliminary phase. in AIP Conference Proceedings. AIP Publishing. \n\n\n\nCite The Article: Sivadass Thiruchelvam, Rahsidi Sabri Muda, Azrul Ghazali, Fatin Faiqah Norkhairi, Kamal Nasharuddin Mustapha, Nora Yahya, Rosnafisah \nSulaiman, Zakaria Che Muda (2018). Inception Of 3es In Promoting Disaster Resilient Communities Living Near Hydropower Dams Of Peninsular Malaysia.  \n\n\n\nMalaysian Journal of Geosciences, 2(2) : 17-21. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 17-21 \n\n\n\n[17] Trogrli\u0107, R.S., Wright, G.B., Adeloye, A.J., Duncan, M.J.,  Mwale, F. 2017. \n\n\n\nTaking stock of community-based flood risk management in Malawi: \n\n\n\ndifferent stakeholders, different perspectives. Environmental Hazards, 1-\n\n\n\n21. \n\n\n\n[18] Feinberg, B., Heinzer, T., Williams, D. 2008. Using the Life Safety Model \n\n\n\nto Estimate Loss from Dam Failure in Urbanized Areas. \n\n\n\nCite The Article: Sivadass Thiruchelvam, Rahsidi Sabri Muda, Azrul Ghazali, Fatin Faiqah Norkhairi, Kamal Nasharuddin Mustapha, Nora Yahya, Rosnafisah \nSulaiman, Zakaria Che Muda (2018). Inception Of 3es In Promoting Disaster Resilient Communities Living Near Hydropower Dams Of Peninsular Malaysia.  \n\n\n\nMalaysian Journal of Geosciences, 2(2) : 17-21. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 88-96 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2022.88.96 \n\n\n\n \nCite The Article: Ajayi Christopher Ayodele, Madukwe Henry Yemagu, Ilugbo Stephen Olubusola, Adebo Babatunde A, Talabi Abel Ojo, Oyedele Akintu nde Akinola, OJO \n\n\n\nOlufemi Felix, Ajisafe Yemisi Christianah, Talabi Joseph Ifeoluwa (2022). Geophysical Investigation for Post-Foundation Assessment \nWithin Ekiti State University, Ado Ekiti, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 88-96. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2022.88.96 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGEOPHYSICAL INVESTIGATION FOR POST-FOUNDATION ASSESSMENT WITHIN \nEKITI STATE UNIVERSITY, ADO EKITI, SOUTHWESTERN NIGERIA \n\n\n\nAjayi Christopher Ayodelea, Madukwe Henry Yemagua, Ilugbo Stephen Olubusolab*, Adebo Babatunde Ab, Talabi Abel Ojoa, Oyedele Akintunde \nAkinolac, OJO Olufemi Felixa, Ajisafe Yemisi Christianaha, Talabi Joseph Ifeoluwaa \n\n\n\na Department of Geology, Ekiti State University, Ado-Ekiti, Nigeria \nb Department of Physics, Lead City University, Ibadan, Nigeria \nc Department of Physics, Ekiti State University, Ado-Ekiti, Nigeria \n*Corresponding Author Email: bussytex4peace44@gmail.com  \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 22 June 2022 \nAccepted 26 September 2022 \nAvailable online 05 October 2022 \n\n\n\n\n\n\n\nThis study has evaluated the post-construction subsoil integrity of a distressed building in Ado-Ekiti, \nSouthwestern Nigeria, to decipher the in-situ lithology and structural disposition. Four traverses of 100 m \nlength were established in approximately E-W direction with an inter-station spacing of 10 m. Two \nGeophysical methods were deployed including the ground magnetic method using Proton Precession \nMagnetometer and Electrical Resistivity method utilizing 2D Electrical Imaging and Vertical Electrical \nSounding (VES) techniques. Twelve (20) VES station points were acquired within the investigated area using \nSchlumberger configuration which gave relevant information on layer stratification and geoelectric \nparameters. The obtained results from the VES showed four geo-electric layers comprises topsoil, clayey \nlayer, weathered layer, fractured bedrock, and fresh basement. Low apparent resistivity was delineated from \nthe 2D imaging exhibiting unfit materials at distance between 50 to 75 m and 80 to 100m. The magnetic \nresults showed a series of bedrock ridges and depressions. The integration of results revealed that the \nmajority of the study areas are unsuitable except for central parts which indicate appreciable competence \nand stability. \n\n\n\nKEYWORDS \n\n\n\nFoundation failure, differential settlement, VES, Resistivity, 2D Electrical Imaging. \n\n\n\n1.   INTRODUCTION \n\n\n\nBuilding failures are mostly attached to the problem of poor-quality \nconstruction materials and foundation design, without the preliminary \nunderstanding of the subsurface lithological conditions which eventually \nprove abortive (Morrow et al., 2001; Bawallah et al., 2019a; Aigbedion et \nal., 2019a; Bawallah et al., 2020a; Adebo et al., 2021; Aigbedion et al., 2021; \nAdebo et al., 2022). Suitability of the subsurface lithologies in terms of the \nmechanical potency which is a function of compatibility and non-friability \ncan be used to ascertain a proposed structure to be erected on them \n(Aigbedion et al., 2019b; Bawallah et al., 2020a). The use of the geophysical \napproach in engineering construction can be justified by directing our \nattention towards it (Ajayi et al., 2022a). The use of the geophysical \napproach in site investigation, engineering construction, subsurface \nlithological properties, and structural setting of a site are discovered \n(Sharma, 1998; Adebiyi et al., 2018; Bawallah et al., 2019b; Bawallah et al., \n2021a; Ajayi et al., 2022b).  \n\n\n\nThe general condition of the subsurface lithologies must be properly \ninvestigated for multi-storey buildings and high load impact \nconstructions. Buildings stability and integrity depends on the mechanical \nstrength of the subsurface properties (Adebo et al., 2019; Ajayi et al., 2020; \nBawallah et al., 2020b). There are several methods in geophysics that can \nbe used for both pre-foundation and post-foundation construction \ninvestigation. The building and construction failure are very rampant due \nto some geological factors which are clayey subgrade soil, lateral \n\n\n\ninhomogeneity, and subsurface geological structures (Akinola et al., 2017; \nIlugbo et al., 2018a; 2018b). Geophysical methods have the potential to \nprovide appreciable information concerning the significant earth \nrocks/soils properties needed in evaluating the potency of subsurface soil \nfor engineering construction purposes (Olorunfemi et al., 2010; Ozegin et \nal., 2013; Oyedele et al., 2020; Bawallah et al., 2021b; Bawallah et al., \n2021c).  \n\n\n\nPoor foundation design is not the only factor affecting building \nconstructions but also foundation inadequacies such as sitting the \nsubstructure on unsuitable materials; so when the \nfoundation/substructure of engineering construction is erected on \nunstable or weak materials, it poses a serious threat to the superstructure \nwhich can also lead to differential settlement (Adelusi et al., 2013; Ilugbo \net al., 2018a; Ozegin et al., 2019a). The durability and stability of the \nsubsurface lithologies can be determined by the condition of the \nsubsurface materials upon which engineering structures will be erected \nvis-\u00e0-vis; dams, buildings, bridges, and bridges (Jiracek, 1990; Tomlingson \nand Boorman, 1999; Fadamiro, 2002; Marrita, 2007; Ademilua et al., 2015; \nIlugbo et al., 2018a; Ozegin et al., 2019b; Adebo et al., 2019). \n\n\n\nDifferential settlements or discontinuities were noticed on the walls of the \nschool buildings which necessitated this post-foundation study. This study \nentails a geophysical approach for the post foundation investigation \ninvolving the Electrical Resistivity and magnetic prospecting methods. The \nFaculty of Social Sciences building in Ekiti State University, Ado-Ekiti has \nbeen evacuated when serious foundation failure was noticed on the \n\n\n\n\nmailto:bussytex4peace44@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 88-96 \n\n\n\n\n\n\n\n \nCite The Article: Ajayi Christopher Ayodele, Madukwe Henry Yemagu, Ilugbo Stephen Olubusola, Adebo Babatunde A, Talabi Abel Ojo, Oyedele Akintu nde Akinola, OJO \n\n\n\nOlufemi Felix, Ajisafe Yemisi Christianah, Talabi Joseph Ifeoluwa (2022). Geophysical Investigation for Post-Foundation Assessment \nWithin Ekiti State University, Ado Ekiti, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 88-96. \n\n\n\n\n\n\n\nbuilding from pronounced cracks and collapse of its section. Therefore, \nthere is a need for a post-foundation investigation to detect the materials \nand geologic structures characterizing the subsurface that contribute to \nthe failure of the building. \n\n\n\n2.   DESCRIPTION AND GEOLOGY OF THE INVESTIGATED AREA \n\n\n\nThe former Faculty of Arts building in Ekiti State University, Ado-Ekiti, \nEkiti State, Southwestern Nigeria lies between Longitude 8o52\u2019861\u201d E and \n8o52\u2019824\u201d E and Latitude 7048\u2019509\u201d N and 7048\u2019413\u201d N in the Universal \nTransverse Mercator scale covering a total area of 0.1 km2 (Figure 1). The \n\n\n\nUniversity was characterized by major and minor roads. The investigated \narea falls within the Nigeria basement complex which comprises \nquartzites, slightly migmatised to unmigmatised metasedimentary schists, \nmigmatitic and granitic gneisses, and metaigneous rocks; gabbroic, \ncharnockitic, and dioritic rocks (Rahaman, 1976). The study area is \npredominantly dominated by migmatite-gneiss (Figure 2). They occur as \nlow-lying outcrops and are predominantly found in the northern part of \nthe study. The migmatite gneiss contained dark coloured bands \n(melanosome), described as \u201cearly gneiss\u201d and \u201cmafic-ultramafic bands\u201d \nand streaks of leucotomies (Rahaman, 1976) as \u201cgranitic or felsic \ncomponents\u201d, which is indicative of anatexis (Odusanya and Amadi, 1989). \n\n\n\n\n\n\n\nFigure 1: Map of Ekiti-State University showing the study area \n\n\n\n\n\n\n\nFigure 2: Geology map of Ado-Ekiti showing rock types and the study area (Modified after Ajayi et al., 2019) \n\n\n\n3.   METHODOLOGY \n\n\n\nFour traverses of 100 m length were established along the E-W direction \n(Figure 3). Magnetic and the Electrical Resistivity methods were adopted \nfor this study. The Electrical Resistivity method involved Combined \nHorizontal and Vertical Electrical Sounding (VES) techniques utilizing \ndipole-dipole configuration along the three traverses. The VES involved \nthe use of Schlumberger configuration. Twelve sounding stations were \noccupied along traverse 1 to 3 and the current electrode spacing (AB/2) \n\n\n\nvaried from 1 to 100 m. The Vertical Electrical Sounding data acquired \nfrom the field was processed qualitatively and quantitatively by plotting \nthe apparent resistivity data against (AB/2) electrode spread with partial \ncurve matching techniques, and computer-assisted 1-D forward modeling \nwith WinResist 1.0 version software (Vander Velpen, 2004). The inter-\nelectrode spacing of 5 m was adopted while the inter-dipole expansion \nfactor (n) was varied from 1 to 5. The dipole-dipole data were inverted \ninto 2-D subsurface images using the DIPPRO 4.0 inversion software \n(Dippro, 2000).  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 88-96 \n\n\n\n\n\n\n\n \nCite The Article: Ajayi Christopher Ayodele, Madukwe Henry Yemagu, Ilugbo Stephen Olubusola, Adebo Babatunde A, Talabi Abel Ojo, Oyedele Akintu nde Akinola, OJO \n\n\n\nOlufemi Felix, Ajisafe Yemisi Christianah, Talabi Joseph Ifeoluwa (2022). Geophysical Investigation for Post-Foundation Assessment \nWithin Ekiti State University, Ado Ekiti, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 88-96. \n\n\n\n\n\n\n\nOhmega Campus SAS 1000 resistivity meter was used to acquire resistivity \ndata. The total field component of the earth\u2019s magnetic field was measured \nusing GEM 8 Proton Precession Magnetometer for the Ground Magnetic \nmethod along the three traverses. The long-wavelength component \n(33152 nT) of the total magnetic field intensity data, which is considered \nto be a deep-seated regional effect, was separated from the observed field \nto obtain the residual effect, which is the short-wavelength component \nand anomaly of geological interest (Lowrie, 2007). Some magnetic data \nprocesses were carried out which includes; Reduction-to-Equator (RTE) \ntransformation filter to center anomalous bodies over their exact \npositions, Noise filtering by upward continuing the reduced to equator \n(RTE) data to 15 m to increase signal-to-noise ratio; and source parameter \nimaging (SPI) method was applied to the upward continued magnetic data \nto estimate the depth to magnetic sources and basement zone using Oasis \nMontaj software. The final results from the two methods were correlated \ntogether to detect the materials and geologic structures characterizing the \nsubsurface that contribute to the failure of the building. \n\n\n\n\n\n\n\nFigure 3: Acquisition map of the investigated area \n\n\n\n4.   RESULTS AND DISCUSSION \n\n\n\n4.1   Electrical Resistivity Method \n\n\n\n4.1.1   Characteristic of The VES Curves \n\n\n\nFive different curves types were identified ranges from HA, AA, HKH, KH, \nand H varying from three to five geoelectric layers. The HA curve type was \npredominated (Figure 4). \n\n\n\n\n\n\n\nFigure 4: Typical VES Sounding Curves from the study area (a=HA, b=AA, \nc=HKH d=KH e=H). \n\n\n\n4.1.2   Geoelectric Section Along The Three Traverses  \n\n\n\nTo understand the nature of the rock underlying the research location, and \nto have a detailed description of the lateral and vertical variation in \nresistivity, thickness, and topography of the subsurface rocks, three (3) \ngeoelectric sections were prepared using the VES interpretation results \nacross the study area, that align approximately along E-W direction as \nshown in the Figure 5a to 5c. Three to five subsurface layers were \ndelineated namely: the topsoil, clay/weathered layer, partly fractured \nbasement, and fresh basement. The geoelectric section along Traverse 1 \n(Figure 5a) delineates four subsurface geologic layers which are the \ntopsoil, clayey/weathered layer, partly fractured basement, and fresh \nbedrock. The topsoil (resistivity varies from 159.2 to 1054.9 \u2126m and \nthickness ranges from 0.6 to 4.0 m); clayey formation (resistivity varies \nfrom 141.7 to 367.3\u2126m and thickness ranges from 3.0 to 4.8 m), \nweathered bedrock (resistivity varies from 92.1 to 328.1 \u2126m and \nthickness ranges from 6.2 to 16.4 m), fresh bedrock resistivity (1177.1 to \n1563.0 \u2126m). On Traverse 2 (Figure 5b), three subsurface geologic layers \nwere also delineated along this traverse. From the geoelectric section, the \ntopsoil, clayey/weathered zone, and fresh bedrock were determined. The \ntopsoil (resistivity varies from 302.8 to 392.1\u2126m and its thickness ranges \nfrom 1.5 to 4.3 m); clayey/weathered zone (resistivity varies from 36.9 to \n178.5 \u2126m and its thickness ranges from 2.1 to 7.8 m, fresh basement \nresistivity varies from 850.8 to 1563.0 \u2126m. Four subsurface layers were \nalso delineated along traverse 3 (Figure 5c); the topsoil, the fractured \nbedrock, weathered bedrock, and fresh bedrock. \n\n\n\n\n\n\n\nFigure 5a: Geoelectric section along traverse one \n\n\n\n\n\n\n\nFigure 5b: Geoelectric section along traverse two \n\n\n\n\n\n\n\nFigure 5c: Geoelectric section along traverse three   \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 88-96 \n\n\n\n\n\n\n\n \nCite The Article: Ajayi Christopher Ayodele, Madukwe Henry Yemagu, Ilugbo Stephen Olubusola, Adebo Babatunde A, Talabi Abel Ojo, Oyedele Akintu nde Akinola, OJO \n\n\n\nOlufemi Felix, Ajisafe Yemisi Christianah, Talabi Joseph Ifeoluwa (2022). Geophysical Investigation for Post-Foundation Assessment \nWithin Ekiti State University, Ado Ekiti, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 88-96. \n\n\n\n\n\n\n\n4.2   Dipole-Dipole Pseudosections \n\n\n\nThe 2-D Pseudo-section was generated along the three traverses using \ndipole-dipole data (Figure 6(a-c)). It was set up to have a 2-Dimensional \nsection of the subsurface as it indicates the interpretation of unilateral \ndata and its contours. The 2D Pseudo-section correlates with the geo-\nelectric section. From the 2D pseudo-sections topsoil, \nweathered/fractured layer (thickness 10 to 25 m), and the fresh bedrock \nwere delineated. The fresh bedrock in the lower region of the pseudo-\nsection is highly resistive while the fractured part of the section consists \nof both the green and blue colour. A suspected linear structure was noticed \nbetween 50 to 70 m (Figure 6a). The clayey formation that can be inimical \n\n\n\nto the integrity of the foundation was delineated around distances 30 \u2013 75 \nm to a depth range of about 10 m. The 2-D pseudo-section along Traverse \n2, delineate the same information as the geo-electric section. Topsoil, \nweathered layer, and fresh bedrock were delineated. It was observed that \ntraverse two is the most stable without any linear features but \ncharacterized with clayey materials dominating the topsoil to a depth of \n2.5 m. The upper 2.5 m along traverse two was not put into cognizance \nbefore the erection of the collapsed building. Traverse 3 shows a formation \nwith more than three geologic features (fracture) within the basement \nwith a depth range of 10 \u2013 25 m across the study area. Fractures observed \nwithin this traverse contribute to the failure of the foundation of the \npreviously existing building (Figure 6c). \n\n\n\n\n\n\n\nFigure 6a: Dipole-dipole Pseudo-section along traverse one \n\n\n\n\n\n\n\nFigure 6b: Dipole-dipole Pseudo-section along traverse two \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 88-96 \n\n\n\n\n\n\n\n \nCite The Article: Ajayi Christopher Ayodele, Madukwe Henry Yemagu, Ilugbo Stephen Olubusola, Adebo Babatunde A, Talabi Abel Ojo, Oyedele Akintu nde Akinola, OJO \n\n\n\nOlufemi Felix, Ajisafe Yemisi Christianah, Talabi Joseph Ifeoluwa (2022). Geophysical Investigation for Post-Foundation Assessment \nWithin Ekiti State University, Ado Ekiti, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 88-96. \n\n\n\n\n\n\n\n\n\n\n\nFigure 6c: Dipole-dipole Pseudo-section along traverse three \n\n\n\n4.3   Magnetic Maps \n\n\n\nThe magnetic data obtained in the study area were presented in form of \nTotal Magnetic Intensity (TMI) map after diurnal correction (Figure 7). \nThe regions of low magnetic values on the TMI maps indicates high \nmagnetic susceptibility (blue) while high magnetic values depicted low \nmagnetic susceptibility regions (pink colour). The map showed variation \nin the total magnetic field intensity ranging from about 34245 nT to \n35655nT suggesting contrasting magnetic susceptibilities or variation in \nstructural extends of the rock types.  The long-wavelength component of \nthe total magnetic field intensity data, which is considered to be a deep-\nseated regional effect, was separated from the observed field to obtain the \nresidual effect, which is the short-wavelength component and anomaly of \ngeological interest (Thurston and Smith, 1997, Lowrie, 2007).  \n\n\n\nThe residual magnetic intensity data was used to produce the residual \nmagnetic intensity (RMI) map which ranged from -735.2 to 490.5 nT \n(Figure 8). Positive and negative anomalies are distributed alternately \nfrom the western to the eastern part of the map. Reduced-to-Equator \n(RTE) transformation filter was applied to the residual magnetic intensity \n\n\n\ndata with inclination and declination of -10.4090 and -0.9750 to produce an \nRTE map (Figure 9). This transformation became necessary to centre \nanomalous bodies over their exact positions since the study area falls \nwithin the magnetic equatorial regions where the inclination is less than \n15\u00b0.  \n\n\n\nNoise filtering by upward continuing the reduced to equator (RTE) data to \n15 m, which is the grid cell size of the data, was carried out to increase the \nsignal-to-noise ratio. Two major magnetic zones were delineated base on \nthe magnetic intensity variation within the investigated areas. The \nSouthwestern and northeastern parts are dominated by high amplitude \nmagnetic anomalies values (between 67.1 nT and 327.2 nT). However, the \nnorthwestern, southeastern, and central part of RTE was characterized by \nlow amplitude magnetic intensity values (between -613.2 nT and -22.0 nT) \nsuggesting geological structures (fracture/fault) with low magnetic \ncontents (Figure 10). To estimate the depth to magnetic sources and \nbasement zone, the source parameter imaging (SPI) method was applied \nto the upward continued magnetic data (GEOSOFT, 2005). The SPI map of \nthe study area showed variations in the depths of the magnetic basement \nfrom 14.5 to 103.7 m (Figure 11). \n\n\n\n\n\n\n\nFigure 7: Map of Total Magnetic Intensity \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 88-96 \n\n\n\n\n\n\n\n \nCite The Article: Ajayi Christopher Ayodele, Madukwe Henry Yemagu, Ilugbo Stephen Olubusola, Adebo Babatunde A, Talabi Abel Ojo, Oyedele Akintu nde Akinola, OJO \n\n\n\nOlufemi Felix, Ajisafe Yemisi Christianah, Talabi Joseph Ifeoluwa (2022). Geophysical Investigation for Post-Foundation Assessment \nWithin Ekiti State University, Ado Ekiti, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 88-96. \n\n\n\n\n\n\n\n\n\n\n\nFigure 8: Map of Residual Magnetic Intensity \n\n\n\n\n\n\n\nFigure 9: Map of Reduction to Equator \n\n\n\n\n\n\n\nFigure 10: Map of Upward Continuation. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 88-96 \n\n\n\n\n\n\n\n \nCite The Article: Ajayi Christopher Ayodele, Madukwe Henry Yemagu, Ilugbo Stephen Olubusola, Adebo Babatunde A, Talabi Abel Ojo, Oyedele Akintu nde Akinola, OJO \n\n\n\nOlufemi Felix, Ajisafe Yemisi Christianah, Talabi Joseph Ifeoluwa (2022). Geophysical Investigation for Post-Foundation Assessment \nWithin Ekiti State University, Ado Ekiti, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 88-96. \n\n\n\n\n\n\n\n\n\n\n\nFigure 11: Map of Source Parameter Imaging \n\n\n\n4.4   Correlation of Results \n\n\n\nThe integration of the results from the two geophysical methods further \nrevealed the unstable portions of the subsurface within the study area. The \ntwo methods confirmed that linear features (fracture) were observed \nbetween distances 45 \u2013 65 m and 85 \u2013 100 m to a depth beyond 25 m on \ntraverse one and 0 \u2013 100 m with a depth range of 3 \u2013 25 m on traverse two; \nclayey formations were also authenticated between distances 30 \u2013 78 m \nwith a depth range of 0 \u2013 12 m on traverse one and 0 \u2013 100 m within the \nupper 2.5 m depth across traverse two. These factors contribute to an \nuneven distribution of load from the building to the subsurface; thereby \nleading to differential settlement and collapse of some parts of the \nbuilding. \n\n\n\n5.   CONCLUSION \n\n\n\nThe two Geophysical methods proved successful for post foundation \nstudies within the old block of the Faculty of Social Sciences, Ekiti-State \nUniversity Ado-Ekiti. The interpretation of VES and the dipole-dipole \npseudo-section in the study area has allowed the delineation of \nincompetent zones within the study area. Fractured zones and the clayey \nformations were suspected to be major problems that contributed to the \nfailure of the foundation of the buildings in the investigated area. The \nformation along traverses one and three was highly incompetent \ncompared to traverse two as a result of the occurrence of fractures within \nit.  Also, the clayey formation that characterizes the topsoil and part of the \nweathered layer along traverses one and two would be unfavorable for \nbuilding construction; this could have led to the sheared settlement which \ncharacterized the failure of the structure. The inhomogeneous nature of \nthe subsurface in the area as characterized by varying depth to bedrock, \nsoil types coupled with uneven distribution of fractures as indicated by the \nresults also contributed to the displacement of the foundation. Only the \ncentral part along the east-west flange of the study was found to be stable \njudging from the integration of the two methods. Any future construction \nwithin the investigated area should be erected on competent materials \nwith a well-constructed piled foundation to ensure adequate stability of \nthe building. The geophysical methods involving the Vertical Electrical \nSounding (VES), 2-D Electrical imaging, and Ground Magnetic method \nhave served as a useful tool for post foundation studies to show the \ninhomogeneous subsurface geologic setting underlying any civil \nengineering construction work.  \n\n\n\nDATA AVAILABILITY STATEMENT \n\n\n\nAll the data supporting these findings are made available within the \nmanuscript and its supplementary materials. \n\n\n\nCONFLICT OF INTEREST \n\n\n\nAuthors have declared that no competing interests exist and the data was \nuse for advancement of knowledge and not for litigation. \n\n\n\nFUNDING \n\n\n\nThis research did not receive any specific grant from funding agencies in \nthe public, commercial, or not-for-profit sector. \n\n\n\nACKNOWLEDGMENT  \n\n\n\nThe authors gratefully acknowledge the reviewers and editor for their \nvaluable contribution during data acquisitions and improving the quality \nof this research work. \n\n\n\nREFERENCES \n\n\n\nAdebiyi, A.D., Ilugbo, S.O., Ajayi, C.A., Ojo, A.O., Babadiya, E.G., 2018. \nEvaluation of pavement instability section using integrated \ngeophysical and geotechnical methods in a sedimentary terrain, \nSouthern Nigeria. Asian Journal of Geological Research, 1 (3), Pp. 1-\n13. \n\n\n\nAdebo, B.A., Ilugbo, S.O., Jemiriwon, E.T., Ali, A.K., Akinwumi, A.K., \nAdeniken, N.T., 2022. Hydrogeophysical Investigation Using \nElectrical Resistivity Method within Lead City University Ibadan, Oyo \nState, Nigeria. International Journal of Earth Sciences Knowledge and \nApplications, 4 (1), Pp. 51-62. \n\n\n\nAdebo, B.A., Layade, G.O., Ilugbo, S.O., Hamzat, A.A., Otobrise, H.K., 2019. \nMapping of Subsurface Geological Structures using Ground Magnetic \nand Electrical Resistivity Methods within Lead City University, \nSouthwestern Nigeria, Kada Journal of Physics, 2 (2), Pp. 64-73. \n\n\n\nAdebo, B.A., Makinde, E.O., Ilugbo, S.O., 2021. Application of Electrical \nResistivity Method to Site Characterisation for Construction \nPurposes at Institute of Agriculture Research and Training Moor \nPlantation Ibadan. Indonesian Journal of Earth Sciences, 1 (2), Pp. 49-\n62. \n\n\n\nAdelusi, A.O., Akinlalu, A.A., Nwanchukwu A.I., 2013. Integrated \nGeophysical Investigation for Post-construction Studies of Buildings \naround School of Science Area, Federal University of Technology, \nAkure, southwestern, Nigeria. International Journal of Physical \nScience, 8 (15), Pp. 657-669.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 88-96 \n\n\n\n\n\n\n\n \nCite The Article: Ajayi Christopher Ayodele, Madukwe Henry Yemagu, Ilugbo Stephen Olubusola, Adebo Babatunde A, Talabi Abel Ojo, Oyedele Akintu nde Akinola, OJO \n\n\n\nOlufemi Felix, Ajisafe Yemisi Christianah, Talabi Joseph Ifeoluwa (2022). Geophysical Investigation for Post-Foundation Assessment \nWithin Ekiti State University, Ado Ekiti, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 88-96. \n\n\n\n\n\n\n\nAdemilua, L.O., Eluwolw, A.B., Bawallah, M., 2015. Geophysical \nInvestigation for Subsurface Integrity Assessment and Its \nImplications on Existing and Proposed Building Around the \nSouthwestern Part of Ekiti State University Campus, Ado-Ekiti, \nSouthwestern Nigeria. International Journal of Advancement in \nEngineering Technology, Management and Applied Science, 2 (2), Pp. \n2349-3224. \n\n\n\nAigbedion, I., Bawallah, M., Ilugbo, S., Osaigbovo, A.D., Diana, E.K., \nIhewkwumere, C., Igbinoba, C., Patrick, P.U., Amagbamwan, E., 2019b. \nGeophysical Investigation for Post Foundation Studies at Ikekogbe \nPrimary School,Ekpoma, Edo State, Nigeria. American Journal of\n Environmental and Resource Economics, 4 (2), Pp. 73-83. doi: \n10.11648/j.ajere.20190402.14  \n\n\n\nAigbedion, I., Bawallah, M.A., Ilugbo, S.O., Abulu, F.O., Eguakhide, V., \nAfuaman, E.W., Ukubile, B., 2019a. Geophysical Investigation for Pre-\nFoundation Studies at RCCG, Calvary Love Parish 2, Ukpenu, Ekpoma, \nEdo State, Nigeria. International Journal of Research and Innovation \nin Applied Science (IJRIAS), 4 (5), Pp. 39-45. \n\n\n\nAigbedion, I., Bawallah, M.A., Ilugbo, S.O., Ozegin, K.O., ThankGod, A., \nAtama, J., Nwankwo, B., Oladi, O.O., Oladeji, J.F., Alabi, S.K., 2021. \nEnvironmental Impact Assessment of Structural Defects Using \nGeophysical and Geotechnical Methods in Parts of Ekpoma, \nSouthsouthern Nigeria. International Journal of Earth Sciences \nKnowledge and Applications, 3 (2), Pp. 124-133. \n\n\n\n Ajayi, C.A., Aderemi, S.A., Ilugbo, S.O., Adebo, B.A., and Adewumi, O.A., \n2022b. Geophysical Post-Foundation Studies of Minisrty of Justice \nBuilding, Ekiti-State Secretariat, Ado Ekiti, Ekiti-State, Nigeria. \nGeological Behaviour, 6 (2), Pp. 65-69. \n\n\n\nAjayi, C.A., Akintorinwa, O.J., Ademilua, L.O., 2020. Empirical Relationship \nbetween Gravimetric and Mechanical Properties of Basement Rocks \nin Ado-Ekiti, Southwestern Nigeria. Journal of Geology and \nGeophysics, 9 (1), Pp. 470. \n\n\n\nAjayi, C.A., Akintorinwa, O.J., Ademilua, O.L., Adeoye, A.S., 2019. \nRelationship between Magnetic Susceptibility and Gravity of \nBasement Rocks in Southwestern Nigeria. Journal of Applied Geology \nand Geophysics (IOSR-JAGG), 7 (2), Pp. 46-57. \n\n\n\nAjayi, C.A., Ilugbo, S.O., Bayode, S., Aderemi, S.A., Adebo, B.A., Talabi, A.O.., \nOjo, O.F., and Talabi, J.I., 2022a. Assessment of Probable Foundation \nProblems Using Geophysical and Remotely Sensed Data in A \nTypical Basement Complex, Southwestern Nigeria. Earth Sciences \nPakistan, 6 (2), Pp. 65-75. \n\n\n\nAkinola, M.A., Akinlalu, A.A., and Adelusi, O.A., 2017. Integrated \nGeophysical Investigation for Pavement Failure along a Dual Carriage \nWay, Southwestern Nigeria: A Case Study. Kuwait Journal of Science, \n44 (4), Pp. 135-149. \n\n\n\nBawallah M.A., Ilugbo S.O., Adebo B.A., Adedapo J.O., Ofomola M.O., Oladeji, \nJ.F., Raji, I., Chinyem, F.I., Hadiza, M.B., Bello, K., and Imolore, M.O., \n2021a. Application of Vulnerability Determination for Foundation \nParameters: A Case Study of Issele Mkpitime Area of Delta State, \nNigeria. Pakistan Journal of Geology, 5(1), Pp. 73-90. DOI: \n10.2478/pjg-2021-0009 \n\n\n\nBawallah, M.A., Ayuks, M.A., Ilugbo, S.O., Ozegin, K.O., Oyedele, A.A., \nAigbedion, I., Aina, A.O., Whetode, J.M., Ladipo, K.O., 2019a. \nGeodynamics and its implications to environmental protection: A \ncase study of Aule area, Akure, Ondo State, Southwestern, Nigeria. \nApplied Journal of Physical Science, 1 (3), Pp. 37-53. \n\n\n\nBawallah, M.A., Ilugbo, S.O., Aigbedion, I., Aina, A.O., Oyedele, A.A., 2019b. \nModeling of subsurface integrity using Dar-Zarrouk parameters: A \ncase study of Ikekogbe UBE Primary School, Ekpoma, Edo State, \nNigeria. Journal of Geography, Environment and Earth Science \nInternational, 22 (1), Pp. 1-17. \n\n\n\nBawallah, M.A., Ilugbo, S.O., Aina, A.O., Olufemi, B., Akinluyi, F.O., Ojo, B.T., \nOyedele, A.A., Olasunkanmi, N.K., 2020b. Hydrogeophysical Studies of \nCentral Kwara State Basement Complex of Nigeria. International \nJournal of Earth Sciences Knowledge and Application, 2 (3), \n Pp. 146-164 \n\n\n\nBawallah, M.A., Ilugbo, S.O., Ozegin, K.O., Adebo, B.A., Aigbedion, I., Salako, \nK.A., 2021b. Electrical Resistivity and Geotechnical Attributes and \nThe Dynamics of Foundation  Vulnerability. Indonesian \nJournal of Earth Sciences, 1 (2), Pp. 84-97. \n\n\n\nBawallah, M.A., Ilugbo, S.O., Ozegin, K.O., Adebo, B.A., Aigbedion, I., Salako, \nK.A. 2021c. Electrical Resistivity and Geotechnical Attributes and the \nDynamics of Foundation Vulnerability. Indonesian Journal of Earth \nSciences, 1 (2), Pp. 84-97. \n\n\n\nBawallah, M.A., Oyedele, A.A., Ilugbo, S.O., Ozegin, K.O., Ojo, B.T., \nOlutomilola, O.O., Airewele, E., Aigbedion, I., 2020a.  Evaluation of \nstructural defects and the dynamic of stress and strain on a building \nalong Oluwole Area, Southwestern Nigeria. Applied journal of \nphysical science, 2 (2), Pp. 23-37. doi.org/10.31248/AJPS20207 \n\n\n\nDippro for Windows. 2000. DipproTM Version 4.0 Processing and \nInterpretation software for Dipole \u2013 Dipole electrical resistivity data. \nKIGAM, Daejon, South Korea. \n\n\n\nFadamiro, J.A., 2002. An assessment of building regulations and standards \nand the implication for building collapse in Nigeria. Nigeria Institute \nof Building, Ondo-State, Nigeria. Pp. 28 \u2013 39. \n\n\n\nGEOSOFT, 2005. Processing, Analysis and Visualization System for 3D \nInversion of Potential Field Data for Oasis Montaj v6.1. Tutorial and \nUser Guide. Geosoft Incorporated, Toronto. \n\n\n\nIlugbo, S.O., Adebiyi, A.D., Olaogun, S.O., Egunjobi, T., 2018a. Application of \nElectrical Resistivity Method in Site Characterization along Ado \u2013 \nAfao Road, Southwestern Nigeria. Journal of Engineering Research \nand Reports, 1 (4), Pp. 1-16. \n\n\n\nIlugbo, S.O., Adebo, A.B., Ajayi, O.A., Adewumi, O.O., Edunjobi, H.O., 2018b. \nGeophysical and geotechnical studies of a proposed structure at \nAkure, Southwestern Nigeria. Journal of Engineering Research and \nReports, 2 (2), Pp. 1-12. \n\n\n\nJiracek, G.R., 1990. Near surface and topographic distortions in \nelectromagnetic induction. Surveys in Geophysics, 11, Pp. 163-203. \n\n\n\nLeucci, G., De Girogi, L., 2005 Integrated Geophysical Surveys to assess the \nStructural Conditions of a Karstic Cave of Archaeological Importance. \nNatural Hazards and Earth System Sciences, 5, Pp. 17-22. \n\n\n\nLowrie, W., 2007. Fundamental of Geophysics, second edition, Cambridge \nUniversity Press, Cambridge. \n\n\n\nMarrita, L., 2007. Paper presentation on Surface Exploration for \nGeothermal Resources, organized by UNU-GTP and KenGen, at Lake \nNaivasha, Kenya, Pp. 2-17. \n\n\n\nMorrow, C.A., Moore, D.E., Lockner, D.A., 2001. Permeability Reduction in \nGranite Under Hydrothermal Conditions. Journal of Geophysics \nResearch and Solid Earth. Vol 106:30551-30560. \n\n\n\nOdusanya, B.O., Amadi, U.M.P., 1989. An Empirical Resistivity Model for \nPredicting Shallow Groundwater Occurrence in the Basement \nComplex. Water Res. J. Nig. Asso. Hydrogeol., 2, Pp. 77-87.  \n\n\n\nOlorunfemi, M., Inumidun, B.I., Gabriel, O.B., Akintorinwa, J.O., 2010. Micro-\nResistivity Measurements, Near-Surface Sequence Delineation, and \nEmpirical Relationships with Engineering Geotechnical Parameters. \nThe Pacific Journal of Science and Technology, 11 (1), Pp. 537-544. \n\n\n\nOyedele, A.A., Bawallah, M.A., Ozegin, K.O., Ilugbo, S.O., Ajayi, C.A., \nAigbedion, I., 2020. Probability functions of road failures in a typical \nbasement complex region, Southwestern Nigeria: A case study of \nAkure - Oba Ile Airport Road. International Journal of Water \nResources and Environmental Engineering, 12 (2), Pp. 10-21. \n\n\n\nOzegin, K.O., Bawalla, M.A., Ilugbo, S.O., Olaogun, S.O., Oyedele, A.A., Iluore, \nK., 2019b. Susceptibility test for road construction: A case study of \nShake Road, Irrua, Edo State. Global Journal of Science Frontier \nResearch: H Environment & Earth Science, 19 (1), Pp. 45-53. \n\n\n\nOzegin, K.O., Bawallah, M.A., Ilugbo, S.O., Oyedele, A.A., Oladeji, J.F., 2019a. \nEffect of geodynamic activities on an existing dam: A case study of \nOjirami Dam, Southern Nigeria. Journal of Geoscience and \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 88-96 \n\n\n\n\n\n\n\n \nCite The Article: Ajayi Christopher Ayodele, Madukwe Henry Yemagu, Ilugbo Stephen Olubusola, Adebo Babatunde A, Talabi Abel Ojo, Oyedele Akintu nde Akinola, OJO \n\n\n\nOlufemi Felix, Ajisafe Yemisi Christianah, Talabi Joseph Ifeoluwa (2022). Geophysical Investigation for Post-Foundation Assessment \nWithin Ekiti State University, Ado Ekiti, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 88-96. \n\n\n\n\n\n\n\nEnvironment Protection, 7 (9), Pp. 200-213. \n\n\n\nOzegin, K.O., Oseghale, A.O., Audu, A.L., Ofotokun, E.J., 2013. An application \nof the 2-D D.C. Resistivity Method in Building Site Investigation- A \nCase Study: South-south Nigeria. Journal of Environment and Earth \nScience, 3 (2), Pp. 108 \u2013 112. \n\n\n\nRahaman, M.A., 1976. Review of the Basement geology of Southwestern \nNigeria: In Geology of Nigeria (Kogbe, C.A. Ed). Elizabethan Publ. Co., \nNigeria. Pp. 47-58. \n\n\n\nRahaman, M.A., 1979. Review of Basement Geology of Southwestern \nNigeria, Department of Geology, Obafemi Awolowo University, Ile-\nIfe, Osun-State Nigeria.  \n\n\n\nSharma, V.P., 1998. Environmental and Engineering Geophysics, published \nby Cambridge University Press, United Kingdom, Pp. 40 \u2013 45. \n\n\n\nThurston, J.B., Smith, R.S., 1997. Automatic conversion of magnetic data to \ndepth, dip, and susceptibility contrast using the SPI method. \nGeophysics, 62 (3), Pp. 807-813. \n\n\n\nTomlingson, M.J., Boorman, R., 1999. Foundation Design and Construction. \nLongman Scientific and Technical: Singapore. Pp. 1- 125. \n\n\n\n Vander-Velpen, B.P.A., 2004. WinRESIST Version 1.0 Resistivity \nDepth Sounding Interpretation Software. M. Sc Research Project, ITC, \nDelf Netherland. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geoscien  ces 2(1) (2018) 18-29 \n\n\n\nCite the Article: Edgar Jr. Joe, Felix Tongkul, Rodeano Roslee (2018). Relationship Between Rainfall and Debris Flow Occurren ce in the Crocker Range of Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 18-29. \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nDebris flow occurences are quite common in mountainous areas such as those in the Crocker Range of Sabah, \n\n\n\nMalaysia especially during prolonged heavy rainfall. Despite the recurrence of debris flows in the area, not much \n\n\n\ninformation is known about the effect of rainfall on their occurrences. Based on the rainfall intensity-duration of \n\n\n\ntwo selected case studies at Jalan Penampang-Tambunan KM38.80 and Jalan Tamparuli-Ranau KM83.90, the debris \n\n\n\nflow threshold for the Crocker Range cases are approximate to that proposed by Montgomery et al. (2000) given by \n\n\n\nthe equation of I = 9.9D-0.52 which is generally low. This implies that low-intensity rainfall is sufficient to trigger \n\n\n\ndebris flows due to high availability of loose material and weak geological condition. \n\n\n\nKEYWORDS \n\n\n\nDebris flow, rainfall, threshold, Crocker Range.\n\n\n\n1. INTRODUCTION \n\n\n\nIn tropical climate, it has been recognized that rainfall is one of the main \n\n\n\ntriggering factors of debris flows. Although many studies have been \n\n\n\nconducted, the effect of rainfall on debris flow remains poorly understood \n\n\n\n[1]. Similar to landslide occurrence, this is due to various factors which \n\n\n\ncontrol slope instability in debris flow initiation. The debris flow \n\n\n\noccurrence in the Crocker Range of Sabah which usually coincided with \n\n\n\ndownpour has raised a great concern among the public, as well as the \n\n\n\ngovernment. Several trunk roads which connect various important towns \n\n\n\nin the state which pass through the Crocker Range are highly prone to \n\n\n\ndebris flow. The debris flow incidents have not only cut off the road, but \n\n\n\nalso disrupt the socio-economic activities of the people. Thus, this study \n\n\n\naims to understand the relationship between rainfall and debris flow \n\n\n\nincident by selecting two debris flow-prone locations along the major road \n\n\n\nas case studies. The identified rainfall intensity for all events is compared \n\n\n\nwith findings by other researchers in order to approximate the threshold \n\n\n\nin local context. The identified threshold will serve as a basis of study in \n\n\n\nestablishing debris flow warning system at the concerned areas.  \n\n\n\n2. METHODOLOGY \n\n\n\nTwo well-known locations which are prone to debris flow along the \n\n\n\nCrocker Range have been selected for case studies. They are Jalan \n\n\n\nPenampang-Tambunan KM38.80 and Jalan Tamparuli-Ranau KM83.90 \n\n\n\n(Figure 1). The selection of the two sites is made based on the following \n\n\n\nconsiderations: (a) Important information such as date and time of \n\n\n\nincident is available to identify correlation between rainfall and debris \n\n\n\nflow occurrence; (b) Site is accessible for field investigation and on-site \n\n\n\ndata collection; and (c) Frequent incidents of debris flow which pose \n\n\n\nmedium to high risk impact to the public. Table 1 tabulates the location \n\n\n\ncoordinates, elevation as well as the time and date of incident. Some of the \n\n\n\npictures taken during incident of debris flows are shown in Photographs 1 \n\n\n\nto 4.  Figure 1: Location of debris flow and rainfall gauge station in the Crocker \nRange (Inset: Locality of sites in Sabah). \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : https://doi.org/10.26480/mjg.01.2018.18.29\n\n\n\nRELATIONSHIP BETWEEN RAINFALL AND DEBRIS FLOW OCCURRENCE IN \n\n\n\nTHE CROCKER RANGE OF SABAH, MALAYSIA \nEdgar Jr. Joe1,3*,Felix Tongkul1,2, Rodeano Roslee1,2 \n1 Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia. \n2 Natural Disaster Research Centre (NDRC), Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia. \n\n\n\n*Corresponding Author's Email: Edgar.Joe@sabah.gov.my\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \n\n\n\nCODEN : MJGAAN \n\n\n\n3 Slope Branch, Public Works Department of Sabah, Jalan Sembulan, 88582 Kota Kinabalu, Sabah, Malaysia.\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 18-29\n\n\n\nCite the Article: Edgar Jr. Joe, Felix Tongkul, Rodeano Roslee (2018). Relationship Between Rainfall and Debris Flow Occurrence in the Crocker Range of Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 18-29. \n\n\n\nTable 1: Coordinates, date, and time of debris flow incidents. The incidents occurred between year 2012 and 2015 which mostly happen in the afternoon \nand evening times. \n\n\n\nLocation Elevation Coordinate Date & Time of Incident \n\n\n\nJalan Penampang-Tambunan \n\n\n\nKM38.80 (Penampang District) \n\n\n\n910m  \n\n\n\nAMSL \n\n\n\nN 5.8593710, \n\n\n\nE 116.2654890 \n\n\n\n05 Apr 2013 @ 4.30 pm \n\n\n\n28 March 2014 @ 5.33 pm \n\n\n\n29 Apr 2014 @ 7.30 pm \n\n\n\n16 Jan 2015 @ 3.40 pm \n\n\n\n21 Jan 2015 @ 7.30 pm \n\n\n\n18 May 2015 @ 5.15 pm \n\n\n\nJalan Tamparuli-Ranau KM83.90 \n\n\n\n(Ranau District) \n\n\n\n1,340m AMSL N 6.0131200, \n\n\n\nE 116.5124300 \n\n\n\n11 Apr 2012 @ 4.00 pm \n\n\n\n04 May 2012 @ 7.08 pm \n\n\n\n05 May 2012 @ 5.00 pm \n\n\n\n06 May 2012 @ 4.00 pm \n\n\n\n10 Oct 2014 @ 8.00 am \n\n\n\n05 Nov 2014 @ 2.30 pm \n\n\n\nPhoto 1 \n\n\n\nPhoto 2\n\n\n\nPhotos 1 & 2: The worst debris flow incident at Jalan Penampang-\n\n\n\nTambunan KM38.80 which occurred on 16th January 2015. The road was \n\n\n\nimpassable to traffic for more than 17 hours as big loose boulders and mud \n\n\n\nwere deposited along the pavement. \n\n\n\nPhoto 3 \n\n\n\nPhoto 4 \n\n\n\nPhotos 3 & 4: The worst debris flow incident at Jalan Tamparuli-Ranau \n\n\n\nKM83.90 which occurred on 6th May 2012. The road was impassable to \n\n\n\ntraffic for more than 5 hours due to deposition of about 5m thick sediment \n\n\n\non road. \n\n\n\nThe rainfall records are acquired from the nearest Rainfall Gauge Station \n\n\n\n(RGS) which are managed by the Department of Irrigation and Drainage of \n\n\n\nSabah. Location of the stations are shown in Figure 1 and explained in \n\n\n\nTable 2.  \n\n\n\n19\n\n\n\n\n\n\n\n\nMalaysian Journal of Geoscie n ces 2(1) (2018) 18-29\n\n\n\nCite the Article: Edgar Jr. Joe, Felix Tongkul, Rodeano Roslee (2018). Relationship Between Rainfall and Debris Flow Occurren ce in the Crocker Range of Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 18-29. \n\n\n\nTable 2: Adopted rainfall gauge station within 10km from the debris flow sites. \n\n\n\nDebris Flow Site RGS Coordinate of RGS \nRadial Distance from RGS to \n\n\n\nSite \n\n\n\nElevation of RGS AMSL \n\n\n\nKM38.80 Ulu Moyog 5.87140 N, 116.25040 E 2.15km 730m \n\n\n\nKM83.90 Dalas 6.03390 N, 116.45530 E 6.75km 1,072m \n\n\n\nAs commonly used in various studies, the rainfall intensity-duration \n\n\n\n(Figure 2) is analysed to identify the relationship between rainfall and \n\n\n\ndebris flow occurrence [2]. Intensity-duration threshold which pertains to \n\n\n\nhourly rainfall intensity prior to the debris flow incident is adopted in this \n\n\n\nstudy due to the reason that shallow landslide like debris flow in tropical \n\n\n\narea can occur without significant antecedent rainfall [3]. Graph of \n\n\n\nmaximum hourly rainfall (mm/h) against duration (h) and cumulative \n\n\n\nrainfall (mm) against duration (h) are then plotted to identify the recorded \n\n\n\nmaximum hourly rainfall (mm/h) and critical duration (h). The maximum \n\n\n\nhourly rainfall (mm/h) and critical duration (h) for all events are plotted \n\n\n\ntogether in comparison with findings by other researchers to approximate \n\n\n\nthe closest rainfall threshold. The threshold refers to the minimum or \n\n\n\nmaximum level of causing and controlling parameter required to initiate \n\n\n\nlandslide [1]. In this study context, the minimum threshold concept is \n\n\n\nadopted as it defines the lowest rainfall intensity required for the debris \n\n\n\nflow to happen. \n\n\n\nFigure 2: Definition of rainfall parameters. Critical intensity (mm/h) \n\n\n\nmarks sharp increase in cumulative rain and triggers landslide occurrence. \n\n\n\nIt is equivalent to the value of critical rainfall (mm) which happens within \n\n\n\nthe critical duration (h) [4]. \n\n\n\nIn order to categorize the rainfall intensity-duration thresholds, the \n\n\n\nrainfall intensity is classified in reference to Table 1 below. \n\n\n\nTable 1: Rainfall intensity \n\n\n\nRainfall Category Intensity (mm/h) \nLight rain 1 \u2013 10  \nModerate rain 11 \u2013 30  \nHeavy rain 31 \u2013 60  \nVery heavy rain >60 \n\n\n\nSource: Department of Irrigation and Drainage Malaysia (2006). \n\n\n\n3. RESULTS \n\n\n\n3.1 Case Study 1 \n\n\n\nThe debris flow at Jalan Penampang-Tambunan KM38.80 is underlain by \nCrocker Formation of Eocene-Oligocene age. Observation on some \noutcrops shows occurrence of massive sandstone, as well as interbedded \nsandstone with thin layer of shale. Most of the sandstone units are \ndissected by numerous conjugate shear joint sets which are closely \nspaced of up to about 50mm wide. Due to the presence of joint sets, the \nrocks are highly fractured. The highly loose rock materials along the \nsteep channel have mostly undergone intense physical weathering \nbetween Grade V (completely weathered) and Grade VI (residual soil). \nOccurrences of  several secondary landslides also supply abundant loose \nsediment. Some of the channel conditions are shown in Photographs 5 to \n7.  \n\n\n\nPhoto 5 \n\n\n\nPhoto 6 \n\n\n\nPhoto 7 \n\n\n\nPhoto 5: Loose sandstone fragments at the initiation zone. \n\n\n\nPhoto 6: Abundant highly weathered rock within the runout zone. \n\n\n\nPhoto 7: Secondary landslide supplies loose sediment. \n\n\n\n20\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 18-29\n\n\n\nCite the Article: Edgar Jr. Joe, Felix Tongkul, Rodeano Roslee (2018). Relationship Between Rainfall and Debris Flow Occurrence in the Crocker Range of Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 18-29. \n\n\n\nIn the six events of the debris flow incident at Jalan Penampang-Tambunan \nKM38.80, the critical rainfall and critical duration prior to the occurrence \nrange between 13.00mm to 101.70mm and 1.55h to 7.50h respectively. \nRatio between the critical rainfall and critical duration gives critical \n\n\n\nintensity from 5.20mm/h to 29.06mm/h with the mean value of \n15.85mm/h. The maximum hourly rainfall intensity recorded between \n12.30mm/h and 60.50mm/h. The hourly and cumulative rainfalls during \nthe incidents are plotted in Figure 3 to Figure 8. \n\n\n\n3.1.1 Event 1 \n\n\n\nFigure 3: Rainfall duration and depth at Ulu Moyog on 5th April 2013. The graph shows critical rainfall of 50.30mm over critical duration of 7.50h \ngives critical intensity of 6.71mm/h, with recorded maximum hourly rainfall of 43.60mm/h. \n\n\n\n3.1.2 Event 2 \n\n\n\nFigure 4: Rainfall duration and depth at Ulu Moyog on 28th March 2014. The graph shows critical rainfall of 34.30mm over critical duration of 1.55h gives \ncritical intensity of 22.13mm/h, with recorded maximum hourly rainfall of 34.30mm/h. \n\n\n\n21\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 18-29\n\n\n\nCite the Article: Edgar Jr. Joe, Felix Tongkul, Rodeano Roslee (2018). Relationship Between Rainfall and Debris Flow Occurrence in the Crocker Range of Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 18-29. \n\n\n\n3.1.3 Event 3 \n\n\n\nFigure 5: Rainfall duration and depth at Ulu Moyog on 29th April 2014. The graph shows critical rainfall of 13.00mm over critical duration of 2.50h gives \ncritical intensity of 5.20mm/h, with recorded maximum hourly rainfall of 12.30mm/h. \n\n\n\n3.1.4 Event 4 \n\n\n\nFigure 6: Rainfall duration and depth at Ulu Moyog on 16th January 2015. The graph shows critical rainfall of 39.60mm over critical duration of 1.67h gives \ncritical intensity of 23.71mm/h, with recorded maximum hourly rainfall of 39.60mm/h. \n\n\n\n22\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 18-29\n\n\n\nCite the Article: Edgar Jr. Joe, Felix Tongkul, Rodeano Roslee (2018). Relationship Between Rainfall and Debris Flow Occurrence in the Crocker Range of Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 18-29. \n\n\n\n3.1.5 Event 5 \n\n\n\nFigure 7: Rainfall duration and depth at Ulu Moyog on 21st January 2015. The graph shows critical rainfall of 101.70mm over critical duration of 3.50h \ngives critical intensity of 29.06mm/h, with recorded maximum hourly rainfall of 60.50mm/h. \n\n\n\n3.1.6 Event 6 \n\n\n\nFigure 8: Rainfall duration and depth at Ulu Moyog on 18th May 2015. The graph shows critical rainfall of 18.60mm over critical duration of 2.25h gives \n\n\n\ncritical intensity of 8.27mm/h, with recorded maximum hourly rainfall of 18.60mm/h. \n\n\n\n23\n\n\n\n\n\n\n\n\nMalaysian Journal of Geoscie n ces 2(1) (2018) 18-29\n\n\n\nCite the Article: Edgar Jr. Joe, Felix Tongkul, Rodeano Roslee (2018). Relationship Between Rainfall and Debris Flow Occurren ce in the Crocker Range of Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 18-29. \n\n\n\n3.2 Case Study 2 \n\n\n\nThe debris flow at Jalan Tamparuli-Ranau KM83.90 is similarly underlain by Crocker Formation of Eocene-Oligocene age. Based on photographs of \n\n\n\nfresh debris flow incident, highly loose rock material along the steep channel have mostly undergone intense physical weathering between Grade V \n\n\n\n(completely weathered) and Grade VI (residual soil). Some of the previous channel conditions are shown in Photographs 8 to 10.  \n\n\n\nPhoto 8 \n\n\n\nPhoto 9 \n\n\n\nPhoto 10 \n\n\n\nPhoto 8: Loose sandstone fragments at the initiation zone. \n\n\n\nPhoto 9: Abundant highly weathered rock within the runout zone. \n\n\n\nPhoto 10: Thick sediment at the upper slope nearby the deposition zone. \n\n\n\nIn the six events of the debris flow incident at Jalan Tamparuli-Ranau KM83.90, the critical rainfall and critical duration prior to the occurrence range \nbetween 11.00mm to 80.90mm and 1.50h to 10.13h respectively. Ratio between the critical rainfall and critical duration gives critical intensity from \n1.09mm/h to 10.11mm/h with the mean value of 6.69mm/h. The maximum hourly rainfall intensity recorded between 7.80mm/h and 42.20mm/h. The \nhourly and cumulative rainfalls during the incidents are plotted in Figure 9 to Figure 14. \n\n\n\n24\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 18-29\n\n\n\nCite the Article: Edgar Jr. Joe, Felix Tongkul, Rodeano Roslee (2018). Relationship Between Rainfall and Debris Flow Occurrence in the Crocker Range of Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 18-29. \n\n\n\n3.2.1 Event 1 \n\n\n\nFigure 9: Rainfall duration and depth at Dalas on 11th April 2012. The graph shows critical rainfall of 52.60mm over critical duration of 7.00h gives critical \n\n\n\nintensity of 7.51mm/h, with recorded maximum hourly rainfall of 35.90mm/h. \n\n\n\n3.2.2 Event 2 \n\n\n\nFigure 10: Rainfall duration and depth at Dalas on 4th May 2012. The graph shows critical rainfall of 11.00mm over critical duration of 10.13h gives critical \n\n\n\nintensity of 1.09mm/h, with recorded maximum hourly rainfall of 7.80mm/h. \n\n\n\n25\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 18-29\n\n\n\nCite the Article: Edgar Jr. Joe, Felix Tongkul, Rodeano Roslee (2018). Relationship Between Rainfall and Debris Flow Occurrence in the Crocker Range of Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 18-29. \n\n\n\n3.2.3 Event 3 \n\n\n\nFigure 11: Rainfall duration and depth at Dalas on 5th May 2012. The graph shows critical rainfall of 80.90mm over critical duration of 8.00h gives critical \nintensity of 10.11mm/h, with recorded maximum hourly rainfall of 38.50mm/h. \n\n\n\n3.2.4 Event 4 \n\n\n\nFigure 12: Rainfall duration and depth at Dalas on 6th May 2012. The graph shows critical rainfall of 58.70mm over critical duration of 7.00h gives critical \n\n\n\nintensity of 8.39mm/h, with recorded maximum hourly rainfall of 42.20mm/h. \n\n\n\n26\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 18-29\n\n\n\nCite the Article: Edgar Jr. Joe, Felix Tongkul, Rodeano Roslee (2018). Relationship Between Rainfall and Debris Flow Occurrence in the Crocker Range of Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 18-29. \n\n\n\n3.2.5 Event 5 \n\n\n\nFigure 13: Rainfall duration and depth at Dalas on 10th October 2014. The graph shows critical rainfall of 21.60mm over critical duration of 7.00h gives \n\n\n\ncritical intensity of 3.09mm/h, with recorded maximum hourly rainfall of 14.70mm/h. \n\n\n\n3.2.6 Event 6 \n\n\n\nFigure 14: Rainfall duration and depth at Dalas on 5th November 2014. The graph shows critical rainfall of 14.90mm over critical duration of 1.50h gives \ncritical intensity of 9.93mm/h, with recorded maximum hourly rainfall of 14.90mm/h. \n\n\n\n27\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 18-29\n\n\n\nCite the Article: Edgar Jr. Joe, Felix Tongkul, Rodeano Roslee (2018). Relationship Between Rainfall and Debris Flow Occurrence in the Crocker Range of Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 18-29. \n\n\n\n4. DISCUSSIONS \n\n\n\nIn order to approximate the rainfall intensity-duration threshold of debris \n\n\n\nflow in the Crocker Range, the 12 events of debris flow for both case \n\n\n\nstudies are plotted together by taking the rainfall intensity against \n\n\n\nduration criteria (Table 3 and 4) and compared with other studies (Figure \n\n\n\n15). \n\n\n\nTable 3: Recorded maximum rainfall intensity for given duration at Jalan \nPenampang Tambunan KM38.80.  \n\n\n\nIncident \n\n\n\nDate \n\n\n\nCritical \n\n\n\nDuration (h) \n\n\n\nRecorded Maximum Rainfall \n\n\n\nIntensity (mm/h) \n\n\n\n5 Apr \n\n\n\n2013 \n7.50 43.60 \n\n\n\n28 Mac \n\n\n\n2014 \n1.55 34.30 \n\n\n\n29 Apr \n\n\n\n2014 \n2.50 12.30 \n\n\n\n16 Jan \n\n\n\n2015 \n1.67 39.60 \n\n\n\n21 Jan \n\n\n\n2015 \n3.50 60.50 \n\n\n\n18 May \n\n\n\n2015 \n2.25 18.60 \n\n\n\nTable 4: Recorded maximum rainfall intensity for given duration at Jalan \nTamparuli-Ranau KM83.90. \n\n\n\nIncident \n\n\n\nDate \n\n\n\nCritical \n\n\n\nDuration (h) \n\n\n\nRecorded Maximum Rainfall \n\n\n\nIntensity (mm/h) \n\n\n\n11 Apr \n\n\n\n2012 \n\n\n\n7.00 35.90 \n\n\n\n4 May \n\n\n\n2012 \n\n\n\n10.13 7.80 \n\n\n\n5 May \n\n\n\n2012 \n\n\n\n8.00 38.50 \n\n\n\n6 May \n\n\n\n2012 \n\n\n\n7.00 42.20 \n\n\n\n10 Oct \n\n\n\n2014 \n\n\n\n7.00 14.70 \n\n\n\n5 Nov \n\n\n\n2014 \n\n\n\n1.50 14.90 \n\n\n\nFigure 15: Comparison of landslide-triggering rainfall intensity\u2013duration thresholds from various studies. As the lowest recorded rainfall intensities are \n\n\n\n12.30mm/h and 7.80mm/h at Location 1 and 2 respectively, the boundary of triggering condition (marked by red line) is closer to line 10. Thus, the \n\n\n\nminimum rainfall intensity-duration threshold which is considered sufficient to trigger debris flow is approximate to that proposed by some researcher \n\n\n\n[5]. Equation of each threshold is explained in Table 5 [1]. \n\n\n\nAs shown by line 8 in Figure 15, the equation by a group researcher \n\n\n\nsuggested the highest threshold in which shorter rainfall duration is \n\n\n\nadequate to produce high rainfall intensity in order to initiate the debris \n\n\n\nflow [6]. On the contrary, the equation by a researcher as indicated by line \n\n\n\n5 in Figure 15 proposed the lowest threshold in which longer rainfall \n\n\n\nduration is required to achieve the minimum rainfall intensity needed for \n\n\n\nthe debris flow to occur [7].  \n\n\n\nTable 5: Several equations of rainfall intensity\u2013duration threshold for \nglobal, regional, and local scales. \n\n\n\nResearcher Area Equation \n\n\n\nCaine (1980) World I = 14.82D-0.39 \n\n\n\nCeriani et al. (1992) Lombardy, North \n\n\n\nItaly \n\n\n\nI = 20.1D-0.55 \n\n\n\nLarsen & Simon \n\n\n\n(1993) \n\n\n\nPuerto Rico, \n\n\n\nCaribbean Sea \n\n\n\nI = 91.46D-0.82 \n\n\n\n28\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 18-29\n\n\n\nCite the Article: Edgar Jr. Joe, Felix Tongkul, Rodeano Roslee (2018). Relationship Between Rainfall and Debris Flow Occurrence in the Crocker Range of Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 18-29. \n\n\n\nCannon & Ellen \n\n\n\n(1985) \n\n\n\nSan Francisco Bay \n\n\n\nRegion, California \n\n\n\nI = 6.9+38D-1.00 (High \n\n\n\nmean annual \n\n\n\nprecipitation); \n\n\n\nI = 2.5+300D-2.00 (Low \n\n\n\nmean annual \n\n\n\nprecipitation); \n\n\n\nInnes (1983) World I = 4.93D-0.50 \n\n\n\nCancelli & Nova \n\n\n\n(1985) \n\n\n\nValtellina, \n\n\n\nNorthern Italy \n\n\n\nI = 44.668D-0.78 \n\n\n\nWieczorek (1987) California I = 1.7+9D-1.00 \n\n\n\nWilson et al. (1992) Oahu, Hawai I = 121.4D-0.602 \n\n\n\nMontgomery et al. \n\n\n\n(2000) \n\n\n\nMettman Ridge, \n\n\n\nOregon \n\n\n\nI = 9.9D-0.52 \n\n\n\nSource: Guzzetti et al. (2007); Brunetti et al. (2010)  [2,8]. \n\n\n\nBased on Figure 15, the empirical threshold equation for Crocker Range \ncases are close to that proposed by Montgomery et al. (2000) as shown in \nEquation 1 below [5]. \n\n\n\nI = 9.9D-0.52 \u2026 Equation 1 \n\n\n\nwhere  I = Highest rainfall intensity during rainfall event (mm/h);  \n\n\n\nD = Duration of rain (h)  \n\n\n\nBy applying Equation 1, the threshold values for the events are \n\n\n\ncalculated as indicated in Tables 6 and 7 for each case.  \n\n\n\nTable 6: Calculated maximum rainfall intensity for given duration at \nJalan Penampang-Tambunan KM38.80. \n\n\n\nIncident \n\n\n\nDate \n\n\n\nCritical Duration \n\n\n\n(h) \n\n\n\nCalculated Rainfall Intensity \n\n\n\n(mm/h) \n\n\n\n5 Apr 2013 7.50 3.47 \n\n\n\n28 Mac \n\n\n\n2014 \n1.55 \n\n\n\n7.88 \n\n\n\n29 Apr \n\n\n\n2014 \n2.50 \n\n\n\n6.15 \n\n\n\n16 Jan 2015 1.67 7.58 \n\n\n\n21 Jan 2015 3.50 5.16 \n\n\n\n18 May \n\n\n\n2015 \n2.25 \n\n\n\n6.49 \n\n\n\nTable 7: Calculated maximum rainfall intensity for given duration at \nJalan Tamparuli-Ranau KM83.90. \n\n\n\nIncident \n\n\n\nDate \n\n\n\nCritical Duration \n\n\n\n(h) \n\n\n\nCalculated Rainfall Intensity \n\n\n\n(mm/h) \n\n\n\n11 Apr \n\n\n\n2012 \n\n\n\n7.00 \n3.60 \n\n\n\n4 May 2012 10.13 2.97 \n\n\n\n5 May 2012 8.00 3.36 \n\n\n\n6 May 2012 7.00 3.60 \n\n\n\n10 Oct 2014 7.00 3.60 \n\n\n\n5 Nov 2014 1.50 8.02 \n\n\n\nAs shown in Tables 6 and 7, the calculated rainfall intensity is obtained by \n\n\n\napplying the value of recorded maximum rainfall intensity (Table 3 and 4) \n\n\n\ninto Equation 1 [9]. The recorded maximum rainfall intensity refers to the \n\n\n\nidentified maximum hourly rainfall within the critical duration (Figure 2) \n\n\n\nprior to the debris flow occurrence. The calculated rainfall intensity can be \n\n\n\ndefined as the intensity of precipitation needed to trigger the debris flow, \n\n\n\nalso known as the threshold [10]. \n\n\n\nIt is observed that the debris flow can be triggered by rainfall intensity \n\n\n\nwithin the range of 2.97mm/h to 8.02mm/h which can be categorized as \n\n\n\nlight rain in Table 1. This light rain indicated low-intensity rainfall. \n\n\n\n5. CONCLUSION \n\n\n\nThis study shows that rainfall intensity-duration thresholds for debris \n\n\n\nflow to occur in the Crocker Range are generally lower which implies \n\n\n\nthat low-intensity rainfall will be sufficient to trigger the incidents. The \n\n\n\nlower threshold is associated with high availability of loose material and \n\n\n\nweak geological condition. The identified threshold in this study will \n\n\n\nserve as a fundamental element in the debris flow warning system \n\n\n\napplication at Crocker Range area in the state which displays similar \n\n\n\ntopographical and geological setting. This however requires further \n\n\n\nstudy. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe authors would like to thank the Department of Irrigation and \n\n\n\nDrainage, Sabah for supplying rainfall data for this study purpose. \n\n\n\nREFERENCES \n\n\n\n[1] Crosta, G., Frattini, P. 2001. Rainfall thresholds for triggering soil slips \nand debris flow. In A. Mugnai, F. Guzzetti , & G. Roth (Ed.), Proceedings \n2nd EGS Plinius Conference on Mediterranean Storms,(pp. 463-487). \nSiena.\n\n\n\n[2] Guzzetti, F., Peruccacci, S., Rossi, M., & Stark, C. P. 2007. Rainfall \nthresholds for the initiation of landslides in central and southern Europe. \nMeteorology and Atmospheric Physics, 98 (3-4), 239-267.\n\n\n\n[3] Corominas, J., Moya, J. 1999. Reconstructing recent landslide activity in \nrelation to rainfall in the Llobregat River basin, Eastern Pyrenees, Spain. \nGeomorphology, 30 (1-2), 79-93. \n\n\n\n[4] Aleotti, P. 2004. A warning system for rainfall-induced shallow failures. \nEngineering Geology, 73, 247-265. \n\n\n\n[5] Montgomery, R.D., Schmidt, M.K., Greenberg, H.M., Dietrich, W.E. 2000. \nForest clearing and regional land sliding. Geology, 28 (4), 311-314. \n\n\n\n[6] Norhidayu, K., Kamarudin , A. T., Muhammad, M., & Anuar, K. 2016. \nTrigerring mechanism and characteristic of debris flow in Peninsular \nMalaysia. American Journal of Engineering Research, 5 (4), 112-119.\n\n\n\n[7] Nettleton, I. M., Martin, S., Hencher, S., & Moore, R. 2005. Debris flow \ntypes and mechanisms. In M. G. Winter, F. Macgregor, & L. Shackman, \nScottish road network landslides study (pp. 45-67). Edinburgh: The \nScottish Executive.\n\n\n\n[8] Brunetti, M.T., Peruccacci, S., Rossi, M., Luciani, S., Valigi, D., Guzzetti, F. \n2010. Rainfall thresholds for the possible occurrence of landslides in Italy. \nNatural Hazards and Earth System Sciences, 10, 447-458. \n\n\n\n[9] Giraud, R.E. 2005. Guidelines for the Geologic Evaluation of Debris-\nFlow Hazards on Alluvial Fans in Utah. Utah: Utah Geological Survey. \n\n\n\n[10] Department of Irrigation and Drainage Malaysia. 2006. Info Banjir. \nRetrieved 2017, from On-Line Rainfall Data: \nhttp://infobanjir.water.gov.my/rainfall_page.cfm?state=SAB\n\n\n\n29\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 36-44 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2022.36.44 \n\n\n\n \nCite The Article: Matthew Coffie Wilson, Chiri G. Amedjoe, Simon K. Y. Gawu  (2022). Structures of Birimian and Tarkwaian \n\n\n\nRocks at North-West New Drobo \u2013 Implication on Deformation. Malaysian Journal of Geosciences, 6(1): 36-44. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2022.36.44 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSTRUCTURES OF BIRIMIAN AND TARKWAIAN ROCKS AT NORTH-WEST NEW \nDROBO \u2013 IMPLICATION ON DEFORMATION \n\n\n\nMatthew Coffie Wilson1, Chiri G. Amedjoe2, Simon K. Y. Gawu3 \n\n\n\n1Lecturer, Kwame Nkrumah University of Science and Technology, Department of Geological Engineering, University Post Office, Kumasi \u2013 Ghana. \n2Senior Lecturer, Kwame Nkrumah University of Science and Technology, Department of Geological Engineering, University Post Office, Kumasi \n\u2013 Ghana \n3Associate Professor, Kwame Nkrumah University of Science and Technology, Department of Geological Engineering, University Post Office, \nKumasi \u2013 Ghana.  \n*Corresponding Author Email: regimatt2003@yahoo.co.uk, mcwilson.coe@knust.edu.gh \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 10 August 2022 \nAccepted 20 September 2022 \nAvailable online 23 September 2022 \n\n\n\n\n\n\n\nThis research leads to identifying the microstructures, the deformation episodes (D) and foliation grades (S) \nof rocks in the study area. Different geological structures such as shear zones, foliations, micro-faults, crack-\nseal events, etc. have been identified in the study area. Different phases of deformation episodes and foliation \ngrades have been studied to determine the deformation environments. Senses of shear such as dextral, CS, \nand CS' as well as antithetic and synthetic micro-faults and boudins may be identified at the north-west New \nDrobo. Three (3) main deformational (D) events and foliation grades (S) in both the Tarkwaian sandstones \nand Birimian volcanic rocks may be identified as D1, D2 and D3 as well as S1, S2 and S3. Recrystallization of \nquartz and feldspar through bulging (BLG), subgrain boundary rotation (SBR) and grain boundary migration \n(GBM) have been observed at the study area. The overprinting relationships in the Birimian Supergroup may \nbe identified with three (3) different deformational phases. The first deformation (D1) defines the formation \nof a vertical shortening, whilst the second deformation (D2) defines oblique shortening. Moreover, the third \ndeformation (D3) is due to high strain rate causing brittle faulting. \n\n\n\nKEYWORDS \n\n\n\nStructures, Deformation, Foliation, Recrystallization, Petrography \n\n\n\n1.   INTRODUCTION \n\n\n\n\n\n\n\nFigure 1: Geological map of the Man Shield showing the study area \n(Modified after Attoh et al., 2006). \n\n\n\nThe Birimian in Ghana covers a wider area where the deformation \nactivities vary from area to area and controls the mineralization. The \nmagnitude of deformation in the Birimian rocks may not be the same as \nthe magnitude of deformation in the Tarkwaian rocks. The structural \nfeatures in the Birimian and Tarkwaian vary from belt to belt. For instance, \nthe structures in the Wa-Lawra Belt would not be the same as that in the \nNangodi, Bui-Banda, Ashanti, etc. Each of the belts has its own peculiar \nstructures. For instance, in the Ashanti Belt, both the Birimian and \nTarkwaian Groups have undergone multiple deformational events, \nnamely, D1, D2, D3, D4, D5 and D6 (Perrouty et al., 2012, 2015). Therefore, \nthe episodes of deformation (D1, D2, D3, etc) in the study area ought to be \ninvestigated. \n\n\n\nIn the Birimian environment, there are a number of deformational \nstructures which have in turn affected the Tarkwaian supracrustal rocks \nthat are lying on it. However, some of the deformation activities folded the \nTarkwaian supracrustal rocks alongside. This thus explains the need for \nstructural analyses. The volcanic rocks of the six gold belts in Ghana (with \nexception of Wa-Lawra belt which is oriented in the N-S direction), are \ntectonically oriented in the NE-SW directions (Fig. 1). However, at the very \ntail end of the Bui - Banda Belt entering La Cote d\u2019Ivoire, NW of Drobo at \nthe borderland, the strike direction turns north, in the same orientation as \nthe Wa-Lawra Belt in the northwest part of the country. It is unclear \nwhether the result is due to deformation or not. Different microtectonic \nstructures and foliation-bearing grades (S) suggesting phases of \ndeformation (D), ought to be identified to understand the deformation and \nmetamorphic processes in the environment. This research leads to \n\n\n\n\nmailto:mcwilson.coe@knust.edu.gh\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 36-44 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Chiri G. Amedjoe, Simon K. Y. Gawu  (2022). Structures of Birimian and Tarkwaian \n\n\n\nRocks at North-West New Drobo \u2013 Implication on Deformation. Malaysian Journal of Geosciences, 6(1): 36-44. \n \n\n\n\nidentifying the structures and the deformation episodes at the study area. \nThe Birimian basaltic flow and the volcaniclastics are in contact with \nTarkwaian sandstones along the Bui Belt (Fig. 1). This suggests the \ndifferent rock competencies to result in a shear zone. Different phases of \ndeformation (D) and foliation grades (S) would have to be identified to \nunderstand the deformation and metamorphic processes in the \nenvironment. At the area of research (Map Sheet 0703A-Sampa), the \ndeformation episodes and the types of foliations are not well known.  \n\n\n\n2.   GEOLOGIC SETTING  \n\n\n\nRocks of the Birimian Supergroup which are of Paleoproterozoic age \n(2000 Ma) are highly defined at the eastern part of the Man Shield (Peucat, \n2005). However, the project area in Ghana is located at the southeastern \npart of the West African Craton (WAC) (Fig. 1). The volcanic belts of the \nWAC alternate with sedimentary basins which consist of the Birimian and \nTarkwaian Supergroups with granitoid intrusions (Kesse, 1985). The \nbatholith or granitoid intruded at a later stage and thus post-dated the \nformation of the Tarkwaian Supergroup (Griffis et al., 2002).  \n\n\n\nAccording to Bonhomme (1962), the Man Shield consists of two domains \nnamely, western domain which defines the Archean rocks of Liberian age \n(3.0-2.5 Ga) and an eastern domain describing Birimian rocks of Early \nProterozoic age (2.4-2.0 Ga) which have been folded, metamorphosed, and \nintruded by granitoids during the Eburnean event at about 2.0-1.8 Ga. \nGhana falls in the eastern part of the Man Shield (Fig. 1) where a geological \nterrain such as Paleoproterozoic (Birimian/Tarkwaian) terrain hosts gold \ndeposits (Hirdes et al., 1992). The early Proterozoic which defines the \nBirimian Supergroup consists of large sedimentary basins and volcanic \nbelts refers to an accretionary period of age 2.1 Ga (Abouchami et al., \n1990). This activity occurred during the Eburnean orogeny at a period of \n2.1 to 2.0 Ga (Bonhomme, 1962). The Birimian rocks of the Baoul\u00e9-Mossi \ndomain of the Man Shield are mostly found in Ghana, Niger, Mauritania, \nGuinea. Kesse (1985) on the Geology of Ghana was of the view that the \nBirimian rocks overlain dominantly by clastic Tarkwaian rocks and were \nextensively intruded by granitoids during the Eburnean orogeny. The \nLower Birimian stratigraphy consists of predominantly metasedimentary \nrocks, whilst the Upper Birimian stratigraphic column defines \nmetavolcanics such as lava flows, basaltic and andesitic dykes which \nmostly have been metamorphosed to hornblende actinolite-schists, \ncalcareous chlorite schists and amphibolites (the greenstones) (Eisenlohr \nand Hirdes, 1992). Felsic volcanic rocks also occur in this succession as \nwell as in the predominantly sedimentary sections. The felsic units include \ndacitic pyroclastic rocks, minor andesitic and rhyolite flows, and \nundifferentiated volcaniclastic rocks (Eisenlohr and Hirdes, 1992). \n\n\n\nThe Tarkwaian rocks are located at the South-western part of Ghana. The \nTarkwaian rocks rest upon the Birimian rocks which in Ghana have a \nminimum age of 1915 \u2013 2110 million years. The Tarkwaian system is made \nup of four stratigraphic units. They are Huni sandstones, Tarkwa phyllite, \nBanket series, and Kawere group. The Huni sandstone is the topmost and \nyoungest rock unit of the Tarkwaian super group. Huni sandstones are \ngrey in color and are a representation of weathered feldspathic quartzites. \nThe second stratigraphic unit of the Tarkwaian super group is the Tarkwa \nphyllite. Tarkwa phyllite consists of phyllite with and phyllite without \nchloritoid. The Banket series is the third stratigraphic unit of the \nTarkwaian super group. The Banket series is made of reefs that hold gold \nmineralization. (Eisenlohr and Hirdes, 1992). Kawere group is the basal \nand oldest unit of the Tarkwaian super group. \n\n\n\n2.1   Location of the Study Area  \n\n\n\nThe NW New Drobo is located at the southwestern part of Ghana, in the \nsouth of Jaman District in the Bono Region of Ghana, and covers the \nsouthwest part of the Bui Belt and lies between 7\u00b030' and 8\u00b0 N and from \n2\u00b030' W (Fig. 1) up to the Cote d\u2019Ivoire boundary (Zitzmann et al., 1997) \non Map Sheet 0703A - Sampa. The lithologies of the study area identify \nboth the Birimian and Tarkwaian Supergroups striking on NE-SW. The \nBirimian Supergroup consists of a metavolcanic and metasedimentary \nrock assemblage. The Birimian metasedimentary rocks comprise of \nargillite-argillite/wacke-wacke-volcaniclastic-argillite/volcaniclastic and \nchert (Zitzmann et al., 1997). The fine-grained clastic facies of mostly \nsericite phyllite is typical for the sedimentary basins, while the coarser \nwacke facies accumulated closer to the volcanic belts.  \n\n\n\nThe Birimian metavolcanic rocks are basalt-andesite-rhyodacite lavas, \nwith elevated Mg-Ca-Na contents, and volcaniclastics (Zitzmann et al., \n1997). The NE-SW striking Birimian volcanic belts which are separated by \nsedimentary basins are very pronounced in Ghana and eastern part of \nIvory Coast. However, in Burkina Faso the Birimian volcanic belts strike \nalong ENE-WSW. The lithologies of the volcanic belts in both Ghana and \n\n\n\nIvory Coast can be identified as metamorphosed lavas, mafic intrusions, \nminor volcaniclastics as well as intruded granitoids (Hirdes et al., 1992, \n1996). The geology of Afema District in C\u00f4te d\u2019Ivoire (close to the C\u00f4te \nd\u2019Ivoire-Ghana border) is characterized by NE-SW striking lithologies of \nBirimian Supergroup (Assie, 2008). The Afema tectonic zone is a major \nfault 700 m to 3 km wide and several tens of km long from the Afema \nDistrict in C\u00f4te d\u2019Ivoire up to Ghana and thus the study area. This major \nfault or geologic structure strikes NNE and dips 70-80\u00b0to the southeast \n(Assie, 2008). \n\n\n\n3.   MATERIALS AND METHODOLOGY  \n\n\n\nMapping phases and traverses were conducted along rivers and streams, \nroad cuts and on top of hills in search of outcrops. The colour of soils and \nant hills were closely examined to help in the identification of the various \nlithologies. Lithological logging, structural logging and geological mapping \nwere conducted where moderately fresh samples were mapped and \nlogged. Topographical map (Map Sheet 0703A \u2013 Sampa) was used to \nidentify the physical features of a place which are very useful in geological \nmapping. Detailed descriptions of outcrops were done at each location. \n\n\n\n3.1   Thin Section Preparation  \n\n\n\nOver hundred rocks were sampled from the field of which sixty-three (63) \nrepresentative rocks were used to prepare thin sections at the KNUST \nGeological Engineering Laboratory. Rocks were first cut using the Hillquist \nRock Cutting machine. A particular face of the rock sample to be analyzed \nwas selected and smoothened. The smoothening begun with the roughest \nsurface (P60) of the abrasive papers through P80, P120, P180, P240, P400, \nP600 and then the finest surface using P1000. The glass slide of size 24mm \n* 48mm on which the specimen was to be bonded was roughened using a \nslurry made of a mixture of silicon powder 220 and water. The slide was \nthen thoroughly cleaned and made to dry. A mixture of Epoxy and \nHardener in the ratio of 15:2 was prepared and used to bond the prepared \nsmoothened face of the rock specimen to the roughened surface of the \nglass slide and the bond was left for 48 hours (2 days) to properly dry and \nharden. The bonded slide was placed on the cutting edge of the cutting and \npolishing machine to cut off the unwanted part of the rock specimen and \nthe surface trimmed and polished using the Grinding machine. The \nprepared thin section was finalized to a size of 30 \u03bcm. The mineral \ncomponents as well as the geological structures were identified using the \npolarizing microscope with 4x and 5x objective lens magnification. \nHowever, in the determinations of mineral optic signs, higher \nmagnification (40x) was used. The minerals present and their structural \ncharacteristics were used to appraise the deformations and foliations of \nthe rock. \n\n\n\n4.   RESULTS AND DISCUSSIONS \n\n\n\n4.1   Petrography of Volcanic Rocks  \n\n\n\nThe porphyritic igneous textures and phenocrysts in aphanitic \ngroundmass and the mineralogical composition indicate a volcanic rock \n(Fig. 2A) for these samples. The modal percentages of the various minerals \nas well as the quantity of quartz minerals in the volcanic rocks help to \nidentify the type of volcanic rock. Volcanic and volcaniclastic rocks such as \nbasalt (Fig. 2A), andesite (Fig. 2C), dacite (Fig. 2D), rhyo-dacite, etc. were \nidentified in the study area. \n\n\n\n4.1.1   Basalt \n\n\n\nPhenocrysts such as hornblende, plagioclase feldspars, and quartz, though \nof varying sizes, are euhedral to subhedral and angular in shapes. The \npattern of non-directional texture of the aphanitic groundmass as \nobserved in the framework (Fig. 2A), shows interlocking arrangement of \nthe mineral crystals arising from solidification of molten rock material. \nThus, the general porphyritic texture of the sample, is an affirmation of its \nigneous origin and the fineness of the groundmass implies that they are \nvolcanic. The plagioclase feldspars seem to be altering relatively faster. \nPlagioclase feldspars with granulated edges signify alteration. The \nalterations have produced sericites that occurred as granulated patches in \nthe framework. \n\n\n\nAlso, subhedral hornblende crystals which appear somewhat rotated and \nbrecciated with blurred edges between the platy minerals may be \nresponsible for the wavy foliation observed in the framework. The \napparent rotation of the subhedral hornblende crystals signifies shearing. \nThe quartz crystals within the framework are subhedral to anhedral and \nexhibit polycrystalline and undulose extinction. Sericitization in some \nportions of the thin section probably result from alteration of some \nfeldspars. It may be inferred that the sample is a volcanic rock and may be \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 36-44 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Chiri G. Amedjoe, Simon K. Y. Gawu  (2022). Structures of Birimian and Tarkwaian \n\n\n\nRocks at North-West New Drobo \u2013 Implication on Deformation. Malaysian Journal of Geosciences, 6(1): 36-44. \n \n\n\n\ncoming from either weakly sheared zone or near a sheared zone that is \nmildly metamorphosed. \n\n\n\n4.1.2   Dacite \n\n\n\nThe specimen is greyish in colour and has coarse-grained texture. The \nspecimen is composed of large phenocrysts of sharp greenish and angular \nclasts of pyroxene, larger phenocrysts of brownish and angular iron oxide, \nand also medium-sized phenocrysts of milky lenticular plagioclase \nfeldspar in a fine-grained matrix. It is possible that these greenish and \nbrownish angularly shaped minerals were formed after deformation and \n\n\n\nkinematic activities in the parent rock (Fig. 2D). That is to say that the \npyroxene and iron oxide are post kinematic and post tectonic. The \npyroxene dominates the iron oxide mineral in terms of quantity. Dark \ngreenish mineral, which may be amphibole, greenish pyroxene and \nchlorite (Fig. 2D), are seen to have a sharp contact and intrusion in the \nrock. These minerals are said to be post kinematic as well as post tectonic \nformed minerals. These flake and angular minerals are both \nmicroscopically and macroscopically identified as large and fresh \nphenocrysts which took some time to crystallize after the normal fast \ncrystallization of the volcanic rock. The dark greenish post mineral may be \nintruded with quartz veinlet (Fig. 2D). \n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Photomicrographs showing minerals of: (A) basaltic volcanic rock in XPL showing porphyritic texture consisting of interlocking aphanitic \ngroundmass with phenocrysts of hornblende, augite (Cpx), plagioclase feldspar, quartz, olivine; (B) mineralogical banding of light and dark minerals in \n\n\n\nXPL, in a quartz-feldspar-graphite schist, section parallel to the aggregate lineation and normal to the foliation (Locality: Bepoase); (C) andesitic volcanic \nrock in XPL showing porphyritic texture consisting of interlocking aphanitic groundmass with phenocrysts of hornblende (amphibole), plagioclase \n\n\n\nfeldspars, pyroxene (augite), quartz, and chlorite. Samples taken from Adamsu; (D) dacitic volcanic rock in XPL showing porphyritic texture of \nhornblende (amphibole), pyroxene, feldspars (plagioclase and orthoclase), quartz, rutile in a fine groundmass. The phenocrysts are post-mineral deposits. \n\n\n\nLocation: Kwamepim.\n\n\n\n4.1.3   Schist  \n\n\n\nQuartz-feldspar-graphite schist (Fig. 2B) consists of medium-grained \nplagioclase and orthoclase feldspars and vitreous quartz crystals. Though \nthe quartz is mostly monocrystalline, some polycrystalline grains were \nobserved suggesting that the rock has suffered straining to a certain \ndegree. The feldspar, quartz and rutile form the microlithon of the foliated \nrock. The feldspar may be identified as feldspar fish arranged between C-\ntype shear bands and a dextral shear sense (Fig. 2B). Again, very few \ngranulated sericitic patches were observed in the framework, suggesting \nweathering of some primary minerals. The feldspar crystals show cleavage \nplanes in a direction parallel to their lengths. There is recrystallization as \nshown by the merging of some quartz crystals. Structurally foliation in the \nrock is shown by the schistose characteristics where the light minerals \nhave been separated from the dark minerals. Plagioclase feldspars with \ngranulated edges signify alteration. The quartz stringers in the framework \nparallel the preferred orientation of the platy minerals. The observed \nwavy nature of the foliation, may be due to the growth of secondary quartz \ncrystals between the platy minerals. \n\n\n\n4.1.4   Tarkwaian Sandstone  \n\n\n\nThe Tarkwaian sandstone (Fig. 10) consists of medium-grained \nplagioclase and alkaline feldspars and vitreous quartz crystals. The sample \nagain show a little high content of dark brown platy biotite flakes (Fig. 10) \ndispersed within the rock. The platy minerals show preferred alignment \nin the sample in portions where they are clustered. There is a moderate \npercentage of platy minerals where the dark and brownish platy minerals \nshow foliation. Though the quartz is mostly monocrystalline, some \npolycrystalline grains were observed suggesting that the rock has suffered \nstraining to a certain degree. The oriented biotite flakes (Fig. 10) may be \nidentified as the domain of the deformation whilst feldspar, quartz and \nrutile form the microlithon of the foliated rock. Biotite flakes which are \nmost irregular small flakes but sometimes elongated are pleochroic \nshowing range of brown colours in PPL. The biotite flakes are mostly \ninterstitial and dispersed in the framework. While some biotite flakes have \nsharp edges, others show blurred edges resulting from alterations (Fig. \n10). Again, very few granulated sericitic patches were observed in the \nframework, suggesting weathering of some primary minerals. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 36-44 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Chiri G. Amedjoe, Simon K. Y. Gawu  (2022). Structures of Birimian and Tarkwaian \n\n\n\nRocks at North-West New Drobo \u2013 Implication on Deformation. Malaysian Journal of Geosciences, 6(1): 36-44. \n \n\n\n\nMetamorphism in the rock relying on the chlorite index mineral is \nadvanced probably approaching high-grade rendering the schist rock into \ngreenschist facies. Plagioclase feldspars with granulated edges signify \nalteration. The alterations have produced sericites that occurred as \ngranulated patches in the framework. The sample is foliated and \ndominated by platy minerals \u2013 elongated biotite (Fig. 10). The striations \nobserved in this mineral (biotite) is an indication of shearing. The quartz \nstringers in the framework parallel the preferred orientation of the platy \nminerals. \n\n\n\n4.2   Structures \n\n\n\n4.2.1   Brecciation  \n\n\n\nThe sample (volcanic breccia, Figure 3) is composed of high-strained \nquartz mineral depicting a silica-alkali reaction and also high deformation \n\n\n\nmechanism. The specimen is composed of phenocrysts of angular clasts in \na fine-grained matrix. The sample is observed to be highly brecciated with \nbands of shearing. The senses of shear are both dextral and C'S. The C'-\ntype shear band is oblique to the main foliation (S). This highly deformed \nmaterial can be found in a shear zone or close to a shear zone. The breccia \ndepicts an intense D2 deformation zone. The orientation of the breccia \nzones suggests that their localization is determined by tensional \ncomponents within the overall D2 stress field. The cyclical pattern of the \nbrecciation during the D2 deformation is considered to represent rapid \nbrittle transitions during the ductile deformation. Figure 3C demonstrates \na right stepping arrangement where one fault segment occurs to the right \nof the adjacent segment. The dextral sense of shear depicts restraining \nbends and offsets and this demonstrates that the rock material is pushed \ntogether by the dominant fault movement. \n\n\n\n\n\n\n\n\n\n\n\nFigure 3: Brecciated rock from Adamsu locality showing: (A) an intense deformation zone; (B) new quartz veinlet crosscutting old veinlets of quartz and \nalso showing C'S sense of shear; (C) dextral sense of shear; (D) different grain sizes of quartz depicting straining and deformation\n\n\n\n4.2.2   Crack-Seal Event  \n\n\n\nWhen a crack in a rock opens up as a result of differences in temperature \nand pressure, the mineral aggregates that form in the opened crack leads \nto the formation of veins. Normally these cracks form when the fluid \npressure increases. However, precipitation of minerals (example, quartz) \nin those cracks seal the cracks. The differences in stiffness and strength of \nthe host rock cause propagation of crack to contain the veins. Precipitation \nof minerals seal the cracks and causes permeability to reduce and also \nrestores the mechanical strength of the rocks. This sealed minerals or \nveins identifies and defines the kinetics of the growth of the crystals and \nalso the properties of the host rocks. Two veins in cracks of same quartz \nmaterial but oriented in different directions are identified and studied \u2013 \nold and new quartz veins. The new veins may be observed to cut through \nthe old veins (Figures 3B and 4) at both Adamsu and Adiakor No.1 \nrespectively. However, the old veins may be seen to continually grow even \nafter formation of the new veins. The structures and/or mineral \nassemblages overprinting or superposing on each other reveal different \n\n\n\ndeformation intensities and thus predicts different ages. The Bui-Banda \ngold Belt strikes in two different directions, namely, NE-SW and N-S. Since \nthe volcanic rocks intruded the sedimentary rocks in the environment, it \nis inferred that the quartz veins which intrude in two different directions \n(NNE-SSW and NNW-SSE) are as a result of the volcanic rocks in two \ndifferent directions. The old and new veins intrusions are almost \nperpendicular to one another. Highly weathered minerals portray high \nalteration of minerals as a result of the entire minerals being coated with \niron oxide. In other words, the entire rock is iron oxide dominated with \nintrusion of quartz stringers (Fig. 4A). Each band of the contacts of the \nquartz vein intrusion probably represents a separate crack-seal event. \n\n\n\nThe study of veins as microstructures in rocks help to deduce the \ndeformation episodes and deformation history of the rocks in an area. \nVeins may form by local alteration of the wall rock along a fracture or rigid \nobject, or by deformation and recrystallization of veins with sharp \nboundaries (Passchier and Trouw, 2005). The development of veins is \nassociated with the circulation of fluids in rocks, both for transport of \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 36-44 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Chiri G. Amedjoe, Simon K. Y. Gawu  (2022). Structures of Birimian and Tarkwaian \n\n\n\nRocks at North-West New Drobo \u2013 Implication on Deformation. Malaysian Journal of Geosciences, 6(1): 36-44. \n \n\n\n\nmaterial and for propagation and opening of the vein. The quartz veinlets \nare aligned in various forms. The quartz-veinlet trends NW-SE along the \nstrike. The sigmoidal arrangement of the quartz-vein-filled fractures of the \nrocks help to deduce the orientations of the principal stresses. The quartz-\nveinlet identifies the arrangement of the principal axis, \u03c31, as nearly \nvertical. Fringe structures are informative and may be used to estimate \n\n\n\nsense of shear and finite strain (Ramsay and Huber, 1983). Euhedral \npyrite, like a rectangular pyrite, in a mineralized zone in Bepoase may have \ngrown after diagenesis. A fringe adjacent to another rectangular pyrite \nframboid is located in the same outcrop (Figure 5). The suture defines an \nS-shape (not Z-shape as sinistral).  \n\n\n\n\n\n\n\nFigure 4: The figures depict a cross-cutting feature, where a later fracture cuts a former structure to form an X-node in the localities of: (A) Adiakor \nNumber 1; (B) Adamsu \n\n\n\n\n\n\n\nFigure 5: Figure A shows tension gases in en-echelon arrangement in a shear zone and also the growth of veins trending NW-SE in a dextral shear zone; B \nshows Quartz-calcite fringes around a pyrite grain in carbonaceous slate \n\n\n\n\n\n\n\nFigure 6: (A) Antitaxial shear vein of quartz. There is a reflection of fault plane where chromite forms a fault plane between biotite levels. Bands of solid \ninclusions trend from top left to bottom right. Each of the band represents a separate crack seal event. (B) Two different asymmetric boudins indicate \n\n\n\nshear senses \u2013 synthetic (S-slip) and antithetic (A-slip) asymmetric boudins \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 36-44 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Chiri G. Amedjoe, Simon K. Y. Gawu  (2022). Structures of Birimian and Tarkwaian \n\n\n\nRocks at North-West New Drobo \u2013 Implication on Deformation. Malaysian Journal of Geosciences, 6(1): 36-44. \n \n\n\n\nZonation of quartz in a biotite mineral implies some of the biotite minerals \ndisplay alteration. The rock is foliated and has a deformation band of C'S \nshear sense (Fig. 6A). The specimen depicts antitaxial shear vein of quartz. \nFigure 6A portrays bands of solid inclusions which may represent a \nseparate crack-seal event and trends from top left to bottom right. The \ninclusion bands which are oblique to the elongate quartz crystals are \nthinly-packed layers and may indicate narrow crack events. Figure 6B \nshows two different asymmetric boudins which indicate shear senses \u2013 \nsynthetic (S-slip) and antithetic (A-slip) asymmetric boudins. They both \nindicate different geometry, motion and directions. The synthetic boudins \nportray long and curved lenticular shape and large relative displacement. \nThe inter-boudin surface is gently inclined. Antithetic boudins reveal an \nangular shape with small relative displacement. The inter-boudin surface \nof the A-slip asymmetric boudins is steeply inclined (Goscombe and \nPasschier, 2003). \n\n\n\n4.2.3   Foliations \n\n\n\nIn Figure 7, there is a syntectonic porphyroblasts which shows sigmoidal \nS1 pattern and asymmetric strain shadows of quartz. This asymmetry of \nthe strain shadows of quartz indicates dextral sense of shear and this \nimplies that the feldspar rotated in a clockwise sense with respect to the \nkinematic frame during its growth. In the quartz-feldspar schist, a \ncrenulation cleavage (S1) develops along the limbs of microfolds \ndeforming an earlier planar fabric. A new foliation (S2) positions itself \nperpendicular to the maximum shortening (D1) and bands of the chlorites \nat the limb sites alternate with bands of quartz and / or feldspar at the \nhinge sites and turns to be parallel to the new foliation (S2). The first phase \nof deformation (D1) is marked by the foliation (S1) and defines schistosity. \nThe second phase of deformation D2 is marked by tectonism and thus \ndepicts a fold structure (P2) and Lineation (L2). A foliation domain \ndisplaying a sub-parallel wall in which high deformations of minerals such \nas the chlorite, feldspar and quartz are localized. These large and \nfragmented rigid grains, which form the microlithons, are feldspar and \nquartz porphyroclasts. This shear zone or microfaults depict a synthetic \nrotation of asymmetric quartz boudins (Fig. 7). The graphite, eye-shaped \nfeldspar and rotational quartz are seen to be the microlithons localized in \nthe D2 deformation of the quartz-feldspar-graphitic schist (Figure 7). Due \nto the sufficient viscosity contrast between the veins and matrix, the veins \nare identified as boudinaged on extension or fold on shortening (Passchier \nand Trouw, 2005). S0 is almost parallel to S1. The D1 deformation releases \nnew mineral formation. Quartz shows rotational deformation. The \nminerals are syntectonic deformed. The rock is through compression \ndeformed and thus depict pure shear. \n\n\n\n\n\n\n\nFigure 7: The Figure determines the direction and sense criteria and also \nminor structures indicating the sense of relative movement as dextral. \n\n\n\nThe foliation is defined by cleavage domain and microlithons of the \nquartz-feldspar schist in Bepoase. There is also synthetic microfaults or \n\n\n\nshear zones in grains (synthetic rotated asymmetric boudins). \n\n\n\nThe displacements along shear zones curve and shear foliation (S) are at a \nshallow angle to the shear plane. The deformation is sufficiently strong, so \npenetrative S-surfaces display a sinusoidal shape that rotates into \nparallelism with discrete and regularly spaced zones of concentrated \nshear parallel to the gross shear plane: the C-surfaces. Each individual C-\nshear zone is relatively planar and develops its own curved foliation \npattern on small scale, so that the sense of deflection of S into C is the same \nas the general sense of shear (Fig. 8). The rocks where ductile shear zones \nare sufficiently abundant to constitute a fabric have S-C structures (or \nfabrics). The foliation S is leaning over in the direction of shear and has an \nacute bulk inclination to the C-surfaces. Parting of the rock is easier along \nthe C planes. Note that owing to additional and localized displacements on \nC surfaces, the foliation planes S do not record the total shear-strain of the \nrock.   \n\n\n\n\n\n\n\nFigure 8: S-C Fabric in the XZ-Plane of finite strain ellipsoid \n\n\n\n4.2.4   Deformation Zones  \n\n\n\nFigure 9 shows crenulation cleavage (S2 subvertical) overprinting a slaty \ncleavage (S1) that is parallel to bedding (S0). The extreme attenuation of \nthe vertical fold limb in the quartz-rich (light-coloured) layer coincides \nwith the presence of accentuated dark seams along the S2 plane in adjacent \nmicaceous layers. The microscopic or micro-structural analysis of the \nstudy area are identified with three (3) main deformational events which \ninclude D1, D2 and D3 phases. The D1 event may be identified in the basalts \nand defines a foliation grade of S1 in the E-W direction which is parallel to \nthe primary bedding (S0). Field mapping of the various different geological \nstructural work and structural analysis identifies different foliation planes \n(S0, S1, S2 and S3). At the first deformation stage, D1 is marked by the \nfoliation (S1) depicting slaty cleavage, and is developed at varying angles \nwith bedding. Due to tectonism, the second deformation, D2, indicates a \nfold structure (P2) and thus a secondary foliation (S2). The S1 is related to \nthe S2 at an oblique angle. The deformation D2 event produces a \ncrenulation cleavage, folding S1 or isoclinal folds, trending towards the \nNNE-SSW. During the D3 deformation, the S2 is folded into a crenulation \ncleavage. This third and final deformation (D3) event defines the brittle \nfault in NE-SW trend folds. That is to say the deformation phase D3 depicts \nductile tectonism and thus indicates large folds. \n\n\n\nThe Figure 10 shows a medium to coarse-grained metamorphosed \nsandstone which is foliated. The specimen depicts a lepidoblastic texture. \nThe texture is due to the parallel orientation during recrystallization of \nminerals with a flaky habit, e.g. mica, chlorite. Domainal spaced cleavage \nwith micaceous stacks in microlithons are identified in the specimen as it \nportrays dextral shear sense of deformation. The deformation depicts C'-\ntype shear band cleavage transecting the main foliation in a mica-schist \n(Figure 10). The cleavage does not continue into the quartz ribbons in the \ncenter of the photograph and depicts a dextral shear sense. The dextral \nsense of shear of the Tarkwaian sandstone (Fig. 10) depicts contractional \nor restraining bends and offsets and this demonstrates that the rock \nmaterial is pushed together by the dominant fault movement. The rock \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 36-44 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Chiri G. Amedjoe, Simon K. Y. Gawu  (2022). Structures of Birimian and Tarkwaian \n\n\n\nRocks at North-West New Drobo \u2013 Implication on Deformation. Malaysian Journal of Geosciences, 6(1): 36-44. \n \n\n\n\ndemonstrates later stages of deformation of planar fabrics. A foliation \ndomain of biotite displaying sub-parallel walls in which high deformations \nof minerals are localized and the rock is thus said to have retrograde \nmetamorphism. This shear zone or microfaults depict a synthetic rotation \nof asymmetric quartz boudins. (Fig. 10). The eye-shaped feldspar and \nrotational quartz are seen to be the microlithons localized in the D2 \ndeformation of the metamorphosed Tarkwaian sandstone (Fig. 10). \n\n\n\n\n\n\n\nFigure 9: Deformational episodes and foliation planes of a volcanic rock \n\n\n\n\n\n\n\nFigure 10: The shear zone or microfaults depicts a synthetic rotation of \nasymmetric quartz boudins. The eye-shaped feldspar and rotational \n\n\n\nquartz are seen to be the microlithons localized in the D2 deformation of \nthe metamorphosed Tarkwaian sandstone. \n\n\n\n4.2.5   Overprinting Relations  \n\n\n\nFigure 11A depicts an ellipsoidal and a flame-shaped albite-lamellae in \nperthitic K-feldspar in the volcanic rock. Two sets of ellipsoidal lamellae of \ndifferent size are present, possibly reflecting two stages of dissolution at \ndifferent temperature. The flame-shaped perthite lamellae result from \nunmixing during greenschist facies deformation (Passchier and Trouw, \n2005). Figure 11 B shows domainal spaced cleavage of amphibole with \nchlorite and feldspar stacks in microlithons. There is a reflection of fault \nmovement where chlorite forms a fault plane between amphibole levels. It \nindicates that an initial deformation phase with a component of vertical \nshortening formed a foliation under conditions suitable for growth of \namphibole; a second deformation phase of oblique shortening formed a \nfoliation condition suitable for growth of chlorite under low-grade \nmetamorphic conditions. A third deformation phase affected both earlier \nstructures at very low grade or high-strain rate, to cause brittle faulting.\n\n\n\n\n\n\n\nFigure 11: A diagram of a hornblende foliation (horizontal), a chlorite foliation (inclined) and a brittle fault (B). There is a reflection of fault movement \nwhere chlorite forms a fault plane between hornblende levels. (A) depicts two sets of ellipsoidal lamellae of different size, possibly reflecting two stages of \n\n\n\ndissolution at different temperature \n\n\n\nIn Figure 11B a deformational event as well as a metamorphic event are \nidentified as a horizontal amphibole foliation which is being overprinted \nby a steeply dipping chlorite foliation (parallel to the fault) and both are \ncut by a brittle fault. Based on these overprinting relations, it may be \nargued that a first deformation phase with a component of vertical \nshortening formed a foliation under conditions suitable for growth of \namphibole, whilst a second \u2018deformation phase\u2019 of oblique shortening was \naccompanied by chlorite growth under low-grade metamorphic \nconditions. A third \u2018deformation phase\u2019 affected both earlier structures at \nvery low-grade or high strain rate to cause brittle faulting (Passchier and \n\n\n\nTrouw, 2005). The sequence of overprinting relations is: amphibole \nfoliation-chlorite foliation-fault. The three structures may represent \ndifferent deformation phases since they overprint each other, have \ndifferent orientation and represent probably different metamorphic \nconditions. \n\n\n\n4.2.6   Grain Deformation \n\n\n\nIn the study area, however, recrystallization processes which lead to the \nreduction of dislocation density are well pronounced. The different \n\n\n\n(A) (B) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 36-44 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Chiri G. Amedjoe, Simon K. Y. Gawu  (2022). Structures of Birimian and Tarkwaian \n\n\n\nRocks at North-West New Drobo \u2013 Implication on Deformation. Malaysian Journal of Geosciences, 6(1): 36-44. \n \n\n\n\nrecrystallization processes at the study area may be bulging \nrecrystallization (BLG) and sub grain rotation (SGR) recrystallization \nrespectively (Figures 12 A & B). A recrystallization deformation of two or \nmore crystals with different dislocation densities of atoms along the grain \nboundary in the crystal with high dislocation density may be displaced \nslightly in order to fit into the lattice of the crystal with low dislocation \n\n\n\ndensity. Thus, the bulging (BLG) recrystallization operates at low \ntemperature whereby the grain boundary bulges into the crystal with high \ndislocation density and forms new, independent small crystals. This \nresults in local displacement of the grain boundary and growth of the less \ndeformed crystal at the cost of its more deformed neighbor (Passchier and \nTrouw, 2005). \n\n\n\n\n\n\n\nFigure 12: (A) Fabric of dynamic recrystallization in quartz. Relics of large old quartz grains with undulose extinction and elongate subgrains pass \nlaterally into domains of small, new grains formed by bulging (BLG) recrystallization and located at Asuokor; (B) Polycrystalline quartz with irregular \n\n\n\ngrain boundaries formed in response to grain boundary migration recrystallization in Kofitiakrom.\n\n\n\nThis recrystallization type (as located in Asuokor and Tainso) is the BLG. \nThere is thus a displacement of new grains to the old grain. The length of \nthe grain boundary increases (Fig. 12B). With increase in temperature, \ndislocations are continuously added to subgrain boundaries and the angle \nbetween the crystal lattice on both sides of the subgrain boundary \nincreases until gradually the subgrain can no longer be classified as part of \nthe same grain. The older grains tend to be ductile deformed and elongate. \nThis type of dynamic recrystallization is known as subgrain boundary \nrotation (SBR) recrystallization (Passchier and Trouw, 2005; Fig. 12A). \n\n\n\n5.   CONCLUSION  \n\n\n\nThe three (3) different senses of shear namely, dextral, CS and CS' may be \nidentified in the study area. The dextral sense of shear may be identified \nin both the Birimian metavolcanic rocks and Tarkwaian sandstones. The \ndextral shear sense movement depicts that the affected rocks are being \npushed together by the movement of the dominant fault. \n\n\n\nOverprinting of one rock over the other defines different deformation \nepisodes and relative ages of the rocks. Different asymmetric boudins may \nbe identified, namely, synthetic and antithetic boudins of quartz which \nindicate shear senses at different geometry, motion and directions. \n\n\n\nIn the quartz-feldspar-graphite schist rock, the microlithons comprise of \nthe eye-shaped feldspar and the graphite which may be localized in the D2 \ndeformational episode.  \n\n\n\nThree (3) main deformational (D) events and foliation episodes (S) at the \nstudy area may be identified as D1, D2 and D3 as well as S0, S1, S2 and S3. The \nD3 deformation defines the brittle fault.  \n\n\n\nThe Birimian Supergroup in this study area with respect to overprinting \nrelationships concerning hornblende, chlorite and fault plane may be \nidentified with three (3) different deformational phases. Firstly, formation \nof a vertical shortening for hornblende mineral, a second deformation \nphase on an oblique shortening with chlorite, whilst the third deformation \nphase is as a result of high strain rate causing brittle faulting. The sequence \nof overprinting thus reads hornblende foliation-chlorite foliation-fault. \n\n\n\nAt low temperatures, new independent small crystals take over from old \nones. This is as a result of recrystallization through bulging (BLG) found in \nTainso and Asuokor. At increase in temperature the old grains tend to be \ndynamically recrystallized (SGR). At a higher temperature, dislocations \nand subgrain boundaries of crystals are removed. \n\n\n\nACKNOWLEDGEMENT  \n\n\n\nThanks go to my co-authors for their contributions. \n\n\n\nFUNDING SOURCE  \n\n\n\nPublication of this paper did not receive any specific grant from funding \nagencies in the public, commercial, or not-for-profit sectors. \n\n\n\nCONFLICT OF INTEREST  \n\n\n\nThere is no conflict of interest with respect to this article \n\n\n\nREFERENCES \n\n\n\nAbouchami, W., Boher, M., Michard, A., Albarede, F. (1990). A major 2.1 Ga \nevent of mafic magmatism in West Africa: an Early stage of crustal \naccretion. J. Geophys. Res. Solid Earth 95, 17605\u201317629.  \n\n\n\nAssi\u00e9, K. E. (2008). Lode Gold Mineralization in the Paleoproterozoic \n(Birimian) Volcanosedimentary Sequence of Afema Gold District, \nSoutheastern C\u00f4te d\u2019Ivoire. Thesis, Faculty of Energy and Economic \nSciences Technical University of Clausthal, Germany, 198 p.  \n\n\n\nAttoh, K., Evans, M. J. and Bickford, M. E. (2006). Geochemistry of an \nUltramafic-Rodingite Rock Association in the Palaeoproterozoic \nDixcove Greenstone Belt Southwestern Ghana, Journal of African Earth \nSciences, pp. 333 \u2013 346.  \n\n\n\nBonhomme, M. (1962). Contribution \u00e0 l\u2019\u00e9tude g\u00e9ochronologique de la \nplateforme de l\u2019Ouest africain. In G\u00e9ologie et Min\u00e9ralogie (Vol. 5, pp.62). \nGap: Impriemerie Louis-Jean.  \n\n\n\nEisenlohr B. N., Hirdes, W (1992). The structural development of early \nProterozoic Birimian and Tarkwaian rocks in southwest Ghana, West \nAfrica. J. Afri. Earth Sci., 14 (3):313\u2013325 \n\n\n\nGoscombe, B., Passchier, C. W. (2003). Asymmetric boudins as shear sense \nindicators\u2013An assessment from field data. Journal of Structural Geology, \n25(4): 575-589. DOI:10.1016/S0191-8141(02)00045-7 \n\n\n\nGriffis, R. J., Barning, K., Agezo, F. L., Akosah, F. K. (2002). Gold deposits of \nGhana. Minerals Commission Report.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 36-44 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Chiri G. Amedjoe, Simon K. Y. Gawu  (2022). Structures of Birimian and Tarkwaian \n\n\n\nRocks at North-West New Drobo \u2013 Implication on Deformation. Malaysian Journal of Geosciences, 6(1): 36-44. \n \n\n\n\nHirdes, W., Davis, D. W., L\u00fcdtke, G., Konan, G. (1996). Two generations of \nBirimian (Paleoproterozoic) volcanic belts in northeastern C\u00f4te \nd'Ivoire (West Africa): consequences for the \u2018Birimian controversy\u2019. \nPrecambr. Res. 80, 173\u2013191.  \n\n\n\nHirdes, W., Davis, D. W. and Eisenlohr, B. N. (1992). Reassessment of \nProterozoic granitoid ages in Ghana on the basis of U/Pb zircon and \nmonazite dating. Precambrian Research, 56, 89-96.  \n\n\n\nKesse, G. O. (1985). The Mineral and Rock Resources of Ghana. A.A. \nBalkema, Rotterdam.  \n\n\n\nPasschier, C. W., Trouw, R. A. J. (2005). Microtectonics. Springer-Verlag \nBerlin Heidelberg. http://dx.doi.org/10.1007/3-540-29359-0.  \n\n\n\nPerrouty, S., Aill\u00e8res, L., Jessell, M. W., Baratoux, L., Bourassa, Y., Crawford, \nB. (2012). Revised Eburnean geodynamic evolution of the gold-rich \nsouthern Ashanti Belt, Ghana, with new field and geophysical evidence \nof pre-Tarkwaian deformations. Precambrian Res. 204-205, 12\u201339.  \n\n\n\nPerrouty, S., Jessell, M. W., Bourassa, Y., Miller, J., Apau, D., Siebenaller, L., \nVel\u00e1squez, G., Baratoux, L., Aill\u00e8res, L., B\u00e9ziat, D., Salvi, S. (2015). The \nWassa deposit: a poly-deformed orogenic gold system in southwest \nGhana \u2014 Implications for regional exploration. J. Afr. Earth Sci. \nhttp://dx.doi.org/10.1016/j.jafrearsci.2015.03.003.  \n\n\n\nPeucat, J. J., Capdevila, R. R., Drareni, A., Kahoui, M. (2005). The Eglab \nmassif in the West African Craton (Algeria), an original segment of the \nEburnean orogenic belt: Petrology, geochemistry and geochronology. \nPrecambrian research 136 (3): 309-352. \nDOI:10.1016/j.precamres.2004.12.002.  \n\n\n\nRamsay, J. G. and Huber, M. I. (1983). The techniques of modern structural \ngeology, I: Strain analysis. Academic Press, London. \n\n\n\nZitzmann, A., Kiessling, R., and Loh., G. (1997). Geology of the Bui Belt area \nin Ghana. In A. Zitzmann (ed), Geological, Geophysical and Geochemical \nInvestigation in the Bui Belt Area in Ghana., page 269. Geol. Jb. Reihe B, \nHeft 88. \n\n\n\n \n\n\n\n\nhttp://dx.doi.org/10.1007/3-540-29359-0\n\n\nhttp://dx.doi.org/10.1016/j.jafrearsci.2015.03.003\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 43-51 \n\n\n\nCite The Article: Obioma Umunna, Etim D. Uko, Idara O. Akpabio (2019). Delineation Of Subsurface Structures In Toja Field In The Niger Delta Using Well-Logs And Seismic \nData. Malaysian Journal Of Geosciences, 3(2): 43-51. \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 01 May 2019 \nAccepted 18 June 2019 \nAvailable online 20 June 2019\n\n\n\nABSTRACT\n\n\n\nThe subsurface structures delineation of TOJA Field southwest Niger Delta using well-log and seismic data is \n\n\n\nhere presented. The reflectivity seismic amplitude and acoustic impedance, spectra decomposition volume \n\n\n\nderivatives were used for reservoir delineation. Seismic data and well logs have been integrated through \n\n\n\nseismic inversion as part of the techniques deployed in the delineation of subsurface structures in the Niger \n\n\n\nDelta basin. Well logs were tied to seismic data using four wells from four fields in the Niger Delta. Reflectivity \n\n\n\nseismic data was inverted to generate a 3D distribution of P-impedance in the fields of interest. Fluid and \n\n\n\nlithology sensitivity analysis including cross-plotting, forward seismic modelling and Gassmann fluid \n\n\n\nsubstitution was performed to delineate various subsurface structures. The TOJA prospect is a footwall-\n\n\n\nclosure located behind the main bounding fault, north of the TOJA Field. The Field\u2019s structure is a fault-\n\n\n\ndependent footwall closure with a dip component in the shallow levels. The structure is bounded by three \n\n\n\nfaults; a large east-west fault forms the boundary between the TOJA Field to the South and the TOJA North \n\n\n\nField. This fault is relayed by a minor fault that delimits the south-western end of the accumulation. A northeast-\n\n\n\nsouthwest bounding fault separates the TOJA structure from the SATRA accumulations to the east and has a \n\n\n\nthrow of between 200 and 400 ft. The results of this study can lead to a more cost-effective method for defining \n\n\n\nthe Field Development Plan (FDP), through the use of seismically constrained reservoir information that would \n\n\n\nprovide better well placement to achieve improved production. \n\n\n\n KEYWORDS \n\n\n\nReservoir, subsurface structures, porosity, well-logs, seismic, inversion, Niger Delta, Nigeria\n\n\n\n1. INTRODUCTION \n\n\n\nThe Niger Delta is a very prolific hydrocarbon province within the West \nAfrican subcontinent. Exploration activities have been concentrated in \nthe onshore part of this basin, but as the delta becomes better \nunderstood, exploration influences are gradually being shifted to the \noffshore. Although the geology, tectonics and evolution of the Niger Delta \nare fairly well known, new ideas by the use of recent technologies are \nexpected to increase as new analytical tools and concepts evolve. This \nwork is an integrated structural, seismic, and well log study conducted in \nthe TOJA Field, onshore Eastern Niger Delta, and targeted at improving \nthe present understanding of the structural out lay, reservoir extent, \nquality and potential within the TOJA Field. \n\n\n\nThe aim of this research is to delineate subsurface structures in the TOJA \nField of the Niger Delta using well-logs and seismic data. This study would \nhelp in the characterization of subsurface structures, fault zones and \nphysical properties of the formations through integration of geophysical \nand geological measurements. It will further provide better \nunderstanding of the subsurface leading to a better identification of \noptimum well placement position guide for economically viable well \ndrilling activities. Delineation of subsurface structures, reservoir quality \nand lateral extents are of key importance in the determination of \neconomic viability of Fields and exploration opportunities [1-5]. With \nlimited well data control and difficulty in the resolution of some \n\n\n\nembedded events in the seismic data, this study seeks to proffer ways of \nreducing uncertainties in the identification and classification of some \nsubsurface structures by the integration of seismic and well log data. \n\n\n\nFigure 1: Map of the Niger Delta showing the area of study \n\n\n\n2. STUDY AREA AND ITS GEOLOGY\n\n\n\nThe TOJA Field is located about 40 km southeast of Port Harcourt within \nthe swamp area in the Niger Delta, Nigeria Fig. 1). The origin of the Niger \n\n\n\nMalaysian Journal of Geosciences (MJG) \nDOI : http://doi.org/10.26480/mjg.02.2019.43.51 \n\n\n\nRESEARCH ARTICLE \n\n\n\nDELINEATION OF SUBSURFACE STRUCTURES IN TOJA FIELD IN THE NIGER DELTA \n\n\n\nUSING WELL-LOGS AND SEISMIC DATA  \n\n\n\nObioma Umunna1, Etim D. Uko1 and Idara O. Akpabio2\n\n\n\n1Department of Physics, Rivers State University, PMB 5080, Port Harcourt, Nigeria \n2Department of Physics, University of Uyo, Uyo, Nigeria \n\n\n\n*Corresponding Author Email: obi_pat@yahoo.com; e_uko@yahoo.com; idaraakpabio@uniuyo.edu.ng\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\n\nmailto:obi_pat@yahoo.com;\n\n\nmailto:e_uko@yahoo.com\n\n\nmailto:idaraakpabio@uniuyo.edu.ng\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 43-51 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Obioma Umunna, Etim D. Uko, Idara O. Akpabio (2019). Delineation Of Subsurface Structures In Toja Field In The Niger Delta Us ing Well-Logs And Seismic \nData. Malaysian Journal Of Geosciences, 3(2): 43-51. \n\n\n\nDelta basin is related to the continued subsidence of the southern Benue \nTrough as defined by the Chain and Charcot Fracture Zones. Thus, the \nCenozoic Niger Delta Basin is superposed on the southern Benue and the \nAnambra Basins [6,7]. The stratigraphic components of the Niger Delta \ncomprise subsurface and outcropping units. The outcropping units are \npresent in the northern parts of the delta. They continue in the surface as \nthe subsurface equivalents, only getting younger basinward. In fact, all \nthe facies are still being deposited in the present day Niger Delta as \ncontinental, transitional (paralic) and marine sediments. Each of the \nstratigraphic units named in is characterised by a set of sedimentological \nfeatures including lithology, textures, composition and sedimentary \nstructures, the study of which would enable the determination of the \ndepositional environments. The Imo Shales constitute an extensive unit \nof predominantly green grey shales and siltstones with minor limestone \nbeds and some sandstone units. The shales were deposited in a shallow \nmarine setting. \n \nThe Ameki Group overlies the Imo Shales and outcrops over a large area \nof southern Nigeria. In the southeastern parts, especially north of \nUmuahia, it consists of highly fossiliferous calcareous siltstones \nindicating deposition in a shallow marine environment. In the areas \nwithin Anambra and Imo States, the unit consists of loose sands \ndeposited as subtidal sand waves. Being loose and unconsolidated, it is \nhighly erodible. This is why the area it underlies is being intensely \naffected by sheet and gully erosion. Farther westwards around Onitsha \nand Asaba to Okpanam, the unit consists of highly ferruginous, coarse, \npebbly and relatively consolidated sandstones which are thought to have \nbeen deposited in a continental, probably fluviatile setting. \n \nOverlying the Ameki Group is a lignite-bearing set of beds called the \n\n\n\nOgwashi-Asaba Formation. The lignite beds consist of brown coals a few \ncentimetres to 2m thick exposed in a belt that stretches from Asaba area \nsouth- eastwards to north of Calabar. The unit is a depositional product \nof swamp forests. The Benin Formation predominantly sand, with rare \nmudrock beds, Fig. 2. Conglomerates and pebbly horizons are common. \nIt is up to 3000 m thick, as noted in some oil wells. In the Obeakpu-Afam-\nYorla areas of the Niger Delta, the formation has a clay unit over 550 m \nthick (Afam Clay Member). Lignitic horizons are also common in the \nformation, suggesting continental depositional environments that \nincluded both fluviatile and swampy (palludal) settings. \n \nThe subsurface units of the Niger Delta are the Akata and the Agbada \nFormations (Benin Formation being regarded as both surface and \nsubsurface in mode of occurrence), Fig. 2. The Akata Formation is \nconsidered the subsurface stratigraphic equivalent of the Imo Shales. It \nconsists predominantly of shales and subordinately of sands thought to \nbe mostly of turbiditic origin. The unit contains abundant depositional \nwater, i.e. the shales had not been fully dewatered after deposition and \nare said to be under compacted. This accounts for the shale bulge \nphenomenon by which shale ridges have been formed, facilitating the \nstructuration of the overlying Agbada into depobelts. It was deposited in \na fully marine setting and was actually the prodelta for the overlying \nparalic unit. Its equivalent is still being deposited in the present-day in \nthe far offshore. \n \nThe Agbada Formation (Fig. 2), sandwiched between the Akata \nFormation below and the Benin Formation above, is the hydrocarbon-\nbearing unit of the Niger Delta. It consists of alternating sands and \nmudrocks, deposited in a variety of wave-dominated and tide-influenced \nenvironments, including estuarine, beach, and shallow marine. \n\n\n\n\n\n\n\n\n\n\n\nFigure 2: The map of cross-section of Niger Delta showing the stratigraphic columns of the subsurface units \n \n \n3.  MATERIALS AND METHODS \n\n\n\n \n3.1 Seismic acquisition and interpretation  \n \nThe TOJA 3D survey seismic volume was loaded onto the various \ninterpretation platforms. Seismic quality is generally good but \ndeteriorates at deeper levels (below 3500ms). Localized masking from \ncalcite cementation of sands above X10000 level further reduces the \nseismic quality. This occurs above the crest of the TOJA structure, and \nalong the main bounding fault in the TOJA North area. The structural data \nquality was further enhanced by the application of further structural \nhighlighting and denoising techniques over the dataset. \n \nSeismic events in the vicinity of the TOJA North Field are generally well \nresolved, continuous and can be readily mapped down to 3 seconds Two \nWay Time. Key horizons were correlated from the TOJA-North Field and \nfrom nearby wells TOJANorth-001, TOJA-North-002, TOJA-21, BOCH-\n001, and SATRA-1. A number of major faults, some with throw in excess \nof 1000 ft, cross the area of interpretation and impede ability to directly \ncorrelate events between wells in adjacent fault blocks. Despite this, the \ncoverage of the 3D seismic coupled with good seismic character match \nacross faults generally supported confident, unambiguous correlation of \nevents into the TOJA study area and other adjoining blocks. \n\n\n\n \n3.2 Well-logs Data \n \nSynthetic seismograms were used to compare seismic reflection data to \nwell log data. A comparison of the reflectivity or acoustic impedance \nresponses derived from seismic and from wireline logs enabled matching \nof seismic events with geological markers. The objective of this \ncomparison was to identify seismic reflectors as geological units and/or \nto calibrate seismic amplitudes [8-14]. A total of 28 seismic-to-well \nmatches were generated and significant improvements in match quality \nwere achieved. Better validation and editing of sonic and density logs. \nSome of the well-logs are shown in Figure 3. \n \n3.2.1 Log calibration \n \nCalibration started with picking checkshot times. The checkshot times \nseems to favour trough-to-trough picking of VSP times (15-19). For this \nstudy, calibrated sonic and density logs and checkshot data (time/depth \ncalibration) were used. When unconditioned, the logs used (Fig. 4) to \ngenerate well-to-seismic ties, the overall correlation achieved 63%, while \nwhen conditioned (Fig. 5), the overall correlation significantly improved \nto 88%. As a result of performing good well data conditioning, acoustic \nand petrophysical information for reservoir modelling was derived and \ndefinition of geological boundaries and hydrocarbon contacts on the \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 43-51 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Obioma Umunna, Etim D. Uko, Idara O. Akpabio (2019). Delineation Of Subsurface Structures In Toja Field In The Niger Delta Us ing Well-Logs And Seismic \nData. Malaysian Journal Of Geosciences, 3(2): 43-51. \n\n\n\nseismic data was achieved as well as highlighting some subtle subsurface \nstructures. \n  \n \n \n\n\n\n\n\n\n\nWell TOJA North-1 \n \n\n\n\n\n\n\n\nWell TOJA North-2 \n \n\n\n\n\n\n\n\nWell TOJA-21 \n \n\n\n\n\n\n\n\nWell SATRA-1 \n \n\n\n\n\n\n\n\n\n\n\n\nWell Boch-28 \n \nFigure 3: Panel showing well log data (Compressional Velocity, Density, \nGamma Ray, Impedance, Calliper and Resistivity) \n \n\n\n\n \n \nFigure 4: Well-to-seismic tie using unconditioned/conditioned/ \nuncalibrated logs give a lower correlation of 63%. \n \n\n\n\n \n \nFigure 5: Well-to-seismic tie using calibrated logs give a higher \ncorrelation of 88%. \n \n3.3 Constructing the Synthetic Seismogram \n \nThe construction and correlation of the data with the primary reflectivity \nusually produces a satisfactory well tie. A wavelet was estimated to be \nconvolved with the reflection series. A problem in estimating the wavelet \nwas that the transmission response of the earth has to be included if the \nfiltered synthetic seismogram is to match the seismic data. A matching \nprocess was therefore required. In generating a fit for purpose wavelet, it \nwas ensured that the quality of the seismic data has an impact on our \nconfidence in well ties and wavelet extractions. Wavelets were extracted \nalong the boreholes for individual wells to generate synthetics \nseismograms and subsequently overlain on the seismic data to obtain the \nreflectors corresponding to the available check-shots for geological \nmarker and petrophysical log data conversion from depths to time on the \nseismic data. \n \nThe correlation Window (Figure 6) contains several panels which were \npredefined in the cross-correlation tab. Tmin correlates to the Centre \ntime for first cross correlation while Tmax is the centre time for the last \ncross correlation. The panels are as follows: Input seismic (Blue); Well \nsynthetics (Orange) and filtered synthetics (Red); Hanning taper weight \nfunction for seismic (Yellow); Cross correlation filter (Green), and Cross \ncorrelation trace. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 43-51 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Obioma Umunna, Etim D. Uko, Idara O. Akpabio (2019). Delineation Of Subsurface Structures In Toja Field In The Niger Delta Us ing Well-Logs And Seismic \nData. Malaysian Journal Of Geosciences, 3(2): 43-51. \n\n\n\n \n \nFigure 6: Cross correlation window derived through the process of \nsynthetics generation. \n \n3.4 Creating a reflection series in time \n \nVelocities and densities were used to derive the bulk and shear moduli, \nwhich were then averaged using the harmonic average. Velocities are re-\nconstituted from the averaged moduli and the arithmetically averaged \ndensity log. The generated synthetic seismograms show that the tops of \nevents suggest distinct peaks for proper horizon loop identification on \nthe seismic data (Figs. 7 \u2013 11). The synthetics were generated by \nextracting a wavelet along the well borehole and using this wavelet to \nconvolve the reflectivity series created by the sonic and density curves of \nthe well. The ensuing synthetic was overlain on the seismic section and \nmatched with a trace at the well location to ensure consistency. Overall \nstable, zero phase wavelets (inset bottom of Fig. 12) were extracted at the \nwell locations and used in the synthetic seismogram generation. \n \nThe review of the generated synthetics and its comparison with the \nseismic suggests that positive amplitudes as displayed in seismic sections \nare soft kicks and displayed in the sections as blue. As a result of the good \nmatches, proper identification of events on seismic data was achieved. \n     \n\n\n\n \n \nFigure 7: Well-to-Seismic tie for TOJA North-001 well. The blue wiggles \nare the seismic data while the red wiggles are the synthetic seismogram.    \n      \n\n\n\n \n \nFigure 8: Well-to-Seismic tie for TOJA North -002 well. The blue wiggles \nare the seismic data while the red wiggles are the synthetic seismogram \n \n\n\n\n \n \nFigure 9: Well-to-seismic tie for SATRA-001 well. The blue wiggles are \nthe seismic data while the red wiggles are the synthetic seismogram. \n \n\n\n\n \n  \nFigure 10: Well-to-seismic tie for SATRA-001 well. The blue wiggles are \nthe seismic data while the red wiggles are the synthetic seismogram. \n                                                        \n\n\n\n\n\n\n\nFigure 11: Well-to-seismic tie for TOJA- 021 well.  The blue wiggles are \nthe seismic data while the red wiggles are the synthetic seismogram. \n \n\n\n\n \n \nFigure 12: The generated input wavelet for SATRA-001 well-to-seismic \ntie. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 43-51 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Obioma Umunna, Etim D. Uko, Idara O. Akpabio (2019). Delineation Of Subsurface Structures In Toja Field In The Niger Delta Us ing Well-Logs And Seismic \nData. Malaysian Journal Of Geosciences, 3(2): 43-51. \n\n\n\n3.5 Seismic and Constrained Sparse Spike Inversions \n \nSeismic inversion was carried out on the TOJA seismic data to generate \n2D acoustic impedance volume. This was calibrated to well data in order \nto understand strengths and limitations of the generated layer properties \nfor quantitative analysis. It was also used to improve geological \ninterpretation, map porosity distribution, optimize well locations, and to \nreduce drilling risks. \n \n4.  RESULTS AND DISCUSSION \n\n\n\n \nSixteen full-stack seismic lines with a total length of 65.4 km containing \n6200 traces were inverted and calibrated to 4 wells (TOJA NORTH-1, \nTOJA NORTH-2, BOCH-1, and SATRA-28). Seven wells were conditioned \nbefore inversion was executed. TOJA-21 and TOJA-24 were used as blind \nwells to validate the inversion results. Well TOJA-21 was a dry well in the \nprimary reservoir interval. To build the low frequency model, the original \ngeological interpretation was used in the first pass inversion and was \nthen updated to generate final inversion results. \n \nA constrained sparse spike inversion (CSSI) was run by integrating the \ninformation from well data, seismic data, and geological interpretation. \nCSSI is a method that integrates full stack seismic data, geological \ninterpretation, and well log data to generate acoustic impedance with \nhigher resolution than the input seismic data. The process works by \nremoving the wavelet from the seismic data, converting seismic interface \nproperty into layer property, and by integrating a low frequency model \n[20,21]. Acoustic impedance is a property of the rock layer itself, unlike \nreflectivity, which is a property of the interface between two acoustic \nlayers. It is used in carrying out more accurate and detailed structural and \nstratigraphic interpretations than can be obtained from seismic (or \nseismic attribute) interpretation. In many geological environments \nacoustic impedance has a strong relationship to petrophysical properties \nsuch as porosity, lithology, and fluid saturation. \n \n4.1 Spectral Decomposition \n \nSpectral decomposition requires the transformation of each individual \n1D seismic trace, s (t) into a 2D time- frequency representation, s (\u03c4,f). \nMany methods exist to achieve this and each has different resolution \ncapabilities in time and frequency. The relative thickness of stratigraphic \nunits of Regional (TOJA, TOJA-North, BOCH and SATRA Fields) \nInterpretation of the X1000 reservoir was observed using spectral \ndecomposition see Fig. 13. As strata become thicker, the peak frequency \nof their seismic response tends to be lower, and vice versa. By taking a \nhorizon slice of a 3D seismic volume, this study was able to identify the \nthickening directions by viewing the slice at progressively lower \nfrequencies. \n \nThe transformed amplitude data was used to delineate the reservoir \nextent and highlight the differentiated sand body (erosional surface \npossibly containing a different depositional material from that of the \nHydrocarbon bearing sands). This was observed to have cut through the \nreservoir as also seen by the Impedance volume and amplitude map. \n  \n\n\n\n \n \nFigure 13: A slice section of the spectrally decomposed reflectivity \nseismic data through the X1000 reservoir at a frequency of 8Hz. \n4.2 Acoustic-impedance Inversion using Constrained Sparse Spike \nInversion \n \n\n\n\n4.2.1 Impedance Model \n \nTo create an Earth Model, the first step in model building is to design the \nstructure. This is done by providing two pieces of information - the \ninterpreted horizons and the model \u201cframework\u201d. The framework, in the \nform of a spreadsheet, describes the ordering of the horizons in space and \ntime and their behaviour at faults. The horizons, which can include \ninterpreted faults, provide structure information. Together, these form a \nblueprint for the model. The model is completed by populating it with \ngeophysical information, usually input in the form of well log data (22). \nThe well log data provides the low frequency component of the \nimpedance since this is needed to complete portion of the seismic \ninversion. The logs are usually provided in depth and the horizons in \ntime. Thus, it is necessary to create a time-depth transformation if these \ntwo pieces of information are to be rationalized. Input sonic logs are \nintegrated, hung on an input time datum (one of the input horizons) and \ndrift-corrected to tie the time horizons. Upon completion, the model \nexists in both time and depth and time-to-depth and depth to-time \ntransformations are possible. Interpolation of the input log information \nbetween wells is done along layers, respecting both stratigraphy and \nfaults. Interpolated data from each horizon-defined interval define the \nconstraints in CSSI and the inversion trend model. The impedance model \nmay be used to construct low-frequency information that cannot be \nreliably estimated from seismic data. \n \n4.2.2 Wavelets Estimation \n \nWavelets are estimated, preferably using well control. All modern seismic \ninversion methods require seismic data and a wavelet estimated from the \ndata. Typically, a reflection coefficient series from a well within the \nboundaries of the seismic survey is used to estimate the wavelet phase \nand frequency Fig. 14. Accurate wavelet estimation is critical to the \nsuccess of any seismic inversion. The inferred shape of the seismic \nwavelet may strongly influence the seismic inversion results and, thus, \nsubsequent assessments of the reservoir quality. \n \nWavelet amplitude and phase spectra are estimated statistically from \neither the seismic data alone or from a combination of seismic data and \nwell control using wells with available sonic and density curves. After the \nseismic wavelet is estimated, it is used to estimate seismic reflection \ncoefficients in the seismic inversion using suitable estimation algorithms \nwhich removes the wavelet effect. \n \nThe wavelet alignment is good boosting confidence on the Impedance \nvolume. When the estimated (constant) phase of the statistical wavelet is \nconsistent with the final result, the wavelet estimation converges more \nquickly than when starting with a zero-phase assumption. Minor edits \nand \"stretch and squeeze\" may be applied to the well to better align the \nevents. Accurate wavelet estimation requires the accurate tie of the \nimpedance log to the seismic. Errors in well tie can result in phase or \nfrequency artifacts in the wavelet estimation. Once the wavelet is \nidentified, seismic inversion computes a synthetic log for every seismic \ntrace. To ensure quality, the inversion result is convolved with the \nwavelet to produce synthetic seismic traces which are compared to the \noriginal seismic. It was also important to ensure that the wavelet matches \nthe phase and frequency of seismic data. \n \n4.2.3 Constraints \n \nInversion is carried out with constraints around the trend model. \nCompressional sonic from the wells were used to generate the well log \nimpedance which introduced constraints across the Fields, honouring \nthese well inputs. The well log impedance is quality checked and \ncompared with the seismically generated Pseudo P-Impedance see Fig. \n15. \n  \n\n\n\n \n  \n \nFigure 14: Wavelets from TOJA-North-002, BOCH-028 and SATRA-1 \nwere input into the acoustic impedance generation. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 43-51 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Obioma Umunna, Etim D. Uko, Idara O. Akpabio (2019). Delineation Of Subsurface Structures In Toja Field In The Niger Delta Us ing Well-Logs And Seismic \nData. Malaysian Journal Of Geosciences, 3(2): 43-51. \n\n\n\n \n  \nFigure 15: Good agreement between the well-log impedance (in blue) \nand pseudo-extracted P- impedance (in pink) authenticating the \nseismically generated acoustic impedance volume.  \n  \n4.2.4 Low Frequency Model \n \nThe low-frequency component of the geologic-impedance is merged with \nthe high frequency component contributed from the seismic data Fig. 16. \nFor the TOJA Field geologic model, the framework is composed of the \nhorizons from the seismic-based structural interpretation, plus density \nand velocity information from four wells. Horizons are interpolated and \nthe layers in the model are populated with interpolated log values \nconsistent with the stratigraphy of each layer. Faults and truncations are \nhonoured. \n \nWavelet estimates were made at wells TOJA-North-002, BOCH-001 and \nSATRA-028 as their individual wavelets are stable and have good phase \nalignment. Selection of the optimum wavelet and its scaling was critically \nconsidered. The above stated wavelets gave the best overall performance \nand were selected along with the match to the seismic Fig. 17. \n \n\n\n\n \n \nFigure 16: A graphical representation of low frequency and high \nfrequencies components of the acoustic impedance volume. \n \n \n\n\n\n\n\n\n\nFigure 17: Well tie for seismic inversion done on SATRA-001 well \n \nThe CSSI inversion used constraints around the trend model. Synthetic \nseismic data were compared to the real seismic to identify any problems. \nFor the CCSI, the correlation was greater than 96-97% in the area of \ninterest, indicating a very good seismic match. Well log impedance results \nwere merged with the geologic-impedance model. Fig. 18 is the final \nimpedance result across the TOJA-North Field. \n\n\n\nIn Figure 19, a cross-section through the final impedance model, the fit \nbetween the well log and inverted acoustic impedance is quite good in the \nTOJA, TOJA North, BOCH and SATRA Fields. The results of the inversion \nwere acoustic impedance and porosity derived from the simple \nrelationship between porosity and acoustic impedance as observed from \nthe well data. The results were used to interpret the facies distribution, \nchannelized structures and to delineate lateral extent of the X1000 \nreservoir sands (Figs. 20-22). By adding careful hydrocarbon distribution \ninterpretation, the inversion results have been used for locating new \nwells positions to drill hydrocarbon. \n  \nThe acoustic impedance volume from our constrained sparse spike \ninversion (CSSI) clearly shows the lateral extent of the X1000 reservoir \nand the incised clay channel fill. The CSSI result has been used to refine \nthe interpretation of the X1000 time horizon and to more confidently \ndelineate the channel edges. \n \n\n\n\n \n \nFigure 18: A cross-section through the final impedance showing the \nTOJA, TOJA North, BOCH and SATRA Fields.  \n \n\n\n\n \n \nFigure 19: Generated impedance overlain with well log impedance   \n \n\n\n\n \n \nFigure 20: A section of the generated impedance showing the digitisation \nand channel feature in X1000 reservoir through TOJA-North. \n \n \n\n\n\n\n\n\n\nFigure 21: Flattened X1000 horizon showing channel incision. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 43-51 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Obioma Umunna, Etim D. Uko, Idara O. Akpabio (2019). Delineation Of Subsurface Structures In Toja Field In The Niger Delta Us ing Well-Logs And Seismic \nData. Malaysian Journal Of Geosciences, 3(2): 43-51. \n\n\n\n \n \nFigure 22: Reservoir lateral extent delineated by applying reservoir \nspecific acoustic properties. \n \n\n\n\n \n \nFigure 23: Stratigraphic slice from a horizon porosity volume, showing \nvery high porosity around an existing well. \n \n4.2.5 Porosity \n \nThe porosity derived from the generated acoustic impedance volume was \nvalidated by firstly extracting a slice from around the already producing \nTOJA Field. Very high porosity (Fig. 23) seen on the slice was compared \nto that encountered by a well and there was a good agreement; and \n\n\n\nfurther validation was done by cross plotting the impedance volume \nagainst the derived porosity Fig. 24. The cross-plotting technique is often \nuseful in the differentiation of lithology types in fluid discrimination \nscenarios [23-25]. \n  \n4.2.6 Blind Well Test \n \nA blind well test is an important tool to investigate whether the inversion \nresult is valid not only at well locations, but also at different places. To do \nthis, the inversion was carried out by not including all wells but only some \nof them. After inversion, the P-impedance of the blind well was overlain \non top of the inverted P-impedance section to compare. Fig. 25 shows a \nblind well (BOCH-01) example. The generated impedance data is overlain \nby Gamma Ray log which discriminates between the sands and shales \nhence authenticating the generated impedance volume. \n \n\n\n\n\n\n\n\nFigure 24: Porosity of hydrocarbon and brine- bearing sand well \ndifferentiated \n\n\n\n\n\n\n\n\n\n\n\nFigure 25: Blind well log (Gamma Ray) overlain on impedance data show a good match with the sand bodies. \n \n4.2.7 Amplitude extraction and analysis \n \nAmplitude extractions were carried out in TOJA Field and the adjacent \nTOJA-North Field, a Field already producing. In TOJA-North Field, the \nX1000 reservoir was used as calibration. The extracted amplitude map \nshowed an amplitude anomaly, which is consistent and conformable to \nstructure see Fig. 26. A polygon was extracted and the seismic amplitude \ncross plotted. \n \nThe amplitude over the TOJA Field at the X1000 level shows consistent \nand structurally conformable amplitude that is gas related. Hydrocarbon \ncontact at this level, which is seen at 1604ms, is easily read off from the \n\n\n\nseismic, amplitude map and cross plot. \n \nThe time interpretation was extended to the TOJA-North area, and the \namplitude maps showed an anomaly similar to that of TOJA over the crest \nof the structure (see Fig. 27). The amplitude-time cross-plot of selected \npolygon reveals a good tuning curve and amplitude build up starting at \nabout 1466ms. \n \nFor the TOJA-North prospect, conformable amplitude anomaly is seen but \nthis amplitude is truncated at the northern part of this reservoir by an \nerosional sand body with different reservoir property. \n \n\n\n\n\n\n\n\n \n \n \nFigure 26: Hydrocarbon contact seen by the Seismic at 1604ms agrees with that seen by the time Amplitude-time crossplot for the X1000 horizon   for \nTOJA Field. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 43-51 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Obioma Umunna, Etim D. Uko, Idara O. Akpabio (2019). Delineation Of Subsurface Structures In Toja Field In The Niger Delta Us ing Well-Logs And Seismic \nData. Malaysian Journal Of Geosciences, 3(2): 43-51. \n\n\n\n \n \n \nFigure 27: Hydrocarbon contact seen by seismic at 1446ms agrees with \nthat seen by the amplitude-time cross-plot for the X1000 horizon for \nTOJA-North Field. \n \n5.  CONCLUSION \n\n\n\n \nReservoir delineation, quality and lateral extent are of key importance in \nthe determination of economic viability of any Fields and exploration \nopportunities. With limited well data control, the lateral uncertainty in \nreservoir properties such as N/G, porosity and reservoir continuity, can \nbe further reduced with the use of 3D seismic data. This project discusses \nthe reliability of the use of well log and seismic data and its attributes in \nthe delineation of subsurface reservoirs. In addition, it discusses the use \nof seismic inversion results to aid in distinguishing different lithologies \n(hydrocarbon sands versus brine sands/shales), and in assisting in the \nstratigraphic interpretation of a reservoir in the Niger Delta. \n \nThe seismic amplitude and other volume derivatives (acoustic \nimpedance, spectra decomposition, etc.) have been used for reservoir \ndelineation. The integration of the 3D seismic is not without its own \nlimitations. The key factors to be considered are data quality and other \ninfluences of the seismic amplitude response with respect to \nhydrocarbon content, such as tuning effect, porosity variation, and \nsaturation changes. The result of this study has led to a more cost-\neffective method for defining the Field Development Plan (FDP), through \nthe use of seismically constrained reservoir information that provides \nbetter well placement to achieve improved production. The use of \nquantitative seismic methods was used to identify and characterize \nhydrocarbon reservoirs from seismic amplitude data and well logs. \nSeismic data and well logs have been integrated through seismic \ninversion to delineate subsurface structures in the Niger Delta basin. Well \nlogs were tied to seismic data using four wells from four Fields in the \nNiger Delta. Seismic data was inverted to generate a 3D distribution of P-\nimpedance in the Fields of interest. Fluid and lithology sensitivity \nanalysis including cross-plotting, forward seismic modelling and \nGassmann fluid substitution was performed to delineate various \nsubsurface structures. \n \nFrom the well log data, useful information on the following has been \nderived: Hydrocarbon Sands discrimination; Events identification. \nSecondly, the use of the seismic data as seen from this study yielded some \npositive business impact in the following areas: Better understanding of \nreservoir extent/lateral continuity; more accurate hydrocarbon contact \ninformation; Highlighting of channels; subtle faults identification; and \nbetter well placement and reduced missed opportunities. \n \nThis study has successfully been delineated the TOJA/TOJA-North X1000 \nreservoir lateral extent. The X1000 reservoir is eroded on the northern \npart by a different sand facie. The hydrocarbon (HC) contact as predicted \nfrom the amplitude studies at the X1000 reservoir is at 1466ms in the \nTOJA-North Field. \n \nReflectivity Seismic Data from Sato field served as an input for both the \nAcoustic Impedance generation and geohazard analysis. Well log cross \nplots for SATO-01 and SATO-03 show significant overlap between the \nsand and shale properties in the P-Impedance space for X1000, X3000 \nand X5000 reservoirs. A slight separation in shale and sand acoustic \nproperties exist for X1000, X3000 and X5000 reservoirs for SATO-01 and \nSATO-03 respectively. Acoustic Impedance attribute extraction shows \nless contrast between sand and shale. However, red areas on the map is \nindicative of areas with better quality sands. Bluish areas could also be \nindicative of sands which are of relatively harder acoustic properties. \n \nThe Sato planned wells are likely to encounter some good sands at X1000 \nand X5000 reservoirs levels. At the X3000 reservoir, the sands are most \nlikely to be of relatively harder acoustic properties. Geohazard \nassessment was carried out for the four planned Sato wells (SATO-CSBT-\n01, SATO-CSBT-02, SATO-KSBK-03 and Sato- KSBK-04). Amplitude and \nsemblance analysis based on seismic data did not indicate any geohazard \nissues around the planned wells surface locations and top-hole sections. \n\n\n\nObservations from the offset well analysis indicate issues with wellbore \nstability ranging from tight holes/spots, stuck pipe and hole pack off \nbelow 5000 ftss. Intervals between surface and 5000ftss are free of any \ngeohazard. \n \nBased on SATO Field sand development modelling, acoustic impedance \nvolume analysis and geohazard assessment, the planned well trajectories \nhave been better designed to generally traverse safely through the target \nreservoirs and landing in good sands. The benefits of bringing in the \nhistorical information from the previously drilled wells provided an \nadded level of safety assurance for successful drilling of the new wells \nand hopefully minimize the possible chance of occurrence of \nunanticipated surprises. \n \nACKNOWLEDGEMENTS \n \nThe authors are grateful to Nigeria National Petroleum Corporation \n(NNPC) and The Shell Petroleum Development Company (SPDC) of \nNigeria for provision of data. \n  \nREFERENCES \n[1] Swan, H.W. 1999. Amplitude-versus-offset measurement errors in a \nfinely layered medium. Geophysics, 56, 41-49. \n \n[2] Dong, W. 1990. Fluid line distortion due to migration stretch: 61st \nAnnual International Meeting, Society of Exploration Geophysics, \nExpanded Abstracts, 1345-1348. \n \n[3] Mahob, P.N., Castagna, J.P., Young, R.A. 1999. AVO inversion of a Gulf \nof Mexico bright spot-a case study. Geophysics, 64, 1480 - 1491. \n \n[4] Malkin, A., Zakhem, U.I., Canning, A. 1999. Amplitude inversion of \nreflectivity type AVO attributes: 69th Annual International Meeting, \nSociety of Exploration Geophysics Expanded Abstracts, 812 - 815. \n \n[5] Larsen, J.A., Margrave, G.F., Lu, H. 1999. AVO analysis by simultaneous \nP-P and P-S weighted stacking applied to 3C-3D seismic data: 69th Annual \nInternational Meeting, Society of Exploration Geophysics, Expanded \nAbstracts, 721 - 723. \n \n[6] Tamunosiki, D., Ming, G.H., Uko, E.D., Tamunobereton-ari, I., \nEmudianughe, J.E. 2014a. Porosity modelling of the south-east Niger \nDelta basin, Nigeria. International Journal of Geology, Earth and \nEnvironmental Sciences, 4(1), 49-60. \n \n[7] Ekeh, C.C., Uko, E.D., Eleluwor, E.F., Sigalo, F.B. 2019. Delineation of \nStratigraphic Units in Xyz Field of Niger Delta Using Geophysical Logs. \nPakistan Journal of Geology, 3(1), 1-12. \n \n[8] Simmons, J.L., Backus, M.M. 1994. AVO modelling and the locally \nconverted shear wave. Geophysics, 59(9), 1237-1248. \n \n[9] Goodway, B., Chen, T., Downton, J. 1997. Improved AVO fluid \ndetection and lithology discrimination using Lam\u00e9 petrophysical \nparameters; \u03bb\u03c1, \u00b5\u03c1 & \u03bb \u00b5 fluid stack from P and S inversions: 67th Annual \nInternational Meeting, Society of Exploration Geophysics, Expanded \nAbstracts, 183-186. \n \n[10] Hilterman, F. 1989. Is AVO the seismic signature of rock \nproperties? 59th Annual International Meeting, Society of Exploration \nGeophysics Expanded Abstracts, 559. \n \n[11] Hilterman, F. 1990. Is AVO the seismic signature of lithology? A \ncase history of Ship Shoal-South addition. Geophysics, 9, 15-22. \n \n[12] Engbers, P. 1995. An overview of synthetic seismograms, SIPM-\nEPX/22, EP89-0080, 26. \n \n[13] Wapenaar, K., van Wijingaarden, AJ., van Geloven, W., van der Leij, \nT. 1999. Apparent AVA effects of fine layering. Geophysics, 64, 1939-\n1948. \n \n[14] Zorasi, C.B., Ekine, A.S., Nwankwo, C.N., Nwosu, L.I. 2019. Well log \nanalysis and cluster analysis for lithology and fluid identification in Wabi \nField, Niger Delta, Nigeria. International Journal of Engineering Sciences \n& Research Technology, 8(6), 50-60. \n \n[15] Engbers, P. 1997. Improving Well-to-Seismic Matches in Brunei, \n96/43/XGP3, 24. \n \n[16] Richards, P.G., Frasier, C.W. 1976. Scattering of elastic waves from \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 43-51 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Obioma Umunna, Etim D. Uko, Idara O. Akpabio (2019). Delineation Of Subsurface Structures In Toja Field In The Niger Delta Us ing Well-Logs And Seismic \nData. Malaysian Journal Of Geosciences, 3(2): 43-51. \n\n\n\ndepth-dependent inhomogeneities. Geophysics, 41, 441-458. \n \n[17] Roy, W., Rob, S. 2002. Phase, polarity and the interpreter's wavelet. \nFirst Break, 20(5), 277-281. \n \n[18] R\u00fcger, A. 1997. P-wave reflection coefficients for transversely \nisotropic models with vertical and horizontal axes of symmetry. \nGeophysics, 62, 713-722. \n \n[19] White, R. 2003. Tying Well-log synthetic seismograms to seismic \ndata: the key factors, Geophysics, 2003-2449. \n \n[20] Ronghe, S., Surarat, K. 2002. Acoustic Impedance Interpretation for \nSand Distribution Adjacent to a Rift Boundary Fault, Suphan Buri Basin, \nThailand. American Association of Petroleum Geologists Bulletin, 86(10), \n1753-1771. \n \n[21] Oldenburg, D.W., Scheuer, T., Levy, S. 1983. Recovery of the \n\n\n\nacoustic impedance from reflection seismograms. Geophysics, 48(10), \n1318-1337. \n \n[22] Tamunosiki, D., Ming, G.H., Wang, L., Uko, E.D., \nTamunonengiyeofori, W. 2014b. Petrophysical characteristics of Coastal \nSwamp Depobelt reservoir in the Niger Delta using Well-Log Data. \nJournal of Applied Geology and Geophysics, 2(2), 76-85. \n \n[23] Boaca, T., Malureanu, I. 2017. Determination of oil reservoir \npermeability and porosity from resistivity, (2), 33-42. \n \n[24] Uko, E.D., Alabraba, M.A., Idahosa, L., Tamunosiki, D. 2017. \nPorosity-Permeability Relationship in the North-West Niger Delta Basin, \nNigeria. World Journal of Applied Science and Technology, 9(2), 150-159. \n \n[25] Orji, C.S., Uko, E.D., Tamunobereton-ari, I. 2019. Permeability-\nPorosity trends in CAWC reservoir sands in the Niger Delta Nigeria, using \nwell-log data. Malaysian Journal of Geosciences, 3(2),33-4  \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 22-25  \n\n\n\nCite The Article: Nordin Sakke, Mohamad Tahir Mapa, Azali Saudi (2018). Analysis Of Several Hydrological-Drought Duration Parameters In Mengalong River \nBasin, Sipitang, Sabah.  Malaysian Journal of Geosciences, 2(2) : 22-25. \n\n\n\n\n\n\n\n    ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 26 June 2018  \nAccepted 2 July 2018  \nAvailable online 1 August  2018 \n\n\n\nABSTRACT\n\n\n\nABSTRACT \n\n\n\nDrought is a phenomenon of water shortage that will impact the wellbeing of human life. The hydrological drought \nis a situation of water shortage compared to normal conditions. The degree of severity of drought events can be \nexplained via duration and water deficits of drought events. The duration is an important parameter in \nunderstanding the event of drought. Duration refers to a period in which the value of river discharge remains below \na certain threshold level. This study attempts to identify the severity of drought based on two drought duration \nparameters namely the duration of drought event (DE) and the inter arrival time (IAT). In the context of this study, \nthe Q90 percentile value was obtained from the flow duration curve and the minimum drought period (MDP) of \ndrought events for 45 days is used as a threshold level of drought events. The 39 year discharge data for Mengalong \nstations is used to determine the Q percentile value. From the analysis, the cumulative period of the drought events \nis recorded around 390 days covers 3.6% of the entire record. There were four drought events throughout the \nrecord that is in 1992, 1998, 2015 and 2016. The lowest duration was 59 days recorded in 1992, while the longest \nwas 135 days recorded in 1998. This long period is associated with the presence of extreme weather phenomena \nsuch as El-Ni\u00f1o.  \n\n\n\n KEYWORDS \n\n\n\nHydrological Drought, Drought Duration.\n\n\n\n1. INTRODUCTION\n\n\n\nIn Malaysia, the presence of two hot sessions during January - March and \n\n\n\nJune - August will result in reduction of rainfall. This situation is further \n\n\n\nintricate by the warming effect of the earth that changed the pattern of \n\n\n\nrainfall. The presence of ENSO phenomenon that links the events of El \n\n\n\nNino / La Nina and Southern Oscillation has also changed the pattern of \n\n\n\nrainfall. The extreme El Nino phenomenon occurred in 1982/1983, \n\n\n\n1997/1998 and the latest in 2015/2016 [1]. The hot and dry sessions that \n\n\n\nhit Malaysia at the end of 2015 until middle of 2016 have reduce the \n\n\n\namount of rainfall between 20 - 60%. This rainfall reduction has \n\n\n\nthreatened the sustainability of water resources. This is due to the \n\n\n\ninterruption of water treatment plant operation due to low and \n\n\n\ninadequate water coupled with the presence of contaminated materials \n\n\n\nthat exceed the permitted standards. Therefore, this paper attempts to \n\n\n\nidentify the drought event in Sipitang, Sabah using duration parameter to \n\n\n\ndescribe the drought properties, identify the patterns and trends of \n\n\n\nhydrological drought.   \n\n\n\n2. DROUGHT: CONCEPT AND DEFINITION\n\n\n\nIn a study, states that droughts have a multiple meanings based on human \n\n\n\ndiversity, their specific needs and interests [2]. Although all types of \n\n\n\ndroughts are due to lack of rainfall, however, drought is seen differently \n\n\n\nby different water users and therefore the definitions are depending on \n\n\n\nthe user [3,4]. For farmer community, drought means lack of moisture in \n\n\n\nplant root zones. For hydrological experts, drought means the state of \n\n\n\nwater in rivers, lakes and ponds that below the normal levels. For \n\n\n\neconomists, it means deficiencies that affect economic growth.  To this \n\n\n\nend, there is a need to define drought differently as it affects various \n\n\n\nsectors of society [3]. \n\n\n\nAlthough there are more than 150 definitions related to drought \n\n\n\npublished in different field of studies (Wilhite & Glantz, 1985), but \n\n\n\ngenerally the drought phenomenon can be depicted as a prolonged \n\n\n\ndeviation from the normal state of water variable such as precipitation, \n\n\n\nstream discharge, groundwater and soil moisture [3,5]. \u201cDuration\u201d is a \n\n\n\nterm that is widely used in describing the definition of drought. Terms \n\n\n\nsuch as \"a duration of inadequate water usage to a particular water supply \n\n\n\nmanagement system\", \"a duration where water is insufficient for the \n\n\n\nnormal use\" and \"duration of current discharge is insufficient \" needs to \n\n\n\nbe understood and analyzed according to spatial and time so the drought \n\n\n\ninterpretation is appropriate and meets the current requirements [6-8]. \n\n\n\nIn general, the drought event (DE) refers to a period in which the value of \n\n\n\nriver discharge remains below a certain average level or the sequence of \n\n\n\nsequential time series that is below specific threshold levels as illustrated \n\n\n\nin Figure 1 [9,10]. The duration of DE is indefinable, and it may involve a \n\n\n\nshort period of one week to several years and the unit may use day, month \n\n\n\nand years [11,12]. \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: https://doi.org/10.26480/mjg.02.2018.22.25\n\n\n\nANALYSIS OF SEVERAL HYDROLOGICAL-DROUGHT DURATION PARAMETERS IN \nMENGALONG RIVER BASIN, SIPITANG, SABAH \n\n\n\nNordin Sakke1, Mohamad Tahir Mapa1, Azali Saudi2\n\n\n\n1Geography Program, Faculty of Humanities, Arts and Heritage, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah. \n2Faculty of Computing and Informatics, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah. \n*Corresponding Author Email: dinums@ums.edu.my\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\n\n\n\n\nISSN : 2521-0920 (Print)\nISSN : 2521-0602 (Online) \nCODEN: MJGAAN\n\n\n\n\nhttps://doi.org/10.26480/mjg.02.2018.11.16\n\n\nmailto:dinums@ums.edu.my\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 22-25 \n\n\n\nFigure 1: Drought Events based on Runoff Theory [13]. \n\n\n\nDuration is an important feature in defining drought. The drought season \n\n\n\nin early 2014 that hit Malaysia is exemplar of a mistake in defining \n\n\n\ndrought. Most local newspapers refer the word drought when describing \n\n\n\nthe hot weather at that time. The word \"drought\" was later translated into \n\n\n\n\"dry season\" after the Malaysian Meteorological Department denied that \n\n\n\nMalaysia was in drought condition [14]. This confusion is due to \n\n\n\nuncertainty regarding the definition of drought used in Malaysia, as most \n\n\n\ncommunity perceives that dry session is a sign of drought phenomenon. \n\n\n\nAlthough there are a number of minimum drought period (MDP) that have \n\n\n\nbeen used such as 10 consecutive days, 15 consecutive days or 25 \n\n\n\nconsecutive days [15,16]. However, based on Regulation of Operation of \n\n\n\nDrought Hazard Control issued by the National Security Council (MKN), \n\n\n\nMalaysia has established that situation of drought occurs when water \n\n\n\nsupplies (rain and rivers flow) are deficient within 3 consecutive months \n\n\n\n[17]. Therefore, in this paper the severity of the drought will be analysis \n\n\n\nusing two duration parameters \u2013 duration of drought event and inter \n\n\n\narrival time (IAT). The DDE refers to a duration when the value of river \n\n\n\ndischarge remains below certain threshold level while IAT is a duration \n\n\n\nbetween the initiation date of current drought until the next \n\n\n\ncommencement date of the drought events as illustrated in Figure 1. \n\n\n\n3. METODOLOGY\n\n\n\n3.1 Study Area \n\n\n\nMengalong river basin is located at longitude 115o 20 '00 \"- 115o 50' 00\" E \n\n\n\nand latitude 4o 50 '00 \"- 5o 10' 00\" N (Figure 2) situated between Sabah, \n\n\n\nSarawak and Brunei boarder. The basin is the main water resource for \n\n\n\nSipitang and surrounding areas such as Mesapol and Sindumin area and \n\n\n\nalso to several mega projects such as Sabah Ammonia Urea (SAMUR) a \n\n\n\nproject by Petronas.  \n\n\n\nFigure 2: Study Area \n\n\n\n3.2 Research Method \n\n\n\nThe Mengalong river (station 4955403) data channel was obtained from \n\n\n\nDID Inanam, Sabah.  The data collected consist of daily discharge data \n\n\n\n(m3s-1) for 29 years from March 1987 to June 2016. Time series data is the \n\n\n\nsequence of reading values or measurement that has different time to \n\n\n\ndescribe certain quantity which involve repeat measurement of discharge \n\n\n\nat river observation station. The discharge station takes place at 4o 59 '33 \n\n\n\n\"U and 115o 34' 40\" T. The daily discharge data is used for consideration \n\n\n\nparticularly in tropical environments when average annual data is not \n\n\n\nable to indicate which event is more severe. The missing data was \n\n\n\ncorrected using reconstruction and filtering process by utilizing simple \n\n\n\ninterpolation, linear regression and average arithmetic. The threshold \n\n\n\nlevel method was used to produce drought events. The Q90 and Q95 \n\n\n\npercentile were used to determine threshold levels with the value of Q90 = \n\n\n\n3.45 m3s-1 and Q95 = 1.69 m3s-1 was obtain using flow duration curve [5, \n\n\n\n18]. Minimum drought period of 45 days and 90 days were selected as the \n\n\n\ndrought event [14,17]. The 7 days moving average method combined with \n\n\n\n7-day inter-event time used for the pooling of mutually dependent \n\n\n\ndroughts and to remove minor drought. \n\n\n\n4. RESEARCH FINDINGS\n\n\n\n4.1 Inter Arrival Time and Drought Frequency \n\n\n\nBased on Figure 3, between a year 1987 and 2016, Sipitang was \n\n\n\nexperienced with drought at Q90 levels in 1992, 1998, 2015 and 2016. This \n\n\n\nrepresents 13.8 per cent of drought event from the total of year reviewed. \n\n\n\nThe frequency and repetition of drought events can be explained with IAT. \n\n\n\nAnalyzing the time series data of Mengalong river station, the duration of \n\n\n\nIAT at Q90 level between each DE are varies. The lowest IAT was recorded \n\n\n\n304 days from 9 February 1992 (initiation of DE1) until 29 December \n\n\n\n1997 (initiation of DE2). The longest duration of IAT was 6,271 days from \n\n\n\n29 December 1997 (initiation of DE2) until 28 February 2015 (initiation \n\n\n\nof DE3). Average duration of IAT was 2909 days. This indicates that at the \n\n\n\nQ90 level, the drought recurrence period is once in every 8 years within 29 \n\n\n\nyears. In contrast to Q95 level, the droughts was recorded in 1998 and \n\n\n\n2016, which accounted for 6.9 per cent of the overall year. Duration of IAT \n\n\n\nbetween DE1 and DE2 was 6,571 days starting on 12 January 1998 until 8 \n\n\n\nJanuary 2016. This implies at Q95 level, the drought events in Sipitang area \n\n\n\ndiscovered once in every 18 years. \n\n\n\nFigure 3: Frequency and inter arrival time (IAT) of drought event. \n\n\n\n4.2 Duration of Drought Events \n\n\n\nTable 1 shows the cumulative period was 395 days at Q90 level. The lowest \n\n\n\ndrought period was recorded for 59 days in 1992 and the longest was 135 \n\n\n\ndays in 1998. The average period of drought in Sipitang area was 99 days. \n\n\n\nThere were two years of severe drought that's are in 1998 and 2016 \n\n\n\n(above average level). In contrast to Q95 level, the overall duration of \n\n\n\ndrought event was around 159 days with the lowest was 51 days in 2016. \n\n\n\nThe highest value was recorded for 108 days in 1998. The average period \n\n\n\nof drought in the study area was around 80 days. In year 1998 severe \n\n\n\ndrought occurred when the duration recorded was exceeding 4 times of \n\n\n\nthe average of Q95 level. \n\n\n\nCite The Article: Nordin Sakke, Mohamad Tahir Mapa, Azali Saudi (2018). Analysis Of Several Hydrological-Drought Duration Parameters In Mengalong River \nBasin, Sipitang, Sabah.  Malaysian Journal of Geosciences, 2(2) : 22-25. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 22-25 \n\n\n\nTable 1: Duration of drought events in Sipitang district \n\n\n\n4.3 Drought Duration Trend \n\n\n\nThe drought events in Sipitang district show a different trend based on Q90 \n\n\n\nand Q95 levels. Through Mann-Kendall Trend and linear regression \n\n\n\nanalysis, the trend at Q90 level has increase as shown in figure 4. The \n\n\n\nincrease was indicate by positive values of b = 0.7322x as well as Mann-\n\n\n\nKendall test S = 2. Contrary to Q95 level, there is a trend of decline as \n\n\n\nillustrated by negative values of coefficient b = -3.1667x and Mann-Kendall \n\n\n\ntest S = -1.  It shows that moderate severe drought (Q90) has increase, but \n\n\n\nsevere drought (Q95) showed a decline pattern. However, these findings \n\n\n\nare not significant at confidence level of \u03b1 = 0.1. \n\n\n\nFigure 4: Drought duration trend in Sipitang \n\n\n\n5. DISCUSSION\n\n\n\nDuration is an important aspect in interpreting the properties of drought. \n\n\n\nDuring the study period, Sipitang area is in a state of drought for 4 years \n\n\n\nand it represents 13.8 percent of the total year. This percentage is lower \n\n\n\ncompared to other studies such as in Langat basin of 31.3 per cent [12]. \n\n\n\nThe number of days recorded is 135 days and exceeds the MDP level set \n\n\n\nby the MKN of 3 months, but this period is lower than the one previously \n\n\n\nstudied in the Langat basin of 250 days. The difference is related to the \n\n\n\nenvironmental factors of the basin. The rapid development rate and high-\n\n\n\nwater demand in the Langat basin become a major factor in severity level. \n\n\n\nIn contrast to Mengalong basin with low development rate, extreme \n\n\n\nweather become the major contributor to severity of the events.  \n\n\n\nBased on the El-Nino pattern plotted the entire drought incidence is \n\n\n\nassociated with the presence of extraordinary weather phenomenon (El-\n\n\n\nNino) [1]. This is shown by Person correlation value of r2 = 0.71 which \n\n\n\nindicates a high correlation at confidence level of \u03b1 = 0.05.   Drought events \n\n\n\nthat took place over a long term period (more than 3 months) are \n\n\n\nassociated with the presence of El-Nino on a very strong scale (scale 2.0 -\n\n\n\n2.5) as shown in  1997/98 and 2015/16 episodes.  While drought that \n\n\n\nlasted less than 3 months occurred on a strong scale (scale 1.5 - 2.5) as \n\n\n\nshown during 1991/92 and 2014/15 episodes.  \n\n\n\nThe study also found that year 1998 and 2016 indicate a severe drought \n\n\n\nin the study area. At Q90 threshold level, the drought years recorded was \n\n\n\n4 DE ie 1992, 1998, 2015 and 2016. However, when Q95 threshold level \n\n\n\nwas used, only year 1998 and 2016 could be consider as drought year.  In \n\n\n\n1998 the period of drought decreased from 135 days to 108 days but \n\n\n\nremains above the level of 3 months. For 2016, the period of drought \n\n\n\ndecreased from 122 days to 51 days. This study also proves that year 1998 \n\n\n\nwas considered a year with an extreme drought due to its long duration \n\n\n\nwith a high river discharge deficit between 100 - 350 m3s-1. In terms of \n\n\n\nfrequency, the occurrence of drought in Sipitang is still acceptable with \n\n\n\nthe occurrence average is once in 8 years. This is in contrast to the Langat \n\n\n\nriver basin that records the occurrence is once in 3 years [12]. \n\n\n\n6. CONCLUSION\n\n\n\nDrought is a phenomenon that can occur in all climatic areas either in wet \n\n\n\nor dry. Malaysia in general and Sabah in particular are not free from \n\n\n\ndrought session. This is evidenced by four droughts that occurred in \n\n\n\nstudy area with repetition rate once in every 8 years. The long period of \n\n\n\ndrought is strongly influenced by the presence of the El-Nino \n\n\n\nphenomenon. The phenomenon is greatly affected the study area \n\n\n\nespecially in 1998 and 2016. This shows that Sipitang districts will faced \n\n\n\nwith raw water supply issues in the future. The trend of drought is also \n\n\n\nexpected to increase with the presence of El-Nino. Hence, stakeholders in \n\n\n\nparticular those who involved in water supply industry required to plan \n\n\n\nthe water needs in the future.  \n\n\n\nREFERENCES \n\n\n\n[1] Null, J. 2017. El Nino and La Nina Years and Intensities Based on \n\n\n\nOceanic Ni\u00f1o Index (ONI). Available at \n\n\n\nhttp://ggweather.com/enso/oni.htm \n\n\n\n[2] Palmer, W.C. 1965. Meteorological drought. Washington, DC, USA: US \n\n\n\nDepartment of Commerce, Weather Bureau, 30.  \n\n\n\n[3] Wilhite, D.A., Glantz, M.H. 1985. Understanding the drought \n\n\n\nphenomenon: the role of definitions. Water Int. 10, 111\u2013120. \n\n\n\n[4] Allaby, M. 2003. Droughts. New York: Facts on File, Incorporated. \n\n\n\n[5] Tallaksen, L.M., Van Lanen, H.A.J. 2004. Introduction. In Tallaksen LM, \n\n\n\nvan Lanen HAJ (eds) Hydrological Drought: Processes and Estimation \n\n\n\nMethods for Streamflow and Groundwater (48th ed.), 3-17. Elsevier B.V.: \n\n\n\nAmsterdam. \n\n\n\n[6] Mishra, A.K., Singh, V.P. 2010. A review of drought concepts. Journal \n\n\n\nof Hydrology. doi: 10.1016/j.jhydrol.2010.07.012 \n\n\n\n[7] Bureau of Meteorology. 2006. Living with Drought. Available at \n\n\n\nhttp://www.bom.gov.au/climate/drought/livedrought.html \n\n\n\n[8] Rossi, G. 2011. Drought risk for water supply systems based on low-\n\n\n\nflow regionalization. (Unpublished PhD Dissertation) Institute of \n\n\n\nTechnology, University of Braunschweig. Available at http://digisrv-\n\n\n\n2.biblio.etc.tu-\n\n\n\nbs.de:8081/docportal/servlets/MCRFileNodeServlet/DocPortal_derivat\n\n\n\ne_00022630/Dissertation.pdf \n\n\n\n[9] Mckee, T.B., Doesken, N.J., Kleist, J. 1993. The relationship of drought \n\n\n\nfrequency and duration to time scales. In. Eighth Conference on Applied \n\n\n\nClimatology (hlm. 17\u201322). Anaheim. Available at \n\n\n\nhttp://clima1.cptec.inpe.br/~rclima1/pdf/paper_spi.pdf \n\n\n\n[10] Byun, H.R., Wilhite, D.A. 1999. Objective Quantification of Drought \n\n\n\nSeverity and Duration. Journal of Climate, 12, 2747\u20132756. \n\n\n\n[11] Zargar, A., Sadiq, R., Naser, B., Khan, F.I. 2011. A review of drought \n\n\n\nindices. Environmental Reviews. DOI: 10.1139/a11-013 \n\n\n\n[12] Sakke, N. 2016. Analisis Pola Kemarau Hidrologi di Lembangan \n\n\n\nSungai Langat, Selangor, Malaysia. (Unpublished PhD Dissertation). \n\n\n\nUniversiti Pendidikan Sultan Idrsis. \n\n\n\n[13] Salas, J.D., Asce, M., Fu, C., Cancelliere, A., Dustin, D., Bode, D., Vincent, \n\n\n\nE. 2005. Characterizing the Severity and Risk of Drought in the Poudre \n\n\n\nRiver. Journal of Water Resources Planning and Management, 131 (5), \n\n\n\n383 \u2013 393. DOI: 10.1061/ (ASCE) 0733-9496(2005)131:5(383) \n\n\n\nCite The Article: Nordin Sakke, Mohamad Tahir Mapa, Azali Saudi (2018). Analysis Of Several Hydrological-Drought Duration Parameters In Mengalong River \nBasin, Sipitang, Sabah.  Malaysian Journal of Geosciences, 2(2) : 22-25. \n\n\n\n\nhttp://ggweather.com/enso/oni.htm\n\n\nhttp://www.bom.gov.au/climate/drought/livedrought.html\n\n\nhttp://digisrv-2.biblio.etc.tu-bs.de:8081/docportal/servlets/MCRFileNodeServlet/DocPortal_derivate_00022630/Dissertation.pdf\n\n\nhttp://digisrv-2.biblio.etc.tu-bs.de:8081/docportal/servlets/MCRFileNodeServlet/DocPortal_derivate_00022630/Dissertation.pdf\n\n\nhttp://digisrv-2.biblio.etc.tu-bs.de:8081/docportal/servlets/MCRFileNodeServlet/DocPortal_derivate_00022630/Dissertation.pdf\n\n\nhttp://digisrv-2.biblio.etc.tu-bs.de:8081/docportal/servlets/MCRFileNodeServlet/DocPortal_derivate_00022630/Dissertation.pdf\n\n\nhttp://clima1.cptec.inpe.br/~rclima1/pdf/paper_spi.pdf\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 22-25 \n\n\n\n[14] Sakke, N., Ithnin, H., Che Ngah, M.S.Y. 2015. Memahami Kemarau \n\n\n\nDalam Konteks Tempoh Yang Berbeza. Kajian Kes Lembangan Langat, \n\n\n\nSelangor. In Hamirdin Ithnin et al. (Ed.). Proceedings of International \n\n\n\nConference of Human Sciences 2015. Tanjong Malim: 567-580. \n\n\n\n[15] Huschke, R.E. 1970. Glossary of Meteorology. American \n\n\n\nMeteorological Society Press, Boston. 638 pp. \n\n\n\n[16] Steila, D. 1986. Drought. In J. E. Oliver, (Ed). The Encyclopaedia of \n\n\n\nClimatology. van Nostrand Reinhold, 386\u2013395. \n\n\n\n[17] MKN. 2011. Peraturan Tetap Operasi Pengendalian Bencana \n\n\n\nKemarau. Putrajaya: Jabatan Perdana Menteri.  \n\n\n\n[18] Zelenhasic, E., Salvai, A. 1987. A Method of Stream flow Drought \n\n\n\nAnalysis. Water Resources, 23 (1), 156-168. DOI: \n\n\n\n10.1029/WR023i001p00156. \n\n\n\nCite The Article: Nordin Sakke, Mohamad Tahir Mapa, Azali Saudi (2018). Analysis Of Several Hydrological-Drought Duration Parameters In Mengalong River \nBasin, Sipitang, Sabah.  Malaysian Journal of Geosciences, 2(2) : 22-25. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 08-11 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2022.08.11 \n\n\n\nCite The Article: Shoukat Ali Shah, Madeeha Kiran, Aleena Nazir, Shaharyar Hassan Ashrafani (2022). Exploring NDVI and NDBI Relationship Using Landsat 8 Oli/Tirs \nin Khangarh Taluka, Ghotki. Malaysian Journal of Geosciences, 6(1): 08-11. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2022.08.11 \n\n\n\nEXPLORING NDVI AND NDBI RELATIONSHIP USING LANDSAT 8 OLI/TIRS IN \nKHANGARH TALUKA, GHOTKI \n\n\n\nShoukat Ali Shaha*, Madeeha Kiranb, Aleena Nazirc, Shaharyar Hassan Ashrafanid  \n\n\n\naInstitute of Water Resources Engineering and Management, Mehran University of Engineering and Technology, Jamshoro, Pakistan. \nbInstitute of Natural Disaster Research, School of Environment, Northeast Normal University, Changchun, China. \ncDepartment of Earth and Environmental Sciences, Bahria University, Islamabad Campus, Pakistan. \ndIrrigation and Drainage, Sindh Agriculture University, Tandojam, Pakistan. \n*Corresponding Author Email: sarkar.sain151@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 25 November 2021 \nAccepted 28 December 2021 \nAvailable online 04 January 2022\n\n\n\nBuilt-up is one of the most significant type of land use-cover linked with urbanization. Computing, classifying, \nand mapping the built-up areas by Landsat image, is on priority demand for municipal and policymakers to \ninvestigate urban extension. Thus, increasing population and conversion of agricultural land into urban is a \nmajor topic understanding the bond between both types of land use. In the context of this, this study \ninvestigates the relationship between Normalized Difference Vegetation Index (NDVI) and Normalized \nDifference Built-up Index (NDBI) in Khangarh taluka. Therefore, satellite images of Landsat 8 OLI/TIRS 2014, \n2016, 2018, and 2021 were downloaded freely from the USGS-GloVIS Earth Explorer website. The images \nwere processed in the ArcGIS 10.3 environment. NDVI was calculated using the Near-Infrared NIR (band 5), \nRed (band 4) and for NDBI, Middle Infrared Reflectance MIR (band 6) and NIR (band 5) was used following \nthe equation of both indices. The calculated values were then exported in SPSS software for correlation \ndetermination and scatter plot development. The results from the case showed that there was a linear and \nnegative correlation between vegetation index and built-up index in all years over the study area. \nFurthermore, in 2014 the coefficient of correlation explicated R2=0.96; in 2016 R2=0.23, in 2018 R2=0.34, and \nin 2021 R2=0.22 which indicated that NDBI could be used to illustrate the evaluation of urban construction \nland. The all-over study recommends that built-up index NDBI not only can be used as a significant indicator \nfor built-up or urban areas estimation but also deliver a consistent source for urban development and \nplanning. \n\n\n\nKEYWORDS \n\n\n\nCorrelation, NDBI, NDVI, Remote sensing, Khan Garh taluka \n\n\n\n1. INTRODUCTION \n\n\n\nThe built-up area mapping is an essential indicator for urban growth, \n\n\n\ndevelopment, and urban sprawl. The demand for new housings, schools, \n\n\n\nhospitals, transportation, parks, and many other basic needs of \n\n\n\ncommunities are increasing with the increasing human population \n\n\n\nparticularly in developing countries. Regular progress of urban hubs \n\n\n\nconsumes cultivated land next to these, resulting in lower agricultural \n\n\n\noutput. In view of these land use-cover dynamics and change detection on \n\n\n\nthe earth's surface. Academics and Scientists are active and worked, an \n\n\n\narray of indicators together with Index based built-up index IBI, Urban \n\n\n\nIndex, Normalized Difference Bareness Index (NDBaI), and Bare Soil Index \n\n\n\nBI for monitoring and mapping the built-up lands (Xu, 2008; Kawamura et \n\n\n\nal., 1996; Zhao and Chen, 2005; Rikimaru, 1997). The mapping process \n\n\n\nspread over several remote sensing (RS) data and spectral digits on the \n\n\n\nland use and land cover classes. Collectively increasing of built areas and \n\n\n\nfluctuation in vegetative lands, it is important to estimate the agricultural \n\n\n\nland, cropping area, forests for future planning to make availability and \n\n\n\ncomfort zone for the population needs.  \n\n\n\nEnhanced Vegetation Index (EVI) and NDVI are the two main vegetation \n\n\n\nindicators for vegetation estimation and monitoring through remotely \n\n\n\nsensed technology.  NDVI is mostly used in semi-arid areas for vegetation \n\n\n\nproduction and moisture estimation. It responds primarily in the high \n\n\n\nabsorption red affected band. Thus, it is a dire need to monitor the \n\n\n\nvegetation areas and built-up indices. The indices are combinations of two \n\n\n\nor more bands associated with spectral appearances of vegetation \n\n\n\n(Matsushita et al., 2007). It has been found that the wide application in \n\n\n\nmonitoring of cropping pattern, phenology, vegetation types and classes, \n\n\n\nand derivation of vegetative bio-physical constraints. A studies has \n\n\n\ndescribed the vegetation index in their research work, NDVI lies between \n\n\n\nthe ranges of -1 to +1 (Shah and Siyal, 2019). Waterbody, snow, barren \n\n\n\nland, and urban areas are under negative values. While the positive values \n\n\n\nindicate agricultural land, crops pasture, and are positively correlated \n\n\n\nwith green vegetation. NDVI mostly used indices for vegetation and crop \n\n\n\nmonitoring because it withdraws the huge portion of the noise produced \n\n\n\nby the topographic effects, clouds shadow, changing sun angles, and \n\n\n\natmospheric conditions (Matsushita et al., 2007). It is accurate, reliable, \n\n\n\nsimply calculated, and convenient for crop mapping, agriculture land \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 08-11 \n\n\n\n\n\n\n\n \nCite The Article: Shoukat Ali Shah, Madeeha Kiran, Aleena Nazir, Shaharyar Hassan Ashrafani (2022). Exploring NDVI and NDBI Relationship Using Landsat 8 Oli/Tirs \n\n\n\nin Khangarh Taluka, Ghotki. Malaysian Journal of Geosciences, 6(1): 08-11. \n\n\n\n\n\n\n\nmapping in tropical environments (Meera et al., 2015). Moreover, it is \n\n\n\nmore saturated at high biomass levels and sensitive to canopy background \n\n\n\nvariations (Gao et al., 2000). Vegetation Index and Built-up indices are the \n\n\n\nmost used indices by remotely sensed techniques in past research work. \n\n\n\nVegetative indicator NDVI is generally utilized to study the relationship \n\n\n\nbetween different indices correlation i.e., NDVI-NDBI (Macarof et al, 2017; \n\n\n\nMalik et al, 2019), LST-NDVI, and NDVI-NDWI (Weng et al, 2004; Smith et \n\n\n\nal, 1990; Julien et al, 2006; Stroppiana et al, 2014; Wen et al, 2017). \n\n\n\nTherefore, this study focuses to investigate the correlation between NDVI-\n\n\n\nNDBI in Khangarh taluka from 2014-2021. \n\n\n\n2.  MATERIALS AND METHODS \n\n\n\n2.1 Study area \n\n\n\nThis study was conducted in Khangarh taluka, district Ghotki. The taluka \nis situated on Latitude N: 28 02' 00' and Longitude E: 69 33' 00' with 77 \nmeters above sea level. The total area covered by taluka is 1626.84 km2 \nwithholding 149,667 population (PBS, 2017; Shah and Siyal, 2019). The \npeople of the taluka engaged in agriculture. The major crops in taluka are \nwheat, sugarcane, cotton, and some vegetables and fruits are excessively \nfound. Qazi Wah irrigates the whole command area, it off takes from the \nGhotki feeder canal. Desert minor also irrigated some agricultural land of \nthe taluka (Shah et al., 2021). Government and private tube wells are also \nused during the shortage period of irrigation water in the study area. \n\n\n\nFigure 1: Layout map of Khangarh taluka, Ghotki, Sindh, Pakistan \n\n\n\n2.2 Data Acquisition and Preprocessing \n\n\n\nRemote sensing data from Landsat 8 OLI/TIRS was employed in this study \n\n\n\nfor the years 2014, 2016, 2018, 2021. The data were freely downloaded \n\n\n\nfrom USGS-GloVIS (https://earthexplorer.usgs.gov/) web portal in Tagged \n\n\n\nImage File (TIF) format with 0% cloud cover. Remote sensing images were \n\n\n\nreferenced to the (UTM) Universal Transverse Mercator projection system \n\n\n\nand covered one scene of Landsat 8 imagery from the worldwide reference \n\n\n\nsystem (WRS-2) of (path 151 row 41) presented in (Table 1). The acquired \n\n\n\nsatellite images were processed in the geospatial tool ArcGIS 10.3 \n\n\n\nenvironment. Then shapefile of Khangarh taluka was taken out from the \n\n\n\nentire scene using the Extract by Mask tool. \n\n\n\nCurrently, many researchers have used NDVI for mapping and monitoring \n\n\n\nthe vegetative zones with remotely sensed data (Sonawane and Bhagat, \n\n\n\n2017; Chen et al, 2017; Shah and Siyal, 2019; Shah and Kiran, 2021). NDVI \n\n\n\nis mostly used for drought monitoring, timely prediction of crop \n\n\n\nproduction, predicting fire zones, and desert offensive maps worldwide. \n\n\n\nSince, it supports paying off for changes in lighting conditions, surface \n\n\n\nslope, exposure, and other peripherical factors. The range of vegetation \n\n\n\nindex values between (-1 to 1) depends on the relative digital number DN \n\n\n\nof Near Infrared and red bands (Sonawane and Bhagat, 2017). The \n\n\n\nnegative values indicate rocks, clouds, snow, surface water, bare land that \n\n\n\nnormally falls within the 0.1-0.2 values corresponding to areas where \n\n\n\nplantation exists. Healthy and dense vegetation canopy always fall within \n\n\n\n0.5 and sparse vegetation falls within 0.2-0.5. Moderate vegetation tends \n\n\n\nto differ from 0.4-0.6. Whatever above 0.6 values indicate the highest \n\n\n\npossible density of green leaves (Shah and Kiran, 2021; Malik et al, 2019). \n\n\n\nThe NDVI is calculated from the following equation.1. \n\n\n\nNDVI=\n\ud835\udc0d\ud835\udc1e\ud835\udc1a\ud835\udc2b\u2212\ud835\udc08\ud835\udc27\ud835\udc1f\ud835\udc2b\ud835\udc1a\ud835\udc2b\ud835\udc1e\ud835\udc1d (\ud835\udc01\ud835\udc1a\ud835\udc27\ud835\udc1d \ud835\udfd3)\u2212\ud835\udc11\ud835\udc1e\ud835\udc1d \ud835\udc01\ud835\udc1a\ud835\udc27\ud835\udc1d (\ud835\udc01\ud835\udc1a\ud835\udc27\ud835\udc1d \ud835\udfd2)\n\n\n\n\ud835\udc0d\ud835\udc1e\ud835\udc1a\ud835\udc2b\u2212\ud835\udc08\ud835\udc27\ud835\udc1f\ud835\udc2b\ud835\udc1a\ud835\udc2b\ud835\udc1e\ud835\udc1d (\ud835\udc01\ud835\udc1a\ud835\udc27\ud835\udc1d \ud835\udfd3)+\ud835\udc11\ud835\udc1e\ud835\udc1d \ud835\udc01\ud835\udc1a\ud835\udc27\ud835\udc1d (\ud835\udc01\ud835\udc1a\ud835\udc27\ud835\udc1d \ud835\udfd2)\n \n\n\n\n                  (1) \n\n\n\nNormalized Difference Built-up Index (NDBI) differs from vegetation \n\n\n\nindex NDVI. It deals with the extraction, mapping, and monitoring of built-\n\n\n\nup/settlements areas from the remotely sensed data. Whereas NDVI only \n\n\n\ndeals with vegetation extraction. The NDBI map and values were extracted \n\n\n\nfrom Landsat 8 OLI/TIRS. In contrast to the other land cover surfaces, \n\n\n\nbuilt-up and settlements lands have higher reflectance in the MIR \n\n\n\nwavelength range 1.55~ 1.75\u03bcm than in the NIR wavelength range 0.76~ \n\n\n\n0.90\u03bcm. Built-up range from (-1 to 1). The greater the built-up values, the \n\n\n\nhigher the proportion of built-up areas. NDBI has been computed using the \n\n\n\nequation. 2.  \n\n\n\n\ud835\udc0d\ud835\udc03\ud835\udc01\ud835\udc08 =\n\ud835\udc0c\ud835\udc08\ud835\udc11(\ud835\udc01\ud835\udc1a\ud835\udc27\ud835\udc1d\ud835\udfd4)\u2212\ud835\udc0d\ud835\udc08\ud835\udc11(\ud835\udc01\ud835\udc1a\ud835\udc27\ud835\udc1d\ud835\udfd3)\n\n\n\n\ud835\udc0c\ud835\udc08\ud835\udc11(\ud835\udc01\ud835\udc1a\ud835\udc27\ud835\udc1d\ud835\udfd4)+\ud835\udc0d\ud835\udc08\ud835\udc11(\ud835\udc01\ud835\udc1a\ud835\udc27\ud835\udc1d\ud835\udfd3)\n   \n\n\n\n                                      (2) \n\n\n\nHere, MIR=Middle Infrared Reflectance (Band 6), NIR=Near Infrared \nreflectance (Band 5) \n\n\n\nTable 1: Illustrate the information of downloaded Landsat images \n2014, 2016, 2018 and 2021 \n\n\n\nS. No Landsat \nName \n\n\n\nLandsat scene ID Path/ \nRow \n\n\n\nDO\nY \n\n\n\nImage \nacquisiti\non \nDate \n\n\n\n1 Landsat 8 \n(OLI/TIRS) \n\n\n\nLC81510412014\n203LGN01 \n\n\n\n151/\n41 \n\n\n\n20\n2 \n\n\n\n2014-07-\n22 \n\n\n\n2 Landsat 8 \n(OLI/TIRS) \n\n\n\nLC81510412016\n353LGN02 \n\n\n\n151/\n41 \n\n\n\n35\n2 \n\n\n\n2016-12-\n18 \n\n\n\n3 Landsat 8 \n(OLI/TIRS) \n\n\n\nLC81510412018\n214LGN00 \n\n\n\n151/\n41 \n\n\n\n21\n3 \n\n\n\n2018-08-\n02 \n\n\n\n4 Landsat 8 \n(OLI/TIRS) \n\n\n\nLC81510412021\n174LGN00 \n\n\n\n151/\n41 \n\n\n\n17\n3 \n\n\n\n2021-06-\n23 \n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\n3.1 Computation of Normalized Difference Vegetation Index (NDVI) \n\n\n\nNDVI values of 2014 imagery (Figure. 2) indicate the highest and lowest \n\n\n\nvegetation 0.40 to -0.13. The maximum values indicate the vegetation \n\n\n\ncanopy over an area is highest, and the minimum values are displayed on \n\n\n\nthe water body, barren land, and sandy areas. While the NDVI of 2016 \n\n\n\nimagery shows that the maximum value of 0.44 and observed as healthy \n\n\n\nvegetation, and the minimum value is -0.14 over the study area. The \n\n\n\nimagery 2018 NDVI represents the highest value on agricultural crop and \n\n\n\npasture 0.43, on the other hand, minimum values showed the NDVI -0.05 \n\n\n\nover the sandy, barren and built-up area. The vegetation index of 2021 \n\n\n\nshowed the maximum value 0.42 and minimum value -0.02 observed on \n\n\n\nbarren land and sandy area over the Khangarh taluka. Comparing NDVI of \n\n\n\n2014-2021, the vegetation canopy is highest in 2016 with full greenery on \n\n\n\nthe agricultural land. While the lowest NDVI was computed in 2021 \n\n\n\nimagery. \n\n\n\n \nFigure 2: Represents NDVI of 2014-2016-2018-2021 \n\n\n\n\nhttps://earthexplorer.usgs.gov/\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 08-11 \n\n\n\n\n\n\n\n \nCite The Article: Shoukat Ali Shah, Madeeha Kiran, Aleena Nazir, Shaharyar Hassan Ashrafani (2022). Exploring NDVI and NDBI Relationship Using Landsat 8 Oli/Tirs \n\n\n\nin Khangarh Taluka, Ghotki. Malaysian Journal of Geosciences, 6(1): 08-11. \n\n\n\n\n\n\n\n3.2 Computation of Normalized Difference Built-up Index (NDBI) \n\n\n\nNDBI values of 2014 imagery (Figure.3) indicate the highest and lowest \n\n\n\nbuilt-up values 0.08 to -0.31. The maximum values indicate the dense \n\n\n\nbuilt-up and barren areas, and the minimum values are displayed on the \n\n\n\nwater body, and vegetative areas. While the NDBI of 2016 imagery shows \n\n\n\nthat the maximum value of 0.55 and observed as the highest built-up, and \n\n\n\nthe minimum value is -0.31 over the study area. The imagery 2018 NDBI \n\n\n\nrepresents the highest value on settlement, sand, and barren land 0.08, on \n\n\n\nthe other hand, minimum values showed the NDBI -0.31over the crops, \n\n\n\nvegetation, and pasture. The built-up index of 2021 showed the maximum \n\n\n\nvalue of 0.07 and a minimum value of -0.28 observed on vegetation and \n\n\n\nwaterbody areas over the Khangarh taluka. Comparing NDBI of 2014-\n\n\n\n2021, the built-up is highest in 2016. While the lowest values were \n\n\n\ncomputed in 2021 imagery. Hence, NDBI was found to be a good index for \n\n\n\ndistinguishing the built-up areas from OLI data. The barren index could be \n\n\n\nactive in several applications concomitants with the application of the \n\n\n\ngeospatial information, mainly in the subject of municipal remote sensing. \n\n\n\n \nFigure 3: Represents NDBI of 2014-2016-2018-2021 \n\n\n\nTable 2: Illustrate NDVI-NDBI values of 2014-2016-2018-2021 \nimageries \n\n\n\nIndices \n2014 2016 2018 2021 \n\n\n\nHigh Low High Low High Low High Low \n\n\n\nNDVI 0.40 \n-\n0.13 \n\n\n\n0.44 \n-\n0.14 \n\n\n\n0.43 \n-\n0.05 \n\n\n\n0.42 \n-\n0.02 \n\n\n\nNDBI 0.08 \n-\n0.31 \n\n\n\n0.55 \n-\n0.31 \n\n\n\n0.08 \n-\n0.31 \n\n\n\n0.07 \n-\n0.28 \n\n\n\n3.3 Relationship between NDVI-NDBI 2014, 2016, 2018, 2021 \n\n\n\nThe correlation between NDVI-NDBI is depicted in (Figure. 4) which \n\n\n\nindicates that the NDVI is negatively correlated with NDBI in all years. The \n\n\n\nNDVI-NDBI relationship in 2014, 2016, 2018, 2021 were computed as \n\n\n\nR2=0.20, R2=0.44, R2=0.91, and R2=0.76. The statistical scatter plots \n\n\n\nexplained the correlation values. Whereas, in 2018-2021 graphs indicated \n\n\n\nthere is a strong negative relationship between vegetation index and built-\n\n\n\nup index. There is a linear and negative correlation between the vegetation \n\n\n\nindex and the built-up index during 2014 and 2016. Hence, it is clear that \n\n\n\nNDBI can be employed to illustrate the evaluation and expansion of the \n\n\n\nbuilt-up index. The linear relationship of NDVI-NDBI is displayed in the \n\n\n\nscatter plots for all selected years (Figure.4). \n\n\n\n \nFigure 4: Represents Correlation between NDVI-NDBI 2014, 2016,2018, \n\n\n\n2021 \n\n\n\n4. DISCUSSION \n\n\n\nGlobally, the increase in built-up areas is due to the different actions of \nhumans i.e., land-use change, urbanization, population growth, and \nindustrialization. It has been confirmed from the satellite images, ground \ntrothing survey and from past literature, the rapid increase in the \npopulation, unplanned built-up, industrialization, and commercialization \nhave affected the vegetative areas especially agricultural land as well as \nenvironmental factors. Land use-cover dynamics have given away to vary \ngreatly in their effects on vegetation indices, and built-up areas. While \ncomputing the NDVI-NDBI in the study area from 2014-2021, it has been \nconfirmed through correlation that due to the spatial size of the cities, and \nvegetation cover there has negative relation between vegetation index and \nbuilt-up index. Hence, we have tried to discuss and compare the results of \nboth indices in four years (2014, 2016, 2018, and 2021). \n\n\n\nNDVI values of 2014 indicated the highest and lowest vegetation 0.40 to -\n0.13. The maximum values indicate the vegetation canopy over an area is \nhighest, and the minimum values are displayed on the water body, barren \nland, and sandy areas. While the vegetation index of 2016 imagery shows \nthat the maximum value of 0.44 and observed as healthy vegetation, and \nthe minimum value is -0.14 over the study area. The 2018 imagery \nrepresents the highest value on cropping and pasture areas as 0.43, on the \nother hand, minimum values recorded -0.05 over the barren land, \nsettlements, and sandy area. The vegetation index of 2021 showed the \nmaximum value 0.42 and minimum value -0.02 observed on sand, built-up \nand barren areas over the Khangarh taluka. Comparing NDVI of 2014-\n2021, the vegetation canopy is highest in 2016 with full greenery on the \nagricultural land. While the lowest NDVI was computed in 2021 imagery.  \n\n\n\nOn the other side, NDBI values of 2014 indicate the highest and lowest \nbuilt-up values 0.08 to -0.31. The maximum values indicate the dense \nbuilt-up and barren areas, and the minimum values are displayed on the \nwater body, and vegetative areas. The Landsat 2016 imagery shows that \nthe maximum value of 0.55, observed as the highest built-up, and the \nminimum value is -0.31 over the study area. The imagery 2018, built-up \nindex represents the highest value on settlement, sand, and barren land \n0.08, while, minimum values showed -0.31over the crops, vegetation, and \npasture. The built-up index of 2021 displayed the maximum value 0.07 and \nminimum value -0.28 observed on vegetation and water body areas.  \n\n\n\nFurthermore, the correlation between both indices has been computed to \nfind what relations occur either positive or negative. The NDVI-NDBI \nrelationship in 2014, 2016, 2018, and 2021 was computed as 0.20, 0.44, \n0.91, and 0.76. The scatter plots in (Figure.4) clearly showed there was \nlinear and strong negative relation in all years. It is obvious from the \nresults that vegetation and urban lands are inversely proportional to each \nother as one grows, the other decrease simultaneously. Now researchers \nturn to work on this perception of sustainable management as with the up-\nto-date step, the urban growth may just cross out the vegetation and \navailability of resources. This would lead to havoc creating cases of \njeopardizing and enormous harm of bio-diversity in the existing \necosystem. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 08-11 \n\n\n\n\n\n\n\n \nCite The Article: Shoukat Ali Shah, Madeeha Kiran, Aleena Nazir, Shaharyar Hassan Ashrafani (2022). Exploring NDVI and NDBI Relationship Using Landsat 8 Oli/Tirs \n\n\n\nin Khangarh Taluka, Ghotki. Malaysian Journal of Geosciences, 6(1): 08-11. \n\n\n\n\n\n\n\n5. CONCLUSION \n\n\n\nIn the present study, NDVI-NDBI has been derived using Landsat 8 \nOLI/TIRS data and the relationship between both indices has been \ninvestigated over the Khangarh taluka. It has been observed that most of \nthe settlement, barren land, and sandy areas have high NDBI values and \non vegetation, low values were recorded during the selected years. There \nwas a strong negative relationship between the vegetation index and the \nbuilt-up index from 2014-2021. From the results, it has been concluded \nthat NDVI and NDBI can be used as good parameters for monitoring \nvegetation, cropping area estimation, built-up, and urban areas. In \nconclusion, a built-up index was anticipated for mapping the built-up \nareas in the study area using OLI/TIRS bands, and the result of the \nresearch study would appearance municipal and cities designers to \ndistinguish evaluate growth for sustainable performs of the urban land \nsystem. It is expected from Government and policymakers that they should \nencourage researchers to conduct more studies on these indices with \nenvironmental parameters and provide more knowledge about the \ncurrent land use cover dynamics particularly urban areas and agricultural \nlands. Other factors including rapid population growth, landscapes, and \nsocio-economics parameters should be considered in their research \nstudies. \n\n\n\nREFERENCES \n\n\n\nChen, F., Yang, S., Yin, Kai., Chan, P., 2017. Challenges to quantitative \napplications of Landsat observations for the urban thermal \nenvironment. J. Environ. Eng.  3 (59), 80-88. \n\n\n\nGao, X., Huete, A.R., Ni, W., Miura, T., 2000. Optical biophysical \nrelationships of vegetation spectra without background contamination. \nRemote Sens. Environ. 74, 609-620. \n\n\n\nJulien, Y., Sobrino, J.A., Verhoef, W., 2006. Changes in land surface \ntemperature and NDVI values over Europe between 1982 and 1999. \nRemote Sens. Environ. (103), 43-55. \n\n\n\nKawamura, M., Jayamana, S., Tsujiko, Y., 1996. Relation between social and \nenvironmental conditions in Colombo Sri Lanka and the urban index \nestimated by satellite remote sensing data. Int. Arch. Photogramm. \nRemote Sens. 31 (Part B7), 321\u2013326. \n\n\n\nMacarof, Paul, Statescu, Florian., 2017. Comparison of NDBI and NDVI as \nindicators of surface urban heat island effect in Landsat 8 imagery: A \ncase study of Lasi. Present Environ. & Sust. Dev. 11 (2), 141-150. \n\n\n\nMalik, M.S., Shukla, J.P., Mishra, S., 2019. Relationship of LST-NDBI and \nNDVI using Landsat 8 data in Kandaihimmat Watershed, Hoshangabad, \nIndia. Indian J. Geo-Marine Sci.  48 (1), 25-31. \n\n\n\nMalik, M.S., Shukla, J.P., Mishra, S., 2019. Relationship of LST-NDBI and \nNDVI using Landsat 8 data in Kandaihimmat Watershed, Hoshangabad, \nIndia. Indian J. Geo-Marine Sci.  48 (1), 25-31. \n\n\n\nMatsushita, B., Yang, W., Chen, J., Onda, Y., Qiu, G., 2007. Sensitivity of the \nenhanced vegetation index EVI and Normalized Difference Vegetation \nIndex NDVI to topographic effects: A case study in high-density Cypress \nForest. Sens. (7), 2636-2651. \n\n\n\nMatsushita, B., Yang, W., Chen, J., Onda, Y., Qiu, G., 2007. Sensitivity of the \nenhanced vegetation index EVI and Normalized Difference Vegetation \nIndex NDVI to topographic effects: A case study in high-density Cypress \nForest. Sens. (7), 2636-2651. \n\n\n\nMeera, G.G., Parthiban, S., Nagaraj. T., Christy, A., 2015. NDVI: Vegetation \nchange detection using remote sensing and GIS- A case study of Vellore \n\n\n\ndistrict. 3rd International Conference on recent trends in Computing \n2015. (ICRTC-2015). Procedia Comput. Sci. 57 (2015), 1199-1210. \n\n\n\nPakistan Bureau of Statistics, P.B.S., 2017. Provisional summary results of \nthe 6th population and housing census. Available from: \nhttp://www.pbscensus.gov.pk. (accessed 12 October 2021) \n\n\n\nRikimaru, A., Miyatake, S., 1997. Development of Forest Canopy Density \nMapping and Monitoring Model using Indices of Vegetation, Bare soil, \nand Shadow. In Proceeding of the 18th Asian Conference on Remote \nSensing (ACRS) 1997 (p.3), Kuala Lumpur, Malaysia, 20\u2013 25 October \n1997. \n\n\n\nShah, S.A., Kiran, M., 2021. GIS-based technique analysis of land use land \ncover change detection in taluka Mirpur Mathelo: a case study in district \nGhotki, Pakistan. Int. Adv. Res. & Eng. J.  5 (2), 231-239. \n\n\n\nShah, S.A., Kiran, M., 2021. GIS-based technique analysis of land use land \ncover change detection in taluka Mirpur Mathelo: a case study in district \nGhotki, Pakistan. Int. Adv. Res. & Eng. J. 5 (2), 231-239. \n\n\n\nShah, S.A., Kiran, M., Khurshid, T., 2021. Seepage losses measurement of \nDesert minor and development of gauge-discharge rating curve: A case \nstudy in district Ghotki, Sind. World Acad. J. Eng. Sci.  8 (1), 13-22. \n\n\n\nShah, S.A., Siyal, A.A., 2019. GIS-based approach estimation of area under \nwheat and other major rabi crops in district Ghotki and corresponding \nirrigation water requirements. ACTA Sci. Agri.  3 (12), 59-70. \n\n\n\nShah, S.A., Siyal, A.A., 2019. GIS-based approach estimation of area under \nwheat and other major rabi crops in district Ghotki and corresponding \nirrigation water requirements. ACTA Sci. Agri.  3 (12), 59-70. \n\n\n\nShah, S.A., Siyal, A.A., 2019. GIS-based approach estimation of area under \nwheat and other major rabi crops in district Ghotki and corresponding \nirrigation water requirements. ACTA Sci. Agri.  3 (12), 59-70. \n\n\n\nSmith, R.C.G., and Choudhury, B.J., 1990. On the correlation of indices of \nvegetation and surface temperature over south-eastern Australia. Int. J. \nRemote Sens. (11), 2113-2120. \n\n\n\nSonawane, K., Bhagat, V., 2017. Improved change detection of forests using \nLandsat TM and ETM+ data. Remote Sens. land. 1 (1), 18-40. \n\n\n\nSonawane, K., Bhagat, V., 2017. Improved change detection of forests using \nLandsat TM and ETM+ data. Remote Sens. land. 1 (1), 18-40. \n\n\n\nStroppiana, D., Antoinette, M., Brivio, P.A., 2014. Seasonality of MODIS LST \nover southern Italy and correlation with land cover, topography, and \nsolar radiation.  European J. Remote Sens.  47 (1), 133-152. \n\n\n\nWen, L.J., Peng, W.F., Yan, H., Wang, H., Dong, L.D., 2017. An analysis of land \nsurface temperature LST and its influencing factors in summer in \nwestern Sichuan Plateau: A case study of Xichang city. Remote Sens. \nLand & Res. 29 (2), 207-214. \n\n\n\nWeng, Q., Lu D., Schurbring, J., 2004. Estimation of land surface \ntemperature vegetation abundance relationship for urban heat island \nstudies. Remote Sens. Environ. 89, 467-483. \n\n\n\nXu, H., 2008. A new index for delineating built-up land features in satellite \nimagery. Int. J. Remote Sens. 29, 4269\u20134276. \n\n\n\nZhao, H.M., Chen, X.L. 2005. Use of Normalized Difference Bareness Index \nin Quickly Mapping Bare Areas from TM/ETM+. In Proceedings of 2005 \nIEEE International Geoscience and Remote Sensing Symposium, Seoul, \nKorea. 3, 25\u201329. \n\n\n\n \n\n\n\n\nhttp://www.pbscensus.gov.pk/\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 01-07 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2023.01.07 \n\n\n\n\n\n\n\n Cite the Article: Sehah, Urip Nurwijayanto Prabowo, Sukmaji Anom Raharjo, Resti Kurniati (2023). Utilization of Satellite Gravity Anomaly Data for  \nTwo-Dimensional Modeling of Subsurface Structure of Slamet Volcano, Central Java, Indonesia. Malaysian Journal of Geosciences, 7(1): 01-07. \n\n\n\n\n\n\n\n\n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\n\n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2023.01.07 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUTILIZATION OF SATELLITE GRAVITY ANOMALY DATA FOR TWO-DIMENSIONAL \nMODELING OF SUBSURFACE STRUCTURE OF SLAMET VOLCANO, CENTRAL JAVA, \nINDONESIA \n\n\n\nSehah*, Urip Nurwijayanto Prabowo, Sukmaji Anom Raharjo, Resti Kurniati \n\n\n\nDepartment of Physics, Jenderal Soedirman University, Street of Dr. Suparno No.61 Karangwangkal Purwokerto Central Java, Indonesia \nCorresponding Author Email: sehah@unsoed.ac.id  \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 04 November 2022 \nRevised 07 December 2022 \nAccepted 10 January 2023 \nAvailable online 16 January 2023 \n\n\n\n Study using the satellite gravity method has been carried out in the Slamet Volcano area, Central Java, \nIndonesia. The gravimetric satellite has produced gravity anomalies data which have been corrected up to \nfree air correction. This study purposes to determine the subsurface structure of Slamet Volcano by modeling \nresidual gravity anomalies data. Data processing which has been conducted includes bouguer correction, \nterrain correction, data reduction to a horizontal surface, separation of regional and residual anomalies data, \nmodeling, and interpretation. The residual gravity anomalies data obtained from the processing range from \n-40.62 \u2013 66.22 mGal. The anomalies data are modeled in two dimensions (2D) to obtain the subsurface \nstructure model of Slamet Volcano. The 2D-subsurface models on the AB and CD trajectories show the \npresence of magma chamber and several lava rocks which form the body of the volcano. The rocks are \ninterpreted as andesitic lava with a density value of 2.72 g/cm3, andesitic-basaltic lava with a density value \nof 2.89 g/cm3, basaltic lava with a density value of 2.97 g/cm3, and magma chamber with a density value of \n1.32 g/cm3. Such low value of magma chamber density relative to the surrounding rocks, indicates that Slamet \nVolcano is still active. \n\n\n\nKEYWORDS \n\n\n\nsatellite gravity method, Slamet Volcano, subsurface structure. \n\n\n\n1. INTRODUCTION  \n\n\n\nOne of the volcanoes that is still active in Central Java, Indonesia is Slamet \nVolcano, that is located in the border area of five regencies, i.e. Brebes, \nTegal, Pemalang, Purbalingga, and Banyumas  (Djafar and Nurlathifah, \n2020). Slamet Volcano has four craters at the top and all of them are still \nactive. Slamet Volcano is an active strato volcano with with 3428 m height \n(Harijoko et.al., 2020). Based on the data from the Department of Energy \nand Mineral Resources of Banyumas Regency, the depth of the magma \nchamber of Slamet Volcano is estimated to be between 5 \u2013 10 km \n(Sumarwoto, 2014). The geology of this volcano consists of old Slamet \nVolcano and young Slamet Volcano. The old Slamet Volcano is located in \nthe western, while the young Slamet Volcano is located in the eastern. The \nyoung Slamet volcano has a crater which is still active today (Arhananta \net.al., 2019). Since 1825, this volcano has had the shortest resting period \nof 1 year and the longest resting period of 19 years (Kusumadinata, 1979). \nIn 2019, there was an increase in alert status but no eruption has occurred. \nThe last eruption of the volcano occurred in 2014 with a strombolian type \n(Triastuty et.al., 2020). \n\n\n\nThe danger arising from volcanic eruptions is very high, considering the \nhigh level of activity and the dense population around the volcano. In \naddition, there are many national assets such as cultural heritage and \ntourist sites, and centers of public economic activity, transportation \nfacilities, agriculture, livestock, and educational facilities in the vicinity. \nTherefore, studies and research to minimize the impact caused by the \neruption of Slamet Volcano need to be done. Pre-mitigation activities can \n\n\n\nbe done through study and research to identify subsurface structure of the \nvolcano, especially the magma chamber (Chasanah et.al., 2021). The \nvolcano subsurface structure plays an important role in the processes that \noccur in it. By knowing the structure under the volcano, then the \ninterpretation related to the volcanic activity can be well understood. \n\n\n\nOne technique to know the subsurface structure of a volcano is through \nprocessing and modeling the satellite gravity anomalies data (Hwang and, \nParsons, 1995). The satellite gravity anomaly is the development of the \nrelative gravity method. The acquisition and processing of satellite gravity \nfield anomaly data is not carried out directly in the field, but can be \naccessed through an already available website. The data acquired are \ngravity field anomalies data which are assumed to have been corrected to \nfree air correction. Hence, for modeling purpose, only the bougeur and \nterrain corrections are carried out in data processing to obtain the \nComplete Bouguer Anomalies (CBA) data (Maulana and Prasetyo, 2019). \nFurthermore, the CBA data are reduced to a horizontal surface and \nseparated from regional anomalies data in order to obtaine residual \nanomalies data (Sehah et.al., 2021). The residual anomalies data can be \nassumed to originate from the local anomalous sources which are the \ntarget of this study. \n\n\n\n2. LITERATURE REVIEW \n\n\n\n2.1   Geological Setting \n\n\n\nSlamet Volcano is a quarter volcano (Bemmelen, 1949), which occupies \nthe westernmost part of the North Serayu Mountains. Slamet Volcano is a \n\n\n\n\nmailto:sehah@unsoed.ac.id\n\n\n\n\n\n\nCite the Article: Sehah, Urip Nurwijayanto Prabowo, Sukmaji Anom Raharjo, Resti Kurniati (2023). Utilization of Satellite Gravity Anomaly Data for  \nTwo-Dimensional Modeling of Subsurface Structure of Slamet Volcano, Central Java, Indonesia. Malaysian Journal of Geosciences, 7(1): 01-07. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 01-07 \n\n\n\n\n\n\n\n\n\n\n\ncomposite volcano with very large dimensions (50 \u2013 60 km in diameter), \nwhich covers tertiary deposit units around this area (Pratomo, 2012). A \ncomposite volcano or stratovolcano is a type of volcanic body formed from \nlava deposits mixed by other pyroclastic materials, such as ash, sand, and \nhardened gravel. The top of this volcano type can continue to grow due to \nheaps of lava and pyroclastic that comes out during an eruption. Volcanic \nactivity in this area is estimated to have been going on since the upper \nmiocene era which is marked by the presence of rocks the Kumbang \nformation units consisting of volcanic rocks both in land and sea \nenvironments, which occupies the central part of the Java Island (Pratomo, \n2012). \n\n\n\nThe stratigraphy of Slamet Volcano area consists of some geological \nformations (Djuri et.al, 1996). However, the three geological formations \n\n\n\nclosest to this volcano are laharic deposits, lava deposits, and \nundifferentiated volcanic rocks. The laharic deposits (Qls) are composed \nof laharic materials with boulders of andesitic-basaltic volcanic rocks \nwhich are produced from old Slamet Volcano, in holocene age. Then, the \nlava deposits (Qvls) are composed of andesitic lava of Slamet Volcano; very \nporous and have lots of cracks (Iswahyudi et.al., 2018), in pleistocene age. \nMeanwhile the undifferentiated volcanic rocks (Qvs) consits of breccias, \nlava, and tuffs, in pleistocene age. The origin of all Slamet Volcano rocks is \nmagma. Magma is a mixture of molten rock with various types of minerals \nand gases dissolved in it. Then, magma which comes out to the earth's \nsurface is called lava, whereas that cools and crystallizes is called igneous \nrock (Gill., 2010). The geological map of the reasearch area is shown in \nFigure 1 (Djuri et.al., 1996). \n\n\n\n\n\n\n\nFigure 1: The geological map of the research area (Djuri et.al., 1996) inserted with an image of Slamet Volcano. \n\n\n\n2.2   Gravity Method \n\n\n\nThe gravity method is based on Newton's law of attraction between two \nmass points. The value of the force between two mass points (m1 and m2) \nseparated by a certain distance (r) can be writen: \n\n\n\n1 2\n\n\n\n2\n( )\n\n\n\nm m\nF r G r\n\n\n\nr\n= \u2212\n\n\n\n\n\n\n\n(1) \n\n\n\nwhere F is the force (N), r is the distance between two masses points (m), \nm1 and m2 are the masses of each point or object (kg), and G is the universal \ngravitational constant (i.e. 6.67 \uf0b4 10-11 Nm2/kg2). Telford et al. (1990) have \nexplained this equation until the gravitational potential is obtained at a \npoint (P) on the Earth's surface with the equation: \n\n\n\n30\n02 22 2\n\n\n\n0 0\n\n\n\n( )\n( )P\n\n\n\nV V\n\n\n\nrG\nU r dm G d r\n\n\n\nr r r r\n\n\n\n\uf072\n= \u2212 = \u2212\n\n\n\n\u2212 \u2212\n\uf0f2 \uf0f2\n\n\n\n\n\n\n\n(2) \n\n\n\nwhere \n\n\n\n2 22 2\n\n\n\n0 0 02 cosr r r r r r \uf067\u2212 = + \u2212\n  \n\n\n\nWhen the volume integral is applied for the overall volume of the Earth, so \nthe gravitational potential at the Earth's surface can be determined. While \nthe gravity field can be acquired by differentiating the gravitational \npotential as shown in the following equation (Telford et.al., 1990): \n\n\n\n( ) ( )PE r U r= \u2212\uf0d1\n \n\n\n\n(3) \n\n\n\nThe Earth's gravitational field is often referred to as the gravitational \nacceleration and is given the symbol g. Based on Equation (3), the value of \nthe earth's gravitational field can be written by the equation (Telford et.al., \n1990): \n\n\n\n( ) ( ) ( )Pg r E r U r= \u2212 = \uf0d1\n \n\n\n\n(4) \n\n\n\nEquation (4) can be stated more fully into equation (5) as shown in the \nequation (Telford et.al., 1990): \n\n\n\n3\n\n\n\n0 0\n\n\n\n2 2 2 3/ 2\n\n\n\n( )\n( )\n\n\n\n( )\nV\n\n\n\nr z d r\ng r G\n\n\n\nx y z\n\n\n\n\uf072\n= \u2212\n\n\n\n+ +\uf0f2\n \n\n\n\n(5) \n\n\n\n\n\n\n\n3\n\n\n\n0 0 0\n\n\n\n3/2\n2 2 2\n\n\n\n0 0 0\n\n\n\n( ) ( )\n( )\n\n\n\n( ) ( ) ( ) )V\n\n\n\nr z z d r\ng r G\n\n\n\nx x y y z z\n\n\n\n\uf072 \u2212\n= \u2212\n\n\n\n\uf0e9 \uf0f9\u2212 + \u2212 + \u2212\uf0eb \uf0fb\n\uf0f2\n\n\n\n\n\n\n\n(6) \n\n\n\nEquation (6) shows that the value of the Earth's gravitational field is \ninfluenced by the position of latitude, longitude, altitude, and the \ndistribution of mass in the subsurface or density of the body mass in the \nsubsurface. Earth's gravitational field on the surface will be influenced by \nrocks with various densities. The geological structure also affects the \nvariation of the gravitational field on the surface, including uneven relief  \nof the earth's surface (rough topography). In a gravity survey method, the \nvalue of the gravitational field which is the data acquisition result is stated \nin gal unit (1 gal = 10-5 m/s2). But the gravity anomaly data measured in \nthe field are generally very small, in the milligal range (Lichoro, 2016). \n\n\n\n\n\n\n\n\nCite the Article: Sehah, Urip Nurwijayanto Prabowo, Sukmaji Anom Raharjo, Resti Kurniati (2023). Utilization of Satellite Gravity Anomaly Data for  \nTwo-Dimensional Modeling of Subsurface Structure of Slamet Volcano, Central Java, Indonesia. Malaysian Journal of Geosciences, 7(1): 01-07. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 01-07 \n\n\n\n\n\n\n\n\n\n\n\n3. RESEARCH METHOD \n\n\n\n3.1   Location and Time of Research \n\n\n\nThis study has been done in April \u2013 July 2021 at Geophysics Laboratory of \nJenderal Soedirman University, Purwokerto, Indonesia. The gravity \nanomalies data and the geographical position data have been accessed and \ndownloaded from URL: http://topex.ucsd.edu/cgi-bin/get_data.cgi, which \nhas been provided by the Scripps Institution of Oceanography, California \nUniversity, San Diego, USA (Sandwell and Smith, 2009). The anomalies \ndata acquired are gridded regularly in the ASCII-XYZ format in accordance \nwith the geographical position data. The spatial resolution for latitude and \nlongitude is 1 minute per grid, meanwhile the accuracy of the gravity \nanomalies data is 0.1 mGal and the altitude data is 1.0 m (Sandwell and \nSmith, 1997).  \n\n\n\n3.2   Research Equipment \n\n\n\nSeveral equipments used in this study consist of a laptop connected to the \ninternet to extract and download the satellite gravity anomalies data and \nthe geographic position data, the Google Earth to acquire the boundaries \nof the research area, and a local geological map as a guide in modeling and \ninterpretation. Meanwhile several softwares used consist of the Microsoft \nExcel 2019 for bouguer correction, the Gravity 900 for terrain correction, \nthe Fortran 77 for processing gravity anomalies data, and the Surfer for \ndepicting gravity anomaly contour map.  \n\n\n\n3.3   Research Procedure \n\n\n\nSatellite data acquired from accessing are free-air gravity anomalies data. \nThe anomalies data don\u2019t require free-air correction because the \nacquisition is done at the same elevation datum. The latitude correction is \nalso not required in the processing, because the satellites have calculated \nthe gravity values effect on differences in latitude positions. In addition, \nwith the distance from the earth mass center to the orbital trajectory of \nthe satellite, the difference in the gravitational acceleration value caused \nby the difference in latitude does not have much effect. Then, several \ncorrections commonly applied to the gravitymeter such as equipment \nheight correction and drift correction are also not required (Maulana and \nPrasetyo, 2019). Hence, only bougeur and terrain corrections were done \nin data processing to acquire the Complete Bouguer Anomalies (CBA) data \n(Putri et.al., 2019).  \n\n\n\nThe CBA data acquired are still distributed on the topographic surface that \nare a function of position of longitude, latitude, and altitude, so that they \ncan be written as \uf044g(\uf06c,\uf04a,h). Reduction of anomalies data to a horizontal \nsurface must be done, because the data must be spread at a horizontal \nsurface for the next processing (Blakely, 1995). One method which can be \napplied to reduce anomalies data to a horizontal surface is Taylor series \napproximation that can be expressed as equation (Blakely, 1995): \n\n\n\n( )\n\n\n\n( )\n( )\n\n\n\n( ) ][\n\n\n\n0\n\n\n\n0\n\n\n\n0\n\n\n\n]1[\n\n\n\n0\n\n\n\n,,\n!\n\n\n\n,,\n\n\n\n,,\n\n\n\ni\n\n\n\nn\n\n\n\nn\n\n\n\nn\n\n\n\nn\n\n\n\ni\n\n\n\nhg\nhn\n\n\n\nhh\nhg\n\n\n\nhg\n\n\n\n\uf04a\uf06c\uf04a\uf06c\n\n\n\n\uf04a\uf06c\n\n\n\n\uf044\n\uf0b6\n\n\n\n\uf0b6\u2212\n\u2212\uf044\n\n\n\n=\uf044\n\n\n\n\uf0e5\n\uf0a5\n\n\n\n=\n\n\n\n+\n\n\n\n\n\n\n\n(7) \n\n\n\nThe basic principle of the Taylor series is to use a derivative function at a \npoint to extrapolate the function around that point. Hence, this method can \nbe utilized to estimate the gravity anomaly values at points outside the \nobservation field. Equation (7) can be stated in the form of iteration; \nwhere \uf044g(\uf06c,\uf04a,h0) are CBA data which are spread on the horizontal surface. \nThe CBA data can be estimated through an approach; i.e. \uf044g(\uf06c,\uf04a,h0) data \nobtained from i-th iteration are used to acquire \uf044g(\uf06c,\uf04a,h0) data in the \n(i+1)-th iteration. The iteration can be carried out sufficiently to reach \nconvergent values (Blakely, 1995). Convergence of Equation (7) can be \nachieved quickly, if z0 is placed at the average topographical elevation of \nthe research area. For the initial guess values before iteration, \uf044g(\uf06c,\uf04a,h0) \non the right of Equation (7) is filled by \uf044g(\uf06c,\uf04a,h) data (Blakely, 1995). \n\n\n\nThe CBA data which are spread on the horizontal surface are still affected \nby subsurface densities originating from the deep and wide sources, that \nare called as regional gravity anomaly. Therefore, the regional gravity \nanomaly must be separated from the CBA data to obtaine the residual \ngravity anomalies data (Sehah et.al., 2020 and Ilapadila et.al., 2019). The \nregional gravity anomalies data can be obtained through the upward \ncontinuation process to a certain height, so that the anomalous data \nintervals have shown very small values and smooth patterns (Guo et.al., \n2013). The regional gravity anomalies data obtained, then corrected to the \nCBA data which have been distributed on the horizontal surface to obtaine \nthe residual gravity anomalies data as stated in the following equation \n\n\n\n(Blakely, 1995 and Sehah et.al., 2020): \n\n\n\n( ) ( )\n\n\n\n( )\n\n\n\n\uf05b \uf05d\n\uf04a\uf06c\n\n\n\n\uf04a\uf04a\uf06c\uf06c\n\n\n\n\uf04a\uf06c\n\n\n\n\uf070\n\n\n\n\uf04a\uf06c\uf04a\uf06c\n\n\n\ndd\n\n\n\nh\n\n\n\nhgh\n\n\n\nhghg res\n\n\n\n\uf0f2 \uf0f2\n\uf0a5\n\n\n\n\uf0a5\u2212\n\n\n\n\uf0a5\n\n\n\n\uf0a5\u2212 \uf044+\u2212+\u2212\n\n\n\n\uf044\uf044\n\n\n\n\u2212\uf044=\uf044\n\n\n\n3222\n\n\n\n0\n\n\n\n00\n\n\n\n)'()'(\n\n\n\n,,\n\n\n\n2\n\n\n\n,,,','\n\n\n\n\n\n\n\n(8) \n\n\n\nThe right term is the regional gravity anomalies data resulting from the \nupward continuation and \uf044h is the height of the upward continuation. The \nresidual anomalies data are assumed to only come from the local \nanomalous sources which are the target of the study (Quesnel et.al., 2008). \nIn this study, the target is the subsurface structure model of Slamet \nVolcano in two dimensions, including the rock layer which is thought to be \nthe location of the magma chamber. \n\n\n\n4. RESULTS AND DISCUSSION \n\n\n\n4.1   Results of Processing Data  \n\n\n\nAccessing the data has resulted satellite gravity anomalies data which are \nequivalent to gravity anomalies data which has been corrected up to free-\nair correction (Maulana and Prasetyo, 2019). These anomalies data area \ndownloaded from the Topex website The number of gravity anomalies \ndata which has been obtained is 648 points with a spacing of 1.8 km \nbetween points and an area of 47 km x 47 km. Free-air gravity anomalies \ndata obtained ranged from 16.10 \u2013 304.40 mGal. The high anomalous is \nconcentrated in the central part of the research area, around the cone of \nSlamet Volcano to be precise. The research area which is Slamet Volcano \nand its surrounding areas has a topographical elevation ranging from 19 \u2013 \n3,017 m based on the Topex data. \n\n\n\nAs explained in the Research Method section, the free-air gravity \nanomalies data were corrected by bouguer and terrain corrections to \nobtain the Complete Bouguer Anomalies (CBA) data. The bouguer \ncorrection is carried out to eliminate the mass influence located between \nthe measurement points on the topography to the datum that is not taken \ninto account, even though this mass greatly affects the gravity anomalies \ndata (Telford et.al., 1990). While terrain correction aims to eliminate the \neffect of mass around the measurement point. This correction arises due \nto the influence of the topography on gravity at the measurement point \n(i.e. due to the difference in elevation between the station and the base \nstation). Rough topography and large elevation differences such as \nmountains and/or hills around the measurement point can reduce the \ngravity field value. The magnitude of the terrain correction can be \ncalculated using the Hammer Chart method (Telford et.al., 1990). The CBA \ncontour map of the research area is shown in Figure 2, with anomalous \nvalues ranging from  8.22 \u2013 156.58 mGal. \n\n\n\nThe CBA data obtained are still distributed on the topographic, so that the \ndata must be reduced to a horizontal surface, so that they can be processed \nfurther. As described in the Research Method, the Taylor Series \napproximation was applied for this aim at the average topographic height \nof the study area, i.e. 513 m. CBA data obtained at an average topographical \nheight ranged from 15.94 \u2013 153.54 mGal. Furthermore the CBA data that \nhave been reduced to a horizontal surface are separated from regional \ncomponent of CBA data. The process of separating regional component \nfrom CBA data was carried out using the upward continuation method, as \ndescribed in the Research Methods section. The upward continuation \nprocess was carried out up to an altitude of 50,000 m to obtain regional \ncomponent of CBA data. The obtained regional component data of the CBA \ndata have values ranging from 74.93 \u2013 75.06 mGal. This regional anomaly \nis a component of the CBA data which provides information on sources of \ninternal anomalies, which are very subtle and have a low frequency, such \nas basements, folds, and regional faults. \n\n\n\nAfter the CBA data is corrected by the regional component data, the \nresidual gravity anomalies data are obtained (Sehah et.al., 2020). This \nlocal anomaly is a component of CBA data which has high frequency and \nrelatively complex closure representing shallow anomalous sources such \nas geothermal reservoirs dan magma chambers of Slamet Volcano. The \ndata have a value ranging from -58.99 - 78.49 mGal with contour map can \nbe seen in Figure 3. Based on this Figure 3, the residual anomalous contour \nmap tends to indicate a local pattern. The lowest anomalous values seen \nbelow the Slamet volcano cone is estimated to be magma chamber. The \ngravity anomalies values which is lower than the surrounding rocks shows \nthat the magma chamber of Slamet Volcano is relatively hot and liquid. \nTherefore, the magma chamber of this volcano is estimated to be still \nactive and productive. \n\n\n\n\n\n\n\n\nCite the Article: Sehah, Urip Nurwijayanto Prabowo, Sukmaji Anom Raharjo, Resti Kurniati (2023). Utilization of Satellite Gravity Anomaly Data for  \nTwo-Dimensional Modeling of Subsurface Structure of Slamet Volcano, Central Java, Indonesia. Malaysian Journal of Geosciences, 7(1): 01-07. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 01-07 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2: The Complete Bouguer Anomalies (CBA) map of Slamet Volcano area. \n\n\n\n\n\n\n\nFigure 3: The residual gravity anomaly map of the research area (include the A-B and C-D trajectories for modeling). \n\n\n\n4.2   Results of Modeling dan Interpretation \n\n\n\nThe modeling has been carried out on the residual anomalies data, because \nthese data represent the geological structures and subsurface rocks with \nrelatively shallow depths (not depths in a regional scale) (Quesnel et.al., \n2008). This modeling aims to deliniate subsurface geological structure in \ntwo dimensions (2D), including the magma chamber of Slamet Volcano. In \norder the modeling results to approach the actual conditions, then \ngeological information support is needed. The input data for this modeling \nis residual anomalies data that are supported by topographical data of the \nstudy area. This modeling has been done using the forward and inverse \nmethod by means of curve matching between the observation anomalous \ncurve and the calculation anomalous curve (Guglielmetti et.al., 2021). \nModeling on the anomalous data along the trajectory has resulted in a \nsubsurface structure cross-sectional model. The trajectories modeled are \nthe AB and CD trajectories such as shown in Figure 3. The results of 2D-\nmodeling on these trajectories can be seen in Figure 4 and Figure 5. These \nfigures show the location and depth of the magma chamber of Slamet \nVolcano and other rock layer structures. Magma chamber is a large \nchamber where molten rock accumulates under the earth's surface. \n\n\n\nTwo dimensional modeling has been carried out on the residual gravity \nanomalies data of the A-B trajectory. This trajectory stretches at \n\n\n\ngeographic position of 109.18\uf0b0 \u2013 109.22\uf0b0 E and 7.05\uf0b0 \u2013 7.38\uf0b0 S with a \n\n\n\nlength of about 32,470 m. The depth of the top of each model ranges from \n0 \u2013 2,053 m from the average topographic. Two dimensional modeling on \nthe residual anomalies data has also been done on the C-D trajectory. This \n\n\n\ntrajectory is located at position of 109.04\uf0b0 \u2013 109.33\uf0b0 E and 7.13\uf0b0 \u2013 7.30\uf0b0 S \n\n\n\nwith a length of about 34,130 m. The top of each model has a depth of 0 \u2013 \n2,110 m measured from the average topographic surface. The results of \nthe interpretation of each model object on the AB and CD trajectories are \nshown in Table 1 and Table 2. The subsurface structure modeling is not \ncarried out until the topographic surface, considering that the anomalies \ndata is at the average topographic elevation after reduction to the \nhorizontal surface is applied.  \n\n\n\nThe modeling results show three rock formations, consisting of basaltic \nlava rocks with a density of 2.97 g/cm3, basaltic-andesite lava rocks with a \ndensity of 2.89 g/cm3, and andesite lava rocks with a density of 2.72 g/cm3. \nIn addition, in the center part of the volcanic body, an anomalous object \n\n\n\n\n\n\n\n\nCite the Article: Sehah, Urip Nurwijayanto Prabowo, Sukmaji Anom Raharjo, Resti Kurniati (2023). Utilization of Satellite Gravity Anomaly Data for  \nTwo-Dimensional Modeling of Subsurface Structure of Slamet Volcano, Central Java, Indonesia. Malaysian Journal of Geosciences, 7(1): 01-07. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 01-07 \n\n\n\n\n\n\n\n\n\n\n\nmodel with a density of 1.32 g/cm3 is also obtained which can be \ninterpreted as a magma chamber of Slamet Volcano. The density value is \nrelatively small compared to other rocks in the Earth's crust, thus \nindicating that the magma is molten with high temperature. This will \nproduce a buoyant force on the magma that tends to push it upwards. This \n\n\n\nmagma is also under great pressure, and after getting enough time and \npressure, it can break the rock above it to make a gap for the magma. If \nmagma finds a gap to the surface, a volcanic eruption can occur. Magma \nwhich has reached the surface can flow along the slopes or immediately \nfreeze at the top. \n\n\n\n\n\n\n\nFigure 4: Schematic of modeling results of subsurface rock section of Slamet Volcano on the trajectory A-B. \n\n\n\n\n\n\n\nFigure 5: Schematic of modeling results of subsurface rock section of Slamet Volcano on the trajectory C-D. \n\n\n\nTable 1: The Results of the Lithological Interpretation of the Modeling Results on the AB Trajectory \n\n\n\nNo. Density (g/cm3) Top Depth (m) Lithological Interpretation \n\n\n\n1 2,72 0 Andesitic lava \n\n\n\n2 2,89 67 Basaltic-andesitic lava  \n\n\n\n3 2,97 255 Basaltic lava \n\n\n\n4 1,32 2053 Magma Chamber \n\n\n\nNote: The depth of the object is measured from the average topographic elevation of the research area. \n\n\n\n \nTable 2: The Results of the Lithological Interpretation of the Modeling Results on the CD Trajectory \n\n\n\nNo. Density (g/cm3) Top Depth (m) Lithological Interpretation \n\n\n\n1 2,72 0 Andesitic lava \n\n\n\n2 2,89 100 Basaltic-andesitic lava \n\n\n\n3 2,97 179 Basaltic lava \n\n\n\n4 1,32 2110 Magma Chamber \n\n\n\nNote: The depth of the object is measured from the average topographic elevation of the research area. \n\n\n\n \n4.3   Analysis and Discussion \n\n\n\nThe residual gravity anomaly contour map shows high anomaly values \naround the volcanic cone as shown in Figure 3. These zones are estimated \nto be rock zones with high density due to magma freezing in the ancient \ntimes. The rock complexes are estimated to be above the tertiary rock \nunits in the form of Miocene marine sediment deposits which later became \nthe basement for the Slamet Volcano area (Pratomo, 2012). The basement \nrocks consist of the Halang and the Rambatan formations which are \nunconformably overlain by volcanic deposits from the Kumbang \nformation in late miocene and the greenish coarse sandstone and \n\n\n\nconglomerate from the Tapak rock formation in pliocene age (Djuri et.al., \n1996). \n\n\n\nThe regional stratigraphy of the the research area is assumed to be \nequivalent to the stratigraphy of Western North Serayu Mountains and \nEastern Bogor Zone, which consist of Halang formation in the middle \nmiocene age; dacitic, andesitic, and dioritic rock intrusions in the late \nmiocene age; old quaternary rock formation in pleistocene age; as well as \nalluvial and young volcanic rocks in the holocene age (Bemmelen, 1949). \nMeanwhile the residual gravity anomaly contour indicates a local pattern \ncontrolled by the quaternary deposits. The deposits are dominated by \n\n\n\n\n\n\n\n\nCite the Article: Sehah, Urip Nurwijayanto Prabowo, Sukmaji Anom Raharjo, Resti Kurniati (2023). Utilization of Satellite Gravity Anomaly Data for  \nTwo-Dimensional Modeling of Subsurface Structure of Slamet Volcano, Central Java, Indonesia. Malaysian Journal of Geosciences, 7(1): 01-07. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 01-07 \n\n\n\n\n\n\n\n\n\n\n\nvolcanic rock deposits which are resulted from the eruption of Slamet \nVolcano, which consists of volcanic rock in the form of pyroclastic falls, \nboth loose or petrified deposits, and basaltic lava that is spread widely. A \npyroclastic fall is a uniform deposit of material which has been ejected \nfrom a volcanic eruption or plume such as an ash fall or tuff (Saucedo, et.al., \n2005). \n\n\n\nBased on the results of modeling and lithological interpretation as shown \nin Table 1 and Table 2, the magma chamber has a lower density (i.e. 1,32 \ng/cm3) than the surrounding rocks. This can cause magma to be pushed \nto the earth's surface, if there is sufficient pressure. Magma is an \nincandescent liquid found in the rock layers of the earth with a very high \ntemperature, which is estimated to be more than 1,000 C (Anonimous, \n2015). Magma which has come out of a volcano crater is called lava, which \nhas a temperature that ranges from 700 -1,200 C (Anonimous, 2018). \nLava and ash that comes out through the eruption every time in a long time \nwill accumulate and form volcano body as shown in the results of the \nmodeling such as Figure 4 and Figure 5. When the volcano is in the marine \nenvironment, the eruption produces basaltic lava with a density value of \n2.97 g/cm3. In the next time there was a change from basaltic lava to \nandesitic-basaltic lava (2.89 g/cm3), until finally it became andesitic lava \n(2.72 g/cm3) as shown in those figures, along with changes in the volcanic \nenvironment from sea to land (Pratomo, 2012). \n\n\n\nThe modeling results show andesitic lava near the surface of Slamet \nVolcano with a density of 2.72 g/cm3. The andesite lava deposits are \nestimated to spread to the topographical surface, as shown on the \ngeological map, except the western part of the body of the volcano. In this \narea, the andesite lava rocks have weathered due to exogenous forces on \nundifferentiated volcanic rocks. According to the geological map, the \nupper stratigraphy of the area consists of the andesite lava and \nundifferentiated volcanic rocks as shown in Figure 1 (Djuri et.al., 1996). \nThe two formations are pleistocene in age. The modeling results show that \nunder the andesite lava deposits, there are andesitic-basaltic lava and \nbasaltic lava deposits. The formation of the rock deposits is estimated to \nbe in accordance with the conditions of the volcanic environment (mainly \nat the eruption) in ancient times, as described in the previous paragraph. \n\n\n\nSatellite gravity anomalies data from Topex have been successfully used \nto model the subsurface structure of Slamet Volcano, Indonesia. Gravity \ndata acquisition activity was not carried out in situ, considering that this \nvolcano is very large, with difficult field conditions and very extreme \ntopography. The spatial resolution for the latitude and longitude of the \nTopex data is 1 minute per grid (Sandwell and Smith, 1997); this means \nthat the spacing of 1 grid is about 1.8 km. Given the relatively large spatial \nresolution, the modeling and interpretation in this reserach is only limited \nto estimate the rock formations which make up Slamet Volcano, not \nmodeling the rock types in detail. As for more detailed subsurface \nstructure modeling, satellite gravity anomalies data from GGMplus can be \napplied (Apriliani et.al., 2021). \n\n\n\n5. CONCLUSION \n\n\n\nThe research using the satellite gravity method to interpret the two-\ndimensional subsurface structure model has been carried out for the \nSlamet Volcano area in Central Java, Indonesia. The gravimetric satellite \nhas resulted free-air gravity anomalies data and geographical position \ndata of the research area. Data processing which has been carried out \nincludes bouguer correction, terrain correction, reduction to the \nhorizontal surface, and separation of the regional anomalies data. The \nresults of processing data are the residual gravity anomalies data with \nvalues ranging of -40.62 \u2013 66.22 mGal that describe the subsurface local \nstructure of the research area. The residual anomalies data are modeled in \ntwo dimensions, so that several models of the subsurface structure of \nSlamet Volcano are obtained. The 2D-models under the AB and CD \ntrajectories on the residual anomaly contour map show the presence of \nmagma chamber and some lava rocks that form volcano body. These rocks \nare interpreted as andesitic lava rocks with a density value of 2.72 g/cm3, \nandesitic-basaltic lava rocks with a density value of 2.89 g/cm3, basaltic \nlava rocks with a density value of 2.97 g/cm3, as well as magma chamber \nwith a density value of 1.32 g/cm3. The density of the magma chamber \nthat is relatively lower than the other rocks in the earth's crust indicates \nthat Slamet Volcano is still active. \n\n\n\nACKNOWLEDGMENT  \n\n\n\nThe authors would like to thank the Rector and Chairman of Research and \nCommunity Service Institute of Jenderal Soedirman University for the \nresearch funding provided. We also would like to express gratitude to the \nmember of the research teams of the geophysical interest team of Jenderal \nSoedirman University for the collaboration in the research. \n\n\n\nREFERENCES \n\n\n\nAnonimous, 2015. Volcanoes, Magma, and Volcanic Eruptions. Tulane \nUniversity. USA. Available in: \nhttp://www2.tulane.edu/~sanelson/Natural_Disasters/volcan&m\nagma.htm [Accessed: November 7, 2022]. \n\n\n\nAnonimous, 2018. Lava\u2019s Study; Our work in temperatures from 700 to \n1,200 \u00b0C (1,292 to 2,192 \u00b0F). Available in: \nhttps://sixnfive.com/projects/lavas-study/ [Accessed: November \n7, 2022]. \n\n\n\nApriliani, R., Indriana, R.D., Harmoko, U., Yulianto, T., 2021. The GGMplus \nData Analysis for Modeling of the Kelimutu Volcanic Subsurface. \nInternational Journal of Software & Hardware Research in \nEngineering, 9(6), Pp. 9-15. \n\n\n\nArhananta, Mahyudani A., Barizi, A.R.F., Ahmad, O., Kurniawan, D.R., \nHarjanto, A., 2019. Conservation of Northwest Slope of Mount \nSlamet and Identification of Hot Spring Manifestations in Guci Area, \nTegal, Central Java. Proceedings of the Seminar on Earth and Marine \nTechnology I at the Adhitama Institute of Technology, Surabaya. \nAugust 24, 2019. \n\n\n\nBammelen, R.W.V., 1949. The Geology of Indonesia, Vol. IA: General \nGeology of Indonesia and Adjacent Archipelagoes. Government \nPrinting Office. The Haque. Pp. 732. \n\n\n\nBlakely R.J., 1995. Potential Theory in Gravity and Magnetic Applications. \nCambridge University Press. USA. Pp. 464. \n\n\n\nChasanah, U., Febriani, A.D.A., and Minarto, E., 2021. Estimation of the \nSubsurface Structure of Mount Merapi Based on Satellite Imagery \nGravity Anomaly Data Analysis. Physical Journal \u201cFlux\u201d, 18(1), Pp. \n25-34. \n\n\n\nDjafar, A., and Nurlathifah, A., 2020. Identification of the Geological \nDiversity of the Cinder Cone of Slamet Volcano as a Geotourism \nObject. Bulletin of Scientific Contribution: GEOLOGY, 18(1), Pp. 13-\n24. \n\n\n\nDjuri, M., Samodra, H., and Gafoer, S., 1996. Geological Map of The \nPurwokerto and Tegal Quadrangles, Java, Scale 1:100,000. \nGeological Research and Development Center. Bandung. Indonesia. \n\n\n\nGill, R., 2010. Igneous Rocks and Processes; A Practical Guide. Wiley-\nBlackwell. Malaysia. Pp. 428. \n\n\n\nGuglielmetti, L., and Moscariello, A., 2021. On The Use of Gravity Data in \nDelineating Geologic Features of Interest for Geothermal \nExploration in The Geneva Basin (Switzerland): Prospects and \nLimitations. Swiss Journal of Geosciences, 114, Pp. 2-20.  \n\n\n\nGuo, L., Meng, X., Chen, Z., Li, S., and Zheng, Y., 2013. Preferential Filtering \nfor Gravity Anomaly Sparation. Computers and Geosciences, 51, Pp. \n247-254.  \n\n\n\nHarijoko, A., Milla, A.N., Wibowo, H.E., Setiawan, N.I., 2020. Magma \nEvolution of Slamet Volcano, Central Java, Indonesia Based on Lava \nCharacteristic. IOP Conf. Series: Earth and Environmental Science \n451 (2020) 012092. \n\n\n\nHwang, C., and Parsons, B., 1995. Gravity Anomalies Derived from Seasat, \nGeosat, ERS-1 and TOPEX/POSEIDON Altimetry and Ship Gravity: A \nCase Study Over the Reykjanes Ridge. Geophysical Journal \nInternational, 122, Pp. 551-568. \n\n\n\nKusumadinata, K. 1979. Base Data of Indonesian Volcanoes. Directorate of \nVolcanology. Republic of Indonesia. Available in: \nhttps://vsi.esdm.go.id/index.php/gunungapi/data-dasar-\ngunungapi/529-g-slamet [Accessed: May 7, 2022]. \n\n\n\nIlapadila, Herimei, B., and Maria, 2019. Analysis of Regional Anomaly on \nMagnetic Data Using the Upward Continuation Method. The \nInternational Conference on Geoscience. IOP Conf. Series: Earth and \nEnvironmental Science 279 (2019) 012037. Pp. 6. \n\n\n\nIswahyudi, S., Jati, I.P., and Setijadi, R., 2018. Preliminary Study of the \nGeology of Tirta Marta Lake, Purbalingga, Central Java. Jurnal Ilmiah \nDinamika Rekayasa, 14(2), Pp. 86-91. \n\n\n\nLichoro C.M., 2016. Gravity and Magnetic Method. Presented at SDG Short \nCourse I on Exploration and Development of Geothermal Resources: \n\n\n\n\n\n\n\n\nCite the Article: Sehah, Urip Nurwijayanto Prabowo, Sukmaji Anom Raharjo, Resti Kurniati (2023). Utilization of Satellite Gravity Anomaly Data for  \nTwo-Dimensional Modeling of Subsurface Structure of Slamet Volcano, Central Java, Indonesia. Malaysian Journal of Geosciences, 7(1): 01-07. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 01-07 \n\n\n\n\n\n\n\n\n\n\n\nOrganized by UNU-GTP, GDC and KenGen, at Lake Bogoria and Lake \nNaivasha, Kenya, Nov. 10-31, 2016. \n\n\n\nMaulana, A.D. and Prasetyo, D.A., 2019. Mathematical Analysis on Bouguer \nCorrection and Field Correction on Topex Satellite Gravity Data and \nApplication in Geohazard; A Case Study of the Palu Koro Fault, \nCentral Sulawesi. Jurnal Geosaintek, 5(3), Pp. 91-100. \n\n\n\nPratomo, I., 2012. Geological Diversity of Mount Slamet Volcanic Complex, \nCentral Java. Mount Slamet Ecology: Geology, Climatology, \nBiodiversity and Social Dynamics. Biology Research Center - \nIndonesian Institute of Sciences in collaboration with Jenderal \nSudirman University, Indonesia. Pp. 15-30. \n\n\n\nPutri, D.R., Nanda, M., Rizal, S., Idroes, R., and Ismail, N., 2019. \nInterpretation of Gravity Satellite Data to Delineate Structural \nFeatures Connected to Geothermal Resources at Bur Ni Geureudong \nGeothermal Field. IOP Conference Series: Earth and Environmental \nScience, 364 (2019) 012003. Pp. 6. \n\n\n\nQuesnel, Y., Langlais, B., Sotin, C., and Galdeano, A., 2008. Modelling and \nInversion of Local Magnetic Anomalies. Journal of Geophysical \nEngineering, 5, Pp. 387\u2013400.  \n\n\n\nSandwell D.T. and Smith H.F., 1997. Global Seafloor Topography from \nSatellite Altimetry and Ship Depth Soundings. Science, 277, Pp. \n1957-1962. \n\n\n\nSandwell D.T. and Smith H.F., 2009. Global Marine Gravity from Retracked \nGeosat and ERS\u20101 Altimetry: Ridge Segmentation Versus Spreading \nRate. Journal of Geophysical Research, 114(B1), Pp. 1 \u2013 18. \n\n\n\nSaucedo, R., Macias, J.L., Sheridan, M.F., Bursik, M.I., and Komorowski, J.C., \n2005. Modeling of Pyroclastic Flows of Colima Volcano, Mexico: \nImplications For Hazard Assessment. Journal of Vulcanology and \nGeothermal Research (Elsevier), 139, Pp. 103-115. \n\n\n\nSehah, Raharjo, S.A., and Risyad, A., 2020. A Geophysical Survey with \nMagnetic Method for Interpretation of Iron Ore Deposits in the \nEastern Nusawungu Coastal, Cilacap Regency Central Java \nIndonesia. Journal of Geoscience Engineering, Environment, and \nTechnology (JGEET), 5 (1), Pp. 47-55. \n\n\n\nSehah, Raharjo, S.A., Prabowo, U.N., and Sutanto, D.S., 2021. Interpretation \nof Magnetic Anomaly Data in the Andesitic Rock Prospect Area of \nKutasari Subregency, Purbalingga Regency, Central Java, Indonesia. \nIndonesian Journal on Geoscience, 8(3), Pp. 345-357. \n\n\n\nSumarwoto and Zainal A., 2014. ESDM: Character of Mount Slamet \nEruption Weakens. Antarajateng. Available in: \nhttps://jateng.antaranews.comand/berita/94350/esdm-karakter-\nerupsi-gunung-slamet-melemah [Accessed: February 3, 2022]. \n\n\n\nTelford W.M., Gedaart L.P, and Sheriff R.E., 1990. Applied Geophysics. \nCambridge. New York. Pp. 744. \n\n\n\nTriastuty, H., Mulyana, I., Surmayadi, M., Alfianti, H., Ipmawan, V., Rusdi, \nM., Kriswati, E., Sulton, F., 2020. Comparative Study of Mount Slamet \nActivity: Crisis Period 2019 with 2014 Eruption. Proceedings of the \n2019 Volcanic Studies Colloquium. Center for Volcanology and \nGeological Hazard Mitigation. Geological Agency, Republic of \nIndonesia. Pp. 45-54. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG)  3(1) (2019) 61-65 \n\n\n\nCite The Article: Dora B. Umoetok, Etim D. Uko, Aminayanasa P. Ngeri (2019). Attenuation Of Water-Bottom Multiples: A Case Study From Shallow Marine In The Niger \nDelta, Nigeria. Malaysian Journal of Geosciences, 3(1) : 61-65. \n\n\n\n\n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 10 June 2019 \nAccepted 15 July 2019 \n\n\n\nAvailable online 1 August 2019 \n\n\n\nABSTRACT\n\n\n\nABSTRACT \n\n\n\nThree-dimensional (3D) seismic data in the shallow offshore Niger Delta was acquired and processed. The dataset \nat water-depth of 250m was masked by interbed multiples and water bottom multiples.  The major differences \nexploited for the multiples removal were velocity discrimination, frequency, wavelength, periodicity, and \npredictability using predictive deconvolution and Radon techniques. The dominant frequency of the primary events \nvaries between 3 and 120Hz having dominant amplitude ranging between -12dB and -45dB.  The dominant \nfrequency of the multiples ranges between 8 and 90Hz, while dominant amplitude ranges between -5dB and -45dB. \nMultiples were predominantly short-period with water bottom reverberation having high-frequency and high-\namplitude. With the short-period and high-frequency content, the characteristics of the multiples were quite close \nto those of the primaries. Water-depth and geology are major generators of the waterbottom and interbed multiple \nenergies in a marine environment. The multiple parameters established in this work would be required as inputs in \nthe multiples-attenuation processing program for a better image of the subsurface geology.  \n\n\n\n KEYWORDS \n\n\n\nSeismic, processing, multiples, attenuation, shallow marine, Niger Delta, Nigeria. \n\n\n\n1. INTRODUCTION \n\n\n\nThe occurrence of strong primary reflections necessarily implies the \n\n\n\noccurrence of strong multiples. Multiples are delayed reflections that \n\n\n\ninterfere with the primary reflections. The delay occurs because the \n\n\n\nreflection energy, reverberating between two layers that are highly \n\n\n\nreflective, has taken a more complex and longer ray path from source to \n\n\n\nreceiver. While any two layers with high reflectivity can create a multiple \n\n\n\nimage, it is energy reverberating between the seafloor and the sea surface \n\n\n\nthat is the most important issue to deal with. The three main methods of \n\n\n\nmultiples removal are: treat the multiple as a long, ringing wavelet, and \n\n\n\nuse signal processing to simplify it; create a model of the multiple and try \n\n\n\nand subtract it from the data, and use the difference in stacking velocity of \n\n\n\nthe primary and multiple to remove the multiple [1-5]. \n\n\n\nThe modelling approaches, such as SRME (Surface Related Multiple \n\n\n\nElimination), usually require the primary seafloor reflection to be the first \n\n\n\nclear signal. The velocity discrimination method uses the Parabolic Radon \n\n\n\nTransform (PRT) approach. The Radon transform is a generic \n\n\n\nmathematical procedure where input data in the frequency domain are \n\n\n\ndecomposed into a series of events in the RADON domain. One of the key \n\n\n\nelements of the PRT as a demultiple method is that a Normal Moveout \n\n\n\n(NMO) correction is applied to the data. This helps do two things: (i) target \n\n\n\nthe primaries and multiples \u2013 for example we can use water velocity or a \n\n\n\npicked primary trend, and (ii) approximate the reflections (which are \n\n\n\noriginally hyperbolic) through a series of parabolic curves [6].   \n\n\n\nIn marine areas, the sea-floor (water bottom) and free water surface have \n\n\n\nlarge acoustic impedance contrasts and therefore, generate multiples \n\n\n\nwhich are recorded together with the desired primary reflections [7- 10]. \n\n\n\nThe presence of multiples often has an obscuring effect in the \n\n\n\ninterpretation of target reflections in the seismic section. Multiples always \n\n\n\nreduce the signal-to-noise ratio, interfere with the identification of \n\n\n\nprimaries and lead to difficulties in velocity analysis and in migration. \n\n\n\nMultiples can present pitfalls for the interpreter and cause problems in \n\n\n\nseismic data interpretation. Multiples can result in spurious images and \n\n\n\namplitudes and interpretation uncertainties. The aim of this research is to \n\n\n\nattenuate multiples in order to appropriately image the geology and \n\n\n\nprovide appropriate interpretable image of the subsurface, thereby \n\n\n\nreducing the risk of drilling dry wells.  \n\n\n\n2. THEORETICAL BACKGROUND \n\n\n\nMany different methodologies have been developed based on different \n\n\n\ncharacteristics of the multiples in the history of the multiple attenuation \n\n\n\nresearch [11-15]. Multiple attenuation methods can be briefly classified \n\n\n\ninto three categories: (1) methods which rely on the natural periodicity of \n\n\n\nmultiples or use a transform to introduce periodicity into the data; (2) \n\n\n\nmethods that discriminate primaries and multiples according to some \n\n\n\nspecific features or properties; (3) methods which predict and then \n\n\n\nsubtract the multiple reflections from input seismic data with wave-\n\n\n\nequation theories.  \n\n\n\nMultiple-suppression techniques make use of the different characteristics \n\n\n\nthat distinguish multiple from primary reflections. The characteristics \n\n\n\nmost frequently used are the moveout differences between multiple and \n\n\n\nprimary and the periodic nature of multiples due to the fact that multiples \n\n\n\nare repetitions of some primary reflection [16-18]. The first characteristic \n\n\n\nmay be exploited to attenuate the multiples by stacking the data or \n\n\n\nmoveout filters [19-21]. Short-period multiples are approximately \n\n\n\nperiodic and may be discriminated using predictive and adaptive \n\n\n\ndeconvolution methods [22,23]. Another common approach is \n\n\n\nsuppression based upon wave-equation methods [24-27].  \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : http://doi.org/10.26480/mjg.01.2019.61.65 \n\n\n\n ATTENUATION OF WATER-BOTTOM MULTIPLES: A CASE STUDY FROM SHALLOW \nMARINE IN THE NIGER DELTA, NIGERIA \n\n\n\nDora B. Umoetok1, Etim D. Uko2, Aminayanasa P. Ngeri2 \n\n\n\n1Integrated Data Sciences Limited, Nigeria National Petroleum Corporation, Benin City, Nigeria. \n2Department of Physics, Rivers State University, PMB 5080, Port Harcourt, Nigeria. \n*Corresponding Author Email: umoetok.dorathy@nnpc-idsl.com, e_uko@yahoo.com, paddyngeri@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\n\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\n\nmailto:paddyngeri@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  3(1) (2019) 61-65 \n\n\n\nCite The Article: Dora B. Umoetok, Etim D. Uko, Aminayanasa P. Ngeri (2019). Attenuation Of Water-Bottom Multiples: A Case Study From Shallow Marine In The Niger Delta, \nNigeria. Malaysian Journal of Geosciences, 3(1) : 61-65. \n\n\n\nRadon de-multiple method is one of the methods of multiple attenuation. \n\n\n\nIt generally consists of three stages: decomposition, modelling and \n\n\n\nsubtraction. This scheme usually decomposes data (i.e. both primaries and \n\n\n\nmultiples) into parabolas as this allows the method to operate in the \n\n\n\nfrequency domain. It models the multiples by muting in the Radon domain, \n\n\n\nand then subtracts the resulting model from the original data. Both the \n\n\n\nmodelling and subtraction are generally performed with least squares \n\n\n\nalgorithms in the frequency domain. The Radon de-multiple method \n\n\n\nperforms well with the correct primary velocity and adequate move-out \n\n\n\ndifferences between the primaries and multiples [28-30]. \n\n\n\n3. THE STUDY AREA AND GEOLOGICAL SETTING\n\n\n\nThe area of study is within the offshore portion of the Niger Delta Province. \n\n\n\nIt is delineated by the geology of southern Nigeria and south-western \n\n\n\nCameroon (Figure 1) in the Gulf of Guinea. The Gulf of Guinea consists of \n\n\n\nthe coastal and offshore areas of Cote d\u2019lvoire, Ghana, Togo, and Benin, and \n\n\n\nthe western part of the coast of Nigeria, from the Liberian border east to \n\n\n\nthe west edge of the Niger Delta. The province includes the Ivory Coast, \n\n\n\nTano, Central, Saltpond, Keta, and Benin basins and the Dahomey \n\n\n\nEmbayment. The Gulf of Guinea formed at the culmination of Late Jurassic \n\n\n\nto Early Cretaceous tectonism that was characterized by both block and \n\n\n\ntransform faulting superimposed across an extensive Paleozoic basin \n\n\n\nbreakup of the African, North American, and South American \n\n\n\npaleocontinents [31]. The deep offshore contains Alba, Azurita and Zafiro \n\n\n\nCanyons [32-34]. \n\n\n\nFigure 1: Map of Niger Delta showing the area of study [18, 15]. \n\n\n\n4. MATERIALS AND METHODOLOGY\n\n\n\n4.1 Data Acquisition \n\n\n\nIn the areas under this study, the data are from Shallow Marine (250m \n\n\n\ndeep) in the Gulf of Guinea, off the Niger Delta. Two traverse lines \n\n\n\narbitrarily selected - inline and crossline from sample datasets - are taken \n\n\n\nfrom the offshore Niger Delta area of southern Nigeria to demonstrate the \n\n\n\nimpact of multiple removal from a seismic data. The data is with water \n\n\n\ndepth of 250meters (shallow marine). Acquisition parameters for datasets \n\n\n\nare shown in Table 1. Figure 2 shows the raw Stack for dataset for Inline \n\n\n\n1001 masked by noise and multiples. \n\n\n\nTable 1: Acquisition Parameters for shallow marine \nParameters Shallow Marine \n\n\n\nstreamer \n\n\n\nRecording length 6s \n\n\n\nSample rate 2ms \n\n\n\nBin size 12.5 x 18.75m \n\n\n\nFar offset 4500m \n\n\n\nCoverage (fold) 45 \n\n\n\nChannels 360 \n\n\n\nCables 8 \n\n\n\nCable depth 7m \n\n\n\nCable separation 100m \n\n\n\nGroup interval Group interval 12.5 \n\n\n\nSource \n\n\n\nSource power 2120ci 2000psi \n\n\n\nSource depth 5m \n\n\n\nShot point interval 25m \n\n\n\nFigure 2: Raw Shots for Dataset for Inline 1001 \n\n\n\n4.2 Data Processing  \n\n\n\nThe data processing was executed on a 64 bit processing system \n\n\n\ncomprising of a 32-Node pc cluster with 50tb online storage disk. Job \n\n\n\nsubmission was done using an RS690 engineering workstation as front \n\n\n\nend machine. Proprietary software with client-server architecture \n\n\n\ndeveloped by Information Technology (IT) department of Integrated Data \n\n\n\nServices Limited was used for the processing. The first multiple \n\n\n\nattenuation method applied to the data was filtering based on move-out \n\n\n\nand dip discrimination. This determined the velocities of Multiples energy \n\n\n\nand separates them from primaries. The common-mid-point (CMP) \n\n\n\ngathers, semblance and stack response are all placed side by side for \n\n\n\nquality control purposes. The CMP gathers were NMO-corrected with \n\n\n\nvelocity functions that are between the primary and multiple velocities, \n\n\n\nsuch that the overcorrected primaries map into the negative wave \n\n\n\nnumbers plane and the under-corrected multiples map unto the positive \n\n\n\nwave numbers. By muting the data for the positive wave numbers, \n\n\n\nmultiples were suppressed. \n\n\n\nAn NMO correction was applied to make the originally hyperbolic events \n\n\n\nin CMP gathers nearly parabolic in the x-t domain, thereby mapping them \n\n\n\ninto approximately discrete points after the parabolic transform. Yilmaz \n\n\n\n(1989) improved on Hampson\u2019s method by replacing the NMO correction \n\n\n\nprior to the transform with a stretching of the CMP data. This converts the \n\n\n\nhyperbolic events to exact parabolas, improving the velocity resolution \n\n\n\nand hence the primary-multiple discrimination in the transform domain.  \n\n\n\n5. RESULTS AND DISCUSSION \n\n\n\n5.1 Velocity Discrimination  \n\n\n\nA preliminary 2km by 2km velocity analysis was performed on 8 CMP \n\n\n\nlocations. Inline stack was displayed to validate the result of the velocities. \n\n\n\nCMP velocity function for dataset and the raw data without velocity \n\n\n\napplied are shown in Figures 3 - 5. Figure 5 clearly shows the effect of \n\n\n\nvelocity application on dataset. It is clear from results that stack velocity \n\n\n\nhas resolved some of the structures in the dataset. Improvement in the \n\n\n\ndataset is very pronounced because of the gentle subsurface structures.  \n\n\n\nFigure 3: Velocity analysis for dataset for Inline 1001. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  3(1) (2019) 61-65 \n\n\n\nCite The Article: Dora B. Umoetok, Etim D. Uko, Aminayanasa P. Ngeri (2019). Attenuation Of Water-Bottom Multiples: A Case Study From Shallow Marine In The Niger Delta, \nNigeria. Malaysian Journal of Geosciences, 3(1) : 61-65. \n\n\n\nFigure 4: Inline 1001 stack from dataset after velocity without velocity \n\n\n\napplication \n\n\n\nFigure 5: Inline 1001 from dataset stack Application \n\n\n\n5.2 Amplitude Analysis of Primaries and Multiples  \n\n\n\nSpectral analysis was applied on the dataset to see the difference between \n\n\n\nthe primaries and multiples amplitude values. Amplitude response trend \n\n\n\nindicates that the processing preserved true amplitudes as there was no \n\n\n\nchange in the spectrum (Figure 6). Spectral analysis for the dataset \n\n\n\nshowed that the multiple energies were of higher amplitude than the \n\n\n\nprimaries. This was a major characteristic which was used for its \n\n\n\ndiscrimination and attenuation. The amplitudes of primaries and \n\n\n\nmultiples are significantly different as shown in Figures 7 and 8.  \n\n\n\nFigure 6: Amplitude Response for dataset from inline 1001 \n\n\n\nFigure 7: Pre-demultiple amplitude for dataset \n\n\n\nFigure 8: Overlay of amplitude spectrum for primaries and multiples \n\n\n\npost demultiplex for dataset from inlin 1001 \n\n\n\n5.3 Predictive Deconvolution  \n\n\n\nPredictive deconvolution was performed on the dataset. Various values of \n\n\n\ndeconvolution operator, operator length, and deconvolution window and \n\n\n\nfrequency range were applied. Figures 9 and 10 show the dataset without \n\n\n\nand with deconvolution operations.  \n\n\n\nFigure 9: Inline 1001 stack from dataset (Grey Scale) with events with \ncollapsed diffractions. \n\n\n\nFigure 10: Inline 1001 from dataset (Grey Scale) without deconvolution \nmasked by ringing and deconvolution applied: Sharper events with \n\n\n\ndiffractions. \n\n\n\n5.4 Radon Transformation \n\n\n\nThe objective of the Radon transformation process was to establish \n\n\n\nparameters for the removal of multiples that may be discriminated from \n\n\n\nprimaries through differences in velocity or, more generally, in moveout. \n\n\n\nA high-resolution Radon transform was applied to the dataset in order to \n\n\n\nmodel or remove events based on moveout criteria. Radon parameters for \n\n\n\nminimum and maximum moveout in t-x (ms) were 2200ms, 3000ms \n\n\n\nrespectively. Figures 11 and 12 are stacks with and without Radon \n\n\n\ndemultiple. Figure 13 is the difference plot, after the removal of the \n\n\n\nmultiple energy from dataset. Radon had a lot of significant improvements \n\n\n\non the data. \n\n\n\nFigure 11: Before Radon Demultiple \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  3(1) (2019) 61-65 \n\n\n\nCite The Article: Dora B. Umoetok, Etim D. Uko, Aminayanasa P. Ngeri (2019). Attenuation Of Water-Bottom Multiples: A Case Study From Shallow Marine In The Niger Delta, \nNigeria. Malaysian Journal of Geosciences, 3(1) : 61-65. \n\n\n\nFigure 12: After Radon Demultiple \n\n\n\nFigure 13: Difference after Radon Demultiple \n\n\n\n5.5 Dominant Characteristics of Multiples  \n\n\n\nOwing to the dominant characteristics (velocity, frequency wavelength, \n\n\n\netc.) of the multiples as compared to those of primaries, Velocity \n\n\n\nDiscrimination and Radon transformation was used to eliminate multiples \n\n\n\nin shallow marine data. Multiples were observed to be of predominantly \n\n\n\nshort-periods with some water bottom reverberation, and high frequency \n\n\n\nand high amplitude resulting from short travel time. With the short-period \n\n\n\nand high-frequency content, multiples were quite close to the primaries \n\n\n\nand therefore required a lot of care in the attenuation process. \n\n\n\n5.6 Velocity Responses of Primaries and Multiples  \n\n\n\nFrom the velocity spectrum and semblance stack, it is clear that the \n\n\n\nvelocities of multiples are much lower than those of primary events. This \n\n\n\nis the reason the multiple energy was significantly attenuated when the \n\n\n\ndataset was stacked with higher velocities. \n\n\n\n6. CONCLUSION\n\n\n\nThe shallow marine dataset at depth of 250m had predominantly interbed \n\n\n\nmultiples. The major differences that were exploited for the multiples \n\n\n\nremoval were multiple-attributes of velocity, frequency, wavelength, \n\n\n\nperiodicity, and predictability using predictive deconvolution and Radon \n\n\n\ntechniques. The dominant frequency of the primary events varies between \n\n\n\n3 and 120Hz having dominant amplitude ranging between -12dB and -\n\n\n\n45dB.  The dominant frequency of the multiples ranges between 8 and \n\n\n\n90Hz, while dominant amplitude ranges between -5dB and -45dB. This \n\n\n\nwork is relevant because when multiples are attenuated from a seismic \n\n\n\nsection, a better image of the subsurface geology is obtained, thereby \n\n\n\nreducing the risk of drilling dry oil wells. \n\n\n\nACKNOWLEDGEMENT \nThe authors are grateful to The Nigeria National Petroleum Corporation \n\n\n\nfor provision of data. Wale Adelodun,   Ya\u2019u   Auwal,   Tope Omotoso and \n\n\n\nNelson Ebini are also appreciated for provision of processing technical \n\n\n\nassistance. \n\n\n\nREFERENCES \n\n\n\n[1] Weglein, A. 1999. Multiple attenuation: an overview of recent advances \nand the road ahead. The Leading Edge, 18 (1), 40 \u2013 44. \n\n\n\n[2] Niu, B.H., Sheng, C., Huang, X.W. 2002a. Progress in multiple \nattenuation techniques based on wave equation. Progress. Geophysics, 17 \n(3), 480-485. \n\n\n\n[3] Niu, B.H., Sheng, C., Huang, X.W. 2002b. Multiple attenuation \ntechniques based on wave equation. Earth Science Frontiers, 9 (2), 511-\n517. \n\n\n\n[4] Huang, X.W., Wu, L., Niu, B.H. 2003. Reconstruction of seismic traces by \nparabolic Radon transform. Journal of China University of Mining Science \n& Technology, 32 (5), 534-539. \n\n\n\n[5] Ogagarue, D.O., Ebeniro, J.O. 2014. Water Bottom Multiple Elimination \nand Data Quality Enhancement Using Parabolic Radon Transform: A Case \nStudy of 3D Seismic Data from Offshore Niger Delta. Journal of Geosciences \nand Geomatics, 2 (4) 172-177. \n\n\n\n[6] Ogagarue, D.O., Nwankwo, C.N. 2016. A Statistical Amplitude Analysis \nMethod with Application to Streamer Data from the Niger Delta Offshore. \nJournal of Applied Geology and Geophysics, 4 (2), 10-14. \n\n\n\n[7] Umoetok, D.B., Uko, E.D. 2015. Characteristics of multiples in shaloow \nand deep marine seismograms in Gulf of Guinea. Petroleum Technology \nDevelopment Journal, 5 (2), 72 \u2013 83. \n\n\n\n[8] Meles, G.A., L\u00f6er, K., Ravasi, M., Curtis, A., Carlos Alberto da Costa Filho, \nC.A.C. 2015. Internal multiple prediction and removal using Marchenko \nautofocusing and seismic interferometry. Geophysics, 80 (1), A7\u2013A11. \n\n\n\n[9] Wang, J., Wang, S. 2013. Practical implementation of SRME for land \nmultiple attenuation.  GeoConvention Integration, 1-5. \n\n\n\n[10] Umoetok, D.B. 2019. Land multiples prediction and attenuation in \nBornu-Chad Basin, Nigeria. Unpublished PhD Thesis, Rivers State \nUniversity, Port Harcourt, Nigeria.  \n\n\n\n[11] Hansen, R.F., Johnson, C.H. 1948. Multiple reflections of seismic \nenergy. Geophysics, 13, 58-85. \n\n\n\n[12] Yilmaz, O. 1987. Seismic Data Processing. SEG Tulsa, Oklahoma. \n\n\n\n[13] Foster, D.J., Mosher, C.C. 1992. Suppression of multiple reflections \nusing the Radon transform. Geophysics, 57 (3), 386 - 395. \n\n\n\n[14] Huang, X.W., Sun, C.Y., Niu, B.H., Wang, H.D., Zeng, M.S. 2005. Surface-\nrelated multiple prediction and suppression based on data-consistence: a \ntheoretical study and test. Chinese Journal of Geophysics, 48 (1), 188-196. \n\n\n\n[15] Bruso, J.M., Wallace, R.L. 2004. Gulf of Guinea geology. Oil and gas \nJournal. \n\n\n\n[16] Kulke, H. 1995. Nigeria In Kulke, H. (Ed), Regional Petroleum Geology \nof the World. Part II: Africa, America, Australia and Antarctica: Berlin, \nGebr\u00fcder Borntraeger, 143 - 172. \n\n\n\n[17] Klett, T. R., Ahlbrandt, T.S., Schmoker, J.W., Dolton, J.L. 1997. Ranking \nof the world\u2019s oilroleumand gas pr volumes. U.S. Geological Survey Open-\nfile Report-97-463, CD-ROM. \n\n\n\n[18] Doust, H., Omatsola, E. 1990. Niger Delta In Edwards, J. D. and \nSantogrossi, P. A., (Eds), Divergent/passive Margin Basins, AAPG Memoir \n48: Tulsa. American Association of Petroleum Geologists, 239 - 248. \n\n\n\n[19] Hospers, J. 1965. Gravity field and structure of the Niger Delta, \nNigeria, West Africa. Geological Society of American Bulletin, 76, 407 - 422. \n\n\n\n[20] Kaplan, A., Lusser, C.U., Norton, I.O. 1994. Tectonic map of the world, \npanel 10. American Association of Petroleum Geologists, Tulsa. \n\n\n\n[21] Naija247 News. 2011. Group wants urgent oil Exploration in the \nNorth. \n\n\n\n[22] Schneider, W., Prince, E., Giles, B. 1965. A new data-processing \ntechnique for multiple attenuation exploiting differential moveout. \nGeophysics, 30, 348 \u2013 362.  \n\n\n\n[23] Hardy, R.J.J., Warner, M.R., Hobbs, R.W. 1989. Labelling long-period \nmultiple reflections. Geophysics, 54, 122 \u2013 126.  \n\n\n\n[24] Yilmaz, O. 1989. Velocity stack processing. Geophysical Prospecting, \n37, 357 \u2013 382. \n\n\n\n[25] Backus, M.M. 1959. Water reverberations, their nature and \nelimination. Geophysics, 55, 1508 \u2013 1511. \n\n\n\n[26] Peacock, K., Treitel, S. 1969. Predictive deconvolution \u2013 theory and \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  3(1) (2019) 61-65 \n\n\n\nCite The Article: Dora B. Umoetok, Etim D. Uko, Aminayanasa P. Ngeri (2019). Attenuation Of Water-Bottom Multiples: A Case Study From Shallow Marine In The Niger Delta, \nNigeria. Malaysian Journal of Geosciences, 3(1) : 61-65. \n\n\n\npractice. Geophysics, 34, 155 \u2013 169. \n\n\n\n[27] Berryhill, J.R., Kim, Y.C. 1986. Deep water peg-legs and multiples: \nelimination and suppression. Geophysics, 51, 2177 \u2013 2184. \n\n\n\n[28] Fokkema, J., Berg, P.V. 1990. Removal of surface-related wave \nphenomena: the marine case. 60th SEG meeting, San Francisco, USA, \nExpanded Abstracts, 983 \u2013 988. \n\n\n\n[29] Verschuur, D.J., Berkhout, A.J. 1992. Surface-related multiple \neliminations: Practical Aspects. 62nd SEG Metting, New Orleans, USA, \nExpanded Abstracts, 1100 \u2013 1103. \n\n\n\n[30] Keydar, S., Gelchinhinsky, B., Berkovitch, A. 1996. Common shot-point \n\n\n\nstacking and imaging method. Journal of Seismic Exploration, 5, 261 \u2013 274.  \n\n\n\n[31] Dix, C.H. 1948. The existence of multiple reflections. Geophysics, 13, \n49 - 50. \n\n\n\n[32] Gutenberg, B., Fu, C.Y. 1948. Remarks on multiple reflections. \nGeophysics, 13, 45 - 48. \n\n\n\n [33] Waterman, J.C. 1948. Multiple-reflection evidence. Geophysics, 13, 41 \n- 44. \n\n\n\n[34] Burg, K.E., Ewing, M., Press, F., Stulken, E.J. 1951. A seismic wave guide \nphenomenon. Geophysics, 16, 594 - 612. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 52-60 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2023.52.60 \n\n\n\n\n\n\n\n Cite the Article: Akpan, Mfon Joseph, George, Nyakno Jimmy, Ekanem, Aniekan Martin, Nathaniel, Ekong Ufot (2023).  Deploying Geostatistical Stochastic Inversion \nModeling in The Characterization of Reservoirs: A Case Study from an Onshore Niger Delta Field. Malaysian Journal of Geosciences, 7(2): 52-60. \n\n\n\n\n\n\n\n\n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2023.52.60 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDEPLOYING GEOSTATISTICAL STOCHASTIC INVERSION MODELING IN THE \nCHARACTERIZATION OF RESERVOIRS: A CASE STUDY FROM AN ONSHORE NIGER \nDELTA FIELD \n\n\n\nAkpan, Mfon Joseph, George, Nyakno Jimmy*, Ekanem, Aniekan Martin and Nathaniel, Ekong Ufot  \n\n\n\nDepartment of Physics (Geophysics Research Group), Akwa Ibom State University, Mkpat Enin, PMB 1162, Uyo, Nigeria \n*Correspondence Author Email: nyaknojimmyg@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 15 April 2023 \nRevised 18 May 2023 \nAccepted 22 June 2023 \nAvailable online 23 June 2023 \n\n\n\n Three-dimensional (3D) seismic data and well logs were conjointly used to characterize reservoirs in the \nOnshore field Niger Delta Field of South-eastern Nigeria. The goal was to model and improve on the \nlimitations of seismic resolution in conventional inversion techniques. This is necessitated by the quest to \nbetter off understanding of the distribution of lithology and pore fluid. Both geo-statistical and deterministic \ninversion techniques were applied in this study to appreciate the impact of each process on reservoir \nrecognition and demarcation. The methodology began with well log conditioning and well-to-seismic tie. \nRock-Physics feasibility analysis prior to the seismic inversion was done to estimate the elastic attributes \nappropriate for distinguishing between different fluid types and lithologies. The rock Physics results revealed \nthe separation of the Velocity ratio (Vp/Vs) versus Acoustic impedance (Ip) cross plot into three regions, which \nare hydrocarbon, brine and shale zones. This suggests that the inversion results would be capable of \ndistinguishing hydrocarbon sands from shale. Cross-plots of Mu-Rho (\u03bc\u03c1) versus Lambda-Rho (\u03bb\u03c1) showed \nthat clusters of data, which are separated into three different zones inferred to be potential hydrocarbon, oil, \nbrine and shale zones. The geo-statistical stochastic inversion was done by integrating variogram models and \nprobability density functions (PDFs) to identify high frequencies or low periods in the output inversion \noutcome. The deterministic inversion results show that hydrocarbon saturated-sands are identified from the \nfollowing attributes: low values of acoustic impedance (2.11 \u2013 2.24 \u00d7104 ft/s*g/cc), low velocity ratio (1.70\u2013\n1.82), low Lambda-Rho (14.4 \u201319.7 Gpa*g/cc).  In all cases, the results of geo-statistical inversion provided \nmore detailed and increased resolution than deterministic inversion, allowing for detailed reservoir \ncharacterization. In the geo-statistical inversion, some regions in deterministic inverted sections with low \nacoustic impedance, velocity ratio, and Lambda-Rho attribute values inferred to be hydrocarbon sands \nappeared as either shale or brine. The inversion shows that the lambda-Rho quality is more useful in \ndetermining fluid classification whereas the acoustic impedance attribute is a good lithology discriminator. \nThe overall results demonstrate the workflow's ability to accurately map the rock properties with higher \nresolution and the delineation of new forecast as an effective, cost-effective and decision-making instrument. \n\n\n\nKEYWORDS \n\n\n\nGeo-statistical Stochastic Inversion; Variogram; Partial-Angle Stacks; Deterministic inversion; Sigma field; \nCoastal Swamp Depobelt \n\n\n\n1. INTRODUCTION  \n\n\n\nHydrocarbon exploration, development, and production are protracted \nand difficult journey. Finding suitable hydrocarbon sources in commercial \namounts are the first steps in any hydrocarbon exploration and \ndevelopment project. Size, complexity, productivity and fluid type of the \nreservoir are all important factors in the development of any oil and gas \nproduction. For the development approach to be effectively utilized, these \nqualities need to be precisely specified (Umoren and George, 2018; \nAdesun et al., 2019). Due to the inherent risks and uncertainties involved \nin hydrocarbon exploration and extraction, improved seismic \ninterpretation, lithology and fluid discrimination are also necessary \n(Odegaard and Avseth, 2004; Akpan et al. 2020a). To overcome these \nanticipated exploratory hurdles, newer and more cost-effective \ntechnologies such as neural network and geo-statistical inversion are \n\n\n\nrequired (Yuan and Wang, 2018). \n\n\n\nSeismic inversion has been utilized to improve reservoir characterization \nsince its inception in the early 1980s (Pendrel, 2001). It can extract \nsubsurface structures and petrophysical data from seismic and well log \ndata (Chen et al., 2017). The conventional seismic inversion techniques \nlike generalized linear inversion or sparse spike inversion can generate a \nsmooth outcome that ignores marginal heterogeneities (Cooke and \nSchneider, 1983; Oldenburg et al., 1983). Geo-statistical inversion \ncombines stochastic simulation with seismic inversion to recover the \nmarginal heterogeneities that were not picked by the seismic statistics \n(Azevedo and Soares, 2017). To identify high frequencies in the outcome \nof inversion results, geo-statistical seismic inversion, an integrated \napproach that uses PDFs and variogram models can be useful (Pendrel, \n2001). Geo-statistical inversion combines high-resolution well data with \n\n\n\n\n\n\n\n\nCite the Article: Akpan, Mfon Joseph, George, Nyakno Jimmy, Ekanem, Aniekan Martin, Nathaniel , Ekong Ufot (2023).  Deploying Geostatistical Stochastic Inversion Modeling in \nThe Characterization of Reservoirs: A Case Study from an Onshore Niger Delta Field. Malaysian Journal of Geosciences, 7(2): 52-60. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 52-60 \n\n\n\n\n\n\n\n\n\n\n\nlow-resolution 3-D seismic to create a model with high vertical features \nnear and far from the well. This produces geologically credible reservoir \nmodels and a clear quantification of uncertainty for risk assessment. \nHighly detailed petrophysical models are created and are ready to be used \nin reservoir flow simulations. Geo-statistics differs from statistics in that it \nrecognizes that only a few geologically viable possibilities exist. Geo-\nstatistical inversion combines data from a variety of sources to build \nmodels that are more detailed than the original seismic, reflect known \ngeological patterns and can be used to assess and reduce risk of finding \nhydrocarbons. \n\n\n\nIn order to predict 3D high-resolution reservoir characteristics and \ndelineation on a small region of the Sigma Field, an integrated \nmethodology that combines rock Physics analysis, deterministic and geo-\nstatistical inversions are applied in this study. The sigma field is situated \nin the arcuate Niger Delta regarded as one of the prominent Tertiary deltas \nas well as the most productive hydrocarbon basins in the world. The main \nobjective of this study is to qualitatively understand the degree, quality \nand potential of the reservoirs as well as their fluid characteristics. \nNumerous published case studies as opined by researchers provided \nevidence for the validity and accuracy of the stochastic technique (Pereira \net al., 2017; Mohamed et al., 2019; El-Behiry et al., 2019). \n\n\n\n2. GEOLOGIC SETTING OF THE STUDY AREA  \n\n\n\nThe Sigma field is stationed in the eastern part of the Coastal Swamp \ndepositional belt (CSDP), an onshore regional sub-basin of the arcuate \nNiger Delta Basin positioned in the Atlantic Ocean within the West African \nmargin (Figure 1a, b). The survey area is stationed within the limit of \nlongitudes 7o to 8oE, and latitudes 4o to 4.5o N. The CSDP abuts on the \nNorthern and Central belts by regional faults. The faults are trending in the \nNorth-west and South-East directions. It has counter-regional fault, which \n\n\n\nseparates it from the Offshore Depobelt in the southern part. The Volcanic \nLine of Cameroon in the east, the basin of Dahomey in the west as well as \nthe 4 km bathymetric contour in the southern region describes the Niger \nDelta Basin (Onuoha, 1999). The Benue Trough, generally known as a \nfailed wing of a rift, which is not rifting anymore in the Late Cretaceous \nand the Niger Delta Basin are positioned at its southern edge (Onuoha, \n1999; Adojoh et al., 2017; Ekanem et al., 2021 a, b). Consequent upon the \nEarly Cretaceous depression of the African continental border and \ndeposition of clastic sediments, the Niger Delta was created by two arms \nof a triple junction characterized by the collapsed South Atlantic border \n(Edwards, 2000; 1999; George et al., 2010; Akpan et al. 2020a, b). The delta \ncame to existence at the time of Late Paleocene/Eocene when sediments \nwere formed from the weathering flanks of the Niger\u2013Benue drainage \nsystem built from the troughs separating basement horst blocks on the \nnorthern border of the present delta area (Doust and Omatsola, 1990). \nPre- and syn-sedimentary tectonics related to the interaction between \nrates of supply of sediment and subsidence, which produced the \nstructures of the basin and stratigraphy (Ejedawe, 1981; Stacher, 1995; \nGeorge et al., 2017; George, 2021). The stratigraphic succession of the \nNiger Delta Basin consists of three lithostratigraphic units known as Akata, \nAgbada and Benin Formations (Stacher, 1995; Ekanem et al., 2021a). The \nAkata Formation's marine shales or source rock, the paralic Agbada \nFormation's inter-stratified siltstones, sandstones and shales (reservoir \nand source rocks) and the Benin Formation's continental alluvial/Coastal \nPlain sands and clays (overburden rocks) are all diachronous (Short and \nStauble, 1967; Avbovbo, 1978; Doust and Omatsola, 1990; Ekanem et al., \n2022 a, b; Umoh et al., 2022). Roll-over anticlines, shale diapirs, back-to-\nback features, collapsed growth fault crests, sharply dipping and closely \nspaced flank faults generally regarded as structural characteristics  in the \nNiger Delta Basin known to provide excellent migration conduits and \nentrapment mechanisms for hydrocarbon accumulation (Evamy, 1978; \nAkpan et al., 2022). \n\n\n\n\n\n\n\nFigure 1: (a) Map of Africa displaying Nigeria and the Niger Delta location considered. (b) Depositional belt of Niger Delta showing the study area in a \nyellow box (Ejedawe et al., 2007). (c) Indication of Locations and distribution of well considered across the study area. \n\n\n\n3. THEORETICAL BACKGROUND OF GEO-STATISTICAL \n\n\n\nSTOCHASTIC INVERSION (GSI) \n\n\n\nGSI carries out statistical inversions to estimate petrophysical properties \nfrom seismic and log data. This method assumes that the seismic trace and \nthe preliminary guess impedance are the two pieces of (probably \nopposing) data, that must be combined to generate the final inversion \noutcome. Geo-statistical stochastic inversion algorithm is an integration of \nthe Bayesian linearized amplitude and angle inversion approached \naccording to (Buland and More, 2003). This sequential Gaussian \nsimulation (SGS) technique was first used by (Haas and Dubrule, 1994). \nTaking into consideration the assumptions spelt out by (Escobar et al., \n2006), it is practicable to compute a combined posterior distribution for \nthe impedances generated by P and S velocities, which controlled the \n\n\n\nseismic amplitudes. It is possible to use the SGS inversion to break the joint \nposterior into a series of local Gaussian posterior distributions for each \nsampled seismic trace sequentially generated in order realize elastic \nproperties opined by (Moyen and Doyen, 2009). proposed the possibility \nof using geo-statistical stochastic inversion algorithm through a Bayesian \nframework and a linearized approximation of the Zoeppritz equation to \ncompute a joint posterior distribution for the shear and acoustic \nimpedances (Escobar et al., 2006). For a sole interface, the rough \ncalculation of the angle-variant reflectivity \ud835\udc5f\ud835\udf03 for offset angle \ud835\udf03 is according \n(Fatti et al., 1994) given by equation 1: \n\n\n\n\ud835\udc5f\ud835\udf03 \u2248\n1\n\n\n\n2 \ud835\udc50\ud835\udc5c\ud835\udc602 \ud835\udf03\n\ud835\udee5 \ud835\udc59\ud835\udc5b(\ud835\udc3c\ud835\udc5d) \u2212 4\n\n\n\n\ud835\udc3c\ud835\udc46\n2\n\n\n\n\ud835\udc3c\ud835\udc43\n2\n\ud835\udc60\ud835\udc56\ud835\udc5b2 \ud835\udf03 \ud835\udee5 \ud835\udc59\ud835\udc5b(\ud835\udc3c\ud835\udc46)  (1) \n\n\n\n\n\n\n\n\nCite the Article: Akpan, Mfon Joseph, George, Nyakno Jimmy, Ekanem, Aniekan Martin, Nathaniel , Ekong Ufot (2023).  Deploying Geostatistical Stochastic Inversion Modeling in \nThe Characterization of Reservoirs: A Case Study from an Onshore Niger Delta Field. Malaysian Journal of Geosciences, 7(2): 52-60. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 52-60 \n\n\n\n\n\n\n\n\n\n\n\nwhere \ud835\udc3c\ud835\udc46   and \ud835\udc3c\ud835\udc5d are the S-wave and P-wave acoustic impedances, \n\n\n\nrespectively. Accordingly, for the stratigraphic grid, the equation will be \nmade to extend to a full vertical column of ith unit layers, where \ud835\udc3c\ud835\udc5dand \ud835\udc3c\ud835\udc46  \n\n\n\nlogarithms are stored in a vector (\ud835\udc5a\ud835\udc56). The series of reflectivity can be \nexpressed in equation 2: \n\n\n\n\ud835\udc5f\ud835\udc56,\ud835\udf03 \u2248 \ud835\udc34\ud835\udc56,\ud835\udf03\ud835\udc5a\ud835\udc56   (2) \n\n\n\nwhere the \ud835\udc5a\ud835\udc56 vector contains the logarithms of \ud835\udc3c\ud835\udc5d and \ud835\udc3c\ud835\udc46 .\ud835\udc34\ud835\udc56,\ud835\udf03 is the \n\n\n\namplitude coefficient of reflectivity. The \ud835\udc34\ud835\udc56,\ud835\udf03matrix computes the \n\n\n\ndifferences of ln (\ud835\udc3c\ud835\udc5d) and ln (\ud835\udc3c\ud835\udc46) and multiplies them with amplitude versus \n\n\n\noffset angle (AVA) coefficients (i.e., coefficients (\ud835\udc34\ud835\udc56,\ud835\udf03) of equation 2).  \n\n\n\nHere, equation 1 is linear in log impedances when the squared impedance \nratios are known. Generally, a post processing step is essential to \nsurmount this supposition. If this step is avoided, high disparities crops up \nbetween the synthetics data and input seismic data. The correction \nprocedure was employed in each trace and each realization after the \ninversion process. By applying the initial impedance ratio, the simulated \nreflectivities were adjusted to reflect the computed reflectivities. \nAccording to previous research, inverse problem can be represented to \nreflect a Bayesian framework with Gaussian PDFs (Buland and More, \n2003). The probability of seismic data s, is given in equation 3: \n\n\n\n\ud835\udc43(\ud835\udc5a|\ud835\udc60) \u221d \ud835\udc52\ud835\udc65\ud835\udc5d (\u2212\n1\n\n\n\n2\n\u2211 \u2211 (\ud835\udc56 \ud835\udc60\ud835\udc56,\ud835\udf03 \u2212 \ud835\udc3a\ud835\udc56,\ud835\udf03\ud835\udc5a\ud835\udc56)\n\n\n\n\ud835\udc47\ud835\udc36\ud835\udc46\ud835\udc56,\ud835\udf03\n\u22121\n\n\n\n\ud835\udf03 (\ud835\udc60\ud835\udc56,\ud835\udf03 \u2212 \ud835\udc3a\ud835\udc56,\ud835\udf03\ud835\udc5a\ud835\udc56))  (3) \n\n\n\nwhere \ud835\udc36\ud835\udc46\ud835\udc56,\ud835\udf03coefficients are the noise covariance matrices and \ud835\udc3a\ud835\udc56,\ud835\udf03are the \nrectangular matrices that combine  together \ud835\udc34\ud835\udc56,\ud835\udf03with the wavelet \nconvolution and then  transform the irregular model traces or layers into \nregular samples of seismic traces. Again, the representation of the \nGaussian prior PDF for \ud835\udc5a is given in equation 4: \n\n\n\n\ud835\udc43(\ud835\udc5a) \u221d \ud835\udc52\ud835\udc65\ud835\udc5d (\u2212\n1\n\n\n\n2\n(\ud835\udc5a \u2212 \ud835\udf07\ud835\udc5a)\n\n\n\n\ud835\udc47\ud835\udc36\ud835\udc5a\n\u22121(\ud835\udc5a \u2212 \ud835\udf07\ud835\udc5a))  (4) \n\n\n\nwhere \ud835\udc36\ud835\udc5a is the prior covariance matrix and \ud835\udf07\ud835\udc5a is the prior mean. The \ud835\udc36\ud835\udc5a \nis dependent upon vertical correlations and the prior variograms. \n\n\n\nAdditional likelihood term was added when a logged well intersected a \ntrace. The Gaussian posterior PDF \ud835\udc43(\ud835\udc5a|\ud835\udc60) constructed by combining \nequations 3 and 4. Consequently, an SGS algorithm was used to trace by \ntrace decompose the posterior PDF into local PDFs. The resulting PDFs \nwere modelled according to Escobar et al. (2006) to generate the possible \nrealizations of the elastic parameters.  \n\n\n\n4. DATASET AND METHODS \n\n\n\nThe field, for proprietary reasons, is renamed in this study as Sigma. Based \non the approval by the Department of Petroleum Resources (DPR), Port \nHarcourt, the seismic and well   data were provided by an international oil \ncompany (IOC). Available data included a full offset seismic stack and \npartial-angle stacks of far (30\u00b0\u201345\u00b0), mid (15\u00b0\u201330\u00b0) and near (0\u201315\u00b0) \nangles in SEG Y format. The surveys had inline spacing of 9.37 m and the \ncrossline spacing of 12.5 m. The 3D seismic data have an area of 1015m \u00d7 \n772 m spacing and a 4 ms sampling rate.  The seismic data had a maximum \nfrequency of 60 Hz. The inline and crossline numbers ranged between \n4485-5524 and 1713-2033  (Figure 2) respectively. The two-way travel \ntime (TWT) seismic volumes extended to 5000 milliseconds. The quality \nof the seismic record showed variations with depth. Below 3000 ms TWT \nconsidered as the basal part of the seismic record is found disrupted by \nmany zones of transparent to highly-discontinuous reflection outlines, \nwhich spread higher inside the seismic volume under footwalls of \nprominent faults. Reflections inside this portion have moderate to good \ncontinuities with high amplitude disparities. Within the sigma field, the \nseismic volume showed characteristic prominent normal faults \nmanifested in the cross-line section across the volume and roll over, \ncollapsed crest and normal faults revealed in the inline section across the \nvolume. The study area was penetrated by five vertical wells logged with \nthe log suites presented in Table 1. The logs available from the wells were \nCaliper log (CAL in inches), gamma ray log (GR in API unit), resistivity log \n(RT in Ohm-m), density log (DEN in g/cm3) and sonic log (DT in \u00b5s/ft). \nThe well logs, which were provided in ASCII digital format, also showed \ninformation such as rig floor elevation, well tops, fluid fill, lithology etc. In \nthis study, the wells were renamed as Well 25, Well 26, Well 27, Well 30 \nand Well 48 for proprietary reasons. \n\n\n\n\n\n\n\nFigure 2: Quality of Full stack Seismic Data Used for interpretation \n\n\n\nTable 1: Well Log Data Were Used in This Study. \nWell Gamma Ray (API) Resistivity (Ohmm) Density (g/cm3) Vp (m/s) Vs (m/s) \n\n\n\nWell 24 YES YES YES YES NO \nWell 26 YES YES YES YES NO \nWell 27 YES YES YES YES NO \nWell 30 YES YES YES YES NO \nWell 48 YES YES YES NO NO \n\n\n\n4.1   Methodology \n\n\n\nEvery quantitative seismic reservoir description project portrays a \ndistinct challenge that requires a comprehensive evaluation. Well \nexploration and development risks can be reduced through integrated \nquantitative interpretation (QI) method by extracting more significant \nhigh-value information from well and seismic data. This work employed \nan integrated method that includes a rock-physics feasibility assessment, \nthe creation of a stratigraphic geocellular grid variogram modeling, pre-\n\n\n\ninversion analysis, deterministic and geo-statistical inversion. \n\n\n\n4.2   Well Log Loading and Conditioning \n\n\n\nA typical QI work begins with well log loading and editing. A database of \nfive wells each with log suites was compiled in a new Hampson Russell \nproject. In practice, well logs typically have a number of inherent issues, \nparticularly problems of spikes. Well logs must undergo quality control \nand editing to ensure reliable and correct values before being used in any \n\n\n\n\n\n\n\n\nCite the Article: Akpan, Mfon Joseph, George, Nyakno Jimmy, Ekanem, Aniekan Martin, Nathaniel , Ekong Ufot (2023).  Deploying Geostatistical Stochastic Inversion Modeling in \nThe Characterization of Reservoirs: A Case Study from an Onshore Niger Delta Field. Malaysian Journal of Geosciences, 7(2): 52-60. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 52-60 \n\n\n\n\n\n\n\n\n\n\n\nstudy. Checkshot correction and median filtering are two log editing \noperations used in this work. Prior to cross-plot analysis, erroneous spikes \nthat could impact clusters were eliminated to ensure accurate results \nusing a median filter characterised by operator length of 5. Checkshot \ncorrections, on the other hand were used to match well log values (in the \ndepth domain) with seismic data (in the time domain). As a result, the \ncheckshot corrections were deployed with the STRATA-CGG tool to adjust \n\n\n\nthe associated depth to time curve with a sonic log. This was done to \nimprove the relationship between actual seismic and synthetic data. The \nadjusted synthetic seismogram was thereafter coupled with the seismic \nvolume in the well site to maximize dept to time conversion. The checkshot \ncorrection for Well 26 is shown in Figure 3. When a smoother length of \n2000 is used, a correction to actual time-depth is observed where the \ncheckshot data is so closely related to the P-wave log. \n\n\n\n\n\n\n\nFigure 3: Check shot correction applied to Well 26. The tracks show the Input time curve (black), corrected time curve (red), drift curve (blue) and the \noutput sonic log (orange). \n\n\n\n4.3   Shear Wave Prediction \n\n\n\nShear wave velocities were used for further rock Physics analysis, elastic \nmodulus calculation and low frequency/high period building models. \nHowever, S-wave velocity was not provided in any of the logs and as such, \nit values were predicted. S-wave velocity was estimated from P-wave \nvelocity according to (Greenberg and Castagna, 1992) using the linear \nregression modified equation. S-wave velocity is proportionally computed \nfrom the two derived relationships in shale and sandstone based on the \nshale volume log. This formed the basis for empirical relationship used in \nextracting shear wave velocity in a multi-mineral brine saturated rocks. \nThe relation also contains a polynomial characterized by a polynomial \nregression coefficient for mono-mineralic lithology given by equation 5. \n\n\n\n\ud835\udc49\ud835\udc60 = \ud835\udc4e\ud835\udc49\ud835\udc5d\n2 + \ud835\udc4f\ud835\udc49\ud835\udc5d + \ud835\udc58  (5) \n\n\n\nwhere a, b and k are the coefficient of regression, Vs is the shear wave \nvelocity and Vp is the P-wave/compressional wave velocity associated with \nthe density attributes employed in generating the rock properties.  \n\n\n\n4.4   Generation of Elastic Properties \n\n\n\nBy the application of rock Physics algorithm, rock qualities for acoustic \nimpedance, Vp/Vs rock property volumes and lambda-Rho were extracted \nfrom the seismic and well data. The rock attributes have the capacity to \ndiscriminate the reservoir in terms of lithology and its fluid content. \nAcoustic impedance is contextually defined as the product of density and \nP-wave velocity and shear impedance is considered to be the product of \ndensity (\ud835\udf0c) and S-wave velocity. The lambda-Rho (\ud835\udf06\ud835\udf0c)is mathematically \nexpressed in equation 6: \n\n\n\n\ud835\udf06\ud835\udf0c = (\ud835\udf0c\ud835\udc49\ud835\udc5d)\n2\n\u2212 \ud835\udc50(\ud835\udf0c\ud835\udc49\ud835\udc60)\n\n\n\n2  (6) \n\n\n\nwhere \ud835\udf0c, \ud835\udc49\ud835\udc60, \ud835\udc49\ud835\udc5dand c are respectively the density, shear wave velocity, \n\n\n\ncompressional wave velocity and c is a constant fluid factor with a value of \n2 (Akpan et al. 2020b). The estimation of the fluid constituent was affected \nby c, which is changed with different lithological environments. Again \nanother attribute, Mu-Rho (\ud835\udf07\ud835\udf0c) is expressed as the square of the product \nof the density and the shear wave velocity (the square of shear impedance) \ngiven in equation 7:  \n\n\n\n\ud835\udf07\ud835\udf0c = (\ud835\udf0c\ud835\udc49\ud835\udc60)\n2  (7) \n\n\n\n4.5    Feasibility Study of Rock-Physics \n\n\n\nBefore the seismic inversion process, rock-physics feasibility study is \noften conducted to identify the more efficient elastic properties that can \nbe employed in distinguishing between various lithologies and heir fluid \n\n\n\ncompositions. This is important in order to appreciate how rock \nproperties are associated with seismic velocities in terms of rock Physics \nmodel. A number of cross-plots were created to distinguish between the \nlithologies of shale, gas sands and brine sands in order to classify the \nlithology model in the Sigma field. Cross-plots, which are used to spot out \nmajor deviations from a background trend, are usually used to detect the \nvisual representations of the relationship between any two variables with \na third dimension represented as colour code. The objective is to evaluate \nthe viability of differentiating facies changes in the reservoir, using seismic \nattributes (Mohamed et al., 2019). In this work, cross-plots of acoustic \n\n\n\nimpedance (\ud835\udf0c\ud835\udc49\ud835\udc5d) against velocity ratio (Vp/Vs) with colour code of water \n\n\n\nsaturation and cross plots of \ud835\udf06\ud835\udf0cagainst \ud835\udf07\ud835\udf0c with shale-volume as colour \nbars were performed utilizing data from three wells. \n\n\n\n4.6   Wavelet Estimation and Seismic-Well Tie \n\n\n\nPractically, accurate well to seismic tie and unswerving wavelet \ndetermination are generally performed before seismic inversion can \nreliably provide reasonable confidence on quantitative interpretation. To \nalign the well log response with the corresponding events seen in the \nseismic data, well to seismic tie is absolutely necessary (Akpan et al. 2020 \na, b). Seismic wavelet furnishes a significant connection between the \nunknown earth reflectivity and seismic data, which are known (Wang, \n2001). In this survey, wavelets are determined by using the following \nprocedures (i) Determination of approximate phase of the wavelet (ii) \nExtraction of the wavelet statistically from the seismic; (iii) Correlateion \nof the logs to tie the seismic data and (iv) Extraction of a new wavelet using \nthe correlated well logs. Figure 4a shows the Statistical wavelet with \nextracted phase angle of 0 and frequency bandwidth of input seismic data \nfrom 10 to 100Hz. This shows the loss of low frequency below 10Hz. \nFigure 4b shows the deterministic wavelet. The computed reflection \ncoefficient was convolved with the statistical wavelet to perform a well-\nto-seismic tie and time shift between synthetic and composite traces. After \nthe stretching and squeezing of the synthetic trace, a correlation was made \nfor wavelet extracted from well to give a good estimate of both amplitude \nand phase spectra of the wavelet and maximum correlation. It was \nobserved  that the correlation coefficient of 34% is low in Figure 5a and \n57%, which is higher in Figure 5b. \n\n\n\n4.7   Geo-Statistical Stochastic Inversion  \n\n\n\nThe geo-statistical stochastic inversion, a procedure for integration of the \nBayesian linearized amplitude versus angle inversion is a sequential \nGaussian simulation (SGS) technique according to Buland and More \n(2003). As opined by Escobar et al. (2006), it computes a joint posterior \ndistribution for the S- and P-impedances and constrains by the amplitudes \n\n\n\n\n\n\n\n\nCite the Article: Akpan, Mfon Joseph, George, Nyakno Jimmy, Ekanem, Aniekan Martin, Nathaniel , Ekong Ufot (2023).  Deploying Geostatistical Stochastic Inversion Modeling in \nThe Characterization of Reservoirs: A Case Study from an Onshore Niger Delta Field. Malaysian Journal of Geosciences, 7(2): 52-60. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 52-60 \n\n\n\n\n\n\n\n\n\n\n\nof seismic data. As theorized by Moyen and Doyen, (2009), it employs the \nSGS to break the joint posterior into a sequence of informative local \nGaussian posterior distributions for each of the seismic trace sequentially \n\n\n\nsampled to generate realizations of the earth\u2019s elastic properties. The \ndiagram in Figure 6 shows the summary of the steps in geo-statistical \ninversion technique deployed in this study.  \n\n\n\n\n\n\n\nFigure 4: Statistical wavelet extraction from input seismic data showing the time and frequency response in (a) and (b) respectively for wavelet extracted \nfor all wells with their phases in the Sigma field. \n\n\n\n\n\n\n\nFigure 5: Correlations: Well-to-seismic tie (a) Before tie (b) after tie. \n\n\n\n\n\n\n\n\nCite the Article: Akpan, Mfon Joseph, George, Nyakno Jimmy, Ekanem, Aniekan Martin, Nathaniel , Ekong Ufot (2023).  Deploying Geostatistical Stochastic Inversion Modeling in \nThe Characterization of Reservoirs: A Case Study from an Onshore Niger Delta Field. Malaysian Journal of Geosciences, 7(2): 52-60. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 52-60 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 6: Flowchart showing a summary of the geo-statistical inversion steps. \n\n\n\n\n\n\n\nFigure 7:  Filtered P-impedance property derived from deterministic inversion along well-26. \n\n\n\nThe process began by building the stratigraphic grid within the reservoir \nchannel horizons, utilizing the identified top and base of the reservoirs. \nThe micro-layers were patterned to proportionally follow the depositional \nmodels. Hence, within the reservoir average thickness of 1 ms was \nadopted while 4 ms above and below the reservoir upheld. Moreover, \nthree-two angle-dependent deterministic wavelets were assigned to the \ntwo-input seismic partial stacks. The near and far angles represented the \ntwo wavelets. The prior horizontal and vertical variogram models within \nthe gamut of stratigraphic grid were defined. The vertical variogram was \ncomputed through the well logs and a set of horizontal variograms follow \nthe trend of delineated geology in the sigma field. The Bayesian joint \nposterior distribution of the elastic properties for every trace was \ncomputed by combining the initial model, seismic pre-stack data, and well \nlogs to be deployed in the inversion. The P- and S-impedance realizations \nfor each trace can generate the sequential sampling displayed in the \ndistribution of the joint posterior. The diagram in Figure 7 represents a \nfiltered P-impedance generated in this work.  \n\n\n\n5. RESULTS AND DISCUSSIONS \n\n\n\nThe final deliverables of this study includes rock Physics cross-plot \nanalysis, Deterministic inversion and geo-statistical inversion cross-\nsections and map sections.  \n\n\n\n5.1   Cross-plots Analysis \n\n\n\nThe cross plot for the Vp-Vs ratio versus \ud835\udf0c\ud835\udc49\ud835\udc5d using the water saturation \n\n\n\ncolour bar (Figure 8a) separates the SAND F reservoir into three zones: \npotential gas-sand points (red polygon), brine sand points (blue polygon), \nand shale points (black polygon). The gas-sand reservoir is defined by less \nthan 0.5 water saturation values. The cross plot of Vp-Vs ratio versus \ud835\udf0c\ud835\udc49\ud835\udc5d \n\n\n\nusing the volume of shale colour bar (Figure 8b) distinguishes the SAND F \nreservoir into two zones: potential hydrocarbon-sand points (red eclipse) \nand shale points (black eclipse). The lowest values of Vp/Vs (< 2) and \nagainst P-impedance [< 25600 (ft/s)*(g/cc)] associated with \nhydrocarbons sands are validated by volume of shale of less than 40%. \nThis cross-plot show good fluid as well as lithology discrimination. Cross-\nplots of  \ud835\udf07\ud835\udf0c against \ud835\udf06\ud835\udf0c with colour bar water saturation in Figure 9 a, shows \nseparation into three zones that can be delineated as potential gas-sand \npoints (red polygon), brine sand points (blue polygon), and shale points \n(black polygon). On the other hand, \ud835\udf07\ud835\udf0c -\ud835\udf06\ud835\udf0ccross-plot with  shale volume as \ncolour code in Figure 9 delineated the SAND F reservoir into two zones \nidentified as possible hydrocarbon-sand points (red eclipse) and shale \npoints (black eclipse).  It is realizable that the cross-plot of Vp-Vs ratio \nversus \ud835\udf0c\ud835\udc49\ud835\udc5d in (Figure 9) is the most consistent discriminator amongst the \n\n\n\nknown litho-fluid facies changes because the points spread away uniquely. \n\n\n\n\n\n\n\nFigure 8: Cross plot of P-impedance versus Vp-Vs ratio using data from three wells (reservoir F) with colour bars  (a) water-saturation and (b) shale-\nvolume colour bars \n\n\n\n\n\n\n\n\nCite the Article: Akpan, Mfon Joseph, George, Nyakno Jimmy, Ekanem, Aniekan Martin, Nathaniel , Ekong Ufot (2023).  Deploying Geostatistical Stochastic Inversion Modeling in \nThe Characterization of Reservoirs: A Case Study from an Onshore Niger Delta Field. Malaysian Journal of Geosciences, 7(2): 52-60. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 52-60 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 9: Cross plot of lambda-rho versus mu-rho using data from three wells (reservoir F) colour coded using (a) water-saturation and (b) shale-volume \ncolour bars \n\n\n\n5.2   Deterministic and Geo-statistical Inversions \n\n\n\nGeo-statistical seismic inversion when compared with deterministic \ninversion has shown an improvement in resolution (Pendrel, 2001). In this \nstudy, more detailed reservoir characterization was made possible using \ngeo-statistical inversion, which offers more information than \ndeterministic inversion. As part of the quality assurance processes, the \noutcomes of the geo-statistical inversion are visually compared with those \nof the deterministic inversion. Figure 10 compares the cross-sectional \nresults of the high-resolution deterministic inversion (upper) with geo-\nstatistical inversion (lower) in the acoustic impedance domain where (a) \nand (b) are \ud835\udf0c\ud835\udc49\ud835\udc5d sections and (c) and (d) are Vp-Vs ratio sections \n\n\n\nrespectively. The log inset is the gamma-ray log which shows zones of low \ngamma ray values corresponding to areas of low acoustic impedance in \nthe section. The segments in purple and blue colours are in elevated order \nof acoustic impedances, while segments in yellow to green colours \ncorrespond to low impedance values. The segments with red colour have \nintermediate acoustic impedance values. The acoustic impedance (Zp) \nhave elevated values, which ranged from 2.61 to 2.74 \u00d7104 ft/s * g/cc with \npurple and blue colours corresponding to shaly sands. The intermediate \nvalues of acoustic impedance ranged between 2.32 and 2.45\u00d7104 ft/s * \ng/cc with red corresponding to shaly sands. The lowest acoustic \nimpedance values are associated with hydrocarbon saturated sands, \nwhich have green and yellow colors in the range of  2.11 and 2.24 \u00d7104 \n\n\n\nft/s*g/cc. Acoustic impedance generally has high  sensitivity to lithology \nand has the potential to fairly discriminate brine sands from  hydrocarbon-\ncharged sands. It is observed that the hydrocarbon sands units are well \nresolved in the geo-statistical stochastic inversion output when compared \nto the alternative deterministic inversion. Some regions in the \ndeterministic inversion (Figure 11c) c around well 30 that had low \nacoustic impedance values inferred to be hydrocarbon sands appear as \neither shale or brine in the geo-statistical inversion (Figure 11d). The ratio \nof Ip and Is was used to calculate Vp/Vs. The Vp-Vs ratio prominently \nindicated fluid presence due to the sensitivity of the P-wave to contrast in \nfluids, whereas the S-wave is not sensitive to fluid with the exception of \nhigh viscosity oil (Avseth et al., 2005). Differences in the Vp-Vs ratio are \ncaused by fluid saturations and fluid type contrasts (Ostrander, 1984; \nCastagna et al., 1993; Wang, 2001). Figure 12 depicts Vp/Vs cross-section \nand inverted horizon slices for single reservoir sand. As seen in the Figure \n12, the Vp-Vs ratio cross section has locally low values (1.70-1.82) \ncorresponding to hydrocarbon bearing sands. At various sections of both \nvolumes (deterministic and geo-statistical inversions), the regions of \nmoderate (1.87-1.97) to high (2.08-2.20) Vp-Vs ratio correspond to shaly \nsands. Three main zones in the seismic inverted section are inferred to be \nprobable hydrocarbon, shales, and brine zones with the aid of cross-plots \nof Vp/Vs with P-impedance. There is an increase in the image resolution \nwhen comparing the output of the geo-statistical stochastic inversion to \nthat of the deterministic inversion. \n\n\n\n\n\n\n\nFigure 10: Comparison of cross-sections of deterministic (upper) and high-resolution geo-statistical inversion (lower) outcomes in the acoustic \nimpedance domain: (a and b) P-impedance sections and (c and d) Vp /Vs sections. \n\n\n\n\n\n\n\n\nCite the Article: Akpan, Mfon Joseph, George, Nyakno Jimmy, Ekanem, Aniekan Martin, Nathaniel , Ekong Ufot (2023).  Deploying Geostatistical Stochastic Inversion Modeling in \nThe Characterization of Reservoirs: A Case Study from an Onshore Niger Delta Field. Malaysian Journal of Geosciences, 7(2): 52-60. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 52-60 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 11: Comparison of horizon of deterministic (upper) and high-resolution geo-statistical inversion (lower) outcomes in the acoustic impedance \ndomain: (a and b) P-impedance sections and (c and d) Vp/Vs sections. \n\n\n\n\n\n\n\nFigure 12: Comparison of cross-section and horizon section respectively in deterministic (a and b) and high-resolution geo-statistical inversion (c and d) \noutcomes in the Lambda-Rho domain. \n\n\n\nThe \ud835\udf06\ud835\udf0c attribute according to recent study, explains the incompressibility \nof the lithology units. Figure 11 show inverted \ud835\udf06\ud835\udf0c  cross-section and \nhorizon slice for both deterministic (a and c) and geo-statistical inversion \n(b and d) (Goodway et al., 1997). The lithology shows shale and sand \nregions and sand- shale intercalation archetypal of the Niger Delta \ngeologic formations. The low incompressibility values are prominent \nwithin the zones charged with hydrocarbon fluids. The embedded shale \nhave higher \ud835\udf06\ud835\udf0c values (34.3-39.6 Gpa*g/cc: purple and blue colours) that \nare suggestive of incompressible lithology. The estimated \ud835\udf06\ud835\udf0c zone ranged \nfrom 14.4 to19.7 Gpa*g/cc with Green and light yellow colours and this is \nconsistent with the economic availability of hydrocarbon. The values of \n\ud835\udf06\ud835\udf0cis low for oil sands and extremely low for gas sands (Akpan et al., \n2020b). The reddish colour with \ud835\udf06\ud835\udf0c ranging from 22.8-29.1 Gpa*g/cc \nnoticed indicates the effect of brine on \ud835\udf06\ud835\udf0cvalue. The \ud835\udf06\ud835\udf0c has a good \nsensitivity potential to discriminate the reservoir fluids. Low values are \nsymptomatic of hydrocarbon-charged sands while higher values indicate \nbrine sands. Geo-statistical inversion has well resolved information than \ndeterministic inversion and so reservoir characterization is more \nachievable in geo-statistical inversion than deterministic inversion.  \n\n\n\nThe incompressible character of the lithology is explained by the lambda-\nrho feature (Goodway et al., 1997). Figure 12 depicts deterministic (a and \nc) and geo-statistical (b and d) inverted lambda-rho (\u03bb\u03c1) cross-sections \nand horizon slices. Low values of lambda-rho denote hydrocarbon-\ncharged sands, while elevated values denote brine sands. The lowest \nvalues of  \u03bb\u03c1  are found in hydrocarbon-charged sands. In the hydrocarbon \ncharged precincts,  reduced incompressibility values are prominent. The \nembedding shale has higher incompressibility values of 34.3 - 39.6 \nGpa*g/cc which coressponds to blue and purple colours. Low \ud835\udf06\ud835\udf0c presinct \nranged from 14.4 to 19.7 Gpa*g/cc with light-yellow and green colours is \n\n\n\ncharged with economic  hydrocarbon presence. Generally,\ud835\udf06\ud835\udf0cis reduced in \noil sands and extremely resuced in gas sands. The noticeably red \ncolouration with values between 22.8 and 29.1 Gpa*g/cc is indicative of \nthe brine effect on \ud835\udf06\ud835\udf0c values. Since  geo-statistical inversion offers \nadditional and well resolved information than the alternative \ndeterministic inversion, relying on inversion of the former than the later \nhas can reduce the challenges associated with characterizing  reservoirs in \nthe arcuate Niger Delta. \n\n\n\n6. CONCLUSION \n\n\n\nThe use of geo-statistical inversion on the Sigma Field has been effective \nin defining reservoir boundaries and has revealed new information about \nsubsurface uncertainty. The methodology used to compare geo-statistical \ninversion with deterministic inversion has made known that the former \ngreatly enhances the seismic inversion outcome. The loading of the data, \nthe seismic-to-well tie, the wavelet extraction, the horizon interpretation, \nthe Rock-Physics Feasibility Study, the construction of the stratigraphic \ngrid, the definition of the prior variogram models, the creation of the initial \nmodel, and the geo-statistical inversion were the steps in the inversion \nprocess. Low values for lambda-rho, Vp/Vs, and acoustic impedance are \nindicators of sands saturated with hydrocarbons. This study can be used \nto classify rock qualities, characterize lithology and fluids and avoid \nuncertain amplitude interpretations that can result in the drilling of \nunproductive or dry wells. The technique, which is useful as economic and \ndecision-making tool can identify new prospects in the Sigma Field. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe authors kindly acknowledge the reviewers for their contributions and \n\n\n\n\n\n\n\n\nCite the Article: Akpan, Mfon Joseph, George, Nyakno Jimmy, Ekanem, Aniekan Martin, Nathaniel , Ekong Ufot (2023).  Deploying Geostatistical Stochastic Inversion Modeling in \nThe Characterization of Reservoirs: A Case Study from an Onshore Niger Delta Field. Malaysian Journal of Geosciences, 7(2): 52-60. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 52-60 \n\n\n\n\n\n\n\n\n\n\n\ncomments towards improving our research article. \n\n\n\nREFERENCES \n\n\n\nAdojoh O., Marret, F., Duller, R. and Osterloff, P. 2017. Tropical \npalaeovegetation dynamics, environmental and climate change \nimpact from the low latitude coastal offshore margin, Niger Delta, \nGulf of Guinea. Palaeoecology of Africa, Vol. 34, pp. 107-144. \n\n\n\nAkpan, A. S., Obiora, D. N., Okeke, F. N., Ibuot, J. C., & George, N. J. 2020a. \nInfluence of wavelet phase rotation on post stack model based \nseismic inversion: A case study of X-field Niger Delta, Nigeria. \nJournal of Petroleum and Gas Engineering UK, 11(1), 57\u201367. \nhttps:// doi.org/ 10.5897/ JPGE2 019. 0320 \n\n\n\nAkpan, A. S., Okeke, F. N., Obiora, D. N., & George, N. J. 2020b. Modelling and \nmapping hydrocarbon saturated sand reservoir using Poisson\u2019s \nimpedance (PI) inversion: A case study of Bonna field Niger Delta \nSwamp Depobelt, Nigeria. Journal of Petroleum Exploration and \nProduction Technology Germany, 11, 117\u2013132. https:// doi. org/ \n10. 1007/ s13202- 020- 01027-8 \n\n\n\nAkpan, MJ. George, NJ. Ekanem, AM, Ekong, UN. 2022 Ekong Petrophysical \nappraisal and 3\u2011D structural interpretation of reservoirs in an \nOnshore Niger Delta Field, Southeastern Nigeria, International \nJournal of Energy and Water Resources \nhttps://doi.org/10.1007/s42108-022-00218-9 \n\n\n\nAvbovbo, A. A. 1978. Tertiary Lithostratigraphy of Niger Delta. American \nAssociation of Petroleum Geologist Bulletin, 62(2), 295\u2013300. \n\n\n\nAvseth, P., Mukerji, T., & Mavko, G., 2005. Quantitative Seismic \nInterpretation \u2013Applying Rock Physics Tools to Seismic \nInterpretation Risk: Cambridge University press. \n\n\n\nBuland, A., and H. Omre, 2003, Bayesian linearized AVO inversion: \nGeophysics, 68, 185\u2013198, doi: 10.1190/1.1543206. \n\n\n\nCastagna, J.P.; Batzle, M.L.; Kan, T.K. 1993. Rock physics\u2014The link between \nRock Properties and AVO Response, in Offset Dependent \nReflectivity\u2014Theory and Practice of AVO Analysis. In: Castagna, J.P., \nBackus, M. (eds.) Investigations in Geophysics, No. 8, Society of \nExploration Geophysics, pp. 135\u2013171. Tulsa, Oklahoma. \n\n\n\nDoust, H., and Omatsola, E., 1990. Niger Delta: In: Edwards, J. D., \nSantogrossi, P.A., (Eds.), Divergent/Passive Margin Basins. \nAmerican Association of Petroleum Geologists Bulletin Memoir. 48, \n239\u2013248. \n\n\n\nEjedawe, J., Love, F., Steele, D., & Ladipo, K. 2007. Onshore to Deep-Water \nGeologic Integration, Niger Delta. An In-House Presentation Pack of \nthe Shell Exploration and Production Limited, Port-Harcourt. \n\n\n\nEkanem, A. M., George, N. J., Thomas, J. E., & Ekpenyong, NE. 2021a. Seismic \nmodelling study of CO2 effects on P-wave amplitude. Modeling \nEarth Systems and Environment China. https:// doi. org/ 10. 1007/ \ns40808- 020- 00904-9 \n\n\n\nEkanem, A. M., Ikpe, E. O., George, N. J. & Thomas, J. E. 2022a. Integrating \ngeoelectrical and geological techniques in GIS\u2011based DRASTIC \nmodel of groundwater vulnerability potential in the raffia city of \nIkot Ekpene and its environs, southern Nigeria. International \nJournal of Energy and Water Resources. https:// doi. org/ 10. 1007/ \ns42108- 022- 00202-3 \n\n\n\nEkanem, AM; Akpan, AE; George, NJ; Thomas, JE 2021b. Appraisal of \nprotectivity and corrosivity of surficial hydrogeological units via \ngeo-sounding measurements Environmental Monitoring and \nAssessment 193, 1-22, 2021 \n\n\n\nEkanem, K. R., George, N. J., & Ekanem, A. M. 2022b. Parametric \ncharacterization, protectivity and potentiality of shallow \nhydrogeological units of a medium-sized housing estate, Shelter \nAfrique, Akwa Ibom State, Southern Nigeria. Acta Geophysics. \nhttps:// doi. org/ 10. 1007/ s11600- 022- 00737-3 \n\n\n\nEscobar, I., P. Williamson, A. Cherrett, P. Doyen, R. Bornard, R. Moyen, and \nT. Crozat, 2006. Fast geostatistical stochastic inversion in a \nstratigraphic grid: 76th Annual International Meeting, SEG, \nExpanded Abstracts, 2067\u20132071, doi: 10.1190/1.2369943 \n\n\n\nEvamy, B. D., Haremboure, J., Kamerling, P., Knaap, W. A., Molloy, F. A., & \nRowlands, P. H. 1978. Hydrocarbon habitats of Tertiary Niger-Delta. \nAmerican Association of Petroleum Geologists Bulletin, 62, 277\u2013\n298. \n\n\n\nFatti, J. L., G. Smith, P. Vail, P. Strauss, and P. Levitt. 1994. Detection of gas \nin sandstone reservoirs using AVO analysis: A 3-D seismic case \nhistory using the geostack technique: Geophysics, 59, 1362\u20131376, \ndoi: 10.1190/1.1443695. \n\n\n\nGeorge, NJ, Akpabio, GT; Udofia, KM. 2010. The implication of oil spillage \non the thermal properties of soil samples in the Niger Delta, \nsouthern Nigeria, Archives of Physics Research 1 (4), 64-72 \n\n\n\nGeorge, NJ. 2021. Modelling the trends of resistivity gradient in \nhydrogeological units: a case study of alluvial environment, \nModeling Earth Systems and Environment 7, 95-104 \n\n\n\nGeorge, NJ., Ekanem, AM., Thomas, JE., Ekong, SA. 2021. Mapping depths of \ngroundwater-level architecture: implications on modest \ngroundwater-level declines and failures of boreholes in \nsedimentary environs, Acta Geophysica 69 (5), 1919-1932 \n\n\n\nGeorge, NJ; Atat, JG; Udoinyang , IE, Akpan, AE; George, AM. 2017. \nGeophysical assessment of vulnerability of surficial aquifer in the oil \nproducing localities and riverine areas in the coastal region of Akwa \nIbom state, southern Nigeria, Current Science, 430-438  \n\n\n\nGoodway, B., Chen, T. & Downton, J. 1997. Improved AVO Fluid Detection \nand Lithology Discrimination Using Lame Petrophysical \nParameters: \u2018\u03bb\u03c1', \u2018\u03bc\u03c1', & \u2018\u03bb/\u03bc fluid stack' from P and S inversions: \nPresented at the 67th Annual International Meeting, Society of \nExploration Geophysics Expanded Abstracts. Pp. 183 \u2013 186. \n\n\n\nGreenberg, M.  L. &Castagna, J.P. 1992. Shear-Wave Estimation in Porous \nRocks:  Theoretical Formulation, Preliminary Verification and \nApplications. Geophysical Prospecting, 40, No.  2, 195\u2013209.  \n\n\n\nHaas, A., and O. Dubrule, 1994. Geostatistical inversion: A sequential \nmethod of stochastic reservoir modeling constrained by seismic \ndata: First Break, 12, 561\u2013569, doi:10.3997/1365-2397.1994034.  \n\n\n\nMoyen, R., and P. Doyen. 2009. Reservoir connectivity uncertainty from \nstochastic seismic inversion: 79th Annual International Meeting, \nSEG, Expanded Abstracts, 2378\u20132382, doi: 10.1190/1.3255337. \n\n\n\n\u00d8degaard, E. and Avseth, P. 2004. Well Log and Seismic Data Analysis \nUsing Rock Physics Templates. First Break, 22, 37-43. \nhttps://doi.org/10.3997/1365-2397.2004017 \n\n\n\nOnuoha, KM. 1999. Structural features of Nigeria\u2019s coastal margin: An \nassessment based on age data from wells. Journal of African Earth \nSciences, 29, 485\u2013499. \n\n\n\nOstrander, WJ. 1984. Plane-Wave Reflection Coefficients for Gas Sands at \nNon-Normal Angles of Incidence. Geophysics, 49, 1637 \u2013 1648. \n\n\n\nPendrel, J., 2001, Seismic inversion \u2014 The best tool for reservoir \ncharacterization: CSEG, 26, no. 1. \n\n\n\nShort, KC., & Stauble, A. J. 1967. Outline of geology of Niger Delta. American \nAssociation of Petroleum Geologists Bulletin, 51, 761\u2013779. \n\n\n\nStacher, P. 1995. Present understanding of the Niger Delta hydrocarbon \nhabitat. In M. N. Oti & G. Postma (Eds.), Geology of Deltas. Pp. 257\u2013\n267. \n\n\n\nUmoh, JA; George, NJ, Ekanem, AM; Emah, JB. 2022. Characterization of \nhydro-sand beds and their hydraulic flow units by integrating \nsurface measurements and ground truth data in parts of the \nshorefront of Akwa Ibom State, International Journal of Energy and \nWater Resources, 1-17. \n\n\n\nUmoren, E. B., & George, N. J. 2018. Time lapse (4D) and AVO analysis: A \ncase study of Gullfaks field, Northern North Sea, NRIAG. Journal of \nAstronomy and Geophysics, Egypt, 7(1), 62\u201377. https://doi. org/ 10. \n1016/j.nrjag. 2018.03.004. \n\n\n\nWang, Z. 2001. Fundamentals of Seismic Rock Physics, Geophysics, 66 (2), \n2001, 398 \u2013 412. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 64-68 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2021.64.68 \n\n\n\n \nCite The Article: Innocent Kiani, Aniefiok Sylvester Akpan (2021). Delineation of Hydrocarbon Saturated Reservoir Sand Using Integrated 3D Pre-Stack Seismic and \n\n\n\nWell Log Data in Bonga \u2013 Field, Central Swamp Depobelt, Onshore Niger Delta, Nigeria. Malaysian Journal of Geosciences, 5(2): 64-68. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nREVIEW ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2021.64.68 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDELINEATION OF HYDROCARBON SATURATED RESERVOIR SAND USING \nINTEGRATED 3D PRE-STACK SEISMIC AND WELL LOG DATA IN BONGA \u2013 FIELD, \nCENTRAL SWAMP DEPOBELT, ONSHORE NIGER DELTA, NIGERIA \n \nInnocent Kiania, Aniefiok Sylvester Akpanb* \n \na Department of Science Laboratory Technology, Captain Elechi Amadi Polytechnic, Rumuola, Rivers State, Nigeria \nb Department of Physics and Astronomy, University of Nigeria, Nsukka, Enugu State, Nigeria. \n*Corresponding Author E-Mail: aniefiok.akpan.pg79875@unn.edu.ng \n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 05 September 2021 \nAccepted 18 October 2021 \nAvailable online 22 October 2021 \n\n\n\n\n\n\n\nThis study has successfully delineated the lateral continuity of hydrocarbon saturated sand reservoir in \nBonga field, Niger Delta. 3D pre-stack seismic volume and well logs from two (2) exploratory wells were \nemployed in the pre-stack seismic inversion analysis. The delineated BGA reservoir sand spans across the \ntwo (2) wells labelled Bonga-26 and Bonga-30. The reservoir depth ranges from 10490 ft to 10620 ft in \nBonga-26 while the reservoir depth ranges from 10390 ft to 10490 ft in Bonga-30. The delineated reservoir \nis characterized by low gamma ray (< 75 API), water saturation, shale volume and high resistivity as \ndeciphered in their respective well log curves signature. Rock attribute crossplot was carried out to \ndiscriminate between the formation fluid and lithology. The crossplot space of VP-VS ratio versus acoustic \nimpedance (AI), discriminates the formation properties into lithology and fluid (gas and brine sand) based \non clusters inferring the presence of each formation fluid properties. The inversion cross sections of P-\nimpedance, S-impedance, density (\u03c1) and VP-VS ratio depicts the spread and lateral continuity of the reservoir \nsand across the well locations. The delineated zones reveal low P-impedance, density, VP-VS ratio and slight \nincrease in S-impedance which further validate the presence of hydrocarbon in the field. \n\n\n\nKEYWORDS \n\n\n\nWell log, P-impedance, Density, Vp-Vs ratio and Crossplots  \n\n\n\n1. INTRODUCTION \n\n\n\nThe Niger Delta sedimentary Basin in Nigeria has over the years been a \nblessing to the Nation. The discovery and production of oil and natural gas \nfrom the region has made the Niger Delta a globally recognized \nhydrocarbon production Basin. In spite of the several oil/gas productions \ncarried out in the various field discovered, reports reveals that quite an \nappreciable volume of hydrocarbon is inherent in the basin. The \napplication of geophysical methods of exploration ranging from seismic, \nwell logs, gravity and magnetic has contributed immensely in \nunderstanding the nature and formation of the basin. Seismic methods \nhave predominately aided in the discovery of several oil fields in the basin. \nThe principle of seismic inversion is to convert the seismic interpreter\u2019s \nview of the earth reflections as a function of time to the geologist\u2019s view of \nthe earth velocity as a function of depth (Nanda, 2016).  \n\n\n\nSeismic inversion is a procedure that helps extract underlying models of \nthe physical characteristics of rocks and fluid from seismic and well log \ndata (Maurya and Sarkar, 2016). In other words, inversion uses the \nseismic data to determine the geology which caused that seismic event. \nWith the aid of inversion we can obtain petrophysical model which fits our \nacquired seismic data. Seismic amplitude inversion uses reflection \namplitudes, calibrated with well data to extract details that can be \ncorrelated with porosity, lithology and fluid saturation (Akpan et al., 2020; \nHampson, 2015; Domagoj and Stipica, 2015). Seismic inversion provides \nthe most detailed view of the subsurface and because of this efficiency; \n\n\n\ninversion method is employed by oil and gas companies to increase the \nresolution and reliability of the data which improve estimation of rock \nproperties such as porosity and net pay.  \n\n\n\nOne key benefit of seismic inversion process pointed out is the ability to \nincrease the frequency content of the seismic data which is band limited \n(Latimer et al., 2000). The result of inversion permits lithology \ndifferentiation and hopefully the estimation of the fluid content, and not \nonly the interface geometry as given by the amplitude attribute (Veeken \nand Silva, 2004; Avseth et al., 2005). Hence, the current research employed \nseismic inversion to delineate hydrocarbon saturated sand reservoir. The \nstudy inspects P-impedance, S-impedance, VP-VS ratio and density to \ndelineate the probable reservoir zone. It reveals the superiority of seismic \ninversion method of geophysical prospecting in the interpretation of \nseismic data to infer geologic features of the earths subsurface. The \ninverted cross sections deciphered the lateral continuity of hydrocarbon \nsaturated bed. \n\n\n\n2.  LOCATION AND GEOLOGY OF THE STUDY AREA \n\n\n\nBonga field is one of the oil/gas fields located Onshore in Central Swamp \nDepobelt of the prolific Niger Delta Basin (Figure 1). According to the \nboundaries of Niger Delta Basin lies between latitudes 3oN and 6oN and \nlongitudes 4oE and 8oE (Ejedawe et al., 2007). The Niger Delta Basin is one \nof the most prolific deltaic hydrocarbon provinces in the world with \nreservoir rocks reported to be dated to Middle Miocene deposited in para-\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 64-68 \n\n\n\n\n\n\n\n \nCite The Article: Innocent Kiani, Aniefiok Sylvester Akpan (2021). Delineation of Hydrocarbon Saturated Reservoir Sand Using Integrated 3D Pre-Stack Seismic and \n\n\n\nWell Log Data in Bonga \u2013 Field, Central Swamp Depobelt, Onshore Niger Delta, Nigeria. Malaysian Journal of Geosciences, 5(2): 64-68. \n\n\n\n\n\n\n\nsequence of shallow marine and deltaic plain (Reijers, 2011). The Basin \nhas one petroleum system identified as the Akata-Agbada system of the \nTertiary Niger Delta. The Deltaic is made up of three lithostratigraphic \nunits \u2013 the oldest Akata Formation, Agbada Formation and the youngest \nBenin Formation. Along the stratigraphic intervals, hydrocarbon is chiefly \nproduced from sandstone and unconsolidated sands of the Agbada \nFormation (Ehirim and Chikezie, 2016; Emujakporue and Ngwueke, \n2013).  \n\n\n\nThe Niger Delta Basin covers an area of about 75,000 square kilometres \nwhich extends from the Calabar flank and the Abakaliki Trough in Eastern \nNigeria to the Benin flank in the west and opens to the Atlantic Ocean in \nthe south. The Akata Formation is the oldest lithostratigraphic unit in the \nNiger Delta Basin. The shales of the Akata Formation serve as the main \nsource rock in the basin while the turbidite sands serve as reservoirs in \nthe deep offshore (Doust and Omatsola, 1990). The Agbada Formation is \ncharacterized by paralic interbedded sandstone and shale (Reijers, 1996). \nThe Benin Formation comprises the top part of the Niger Delta clastic \nwedge from the Benin-Onitsha area in the north to beyond the coastline \n(Short and Stauble, 1967). Whitman estimated the thickness of the Agbada \nFormation to be about 280 m and upto 2100 m in region with maximum \nsubsidence (Whitman, 1982).   \n\n\n\n \nFigure 1: Niger Delta Depobelt map where the study area is situated \n\n\n\n(Ebong et al., 2019) \n\n\n\n3. THEORY AND DATA PROCESSING \n\n\n\nThe acoustic impedance (AI), which determines the reflection or \nrefraction from the interface of the various layers in the subsurface is a \nproduct of the velocity (v) and the density (\ud835\udf0c) given as (Castagna and \nBachus, 1993): \n\n\n\n \n\uf072vAI =\n\n\n\n                                                                                                        (1) \n\n\n\nThe reflected signals to the surface are recorded as \u201cwiggles\u201d. The wiggles \nare made up of peaks and troughs which give an indication of the \nconditions at the boundaries of reflection. Statoil processing report, \nopined that a peak is an indication of transition from a lower AI to a higher \nAI while a trough indicates a transition from a higher AI to a lower AI \nwhich is the SEG (Society of Exploration Geophysics) normal polarity \n(Statoil, 2007). At the boundaries, the amplitude of reflection is based on \nthe reflection coefficient otherwise referred to as the reflectivity given as \n(Vekeen, 2007): \n\n\n\nkk\n\n\n\nkk\n\n\n\nkkkk\n\n\n\nkkkk\n\n\n\nAIAI\n\n\n\nAIAI\n\n\n\nvv\n\n\n\nvv\nR\n\n\n\n+\n\n\n\n\u2212\n=\n\n\n\n+\n\n\n\n\u2212\n=\n\n\n\n+\n\n\n\n+\n\n\n\n++\n\n\n\n++\n\n\n\n1\n\n\n\n1\n\n\n\n11\n\n\n\n11\n\n\n\n\uf072\uf072\n\n\n\n\uf072\uf072\n\n\n\n  \n                                                                                                                  (2) \n\n\n\nwhere \ud835\udc63\ud835\udc58 and \ud835\udc63\ud835\udc58+1 are the velocities of the first and second layers and \ud835\udf0c\ud835\udc58 \nand \ud835\udf0c\ud835\udc58+1 are the densities in the first and second layers respectively. \n\n\n\nThe data employed in this study were obtained from Shell Petroleum \nDevelopment Company (SPDC) of Nigeria Limited. The data include 3D \nprestack seismic volume and suite of well logs (gamma ray, caliper, \nresistivity, density and P-wave) in addition to checkshot log. Although the \nBonga field has five (5) exploratory wells, complete log information is \navailable in two (2) wells which limits the number wells involved in the \nanalysis to Bonga-26 and Bonga-30. The Bonga-26 and Bonga-30 wells \nhave a total logged depth of 11661 ft and 12000 ft respectively. The well \nlog data were checkshot corrected to align the arrival time in milliseconds \n(ms) on the seismic section with the well data logged depth in feet (ft).  \n\n\n\n \nFigure 2: Bonga \u2013 26 well log showing caliper, density, gamma ray and \n\n\n\nresistivity log signatures \n\n\n\n \nFigure 3: Bonga \u2013 30 well log showing caliper, density, gamma ray and \n\n\n\nresistivity log signatures \n\n\n\nMedian filtering was carried out on the well log signatures to correct for \nborehole irregularities such washout, cave in which could introduce \nanomalous spikes in the data. After series of iterations, an operator length \nof seven (7) was observed to be suitable to preserve the signal. Shear wave \nvelocity (Vs) was estimated for Bonga-26 and Bonga-30 wells using \nrelation given in equation 3 as (Greenberg and Castagna, 1992): \n\n\n\n36.116.1 += SP VV\n    \n\n\n\n                                                                            (3) \n\n\n\nwhere VP is the compressional wave velocity derived from sonic log data. \n\n\n\nStatistical wavelet was extracted (Figure 4a) from the seismic volume and \ntie to wells. This was done to properly calibrate the time event on both \nseismic and well logs. BN horizon which corresponds to the delineated \nreservoir in the well log was picked across the seismic section. The horizon \nrun across the section within a time window of 2000 ms to 2400 ms as \nshown in Figure 4c. This was followed by cross correlation were series of \niterations were carried out till a good match was obtained between the \nseismic and well log. An excellent correlation coefficient of 85% was \nobtained as shown in Figure 4b which ascertains the reliability of the \ninverted results presented in this study. P-impedance model (Figure 4c) \nwas established using the relation given in equation 1 and inversion were \ncarried out for P-impedance, S-impedance, density (\u03c1) and VP-VS and \ncompared to ascertain the zone delineated as probable hydrocarbon sand. \nThe summarized workflow illustrating the study methodology is shown in \nFigure 5. \n\n\n\n \n(a) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 64-68 \n\n\n\n\n\n\n\n \nCite The Article: Innocent Kiani, Aniefiok Sylvester Akpan (2021). Delineation of Hydrocarbon Saturated Reservoir Sand Using Integrated 3D Pre-Stack Seismic and \n\n\n\nWell Log Data in Bonga \u2013 Field, Central Swamp Depobelt, Onshore Niger Delta, Nigeria. Malaysian Journal of Geosciences, 5(2): 64-68. \n\n\n\n\n\n\n\n \n(b) \n\n\n\n\n\n\n\n \nFigure 4: (a) Extraceted wavelet (b) well to seismic correlation result (c) \n\n\n\nP-impedance model section within time window of 2000 ms \u2013 2400 ms \n\n\n\n \nFigure 5: The overall workflow of study methodology \n\n\n\n4. RESULTS AND DISCUSSION \n\n\n\nThe results of the seismic and well log data interpretation which include \nrock attribute crossplot, inverted P-impedance, S-impedance, Density and \nVP-VS ratio cross sections are presented in Figures 6\u201311. The crossplot \nspace of Gamma ray versus Poisson impedance is shown in Figures 6 and \n7 for Bonga-26 and Bonga-30 wells respectively. The results decipher \nconverging clusters which discriminates the probable fluid in the \ndelineated BGA reservoir into oil/gas sand, wet sand and shale bed with \nGamma Ray colour key. As stated crossplots are visual representation of \nthe relationships between two or more variables which are employed in \nidentifying anomalies that could be interpreted to infer the presence \nhydrocarbon or other fluid (Omodu and Ebeniro, 2007). Within the \ncrossplot space of Figure 6, oil/gas sand corresponds to clusters in green \nand yellow circled in red polygon for Bonga-26 crossplot established to \ndiscriminate the formation fluid and lithology. The presence of wet sands \nwhich could be saturated with brine are deciphered in converging clusters \nof orange to cyan colours circled in blue. The cap/seal rocks are the \nclusters in blue to purple circled in black polygon. In Bonga-30 well \ncrossplot space (Figure 7), the converging clusters corresponding to \noil/gas sand, wet sand and shale beds are grouped using a trapezium \nshape in purple colour, orange to cyan circled in blue and blue to purple \ncircled in black polygon. \n\n\n\n \nFigure 6: Crossplot of Gamma Ray versus Poisson Impedance for Bonga-\n\n\n\n26 \n\n\n\n \nFigure 7: Crossplot of Gamma Ray versus Poisson Impedance for Bonga-\n\n\n\n26 \n\n\n\nThe inverted P-impedance cross section of the target window of 400 ms \nwhich ranges from 2000 \u2013 2400 ms is shown in Figure 8. The target \nwindow incorporates the BGA reservoir top and base in red and black \ncolour respectively. The Horizon spans across the inverted seismic \nsections. The reservoir base (red colour) cuts across Bong-24, Bonga-26 \nand Bonga-30 wells while the reservoir top in black colour spans across \nall the wells in the field as deciphered in the section of Figure 8. P-\nimpedance is the product of compressional wave velocity and density. The \nresult depicts low impedance in green to yellow which is attributed to \nhydrocarbon saturated sand. The impedance values of these zone which \nlies beneath the shale bed in purple colour code ranges from \n\n\n\n)/)(/(101.2 4 ccgsft\uf0b4 to )/)(/(103.2 4 ccgsft\uf0b4 .  \n\n\n\nThe shale bed which acts as a seal to the hydrocarbon sand is deciphered \nin purple colour. The shale bed P-impedance value ranges from \n\n\n\n)/)(/(106.2 4 ccgsft\uf0b4  to )/)(/(107.2 4 ccgsft\uf0b4 . This zone is also \ndeciphered in the S-impedance section shown in Figure 9. For the S-\nimpedance inverted section (Figure 9), the shale beds value ranges from \n\n\n\n)/)(/(104.1 4 ccgsft\uf0b4  to )/)(/(105.1 4 ccgsft\uf0b4 while the zone \ncorresponding to hydrocarbon sand with low S-impedance values ranges \n\n\n\nfrom )/)(/(101.1 4 ccgsft\uf0b4  to )/)(/(102.1 4 ccgsft\uf0b4 . The variation in \nthe impedance values between P-impedance and S-impedance is due to \nthe different properties in which the parameters measures. As stated by \nlow P-impedance values denote a sand lithologic unit with hydrocarbon \nsaturation (Oyeyemi et al., 2017). \n\n\n\nThe density which gives a measure of the reservoir bulk density is shown \nin Figure 10. The inverted section depicts zones with low density which \ncorresponds to the area inferred to be hydrocarbon sands in the P- and S-\nimpedance inverted cross sections. The density values within this zone \nranges from 1.21 g/cc to 1.53 g/cc for the hydrocarbon delineated bed \nwhile the shale bed density values in purple colour ranges from 2.21 g/cc \nto 2.31 g/cc. The VP-VS ratio which serves as a fluid indicator was estimated \nto validate the inference made from the P-impedance, S-impedance and \ndensity is shown in Figure 11. The inverted section is characterized by low \nVP-VS ratio within the delineated reservoir zone marked by the BGA \nreservoir top and base. A well pronounced reservoir region is deciphered \nin the section with VP-VS ratio values ranging from low in green to yellow \nand high values in purple colour. The hydrocarbon sand reservoir channel \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 64-68 \n\n\n\n\n\n\n\n \nCite The Article: Innocent Kiani, Aniefiok Sylvester Akpan (2021). Delineation of Hydrocarbon Saturated Reservoir Sand Using Integrated 3D Pre-Stack Seismic and \n\n\n\nWell Log Data in Bonga \u2013 Field, Central Swamp Depobelt, Onshore Niger Delta, Nigeria. Malaysian Journal of Geosciences, 5(2): 64-68. \n\n\n\n\n\n\n\nVP-VS ratio values in green to yellow colour range from \n\n\n\n)/)(/(105.1 4 ccgsft\uf0b4 to )/)(/(106.1 4 ccgsft\uf0b4 while the shale bed is \ndeciphered in purple colour with values ranging from \n\n\n\n)/)(/(1092.1 3 ccgsft\uf0b4 to )/)(/(1098.1 3 ccgsft\uf0b4 . \n \n\n\n\n \nFigure 8: P \u2013 impedance inverted section showing the delineated \n\n\n\nhydrocarbon sand and wells in Bonga field \n\n\n\n \nFigure 9: S \u2013 impedance inverted section showing the delineated \n\n\n\nhydrocarbon sand and wells in Bonga field \n\n\n\n \nFigure 10: Inverted density section showing the delineated hydrocarbon \n\n\n\nsand and wells in Bonga field \n\n\n\n \nFigure 11: Inverted VP-VS ratio section showing the delineated \n\n\n\nhydrocarbon sand and wells in Bonga field \n\n\n\n5. CONCLUSION \n\n\n\nThe lateral continuity of delineated hydrocarbon sand has successfully \ndeciphered in Bonga field. The study accessed P-impedance, S-impedance, \ndensity and Vp-VS ratio in delineating potential prospect saturated with \nhydrocarbon. The crossplot of gamma ray plotted against derived rock \nattributes of Poisson\u2019s impedance discriminates the formation fluid into \noil/gas, wet sands and shale bed which acts as a seal to the fluid saturated \nrock. In general, the lateral continuity of the of the suspected hydrocarbon \nsand are present in all the attributes considered in this study. \n\n\n\nACKNOWLEDGEMENTS  \n\n\n\nThe authors are grateful to Shell Petroleum Development Company of \nNigeria Limited for providing the dataset employed in carrying out this \nstudy. We also thank the Hampson Russell Corporation for software used \nin analysing the data. \n\n\n\nREFERENCES \n\n\n\nAkpan, A.S., Obiora, D.N., Okeke, F.N., Ibuot, J.C., George, N.J., 2020. \nInfluence of Wavelet Phase Rotation on Post Stack Inversion: A case \nstudy of X \u2013 field, Niger Delta, Nigeria. Journal of Petroleum and Gas \nEngineering, 11 (1), Pp. 57\u201367.  \nhttps://doi.org/10.5897/JPGE2019.0320   \n\n\n\nAvseth, P., Mukerji, T., Mavko, G., 2005. Quantitative Seismic \nInterpretation. Cambridge University Press. \n\n\n\nCastagna, J., and Backus, M., 1993. Offset Dependent Reflectivity \u2013 Theory \nand Practice of AVO analysis. SEG, Tusla, Investigations in Geophysics, Pp. \n571-581. \n\n\n\nDomagoj, V., Stipica, B., 2015. Acoustic Impedance Inversion Analysis: \nCroatia Offshore and Onshore Case Studies. SPE Conference \u2013 Hungarian \nSection. \n\n\n\nDoust, H., Omatsola, E., 1990. Niger Delta, in Edwards, J.D., Santogrossi, \nP.A., eds., Divergent/Passive Margin Basin, AAPG Memoir 48: Tulsa, \nAmerican Association of Petroleum Geologists, Pp. 239-248. \n\n\n\nEbong, E.D., Akpan, A.E., Urang, J.G., 2019. 3D Structural Modeling and \nFluid Identification       in parts of Niger Delta Basin, Southern Nigeria. \nJournal of African Earth Science. \n\n\n\nEhirim, C.N., Chikezie, N.O., 2016. Anisotropic AVO Analysis for Reservoir \nCharacterization in Derby Field Southeatern Niger Delta. International \nJournal of Applied Physics, 9, Pp. 67 \u2013 73. \n\n\n\nEjedawe, J., Love, F., Steele, D., Ladipo, K., 2007. Onshore to Deep-water \nGeologic Integration, Niger Delta. Shell Exploration and Production \nLimited, Port-Harcourt. \n\n\n\nEmujakporue, Godwin, O., and Ngwueke, Marcel L., 2013. Structural \ninterpretation of seismic data from XY field, onshore Niger Delta, Nigeria. \nJ. Appl. Sci. Environ. Manage, 17 (1), Pp. 153-158. \n\n\n\nGreenberg, M.L., Castagna, J.P., 1993. Shear Wave Velocity Estimation in \nPorous Rocks; Theoritical Formulation, Preliminary Verification and \nApplications. Geophysical Prospecting, Pp. 195-209. \n\n\n\nHampson, R., 2015. Hampson Russell Software Theory. CGG Veritas \nCaractere, France. \n\n\n\nLatimer, R.B., Davidson, R., Van-Riel, P., 2000. An Interpreter\u2019s Guide to \nUnderstanding and Working with Seismic Derived Acoustic Impedance \nData. The Leading Edge, 19, Pp. 242 \u2013 256. \n\n\n\nMaurya, P.S., Sarkar, P., 2016. Comparison of Post-Stack Seismic Inversion \nMethods: A case study of Blackfoot Field, Canada. International Journal \nof Scientific and Engineering Research, 7, Pp. 1091 \u2013 1101. \n\n\n\nNanda, C.N., 2016. Seismic Data Interpretation and Evaluation for \nHydrocarbon Exploration and Production: A practitioner\u2019s Guide. \nSpringer International Publishing, Switzerland. \n\n\n\nOmudu, L.M., Ebeniro, J.O., 2007. Cross plot and Descriptive Statistics for \nLithology and Fluid Discrimination: A case study from Onshore Niger \nDelta. Presented at the Annual Meeting of NAPE, Abuja. \n\n\n\nOyeyemi, K.D., Olowokere, M.T., Aizebeokhai, A.P., 2017. Evaluation of \nOptimal Reservoir Prospectivity using Acoustic Impedance Model \n\n\n\n\nhttps://doi.org/10.5897/JPGE2019.0320\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 64-68 \n\n\n\n\n\n\n\n \nCite The Article: Innocent Kiani, Aniefiok Sylvester Akpan (2021). Delineation of Hydrocarbon Saturated Reservoir Sand Using Integrated 3D Pre-Stack Seismic and \n\n\n\nWell Log Data in Bonga \u2013 Field, Central Swamp Depobelt, Onshore Niger Delta, Nigeria. Malaysian Journal of Geosciences, 5(2): 64-68. \n\n\n\n\n\n\n\nInversion: A case study of an offshore field, Western Niger Delta, Nigeria. \nNRIANG Journal of Astronomy and Geophysics, 6, Pp. 300 \u2013 310. \n\n\n\nReijers, T.J., Petter, S.W., Nwajide, C.S., 1996. The Niger Delta Basin, in: T. J. \nA. Reijers (eds), Selected Chapters on Geology: Sedimentary geology \nsequence stratigraphy of Anambra basin, Pp. 103-117. Warri, Nigeria: \nSPDC Corporate Reprographic Services. \n\n\n\nReijers, T.J.A., 2011. Stratigraphy and sedimentology of the Niger Delta. \nGeologos, 17 (3), Pp. 133-162. \n\n\n\nShort, K.C., Stauble, A.J., 1967. Online of the Niger Delta. American \n\n\n\nAssociation of Petroleum Geology, Bulletin., 51, Pp. 761 \u2013 779. \n\n\n\nStatoil, 2007. Quantitative Time-lapse (4D) Seismic Imaging and \nMonitoring. www.statoil.com \n\n\n\nVeeken, P.C., 2007. Seismic Stratigraphy, Basin Analysis and Reservoir \nCharacterization. Handbook of Geophysical Exploration, Seismic \nExploration. Elsevier Ltd. Netherlands.  \n\n\n\nWhiteman, A., 1982. Nigeria, Its Petroleum Geology, Resources and \nPotential. Edinburgh Graham & Trotman Ltd.\n\n\n\n \n\n\n\n\n\n"
"\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 06-09 \n\n\n\nCite the article: Noor Sheena Herayani Binti Harith, Azlan Adnan (2017). Estimation Of Peak Ground Acceleration Of Ranau Based On Recent Eartqhuake Databases . \nMalaysian Journal Geosciences, 1(2) : 06-09. \n\n\n\n  ARTICLE DETAILS \n\n\n\nARTICLE HISTORY: \n\n\n\nReceived 12 May2017  \nAccepted 12 July 2017  \nAvailable online 10 September 2017 \n\n\n\nKEYWORDS: \n\n\n\nRanau, low to moderate \nearthquake, peak ground \nacceleration, probability of \nexceedance\n\n\n\nABSTRACT\n\n\n\nThe occurrence of earthquake with magnitude MW 6.0 in Ranau recently has triggered many questions regarding \ntheir nature of recurrence, characteristics in size and mechanism in and its surrounding region. In recent years, \nSabah has witnessed an increase in low to moderate seismic activities due to the causative ground structures which \nreflected in their seismic productivities. Over the past years between 1900 until recently, magnitudes ranging from \nMW 2.9 to 6.0 were known to have occurred.  While large magnitude earthquakes are fortunately rare, in the history \nof earthquakes, the region already experienced devastating earthquake including a magnitude of MW 5.8 on 26th \nJuly 1976 centred in Lahad Datu. The observation on earthquake catalogue spanning from 1900 to 2014 has been \nobtained from various earthquake data centers, Ranau previously recorded an earthquake with magnitude MW 5.1, \nthe repeat over intervals of sudden large earthquake is considered to have much shorter recurrence intervals. This \npaper discusses the procedure for evaluating the probabilistic seismic hazard analysis (PSHA) whereas the peak \nground acceleration (PGA) on bedrock of Ranau area for 10% and 2% probability of exceedance is taken into \naccount. By analysing the correlation between the tectonic features and the available data on past seismicity, the \nestimation of PGA is based on smoothed-gridded seismicity with a subjectively chosen correlation distance of 50 \nkm. The PGA estimation values for Ranau are approximately in the range of 80 to 140 cm/s2 that will be exceeded \n10% probability of exceedance and 140 to 250 cm/s2 for 2% probability of exceedance.  \n\n\n\n1. INTRODUCTION \n\n\n\nSabah is considered as a stable continental shield region at the triple \njunction zone of convergence between the Philippine, Indian-Australian \nand Eurasian Plates. The seismicity is classified as a low to moderate \nearthquake with damaging earthquakes are fortunately rare however, in \nrecent years, Sabah has witnessed an increase in low to moderate seismic \nactivities due to a few active local fault lines including the Belait, Crocker, \nJerudong Fault, Mensaban, Mulu faults and the Pegasus Tectonic Line as \nillustrated in Figure 1.1 [1]. The recent earthquake occurred in Ranau with \nmoment magnitude (MW) of 6.0 at a depth of approximately 10 km and \nlasted 30 seconds caused serious damage to many infrastructure and can \nfelt as far as 400 km from the epicentre. This was the strongest and worst \nearthquake ever affected Malaysia since 1976 with approximately 8 people \ndied due to this event.  \n\n\n\nFigure 1.1:Seismic geometry of local earthquake around Sabah [1]  \n\n\n\nThe national catalogue compiled from historical and instrumental \nrecordings from 1874 to 2014 for a magnitude of 2.0 or above in the vicinity \nof East Malaysia is taken into consideration, with total data accumulated of \n159 earthquakes (Figure 1.2). The oldest report made in 1985 which \nmentioned the lack of accuracy of earthquakes recorded before 1900, since \nthere were only two earthquake events [2]. At present, it is not possible to \ncheck the validity of these data; however, a continuing monitoring will help \nto bring insight to this issue in the future. Therefore, it is omitted in the \ncurrent study. The catalogue made by the same researcher also contains \ndetailed information about the damage experienced based on public \nobservations in terms of intensity [2]. \n\n\n\nFigure 1.2: Distribution of local background epicentres with a \nmagnitude ranging from 2.0\u20137.9 around East Malaysia from 1874 until \n2014 \n\n\n\nThere is some researcher also has mentioned the local background \n\n\n\nContents List available at RAZI Publishing \n\n\n\nMalaysian Journal of Geosciences \nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-eosciences-mjg/ \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (online)\n\n\n\n1Civil Engineering Program, Engineering Faculty, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, \nMalaysia. 2Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia. \n\n\n\nNoor Sheena Herayani Binti Harith1, Azlan Adnan2 \n\n\n\nESTIMATION OF PEAK GROUND ACCELERATION OF RANAU BASED ON \nRECENT EARTQHUAKE DATABASES \n\n\n\nhttps://doi.org/10.26480/mjg.02.2017.06.09\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\nhttps://doi.org/10.26480/mjg.02.2017.06.09\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 06-09 \n\n\n\nCite the article: Noor Sheena Herayani Binti Harith, Azlan Adnan (2017). Estimation Of Peak Ground Acceleration Of Ranau Based On Recent Eartqhuake Databases . \nMalaysian Journal Geosciences, 1(2) : 06-09. \n\n\n\n7 \n\n\n\nsource having occurred across East Malaysia and the lack of information \nregarding the events [3]. Previous studies have mentioned some local \nearthquakes having occurred from local active faults [1,2,4]. In a study, \nthe seismicity that was scattered heterogeneously was grouped by the \nsame seismic characteristics in a space [5]. The earthquake scattered \naround Sabah is divided into three seismic zones: Central-North, Labuk \nBay-Sandakan Basin, and Dent-Semporna Peninsular (Figure 1.3).  \n\n\n\nFigure 1.3: Seismic zone of Sabah, as illustrated in the MMD \nreport [5] \n\n\n\nThe earliest seismic hazard study for East Malaysia seismicity was based \non intensities by a researcher [2]. Intensity is a simple classification of \nindirect and non-objective strength of ground motion based on observed \neffects. Only in 2007 was a conversion of this seismic map to peak ground \nacceleration performed by MMD. In 1999, the seismic hazard map for the \nEast Malaysia region can be found from the Global Seismic Hazard \nAssessment Program (GSHAP) [6]. In accordance with the map, Ranau \nregion shows that the peak ground acceleration (PGA) estimation for the \nregion which is available for 10% probability of exceedance is between \n80 and 160 cm/s2. There have been few recent hazard maps published \nfor the region. As an example, the seismic hazard map used probabilistic \nanalysis and the website of the USGS [7,8,3]. A researcher has produced a \nseismic hazard map by considering the earthquake hazard associated \nwith Sumatran subduction and faults [7,8].  \n\n\n\nThe PGA maps show that the PGA trends are between 1.0 and 294 cm/s2 \nfor 2% PE and between 19.6 and 98.1 cm/s2 for 10% PE [7,8]. One of study \npresents the hazard map for 2% PE being between 160 and 180 cm/s2 \nand for 10% PE trends being between 60 and 80 cm/s2 by applying the \nseism tectonic area within Sulawesi and Kalimantan [3]. In the USGS \nhazard map, the 10% PE shows the PGA values being between 29.4 and \n147.2 cm/s2. Concerning the latest PGA analyzed by applying the \nPreliminary Hybrid Seismic Response Spectrum Model, the 2% PE is \ngiven between 100 and 180 cm/s2 (with no further result on 10% PE) \n[9]. \n\n\n\nThe inconsistency in terms of PGAs might be due to seism tectonic \nzoning or GMPE selection in estimating the hazard level. In order to \nremedy this situation, a new analysis of PSHA using a consistent set of \nlow-to-moderate earthquake recordings is presented in this study. \nTable 1.1 shows the summary of PGAs estimated from different \npublications with 10% and 2% probability of exceedance for an \nexposure time of 50 years, which corresponds to a return period of \n475 and 2,475 years, respectively, with site classification being \nbedrock. \n\n\n\nTable 1.1: \n\n\n\nPGA predicted values for 10% and 2% probability of \n\n\n\nexceedance in Ranau \n\n\n\nReference 10% \n(cm/s2) \n\n\n\n2% \n(cm/s2) \n\n\n\nGiardini et al. (1999) 80.0 \u2013 \n160.0 \n\n\n\nN/A \n\n\n\nPetersen et al. (2007, 2008) 19.6\u2013\n98.1 \n\n\n\n1.0 \u2013 \n294.0 \n\n\n\nUnited States Geological \nSurvey (2008) \n\n\n\n29.4\u2013\n147.2 \n\n\n\nN/A \n\n\n\nAdnan et al. (2008) 60.0\u2013\n80.0 \n\n\n\n160.0\u2013\n180.0 \n\n\n\nHee (2014) N/A 100.0\u2013\n\n\n\n180.0 \n\n\n\nNote: N/A = Data not available \n\n\n\nLow-to-moderate numbers from MW 2.8\u20137.4 shallow-depth \nearthquake events of the Modified Mercalli Intensities Scale range from \nIII to VIII [10,11]. A statement made by a researcher mentioned that the \nconditions with low magnitude may have a significant impact on the \nhazard level [12].  The range of intensity approximately correlates with \nPGA in the range of 150 to 160 cm/s2. However, the MMI scale was \nincreased to VIII, where the PGA was predicted as being between 340 and \n650 cm/s2, as illustrated in Figure 1.4.  \n\n\n\nFigure 1.4: Modified Mercalli Intensity (MMI) of East Malaysia [5]  \n\n\n\n2. SEISMIC HAZARD ANALYSIS \n\n\n\nThe earthquake sources generally are not well defined in regions \nof low-to-moderate seismicity, and generally the maximum \nmagnitude estimates have relatively long return periods. Based on this, \nprobabilistic seismic hazard analysis (PSHA) is considered to be the \nbest means of representing the uncertainties and defining the peak \nground acceleration for the region. The PSHA is a mathematical \nprocedure involving a probabilistic study to assess the answer for \nuncertainties about seismic location, earthquake size and shaking \nintensity that might happen in the future. Two of common methodology \nused describing the PSHA procedure can be seen in two studies \n[13,14]. The method also being updated in study which describe the \nprocedure of PSHA clearly [15]. \n\n\n\nThe earthquake data catalogue is processed by three common analyses, \nsuch as earthquake size analysing, decluttering earthquake events, which \nis eliminating foreshock and aftershock from the main earthquake event, \nand lastly incompleteness analysis. Then, the potential seismic source is \nidentified, including low rate seismic activity and regional seismic source \nzones that are believed to be tectonically active. A common practice in \nseismic hazard analysis is to define earthquake sources, so therefore it is \nnecessary to make some scientific earthquake studies in estimation of \nseismicity parameters of defined source zones. By estimating the \namplitudes of a parameter describing the ground motion or the \nearthquake effect based on smoothed-gridded seismicity with a \nsubjectively chosen correlation distance of 50 km covering the complete \narea of a big city or an entire state, zoning maps can be developed by \ncontouring the sub-areas with equal hazard as shown in Figure 1.6. \n\n\n\nThe basic input for the seismic hazard analysis is the source model, \nexpressed through the Gutenberg-Richter activity parameters a- and b-\nvalue for each of the seismic zones. The a-value gives the rate of \noccurrence of events larger than a certain magnitude (intercept of the \ncurve of the sum of all zones) and b-value gives the relative distribution \nof small and large events or the slope of the line. The current study used \nthe 31 seismic zones including isolated and local seismic sources. The \nseismicity can be characterized with the Gutenberg and Richter relation \nusing the formula: Log10 \u03bb = a \u2013 bM where \u03bb is the rate of earthquake with \na and b are constant [16].   \n\n\n\nDue to the small dataset available in background sources, the \ndetermination of b for separate zones are determined as well as the \nmaximum magnitude for each zone by means of the earthquake \ncatalogue. In each zone a maximum magnitude that has ever been \nrecorded can be found. For the choice of minimum magnitude, one would \n\n\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 06-09 \n\n\n\nCite the article: Noor Sheena Herayani Binti Harith, Azlan Adnan (2017). Estimation Of Peak Ground Acceleration Of Ranau Based On Recent Eartqhuake Databases . \nMalaysian Journal Geosciences, 1(2) : 06-09. \n\n\n\n8 \n\n\n\nexpect that it does not strongly influence the hazard estimate, because \nsmall magnitudes do not cause damage. The identification of the \nmaximum and minimum magnitude (Mmax and Mmin) in each seismic \nsource is expected to be varies and it is defined by referring to the \nearthquake catalogue. The a- and b-value were converted to \u03b1 and \u03b2 \nwhere \u03b1 = 2.303a and \u03b2 = 2.303b which imply that the earthquake \nmagnitudes are exponentially distributed. The input parameters that \nwere considered for the final seismic hazard, such as \u03b1, \u03b2, recurrence rate, \n\u03bb and maximum size of future earthquakes for each source, are given in \nTable 1.2. Since hazard calculations are quite sensitive to the b-value, the \nuse of these values will be separately for each zone. \n\n\n\nFigure 1.5: Background source proposed in this study \nTable 1.2: Recurrence parameters East Malaysia source zones \n\n\n\nSource Typ\ne \n\n\n\nName \u03b1 \u03b2 \u03bb Mmi\nn \n\n\n\nMma\nx \n\n\n\nBackgrou\nnd \n\n\n\nArea Sarawak 4.67\n2 \n\n\n\n1.44\n1 \n\n\n\n0.42\n4 \n\n\n\n3.5 5.3 \n\n\n\nUpper \nBorneo 1 \n\n\n\n3.14\n1 \n\n\n\n1.07\n6 \n\n\n\n0.23\n8 \n\n\n\n5.0 7.0 \n\n\n\nUpper \nBorneo 2 \n\n\n\n4.67\n2 \n\n\n\n1.44\n1 \n\n\n\n0.42\n4\n\n\n\n3.5 5.8 \n\n\n\nCentral \nSabah \n\n\n\n5.29\n1 \n\n\n\n1.79\n0 \n\n\n\n1.25\n8 \n\n\n\n2.9 6.0 \n\n\n\nLabuk \nBay \nSandaka\nn \n\n\n\n4.52\n7 \n\n\n\n1.40\n3 \n\n\n\n0.41\n2 \n\n\n\n3.5 6.7 \n\n\n\nDent \nSemporn\na \n\n\n\n7.47\n2 \n\n\n\n2.18\n4 \n\n\n\n8.32\n7 \n\n\n\n3.0 6.3 \n\n\n\nSince this study does not aim at considering the full epistemic \nuncertainties usually contained in a PSHA, this study is restricted to \napplying few GMPEs to fulfil the criteria of Ranau being affected mostly \nby shallow crustal faults and evaluated to highlight their limitation in \nterms of magnitude and distance. These include equations [17-21].  \n\n\n\n5. RESULTS \n\n\n\nThe mean annual rate of incidence for PSHA was calculated from the \ncombination of all input parameters (i.e. earthquake catalogue, seismic \nhazard parameters and GMPE). The hazard maps are depicted in terms of \npeak ground acceleration (PGA) at bedrock level of 10% and 2% \nprobability of exceedance in 50-year return period corresponds to 475 \nand 2,475 years, respectively. Figure 1.7 shows the results of this test. The \nPGA values are approximately in the range of 80 to 140 cm/s2 that will be \nexceeded in a period of 475-years. Obviously, the image is very similar to \nthe Modified Mercalli Intensity (MMI) scale of East Malaysia prepared by \nLeyu [5]. However, the PGAs are still low in Sarawak area. The PGA values \ninvestigated are still in the expected range, except for other studies which \nfall outside the targeted range [6]. \n\n\n\nFigure 1.6: PGA hhazard map with 10% probability of \nexceedance for 50 years (475-year return period) for zonation of \nbackground point sources \n\n\n\nThe resulting seismic hazard map for a 2% probability of exceedance \nfrom background source zonation is as shown in Figure 1.8. The highest \nPGA values obtained is approximately 140 to 250 cm/s2. The results are \ndifficult to compare to the hazard map of this study, since the seismic \nhazard assessment was based on intensities, rather than the PGA [5]. \nHowever, the conversion of intensities to PGA seems to agree fairly well \nwith the accelerations found in this study. \n\n\n\nFigure 1.7: PGA hhazard map with 2% probability of \nexceedance for 50 years (2,475-year return period) for zonation of \nbackground point sources. \n\n\n\n4. CONCLUSION \n\n\n\nThe outcome described in this research forms the definition of PSHA \nresults using the most updated earthquake data records between year \n1900 to 2014. The contour maps were produced for mean PGA at 5% \ncritical damping ratio of 10% and 2% probability of exceedance \n(corresponding to 475 and 2,475 year return periods, respectively) \nin design time period of 50 years. The probabilistic hazard \nmaps represented by PGA for Ranau is in the range of 80 to 140 \ncm/s2, corresponding to the 475-year return period and in the range of \n140 to 250 cm/s2, corresponding to the 2,475-year return period. \nAlthough the earthquakes in Sabah is characterized by a fairly low \nmagnitude with fortunately a rare occurrence of a large earthquake, the \nseismic hazard must be considered for important structures such as \nnuclear power plants, dams, oil platforms, etc. \n\n\n\nREFERENCES \n\n\n\n[1] Alexander, Y., Suratman, S., Liau, A., Hamzah, M., Ramli, M. Y., Ariffin, \nH., Abd. Manap, M., Mat Taib, M. B., Ali, A. and Tjia, H. D. 2006. Study on \nthe Seismic and Tsunami Hazards and Risks in Malaysia. In: (JMG), M. A. \nG. D. M. (ed.) Report on the Geological and Seism Tectonic Information of \nMalaysia. Kuala Lumpur: Ministry of Natural Resources and \nEnvironment. \n\n\n\n[2] Leyu, C. H., Chong, C. F., Arnold, E.P., Kho, Sai-L., Lim, Y. T., \nSubramaniam, M., Ong, T. C.,Tan, C. K., Yap, K. S., Shu, Y. K. and Goh, H. L. \n1985. Series on Seismology Malaysia. In: Arnold., E. P. Southeast Asia \nAssociation of Seismology and Earthquake Engineering (SEASEE). \n\n\n\n[3] Adnan, A., Hendriyawan, A. M., and Selvanayagam, P.N. and Marto. A. \n(ed.) 2008. Development of Seismic Hazard Maps of East Malaysia: \nAdvances in Earthquake Engineering Application, UTM. \n\n\n\n[4] Mohd Hazreek, Z. A., R. S., Fauziah Ahmad, Devapriya Chitral \nWijeyesekera and Mohamad Faizal Tajul Baharuddin 2012. Seismic \n\n\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 06-09 \n\n\n\nCite the article: Noor Sheena Herayani Binti Harith, Azlan Adnan (2017). Estimation Of Peak Ground Acceleration Of Ranau Based On Recent Eartqhuake Databases . \nMalaysian Journal Geosciences, 1(2) : 06-09. \n\n\n\n9 \n\n\n\nRefraction Investigation on Near Surface Landslides at the Kundasang \narea in Sabah, Malaysia. Procedia Engineering, 50, 516-531. \n\n\n\n[5] Leyu, C. H. 2009. Seismic and Tsunami Hazards and Risks Study in \nMalaysia. In: MOSTI (ed.) Summary for Policy Makers. \n\n\n\n[6] Giardini, D., Gr\u00fcnthal, G., Shedlock, K. M. and Zhang, P. 1999. The \nGSHAP Global Seismic Hazard Map. \n\n\n\n[7] Petersen, M. D., Harmsen, S., Mueller, C., Haller, K., Dewey, J., Luco, N., \nCrone, A., Lidke, D. and Rukstales, K. 2007. Documentation for the \nSoutheast Asia seismic hazard maps. Administrative Report September \n30, 2007. \n\n\n\n[8] Petersen, M. D., Harmsen, S., Mueller, C., Haller, K., Dewey, J., Luco, N., \nCrone, S., Rukstales, K. and Lidke, D. 2008. New Usgs Southeast Asia \nSeismic Hazard Maps. In: WCEE, ed. World Conference on Earthquake \nEngineering, October 12-17, Beijing, China. WCEE. \n\n\n\n[9] Hee, M. C. 2014. Preview of Natinal Annex to EC8: Seismic Loadings \nfor Peninsular Malaysia, Sabah and Sarawak. Jurutera: The Monthly \nBulletin of the Institution of Engineers, Malaysia. Institution of Engineers, \nMalaysia. \n\n\n\n[10] Majid, T. A., Zaini, S. S., Nazri, F. M., Arshad, M. R. and Suhaimi, I. F. \nM. 2007. Development of Design Response Spectra for Northern \nPeninsular Malayisa Based on UBC 97 Code. The Institution of Engineers \nMalaysia, 68, 7. \n\n\n\n[11] United State Geological Survey, U. and N. E. I. C., NEIC. 2008. Seismic \nHazard of Western Indonesia [Online]. USGS and NEIC. Available: \nhttp://earthquake.usgs.gov/research/hazmaps/products_data/ \n[Accessed 15 February 2013] \n\n\n\n[12] Delavaud, E., Cotton, F., Akkar, S., Scherbaum, F., Danciu, L., \nBeauval, C., Drouet, S., Douglas, J., Basili, R. and Sandikkaya, M. A. 2012. \nToward a ground-motion logic tree for probabilistic seismic hazard \nassessment in Europe. Journal of Seismology, 16 (3), 451-473. \n\n\n\n[13] Cornell, C. A. 1968. Engineering seismic risk analysis. Bulletin of the \nSeismological Society of America, 58 (5), 1583-1606. \n\n\n\n[14] Reiter, L. 1991. Earthquake Hazard Analysis: Issues and Insights. \nNew York, NY: Columbia University Press. \n\n\n\n[15] Baker, J. W. 2013. Probabilistic Seismic Hazard Analysis, White \nPaper Version. \n\n\n\n[16] Gutenberg, B., and Richter, C. F. 1956. Earthquake Magnitude, \nIntensity, Energy, and Acceleration (Second Paper). Seismological Society \nof America Bulletin, 46 (2), 105-145. \n\n\n\n[17] Sadigh, K., Chang, C.-Y., Egan, J.A., Makdisi, F. and Youngs, R.R. 1997. \nAttenuation Relationships for Shallow Crustal Earthquakes Based on \nCalifornia Strong Motion Data. Seismological Research Letters, 68 (1), \n180-189. \n\n\n\n[18] Fukushima, Y., K\u00f6se, O., Y\u00fcr\u00fcr, T., Volant, P., Cushing, E., and \nGuillande, R. 2002. Attenuation Characteristics of Peak Ground \nAcceleration from Fault Trace of the 1999 Kocaeli (Turkey) Earthquake \nand Comparison of Spectral Acceleration with Seismic Design Code. \nJournal of Seismology, 6 (3), 379-396. \n\n\n\n[19] Pan, T.-C., and Megawati, K. 2002. Estimation of peak ground \naccelerations of the Malay Peninsula due to distant Sumatra earthquakes. \nBulletin of the Seismological Society of America, 92 (3), 1082-1094. \n\n\n\n[20] Campbell, K. W. 2003a. Erratum to Prediction of Strong Ground \nMotion Using the Hybrid Empirical Method and Its Use in the \nDevelopment of Ground-Motion (Attenuation) Relations in Eastern North \nAmerica. Bulletin of the Seismological Society of America, 93, 1012-1033. \n\n\n\nCampbell, K. W. 2003b. Prediction of Strong Ground Motion Using the \nHybrid Empirical Method and Its Use in the Development of Ground-\nMotion (Attenuation) Relations in Eastern North America. Bulletin of the \nSeismological Society of America, 93, 1012-1033.\n\n\n\n\n\n"
"\n\n  \nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 12-20 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Ali Rezaei, Hossein Hassani, Seyedeh Belgheys Fard Mousavi, Nima Jabbari (2019).Evaluation  Of Heavy Metals Concentration In Jajarm Bauxite Deposit In \nNortheast Of Iran Using Environmental Pollution Indices. Malaysian Journal Of Geosciences, 3(1) : 12-20. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    ARTICLE DETAILS \n  \n \n\n\n\n Article History: \n \n\n\n\nReceived 20 November 2018  \nAccepted 21 December 2018 \nAvailable online 4 January 2019 \n \n\n\n\nABSTRACT \n\n\n\n\n\n\n\nHeavy metals are known as an important group of pollutants in soil. Major sources of heavy metals are modern \nindustries such as mining. In this study, spatial distribution and environmental behavior of heavy metals in the Jajarm \nbauxite mine have been investigated. The study area is one of the most important deposits in Iran, which includes about \n22 million tons of reserve. Contamination factor (CF), the average concentration (AV), the enrichment factor (EF) and \ngeoaccumulation index (GI) were factors used to assess the risk of pollution from heavy metals in the study area. Robust \nprincipal component analysis of compositional data (RPCA) was also applied as a multivariate method to find the \nrelationship among metals. According to the compositional bi-plots, the RPC1 and RPC2 account for 57.55% and 33.79% \nof the total variation, respectively. The RPC1 showed positive loadings for Pb and Ni. Also, the RPC2 showed positive \nloadings for Cu and Zn. In general, the results indicated that mining activities in the bauxite mine have not created \nserious environmental hazards in the study area except for lead and nickel. Finding potential relations between mining \nwork and elevated heavy metals concentrations in the Jajarm bauxite mine area necessitates developing and \nimplementing holistic monitoring activities. \n \n KEYWORDS \n \nEnvironmental behavior, Contamination criteria, Heavy metals, Multivariate statistical analyses, Jajarm bauxite deposit.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1. INTRODUCTION \n \nBased on a study, soil contamination has always been a matter of \ndiscussion as an important environmental issue in both developed and \ndeveloping countries, mainly because of the effects of soil pollution on \nchanges in the land use patterns and also due to the complicated cleanup \nprocesses once a soil is contaminated [1]. Among numerous soil \npollutants, heavy metals are especially of high importance as they are \nhighly carcinogenic, toxic and persistent in the environment. According to \nresearch, heavy metals are naturally occurring elements that have a high \natomic weight and a density of at least five times greater than that of water \n[2]. In the environment, heavy metals are spatially distributed in forms of \nores [3]. Based on a study, heavy metal contamination is a serious threat \nto aquatic systems due to their toxicity, abundance, persistence in the \nenvironment [4]. Their multiple industrial, domestic, agricultural, medical, \nand technological applications have resulted in widespread distribution of \nheavy metals in the environment which in turn has been raising concerns \nregarding potential effects on human health and the environment. \nAccording to a scholar, accumulation of heavy metals in soil and water \nresources is a function of both anthropogenic activities and lithogenic \nresources [5]. Two primary sources have been identified for heavy metals \npollution: natural or geological inputs including rock weathering and \nthermal springs, and anthropogenic sources including metalliferous \nmining and associated industries [6]. In many countries without stringent \nenvironmental regulations, mining is a practice with potential impacts on \nhuman health. Resource extraction and mining activities may lead to \nrelease of highly mobile metals into the environment particularly in areas \nnear mines [7,8]. Impact of the mining industry on the environment has \nbeen a public concern and has increased awareness of the possible \nharmful effects of the industry. As an anthropogenic activity, mining has \nfacilitated the movement and distribution of heavy metals in natural \n\n\n\nformations. The extractive nature of mining operations creates a variety of \nimpacts on the environment before, during and after mining operations \n[9]. The extent and nature of impacts can range from minimal to significant \ndepending on a range of factors associated with each mine. Mining \nactivities, in particular, open-pit mining, cause environmental pollution \nand heavy metals contamination with accentuated effects in the \nsurrounding areas. In previous study, environmental impact assessments \nfor mining are, thus, imperative to identify the magnitude and spatial \nextension of the pollution [10]. Variability and uncertainty in the \nextraction of ore, operational, and health parameters are among the most \nimportant factors that significantly affect the movement of pollutants \n[11,12]. \n \nIn this study, we aim to investigate the distribution and environmental \nbehavior of heavy metals and evaluate the anthropogenic and lithogenic \ncontribution in the Jajarm bauxite mine in Iran using environmental \npollution indices. The main goals of this research are to assess the risk of \npollution from heavy metals through quantitative criteria and then to \nevaluate spatial frequencies and distributions of heavy metals \nconcentration by applying multivariate statistical methods (Principal \nComponent Analysis). \n \n2. MATERIALS AND METHODS \n\n\n\n \n2.1 Study Area \n \nThe Jajarm bauxite deposit is the largest deposit in Iran and located in \nNorth Khorasan Province (northeast Iran) and 15 km North-East Jajarm \ntown. The deposit is situated in 56\u25e6 27' 30\" longitude and 37\u25e6 2' to 37\u25e6 4' \nlatitude (Fig.1) and is more than 8 km long and 20 m thick and has over 22 \nmillion tons of storage. This region has a dry desert climate and low \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \nDOI : http://doi.org/10.26480/mjg.01.2019.12.20 \n\n\n\n\n\n\n\n\n\n\n\n \n \nREVIEW ARTICLE \n\n\n\nEVALUATION OF HEAVY METALS CONCENTRATION IN JAJARM BAUXITE DEPOSIT \nIN NORTHEAST OF IRAN USING ENVIRONMENTAL POLLUTION INDICES \n \nAli Rezaei1*, Hossein Hassani1, Seyedeh Belgheys Fard Mousavi2, Nima Jabbari3 \n\n\n\n \n1Department of Mining and Metallurgy Engineering, Amirkabir University of Technology, Tehran, Iran  \n2Department of Agriculture and Environmental Engineering, Tehran University, Tehran, Iran \n3Department of Civil and Environmental Engineering, Southern California, USA \n*Corresponding Author E-mail: Alirezaei2013@aut.ac.ir, hhassani@aut.ac.ir \n \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\n\n\n\n\n\n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\n\nmailto:Alirezaei2013@aut.ac.ir\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 12-20 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Ali Rezaei, Hossein Hassani, Seyedeh Belgheys Fard Mousavi, Nima Jabbari (2019).Evaluation Of Heavy Metals Concentration In Jajarm Bauxite Deposit In \nNortheast Of Iran Using Environmental Pollution Indices. Malaysian Journal Of Geosciences, 3(1) : 12-20. \n\n\n\n\n\n\n\nrainfall, about 150 mm a year. The population of this region is close to 12 \nthousand people [13]. \n \nThere are a number of reasons why bauxite mining in Iran can cause an \nenvironmental problem which will subsequently propagate to human \nhealth hazards if the issue is not resolved or controlled. One of the reasons \nis related to the location of mine which is close to a human settlement area. \nAnother reason is associated with unsustainable mining practices that \nhave led to very extensive and aggressive mining activities and yet \nenvironmentally unfriendly. Potential impacts on human health can be \ndirect and indirect as shown in Figure 2. \n \n\n\n\n \n \nFigure 1: Location of the Jajarm bauxite deposit on the geological map of \nIran [14]. \n \n\n\n\n \n \nFigure 1: Linkages between bauxite mining activities and potentials \nimpacts [15]. \n \n2.2 Geological Setting \n \nThe Jajarm bauxite deposit is situated in the eastern part of the Alborz \nstructural zone (Fig.3). One of the most important characteristics of this \ndeposit is its asymmetrical morphology along the tectonic structure of the \narea. Lower Devonian sandstone evaporates, and limestone of the Padha \nformation are the oldest rocks in the area [16]. The upper Devonian Khosh \nYeylagh formation consists of fossiliferous limestone, dolomite, shale, and \nsandstone, and is overlain by Lower Carboniferous shale and carbonate of \nthe Mobarak formation (Fig.3). There are no Middle and Upper \nCarboniferous sediments in the area. Brown indurated claystone and \nsiltstones with small iron concretions overlie the Mobarak formation. In \nthe sense of a scholar, this layer is equivalent of Sorkh Shale formation \nnamed by othe scholar in eastern central Iran (Tabas area and Shotori \nRange) [17]. In this area, Shemshak formation is located as discontinuities \nover the Elika formation (approximately 215 m thick) and bauxite horizon \nis formed between the two formations (Fig.4). The karstified carbonate-\nhosted Jajarm bauxite is buried by several thousand meters of younger \nsediments, beginning with the Jurassic Shemshak formation and other \nyounger units [18]. \n \nThe Jajarm bauxite deposit is located in an area folded into an E-W \ntrending anticline cut by several reverse faults that its northern extension \nis thrust on to the southern part. This over thrusting has hidden the \nbauxite deposit beneath Quaternary units. As a result, the bauxite deposit \nis only exposed on the northern flank of the anticline in a length of about \n8 km. Exposure of the ore body is discontinuous along its length, with the \n\n\n\ndeposit occurring as isolated blocks subdivided into eight blocks in the \nGolbini area and four in the Zoo area for mining purposes (Fig. 3). Based \non the obtained information of analysis results, the Al2O3: SiO2 ratio varies \nfrom 0.87 to 7.52 throughout the deposit so the ore grade is locally \nheterogeneous. Natural bauxite ore consists of aluminum hydroxide, iron \noxide, titanium oxide, and reactive silica.  \n \n\n\n\n \n \nFigure 2: Simplified geological map of the Jajarm bauxite deposit and its \nsurrounding units. \n \n\n\n\n \n \nFigure 3: Outcrops of the Jajarm bauxite with its footwall (Elika \nformation) and hanging wall (Shemshak formation) \n \n2.3 Sampling and analytical methods \n \nNinety-three soil samples were collected from Jajarm bauxite area in clean \npolythene covers avoiding the all possible contamination. Soil samples \nwere collected from the top 5-30 cm layer of the soil using a plastic spatula. \nThe soil samples were then transferred to the laboratory and were dried \nfor 5 days at 60\u00b0C to avoid the moisture content. The dry soil sample was \npowdered to -200 mesh size (US Standard) using a swing grinding mill and \nhomogenized. In order to determine the heavy metals concentration, soil \nsamples were analyzed using Inductively Coupled Plasma-Mass \nSpectrometer (ICP- MS) method. Cadmium (Cd), copper (Cu), nickel (Ni), \nlead (Pb) and zinc (Zn) were selected as priority control heavy metals \nbased on the results of pollution and health risk assessments. Chemical \nanalyses were carried out at the Lab West Laboratories, Australia. The \nlocation of sample collected points is shown in Fig. 5. \n \n2.4 Environmental pollution indices \n \nVarious methods and factors have been proposed to assess the heavy \nmetal contamination in a mining district [19]. For this study selected \nenvironmental pollution parameters are as follows: the enrichment factor \n(EF), contamination factor (CF), Geo-accumulation index (Igeo) and \npollution load index (PLI). \n \n2.4.1 Enrichment factor (EF) \n \nBased on a study, the enrichment factor (EF) is broadly used to estimate \nthe anthropogenic impacts on sediments and soils [20-22]. This factor \ncompares the concentration of an element in samples with the \nconcentration of the same element in non-contaminated areas [23]. In \norder to evaluate natural or anthropogenic sources of heavy metal content \nin samples, an enrichment factor is calculated as follows: \n\n\n\n\n\n\n\n\n  \nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 12-20 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Ali Rezaei, Hossein Hassani, Seyedeh Belgheys Fard Mousavi, Nima Jabbari (2019).Evaluation  Of Heavy Metals Concentration In Jajarm Bauxite Deposit In \nNortheast Of Iran Using Environmental Pollution Indices. Malaysian Journal Of Geosciences, 3(1) : 12-20. \n\n\n\n\uf05b \uf05d \uf05b \uf05d\n\uf05b \uf05d \uf05b \uf05dcrustcrust\n\n\n\nsamplesample\n\n\n\nEl\nXEl\n\n\n\nXEl\nEF\n\n\n\n/\n\n\n\n/\n=                                                               (1) \n\n\n\n                                                                                                    \nwhere \u201cEl\u201d refers to the element under consideration, the square brackets \nindicate concentrations (usually in mass/ mass units, such as mg/ kg), and \n\u201cX\u201d is the selected reference element. Crust subscription in equation 1 \nrefers to Clarke of Earth\u2019s crust, most often Continental or Upper \nContinental Crust (UCC). \n \n\n\n\n \n \nFigure 4: Map showing the sampling stations in the Jajarm bauxite mine \n \n2.4.2 Contamination Factor (CF) \n \nContamination factor (CF) is an indicator of soil and sediment heavy metals \ncontamination ratio and is obtained by dividing the concentration of the \nelement in the sample taken by the concentration of the same element in \nthe background [24]. \n \n\n\n\nBackground\n\n\n\nSample\n\n\n\nC\n\n\n\nC\nCF =                                                                                                          (2) \n\n\n\nwhere Csample is the concentration of an element in the sample and \n\n\n\nCbackground is the concentration of the element in global shale. If CF is higher \n\n\n\nthan 1, indicating the increased concentration of pollutant due to human \nfactors. \n \n2.4.3 Geoaccumulation index \n \nAccording to research, geoaccumulation index was first introduced by \nMuller and was initially named as the Muller index [25]. The Muller index \nis used to measure the amount of contamination with heavy metals in the \nsoil. This assessment index was used in soil and sediment contamination \nstudies [26,27]. Geoaccumulation index is used for classification of soils, \nfrom non-contaminated to heavily contaminate and is calculated using the \nfollowing formula [28]: \n \n\n\n\n\uf0fa\n\uf0fa\n\n\n\n\uf0fb\n\n\n\n\uf0f9\n\n\n\n\uf0ea\n\uf0ea\n\n\n\n\uf0eb\n\n\n\n\uf0e9\n\n\n\n=\nnBnC\n\n\n\ngeoI\n5.1/\n\n\n\n2\nlog\n\n\n\n                                                                      (3)                                                                                                                       \n\n\n\n \nIn equation 3, Cn is the measured concentration of the element in the \n\n\n\ncollected sample and Bn represents the concentration of the element in \n\n\n\nthe background sample. The coefficient of 1.5 is used to eliminate \npossible changes in the background due to the geological effects [29,30]. \n \n2.4.4 The Modified degree of contamination (mCd) \n\n\n\n \nA scholar presented a modified and generalized form of the previous \nscholar equation for the calculation of the overall degree of contamination \nas below [31,32]:  \n\n\n\nn\n\n\n\nCf\n\n\n\nmC\n\n\n\nn\n\n\n\ni\n\n\n\ni\n\n\n\nd\n\n\n\n\uf0e5\n== 1                                                                                                                   (4)                                                                                                                    \n\n\n\n \nwhere n is the number of analyzed elements,  i refers to the ith element \n(or pollutant) and Cf is the contamination factor. Using this generalized \n\n\n\nformula to calculate the mCd allows the incorporation of as many metals \n\n\n\nas possible with no upper limit. The expanded range of possible \npollutants can, therefore, include both heavy metals and organic \npollutants should the latter be available for the studied samples. For the \nclassification and description of the modified degree of contamination \n(mCd) in sediments and soil, the following gradations were proposed by a \nscholar: \nmCd < 1.5 nil to the very low degree of contamination \n\n\n\n1.5 \u2264 mCd < 2 low degree of contamination \n\n\n\n2 \u2264 mCd < 4 moderate degree of contamination \n\n\n\n4 \u2264 mCd < 8 high degree of contamination \n\n\n\n8 \u2264 mCd < 16 very high degree of contamination \n\n\n\n16 \u2264 mCd < 32 extremely high degree of contamination \n\n\n\nmCd \u2265 32 ultra-high degree of contamination \n\n\n\n \nAn intrinsic feature of the mCd calculation is that it produces an overall \n\n\n\naverage value for a range of pollutants. As with any averaging \nprocedure, care must,  however ,  be taken in evaluating the final results \nas the effect of significant metal enrichment spikes for individual samples \nmay be hidden within the overall average result [33]. \n \n2.4.5 Pollution load index (PLI) \n \nPollution load index (PLI) is often used to evaluate and estimate the degree \nof pollution in soils and sediments. This index is based on the coefficient of \neach element in soil and is calculated by dividing the concentration of each \nelement in a soil sample by its concentration in the reference sample (CF) \n[34]. PLI can, then, be calculated for a set of contaminant metals as the \ngeometric mean of the concentration of all metals. If the PLI concentration \nis close to 1, this indicates that the concentrations are close to the \nbackground concentration, while the PLI concentrations above 1 show soil \ncontamination [35,36]. The total heavy metal contamination in the region \nis obtained using this indicator, and by equation 5 [37]: \n \n\n\n\nn\nnCFCFCFCFPLI \uf0b4\uf0b4\uf0b4\uf0b4= ...321                                     (5)                                                                                    \n\n\n\n \n2.5 Statistical analyses \n \nIn this research, multivariate and basic statistical analyses were applied to \ndetermine the relationship among heavy metals. Application of \nmultivariate statistical techniques facilitates interpretation of complex \ndata matrices for a better understanding a variety of environmental \nfactors [38]. Correlation analysis and principal component analysis (PCA) \nare performed using the commercial statistical software package SPSS \nversion 18.0 for Windows [39]. Principal component analysis (PCA) was \nimplemented to reduce the number of variables and to detect the \nrelationship between variables. This method allows us to display most of \nthe original variability in a smaller number of dimensions and has been \nwidely used in geochemical and hydrochemical studies [40]. \n \nMultivariate statistical methods are used in analytical chemistry to \nquantify relationships between more than two variables under \nsimultaneous consideration of their interactions [41]. Heavy metals \nusually have complex relationships among them [42]. The identification of \npollutant sources is often determined with the aid of multivariate \nstatistical analysis methods, such as correlation analysis and principal \ncomponent analysis (PCA). \n \nThe correlation coefficient between each pair of variable elements in the \nsoil samples was calculated using the Pearson\u2019s correlation matrix \napproach to quantitatively analyze and confirm the relationship among \nvarious metal. In general, significant correlations between pairs of heavy \nmetals suggest a common or combined origin, whereas weak correlations \nindicate different origins [43]. \n \nBased on a study, principal component analysis (PCA) is the most common \nmultivariate statistical method used in environmental studies [44]. The \nPCA method is widely used to reduce data and to extract a small number \nof latent factors for analyzing relationships among the observed variables. \nIt has been reported that PCA methods have been widely used in \n\n\n\n\n\n\n\n\n  \nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 12-20 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Ali Rezaei, Hossein Hassani, Seyedeh Belgheys Fard Mousavi, Nima Jabbari (2019).Evaluation  Of Heavy Metals Concentration In Jajarm Bauxite Deposit In \nNortheast Of Iran Using Environmental Pollution Indices. Malaysian Journal Of Geosciences, 3(1) : 12-20. \n\n\n\ngeochemical applications to identify soil pollution sources and distinguish \nnatural versus anthropogenic contribution [45]. \n \nAccording to recent studies, the PCA is a versatile tool for the integration \nof multi-element concentration values into single principle components \n(PCs) and for the reduction of dimensionality of data sets into \nuncorrelated PCs based on the correlation matrix of variables [46,47]. \nOrdinary PCA decomposes the correlation matrix of variables into two \nmatrices of scores and loadings using eigenvectors and eigenvalues. The \nmutually independent PCs are determined by the scores and loading \nmatrices [48]. The information about the relationship between PCs and \noriginal variables is described by loadings which are the correlations \nbetween PCs and variables. Moreover, the information about the \nrelationship between PCs and samples is described by the scores which \nare a linear combination of variables weighted by eigenvectors. The value \nof variance explained by each PC is expressed by eigenvalues. Significant \nPCs could be retained based on the eigenvalues of greater than 1 [49]. \nGenerally, most of the total variance and information about the data set \nare summarized in the first PC, and thus, the first PC is the most significant \ncomponent [50,51]. \n \nIn this study, we applied a robust principle component analysis of \ncompositional data (RPCA), as a multivariate method, to find a multi-\nelement geochemical signature [52]. Initially, the raw data of five analyzed \nelements (Cd, Cu, Ni, Pb, and Zn) were transformed using the Isometric log \nratio (ilr) transformation to address the data closure problem [53,54]. \nRobust principle component analysis was then applied on ilr-transformed \ndata to integrate geochemical variables into robust PCs (RPCs) and to \nreduce the dimensionality of the data set. Because the ilr-transformation \ndoes not yield into a one-to-one transformation from simplex space to \nEuclidean space, the resulting loading matrix and scores were back-\ntransformed to the Centered log ratio (clr) space, where interpretations \nare possible via compositional biplots [55-57]. The Rob Compositions \nsoftware package of R free software environment was employed for ilr \ntransformation of the data and performing the RPCA [58]. \n \n \n \n\n\n\n3. RESULTS AND DISCUSSION \n \n\n\n\n3.1 Environmental assessment of heavy metal contamination \n \nTo determine the extent of mining contamination with heavy metals the \nelements of a studied area are compared with thresholds defined by \ninternational standards (Table 1). Calculated environmental pollution \nindices are listed in Table 1. The mean EF of Cd, Cu, Ni, Pb, and Zn are close \nto or higher than 3. The EF values vary from non-enriched (Cd, Cu, and Zn) \nto low- enriched (Ni and Pb) for the Jajarm bauxite mine samples. This \nindicates that the anthropogenic origin is probable for Ni and Pb in the \nstudy area. \n \nThe lowest contamination with a CF value (i.e. less than 1) is related to Cd, \nCu, and Zn. Also, the elements such as Ni and Pb, based on the average \nvalues have contamination coefficients 1.85 and 2.10, respectively which \nindicates the increased concentration of these pollutants due to human \nfactors. The obtained results show the anthropogenic (mining activities) \norigin of Pb and Ni in the study area. \n \nTable 1: Enrichment factor, concentration factor, and an average of \nelements in Earth's crust and global shale [59,60] \n \n\n\n\nElement Cd Cu Ni Pb Zn \nEF 0.009 0.47 1.99 3.58 0.33 \nCF 0.007 0.46 1.85 2.10 0.28 \nAverage(Crust) 12.5 41 40 14.8 50 \nAverage(Shale) 2.6 100 68 20 95 \n\n\n\n \nThe Igeo classes were calculated for each sampling station. Results of \ngeoaccumulation index calculation show that the environment and \ncontamination levels ranged from non- contaminated (Cd, Cu, and Zn, Igeo \n\n\n\n< 0, natural origins) to low contamination (Pb and Ni, Igeo > 0, \nanthropogenic sources). Further, the analysis of the modified degree of \ncontamination (mCd) indicates Nil to the very low degree of \n\n\n\ncontamination (Table 2). \n \n\n\n\nTable 2: Modified degree of contamination (mCd) and contamination factors (CF) for heavy metals in the soil samples of the Jajarm bauxite deposit \n \n\n\n\nBaseline Contamination Factor Sum CF mcd \nElement Cd Cu Ni Pb Zn  \nCF (Average continental crust) 0.1 0.025 0.04 0.06 0.01 0.236 0.05 \nCF (Background) 0.007 0.46 1.85 2.10 0.28 4.697 0.94 \n\n\n\n \nThe PLI average value calculated for all samples is 0.31. As presented in \nFigure 6, the PLI values in the samples are below the background \nconcentration (PLI < 1) showing that the Jajarm bauxite mine is not \ncontaminated. \n \n\n\n\n\n\n\n\nFigure 5: PLI calculated values for samples in Jajarm bauxite mine \n \n3.2 Background values from average crustal concentration \n \nAccording to Figure 7, a comparison of the mean concentrations of \npotentially toxic metals in samples with the average crust values for non-\ncontaminated soils and average shale shows that the higher levels of \ncontaminated metals are Ni and Pb compared to the average crust values. \nCadmium, copper, and zinc are less than the average shale. Therefore, \nwhen compared with the background values of world soils the elevated \nconcentrations of Pb and Ni in the Jajarm bauxite mine suggest \nanthropogenic sources for these elements. \n\n\n\n \n \nFigure 6: A comparisons of the mean concentrations of potentially toxic \nmetals in all samples of the Jajarm bauxite mine with the average crust \nvalues for non-contaminated soils and average shale \n \n3.3 Spatial distribution of heavy metals \n \nThe ordinary inverse distance weighting (IDW) method was used to \npopulate spatially distributed results in the study area based on raw \nsamples. Figures 8, 9, 10, 11 and 12 illustrate the spatial distribution from \ndifferent metals as discussed in the following sections. \n \n3.3.1 Cadmium \n \nBased on a study, cadmium is a non-essential element that negatively \naffects plant growth and development [61]. Cadmium is released into the \nenvironment by natural weathering processes, atmospheric deposition, \nuse of phosphate fertilizers and sewage treatment plants [62,63]. \nAccording to a scholar, natural Cd concentration found in the Earth's crust \nis in the range of 0.1-0.5 mg/kg [64,65]. Cadmium concentrations (mg/kg) \n\n\n\n0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0.3\n\n\n\n0.4\n\n\n\n0.5\n\n\n\n0.6\n\n\n\n0.7\n\n\n\nj1 j6\n\n\n\nj1\n1\n\n\n\nj1\n6\n\n\n\nj2\n1\n\n\n\nj2\n6\n\n\n\nj3\n1\n\n\n\nj3\n6\n\n\n\nj4\n1\n\n\n\nj4\n6\n\n\n\nj5\n1\n\n\n\nj5\n6\n\n\n\nj6\n1\n\n\n\nj6\n6\n\n\n\nj7\n1\n\n\n\nj7\n6\n\n\n\nj8\n1\n\n\n\nj8\n6\n\n\n\nj9\n1\n\n\n\nP\nL\n\n\n\nI \nV\n\n\n\na\nlu\n\n\n\ne\n\n\n\nSample Stations\n\n\n\nPolution Load Index (PLI)\n\n\n\n0\n\n\n\n50\n\n\n\n100\n\n\n\n150\n\n\n\nCd Pb Ni Cu ZnC\no\n\n\n\nn\nce\n\n\n\nn\ntr\n\n\n\na\nti\n\n\n\no\nn\n\n\n\n(m\ng\n\n\n\n/\nK\n\n\n\ng\n)\n\n\n\nHeavy metals\n\n\n\nAverage continental crust Average continental shale\n\n\n\nAverge Soil Samples\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 12-20 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Ali Rezaei, Hossein Hassani, Seyedeh Belgheys Fard Mousavi, Nima Jabbari (2019).Evaluation Of Heavy Metals Concentration In Jajarm Bauxite Deposit In \nNortheast Of Iran Using Environmental Pollution Indices. Malaysian Journal Of Geosciences, 3(1) : 12-20. \n\n\n\n\n\n\n\nranged between 0.01 to 0.25 in the study area which is lower than average \ncrustal values (Fig. 8). \n \n\n\n\n \n \nFigure 7: Spatial distribution map of Cd metal \n \n3.3.2 Copper \n \nBased on a study, copper is released into the environment from natural \nsources such as volcanic eruptions, decaying vegetation, forest fires, and \nsea spray etc. up to 50 mg/kg and anthropogenic activities, including \nmunicipal and industrial wastewater [66-69]. The results show that Cu \nconcentrations (mg/kg) in the soils of the study area ranged from 1 to 64. \nThe comparison between Cu concentrations in the soil of the Jajarm \nbauxite mine shows that Cu levels in the near mine and waste dumps had \nhigher levels than other measured stations in the study area (Fig. 9). \n \n3.3.3 Nickel \n \nNickel is a transition element that occurs in the environment only at very \nlow levels. According to research, the major sources of nickel \ncontamination in the soil are metal plating industries, combustion of fossil \nfuels, and nickel mining and electroplating [70]. The results show that Ni \nconcentrations (mg/kg) in the soils ranged from 10 to 161. The \ncomparison between Ni concentrations in the soils of the Jajarm bauxite \nmine shows that Ni levels in the near mine and waste dumps had higher \nlevels than other measured stations in the study area (Fig. 10). \n \n\n\n\n\n\n\n\nFigure 8: Spatial distribution map of Cu metal \n \n\n\n\n\n\n\n\nFigure 9: Spatial distribution map of Ni metal \n\n\n\n3.3.4 Lead \n \nLead, a non-essential and toxic element, is released from natural and \nanthropogenic activities. Major sources include vehicular emissions, \nvolcanoes, airborne soil particles, forest fires, waste incineration, effluents \nfrom leather industry, lead-containing paints and pesticides. Study \nshowed natural concentration of Pb in the earth's crust varied from 15 to \n20 mg/kg [71]. The results show that Pb concentrations (mg/kg) in the \nsoils ranged from 10 to 128. The comparison between Pb concentrations \nin the soils of the Jajarm bauxite mine shows that Pb levels in the near mine \nand waste dumps had higher levels than other measured stations in the \nstudy area (Fig. 11) and suggest anthropogenic sources for this element \n(mining activities). \n \n\n\n\n\n\n\n\nFigure 10: Spatial distribution map of Pb metal \n \n\n\n\n3.3.5 Zinc \n \nBased on a  research, natural background levels of zinc are usually found \nup to 100 mg/kg in soils [72]. Sources of Zn are natural processes and \nhuman activities. The concentrations (mg/kg) of Zn in the study area \nranged from 3.0 to 85.0, which are lower than average crustal values (Fig. \n12). \n \n\n\n\n\n\n\n\nFigure 11: Spatial distribution map of Zn metal \n \nSpatial distribution of the metals in the soils is not uniform over the entire \nsection of the study area. Changes in concentration are pertinent to the \nmagnitude and temporal and spatial extension of the release of heavy \nmetals from different natural and anthropogenic sources. Heavy metals \nconcentration levels and distribution were found higher at the sites \nlocated in the vicinity of mine pits and waste dumps that are probable \nsources of metal pollution. As shown in Figures 8, 9, 10, 11 and 12, the \nspatial distribution patterns of all of the heavy metals tested are quite \nsimilar and relatively enriched in the near waste dumps and mines regard \nto Fig.5. \n \n3.4 Statistical Analysis Methods \n \n3.4.1 Descriptive basic statistics \n \nThe descriptive statistics for soil samples of the study area are given in \nTable 3. The lowest mean concentration belongs to Cd and the highest of \nPb. The average abundance order of heavy metal contents in the soil \nsamples is Pb > Ni > Zn > Cu > Cd. \n\n\n\n\n\n\n\n\n  \nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 12-20 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Ali Rezaei, Hossein Hassani, Seyedeh Belgheys Fard Mousavi, Nima Jabbari (2019).Evaluation  Of Heavy Metals Concentration In Jajarm Bauxite Deposit In \nNortheast Of Iran Using Environmental Pollution Indices. Malaysian Journal Of Geosciences, 3(1) : 12-20. \n\n\n\nTable 3: Descriptive basic statistics of the Raw data of C d ,  C u ,  N i ,  Pb, and Zn in the study area (mg/kg) \n \n\n\n\nElement Valid N Minimum Maximum Mean Std. Deviation Variance Skewness Kurtosis \n\n\n\nCd 93 0.001 0.25 0.09 0.057 0.003 0.75 0.446 \n\n\n\nCu 93 1.6 63.7 18.7 10.98 120.49 1.16 2.3 \n\n\n\nNi 93 10.0 161.0 48.86 30.50 931.40 1.1 1.5 \n\n\n\nPb 93 10.8 128.0 58.60 31.1 967.6 0.285 -0.98 \n\n\n\nZn 93 3.7 85.0 25.7 14.8 220.08 1.1 1.8 \n\n\n\nThe statistical characteristics of the heavy metals, such as the Skewness \nand kurtosis, suggest that the raw data (i.e. data from analysis of samples, \nwithout any transformations) do not follow normal distributions (Table \n3). Histograms of the raw data (Fig. 14), obviously demonstrate that the \nelements follow positively skewed distributions. \n \nTo explore whether the data are log-normally distributed, the individual \nraw data were logarithmically transformed. The Q-Q plots of the ln-\ntransformed data (Fig. 15) show that there are some outliers in the log-\ntransformed data set. Based on a study, it could be inferred that there are \nmultiple populations, which may be related to the influence of a variety of \ngeological processes and anthropogenic factors. Box and whisker plot the \ndata are presented in Fig. 13 [73,74].  \n \n\n\n\n \n \nFigure 12: Box-whisker plots showing heavy metal concentration ranges \nin the soil of the study area (outliers are indicated by rhomboid-shaped \npoints) \n \n\n\n\n \n \nFigure 13: Histogram of the heavy metals for Cd, Cu, Ni, Pb and Zn \n\n\n\n \n \nFigure 14: Q-Q plots of the ln-transformed data of Cd, Cu, Ni, Pb and Zn \n \n3.4.2 Correlation analysis \n \nThe correlation coefficients among the heavy metals are shown in Table 4. \nNickel with Pb and Cu with Zn are significantly correlated according to \nPearson\u2019s coefficient since data normality has been checked. The strong \ncorrelation is an indication of a similar behavior and common origin. \nPearson\u2019s coefficients suggest that Cd does not show a significant \ncorrelation with any of the metals. Cadmium has a high transfer rate and \nhigh mobility in the environment so it can accumulate in relatively large \namounts in plants without any apparent effects on the plants. \n \nTable 4: Pearson\u2019s correlation coefficients among selected metals of the \nstudy area \n \n\n\n\nElement Cd Cu Ni Pb Zn \n\n\n\nCd 1 - 0.008 - 0.48 0.09 0.06 \n\n\n\nCu  1 0.14 0.16 0.86 \n\n\n\nNi   1 0.88 0.14 \n\n\n\nPb    1 0.18 \n\n\n\nZn     1 \n\n\n\n \n3.4.3 Principal component analysis (PCA) \n \nIn the study area, ordinary PCA was used based on the correlation matrix \nof variables [74]. Also, robust principal component analysis of \ncompositional data (RPCA) was applied as a multivariate method to derive \na multi-element geochemical signature of relationships among the \nobserved variables [52, 55]. As expected, two factors were acquired. \nAmong these components, PC1 was of the eigenvalue of greater than 1 (Fig. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 12-20 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Ali Rezaei, Hossein Hassani, Seyedeh Belgheys Fard Mousavi, Nima Jabbari (2019).Evaluation Of Heavy Metals Concentration In Jajarm Bauxite Deposit In \nNortheast Of Iran Using Environmental Pollution Indices. Malaysian Journal Of Geosciences, 3(1) : 12-20. \n\n\n\n\n\n\n\n16). Figure 16 further depicts the relative importance of the two \ncomponents. In the first component, strong and positive loadings related \nto Pb and Ni can be observed. The high correlations between heavy metals \nmay reveal that the two metals had a similar origin in the second group of \n\n\n\nelements consists of Zn and Cu (Fig. 16). Correlation coefficient and PCA \nanalyses results indicated a strong correlation between Zn and Cu. \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nFigure 15: Graph of PCA (left) and Scree plot (right) in the soil samples of the study area \n\n\n\n \n \nAccording to the compositional biplots (Fig. 17), the RPC1 and RPC2 \naccount for 57.55% and 33.79% of the total variation (Table 5), \nrespectively. Besides, the RPC1 shows positive loadings for Pb and Ni (Fig. \n17). Also, the RPC2 shows positive loadings for Cu and Zn. \n \nThese results indicate that principal component 1 is originating from \ncommon anthropogenic sources, whereas, principal component 2 might \nbe from natural origins. The main anthropogenic sources in the region \ninclude mining activities. \n \n\n\n\n \n \nFigure 16: Bi-plots of robust PC1 versus robust PC2 of the ilr transformed \nraw data \n \nTable 5: Rotated component matrix of robust factor analysis. Significant \nloadings (bolded values) are selected based on the absolute threshold \nvalues of 0.5 \n \n\n\n\nElement Component 1 Component 2 \n\n\n\nCd -0.498 -0.866 \n\n\n\nCu -0.481 0.804 \nNi 0.942 - 0.127 \nPb 0.938 -0.153 \nZn - 0.427 0.834 \n% of Variance 57.55 33.79 \nCumulative % 57.55 91.34 \n\n\n\n \n4. CONCLUSIONS \n\n\n\n \nIn this research, we analyzed the heavy metals concentration and their \nsource in soil samples of the Jajarm bauxite mine, using multivariate \nstatistical techniques combined with metal concentrations analysis and \ncorrelation analysis that has been proven to be an effective tool for source \nidentification of heavy metals. In soil samples of the study area, the \naverage of the recorded concentration of elements for cadmium, copper, \nnickel, lead, and zinc are 0.09, 18.70, 48.80, 58.60 and 25.70 (mg/kg), \nrespectively. The comparison of the mean concentrations of potentially \ntoxic metals in samples with the average crust values for non-\ncontaminated soil and average shale showed that the higher levels of \ncontaminated metals are Ni and Pb compared to the average crust values \n\n\n\n. The Cd, Cu and Zn metals are less than the average shale. To ensure a \nmore comprehensive and accurate assessment of heavy metals \ncontamination results, three evaluation methods of enrichment factor, \ngeoaccumulation index, and the contamination factor was applied. Based \non the classification, the lowest contamination with a CF value of less than \n1 was related to the elements such as Cd, Cu, and Zn. Also, the other \nelements such as Ni and Pb, based on the average values have \ncontamination coefficients 1.85 and 2.10, respectively. The PLI average \nvalue for all samples was equal to 0.31. According to the calculation and \nclassification of geoaccumulation index, the Jajarm bauxite mine \ncontamination levels were from non-contaminated (Cd, Cu, and Zn, Igeo < 0, \nnatural origins) to low contamination (Pb and Ni, Igeo > 0, anthropogenic \nsources). The distribution of heavy metals in the soil was not uniform over \nthe whole section of the study area and the change in concentration was \ndue to the release of these metals from different natural and \nanthropogenic sources. Heavy metals levels and distribution was found \nhigher at that sites which were in the vicinity of mine pits and waste \ndumps and were probable sources of metal pollution. In this research, we \napplied the robust principal component analysis of compositional data \n(RPCA). According to the compositional biplots, the RPC1 and RPC2 \naccount for 57.55% and 33.79% of the total variation, respectively. The \nRPC1 showed positive loadings for Pb and Ni while the RPC2 showed \npositive loadings for Cu and Zn. The results indicated that extract the \nmineral from the bauxite mine except for Pb and Ni, have not created more \nenvironmental hazards in the study area. Therefore, heavy metals \ncontaminant, in the Jajarm bauxite mine should be carefully monitored \nand controlled in the future. In order to conduct successful plans and \nmethods of control and prevention and for better management of \nwastewater and sewage contaminated in the Jajarm bauxite mine with \nheavy metals, it is important to observe the following points: Public \neducation for disposal of waste containing heavy metals and compounds; \nInstitutionalize strategies, including environmental monitoring, \nimplementation of environmental regulations and tracking heavy metals \nfrom generation time to becoming waste  \n \nACKNOWLEDGMENTS \n \nThe authors would like to thank the Amirkabir University of Technology \n(Polytechnic Tehran), Department of Mining and Metallurgy Engineering \nfor supporting this research. The contribution of Samira Rezaei and \nMohammad Parsa Sadr is appreciated. \n \nREFERENCES \n \n[1] Chen, C.W., Kao, C.M., Chen, C.F., Dong, C.D. 2009. Distribution and \naccumulation of heavy metals in the sediments of Kaohsiung Harbor, \nTaiwan. Chemosphere, 66(8), 1431-1440. \n \n[2] Tchounwou, P., Yedjou, C., Patlolla, A.,  Sutton, D. 2012. Heavy Metals \nToxicity and the Environment, 10, 133-164. \n \n[3] ABS (Australian Bureau of Statistics). 1999. Environment Protection \nExpenditure, Australia. \n \n[4] Hanif, N., Musstjab Akber Shah Eqani, S.A., Syeda Maria, A., Cincinelli, \nA., Nadeem Ali, N., Ioannis, A.K., Tanveer, Z.I., Bokhari, H. 2015. Geo \naccumulation and enrichment of trace metals in sediments and their \nassociated risks in the Chenab River, Pakistan Journal of Geochemical \nExploration. \n \n\n\n\n\n\n\n\n\n  \nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 12-20 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Ali Rezaei, Hossein Hassani, Seyedeh Belgheys Fard Mousavi, Nima Jabbari (2019).Evaluation  Of Heavy Metals Concentration In Jajarm Bauxite Deposit In \nNortheast Of Iran Using Environmental Pollution Indices. Malaysian Journal Of Geosciences, 3(1) : 12-20. \n\n\n\n[5] Sargaonkar, A., Deshpande, V. 2003. Development of an overall index \nof pollution for surface water based on a general classification scheme in \nIndian context. Environmental Monitoring and Assessment, 89, 43-67. \n \n[6] Rodr\u00edguez Mart\u00edn, J.A., Lopez Arias, M., Grau Corb\u00ed, J.M. 2007. Heavy \nmetal contents in agricultural topsoils in the Ebro basin (Spain). \nApplication of multivariate geostatistical methods to study spatial \nvariations. Environmental Pollution, 144, 1001\u20131012. \n \n[7] Hosseinpoor, A. 2008. Chemistry and soil fertility. Payame Noor \nUniversity, 214. \n \n[8] Samuel, K., Christiana, M.A.O. 2012. Heavy metal pollution around \nItakpe mine, Kogi State, Nigeria. International Journal of Physical Sciences, \n7, 5062-5068. \n \n[9] EA (Environment Australia). 2002. An outline of the Commonwealth \nEnvironmental Impact Assessment process. \n \n[10] Li, X., Feng, L. 2012. Multivariate and geostatistical analyzes of metals \nin urban soil of Weinan industrial areas, Northwest of China. Atmospheric \nEnviron 47, 58\u201365. \n \n[11] Jabbari, N., Aminzadeh, F., Barros, P.J. 2016. Hydraulic fracturing and \nthe environment: risk assessment for groundwater contamination from \nwell casing failure. Stochastic Environmental Research and Risk \nAssessment. \n \n[12] Rezaei, A., Hassani, H., Hayati, M., Jabbari, N., Barzegar,R. 2017a. Risk \nAssessment and Ranking of Heavy Metals Concentration in Iran\u2019s Rayen \nGroundwater Basin Using Linear Assignment Method. Stochastic \nEnvironmental Research and Risk Assessment. \n \n[13] Esmaeily, D., Rahimpour-Bonab, H., Esna-Ashari, A. 2010. \nPetrography and Geochemistry of the Jajrm Karst Bauxite Ore Deposit, NE \nIran: Implications for Source Rock Material and Ore Genesis, Turkish \nJournal of Earth Sciences, 19, 267- 284. \n \n[14] Nouri, F., Azizi, H., Stern, R.J, Asahara, Y., Khodaparast, S., Madanipour, \nS., Yamamoto, K. 2018. Zircon U-Pb dating, geochemistry and evolution of \nthe Late Eocene Saveh magmatic complex, central Iran: Partial melts of \nsub-continental lithospheric mantle and magmatic differentiation. Ore \nGeology Reviews, 84, 116\u2013133. \n \n[15] Noor Hisham, A., Norlen, M., Lokman, H.S., Thahirahtul, A.Z., Daud, A.R. \n2016. Potential health impacts of bauxite mining in Kuantan. Malaysian \nJournal of Medical Sciences, 23(3), 1-8. \n \n[16] Stocklin, J., Eftekhar-Nezhad, J., Hushmand-Zadeh, A. 1981. Geology of \nthe Shotori range, Geological Survey of Iran, Tehran, 3.  \n \n[17] Bronnimann, P., Zaninetti, L., Moshtaghian, A., Huber, H. 1973. \nForaminifera from the Sorkh Shale formation of the Tabas area, East- \nCentral Iran, Rivista Italiana Dipaleontologia e Stratigrafia, 79, 1-32. \n \n[18] Nabavi, M.H., Seyed- EmamiM K. 1977. Sinemudan ammonites from \nthe Shemshak formation of North Iran, Neues Jahrbuch fur Geologie und \nPalaontologie- Abhandlngen, 153, 70-85. \n \n[19] Govil, P., Reddy, G., Krishna, A. 2001. Contamination of soil due to \nheavy metals in the Patancher industrial development area, Andhra \nPradesh, India, Environmental Geology, 41,461- 469. \n \n[20] Kabata-Pendias, A., Mukherjee, A.B. 2007. Trace elements from soil to \nhuman, Springer Berlin Heidelberg New York. \n \n[21] Zhang, W., Feng, H., Chang, J., Qu, J., Xie, H., Yu, L. 2009. Heavy metal \ncontamination in surface sediments of Yangtze River intertidal zone: an \nassessment from different indexes, Environmental Pollution, 157, 1533- \n1543. \n \n[22] Adama, P., Arienzo, M., Imporato, M., Noimo, D., Nardi, G., Stanzione, \nD. 2005. Distribution and partition of heavy metals in surface and \nsubsurface sediments of Naples city port. Chemosphere, 61, 800\u2013809. \n \n[23] Hayaty, M., Tavakoli mohammadi, M.R, Rezaei, A., Shayestehfar, M.R. \n2014. Risk Assessment and Ranking of Metals Using FDAHP and TOPSIS, \nMine Water and the Environment, 33,157-164. \n \n[24] Abrahim, G.M.S., Parker, R.J. 2008. Assessment of heavy metal \nenrichment factors and the degree contamination in marine sediments \n\n\n\nfrom Tamaki. Estuary, Auckland, New Zealand. Environmental Monitoring \nand Assessment, 136, 227- 238. \n \n[25] Muller, G., 1979. Schwermetalle in den sedimenten des Rheins \nVeranderungen seit. Umschau, 79(24), 778- 783. \n \n[26] Bhuiyan, M.A.H., Parvez, L., Islam, M.A., Dampare, S.B., Suzuki, S. 2010. \nHeavy metal pollution of coal mine-affected agricultural soils in the \nnorthern part of Bangladesh. Journal of Hazard Material, 173, 384\u2013392. \n \n[27] Shi, G., Chen, Z., Bi, C., Li, Y., Teng, J. 2010. Comprehensive assessment \nof toxic metals in urban and suburban street deposited sediments (SDSs) \nin the biggest metropolitan area of China. Environmental Pollution, 158, \n694\u2013703. \n \n[28] Zhang, L., Ye, X., Feng, H., Jing, Y., Oyang, T., Yu, X., Liang, R., Gao, C., \nChen, W. 2007. Heavy metal contamination in western Xiamen Bay \nsediments and its vicinity, China, Marine Pollution Bulletin, 54, 974- 982. \n \n[29] Gonzales-Macias, C., Schifter, I., Liuch-Cota, D.B., Mendez-Rodriguez, \nL., Hernandez-Vazquez, S. 2006. Distribution, enrichment and \naccumulation of heavy metals in coastal sediments of Salina Cruz Bay, \nMexico. Environmental Monitoring and Assessment, 118, 211- 230. \n \n[30] Ghrefat, H., Yusuf, N. 2006. Assessing Mn, Fe, Cu, Zn and Cd pollution \nin bottom sediments of Wadi AL- Arab Dam, Jordan. Chemosphere 65, \n2114-2121. \n \n[31] Abrahim, G.M.S. 2005. Holocene sediments of Tamaki Estuary:  \nCharacterisation and impact of recent human activity on an urban estuary \nin Auckland, New Zealand. Ph.D. thesis, University of Auckland, Auckland, \nNew Zealand, 361. \n \n[32] Hakanson, L. 1980. Ecological Risk Index for Aquatic Pollution \nControl, a Sedimentological Approach, Water Resources, 14, 975-1001. \n \n[33] Rahman, S.H., Adyel, T.M., Akbur, M.A. 2012. Assessment of Heavy \nMetal Contamination of Agricultural Soil around Dhaka Export Processing \nZone (DEPZ), Bangladesh: Implication of Seasonal Variation and Indices. \nApplied Sciences, 2, 584-601. \n \n[34] Vafabakhsh, K., Kharghany, K. 2000. Effects of treated municipal \nwastewater on quality and yield of cucumber and carrot, In Agricultural \nresource recycling Symp, Isfahan Khorasgan Azad University Agricultural \nCollege, Iran. \n \n[35] Adomako, D., Nyarko, B.J.B., Dampare, S.B., Serfor-Armah, Y., Osae, S., \nFianko, J.R., Akaho, E.H. 2008. Determination of toxic elements in waters \nand sediments from River Subin in the Ashanti Region of Ghana, \nEnvironmental Monitoring and Assessment, 141, 165- 175. \n \n[36] Qishlag, A., Moore, F., Forghani, G. 2007. Impact of untreated \nwastewater irrigation on soils and crops in Shiraz suburban area, SW Iran. \nEnvironmental Monitoring and Assessment, 149, 254- 262. \n \n[37] Mapanda, F., Mangwayana, E., Nyamangara, J., Giller, K. 2007. Uptake \nof heavy metals by vegetables irrigated using wastewater and the \nsubsequent risks in Harare, Zimbabwe, Physics and Chemistry of the \nEarth, 32, 1399-1405. \n \n[38] Rezaei, A., Hassani, H., Jabbari, N. 2017b. Evaluation of Groundwater \nQuality and Assessment of Pollution Indices for heavy metals in North of \nIsfahan Province, Iran. Sustainable Water Resources Management. \n \n[39] Statistical Package for the Social Sciences (SPSS) Inc. 2017. \n \n[40] Razo, I., Carrizales, L., Castro, J., D\u0131\u00b4az-Barringa, F., Monroy, M. 2004. \nArsenic and heavy metal pollution of soil, water and sediments in a semi-\narid climate mining area in Mexico. Water, Air, & Soil Pollution, 152, 129\u2013\n152. \n \n[41] Krzanowski, W.J. 1988. Principles of Multivariate Analysis, Clarendon \nPress. \n \n[42] Sun, Y., Zhou, Q., Xie, X., Liu, R. 2010. Spatial, sources and risk \nassessment of heavy metal contamination of urban soils in typical regions \nof Shenyang, China. Journal of Hazardous Materials, 174, 455\u2013462.  \n \n[43] Zheng, Y., Gao, Q., Wen, X., Yang, M., Chen, H., Wu, Z., Lin, X. 2013. \nMultivariate statistical analysis of heavy metals in foliage dust near \npedestrian bridges in Guangzhou, South China in 2009. Environmental \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 12-20 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Ali Rezaei, Hossein Hassani, Seyedeh Belgheys Fard Mousavi, Nima Jabbari (2019).Evaluation Of Heavy Metals Concentration In Jajarm Bauxite Deposit In \nNortheast Of Iran Using Environmental Pollution Indices. Malaysian Journal Of Geosciences, 3(1) : 12-20. \n\n\n\n\n\n\n\nEarth Sciences, 70(1), 107 \u2013113.  \n \n[44] Yinxian, S., Junfeng, J., Zhongfang, Y., Xuyin, Y., Changping, M., Ray, F., \nGodwin, A. 2011. Geochemical behavior assessment and apportionment of \nheavy metal contaminants in the bottom sediments of lower reach of \nChangjiang River, Catena, 85, 73-81. \n \n[45] Qiao, M., Cai, Y., Huang, Y., Liu, A., Lin, A., Zheng, Y. 2011. \nCharacterization of soil heavy metal contamination and potential health \nrisk in metropolitan region of northern China. Environmental Monitoring \nand Assessment, 172(1-4), 353-36 \n \n[46] Cheng, Q. 2007. Mapping singularities with stream sediment \ngeochemical data for prediction of undiscovered mineral deposits in Gejiu, \nYunnan Province, China. Ore Geology Reviews, 321, 314\u2013324. \n \n[47] Jolliffe, I.T. 2002. Principal Component Analysis. Springer, New York. \n487. \n \n[48] Meglen, R.R. 1992. Examining large databases: a chemometric \napproach using principle component analysis. Marine Chemistry, 39, 217\u2013\n237. \n \n[49] Kaiser, H.F. 1960. The Application of Electronic Computers to Factor \nAnalysis. Edu. Psychol, Meas 20, 141-151. \n \n[50] Cheng, Q., Bonham-Carter, G., Wang, W., Zhang, S., Li, W., Qinglin, X. \n2011. A spatially weighted principle component analysis for multielement \ngeochemical data for mapping locations of felsic intrusions in the Gejiu \nmineral district of Yunnan, China. Computer Geosciences 37, 662\u2013669. \n \n[51] Zuo, R. 2011. Identifying geochemical anomalies associated with Cu \nand Pb\u2013Zn skarn mineralization using principle component analysis and \nspectrum \u2013 area fractal modeling in Gangdese belt. Journal of Geochemical \nExploration, 111,13\u201322. \n \n[52] Wang, J., Zuo, R., 2015. A MATLAB-based program for processing \ngeochemical data using fractal/multifractal modeling. Earth Sci. Inf. 8, \n937-947. \n \n[53] Filzmoser, P., Hron, K., Reimann, C. 2009a. Principal component \nanalysis for compositional data with outliers. Environmetrics, 20, 621 \u2013\n632. \n \n[54] Filzmoser, P., Hron, K., Reimann, C., Garrett, R. 2009b. Robust factor \nanalysis for compositional data. Computer Geosciences 35, 1854 \u20131861. \n \n[55] Zuo, R. 2014. Identification of geochemical anomalies associated with \nmineralization in the Fanshan district, Fujian, China. Journal of \nGeochemical Exploration, 139, 170-176. \n \n[56] Grunsky, E.C. 2010. The interpretation of geochemical survey data. \nJournal of Geochemical Exploration, Environmental Analysis, 10, 27-74. \n \n[57] Levitan, D.M., Zipper, C.E., Donovan, P., Schreiber, M.E., Seal, R.R., \nEngle, M.A., Chermak, J.A., Bodnar, R.J., Johnson, D.K., Aylor, J.G. 2015. \nStatistical analysis of soil geochemical data to identify path finders \nassociated with mineral deposits: an example from Coles Hill Uranium \ndeposit, Virginia, USA. Journal of Geochemical Exploration, 154, 238 \u2013251. \n \n[58] Templ, M., Hron, K., Filzmoser, P. 2011. Rob Compositions: An R-\npackage for robust statistical analysis of compositional data. \nCompositional Data Analysis: Theory and Applications. John Wiley and \n\n\n\nSons, Chichester, 341-355. \n \n[59] Turekian, K.K., Wedepohl, D.H. 1961. Distribution of the elements in \nsome major units of the earth\u2019s crust. Bulletin of the Geological Society of \nAmerica, 72, 175\u2013192. \n \n[60] Harikumar, P., Nasir, U., and Rahman, M.M. 2009. Distribution of \nheavy metals in the core sediments of a tropical wetland system, \nInternational Journal of Environ Science and Technology, 6, 225- 232. \n \n[61] Benavides, M.P., Gallego, S.M., Tomaro, M.L. 2005. Cadmium toxicity \nin plants. Brazilian Journal of Plant Physiology, 17(1). \n \n[62] World Health Organization. 2010. Exposure to cadmium: a major \npublic health concern. Preventing Disease through Healthy Environment, \n27. \n \n[63] Abdullah, M., Fasola, M., Muhammad, A., Malik, S.A., Boston, N., \nBokhari, H., Kamran, M.A., Shafqat, M.N., Alamdar, A., Khan, M., Ali, N., \nEqani, S.A.M.A.S. 2015. Avian feathers as a non-destructive bio-monitoring \ntool of trace metals signatures: a case study from severely contaminated \nareas. Chemosphere, 119, 553-561. \n \n[64] Agency for Toxic Substance and Disease Registry (ATSDR). 2012a. \nToxicological profile for manganese.  \n \n[65] Agency for Toxic Substance and Disease Registry (ATSDR). 2012b. \nToxicological profile for cadmium. \n \n[66] Agency for Toxic Substance and Disease Registry (ATSDR). 2004a. \nToxicological profile for copper.  \n \n[67] Agency for Toxic Substance and Disease Registry (ATSDR). 2004b. \nToxicological profile for cobalt.  \n \n[68] Saleem, M., Iqbal, J., Shah, M.H. 2013. Study of seasonal variations and \nrisk assessment of selected metals in sediments from Mangla Lake, Journal \nof Geochemical Exploration, 125, 144\u2013152. \n \n[69] Ullah, K., Hashmi, M.Z., Malik, R.N. 2014. Heavy-metal levels in \nfeathers of cattle egret and their surrounding environment: a case of the \nPunjab province, Pakistan. Archives of Environmental Contamination and \nToxicology, 66(1), 139 \u2013153. \n \n[70] Khodadoust, A.P., Reddy, K.R., and Matari, K. 2004. Removal of nickel \nand phenanthrene from kaolin soil using different extractants. \nEnvironmental Engineering Science, 21(6), 691- 704. \n \n[71] Agency for Toxic Substance and Disease Registry (ATSDR). 2007. \nToxicological profile for lead.  \n \n[72] World Health Organization. 2001. Environmental Health Criteria 221 \nZinc. World Health Organization, Geneva, Switzerland. \n \n[73] Reimann, C., Filzmoser, P., Garrett, R.G. 2002. Factor analysis applied \nto regional geochemical data: problems and possibilities. Applied \nGeochemistry, 17, 185-206.  \n \n[74] Zuo, R., Cheng, Q., Agterberg, F.P., Xia, Q. 2009. Application of \nsingularity mapping technique to identify local anomalies using stream \nsediment geochemical data, a case study from Gangdese, Journal of \nGeochemical Exploration 101, 225-235. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\n Malaysian Journal of Geosciences 2(1) (2018) 01-08 \n\n\n\nCite the Article: Akingboye A. Sunny (2018). Derivatives And Analytic Signals: Improved Techniques For Lithostructural Classi fications.  \nMalaysian Journal of Geosciences, 2(1) : 01-08. \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nIn this study, magnetic derivatives and Analytic Signal (AS) techniques were employed to reveal the nature of \nrocks and lithostructural relationships that exist within the basement complexes around Ekiti and Ondo States. \nThe derivatives techniques were used to enhance the Reduction to Equator Total Magnetic Intensity (RTE_TMI) \ndata. In order to make the results from derivatives techniques worthwhile and robust, Analytic Signal (AS) \ntechnique was then applied. The results of the derivatives and analytic signals revealed seven different \nlithological suites, namely: migmatite (M), migmatite/granite-gneiss (M/Gn), gneiss and granite (Gn/G), schist \nand quartzite schist (S/Qs), granite-gneiss and charnockite (Gn/Ch), charnockite and granite (Ch/G), and granite \n(G) with their respective edges and trends. Five different major lineaments/faults and lithological contacts were \nalso identified. The lineaments/faults were classified as F1, F2, F3, F4 and F5 with NW-SE, NNE-SSW, NE-SW, E-W \nand NNW-SSE trends respectively. While lithological contacts were classified into C1, C2, C3, C4 and C5 as contact \nof migmatite and granite, migmatite and granite-gneiss/charnockite/granite, migmatite and gneiss/granite, \nmigmatite and schist/quartzite schist, and migmatite and gneiss respectively. It is evident from the study that \nmigmatites and gneisses which form the basement in the area have been highly deformed and evince many \nintrusives.  A detailed correlation image of the study area geology and analytic signal is produced as deduced \nfrom the results analyses. \n\n\n\nKEYWORDS  \n\n\n\nDerivatives, Analytic Signal (AS), Lithological Suites, Structures, Contacts.\n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: https://doi.org/10.26480/mjg.01.2018.01.08\n\n\n\nDERIVATIVES AND ANALYTIC SIGNALS: Improved Techniques for \nLithostructural Classification. \nAkingboye A. Sunny\n\n\n\nDepartment of Earth Sciences, Adekunle Ajasin University, Akungba-Akoko, PMB 001, Ondo State, Nigeria. \n*Corresponding Author\u2019s E-mail: adedibu.akingboye@aaua.edu.ng \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \n\n\n\nCODEN : MJGAAN \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\nmailto:adedibu.akingboye@aaua.edu.ng\n\n\n\n\n\n\nMalaysian Journal of Geosciences 2(1) (2018) 01-08 \n\n\n\nCite the Article: Akingboye A. Sunny (2018). Derivatives And Analytic Signals: Improved Techniques For Lithostructural Classi fications.  \nMalaysian Journal of Geosciences, 2(1) : 01-08. \n\n\n\n1.0 INTRODUCTION \n\n\n\nThe essential aspect of magnetic data processing is to simplify the \ncomplexity of derived information by removing the effects of data artifacts \nand obscured signals emanating from background noises, shallower and \ndeeper crustal bodies and structures. The derived information is usually \nenhanced by using relevant techniques that can identify and map \nlithologies, structures, trends, and contacts among others after necessary \nreduction processes had been applied. \n\n\n\nDerivatives and analytic signal (AS) are improved enhancement \ntechniques and have been adopted by quite number of authors for \nlithostructural purposes, such as mapping of fractures (lineaments, \nfaults and joints), trends, geological contacts, etc. \n\n\n\nThe significance of fractional order derivatives (horizontal and vertical) \nin locating the position of Cordell and Grauch [1]; Cooper et al [2]; Okpoli \nand Akingboye [3]. These techniques enhance shallow wavelength \nfeatures that are results of near surface structures obscured by stronger \neffects of broader regional features. Recently, much interest has been \nshown in the use of derivatives of fractional order, enabling an optimum \nbalance between feature enhancement and noise. The first vertical \nderivative is used instead of the second vertical derivative because it \nsuppresses noisy data [4, 5]. \n\n\n\nAccording to Salem et al [6] and Okpoli and Akingboye [7], tilt \nderivatives is an enhancement technique used to determine structures \n(lineaments, faults and joints), contacts and edges or boundaries of \nmagnetic sources, and to enhance both weak and strong magnetic \nanomalies of the area by placing an anomaly directly over its source. \nOkpoli and Akingboye [7]  used tilt derivative to map different structural \nfeatures and contacts in three quarry sites in Ondo State [7]. \n\n\n\nOn the other hand, Analytic Signal (AS) centers the peak of magnetising \nbodies symmetrically over their sources through transformation of the \nshapes of inclined magnetising bodies. Ansari and Alamdar [8]  showed \nthat analytic signal is formed through the combination of the\n\n\n\nhorizontal and vertical gradients of the magnetic anomaly and it is \napplied either in space or frequency domain to generate a maximum \ndirectly over discrete bodies as well as their edges [8]. This improved \ntechnique have been applied to detect the edge, depth estimation of \nmagnetic bodies and to detect the structures responsible for the \nobserved magnetic anomalies over an area [9-13].  \n\n\n\nAnalytic signal images are useful as a type of reduction to the pole in low \nmagnetic latitude areas where inclination is less than 150, as they are not \nsubjected to the instability that occurs in transformations of magnetic \nfields from low magnetic latitudes [14]. \n\n\n\nTherefore, this present study discusses the use of first order horizontal \nand vertical derivatives, tilt derivative and analytic signal for litho-\nstructural  classification of the basement complex rocks of parts of Ekiti \nand Ondo States, in order to reveal the litho-structural features, trends \nand anomalous zones, as well as to produce a detailed correlation map to \nevince the relationship between the earlier geological map and analytic \nsignal of the study area.  \n\n\n\n2.0 LOCATION AND GEOLOGIC SETTING \n\n\n\nThe study area is located around Ekiti and Ondo States, Nigeria. The \naeromagnetic knitted sheet (244 and 264) used for this study falls within \nLatitude 70 00\u2019 - 80 00\u2019 N (770000 - 885000 mN) and Longitude 50 00\u2019 - 50 \n\n\n\n30\u2019 E (720000 \u2013 777500 mE) of Zone 31N Greenwich Mercator (Figures \n1 and 2). \n\n\n\nThe study area is underlain by rocks of the Precambrian Basement \nComplex of Southwestern Nigeria. The rock types found in the area \ninclude: migmatite-gneiss, schist with minor phyllite, quartzite, \ncharnockite, granite and other minor felsic and mafic intrusives such as \ndyke, sill and vein of dolerite, aplite and pegmatites (Figure 1) [15-18].  \n\n\n\nFigure 1: Geological Map of the Study Area (Created from Geological Map of Nigeria, [19]) \n\n\n\n\n\n\n\n\n Malaysian Journal of Geosciences 2(1) (2018) 01-08 \n\n\n\nCite the Article: Akingboye A. Sunny (2018). Derivatives And Analytic Signals: Improved Techniques For Lithostructural Classi fications.  \nMalaysian Journal of Geosciences, 2(1) : 01-08. \n\n\n\n3.0 MATERIAL AND METHODS \n\n\n\nAeromagnetic data of Sheet 244 and 264 for Ado-Ekiti and Akure \nrespectively were acquired from Nigerian Geological Survey Agency \n(NGSA). The aero-sheets were knitted and processed using Geosoft\u00ae Oasis \nMontaj\u2122 software, but other software like Surfer was also used for this \nwork. \n\n\n\nThe data processing phase involved reductions and enhancements done \nby using the MAGMAP Step-By-Step Filtering. The removal of near \nsurface noise (NSN) and reduction to magnetic equator of the total \nmagnetic intensity gridded data were performed to accentuate \nintensities signals and center the anomalous bodies and structures over \ntheir sources to give an output of RTE_TMI grid. Thereafter, RTE_TMI \ndata enhancement in First Order Derivative in X (horizontal) and Z \n(vertical) directions were performed to produce the first order \nderivative in horizontal (1HD) and vertical (1VD) respectively.  The tilt \nderivatives (TDR) of RTE_TMI and analytic signal (AS) of RTE_TMI, 1HD \nand 1VD were later produced sequentially. \n\n\n\n4.0 RESULTS AND DISCUSSION \n\n\n\n4.1 Reduction to Equator Total Magnetic Intensity (RTE_TMI) \n\n\n\nThe RTE-TMI image (Figure 2) is produced to center the peaks of magnetic \nanomalies over their sources depending on the inclination and declination \nof the local field of the magnetizing body, as this would enable proper \nmapping and delineating of inclined and other aligned form of structures. \n\n\n\nOn comparison, Figure 3 evinces the litho-structural  similarities \nbetween RTE_TMI image and geological map. It is evident that the highly \ndeformed rocks in the area correspond to the Migmatite-Gneiss Complex \nwith evidences of both positive and negative intensity values that ranged \nfrom 13.50 \u2013 162.04 nT and -63.04 to -12.13 nT respectively. These \nanomaly differences envisaged by the rocks are associated with \nferromagnesian minerals that often give rise to very high magnetic \nintensity and intense degree of metamorphism and deformities that \nproduce low and negative intensity values. The RTE_TMI image also \nshow some of the major lineaments/faults and trend as seen in Figure 1. \n\n\n\nFigure 2: Colour Shaded Reduction to Equator Total Magnetic Intensity (RTE_TMI) Image \n\n\n\n\n\n\n\n\n Malaysian Journal of Geosciences 2(1) (2018) 01-08 \n\n\n\nCite the Article: Akingboye A. Sunny (2018). Derivatives And Analytic Signals: Improved Techniques For Lithostructural Classi fications.  \nMalaysian Journal of Geosciences, 2(1) : 01-08. \n\n\n\nFigure 3: Comparing the RTE_TMI Image with Geological Map of the Study Are\n\n\n\n4.2 Derivatives: First Horizontal (1HD), First Vertical (1VD) and Tilt \n(TDR) \n\n\n\nThe derivative in both x (horizontal) and z (vertical) directions sharpen \nthe edges of magnetic anomalies, gives clearer contrast between the \ngeologic units and causative structures such as lineaments/faults joints, \netc. The first horizontal and vertical derivatives were applied on the \nRTE_TMI gridded data to enhance shallow wavelength features, that are \nresults of near surface structures obscured by stronger effects of broader \nregional features and suppresses the long wavelengths (deeper sources/\nregional features) thereby provide a better and clearer picture of the \nsubsurface. The tilt derivative (TDR) other the hand performs similar \nfunctions by accentuating structural deformations such as faults, joints, \nand arched zones or even geological contacts. The techniques were used \nto map and delineate both minor and major structures (lineaments/\nfaults, joints, etc)  in the area, and classify them base on their trends, \noccurrences and tectonic frameworks. \n\n\n\nFigures 4 (a-c) show the derivative images for First Horizontal Derivative \n(1HD_RTE_TMI), First Vertical Derivative (1VD_RTE_TMI) and Tilt \nDerivative (TDR_RTE_TMI) respectively. 1HD_RTE_TMI (Figure 4a) shows  \n\n\n\nthe major lineaments/faults and even contacts between rocks with \nbetter clarity than 1VD_RTE_TMI (Figure 4b), but 1VD does it better for \ntrends identification because it has suppressed the interfering horizontal \nwavelengths to a better range. While the TDR_RTE_TMI (Figure 4c) \nreveals its ability in lithological, structural, and contacts classifications. \n\n\n\nThe derivatives images (Figures 4 a \u2013 c) reveal five (5) different \nlineaments/faults (F), lithological contacts (C) and four (4) types. The \nlineaments/faults are delineated and classified as F1, F2, F3, F4 and F5 \nwith NW-SE, NNE-SSW, NE-SW, E-W (less) and NNW-SSE trends \nrespectively. Comparing Figure 4a with Figure 1, it is evident that F5 is a \nfault with two major displacements along F3 as seen at the northeastern \nsection of both images. While geological contacts are classified into C1, \nC2, C3, C4 and C5 as contact of migmatite and granite, migmatite and \ngneiss/charnockite/granite, migmatite and gneiss/granite, migmatite \nand schist/quartzite schist, and migmatite and gneiss respectively.  \nThese are relatively sharp contacts and not interpretive/inferred \nboundaries, they give a distinctive boundary amongst the rocks in the \narea. \n\n\n\n\n\n\n\n\n Malaysian Journal of Geosciences 2(1) (2018) 01-08 \n\n\n\nCite the Article: Akingboye A. Sunny (2018). Derivatives And Analytic Signals: Improved Techniques For Lithostructural Classi fications.  \nMalaysian Journal of Geosciences, 2(1) : 01-08. \n\n\n\nFigures 4:(a) First Horizontal Derivative (1HD_RTE_TMI), (b) First Vertical Derivative (1HD_RTE_TMI), and (c) Tilt Derivative (TDR_ RTE_TMI). \n\n\n\n*C (1-5)-Contacts, F (1-5)-Lineaments/Faults. \n\n\n\n4.3 Analytic Signal (AS) of RTE_TMI, 1HD_RTE_TMI and 1VD_RTE_TMI \n\n\n\nThe analytic signal (AS) centers the peak of the magnetising bodies \nsymmetrically over their sources through transformation of the shapes \nof inclined magnetising bodies by relying on the magnetisation strength \nand direction of geologic strike with respect to the magnetisation vector, \nthereby making the interpretation of analytic signal amplitude easier to \ndeal with than in the original total field data or reduction to pole. Hence, \nthis technique shows the amplitude strength of respective litho-\nstructural features based on their magnetisation contrast [14, 20] as the \nmajor driving ability that enables easy mapping and for litho-structural \nclassification.\n\n\n\nFigures 5 a \u2013 c show the analytic signal (AS) results generated for \nRTE_TMI and respective derivatives data. Analytic signal of RTE_TMI \n(AS_RTE_TMI) (Figure 5a) was used for delineating the edges and trends \nof the migmatitic rocks based on its high amplitude signal probably due \nto its high magnetisation generated by sources rich in ferromagnesian-\nbearing minerals. It is evident from the image that the migmatite \ncomplex covers relatively large portion of the study area with a regional \ntrend of NW-SE and NS to some extent\n\n\n\nThe analytic signal of the first order horizontal derivative of RTE_TMI \n(AS_1HD) (Figure 5b) was used to map and delineate other lithological \ntrends and contacts in the area. While analytic signal of first order \nvertical derivative of RTE_TMI (AS_1VD) (Figure 5c) was used to map \nand classify the structures in the area. The AS_1VD reveals some \nlineaments/faults that were not mapped on the derivatives images. Note \nthat Figures 5a and 5b can be used for such structural classification, but \nFigure 5c is chosen in order to reduce congestion of annotations on the \nother two images. The images also reveal that most of the complexes \nhave intrusives and inclusions seen as trends of high and low amplitude \nvariations.\n\n\n\nDifferent range of amplitude signals were revealed on the analytic signal \nimages (Figures 5 a \u2013 c), due to varying magnetisation strength evinced \nby various rock types. Based on the amplitude strength of these \nmagnetizing bodies [20, 21], and study area geology [16, 19], the \nfollowing classifications were made:\n\n\n\ni. very high analytic signals; classified as edges and trends of the \nmigmatite (M) complexes. However, some sections of the \nimages reveal varying amplitude signals due probably to \nvarying strength of the magnetising bodies\n\n\n\nii. moderately high to high analytic signals; classified as \nmagnetisation responses from rock bodies like migmatite/\ngneisses (M/Gn), charnockite (Ch) and granite (G) (around \nIkole and within the schists) and granite-gneiss (extreme end \nof Ikole-Ekiti). However, low amplitude signals are seen along \nsome axis of the images\n\n\n\niii. very low to low analytic signals; classified as rocks with low \nmagnetising strength such as some schist (S) and quartzite (Q).\n\n\n\nStructural trends in NW-SE, NE-SW, and NNW-SSE as seen in the study \narea were also identified based on their signals and classified as: \n\n\n\ni. high analytic signal  trends as basic intrusives like doleritic \ndyke, sill, and vein.\n\n\n\nii. very low analytic signal trends as felsic intrusives and veins of \nquartz, pegmatite and aplite.\n\n\n\n\n\n\n\n\n Malaysian Journal of Geosciences 2(1) (2018) 01-08 \n\n\n\nCite the Article: Akingboye A. Sunny (2018). Derivatives And Analytic Signals: Improved Techniques For Lithostructural Classi fications.  \nMalaysian Journal of Geosciences, 2(1) : 01-08. \n\n\n\nFigures 5: Analytic Signal Images; (a) AS_RTE_TMI, (b) AS_1HD, and (c) AS_1VD. \n\n\n\n*VH-Very high, MH/H-Moderately high to high, MH-Moderately high, L/MH-Low to moderately high, VL/L-Very low to low Analytic Signals\n\n\n\nThe correlation of geological and analytic signal image for the study area \nis shown in Figure 6. This proposed correlation image reveals the litho-\nstructural features such as lineaments/faults, contacts, and lithological \ntrends that were not shown in Figure 1 (Study area geology). Some of \nthese features in Figure 1 were not mapped due to some reasons like soil \nand vegetation cover, etc. The rocks in the study area reveal varying \nsignal strength that differs from one area to another, which could likely \n\n\n\nbe attributed to varying magnetisation strength of different sources and \nmineralogical compositions. This variation also led to complexity in \ngrouping the granite-gneissic and granitic rocks to having relatively \nsimilar amplitude strengths\n\n\n\nFigure 6: Correlation Image of the Study Area Geology and Analytic Signal. \nThe inferred summary of the lithostructural relationshipof major lineaments/faults, trends, and lithological contacts of the study area as deduced \n\n\n\nfrom derivatives and analytic signals results for quick and better understanding is shown in Table 1. \n\n\n\nSome of the delineated structures - lineaments/faults trends in NW-SE, NE-SW, and NNW-SSE. These structures and trends are similar to those \nidentified in Figures 4\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences 2(1) (2018) 01-08 \n\n\n\nCite the Article: Akingboye A. Sunny (2018). Derivatives And Analytic Signals: Improved Techniques For Lithostructural Classi fications.  \nMalaysian Journal of Geosciences, 2(1) : 01-08. \n\n\n\nTable 1: Summary of Litho-structural  Classification for the Study Area \n\n\n\n*F1, F2, F3, F4 and F5 indicate NW-SE, NNE-SSW, NE-SW, E-W (less) and NNW-SSE trends respectively. While C1, C2, C3, C4 and C5 as contact of migmatite \nand granite, migmatite and granite-gneiss/charnockite/granite, migmatite and gneiss/granite, migmatite and schist/quartzite schist, and migmatite and \ngneiss respectively\n\n\n\n5.0 CONLUSION\n\n\n\nIn this study, the lithostructural relationship that exist within the \nbasement complexes around Ekiti and Ondo States revealed the following: \n\n\n\n1. Seven litho-structural suites that include migmatite (M), \nmigmatite/granite-gneiss (M/Gn), gneiss and granite (Gn/G), \nschist and quartzite schist (S/Qs), granite-gneiss and \ncharnockite (Gn/Ch), charnockite and granite (Ch/G), and \ngranite (G)\n\n\n\n2. Five lineaments/faults, classified as F1, F2, F3, F4 and F5 with \nNW-SE, NNE-SSW, NE-SW, E-W (less) and NNW-SSE trends \nrespectively\n\n\n\n3. Five contacts (C 1 \u2013 5); as contact for migmatite and granite, \nmigmatite and granite-gneiss/charnockite/granite, migmatite \nand gneiss/granite, migmatite and schist/quartzite schist, and \nmigmatite and gneiss respectively. The contacts further reveal \nthe exact extension of the rocks in the subsurface (i.e. below \nthe overburden) compared to limited range of the \ninterpretive/inferred boundaries used for the surface \nmapping\n\n\n\n4. The correlated geological and analytic signal image clearly \nreveals the litho-structural features such as lithological trends, \nlineaments/faults, trends, arched structures, contacts and \namplitude strengths of respective rocks\n\n\n\n5. Intrusives and inclusions seen as trends of high and low \nintensities \n\n\n\nFurthermore, it is evident from this study that the Migmatite-gneiss \ncomplex of  the area have been highly deformed and extensively \nintruded compared to other complexes as a result of pronounced \nfaulting, shearing, jointing and other geological processes.\n\n\n\nThe images, lithostructural classifications and detailed discussion of the \nresults for this study have shown the worthiness and abilities of these \nimproved techniques as tools for regional litho-structural \ncharaterisation.. Further ground truth is suggested, in order to map out \nand give details of the various rocks within the migmatite suite, as well \nas litho-structures and contacts that were revealed by various images \nproduced for this work.\n\n\n\nREFERENCES \n\n\n\n[1] Cordell, L., Grauch, V.J.S. 1985. Mapping basement magnetization zones \nfrom aeromagnetic data in the San Juan basin, New Mexico. In: HINZE WJ, \neds. The Utility of Regional Gravity and Magnetic Anomaly Maps. Society \nof Exploration Geophysicists, 181-197. \n\n\n\n[2] Cooper, G.R.J., Cowan, D.R. 2004. Filtering using variable order vertical \nderivatives. Computers and Geosciences, 30 (5), 455\u2013459. \n\n\n\n[3] Okpoli, C., Akingboye, A. 2016a. Reconstruction and appraisal of \nAkunu\u2013Akoko area iron ore deposits using geological and magnetic \napproaches. RMZ \u2013 Materials and Geoenvironment (Materiali in \ngeookolje), 63 (1), 19-38. \n\n\n\n[4] Gunn, P.J., FitzGerald, D., Yassi, N., Dart, P. 1997. New algorithms for \nvisually enhancing airborne geophysical data. Exploration Geophysics, 28 \n(1-2), 220\u2013224. \n\n\n\n[5] Cooper, J.R.G., Cowan, D.R. 2003. The application of fractional calculus \nto potential field data. Exploration Geophysics, 34 (4), 51\u201356.  \n\n\n\n[6] Salem, A., Williams, S., Fairhead, J., Ravat, D., Smith, R. 2007. Tilt-depth \nmethod: a simple depth estimation method using first-order magnetic \nderivatives. The Leading Edge, 26 (12), 1502-1505. \n\n\n\n[7] Okpoli, C.C., Akingboye, A.S. 2016b. Magnetic, radiometric and \ngeochemical survey of quarry sites in Ondo State, Southwestern Nigeria. \nInternational Basic and Applied Research Journal, 2 (8), 16-30. \n\n\n\n[8] Ansari, A.H., Alamdar, K. 2009. Reduction to the Pole of Magnetic \nAnomalies Using Analytic Signal. World Applied Sciences Journal, 7 (4), \n405-409. \n\n\n\n [9] Nabighian, M.N. 1972. The analytic signal of two-dimensional \nmagnetic bodies with polygonal cross-section: its properties and use for \nautomated anomaly interpretation. Geophysics, 37 (3), 501\u2013517.  \n\n\n\n[10] Nabighian, M.N. 1984. Toward a three-dimensional automatic \ninterpretation of potential field data via generalized Hilbert transforms: \nfundamental relations. Geophysics, 49 (6), 780-786. \n\n\n\nS/N Rock Type / \nComplex \n\n\n\nLineament/\nFault Type \n\n\n\nStructural/ \nLithological \n\n\n\ntrend \nContact Analytic Signal (Interpretation) \n\n\n\n1 Granite (G) --- NW-SE C1 \nlow to moderately high; depending on the \n\n\n\nvariation of felsic/basic mineral compositions \nwithin the magnetising bodies \n\n\n\n2 Charnockite / \nGranite (Ch/G) \n\n\n\nF4; F5 E-W; NNW-\nSSE \n\n\n\nC2 \nCharnockite (moderately high to high) while \n\n\n\nGranite is low to moderately low  \n\n\n\n3 Granite-gneiss / \ngranite (Gn/G) F2 NNE-SSW C3 \n\n\n\nlow to moderately high, depending on the \nvariation in mineralogical compositions of the \n\n\n\nmagnetising bodies \n\n\n\n4 Granite-gneiss / \nCharnockite \n(Gn/Ch) \n\n\n\nF2; F3; F   C3 moderately high to high \n\n\n\n5 \nSchist (with \n\n\n\nminor phyllite) \n\n\n\nand Quartzite \nschist (S/Qs) \n\n\n\nF1; F3; F5 NW-SE; NE-\nSW; NNW-SSE C4 \n\n\n\nlow to moderately high for schist and quartzite \n(probably due to sources with low \n\n\n\nmagnetisation peaks due to felsic minerals) \n\n\n\n6 Migmatite/Gneiss \n\n\n\n(M/Gn) \n\n\n\nF1; F3; F4  NW-SE; NE-\nSW; E-W C3 and C5 \n\n\n\nmoderately high to high; variations in signals of \nmigmatitic and gneissic rocks are evidence of \n\n\n\ndifferent peaks of magnetising bodies.  \n\n\n\n7 Migmatite (M) All present Possess all the \ntrends. It showsa \nregional trend of \n\n\n\nNW-SE and \napproximately NS \n\n\n\ndirections\n\n\n\nshared \ncontact \n\n\n\nwith all \nrock types \n\n\n\nvery high (due to high magnetisation from \nsources rich in ferromagnesian-bearing \n\n\n\nminerals). Although, some sections show \nvarying amplitude signal probably due to \nvarying strength of magnetising bodies \n\n\n\nNNE-SSW; NE-\nSW; NNW-SSE \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences 2(1) (2018) 01-08 \n\n\n\nCite the Article: Akingboye A. Sunny (2018). Derivatives And Analytic Signals: Improved Techniques For Lithostructural Classi fications.  \nMalaysian Journal of Geosciences, 2(1) : 01-08. \n\n\n\n[11] Roest, W.R., Verhoef, J., Pilkington, M. 1992. Magnetic interpretation \nusing the 3D analytic signal. Geophysics, 57 (1), 116-125. \n\n\n\n[12] Hsu, S., Sibuet, J.C., Shyu, C. 1996. High-resolution detection of \ngeologic boundaries from potential field anomalies: An enhanced analytic \nsignal technique. Geophysics, 61 (2), 373-386. \n\n\n\n[13] Hsu, S., Sibuet, J.C., Shyu, C. 1998. Depth to magnetic source using the \ngeneralized analytic signal. Geophysics, 63 (6), 1947-1957. \n\n\n\n[14] MacLeod, I.N., Jones, K., Dai, T.F. 1993. 3-D analytic signal in the \ninterpretation of total magnetic field data at low magnetic latitudes. \nExploration Geophysics, 24 (3-4), 679-688. \n\n\n\n[15] Rahaman, M.A. 1988. Recent Advances in the Study of the Basement \nComplex of Nigeria. In: Oluyide, P.O., Mbonu, W.C., Ogezi, A.E.O., Egbuniwe, \nI.G., Ajibade, A.C., Umeji, A.C. eds. Precambrian Geology of Nigeria. \nGeological Survey of Nigeria, Kaduna, 11\u201343. \n\n\n\n[16] Ademilua, O.L. 1997. A Geoelectric and Geologic Evaluation of \nGroundwater Potential of Ekiti and Ondo States, Southwestern Nigeria \n\n\n\n[M.Sc. Dissertation]. Department of Geology, Obafemi Awolowo \nUniversity, Ile-Ife, Nigeria, 1-67. \n\n\n\n[17] Oyinloye, A.O. 2011. Geology and Geotectonic Setting of the Basement \nComplex Rocks in Southwestern Nigeria: implications on Provenance and \nEvolution. Earth and Environment Science, 98 \u2013 117. \n\n\n\n[18] Ayodele, O.S. 2013. Geology and Structure of the Precambrian Rocks \nin Iworoko, Are, and Afao Area, Southwestern, Nigeria. International \nResearch Journal of Natural Sciences, 1 (1), 14 \u2013 29. \n\n\n\n[19] Geological Map of Nigeria. 2004. Nigerian Geological Survey Agency \n(NGSA), Garki, Abuja. \n\n\n\n[20] Macleod, I. A., Vieira, S., Chaves, A. C. 1993. Analytic Signal and \nReduction-to the-Pole in the interpretation of Total Magnetic Field Data \nat Low Magnetic Latitudes.3rd Conference of the Brazillian Geophysical \nSociety. pp. 831-835\n\n\n\n[21] Telford, W.M., Geldart, L.P., Sheriff, R.E., Keys, D.A. 1990. Applied \nGeophysics. 2nd ed. Cambridge University Press. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal Geosciences (MJG) 1(1) (2017) 43-49 \n\n\n\nContents List available at RAZI Publishing\n\n\n\nMalaysian Journal of Geosciences\nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/\n\n\n\nSTAKEHOLDERS\u2019 RESPONSE AND PERSPECTIVES ON FLOOD DISASTER OF PAHANG \nRIVER BASIN\nMd Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin bin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Khairul \nNizam Abdul Maulud, Maimon Abdullah, Lee Yook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan\nInstitute for Environment and Development (LESTARI),Faculty of Science and Technology (FST),Faculty of Social and Humanities \n(FSSK),\nFaculty of Engineering and Built Environment (FKAB),Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia\nCorresponding Author: Email: finazsalleh@yahoo.com\n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle history:\n\n\n\nReceived 22 January 2017  \nAccepted 03 February 2017  \nAvailable online 05 February 2017\n\n\n\nKeywords:\n\n\n\nPahang river basin, stakeholders,  \ncatchment area, flood disaster, workshop\n\n\n\nThe Pahang river basin is the largest river basin in the Pahang State, with total catchment area covering 29300km2. \nFloods of Pahang river basin have become an annual natural disaster event where all the stakeholders have their \nown responsibility and parts to take care of it. This study has focused on stakeholders\u2019 response and perspectives \nto verify the issues on flood disaster of Pahang river basin. The methodology used in this study is the stakeholders\u2019 \nconsultation workshop. This workshop was conducted by involving the stakeholders\u2019 representatives from various \nagencies. The result from this workshop has revealed the response and perspectives based on the important parts \nof each stakeholder to face the flood event that occurred in Pahang river basin. Besides, the issues aroused from \nthis workshop have shown the stakeholders\u2019 response and their perspectives on how to reduce the impacts on \nflood disaster of Pahang river basin. According to the workshop, there are two factors contribute to flood event \nwhich are the heavy rainfall and the arising of water level. The causes of these two factors are the reason that we \nneed to involve all aspects in order to reduce the impact of flood disaster. The aspects are to identify the frequent \nproblems to arise during flood event, to improvise the operating systems such as flood forecasting systems, \ntelemetric systems and hydrology system, the plans of each stakeholder on how to cooperate and reduce the impact \nas one team, to provide the proper flood maps at the study level and to review and verify what are the communities\u2019 \ncomplaints and perspectives as they also one of the victims. This study had discussed the proposed actions need to \nbe taken according to the stakeholders\u2019 response and perspectives. The overflow of river water had caused by the \nlow absorption of rainfall from forest which due to deforestation and loggings. The high water level also caused by \nthe high sedimentations which contributed by these activities. The law enforcement with more stringent need to \nbe done on these matters. Besides, the operating systems need to be improvised and added as these approaches \ncan help in reducing the impact of flood events. The flood maps should be provided at study level to identify and \nproduce a valuable case study. Stakeholders\u2019 consultations and involvement are the keys to improvise the weakness \non how to cope with the floods event from the early stage. The proposal and implementations of the development \nshould be done by involving the stakeholders\u2019 response and perspectives in any disaster.\n\n\n\nINTRODUCTION \n\n\n\nThe state of Pahang is the largest State located centrally in the eastern \nregion of Peninsular Malaysia. The Sg. Pahang, Sg. Rompin, and Sg. Kuantan \nare the three principal rivers in the State. The Pahang river basin is the \nlargest river basin in the Pahang State, with total catchment area covering \n29300km2. The length of the river is estimated to be 440 km and it is a \nconfluence of the Sg. Jelai and Sg. Tembeling from the upstream which join \ntogether at Kuala Tembeling, about 304 km from the river mouth at the \neast coast of Pahang state (Muhammad 2007). River Jelai is one of the two \nmain tributaries which drain from the eastern slope of Mountains Banjaran \nand Titiwangsa, the foot of Central mountain range. The Central Mountain \nrange is the largest mountain in the Malaysia Peninsula and separates the \nPeninsula into an eastern and western.\n\n\n\nSg. Tembeling originates from the Besar Mountain Range in the Northeast of \nthe basin. Other main tributaries of the River Pahang are Semantan, Teriang, \nBera, Lepar, Gelugor, and Chini. There are two main natural reservoir sites \nin the basin which are Lake Chini and Lake Bera. Lake Chini is surrounded \nby variously vegetated low hills and undulating lands which constitute the \nwater-shed of the lake and drains north easterly into Sg. Pahang via the \nSg. Chini (Muhammad,et al.,1998). Lake Bera is located at the southwest \nin the basin and is the larger of the two lakes via area. It is shallow and \nseasonal flowing into the River Pahang via River Bera. This lake plays an \nimportant role in flood control, water flow regulation and also provides \nnatural resources for local community. Hence, it is protected under the \ninternational RAMSAR Convention, which was declared in November 1994 \n(Takeuchi,et al 2007). However, the lake is under threat of drying up in the \nnear future as the water source disappears due to increasing conversion of \nnatural forests to palm oil plantations, excessive siltation, and soil erosion \ncaused by uncontrolled logging activities in the area (Takeuchi,et al 2007).\n\n\n\n Pahang experienced an equatorial climate with distinct wet and dry seasons, \ncharacterized mainly by the northeast monsoon which occur between \n\n\n\nNovember to January bringing heavy rainfall and floods to the region and \nhave the average humidity of about 86%. The monsoons are characterized \nby the seasonality, geographical preference and strength. This season is the \nresult of heating patterns by sun and distribution between land and ocean \n(John 1987). Pahang is rich in water resources and receive high total rainfall \nduring northeast monsoon period with almost 40 percent of total rainfall \nannually (JMM 2010). The peak flow increased rapidly in the Pahang river \nbasin because of natural land has been converted to be concrete surface \nand this phenomenon increases in the surface runoff (Muhammad 2007). \nFloods of Pahang river basin became the annual natural disaster event \nwhere all the stakeholders have their own responsibilities to take care of. \nWith the growing awareness and concern over environmental issues, it is \nimperative that water resources development must be undertaken in an \nenvironmentally sustainable manner.\n\n\n\n3.0 OBJECTIVES\nThis study has focused on stakeholders\u2019 response and perspectives to verify \nthe issues on flood disaster of Pahang river basin. Besides, the objective \nof this study also to gather information and identify the source of floods, \nissues, causes, impacts and related factors for pre and post flood events \nfrom the stakeholders.\n\n\n\n4.0 METHODOLOGY\nThe study area involved three main districts which are Jerantut, Temerloh \nand Pekan as shown in Figure 4.1. A stakeholder consultation workshop \nwas conducted at Hotel Darul Makmur, Jerantut, Pahang. This workshop had \ninvited the stakeholders from Land and District Office (PDT), Department \nof Irrigation and rainage (DID), Town and Country Planning Department \n(JPBD) and headmen from the three districts. A representative from DID \nTemerloh District was the main speaker for this workshop.\n\n\n\nCite this article as: Stakeholders\u2019 Response And Perspectives On Flood Disaster Of Pahang River Basin Md Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin Bin Mokhtar, Mohd Ekhwan Toriman, \nAhmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee Yook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Mal. J. Geo 1(1)  (2017) 43-49\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online)\n\n\n\nhttps://doi.org/10.26480/mjg.01.2017.43.49\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\n http://www.razipublishing.com/journals/galeri-warisan-sains-gws/ \n\n\nhttp://doi.org/10.26480/mjg.01.2017.43.49\n\n\nhttps://doi.org/10.26480/mjg.01.2017.43.49\n\n\n\n\n\n\nMd Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin bin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee \nYook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Malaysian Journal of Geosciences 1(1) (2017) 43-49\n\n\n\n44\n\n\n\nCite this article as: Stakeholders\u2019 Response And Perspectives On Flood Disaster Of Pahang River Basin Md Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin Bin Mokhtar, Mohd Ekhwan Toriman, \nAhmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee Yook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Mal. J. Geo 1(1)  (2017) 43-49\n\n\n\n\n\n\n\nThe result of this workshop has revealed the response and perspectives \nbased on the important parts of each stakeholder to face the flood event \nthat occurred in Pahang river basin. Besides, the issues raised from the \nflood event have been discussed. According to the stakeholders, the main \nsources of flood event are extreme and heavy rainfall triggered by the \nnortheast monsoon and resulted to higher river flow started from the water \ndischarged at the upstream and flows into Pahang river basin. This situation \nfinally contributed to serious flood events at Pahang River Basin (DID 2005 \n& DID 2009).\n\n\n\nHowever, not only the natural climatic condition like heavy rainfall had \ncaused the flood event in year 2014 much worse than previous years. The \nworse impacts also caused by the human activities in the form of exploitation \nof natural resources and developments are always the external factors which \naffect and increase the river dynamic process. These changes may continue \nto river degradation (Jackson et al., 1995). The river would involve in the \nprocess of evolution due to its dynamic system (Camporeale et al., 2007; \nRobert, 2003). This is due to river water overflow from low absorption of \nrainfall from forest which caused by developments, deforestation, loggings \nand contributes in high sedimentation. The law enforcement with more \nstringent need to be done to avoid the worst flood event in the future\n\n\n\nOther issues have been discussed in the workshop included the operating \nsystems to function well before, during and after the flood event, the \nintegrated river basin management among agencies, provide the proper \nflood maps at the study level and review the communities\u2019 complaints \nand hopes to avoid the worse flood impact. The Graphs 5.1 to 5.4 have \nshown the results of the questionnaires distributed during stakeholders\u2019 \nconsultation workshop. The management and operating systems are \ninvolving the telemetry system, hydrological system and forecasting \n\n\n\nsystem. The problems related to operating system are such the telemetry \nstations couldn\u2019t function well due to unstable water level reading. This \nsituation also caused by no power supply and communication fail was not \nfunctioning. The stakeholders also suggested and planned to improve the \ndamaged hydrology system.  Most of the gauge and rainfall stations were \nflooded and caused damages to the system.  Rivers and tributaries were not \ncleaned and organized properly. This matter was contributing to blockage of \nthe flow of water. The main issues raised in the workshop and stakeholders \ncomplaints and hopes were summarized in Figure 5.1 and 5.2 respectively.\n\n\n\nThis workshop also was conducted to introduce the Integrated River \nBasin Management (IRBM) to reduce the impact without overlapping \npower and jurisdiction. In order to raise awareness and the involvement \nof all stakeholders in Pahang River Basin, the stakeholders consultation \nworkshop is the approach method should be discussed in reducing the \nimpact of flood events. Integrated River Basin Management (IRBM) is the \nconcept to gain awareness from each stakeholder and to be alert their own \nroles in protecting the Pahang River Basin. The list of stakeholders and their \nroles are shown in Table 5.3.\n\n\n\n\n\n\n\n\nMd Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin bin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee \nYook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Malaysian Journal of Geosciences 1(1) (2017) 43-49\n\n\n\n45\n\n\n\nCite this article as: Stakeholders\u2019 Response And Perspectives On Flood Disaster Of Pahang River Basin Md Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin Bin Mokhtar, Mohd Ekhwan Toriman, \nAhmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee Yook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Mal. J. Geo 1(1)  (2017) 43-49\n\n\n\nFigure 5 Location on the stereonet of wedge weight, W; the normal Ni, NA \nand NB and angle to the normal, \u03b2i (\u03b4i), \u03b4A and \u03b4B (Kliche, 1999). The resisting force on plane A and B according to Mohr-Coulomb criterion \n\n\n\n\n\n\n\n\nMd Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin bin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee \nYook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Malaysian Journal of Geosciences 1(1) (2017) 43-49\n\n\n\n46\n\n\n\nCite this article as: Stakeholders\u2019 Response And Perspectives On Flood Disaster Of Pahang River Basin Md Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin Bin Mokhtar, Mohd Ekhwan Toriman, \nAhmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee Yook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Mal. J. Geo 1(1)  (2017) 43-49\n\n\n\n\n\n\n\n\nMd Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin bin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee \nYook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Malaysian Journal of Geosciences 1(1) (2017) 43-49\n\n\n\n47\n\n\n\nCite this article as: Stakeholders\u2019 Response And Perspectives On Flood Disaster Of Pahang River Basin Md Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin Bin Mokhtar, Mohd Ekhwan Toriman, \nAhmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee Yook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Mal. J. Geo 1(1)  (2017) 43-49\n\n\n\n\n\n\n\n\nMd Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin bin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee \nYook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Malaysian Journal of Geosciences 1(1) (2017) 43-49\n\n\n\n48\n\n\n\nCite this article as: Stakeholders\u2019 Response And Perspectives On Flood Disaster Of Pahang River Basin Md Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin Bin Mokhtar, Mohd Ekhwan Toriman, \nAhmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee Yook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Mal. J. Geo 1(1)  (2017) 43-49\n\n\n\n\n\n\n\n\nMd Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin bin Mokhtar, Mohd Ekhwan Toriman, Ahmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee \nYook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Malaysian Journal of Geosciences 1(1) (2017) 43-49\n\n\n\n49\n\n\n\nCite this article as: Stakeholders\u2019 Response And Perspectives On Flood Disaster Of Pahang River Basin Md Pauzi Abdullah, Syafinaz Salleh, Rahmah Elfithri, Mazlin Bin Mokhtar, Mohd Ekhwan Toriman, \nAhmad Fuad Embi, Khairul Nizam Abdul Maulud, Maimon Abdullah, Lee Yook Heng, Syamimi Halimshah, Maizura Maizan, Nurlina Mohamad Ramzan / Mal. J. Geo 1(1)  (2017) 43-49\n\n\n\nCONCLUSIONS\n\n\n\nAlthough the rainfall is the main natural factor that given impact to the \nchanges of Pahang River, but anthropogenic factor always considered as \nthe cruel factor that causes and worsen the whole natural scenario into \nmore complicated way (Pan Ia Lun 2011). The high rainfall intensity cannot \nbe controlled but other possible flood factors which contribute to higher \nmagnitude of flood can be controlled especially development in sensitive \narea. All agencies should give high cooperation in playing their roles in order \nto preserve and maintain the river while considering all factors involved. \nThe stakeholders\u2019 response and perspectives are the keys to improve \nthe weakness to face the flood of Pahang River Basin.  IRBM is a concept \nof a management field which comprise all factors with considering the \nenvironmental resources, socio-economic and the institutional frameworks. \nThus the Pahang River Basin can be well preserved\n\n\n\n7.0 ACKNOWLEDGEMENT\nThis study is supported by Transdisciplinery Research Grant Scheme \n(TRGS/1/2015/UKM/01/1/1) funded by Ministry of Higher Education, \nMalaysia\n\n\n\nREFERENCES \n\n\n\nPan Ia Lun, Muhd. Barzani Gasim, Mohd. Ekhwan Toriman, Sahibin Abd. \nRahim & Khairul Amri Kamaruddin. 2011. Hydrological Pattern of Pahang \nRiver Basin and Their Relation to Flood Historical Event  Volume 6, Number \n1, 29-37, 2011.  ISSN: 1823-884x\n\n\n\nJohn, A. Y. 1987. Physics of monsoons: The current view. In Fein, J.S. & \nStephens, P.L. Eds.).Monsoons. New York: John Wiley & Sons. pp. 211-243.\nDepartment of Irrigation and Drainage (DID). 2005. Annual Flooding Report \nof Pahang State 2005. Department of Irrigation and Drainage Malaysia (DID \nMalaysia).\nDepartment of Irrigation and Drainage (DID). 2009. Annual Flooding Report \nof Pahang State 2009. Department of Irrigation and Drainage Malaysia (DID \nMalaysia). \n\n\n\nJabatan Meteorologi Malaysia (JMM). 2010. Monsun. Portal Rasmi Jabatan \nMeteorologi Malaysia.http://www.met.gov.my/index.php?option=%20\ncom_content&task=view&id=69&Itemid=160&limit=1&limitstart=0. \nJabatan Meteorologi Malaysia.\n\n\n\nCamporeale, C., Perona, P., Porporato, A., and Ridolfi. L.2007. The hierarchy \nof models for meandering rivers and related morphodynamic processes. \nReviews of Geophysics 45(1): RG1001.\nRobert, A. 2003. River processes: An introduction to fluvial dynamics. \nLondon: Arnold.\nJackson, L.L., Lopoukhine, N. and Hillyard, D. 1995. Ecological restoration: A \ndefinition and comments. Restoration Ecology 3: 71-75\nHydrology and Water Quality Assessment of the Lake Chini\u2019s Feeder Rivers, \nPahang, Faculty of Science and Techinology, School of Environment and \nNat-ural Resource Science, University of Kebangsaan Malaysia. Available at: \nwww.idosi.org/aejaes/jaes2(1)/6.pdf\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal Geosciences (MJG) 1(1) (2017) 01-06 \n\n\n\nContents List available at RAZI Publishing\n\n\n\nMalaysian Journal of Geosciences\nJournal Homepage:http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/\n\n\n\nFlood Potential Analysis (FPAn) using Geo-Spatial Data in Penampang area, Sabah\nRodeano Roslee*1, Felix Tongkul1, Norbert Simon2 & Mohd. Norazman Norhisham1 \n1Geology Programme, Faculty of Science and Natural Resources, University Malaysia Sabah,UMS Road, 88400 Kota Kinabalu, Sabah, Malaysia\n2Department of Geology, Faculty of Science and Technology, University Kebangsaan Malaysia, 43600 Bangi, Selangor\n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle history:\n\n\n\nReceived 22 January 2017  \nAccepted 03 February 2017  \nAvailable online 05 February 2017\n\n\n\nKeywords:\n\n\n\nFlood Potential Analysis (FPAn) \nMulti-Criteria Evaluation (MCE) Sabah, \nMalaysia\n\n\n\nFlooding is one of the major natural disasters in Sabah, Malaysia. Several recent cases of catastrophic flooding were \nrecorded especially in Penampang area, Sabah (e.g. July 1999; October 2010; April 2013; October & December \n2014). Heavy monsoon rainfall has triggered floods and caused great damage in Penampang area. The 2014 floods \nhas affected 40,000 people from 70 villages. The main objective of this study are to analysis the Flood Potential \nLevel (FPL) in the study area. In this study, eigth (8) parameters were considered in relation to the causative factors \nto flooding, which are: rainfall, slope gradient, elevation, drainage density, landuse, soil textures, slope curvatures \nand flow accumulation. Flood Potential Analysis (FPAn) map were produced based on the data collected from the \nfield survey, laboratory analysis, high resolution digital radar images (IFSAR) acquisation, and secondary data. FPL \nwere defined using Multi Criteria Evaluation (MCE) technique integrated with GIS software. The information from \nthis paper can contribute to better management of flood disaster in this study area.\n\n\n\n1. Introduction\n\n\n\nThe Penampang District of Sabah, East Malaysia (Fig. 1) is subjected to \ndevelopment pressure as the urban centre of Kota Kinabalu expands \nonto the Sungai Moyog floodplain. The subsequent transition of land use \nfrom rural development and cultivation of rice paddy to intensive urban \ndevelopment presents a range of social and environmental issues. Of \nparticular concern to the area are the issues associated with flooding.  \n\n\n\nIn 2014 from October 7 to October 10, Penampang suffered its worse flood \never, since the last big flood in 1991 (Figs. 2 & 3). According to the District \nOfficer of Penampang as many as 40,000 people from 70 villages were \naffected by the flood. The flood coincided with continuous heavy rainfall due \nto typhoon Phanfone and typhoon Vongfong.  Another recent flood disaster \nin Penampang occurred on September 2007 and May 2013, affecting several \nvillages (Fig. 3).\n\n\n\nFigure 1: Location of the study area\n\n\n\n*Corresponding Author \nEmail Address: rodeano@ums.edu.my (Rodeano Roslee)\n\n\n\nFigure 2: Some cases of flash flood in Penampang, Sabah (Sources: Pejabat \nDaerah Penampang)\n\n\n\n Figure 3: Daily recorded rainfall of Babagon Agriculture Station from year \nAugust 2002 \u2013 May 2015 (Sources: Department of Drainage and Irrigation).\n\n\n\nThe main objectives of this study are to analysis the Flood Potential Level \n(FPL) in the study area. It is hopes that the outcomes from this study can be \nan important reference document for the local authority and other relevant \nagencies for the purpose of urban planning and flood mitigation.\nAn ad hoc, or reactive, approach to floodplain management has previously \nbeen standard practice. Insufficient control over floodplain development \npractice has led to a worsening of the flood problem. Until recently, \nfloodplain management has only involved structural approaches to \nmodifying flood behaviour. However, without planning, the structural \nflood modification only compensates for the poor development practice by \nrestoring the flood behaviour to pre-development conditions. Ultimately, \nthere is no net benefit. 1\n\n\n\nISSN: 2521-0920  (Print) \nISSN:  2521-0602 (Online)\n\n\n\nCite this article as: Flood Potential Analysis (FPAn) using Geo-Spatial Data in Penampang area, Sabah. Rodeano Roslee, Felix Tongkul, Norbert Simon & \nMohd. Norazman Norhisham /  Mal. J. Geo 1(1) (2017) 01-06\n\n\n\nhttps://doi.org/10.26480/mjg.01.2017.01.06\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\n http://www.razipublishing.com/journals/galeri-warisan-sains-gws/ \n\n\nhttp://doi.org/10.26480/mjg.01.2017.01.06\n\n\nhttps://doi.org/10.26480/mjg.01.2017.01.06\n\n\n\n\n\n\nRodeano Roslee, Felix Tongkul, Norbert Simon & Mohd. Norazman Norhisham / Malaysian Journal of Geosciences 1(1) (2017) 01\u201306 2\n\n\n\nCite this article as: Flood Potential Analysis (FPAn) using Geo-Spatial Data in Penampang area, Sabah. Rodeano Roslee, Felix Tongkul, Norbert Simon & \nMohd. Norazman Norhisham /  Mal. J. Geo 1(1) (2017) 01-06\n\n\n\nAn ad hoc, or reactive, approach to floodplain management has previously \nbeen standard practice. Insufficient control over floodplain development \npractice has led to a worsening of the flood problem. Until recently, \nfloodplain management has only involved structural approaches to \nmodifying flood behaviour. However, without planning, the structural \nflood modification only compensates for the poor development practice by \nrestoring the flood behaviour to pre-development conditions. Ultimately, \nthere is no net benefit. 1 \nIn the recent years, there have been many studies on flood susceptibility/\nhazard/risk mapping using GIS tools2-4  and many of these studies have \napplied using probabilistic methods.5-9  In different ways, hydrological and \nstochastic rainfall method has also been employed in other areas.10-16  \nLikewise neural network methods have been applied in various case \nstudies.17-29\n\n\n\nDetermining the flood susceptible/vulnerable areas is very important \nto decision makers for planning and management of activities. Decision \nmaking is actually a choice or selection of alternative course of action in \nmany fields, both the social and natural sciences. The inevitable problems \nin these fields necessitated a detailed analysis considering a large number \nof different criteria. All these criteria need to be evaluated for decision \nanalysis.30-34  For instance, Multi Criteria Evaluation (MCE) methods has \nbeen applied in several studies since 80% of data used by decision makers \nare related geographically.35-36  Geographic Information System (GIS) \nprovides more and better information for decision making situations. \nIt allows the decision makers to identify a list, meeting a predefined \nset of criteria with the overlay process,37-38  and the multi-criteria \ndecision analysis within GIS is used to develop and evaluate alternative \nplans that may facilitate compromise among interested parties.39 \n \n2. Materials and Method\nFig. 4 shows the framework model used in this study. There are \nthree (3) main phases involved, namely: a) Phase I: Selection \nand evaluation of criteria; b) Phase II: Multi-Criteria Evaluation \n(MCE); and c) Phase III: Flood Potential Analysis (FPAn). \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n \nFigure 4: Framework model of integrating spatial analysis with Multi-\nCriteria Evaluation (MCE) for Flood Potential Analysis (FPAn)\n\n\n\n2.1 Selection and evaluation of criteria\nThe main purpose in Phase I are database development. Firstly, soil samples \nwere collected from the field will be analyzed their types in accordance with \nBS1377-1990. The next step is secondary data compilation and literature \nreview.  Lastly observation of Flood Hazard Identification (FHI) parameters \nwas conducted through the fieldwork study (Fig. 4)\n\n\n\n2.2 Multi-Criteria Evaluation (MCE) technique\nIn Phase II, the choice of criterions that has a spatial reference is an important \nand profound step in Multi-Criteria Evaluation (MCE) technique. Hence, the \ncriteria consider in this study is based on their significance in causing flood \nin the study area. Eigth factors are considered in relation to the causative \nfactors, which are rainfall, slope gradient, topography, drainage density, \nlanduse, soil textures, slope curvatures and flow accumulation (Fig. 4). \nSeveral questionnaires were distributed among experts in hydrology and \nhydraulics. The inputs obtained from those experts were further used in \ncarrying out the pair-wise comparison technique in order to calculate \nthe weights of each criterion. Pair-wise comparison is more appropriate \nif accuracy and theoretical foundations are the main concern.40  The \ntechnique involves the comparison of the criteria and as allows one to \ncompare the importance of two criteria at a time. This very technique, \nwhich was proposed and developed by 41 within the framework of a \n\n\n\ndecision making process known as Analytical Hierarchy Process (AHP) is \ncapable of converting subjective assessments of relative importance into a \nlinear set of weights. The criterion pair-wise comparison matrix takes the \npair-wise comparisons as an input and produces the relative weights as \noutput. Further the AHP provides a mathematical method of translating this \nmatrix into a vector of relative weights for the criteria. Moreover, because \nof the reason that individual judgments will never be agreed perfectly, the \ndegree of consistency achieved in the ratings is measured by a Consistency \nRatio (CR) indicating the probability that the matrix ratings were randomly \ngenerated. The rule-of-thumb is that a CR less than or equal to 0.10 signifies \nan acceptable reciprocal matrix, and ratio over 0.10 implies that the matrix \nshould be revised, in other words it is not acceptable. 41-42\n\n\n\n2.3 Flood Potential Analysis (FPAn)\n\n\n\nThe initial step in Phase III is the delineation and conversion processes of \ndata from the radar images (IFSAR). Phase III also covers the integration \nbetween criteria weights and maps, producing a Flood Potential Analysis \n(FPAn) using spatial analyst, which determine the Flood Potential Level \n(FPL). \nAll of the thematic maps produced were analyzed through the spatial \nanalyst technique (raster calculator) based on Eq. (1) for LSL estimation \nand classification (Tab. 1). The FPL calculation was carried out through a \ncombination of input parametric maps in Eq. (1) with the GIS operations \nusing a grid base.\n\n\n\n[(32.53*Rainfall) + (22.74*Drainage Density) + (15.84*Flow Accumulation) \n+ (11.08*Landuse) + (7.19*Elevation) + (4.89*Slope Gradient) + (3.35*Soil \nTextures) + (2.38*Slope Curvatures)]                                                                        (1)\n\n\n\n\n\n\n\n\nRodeano Roslee, Felix Tongkul, Norbert Simon & Mohd. Norazman Norhisham / Malaysian Journal of Geosciences 1(1) (2017) 01\u201306 3\n\n\n\nCite this article as: Flood Potential Analysis (FPAn) using Geo-Spatial Data in Penampang area, Sabah. Rodeano Roslee, Felix Tongkul, Norbert Simon & \nMohd. Norazman Norhisham /  Mal. J. Geo 1(1) (2017) 01-06\n\n\n\n3. Materials and Method\n\n\n\n3.1 Rainfall\nHeavy rainfalls are one of the major causes of floods. Flooding occurs most \ncommonly from heavy rainfall when natural watercourses do not have \nthe capacity to convey excess water. Floods are associated with extremes \nin rainfall, any water that cannot immediately seep into the ground flows \ndown slope as runoff. The amount of runoff is related to the amount of rain \na region experiences. The level of water in rivers rises due to heavy rainfalls. \nWhen the level of water rises above the river banks or dams, the water starts \noverflowing, hence causing river based floods. The water overflows to the \nareas adjoining to the rivers or dams, causing floods.38 \nIn the study, a rainfall map was developed based on the daily rainfall values \n(short-term intensity rainfall) for the study area (Figs. 3 & 5). Based on \nthe information obtained from the Metrology Department of Malaysia \n(MetMalaysia) and the Sabah Department of Irrigation and Drainage (DID), \na total of four (4) stations were identified, i.e. the Ulu Moyog station, Inanam \nstation, Kota Kinabalu International Airport (KKIA) station and Babagon \nstation. \nA mean annual rainfall for fourteen (14) years (2002\u20132015) was considered \nand interpolated using Inverse Distance Weighting (IDW) to create a \ncontinuous raster rainfall data within and around municipality boundary. \nThe resulting raster layer was finally reclassified into the five classes using \nan equal interval. The reclassified rainfall was given a value < 40 mm \n(weighted = 0.0624) for least rainfall to > 300 mm (weighted = 0.4162) for \nhighest rainfall (Tab. 1 & Fig. 5).\n\n\n\nFigure 5: Rainfall map\n \n3.2 Drainage density\nDrainage is an important ecosystem controlling the hazards as its densities \ndenote the nature of the soil and its geotechnical properties. This means \nthat the higher the density, the higher the catchment area is susceptible to \nerosion, resulting in sedimentation at the lower grounds.38 The first step in \nthe quantitative FSAn is designation of stream order. The Stream ordering in \nthe present study area was done using the method proposed by. 43  \nDrainage density map could be derived from the drainage map. i.e.,drainage\nmap is overlaid on watershed map to find out the ratio of total length of \nstreams in the watershed to total area of watershed and is categorized. \nThe drainage density of the watershed is calculated as: D=L/A, where, D \n= drainage density of watershed; L = total length of drainage channel in \nwatershed (km); A  = total area of watershed (km2). For the study area, \nhigher weighted value (0.4162) were assigned to poor drainage density \nareas and lower weighted value (0.0624) were assigned to areas with \nadequate drainage.  The drainage density layer were reclassified in five \nsub-groups using the standard classification Schemes. Areas with very low \ndrainage density are > 200 mm and those with very high drainage density \nwith value of < 50 mm as depicted in the results Table 1 and Figure 6\n \n\n\n\n\n\n\n\nFigure 6: Drainage density map\n\n\n\n3.3 Flow accumulation\n\n\n\nFlow accumulation is where water accumulates from precipitation with \nsinks being filled. From the flow accumulation of the study area, two (2) \nmain rivers in the study area were derived: Moyog, and Babagon Rivers \n(Fig. 7). For the study area, higher weighted value (0.3612) were assigned \nas highest flow accumulation areas and lower weighted value (0.1238) \nwere assigned as lowest flow accumulation.  The flow accumulation \nlayer were reclassified in five sub-groups using the standard classification \nSchemes (very low to very high as shown in the results Table 1 and Figure 7 \n\n\n\nFigure 7: Flow accumulation map\n\n\n\n3.4 Landuse\n\n\n\nThe land-use of an area is also one of the primary concerns in FSAn \nbecause this is one factor which not only reflects the current use of the \nland, pattern and type of its use but also in relation to infiltration. Land-\ncover like vegetation cover, whether that is permanent grassland or \nthe cover of other crops, has an important impact on the ability of the \nsoil to act as a water store.38 Impermeable surfaces such as concrete, \nabsorbs almost no water at all. Land-use like buildings and roads, \ndecreases penetration capacity of the soil and increases the water \nrunoff. Land-use types work as resistant covers and decrease the water \nhold up time; and typically, it increases the peak discharge of water that \nenhances a fastidious flooding. This implies that land-use and land-\ncover are crucial factors in determining the probabilities of flood.38 & 39 \n\n\n\nIn this study area, land use map shows a few sectors such as the residential \nsector, commercial sector, public infrastructure sector, the industrial sector,\nthe higher education institutions and schools sector, and the agriculture, \nforestry and others sector (Fig. 8 & Tab. 1). Based on the results of the GIS \nspatial analyst conducted, it was found that the agriculture, forestry and \nothers sector cover the widest area in the study area (53.92%). This was \nfollowed by the residential sector (32.98%), the commercial sector (6.00%), \nwater body (2.34%), the higher education institutions and schools sector \n(2.27%), the industrial sector (1.68%), and the public infrastructure sector \n(0.82%). In terms of the progress of the diversity of land use, this means \nthat the study area has been explored for more than 70% as a whole for \ndevelopment and agricultural activities. Exploration mass without control/\nenforcement of the activities of slope cutting can trigger the occurrence of \nflash flood.\n\n\n\nFigure 8: Landuse map\n\n\n\n3.5 Elevation\n\n\n\nA digital elevation model (DEM) of the slope conditions provided by raster \ndatasets on morphometric features (altitude, internal relief, slope angle, \naspect, longitudinal and transverse slope curvature and slope roughness) \nand on hydrologic parameters (watershed area, drainage density, drainage \n\n\n\n\n\n\n\n\nRodeano Roslee, Felix Tongkul, Norbert Simon & Mohd. Norazman Norhisham / Malaysian Journal of Geosciences 1(1) (2017) 01\u201306 4\n\n\n\nCite this article as: Flood Potential Analysis (FPAn) using Geo-Spatial Data in Penampang area, Sabah. Rodeano Roslee, Felix Tongkul, Norbert Simon & \nMohd. Norazman Norhisham /  Mal. J. Geo 1(1) (2017) 01-06\n\n\n\nnetwork order, channel length, etc.) were automatically extracted from the \nDEM (Fig. 9). In addition, the slope angle is also considered as an index of \nslope stability caused by the presence of a digital elevation model (DEM) \nwhich is evaluated numerically and is illustrated by the spatial analysts. 44 \n \nThe elevation of topography in the study area can be divided into three \nmain areas: lowland areas (<10 m), moderately highland areas (11-30 \nm) and hilly areas (> 30 m) (Fig. 9 & Tab. 1). Almost 16.01% of the study \narea consists of lowland areas (<10 m). Lowland areas were concentrated \nin the southwestern and northern parts of the study area with little hills. \nThis region includes the alluvial plains and areas which have undergone \na process of cut and fill slopes activities for urbanization, housing, \nmanufacturing and other infrastructure construction. From the satellite \nimages observations, lowland areas have brighter tone, incorporeal arise \nand flat. The directional trend of lineaments is northeast-southwest. Short \nand intermittent drainages often found in lowland areas and mostly dried \nduring the dried season. In lowland areas also have several small lakes such \nTaman Tuan Fuad and Bukit Padang area. \nModerately highland areas (11-30 m) covered about 42.38% of the entire \nstudy area (Fig. 9). It is located in the northeastern and southwestern \nparts of the study area. Moderately highland areas most widespread has \nchanged from its original height due to the activities of urbanization. From \nthe satellite images observations, moderately highland areas have medium \ndark tone, incorporeal arise with lineaments trends at northeast-southwest. \nModerately highland areas were produced by the process of adoption or \nfolding of the Crocker Formation. In this area there are many rivers flowing \nalong the valley.\nHilly areas (> 30 m) that extends in the northwestern and southeastern \nparts covered about 41.60% of the entire study area (Fig. 9). This area is \npart of the Crocker range that forms a ridge nearly parallel to the strike \nof the bedding planes of the Crocker Formation sedimentary rocks in the \nnortheast-southwest. There are several residential areas (villages) built in \nthis area. Infrastructure and utilities are very limited and not as good as \nlowland or moderately highland areas.\n\n\n\nFigure 9: Elevation map\n\n\n\n3.6 Slope Gradient\n\n\n\nElevation and slope play an important role in governing the stability of a \nterrain. The slope influences the direction of and amount of surface runoff \nor subsurface drainage reaching a site. Slope has a dominant effect on the \ncontribution of rainfall to stream flow. It controls the duration of overland \nflow, infiltration and subsurface flow. Combination of the slope angles \nbasically defines the form of the slope and its relationship with the lithology, \nstructure, type of soil, and the drainage. Steeper slopes are more susceptible \nto surface runoff, while flat terrains are susceptible to water logging. Low \ngradient slopes are highly vulnerable to flood occurrences compared to high \ngradient slopes.38\nIn terms of slope gradient in the study area, the results suggest that 48.37% \nof the area can be categorized as 0o - 5o, 28.45% as a 6o - 15o, 22.41% as \n16o - 30o, 0.75% as 31o - 60o and 0.01% in excess of 60o  (Fig. 10 & Tab. 1). \nRain or excessive water from the river always gathers in an area where the \nslope gradient is usually low. Areas with high slope gradients do not permit \nthe water to accumulate and result into flooding. If the main concern is river \ncaused flood, elevation difference of the various DEM cells from the river \ncould be considered, whereas for pluvial flood local depressions, i.e., DEM \ncells with lower elevation than the surrounding would be more important. \nThis implies that the way in which elevation could be associated with risk \nis important.\n\n\n\nFigure 10: Slope gradient map\n\n\n\n3.7 Soil Textures\nInformation on soil types explaining the diversity of physical characteristics \nfor unconsolidated deposition and weathering production. Soil texture and \nmoisture are the most important components and characteristics of soils. \nSoil textures have a great impact on flooding because sandy soil absorbs \nwater soon and few runoffs occurs. On the other hand, the clay soils are \nless porous and hold water longer than sandy soils. This implies that areas \ncharacterized by clay soils are more affected by flooding.\nBased on the soil types map derived from the Agriculture Department of \nSabah (JPNS), the soils association in the study area can be grouped into ten \n(10) categories, namely the Weston association (very silty sand textured, \nSM) (5.47%), the Tanjung Aru association (sand with little silty textured, \nSW) (2.98%), the Tuaran association (very silty sand textured, SM) (2.03%), \nthe Kinabatangan association (very clayey sand textured, SC) (1.28%), the \nSapi Figure 10: Slope gradient map\n\n\n\n3.7 Soil Textures\nInformation on soil types explaining the diversity of physical characteristics \nfor unconsolidated deposition and weathering production. Soil texture and \nmoisture are the most important components and characteristics of soils. \nSoil textures have a great impact on flooding because sandy soil absorbs \nwater soon and few runoffs occurs. On the other hand, the clay soils are \nless porous and hold water longer than sandy soils. This implies that areas \ncharacterized by clay soils are more affected by flooding.\nBased on the soil types map derived from the Agriculture Department of \nSabah (JPNS), the soils association in the study area can be grouped into ten \n(10) categories, namely the Weston association (very silty sand textured, \nSM) (5.47%), the Tanjung Aru association (sand with little silty textured, \nSW) (2.98%), the Tuaran association (very silty sand textured, SM) (2.03%), \nthe Kinabatangan association (very clayey sand textured, SC) (1.28%), the \nSapi association (peat textured, Pt) (1.28%), the Klias association (organic \ntextured, O) (1.69%), the Brantian association (clay textured, C) (1.07%), \nthe Dalit association (very clayey sand textured, SC) (8.89%), the Lokan \nassociation (very silty sand textured, SM) (26.23%), and the Crocker \nassociation (clayey sand textured, S-C) (49.07%) (Fig. 11 & Tab. 1).\nThe soil types in an area is important as they control the amount of water \nthat can infiltrate into the ground, and hence the amount of water which \nbecomes flow.45  The structure and infiltration capacity of soils will also \nhave an important impact on the efficiency of the soil  to act as a sponge \nand soak up water. Different types of soils have differing capacities. The \nchance of flood hazard increases with decrease in soil infiltration capacity, \nwhich causes increase in surface runoff. When water is supplied at a rate \nthat exceeds the soil\u2019s infiltration capacity, it moves down slope as runoff on \n\n\n\nsloping land, and can lead to flooding.46\n\n\n\nFigure 11: Soil textures map\n\n\n\n\n\n\n\n\nRodeano Roslee, Felix Tongkul, Norbert Simon & Mohd. Norazman Norhisham / Malaysian Journal of Geosciences 1(1) (2017) 01\u201306 5\n\n\n\nCite this article as: Flood Potential Analysis (FPAn) using Geo-Spatial Data in Penampang area, Sabah. Rodeano Roslee, Felix Tongkul, Norbert Simon & \nMohd. Norazman Norhisham /  Mal. J. Geo 1(1) (2017) 01-06\n\n\n\n3.8 Slope curvatures\n\n\n\nSlope shape has a strong influence on flood occurrences in by concentrating \nor dispersing surface and primarily subsurface water in the landscape. \nThere are three basic slope curvature units: convex, straight and concave. \nConvex landform is most stable in steep terrain, followed by concave \nhillslope segment and straight hillslope (least stable). The main reason \nis related to landform structure affecting largely the concentration or \ndispersion of surface and subsurface water. Convex and concave hillslopes \ntend to concentrate subsurface water into small areas of the slope, thereby \ngenerating rapid pore water pressure increase during storms or periods of \nrainfall. Whereas a straight hillslope /flat surface that allows the water to \nflow quickly is a disadvantage and causes flooding, whereas a higher surface \nroughness can slow down the flood response.\nIn this study, the slope curvatures map (Fig. 12) was prepared using the \ndigital elevation model (DEM) and surface analysis tools in ArcGIS software. \nThe slope curvatures classes having less values was assigned higher \nweighted value due to almost flat terrain while the class having maximum \nvalue was categorized as lower weighted value due to relatively high run-off \n(Fig. 12 & Tab. 1). Most of the entire flooding area lies in a straight or flat \nelevation. This implies that slope curvatures may not be the predominant \nfactor in ranking FPL classes.\n \n3.9 Flood Potential Level (FPL)\n\n\n\nIn terms of Flood Potential Level (FPL), of the results of the analyses for \nthe Kota Kinabalu area suggest that 40.49% of the area can be categorised \nas having very low susceptibility (VLS), 35.08% as low susceptibility (LS), \n18.21% as moderate susceptibility (MS), 5.50% as high susceptibility (HS) \nand 0.71% as very high susceptibility (VHS (Fig. 13). In general, the VLS to \nLS areas refer to stable conditions from flood vulnerability/risk. In contrast, \nMS to HS areas are basically not recommended to be developed due to high \nflood vulnerability/risk. However, if there is no choice or the developer or \nthe local authorities really want to develop these areas, some mitigation \nprocedures to be introduced. VHS areas are strictly not recommended to be \ndeveloped and provisions for suitable structural and non-structural works \nplanning controlare recommended.\n\n\n\nFigure 12: Slope curvatures map\n\n\n\n4. Conclusion\n\n\n\nThe results of this study indicate that the integration of MCE and GIS \ntechniques provides a powerful tool for decision making procedures in FPL \nmapping, as it allows a coherent and efficient use of spatial data. The use of \nMCE for different factors is also demonstrated to be useful in the definition \nof the risk areas for the flood mapping and possible prediction. In overall, \nthe case study results show that the GIS-MCE based category model is \neffective in flood risk zonation and management.\nThe developed framework model (Fig. 4) will be a very valuable resource for \nconsulting, planning agencies and local governments in managing hazard/\nrisk, land-use zoning, damage estimates, good governance and remediation \nefforts to mitigate risks. Moreover, the technique applied in this study can \neasily be extended to other areas, where other factors may be considered, \ndepending on the availability of data.\nRecognition that unplanned and uncontrolled development can increase the \nrisk to life and damage to property is fundamental to successful floodplain \nmanagement. Awareness of this issue is not just the responsibility of the \nlocal authorities, but all stakeholders, covering both public and private \nsectors. Whilst the land developer has the social responsibility for flood \ncompatible development, the approving agencies share a portion of that \nresponsibility through effective floodplain management, excised in a \ntransparent, impartial manner.\n\n\n\nFigure 13: Flood Potential Level (FPL) map of study area\n\n\n\n5. Acknowledgement\n\n\n\nDeep gratitude to Universiti Malaysia Sabah (UMS) for providing easy access \nto laboratories and research equipment. Highest appreciations also to the \nMinistry of Higher Education of Malaysia (MOHE) for the fundamental\nresearch grant award (FRG0410-STWN-1/2015) to finance all the costs of \nthis research.\n\n\n\n6. References\n\n\n\n[1] B. Caddis, W. Hong,  C. Nielsen. 2011. The Challenges of Floodplain \nManagement in Malaysian Borneo. 2011 Floodplain Management \nConference, Tamworth, NSW.  22-25 February 2011. 1-11.\n[2] L. L. Hess, J. M. Melack, D. S. Simonett. 1990. Radar Detection of \nFlooding Beneath the Forest Canopy: A Review. International Journal of \nRemote Sensing. 11: 1313-1325. \n[3] L. L. Hess, J. Melack, S. Filoso, Y. Wang. 1995. Delineation of \nInundated Area and Vegetation along the Amazon Floodplain with the SIR-C \nSynthetic Aperture Radar. IEEE Transactions on Geoscience and Remote \nSensing. 33: 896-903. \n[4] T. Le Toan, F. Ribbes, L. F. Wange, N. Floury, N. Ding, K. H. Kong. \n1997. Rice Crop Mapping and Monitoring using ERS-1 Data based on \nExperiment and Modeling Results. IEEE Transactions on Geoscience and \nRemote Sensing. 35: 41-56.\n[5] S. Landau, R. A. C. Mitchell, V. Barnett, J. J. Colls, J. Craigon, R. W.  \nPayne. 2000. A Parsimonious, Multiple-Regression Model of Wheat Yield \nResponse to Environment. Agricultural and Forest Meteorology. 101 (2-3): \n151-166. Elsevier Publi., USA. \n[6] M. Farajzadeh. 2001. The Flood Modeling using Multiple \nRegression Analysis in Zohre & Khyrabad Basins. 5th International \nConference of Geomorphology. Tokyo, Japan. August.\n[7] M. Farajzadeh. 2002. Flood Susceptibility Zonation of Drainage \nBasins using Remote Sensing and GIS, Case Study Area: Gaveh Rod_ Iran. \nProceeding of International Symposium on Geographic Information \nSystems. Istanbul, Turkey. September 23-26. \n[8] M. S. Horritt, P. D. Bates. 2002. Evaluation of 1D and 2D Numerical \nModels for Predicting River Flood Inundation. Journal of Hydrology. 268: \n87\u201399. \n[9] B. Pradhan, M. Shafie. 2009. Flood Hazard Assessment for Cloud \nProne Rainy Areas in a Typical Tropical Environment. Disaster Advances. 2 \n(2): 7-15.\n[10] S. Blazkova, K. Beven. 1997. Flood Frequency Prediction for Data \nLimited Catchments in the Czech Republic using a Stochastic Rainfall Model \nand TOPMODEL. Journal of Hydrology. 195 (1-4): 256-278.\n[11] J. M. Cunderlik, D. H. Burn. 2002. Analysis of the Linkage Between \nRain and Flood Regime and its Application to Regional Flood Frequency \nEstimation. Journal of Hydrology. 261 (1-4): 115-131. \n[12] F. S. Ebisemiju. 1986. Environmental Constrains of the \nInterdependent of Drainage Basin Morphometeric Properties. Proc. of the \nFirst International Conf. on Geomorphology. Manchester, UK. Part 2: 3- 19\n[13] J. H. Heo, J. D. Salas, D. C. Boes. 2001. Regional Flood Frequency \nAnalysis Based on a Weibull Model, Part 2 Simulations and Applications. \nJournal of Hydrology. 242 (3-4): 171-182, IAHS Publ., UK\n[14] R. B. Nageshwar, P. Bhagabat. 1997. Flood Estimation for \nUngauged Catchments using the GIUH. Journal of Water Resources Planning \nand Management. 228-238, Elsevier Publi., USA. \n\n\n\n\n\n\n\n\nRodeano Roslee, Felix Tongkul, Norbert Simon & Mohd. Norazman Norhisham / Malaysian Journal of Geosciences 1(1) (2017) 01\u201306 6\n\n\n\nCite this article as: Flood Potential Analysis (FPAn) using Geo-Spatial Data in Penampang area, Sabah. Rodeano Roslee, Felix Tongkul, Norbert Simon & \nMohd. Norazman Norhisham /  Mal. J. Geo 1(1) (2017) 01-06\n\n\n\n[15] Y. Yakoo, S. Kazama, M. Sawamoto, H. Nishimura. 2001. \nRegionalization of Lumped Water Balance Model Parameters based on \nMultiple Regression. Journal of Hydrology. 246 (1-4): 209-222, IAHS Publ., \nUK. \n[16] A. B. Villiers. 1986. A Multivariate Evaluation of a Group of \nDrainage Basin Variables, African Case Study. Proc. of the First International \nConf. on Geomorphology. Manchester, UK. Part 2: 21-33. \n[17] S. Kute, S. Kakad, V. Bhoye, A. Walunj. 2014. Flood Modelling of \nRiver Godavari using HEC-RAS. IJRET: International Journal of Research \nin Engineering and Technology. 03 (09):  81-87. http://esatjournals.org/\nVolumes/IJRET/2014V03/I21/IJRET20140321017.pdf. (Retrieved 23 July \n2015).\n[18] K. C. Honda, X. J. Francis, V. P. Sah. 1997. Flood Monitoring in \nCentral Plain of Thailand using JERS-1 SAR Data. Proc. 18th Asian Conference \nof Remote Sensing. Malaysia. \n[19] M. M. Islam, K. Sadu. 2001. Flood Damage and Modeling using \nSatellite Remote Sensing Data with GIS: Case Study of Bangladesh. In: \nRemote Sensing and Hydrology 2000, Edited by R. Jerry et al.,  IAHS \nPublication, Oxford. 455-458.\n[20] M. M. Islam, K. Sado. 2002. Development Priority Map for Flood \nCountermeasures by Remote Sensing  Data with Geographic Information \nSystem. Journal of Hydrologic Engineering. September-October 2002. 346-\n355. \n[21] J. Sanyal, X. X. Lu. 2004. Application of Remote Sensing in Flood \nManagement with Special Reference to Monsoon Asia \u2013 A Review. Natural \nHazards. 33, Kluwer Academic Publishers. \n[22] J. Sanyal, X. X. Lu. 2005. Remote sensing and GIS-Based Flood \nVulnerability Assessment of Human Settlements: A Case Study of Gangetic \nWest Bengal, India. Hydrological Processes. 19: 3699-3716.\n[23] P. A. Townsend, S. J. Walsh. 1998. Modelling Flood Plain \nInundation using Integrated GIS with Radar and Optical Remote Sensing. \nGeomorphology. 21 (98): 295-312. \n[24] G. Wadge, A. P. Wislocki, J. Pearson, J. B. Whittow. 1993. Mapping \nNatural Hazards with Spatial Modeling System. In: Geographic Information \nHandling Research and Applications, edited by P.M. Mather, John Wiley and \nSons Inc., New York.\n[25] G. Profeti, H. Machintosh. 1997. Flood Management Though \nLandsat TM and ERS SAR Data: A Case Study. Hydrological Processes. 11: \n1397\u20131408. \n[26] M. R. Knebl, Z. L. Yang, K. Hutchison, D. R. Maidment. 2005. \nRegional Scale Flood Modeling using NEXRAD Rainfall, GIS, and HEC-HMS/\nRAS: A Case Study for the San Antonio River Basin Summer 2002 Storm \nEvent. Journal of Environmental Management. 75: 325\u2013336. \n[27] M. Masmoudi, H. Habaieb. 1993. The Performance of Some \nRealtime Statistical Flood Forecasting Models Seen Through Multicriteria \nAnalysis. Water Resources Manegement. 7: 57\u201367. \n [28] V. Merwade, A. Cook, J. Coonrod. 2008. GIS Techniques for Creating \nRiver Terrain Models for Hydrodynamic Modeling and Flood Inundation \nMapping. Environmental Modelling and Software. 23 (10): 1300-1311. \n[29] A. Zerger. 2002. Examining GIS Decision Utility for Natural Hazard \nRisk Modeling. Environmental Modelling and Software. 17 (3), 287-294. \n[30] R. Sinha, G. V. Bapalu, L. K. Singh, B. Rath. 2008. Flood Risk Analysis \nin the Kosi River Basin, North Bihar using Multi-Parametric Approach of \nAnalytical Hierarchy Process (AHP). Journal Indian Society Remote Sensing. \n36: 335-349.\n\n\n\n[31] S. Yahaya. Multicriteria Analysis for Flood Vulnerable Area in \nHadejia-Jama\u2019are River Basin, Nigeria. ASPRS 2008 Annual Conference \nPortland, Oregon. April 28 - May 2.\n[32] Y. R. Chen, C. H. Yeh, B. Yu. 2011. Integrated Application of the \nAnalytic Hierarchy Process and the Geographic Information System for \nFlood Risk Assessment and Flood Plain Management in Taiwan. Natural \nHazards. 59 (3): 1261-1276.\n[33] D. U. Lawal, A. N. Matori, A. M. Hashim, K. W. Yusof, I. A. Chandio. \n2012. Detecting Flood Susceptible Areas using GIS-based Analytic Hierarchy \nProcess. 2012 International Conference on Future Environment and Energy. \nIPCBEE vol.28. IACSIT Press, Singapore. \n[34] S. S. Saini, S. P. Kaushik. 2012. Risk and Vulnerability Assessment \nof Flood Hazard in Part of Ghaggar Basin: A Case Study of Guhla Block, \nKaithal, Haryana, India. International Journal of Geomatics and Geosciences. \n3 (1): 42-54.\n[35] A. Istigal. 1997. Land and Agrometeorological Information for \nAgricultural Development. In: M.V.K.  Sivakumar, U.S. De, K.C. Sinharay, \nM. Rajeevan (Eds). User Requirement for Agrometeorological Services \nProceedings of an International Workshops Rune, India. 241 \u2013 257. \n[36] G. P. Siddayao, S. E. Valdez, P. L. Fernandez. 2014. Analytic \nHierarchy Process (AHP) in Spatial Modeling for Floodplain Risk \nAssessment. International Journal of Machine Learning and Computing.  4 \n(5): 450-457. http://www.ijmlc.org/papers/453-A015.pdf. (Retrieved 23 \nJuly 2015).\n[37] K. H. Ho, A. H. Ghazali, S. F. Chong. 2002. Calibration and \nEvaluation of Modified Tank Model (Flood Forecasting Model) for Kelantan \nRiver Basin Malaysia. Proceeding of the Water Engineering. 23-24 July. \nConference Malaysia. Kuala Lumpur. 231-245. \n[38] Y. O. Ouma, R. Tateishi. 2014. Urban Flood Vulnerability and Risk \nMapping Using Integrated Multi-Parametric AHP and GIS: Methodological \nOverview and Case Study Assessment. Water 2014. 6 (6): 1515-1545. \nhttp://www.mdpi.com/2073-4441/6/6/1515. (Retrieved 23 July 2015).\n[39] M. G. Collins, F. R. Steiner,  M. J. Rushman. 2001. Land-Use \nSuitability Analysis in the United States: Historical Development and \nPromising Technological Achievements. Environ. Manage. 28 (5): 611\u2013621.\n[40] J. Malczewski. 1999. GIS and multipleYork: John Wiley & Son.\n[41] T. L. Saaty. 1980. The Analytic Hierarchy Process. New York: Mc \nGraw Hill.\n[42] T. L. Saaty.  2008. Decision Making with the Analytic Hierarchy \nProcess. Int. J. Services Sciences. 1 (1).\n[43] A.N. Strahler. Quantitative Geomorphology of Drainage basins \nand Channel network. In Handbook of Applied Hydrology; Mc Graw Hill: \nNew York, NY, USA, 1964. 39\u201376.\n[44] A. Yalcin & F. Bulut. 2007. Landslide susceptibility mapping using \nGIS and digital photogrammetric techniques: a case study from Ardesen \n(NE-Turkey). Nat Hazards 4: 201-226.\n[45] N. Nicholls & K.K. Wong. 1990. Dependence of Rainfall Variability \non Mean Rainfall, Latitude, and the Southern Oscillation. J. Clim. 3, 163\u2013170.\n[46] B. Lowery, W.J. Hickey, M.A. Arshad & R. Lal. Soil Water Parameters \nand Soil Quality. J.W. Doran & A.J Jones (Eds.). Methods for Assessing Soil \nQuality: Madison, WI, USA, 1996. 143\u201355.\n \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 22-25 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2020.22.25 \n\n\n\nCite the Article: Ismail Abd Rahim, Mohamad Saiful Nizam Mohamad (2020). Tunnel Support By Rock Quality Index (Q) System For Ultrabasic Rock: A Case Study In \nTelupid, Sabah, Malaysia. Malaysian Journal of Geosciences, 4(1): 22-25. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2020.22.25 \n\n\n\nTUNNEL SUPPORT BY ROCK QUALITY INDEX (Q) SYSTEM FOR ULTRABASIC ROCK: \nA CASE STUDY IN TELUPID, SABAH, MALAYSIA  \n\n\n\nIsmail Abd Rahim*, Mohamad Saiful Nizam Mohamad\n\n\n\nNatural Disasters Research Unit, School of Sciences & Technology, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia. \n\n\n\n*Corresponding Author Email: arismail@ums.edu.my\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 05 January 2020 \nAccepted 10 February 2020 \nAvailable online 28 February 2020\n\n\n\nThe study area is underlain by the ultrabasic rock of partly Sabah Ophiolite Complex of Cretaceous ages. The \nobjectives of this study are to determine the Q-value and to estimate the permanent support measures for \n20m span, 10m high and eastern direction of the proposed tunnel in the study area. Engineering geological \nmapping (lithological and surface mapping and discontinuity survey), laboratory study (petrographical \nstudy) and testing (Uniaxial Compressive Strength testing) and data analysis (stereographic plots, Q system \nparameters evaluation and support estimation) was used in this study. The results show that the rock mass \nis classified as lherzolite, strong, excellent quality, more than four joint sets, slightly altered discontinuity \nwall, dry excavation and favourable stress condition. The equivalence dimension (De) are 15.4 for the \npermanent roof. The Q-value for permanent roof and wall of the proposed tunnel are 1.4 (Class D or poor and \ntype 5) and 3.5 (Class D or poor and type 3), respectively. The permanent and temporary supports for the \nroof and wall are systematic bolting, 700J energy absorption of fiber reinforce sprayed concrete, 9-12 and 5-\n6 cm thick fiber reinforce shotcrete, respectively.  \n\n\n\nKEYWORDS \n\n\n\nQ-system, ultrabasic rock, Telupid, Tunnel support, Rock bolts, reinforce sprayed concrete.\n\n\n\n1. INTRODUCTION \n\n\n\nThe Q-system was developed at NGI between 1971 and 1974 (Barton et al., \n1974). Since the introduction of the Q-system in 1974 there has been a \nconsiderable development within support philosophy and technology in \nunderground excavations. The types of rock bolts and fiber reinforced \ntechnology had been introduced and continuous develops as support \nprocedure. Two revisions of the support chart have been carried out and \npublished in conference proceedings. An extensive updating in 1993 was \nbased on 1050 examples mainly from Norwegian underground excavation \n(Grimstand and Barton, 1993). Analytical research updating with respect \nto the thickness, spacing and reinforcement of reinforce ribs of sprayed \nconcrete (RRS) as a function of the load and rock mass quality have been \ndone (Grimstand et al., 2002). The most updated guideline for RRS in the \nsupport chart based on case histories in Norway can be found (NGI, 2015). \nThe Q-system can be used as a guideline in rock support design decisions \nand for documentation of rock mass quality. The Q-value is most precise \nwhen mapped in underground openings. However, the system can also be \nused for field mapping, core logging and investigations in borehole, but it \nis important to have in mind that such cases some of the parameters may \nbe difficult to estimate. The majority of the case histories are derived from \nhard, jointed rocks. From weak rocks with few or no joints there are only \nfew examples and by evaluation of support in such types of rocks, other \nmethods should be used in addition to the Q-system for support design. It \nis important to combine application of the Q-system with deformation \nmeasurement and numerical simulation in squeezing rock or very weak \nrock (Q<1). \n\n\n\nSabah, Malaysia is a country that underline by complex geological rocks \n\n\n\nunit and tectonic history. The infrastructure development growth was \ncontribute to construction of public transport especially the major road or \nhighway which is important for communication and transportation. In \nSabah, ultrabasic rock are the rock unit that should being involved when \nconstructing west-east highway. In some cases, tunneling could be \nnecessary for some reason. The application of Q-system for tunneling in \nultrabasic rock in Sabah, Malaysia is never been documented. Then this \nstudy was conducted to determine the Q-value and to estimate the support \nmeasures for a propose 20m span, 10m high and eastern direction of a \ntunnel in ultrabasic rock in Telupid area (Figure 1) \n\n\n\n2. GEOLOGICAL BACKGROUND \n\n\n\nThe Telupid area consists of igneous and sedimentary rocks with minor \noccurrence of metamorphic rocks (Kirks, 1968; Sanudin and Baba, 2007). \nThe oldest dated sedimentary rocks are radiolarian cherts of Early \nCretaceous age (Jasin, 1991). These thinly bedded cherts are closely \nassociated with basic igneous of basaltic/spilitic type. Also closely \nassociated with these two types of rocks are ultrabasic rocks \n(serpentinites/peridotites), intrusive rocks (dolerites) and metamorphic \nrocks (hornblendes schists and gneiss). This association of rock types, \nwhich resembles an ophiolite sequence is interpreted to represent an \noceanic crust of Mesozoic age and it forms the basement rock here. The \nstudy area is only represented part of this ophiolite complex i.e. peridotite \nand basaltic rocks and quaternary alluvium along the river (Figure 1). The \nTelupid area is also dominated by NE-SW and NW-SE ridges. The NW-SE \nridges represent bedding strikes of the Crocker formation and Kulapis \nformation in western and eastern part of the area, respectively. These \nridges are cut through by NE-SW ridges, representing elongate bodies of \nophiolitic rocks. The boundary of two trends are characterizes by a \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 22-25 \n\n\n\nCite the Article: Ismail Abd Rahim, Mohamad Saiful Nizam Mohamad (2020). Tunnel Support By Rock Quality Index (Q) System For Ultrabasic Rock: A Case Study In \nTelupid, Sabah, Malaysia. Malaysian Journal of Geosciences, 4(1): 22-25. \n\n\n\ndisrupted zone. Several horizontal and thrust faults, mostly trending NW-\nSE dissect of ophiolitic rocks, exposing slivers of sedimentary rock mostly \nfrom the Crocker formation.  Thrusting to the northeast affect the Kulapis, \nCrocker and ophiolotic rocks (Tongkul, 1997). \n\n\n\nFigure 1: The geological map, location of the study area and slope \n\n\n\nstudies. \n\n\n\n3. METHODOLOGY \n\n\n\nThis study is about the proposal to build a 20m span and 10m high tunnel \nof East direction in ultrabasic rock and was conducted with the \nassumption bellows; \n\n\n\na. This study was concentrated on ultrabasic rock only and ignoring the \n\n\n\nbasaltic rocks, then study area is relatively small in size and contains \n\n\n\nuniform discontinuities patterns even though experiencing poly-phase \n\n\n\ntectonic compression in NW-SE and NE-SW direction (Tongkul, 1997). \n\n\n\nb. The rock mass characteristics of the study area are represented by\n\n\n\ncombining and analyzing data from fives (5) outcrops (Figure 1 and Photo\n\n\n\n1). \n\n\n\nThe methodology of this study includes field study (geological mapping, \ndiscontinuity survey and rock sampling), laboratory study and testing \n(petrographical study and uniaxial compressive strength test) and data \nanalysis (evaluation of field and laboratory studies results and Q-system \nparameters and support design calculation and determination). In data \nanalysis, the Q-system were fully applied in determining the Q-value and \nsupport procedure for the proposed tunnel in study area (Barton et al., \n1974; NGI, 2015). The Q-system is a quantitative classification system \nbased on a numerical assessment of the rock mass quality using the \nfollowing six parameters: \n\n\n\ni. RQD, Rock quality designation. \n\n\n\nii. Jn, number of joint sets.\n\n\n\niii. Jr, roughness of the most unfavourable joint or discontinuity.\n\n\n\niv. Ja, degree of alteration or filling along the weakest joint.\n\n\n\nv. Jw, water inflow. \n\n\n\nvi. SRF, stress reduction factor\n\n\n\nPhoto 1: Rock cut slope of the ultrabasic rock (peridotite) in study area. \n\n\n\nThe above six parameters are grouped into three quotients to give the \noverall rock mass quality (Equation 1),  \n\n\n\nQ = (RQD/Jn) x (Jr/Ja) x (Jw/SRF)    (1) \n\n\n\nThe ratings of the various input parameters to the Q-value are given in \nBarton et al., (1974). The Q-value is related to tunnel support requirement \nby defining the equivalent dimensions (De) of the underground opening \n(Equation 2). \n\n\n\nDe = Dt / ESR  (2) \n\n\n\nWhere; \n\n\n\nDt = diameter or wall height \n\n\n\nESR = excavation support ratio (ESR)  \n\n\n\nThe important permanent roof support is a pressure roof, Proof which also \ncan be estimated from Q, Jn and Jr by the Equation 3. \n\n\n\nProof = (2\u221a JnQ-1/3)/3Jr    (3) \n\n\n\n4. RESULT AND DISCUSSION \n\n\n\nThe result of lithological and petrographical study shows that the \nultrabasic rock is a massive rock mass and identified as peridotite. The \ncolor is greenish black, holocrystalline, medium to coarse grained and \nslightly altered. The mineralogy is dominated by olivine (78%), followed \nby orthopyroxene (10%), clinopyroxene (8) and other minor mineral \nsuch as serpentine, talc and iron oxides (8%) (Microphotograph 1). This \nperidotite can be classified as Lherzolite (Le Bas and Streckeisen, \n1991; Mohamad, 2018). Result of the UCS test shows that the peridotite \ncan be classified as strong rock (ISRM, 1981). This strong \ncharacteristic may due to the occurrences of olivine and pyroxene as well \nas less micro fracture on and in the rock forming minerals. Summary of the \nQ-system\u2019s parameters and their ratings, Q value and related design \nparameters are shown in Table 1. RQD is classified as excellent quality \nbecause 88.1% of the discontinuities more than 10cm in distance (Deere, \n1963). This shows that the blocks size are medium to big. The joint set \nnumber (Jn) was rated as 15 because there are more than four \ndiscontinuities sets found in stereographic plots. \n\n\n\nMicrophotograph 1: Mineralogy and texture of ultrabasic rock under \n\n\n\ncross polarized light (XPL) in 10x objectives. Note: Oli-olivine; Opx-\n\n\n\northopyroxene; Cpx-clinopyroxene. \n\n\n\nThe rock mass was classified as a rock-wall contact type and \ndiscontinuities roughness that observed by scanline are smooth, slightly \nrough, slickensides, planar and undulating. But the overall roughness (Jr) \nhave been given as smooth, planar with 1 rating value. Field study also \nshows that the contact between discontinuities walls are clean and slightly \naltered. Then the discontinuities alteration (Ja) is classified as experiencing \ndiscontinuities softening or low friction clay mineral coatings i.e. kaolinite, \nmica, chlorite, talc, gypsum, graphite and small quantities of swelling clay \nwith 2 rating value. The joint water reduction factor (Jw) was selected as \ndry excavation or minor inflow, i.e. < 5 l/min locally according to \ndiscontinuity survey result. The rock mass is observed as competent rock \nwith stability problem due to high stresses or lack of stresses category \nwhich characterized as low stress, near surface and open joints. Then, this \ncategory is suitable for 1 value of Stress Reduction Factor (SRF). \n\n\n\n\nsheri\n\n\nHighlight\n\n\nDelete this text: According a diagram, \r\rBegin that paragraph with: This peridotite can be classified as Lherzolite (Le Bas and Streckeisen, 1991; Mohamad, 2018). \r\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 22-25 \n\n\n\n\n\n\n\n \nCite the Article: Ismail Abd Rahim, Mohamad Saiful Nizam Mohamad (2020). Tunnel Support By Rock Quality Index (Q) System For Ultrabasic Rock: A Case Study In \n\n\n\nTelupid, Sabah, Malaysia. Malaysian Journal of Geosciences, 4(1): 22-25. \n \n\n\n\n\n\n\n\nTable 1: Summary of the Q-system\u2019s parameters and ratings, Q-value \n\n\n\nand related design parameters. \n\n\n\nParameters  Remarks  Notes / Rating \n\n\n\n UCS  92.88 MPa Strong \n   \n\n\n\n RQD  Excellent quality 88.1 \n   \n\n\n\n Jn  Four or more joint sets 15 \n   \n\n\n\n Jr  Smooth planar 1 \n   \n\n\n\n Ja  Softening or low friction clay  \n\n\n\n mineral coatings \n\n\n\n4 \n   \n\n\n\nJw  Dry excavation, or minor   \n\n\n\n inflow dry \n\n\n\n1 \n   \n\n\n\n SRF  Low stress, near surface and   \n\n\n\n open joints \n\n\n\n1 \n   \n\n\n\n ESR  Minor road and railway \n\n\n\ntunnels \n\n\n\n1.3 \n   \n\n\n\n Span  20m \n\n\n\n Height  10m \n\n\n\n De  Span or height in m / ESR Roof Wall \n\n\n\n15.4 7.69 \n\n\n\n Q Type Permanent Temporary \n\n\n\nPosition Roof Wall Roof Wall \n\n\n\nValue 1.4 3.5 7 17.5 \n\n\n\n Proof 3.05 MN \n\n\n\n \nThe proposed tunnel span is 20m and 10m high. The category of \nunderground opening type and use are road tunnels with little traffic and \nrated as 1.3 for Excavation Support Ratio (ESR). Then, the De value for the \nproposed permanent tunnel roof is 15.4. The calculated Q-value for \npermanent roof and wall tunnel are 1.4 (Class D or poor and type 5) and \n3.5 (Class D or poor and type 3), respectively. Then the Q-value for \ntemporary roof and wall are 7 and 17.7, respectively (Figure 2). The \nsupport for the permanent and temporary roof and wall tunnel are shown \nin Figure 2 and Table 2. The permanent support on the roof are 20mm \ndiameter, 2.5m space and 4 length bolts and 9-12 cm thick and 700J energy \nabsorption of fiber reinforce sprayed concrete as well as 20mm diameter, \n4m space and 2.75m length and 6-9 cm thick fiber reinforce shotcrete for \nthe wall. The temporary support for the roof are 20mm diameter, 4m \nspace  and 4m length bolts and 6cm thick fiber reinforce shotcrete but \n20mm diameter, 4.5m space and 2.75m length bolts and 5-6cm thick fiber \nreinforce shotcrete in the wall, respectively. The support pressure for \nproposed tunnel roof (Proof) is 3.05MN. \n\n\n\n\n\n\n\nFigure 2: Lines of the result of Q-value and permanent and temporary \n\n\n\nsupport design requirement of tunnel roof (solid lines) and wall (dot \n\n\n\nlines). \n\n\n\nTable 2: Summary of the result of permanent and temporary support \n\n\n\ndesign requirement of tunnel roof and wall. \n\n\n\nPermanent support \n\n\n\nPosition \n\n\n\nin \n\n\n\nTunnel \n\n\n\nRock Mass  \n\n\n\nQuality \nSymbols Rock support Remarks \n\n\n\nRoof D / Poor / \n\n\n\n5 \n\n\n\nSfr  \n\n\n\n(E700) \n\n\n\n+ B \n\n\n\nSystematic \n\n\n\nbolting , fibre \n\n\n\nreinforce \n\n\n\nsprayed \n\n\n\nconcrete,  9-12 \n\n\n\ncm \n\n\n\n20mm diameter, 2.5m \n\n\n\nspace & 4 length bolts  \n\n\n\n9-12cm thick fibre \n\n\n\nreinforce shotcrete, \n\n\n\n700J energy absorption \n\n\n\nin fibre reinforce \n\n\n\nshotcrete \n\n\n\nWall D / Poor / \n\n\n\n3 \n\n\n\nB + Sfr  Systematic \n\n\n\nbolting , fibre \n\n\n\nreinforce \n\n\n\nsprayed \n\n\n\nconcrete,  5-6 \n\n\n\ncm \n\n\n\n20mm diameter, 4m \n\n\n\nspace & 2.75m length \n\n\n\nbolts \n\n\n\n6-9cm thick fibre \n\n\n\nreinforce shotcrete \n\n\n\nTemporary support \n\n\n\nRoof D / Fair / \n\n\n\n3 \n\n\n\nB + Sfr Systematic \n\n\n\nbolting , fibre \n\n\n\nreinforce \n\n\n\nsprayed \n\n\n\nconcrete,  6 \n\n\n\ncm \n\n\n\n20mm diameter, 4m \n\n\n\nspace & 4m length bolts  \n\n\n\n6cm thick fibre reinforce \n\n\n\nshotcrete \n\n\n\nWall D / Good \n\n\n\n/ 3 \n\n\n\nB + Sfr  Systematic \n\n\n\nbolting , fibre \n\n\n\nreinforce \n\n\n\nsprayed \n\n\n\nconcrete,  5-6 \n\n\n\ncm \n\n\n\n20mm diameter, 4.5m \n\n\n\nspace & 2.75m length \n\n\n\nbolts \n\n\n\n5-6cm thick fibre \n\n\n\nreinforce shotcrete \n\n\n\n5. CONCLUSION \n\n\n\nThe conclusions of this study are: \n\n\n\n1. The Q-values are 1.4 (Class D or poor and type 5) and 3.5 (Class D or \n\n\n\npoor and type 3), for permanent roof and wall of the proposed tunnel, \n\n\n\nrespectively.  \n\n\n\n2. The support measures are systematic bolting and 9-12 cm thick and \n\n\n\n700J energy absorption in fiber reinforce sprayed concrete as permanent \n\n\n\nsupport for roof but systematic bolting and 6-9 cm thick fiber reinforce \n\n\n\nshotcrete for the wall.  \n\n\n\nREFERENCES \n\n\n\nBarton, N., Lien, R., Lunde, J., 1974. Engineering classification of rock \n\n\n\nmasses for the design of tunnel support. Rock Mechanics and Rock \n\n\n\nEngineering, 6 (4), pp. 189-236. \n\n\n\nJasin, B., 1991. The Sabah Complex \u2013 a lithodemic unit (a new name for \n\n\n\nChert-Spilite Formation and its ultramafic association. Warta Geologi., \n\n\n\n17, pp. 253-259. \n\n\n\nDeere, D.U., 1963. Technical Description of rock core for engineering \n\n\n\npurposes. Felsmechanic und Ingenieurgeologie, 1, pp. 16-22.  \n\n\n\nGrimstand, E., Barton, N. 1993. Updating of the Q-system for NMT. (Eds \n\n\n\nKopman, Opsahl and Berg.) In: Proceedings of the International \n\n\n\nSymposium on Sprayed Concrete - Modern Use of Wet Mix Speraed \n\n\n\nConcrete for Underground Support, Norwegian Concrete Association, \n\n\n\nOslo, Norway. \n\n\n\nGrimstand, E., Kankes, K., Bhasin, K., Magnussen, A., Kaynia, A., 2002. Rock \n\n\n\nmass quality Q used in designing reinforced ribs of sprayed concrete \n\n\n\nand energy absorption. Proceeding of International Symposium on \n\n\n\nSprayed Concrete, Davos, pp. 134-142. \n\n\n\nInternational Society of Rock Mechanics (ISRM). 1981. Rock \n\n\n\ncharacterization, testing and monitoring. In: Brown, E. T. (Ed.). ISRM \n\n\n\nsuggested methods. Pergamon press, Oxford. \n\n\n\nKirks, H.J.C., 1968. The igneous rock of Sabah and Sarawak. Geological \n\n\n\nSurvey of Borneo Region Bulletin 5, Pp. 210. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 22-25 \n\n\n\n\n\n\n\n \nCite the Article: Ismail Abd Rahim, Mohamad Saiful Nizam Mohamad (2020). Tunnel Support By Rock Quality Index (Q) System For Ultrabasic Rock: A Case Study In \n\n\n\nTelupid, Sabah, Malaysia. Malaysian Journal of Geosciences, 4(1): 22-25. \n \n\n\n\n\n\n\n\nLe Bas, M.J., Streckeisen, A.L., 1991. The IUGS systematics of igneous rocks. \n\n\n\nJournal of the Geological Society, Geological Society of London, 148, pp. \n\n\n\n825-833. \n\n\n\nMohamad, M.S.N., 2018. Geologi am dan aplikasi Sistem Indeks Kualiti \n\n\n\nBatuan (Q) di kawasan Telupid barat, Sabah. B. Sc. (Hons) with Honor \n\n\n\nDissertation, Universiti Malaysia Sabah, Kota Kinabalu. \n\n\n\nNorwegian Geotechnical Institute (NGI). 2015. Using the Q-System: Rock \n\n\n\nmass classification and support design handbook.  Allkopi AS, Oslo, \n\n\n\nNorway, Pp. 54. \n\n\n\nTahir, S., Musta, B., 2007. Pengenalan Kepada Stratigrafi. Universiti \n\n\n\nMalaysia Sabah, Kota Kinabalu. \n\n\n\nTongkul, F., 1997. Polyphase deformation in Telupid area, Sabah, Malaysia. \n\n\n\nJournal of Asian Earth Sciences, 15 (2-3), pp. 175-183.  \n\n\n\nYin, E.H., 1985. Geological Map of Sabah. 3rd Edition. Scale 1:500,000. \nGeological Survey of Malaysia. Kuching, Sarawak. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 01-06 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2020.01.06 \n\n\n\nCite the Article: Atat, J. G., Uko, E. D., Tamunobereton-ari, I., Eze, C. L (2020). Site-Dependent Geological Model For Density Estimation In The Niger Delta Basin, Nigeria. \nMalaysian Journal of Geosciences, 4(1): 01-06. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2020.01.06\n\n\n\nSITE-DEPENDENT GEOLOGICAL MODEL FOR DENSITY ESTIMATION IN THE \n\n\n\nNIGER DELTA BASIN, NIGERIA \n\n\n\nAtat, J. Ga., Uko, E. Db., Tamunobereton-ari, Ib., Eze, C. Lc. \n\n\n\na Department of Physics, University of Uyo, Uyo, Nigeria \nb Department of Physics, Rivers State University, Port Harcourt, Nigeria \nc Institute of Geosciences and Space Technology, Rivers State University, Nigeria \n*Corresponding Author Email: josephatat@uniuyo.edu.ng and e_uko@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 05 December 2019 \nAccepted 12 January 2020 \nAvailable online 23 January 2020\n\n\n\nSeismic and Well-log data covering three wells in tau(\ud835\udf49) Field in the Niger Delta were used for density \nmodelling. Using Hampson Russell Software, Gardner\u2019s and Lindseth\u2019s relations were localized and \nsubsequently transformed to obtain local fits constants for sand and shale lithologies which were used to \nachieved the final models. The seismic inversion was performed using four steps: well-to-seismic tie, geology \nmodel, acoustic impedance inversion and density prediction. A relationship was established between \nimpedance and density. Pairing Gardner with those by Lindseth approaches and also obtained the average, \nthe final models are \ud835\udf0c = 0.1572\ud835\udc4d\ud835\udc5d\n\n\n\n0.2126 \u2212 16997\ud835\udc4d\ud835\udc5d\n\u22121 + 1.5625  and \ud835\udf0c = 0.09045\ud835\udc4d\ud835\udc60\n\n\n\n0.1935 \u2212 7093.5\ud835\udc4d\ud835\udc60\n\u22121 +\n\n\n\n1.4706 for sandstones lithology; \ud835\udf0c = 0.3185\ud835\udc4d\ud835\udc5d\n0.3103 \u2212 6510\ud835\udc4d\ud835\udc5d\n\n\n\n\u22121 + 1.42855 and \ud835\udf0c = 0.16145\ud835\udc4d\ud835\udc60\n0.2308 \u2212\n\n\n\n34203.5\ud835\udc4d\ud835\udc60\n\u22121 + 1.42855 for shale lithology. These models yield a new concept which will contribute to global \n\n\n\nknowledge. In the absence of density log, these equations can be used to estimate density in the area.  \n\n\n\nKEYWORDS \n\n\n\nDensity, Velocity, Parameters, Sandstones, Shale, Seismic Inversion, Seismic Data and Well-Log Data.\n\n\n\n1. INTRODUCTION \n\n\n\nMathematical modelling aims to describe the different aspects of the real \nworld, the interaction and dynamics through mathematics so as to solve \nproblems (Marion and Lawson, 2015). It could be seen as the third pillar \nof science and engineering, with the fulfilment of theoretical analysis and \nexperimentation (Quarteroni, 2009). A model clarifies our views on how \nthe world functions as ideas can be expressed and fundamental \npredictions identified. \n\n\n\nCrossploting density with rock properties, lithology and pore fluid indicate \ndensity with the property that gives the best differentiation between \nhydrocarbon reservoirs and other fluid types (Koughnet et al., 2003). A \nproper estimate of density is required to determine the location of shale \nin the reservoirs in the case of oil sands or heavy oil developments. \nDifferences in densities caused buoyancy of hydrocarbons to occur \nbecause of their respective fluids; the flow through the reservoir is in \nresponse to differential pressures that exist in a reservoir rock. \n\n\n\nThe challenge is that density values overlap for sands and shales with \nshales having a generally lower velocity than sand. This makes density not \na good lithological indicator.  Inversion for a third parameter such as \ndensity is unstable. This requires the inclusion of constants on the \nparameters to achieve a better result.  \n\n\n\nWell logs are necessary to enhance the evaluation of different density-\nvelocity equations with reference to Gardner\u2019s and Lindseth\u2019s relationship \nused to estimate accurate constants for improve density estimates, both \n\n\n\nfrom p- and s-wave velocities. Well-log data originate from a constant \nrecording obtained in a borehole and documents diverse geological \nparameters (Oluwatoyin, 2016). There are two methods for well-log \ntesting, open-hole or Logging-While-Drilling (LWD). Open -hole logs take \nplace when drilling is complete and in the uncased portion of the well \n(Xavier et al., 2015; Lyaka and Mulibo, 2018). This makes accurate density \nestimates significant for characterization of reservoir.  \n\n\n\n3D Seismic data is also required. Seismic waves are generated by a source \nthrough the subsurface. After reflection at geological boundaries, they \nreturn to the surface and are received by geophones and displayed in the \nseismograph as seismic trace. Travel times of the waves at different ranges \nfrom the source are measured and converted to depth values to enable the \nmapping of the subsurface geological interfaces. Seismic methods are used \nfor explorations like detection and mapping of subsurface boundaries; for \nidentification of physical properties of each subsurface unit. \n\n\n\nDensity may be easily predictable via seismic inversion to establish \nrelationships between rock properties. The properties of lithology, fluid of \na medium may not be concluded with only P-wave statistics but also from \nthe S-wave reaction (Barnola and White, 2001). Two parameters \naccurately estimated from PP and PS seismic inversion are P-impedance, \nS-impedance or \ud835\udc49\ud835\udc5d, \ud835\udc49\ud835\udc60  (Wu et al., 2015; Zhang et al., 2017). Inversion for\n\n\n\nrock density even with reasonably noisy data is not stable. This requires a \nconstant to be included on the parameters when using a density-velocity\nrelation to soothe the inversion. \n\n\n\nSeismic inversion eliminates the effect of the wavelet within the seismic \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 01-06 \n\n\n\nCite the Article: Atat, J. G., Uko, E. D., Tamunobereton-ari, I., Eze, C. L (2020). Site-Dependent Geological Model For Density Estimation In The Niger Delta Basin, Nigeria. \nMalaysian Journal of Geosciences, 4(1): 01-06. \n\n\n\nbandwidth and forces well ties to be made; also separates basin properties \nfrom the overburden and offers measureable estimates on the reservoir \nproperties. Moreso, seismic inversion converts the seismic view of the \nearth reflections as a function of time to the earth velocity as a function of \ndepth. This is because seismic data measurements are taken at the earth\u2019s \nsurface and involve sending a sound pulse through the earth and recording \nthe echoes from the reflecting boundary. The more the sound pulse travel, \nthe pulse become more distorted resulting in less evidence due to noise \nand other factors as it returns back to the surface. It helps extract \nunderlying models of the physical characteristics of rocks and fluids from \nseismic and well-log data; it is also used as a tool to locate hydrocarbon \nbearing sections in the subsurface. \n\n\n\nThe survey noted energy that travelled via the deposits of rock and fluids \nin the ground. Two main parameters of seismic inversion are low \nfrequency and wavelet (band pass filter imposed by seismic acquisition). \nThe low frequency component was derived from well data; high frequency \nfrom seismic data. For better interpretation of seismic data, seismic \ninversion increases data efficiency and quality to better rock effects \nassessment. It eliminates the influence of the wavelet within the seismic \nbandwidth. \n\n\n\nSeismic inversion method requires seismic data and wavelet estimated \nfrom the data. To obtain seismic data, a survey is carried out by firing a \nshort at the surface; it travels to the subsurface and the time was measured \nfor the acoustic energy to travel to a reflection surface and return to the \nreceiver surface. This information acquired, leads to seismic data. To \nobtain well information, measurements were taken in proportion to \nequipment on the drill rig (Kelly Bushing).  \n\n\n\nDensity modelling is very necessary to improve density assessments for \nreservoir classification. It provides evidence for describing a reservoir to \nidentify lithology (Kearey et al., 2002). It gives the accurate distinction \namong hydrocarbon basins and other fluids. Bulk density from \npetrophysical investigation is an essential acoustic pointer of the shale \noccurrence helps the determination of quality of coal (Gray et al., 2006). \nDifferences in densities caused buoyancy of hydrocarbons to occur \nbecause of their own media. \n\n\n\n2. LOCATION AND GEOLOGY OF THE STUDY AREA \n\n\n\nThe Gulf of Guinea is where Niger Delta is positioned between latitudes \n30N and 60N; longitudes 50E and 80E (Kafisanwol et al., 2018; Igbinigie and \nAkenzua-Adamczyk, 2018). The investigation area is \ud835\udf0f Field, located in the \nsouthwest of Port Harcourt, Rivers State (Figure 1). The depobelts form \none of the deltas which is characterized by regression and it is the largest \nin the world with an area of 3 x 105km2 region; sediment capacity of 5 x \n105km3; deposit width of above 104m (Ehigiator and Chigbata, 2017; Obi \net al., 2016; Abbey et al., 2018). The oil classification in this basin is Akata-\nAgbada (Ibe and Oyewole, 2019; Olisa, 2018).  Figure 1 also presents Benin \nFlank; Calabar Flank (Didei and Okumoko, 2017). The Akata development \nis made largely of sea shales. The formation has an estimated thickness of \nup to 7 x 103m (Oghenemeruo and efetobore, 2019). The Agbada \ndevelopment remains the main oil deposit component. The Benin zone \nthickness is about 0.28 x 103m but may be up to 2.1 x 103m in the region \nof extreme settling (Lucas and Omodolor, 2018). The grains of these rocks \nare identified due to their shapes, sizes, mineral structures, the age and \ntime of admission (Fozao et al., 2019; Tamunobereton et al., 2011). Niger \nDelta experiences wet and dry seasons; average rain in a month during wet \nseason is about 135 mm and this falls to 65 mm during dry season (Atat \nand Umoren, 2016). \n\n\n\nFigure 1: Geology map of Niger Delta and Study area  \n\n\n\n2.1 Theoretical Basis \n\n\n\n2.1.1 Density Log \n\n\n\nA zone with higher density defines the number of electrons with greater \n\n\n\ndensities. It reduces the GR strongly; thus, a lower count rate of GR is noted \nat the sensors; similarly for a zone with low density. A low density \nformation decreases the GR less than a zone with high density; therefore a \nhigher GR count rate is noted (Tamunobereton-ari eta al., 2013).  Equation \n1 defines the connection. \n\n\n\n\ud835\udc5b\ud835\udc52 =  \n\ud835\udc41\ud835\udc4e\ud835\udc5b\ud835\udc4d\n\n\n\n\ud835\udc34\n\ud835\udf0c  1 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 \ud835\udc5b\ud835\udc52 is the electron density number in electron cm-3; \ud835\udc41\ud835\udc4e\ud835\udc5b is the \nnumber of Avogadro. \n\n\n\n2.1.2 Gamma Ray Log \n\n\n\nThis is a data source used to determine lithology. A scintillation detector \nin the tool used to detect gamma rays and the numbers detected are noted \nin API. Radioactive elements are seen in illite, in organic matter as well as \nthorium in heavy minerals like zircon, sphene and others. Minerals are \nmore abundant in shales than sandstones; therefore, shales have higher \ngamma ray API responses compared to sand. \n\n\n\n2.1.3 Sonic Log \n\n\n\nIt enables the determination of \ud835\udc49\ud835\udc5d. It is the most accurate log; not affected \n\n\n\nby the magnitude of the hole, production temperature and salt content. \nSonic log also accounts for the time of the creation. Where first arrival is \nnot taken as the head waves of the energy refracted along the borehole \nwall are cases of severe borehole damage or fractures, gas is present \nwithin the pore spaces at high porosity.  Caving and rugosity can induce \nspikes on the sonic response (Odofin, 2014). \n\n\n\n2.1.4 Well-Log Data \n\n\n\nWell-log data originate from a constant recording obtained in a borehole \nand documents diverse geological parameters (Oluwatoyin, 2016). The \nmeasurements derive from three techniques: mechanical, spontaneous or \nnatural and induced (Xavier et al., 2015; Moradi et al., 2016). On a large \nscale, lithology, bed thickness, compaction, and reserve estimates may also \nbe ascertained through well-log tests (Moradi et al., 2016). A collection of \nwell-log data encompassing a geographical area provides material to \ndefine reservoir geometry, correlate beds, and map structures (Lyaka and \nMulibo, 2018). Reservoir properties then can be described through \ncombining well-log and core-plug records. \n\n\n\n2.1.5 Description of Seismic Surveying \n\n\n\nSeismic waves are generated by vibrating the ground to produce signal \nwhich travel to different boundary of the earth. After reflection at \ngeological boundaries, they arrived back at the shot location and received \nby detectors and displayed in the seismograph as seismic trace. Travel \nperiods are noted and converted to depth values to enable the mapping of \nthe subsurface geological interfaces. Seismic methods are used for \nexplorations like recognition of rock interfaces; for identification of \ntargets. It is well suited to map layered sedimentary sequences. Seismic \nmethod may be performed on land and in ocean. It is also used mostly in \noffshore and onshore studies (Kearey et al., 2002). \n\n\n\n3. MATERIALS AND METHOD \n\n\n\n3.1 Materials \n\n\n\nHampson Russell Software was used for data loading, processing, cross-\nplots, wavelet extraction, computation of synthetic seismic trace, \ndifferentiation between measured depth and true vertical depth. Data \nacquired from the onshore Niger Delta oilfield are well Location, Suites of \nLogs (figure 2), Seismic data from 3D survey (figure 3), Geology, Check \nshot data, Marker and Base map. \n\n\n\nFigure 2: Suite of introduced logs for well Q showing log signatures of \n\n\n\nCaliper, GR, Resistivity, Density and \ud835\udc49\ud835\udc5d \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 01-06 \n\n\n\nCite the Article: Atat, J. G., Uko, E. D., Tamunobereton-ari, I., Eze, C. L (2020). Site-Dependent Geological Model For Density Estimation In The Niger Delta Basin, Nigeria. \nMalaysian Journal of Geosciences, 4(1): 01-06. \n\n\n\nFigure 3: Inline and Crossline baseline seismic section between 1200-\n\n\n\n3800ms showing p-wave logs \n\n\n\nFigure 4: Workflow for Density estimate using well log and seismic \n\n\n\ninversion \n\n\n\n3.2 Method \n\n\n\nSeismic inversion is the procedure of changing reflection records into a \nnumerical rock phenomenon account of a basin. Geophysicists do seismic \ninvestigations to have data concerning the geology of oil/gas field. The \nsurvey noted energy that travelled via the deposits of rock and fluids in the \nground. Two main parameters of seismic inversion are low frequency and \nwavelet (band pass filter imposed by seismic acquisition). The low \nfrequency component was derived from well data; high frequency from \nseismic data. For better interpretation of seismic data, seismic inversion \nincreases data efficiency and quality to better rock effects assessment. It \neliminates the influence of the wavelet within the seismic bandwidth. \n\n\n\nSeismic inversion method requires seismic data and wavelet estimated \nfrom the data. To obtain seismic data, a survey is carried out by firing a \nshort at the surface; it travels to the subsurface and the time was measured \nfor the acoustic energy to travel to a reflection surface and return to the \nreceiver surface. This information acquired, leads to seismic data. To \nobtain well information, measurements were taken in proportion to \nequipment on the drill rig (Kelly Bushing).  \n\n\n\nThree wells (P, Q and R) with collection of logs such as caliper, gamma ray, \nresistivity, density and sonic velocity (\ud835\udc49\ud835\udc5d) were studied. S-wave sonic was \n\n\n\nmade available from Castagna curve. The Seismic data has a leading \nfrequency of 60Hz. Inline from 4503 \u2013 5563; Cross line 1540 - 2028 per \nthe volume ranging from 350 to 5200ms. Log headers were checked if the \nestimated depths are comparative to Kelly Bushing and subsea. Necessary \nconversions were made with reference to TVDSS. The \ud835\udc49\ud835\udc5d and \ud835\udc49\ud835\udc60 in \n\n\n\nGardner\u2019s and Lindseth\u2019s connections were used to calculate density. A \nlinear regression line was subjected to these relations to assess the factors \nb and n from Gardner\u2019s relation; e and f from Lindseth\u2019s relation for sand \nand shale lithology. Other procedures employed are presented in figure 4. \nWe used Hampson Russell software to process the data and evaluate the \nsignal. We applied negligible edits; stretch and compression to the data to \nlink the seismic and well log reflectors better.  Once the wavelet was \nidentified, a synthetic seismic trace was computed for well log to original \nseismic correlation. Synthetic seismograms (trace) were generated from \noriginal sonic and density well log after the removal of spurious values \n(figure 5); multiply sonic and density logs to have impedance logs as stated \nbefore. We converted well data in unit of depth to unit of time using check \nshot calibrated in time-depth curve; the wavelet must tie the phase and \nfrequency of seismic information. Well to seismic tie was established in \ntime using statistical wavelet extracted from the data. Calculation of \n\n\n\ndensity from impedance requires a known velocity. The already density-\nvelocity relation evaluated from localizing Gardner and Lindseth constants \nto those of local fits is used to estimate the density from impedance.  \n\n\n\nTheoretically, we have localized Gardner and Lindseth equations to \nparameters in our region of interest using \ud835\udc4f and \ud835\udc5b as constant coefficients \nfor local fits and continue with the solution to obtained density models for \nsandstone and shale lithologies from seismic inversion using impedance. \n\n\n\nFigure 5: De-spiking of \ud835\udc49\ud835\udc5d wave, \u03c1 and other logs using log filtering utility \n\n\n\nof HRS (filtered logs in blue, unfiltered logs in red). \n\n\n\n4. RESULTS \n\n\n\nPairing Gardner with those by Lindseth approaches and also obtained the \naverage, the final models are \ud835\udf0c = 0.1572\ud835\udc4d\ud835\udc5d\n\n\n\n0.2126 \u2212 16997\ud835\udc4d\ud835\udc5d\n\u22121 + 1.5625 and \n\n\n\n\ud835\udf0c = 0.09045\ud835\udc4d\ud835\udc60\n0.1935 \u2212 7093.5\ud835\udc4d\ud835\udc60\n\n\n\n\u22121 + 1.4706 for sandstones lithology; \ud835\udf0c =\n0.3185\ud835\udc4d\ud835\udc5d\n\n\n\n0.3103 \u2212 6510\ud835\udc4d\ud835\udc5d\n\u22121 + 1.42855 and \ud835\udf0c = 0.16145\ud835\udc4d\ud835\udc60\n\n\n\n0.2308 \u2212\n\n\n\n34203.5\ud835\udc4d\ud835\udc60\n\u22121 + 1.42855 for shale lithology. The result of inversion is seen \n\n\n\nin figure 6 to 13. The curves show some errors of the predictable densities \nfrom the impedance inversion.  \n\n\n\nFigure 6: Density assessed from the impedance log inversion with \n\n\n\nconstants from \ud835\udc49\ud835\udc60 for sand obtained through Gardner approach with \n\n\n\nestimated errors \n\n\n\nFigure 7: A cross section of density obtained from the impedance \n\n\n\ninversion using local fit constant from \ud835\udc49\ud835\udc60 for sand obtained through \n\n\n\nGardner approach. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 01-06 \n\n\n\nCite the Article: Atat, J. G., Uko, E. D., Tamunobereton-ari, I., Eze, C. L (2020). Site-Dependent Geological Model For Density Estimation In The Niger Delta Basin, Nigeria. \nMalaysian Journal of Geosciences, 4(1): 01-06. \n\n\n\nFigure 8: Density obtained from the impedance log inversion using local \n\n\n\nfit constant from \ud835\udc49\ud835\udc60 for sand obtained through Lindseth approach with \n\n\n\nestimated errors. \n\n\n\nFigure 9: A cross section of density assessed from the impedance log \n\n\n\ninversion using Lindseth\u2019s default parameter obtained through Lindseth \n\n\n\napproach. \n\n\n\nFigure 10: Density estimated from the impedance log inversion using \n\n\n\ndefault parameter of Gardner obtained through Gardner approach with \n\n\n\nestimated errors. \n\n\n\nFigure 11: A cross section of \ud835\udf0c assessed from impedance inversion by \n\n\n\nmeans of default parameter obtained through Gardner approach. \n\n\n\nFigure 12: Density assessed from the impedance log inversion using \n\n\n\nLindseth\u2019s default parameter obtained through Lindseth approach with \n\n\n\nestimated errors. \n\n\n\nFigure 13: A cross section of density assessed from the impedance log \n\n\n\ninversion using Lindseth\u2019s default parameter obtained through Lindseth \n\n\n\napproach. \n\n\n\n5. DISCUSSION\n\n\n\nImpedance relates with velocity as \n\n\n\n\ud835\udc4d =  \ud835\udf0c\ud835\udc49   2 \n\n\n\nEquations were obtained from those of Gardner\u2019s relation and similarly, \nfrom those of Lindseth relations; substituted in equation 2; rearranged for \ndensity as the subject. We obtained equations 3 to 10 from seismic \ninversion as \nGARDNER (Sandstones) from P-impedance \n\n\n\n\ud835\udf0c =  0.3144\ud835\udc4d\ud835\udc5d\n0.2126 3 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 0.3144 = \ud835\udc65 =  \ud835\udc5e\n(\n\n\n\n1\n\n\n\nr+1\n)\n; 0.2126 = \ud835\udc66 =\n\n\n\n\ud835\udc5f\n\n\n\n\ud835\udc5f+1\n, \ud835\udc5e =  \ud835\udc4f =  0.23;  \ud835\udc5f =  \ud835\udc5b =\n\n\n\n 0.27,the coefficient \ud835\udc5e and the constant \ud835\udc5f are local fit constants for sand \nobtained through seismic inversion analysis,  \ud835\udf0c is density and \ud835\udc4d\ud835\udc5d is P-\n\n\n\nimpedance. \nGARDNER (Sandstones) from S-impedance \n\n\n\n\ud835\udf0c =  0.1809\ud835\udc4d\ud835\udc60\n0.1935 4 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 0.1809 = \ud835\udc65\ud835\udc60 =  \ud835\udc5e\n(\n\n\n\n1\n\n\n\n\ud835\udc5f\ud835\udc60+1\n)\n; 0.1935 = \ud835\udc66\ud835\udc60 =  \n\n\n\n\ud835\udc5f\ud835\udc60\n\n\n\n\ud835\udc5f\ud835\udc60+1\n, \ud835\udc5e =  \ud835\udc4f =  0.12; \ud835\udc5f\ud835\udc60 =\n\n\n\n \ud835\udc5b =  0.24, the coefficient \ud835\udc5e and the constant \ud835\udc5f\ud835\udc60 are local fit constants for \nsand obtained through seismic inversion analysis,  \ud835\udf0c is density and \ud835\udc4d\ud835\udc60 is S-\nimpedance. \n\n\n\nGARDNER (shale) from P-impedance \n\n\n\n\ud835\udf0c =  0.6370 \ud835\udc4d\ud835\udc5d\n0.3103 5 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 0.6370 = \ud835\udc65 =  \ud835\udc5e\n(\n\n\n\n1\n\n\n\nr+1\n)\n; 0.3103 = \ud835\udc66 =\n\n\n\n\ud835\udc5f\n\n\n\n\ud835\udc5f+1\n, \ud835\udc5e =  \ud835\udc4f =  0.52;  \ud835\udc5f =  \ud835\udc5b =\n\n\n\n 0.45, the coefficient \ud835\udc5e and the constant \ud835\udc5f are local fit constants for shale \nobtained through seismic inversion analysis,  \ud835\udf0c is density and \ud835\udc4d\ud835\udc5d is P-\n\n\n\nimpedance. \n\n\n\nGARDNER (shale) from S-impedance \n\n\n\n\ud835\udf0c =  0.3229\ud835\udc4d\ud835\udc60\n0.2308 6 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 01-06 \n\n\n\nCite the Article: Atat, J. G., Uko, E. D., Tamunobereton-ari, I., Eze, C. L (2020). Site-Dependent Geological Model For Density Estimation In The Niger Delta Basin, Nigeria. \nMalaysian Journal of Geosciences, 4(1): 01-06 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 0.3229 = \ud835\udc65\ud835\udc60 =  \ud835\udc5e\n(\n\n\n\n1\n\n\n\n\ud835\udc5f\ud835\udc60+1\n)\n; 0.2308 = \ud835\udc66\ud835\udc60 =  \n\n\n\n\ud835\udc5f\ud835\udc60\n\n\n\n\ud835\udc5f\ud835\udc60+1\n, \ud835\udc5e =  \ud835\udc4f =  0.23; \ud835\udc5f\ud835\udc60 =\n\n\n\n \ud835\udc5b =  0.30,  the coefficient \ud835\udc5e and the constant \ud835\udc5f\ud835\udc60 are local fit constants for \nsand obtained through seismic inversion analysis,  \ud835\udf0c is density and \ud835\udc4d\ud835\udc60 is S-\nimpedance. \n\n\n\nLINDSETH (sandstone) from P-impedance \n\n\n\n\ud835\udf0c =  3.125 \u2212 \n33994\n\n\n\n\ud835\udc4d\ud835\udc5d\n   7 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 3.125 =  \n1\n\n\n\n\ud835\udc52\n; 33994 =  \n\n\n\n\ud835\udc53\n\n\n\n\ud835\udc522\n, \ud835\udc52 and \ud835\udc53are local fit constants (\ud835\udc52 =\n\n\n\n0.320, \ud835\udc53 = 3481) for sand obtained from seismic inversion analysis,  \ud835\udf0c is \ndensity and \ud835\udc4d\ud835\udc5d is P-impedance. \n\n\n\nLINDSETH (sandstone) from S-impedance \n\n\n\n\ud835\udf0c =  2.9412 \u2212 \n14187\n\n\n\n\ud835\udc4d\ud835\udc60\n 8 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 2.9412 =  \n1\n\n\n\n\ud835\udc52\n; 14187 =  \n\n\n\n\ud835\udc53\n\n\n\n\ud835\udc522\n, \ud835\udc52 and \ud835\udc53are local fit constants (\ud835\udc52 =\n\n\n\n0.340, \ud835\udc53 = 1640) for sand obtained from seismic inversion analysis,  \ud835\udf0c is \ndensity and \ud835\udc4d\ud835\udc60 is S-impedance. \n\n\n\nLINDSETH (shale) from P-impedance \n\n\n\n\ud835\udf0c =  2.8571 \u2212 \n13020\n\n\n\n\ud835\udc4d\ud835\udc5d\n 9 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 2.8571 =  \n1\n\n\n\n\ud835\udc52\n; 13020 =  \n\n\n\n\ud835\udc53\n\n\n\n\ud835\udc522\n, \ud835\udc52 and \ud835\udc53are local fit constants (\ud835\udc52 =\n\n\n\n0.350, \ud835\udc53 = 1595) for shale obtained from seismic inversion analysis,  \ud835\udf0c is \ndensity and \ud835\udc4d\ud835\udc5d is P-impedance. \n\n\n\nLINDSETH (shale) from S-impedance \n\n\n\n\ud835\udf0c =  2.8571 \u2212 \n68407\n\n\n\n\ud835\udc4d\ud835\udc60\n 10 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 2.8571 =  \n1\n\n\n\n\ud835\udc52\n; 68407 =  \n\n\n\n\ud835\udc53\n\n\n\n\ud835\udc522\n, \ud835\udc52 and \ud835\udc53are local fit constants (\ud835\udc52 =\n\n\n\n0.350, \ud835\udc53 = 8380) for shale obtained from seismic inversion analysis,  \ud835\udf0c is \ndensity and \ud835\udc4d\ud835\udc60 is S-impedance. \n\n\n\nIn order to obtain our final model, we paired our equations obtained by \nGardner approach with those obtained by Lindseth approach using \nseismic inversion analysis and also obtained the average. This procedure \nyields a new concept which will contribute to global knowledge. This \nconcept yields the development of site dependent geological model for \ndensity estimation using seismic inversion analysis in the \ud835\udf0f field, Niger \ndelta sedimentary basin as \n\n\n\nSANDSTONES LITHOLOGY (from P-impedance) \n\n\n\n\ud835\udf0c = 0.1572\ud835\udc4d\ud835\udc5d\n0.2126 \u2212 16997\ud835\udc4d\ud835\udc5d\n\n\n\n\u22121 + 1.5625 11 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 \ud835\udc4d\ud835\udc5d is P-impedance, 0.1572 =  \n\ud835\udc65\n\n\n\n2\n; \ud835\udc65 =  \ud835\udc5e\n\n\n\n(\n1\n\n\n\nr+1\n)\n; 0.2126 = \ud835\udc66 =\n\n\n\n\ud835\udc5f\n\n\n\n\ud835\udc5f+1\n, \ud835\udc5e =\n\n\n\n \ud835\udc4f =  0.23;  \ud835\udc5f =  \ud835\udc5b =  0.27 , 16997 =  \n\ud835\udc53\n\n\n\n2\ud835\udc522\n; 1.5625 =\n\n\n\n1\n\n\n\n2\ud835\udc52\n; \ud835\udc52 = 0.320; \ud835\udc53 =\n\n\n\n3481; the coefficient \ud835\udc5e and the constant \ud835\udc5f are local fit constants for sand. \n\n\n\nSANDSTONES LITHOLOGY (from S-impedance) \n\n\n\n\ud835\udf0c = 0.09045\ud835\udc4d\ud835\udc60\n0.1935 \u2212 7093.5\ud835\udc4d\ud835\udc60\n\n\n\n\u22121 + 1.4706 12 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 \ud835\udc4d\ud835\udc60 is S-impedance, 0.09045 = \n\ud835\udc65\n\n\n\n2\n; \ud835\udc65\ud835\udc60 =  \ud835\udc5e\n\n\n\n(\n1\n\n\n\n\ud835\udc5f\ud835\udc60+1\n)\n; 0.1935 = \ud835\udc66\ud835\udc60 =  \n\n\n\n\ud835\udc5f\ud835\udc60\n\n\n\n\ud835\udc5f\ud835\udc60+1\n, \n\n\n\n\ud835\udc5e =  \ud835\udc4f =  0.12; \ud835\udc5f\ud835\udc60 =  \ud835\udc5b =  0.24, 7093.5 =  \n\ud835\udc53\n\n\n\n2\ud835\udc522\n; 1.4706 =\n\n\n\n1\n\n\n\n2\ud835\udc52\n;  \ud835\udc52 =\n\n\n\n0.340; \ud835\udc53 = 1640; the coefficient \ud835\udc5e and the constant \ud835\udc5f are local fit constants \nfor sand. \n\n\n\nSHALE LITHOLOGY (from P-impedance) \n\n\n\n\ud835\udf0c = 0.3185\ud835\udc4d\ud835\udc5d\n0.3103 \u2212 6510\ud835\udc4d\ud835\udc5d\n\n\n\n\u22121 + 1.42855 13 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 \ud835\udc4d\ud835\udc5d is P-impedance, 0.3185 =  \n\ud835\udc65\n\n\n\n2\n; \ud835\udc65 =  \ud835\udc5e\n\n\n\n(\n1\n\n\n\nr+1\n)\n; 0.3103 = \ud835\udc66 =\n\n\n\n\ud835\udc5f\n\n\n\n\ud835\udc5f+1\n, \ud835\udc5e =\n\n\n\n\ud835\udc4f =  0.52;  \ud835\udc5f =  \ud835\udc5b =  0.45, 6510 =  \n\ud835\udc53\n\n\n\n2\ud835\udc522\n; 1.42855 =\n\n\n\n1\n\n\n\n2\ud835\udc52\n; \ud835\udc52 = 0.350; \ud835\udc53 =\n\n\n\n1595; the coefficient \ud835\udc5e and the constant \ud835\udc5f are local fit constants for shale. \n\n\n\nSHALE LITHOLOGY (from S-impedance) \n\n\n\n\ud835\udf0c = 0.16145\ud835\udc4d\ud835\udc60\n0.2308 \u2212 34203.5\ud835\udc4d\ud835\udc60\n\n\n\n\u22121 + 1.42855 14 \n\n\n\n\ud835\udc64\u210e\ud835\udc52\ud835\udc5f\ud835\udc52 \ud835\udc4d\ud835\udc60 is S-impedance, 0.16145 = \n\ud835\udc65\n\n\n\n2\n; \ud835\udc65\ud835\udc60 =  \ud835\udc5e\n\n\n\n(\n1\n\n\n\n\ud835\udc5f\ud835\udc60+1\n)\n; 0.2308 = \ud835\udc66\ud835\udc60 =  \n\n\n\n\ud835\udc5f\ud835\udc60\n\n\n\n\ud835\udc5f\ud835\udc60+1\n, \n\n\n\n\ud835\udc5e =  \ud835\udc4f =  0.23; \ud835\udc5f\ud835\udc60 =  \ud835\udc5b =  0.30, 34203.5 =  \n\ud835\udc53\n\n\n\n2\ud835\udc522\n; 1.42855 =\n\n\n\n1\n\n\n\n2\ud835\udc52\n;  \ud835\udc52 =\n\n\n\n0.350; \ud835\udc53 = 8380; the coefficient \ud835\udc5e and the constant \ud835\udc5f are local fit constants \nfor shale. \n\n\n\nHowever, well-log data was used to estimate the local fit constants from \nthe transformed equations using Hampson Russall Software. The results \nare shown in Figures 7, 9, 11 and 13 with the black colour. After seismic \ninversion, these same figures show clear evidence of improvement with \nthe blue indication. These models achieved from inversion yield a better \nresult for estimation of density. \n\n\n\n6. CONCLUSION \n\n\n\nWe have obtained constants for specific rocks for the density-impedance \nrelations in the \ud835\udf0f field. The new approach for density estimation (density-\nimpedance model for characterization of zone or basin) is very reliable \nand accurate. The evaluation of equation which fits the data well is \nnecessary; local constraints should be assessed with either \ud835\udc49\ud835\udc5d or \ud835\udc49\ud835\udc60 or both, \n\n\n\nsince these relations may have been solved empirically. Geology may upset \nthese coefficients meaningfully and could result in loss of evidence if \ndefault parameters are not investigated. This new concept (models) \n[equations 11 to 14] will contribute to global knowledge as it yields the \ndevelopment of site dependent geological model for density estimation \nusing seismic inversion analysis in the \ud835\udf0f field, Niger delta sedimentary \nbasin. \n\n\n\nREFERENCES \n\n\n\nAbbey, C.P., Okpogo., Ifeyinwa, E.U., Atueyi, I.O., 2018. Application of Rock \n\n\n\nPhysics Parameters for Lithology and Fluid Prediction of \u2018TN\u2019 Field \n\n\n\nof Niger Delta Basin, Nigeria. Egyptian Journal of Petroleum, 27(4), \n\n\n\n853 \u2013 866. DOI: org/10.1016/j.ejpe.2018.01.001. \n\n\n\nAtat, J.G., Umoren, E.B., 2016. Assessment of Mechanical and Elastic \n\n\n\nProperties of Soils in the South Eastern Part of Niger Delta, Nigeria. \n\n\n\nWorld Journal of Applied Science and Technology, 8(2), 188 \u2013 193. \n\n\n\nBarnola, A.S., White, R.E., 2001. Gardner\u2019s Relation and AVO Inversion. \n\n\n\nFirst Break, 19, 607 \u2013 611. \n\n\n\nDidei, I.S., Okumoko, D.P., 2017. Determining the Hydrocarbon Generative \n\n\n\nPotential of the Turonian Eze-Aku Shale from Ibii, Lower Benue \n\n\n\nTrough in Southeastern Nigeria. International Journal Geology and \n\n\n\nMining, 3(3), 128 \u2013 136. \n\n\n\nEhigiator, M.O., Chigbata, N.C., 2017. Geophysical and Well Corellation \n\n\n\nAnalysis of Ogo Field: A Case Study in Niger Delta Basin of Nigeria. \n\n\n\nNigerian Journal of Technology, 36(3), 729 \u2013 733. \n\n\n\nDOI:org/10.4314/njt.v36i3.10. \n\n\n\nFozao, K.F., Djieto-Lordon, A.E., Ali, E.A.A., Agying, C.M., Ndeh, D.M., Zebaze-\n\n\n\nDjuka, M. K., 2019. Analysis of Shaly Sand Reservoir Rocks in the \n\n\n\nEastern Niger Delta Basin using Geophysical Well Logs. Journal of \n\n\n\nPetroleum and Gas Engineering. 10(1), 1 \u2013 13. DOI: \n\n\n\n10.5897/JPGE2018.0300. \n\n\n\nGray, F.D., Anderson, P., Gunderson, J., 2006. Prediction of Shale Plugs \n\n\n\nbetween Wells in Heavy OilSands using Seismic Attribute. Natural \n\n\n\nResources Research, 15(2), 102 \u2013 109. \n\n\n\nIbe., A.A., Oyewole, T.E., 2019. Hydrocarbon Play Assessment of X-Field \n\n\n\nin an Onshore Niger Delta, Nigeria. Journal of Petroleum \n\n\n\nExploration and Production Technology, 9, 61 \u2013 74. \n\n\n\nDOI:org/10.1007/s13202-018-0497-5. \n\n\n\nIgbinigie, N.S., Akenzua-Adamczyk, A.H., 2018. Palynological Studies of \n\n\n\nMaastrichtian to Paleocene Sediments Exposed at Okpekpe, \n\n\n\nWestern Flank of Anambra Basin, Edo State, Nigeria Journal of \n\n\n\nApplied Science and Environmental Management, 22 (10), 1563 \u2013 \n\n\n\n1566. DOI: org/10.4314/jasem.v22i10.05. \n\n\n\nKafisanwol, O.O., Falade, A.O., Bakare, O.V., Ayokunle A., 2018. Reservoir \n\n\n\nCharacterization and Prospect Identification in Onka Field, Offshore, \n\n\n\nNiger Delta. Environmental and Earth Sciences Research Journal, \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 01-06 \n\n\n\nCite the Article: Atat, J. G., Uko, E. D., Tamunobereton-ari, I., Eze, C. L (2020). Site-Dependent Geological Model For Density Estimation In The Niger Delta Basin, Nigeria. \nMalaysian Journal of Geosciences, 4(1): 01-06. \n\n\n\n5(4), 79 \u2013 86. DOI:org/10.18280/eesrj.050401. \n\n\n\nKearey, P., Brooks, M., Hill, I., 2002. An Introduction to Geophysical \n\n\n\nExploration. Oxford: Blackwell Science. \n\n\n\nKoughnet, V.R.W., Skidmore, C.M., Kelly, M.C., Lindsay, R., 2003. \n\n\n\nProspecting with Density Cube. The Leading Edge, 22, 1038 \u2013 1045. \n\n\n\nLucas, F.A., Omodolor, H.E., 2018. Palynofacies Analysis, Organic Thermal \n\n\n\nMaturation and Source Rock Evaluation of Sedimentary Succession \n\n\n\nfrom Oligocene to Early Miocene Age in X2 Well, Greater Ughelli \n\n\n\nDepobelt, Niger Delta Basin, Nigeria. Journal of Geosciences and \n\n\n\nGeomatics,  Vol. 6(2), 85 \u2013 93. DOI: 10.12691/jgg-6-2-6. \n\n\n\nLyaka, A.L., Mulibo, G.D., 2018. Petrophysical Analysis of the Mpapai Well \n\n\n\nLogs in the East Pande Exploration Block, Southern Coast of \n\n\n\nTanzania: Geological Implication on the Hydrocarbon Potential. \n\n\n\nOpen Journal of Geology, 8, 781 \u2013 802. DOI:10.4236/ojg. \n\n\n\nMarion, G., Lawson, D.T., 2015. An Introduction to Mathematical \n\n\n\nmodelling. Conference Proceedings.  \n\n\n\nMoradi, S., Moeini, M., Ghassem al-Askari, M.K., Mahvelati, E.H., 2016. \n\n\n\nDetermination of Shale Volume and Distribution Patterns and \n\n\n\nEffective Porosity from Well Log Data Based on Cross-Plot Approach \n\n\n\nfor a Shaly Carbonate Gas Reservoir. Earth and Environmental \n\n\n\nScience, 44, 1 \u2013 7. DOI:10.1088/1755-1315/44/4/042002. \n\n\n\nObi, D.A., George, A.M., Ofem, L.O., 2016. Overpressure/ Depositional \n\n\n\nAnalysis of Parts of Onshore (X-Field) Niger Delta Basin Nigeria, \n\n\n\nBased on Well Logs Data. IOSR Journal of Applied Geology and \n\n\n\nGeophysics, 4(5), 1 \u2013 13. DOI: 10.9790/0990-0405030113. \n\n\n\nOdofin, D.K., 2014. High Resolution Overpressure Prediction from Seismic \n\n\n\nVelocity and Field Derived Formation Parameter in the Onshore \n\n\n\nNiger Delta. Unpublished M.Sc Thesis, Department of Geology, \n\n\n\nUniversity of Nigeria. \n\n\n\nOghenemeruo, U.K., Efetobore, M.G., 2019. Petrophysical Evaluation and \n\n\n\nReservoir Geometry Deduction of Idje Field, Offshore Niger Delta \n\n\n\nNigeria. Journal of Geosciences and Geomatics, 7(4), 157 \u2013 171. \n\n\n\nDOI: 10.12691/jgg-7-4-1. \n\n\n\nOlisa, B.A., 2018. Investigation of Petroleum Source Rock Potential and \n\n\n\nMaturity in Pologbene-001 from Resistivity and Density logs, \n\n\n\nNorthern Depobelt Eocene Deposits, Niger Delta, Nigeria. Scientific \n\n\n\nResearch Journal, 4 (8). \n\n\n\nDOI:org/10.31364/SCIRJ/v6.i8.2018.P0818554. \n\n\n\nOluwatoyin, O., 2016. Reservoir Evaluation of \u201cT-X\u201d Field (Onshore, Niger \n\n\n\nDelta) from Well Log Petrophysical Analysis. Bayero Journal of Pure \n\n\n\nand Applied Sciences, 9(2), 132 \u2013 140. \n\n\n\nDOI:org/10.4314/bajopas.v9i2.25. \n\n\n\nQuarteroni, A., 2009. Mathematicals Models in Science and Engineering. \n\n\n\nNotices AMS 56. \n\n\n\nTamunobereton-ari, I., Omubo-Pepple, V.B., Uko, E.D., 2011. \n\n\n\nDetermination of the Variability of Seismic Velocity with Lithology \n\n\n\nin the Southwestern Part of the Niger Delta Basin of Nigeria Well \n\n\n\nLogs. Journal of Basic and Applied Scientific Research, 1(7), 700 \u2013 \n\n\n\n705. \n\n\n\nTamunobereton-ari, I., Uko., E.D., Omubo-Pepple, V.B., 2013. Estimation of \n\n\n\nLithological and Mineralogical Contents of Rocks from Matrix \n\n\n\nDensity in part of Niger Delta Basin Nigeria using Well-log Data. \n\n\n\nJournal of Emerging Trends in Engineering and Applied Sciences, \n\n\n\n4(6), 828 \u2013 836. \n\n\n\nWu, B., Lawton, D.C., Hall, K.W., 2015. Analysis of Multicomponent \n\n\n\nWalkaway Vertical Seismic Profile Data.  GeoConvention: New \n\n\n\nHorizons, 1 \u2013 4. \n\n\n\nXavier, A., Guerra, C.E., Andrade, A., 2015. Fracture Analysis in Borehole \n\n\n\nAcoustic Images using Mathematical Morphology. Journal of \n\n\n\nGeophysics and Engineering, 12 (3), 492 \u2013 \n\n\n\n501. DOI:org/10.1088/1742-2132/12/3/492. \n\n\n\nZhang, S., Huang, H., Li, H, Wang, G., Dong, Y., Luo, Y., 2017. Prestack \nSeismic Facies-Controlled Joint Inversion of Reservoir Elastic and \nPetrophysical Parameters for Sweet Spot Prediction. Energy \nExploration and Exploitation, 35(6), 767 \u2013 791. \nDOI:10.1177/0144598717716286. \n\n\n\n\nhttp://www.sciepub.com/portal/search?q=Lucas%20%20F.A\n\n\nhttp://www.sciepub.com/JGG/content/6/2\n\n\njavascript:;\n\n\njavascript:;\n\n\njavascript:;\n\n\nhttps://journals.sagepub.com/doi/full/10.1177/0144598717716286\n\n\nhttps://journals.sagepub.com/doi/full/10.1177/0144598717716286\n\n\nhttps://journals.sagepub.com/doi/full/10.1177/0144598717716286\n\n\nhttps://journals.sagepub.com/doi/full/10.1177/0144598717716286\n\n\nhttps://journals.sagepub.com/doi/full/10.1177/0144598717716286\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 52-58 \n\n\n\nCite The Article: Oziegbe Ehitua Julius, Olarewaju Victor Ola, Ocan Ojouk Onesmus (2019) Characterization And Utilization Of Clays From Origo And Awo Southwestern \nNigeria. Malaysian Journal Of Geosciences, 3(2): 52-58. \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 29 August 2019 \nAccepted 30 September 2019 \nAvailable online 08 October 2019\n\n\n\nABSTRACT\n\n\n\nThis study discusses the possible industrial applications of clay from south western Nigeria based on mineralogy and \nchemical composition. Qualitative and quantitative X-ray Diffractometric Studies (XRD) was performed on 10 clay \nsamples, while X-ray fluorescence (XRF) spectrometric analysis was performed on 15 clay samples. The XRD was \ncarried out on both unoriented and oriented samples. Mineralogically, kaolinite is the dominant clay mineral while \nsmectite occurs in small amount, and the non-clay mineral identified include quartz, mica, feldspar, goethite, and \ngibbsite. The concentrations of SiO2 range from 42.45 % to 71.56 %, Al2O3 from 14.00 % to 36.73 %, Fe2O3 from 0.18 \n% to 12.43 %, K2O from 0.23 % to 7.24 % and H2O from 1.21 % to 6.5 %. The percentages of K2O, CaO, and MgO are \nin consonance with the relative chemical mobility of the elements during the process of chemical weathering which \naccounts for the high percentage of kaolinite. The clays are residual in nature which is obtained as a result of chemical \nweathering of pegmatite. The high kaolinite content makes the clay suitable for refractory composite. \n\n\n\n KEYWORDS \n\n\n\nKaolinite, refractory, pegmatite, X-ray Diffraction, feldspar.\n\n\n\n1. INTRODUCTION \n\n\n\nThe demand for tiles and bricks is presently on the increase in Nigeria as \nan average Nigerian can now afford to build brick houses with floor tiles \ninstead of concrete floor, hence the need to explore for more clay.  The \nterm \"clay mineral\" refers to phyllosilicate minerals and other minerals \nwhich impart plasticity to clay by making it hard after it has been dried or \nfired [1-3]. Clay is an important raw material found across Nigeria; it is \nalso an important industrial raw material needed to manufacture a wide \nvariety of products like, bricks, fillers, coaters, drilling mud, poetry, paper, \npaint, ink, sorbents and cosmetics [4, 12-15]. The difference in different \nclay types is accounted for by the arrangement of the octahedral and \ntetrahedral structure in the clay [16]. The industrial utilization of clay \ndepends on its geological disposition, mineralogy and chemical properties. \n\n\n\nClays are divided into two classes: \n1. Residual clay \u2013 found in the place of origin \n2. Transported clay \u2013 also known as sedimentary clay, is removed from\nthe place of origin by an agent of erosion and deposited in a new and \npossibly distant position. \n\n\n\nResidual clays are most commonly formed by surface weathering, which \ngives rise to clay in three ways: \n1. Chemical decomposition of rocks such as granite, containing silica and \nalumina. \n2. Solution of rocks such as limestone, containing clayey impurities which \nbeing insoluble, are deposited as clay. \n3. Disintegration and solution of shale.\n\n\n\nClay minerals could result from the chemical weathering of rocks [17]. \nTraditionally, most analysis of clay usually involves X-ray diffraction \n(XRD) method to determine the mineralogy [18-27]. More recent work has \nbeen carried out on both the qualitative and quantitative analyses of clay \n[27]. For chemical analysis, X-ray fluorescence (XRF) has been used for the \nelemental composition analysis [28-30]. Several authors have \ncharacterized clay in different parts of Nigeria using different methods [6, \n8, 10, 11, 31-32]. \n\n\n\n1.1 Geological Setting \n\n\n\nThe rocks of Origo, Awo and Ede belong to the Nigerian basement complex \nwhich forms part of the mobile belt that lies between the Archean to Early \nProterozoic West African and Congo craton. Four of the six lithological \ngroups recognized by Rahaman [33-34] are present in the study area. \nThese are:  \ni) The migmatite gneiss - quartzite complex. \nii) Members of the Schist Belt\niii) Charnockitic rock \niv) Members of the Older Granite Suite.\n\n\n\n1.2 Older Granite \n\n\n\nThe detailed mapping of the area shows that the clay deposits occur on the \npegmatite and pegmatite is a member of the Older granite. There is the \nexhibition of graphic texture in the granite (Figure 1), which is a common \nconstituent of pegmatite. The graphic pegmatite shows interlocking grains \nof quartz and feldspars. Graphic granite occurs only in restricted zones of \nthe pegmatite [35]. In the graphic granite observed, the quartz appears \nembedded in the feldspar (Figure 1).  \n\n\n\nFigure 1: A field photograph from Ede (1 km from Awo) showing graphic \ntexture in granite. It is the 1st phase of (earliest) the pegmatite. Grains of \nfeldspar (f) interlock with that of quartz (q). \n\n\n\nMalaysian Journal of Geosciences (MJG) \nDOI : http://doi.org/10.26480/mjg.02.2019.52.58 \n\n\n\nCHARACTERIZATION AND UTILIZATION OF CLAYS FROM ORIGO AND AWO \nSOUTHWESTERN NIGERIA \nOZIEGBE Ehitua Julius1*, OLAREWAJU Victor Ola2, OCAN Ojouk Onesmus3 \n\n\n\n1Department of Geosciences, Faculty of Science, University of Lagos, Nigeria \n2Department of Geology, Faculty of Science Obafemi Awolowo University, Nigeria \n3Department of Geological Science, College of Science, Engineering and Technology, Osun State University, Nigeria  \n*Corresponding Author E-mail: eoziegbe@unilag.edu.ng\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN : MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\n\nmailto:eoziegbe@unilag.edu.ng\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 52-58 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Oziegbe Ehitua Julius, Olarewaju Victor Ola, Ocan Ojouk Onesmus (2019) Characterization And Utilization Of Clays From Origo And Awo Southwestern \nNigeria. Malaysian Journal Of Geosciences, 3(2): 52-58. \n\n\n\n\n\n\n\nOn the pegmatite are crystals of garnet (Figure 2) which are reddish-\nbrown in colour and are seed-like in shape. The colouration might be due \nto the presence of Fe which makes it almandine garnet. There is the \noccurrence of xenolith of pegmatite (earlier formed pegmatite) in \npegmatite in the study area (Figure 3), which shows phase relationship \nwithin the pegmatite. There are occurrences of crystals of microcline of up \nto 4 cm in length oriented in different directions.  A partially weathered \npegmatite with books of mica (Figure 4) still standing out was observed \non the clay at Ede (1 km from Awo). The mica is still occurring as books \nbecause it is more resistant to weathering than the feldspar which has \nbeen weathered into clay. A dyke of weathered pegmatite can be seen in a \nweathered host rock (Figure 5). The occurrence of the weathered \npegmatite as dykes shows its intrusive nature.  \n \n2. MATERIALS AND METHODS \n \nSamples of clay for unoriented analysis were done at the Central Analytical \nfacility (CAF) Stellenbosch University while oriented analysis was carried \nout at Agricultural Research Council Pretoria (ARC), both in South Africa. \nThe modal abundances of minerals in the raw clay samples were \ncalculated using Reference Intensity Ratio (RIR) method. A geochemical \nanalysis using XRF was done at CAF Stellenbosch University.  \n \n2.1 X-ray diffraction analysis (unoriented sample) \n \n2.2.1 Sample preparation \n \nA representative portion of the clay sample was air dried and milled into \npowder using agate mortar. The milled sample was later pressed into an \naluminum sample holder forming a reflective surface. This was analyzed \nas pressed or unoriented samples. \n \n\n\n\n \n \nFigure 2: A field photograph of pegmatite from Ede (1 km from Awo) \nshowing crystals of brown garnet. \n \n\n\n\n \n \nFigure 3: A field photograph from Ede (1 km from Awo), showing xenolith \nof pegmatite in pegmatite. The coarse-grained portion \u2018A\u2019 is the first phase \nwhile \u2018B\u2019 is the 2nd phase of the pegmatite with microcline \u2018M\u2019 crystals of \nabout 4cm in length. \n\n\n\n \n \nFigure 4: A field photograph from Ede (1 km from Awo) showing books of \nmica embedded in clay. \n  \n\n\n\n \n \nFigure 5: A field photograph from Origo showing a discordant weathered \npegmatite dike \u2018P\u2019. The weathered pegmatite is discordant to a weathered \nrock which is likely to be schist \u2018S\u2019 based on its structure. \n \n\n\n\n2.2.2 Data collection and treatment \n \nTen samples were submitted in powdered form and measurements were \nperformed under ambient conditions (Table 1). Data was collected using \nPANalytical Data Collector Software and analyzed in PANalytical High \nScore Plus.  All raw data were treated and analysed in the same manner to \nensure consistency.  The background was determined using smoothed \ndata, a derivative method with a bending factor of 0 and a granularity of \n13.  The K\u03b12 wavelength from the Cu tube was stripped from the scan data \n\n\n\nusing Rachinger method and a (\n\ud835\udc3e\ud835\udefc2\n\n\n\n\ud835\udc3e\ud835\udefc1\n) = 0.5, as tabulated below.  Using the \n\n\n\nMinimum second derivative method to achieve maximum peak resolution \nas recommended for multi-phase samples, peaks were identified in the \nscans with tips broader than 0.05 2\u03b8 but narrower than 1.00 2\u03b8, and a base \nwidth less than 2.00 2\u03b8.  \n \n2.2 X-ray diffraction analysis (oriented sample) \n \n2.2.1 Pre-treatment of samples \n \nThe clay fraction (< 2um) was extracted by decantation and centrifugation. \nThis fraction was treated with ethylene-glycol under solvation and \nthereafter, thermal treatment. The prepared samples were then scanned \nwith XRD at ARC (Agricultural Research Council) South Africa, using a \nPhilips 1840 XRD instrument with a Co lamp over a range of 2 \u2013 35o\u03b8, \nwhich is an adaptation of the method of B\u00fchmann et al. [36]. \n \n2.3 X-ray fluorescence spectrometric analysis (XRF) \n \nThe X-ray fluorescence spectrometric analysis was employed for the \nchemical analysis of the clay samples. A total of fifteen samples were \nanalyzed for both the major and trace elements. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 52-58 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Oziegbe Ehitua Julius, Olarewaju Victor Ola, Ocan Ojouk Onesmus (2019) Characterization And Utilization Of Clays From Origo And Awo Southwestern \nNigeria. Malaysian Journal Of Geosciences, 3(2): 52-58. \n\n\n\n\n\n\n\nTable 1: Scan Conditions \n \n\n\n\n\n\n\n\nXRF is ideal for rapid and accurate whole bulk elemental analysis in rock \nor soil samples. The instrument is an AXIOS 2.4 KWatt with a Rh X-ray \ntube. Element settings vary from 50 - 50 to 60 - 40 to 25 - 100 mA KV, \ndepending on the fluorescence yield of the element. A range of \ninternational reference material was used to set up the major and trace \nelement analytical methods (for example NIM-G, -S, P, -N and the full range \nof South African Reference Materials). Major elements are analysed on \nfused beads after a H20- loss is performed at 110OC and a LOI is determined \nat 950OC for one hour for each sample. The L.O.I. is made of contributions \nfrom the volatile compounds\u2019 H2O+, OH-, CO2, F, Cl, S; in parts also K, Na (if \nheated for too long); or alternatively added compounds O2 (oxidation, e.g. \nFeO to Fe2O3), later CO2 (CaO to CaCO3). Trace elements were analysed on \npressed powder pellets after the material has been milled to ~50 \nmicrometer with Zibb mill. For Major elements analysis 5 g of the milled \nsample was used while for the Trace elements analysis 100 g was used. \n \n\n\n\n3. RESULTS AND DISCUSSION \n \n\n\n\n3.1 Mineralogy  \n \n\n\n\nThe X-ray diffraction analysis was carried out for the identification and a \nsemi-quantitative estimation of the proportions of the different minerals \npresent in the clay deposits. The XRD patterns of the unonoriented whole \nclay samples have kaolinite and quartz (Figures 6 \u2013 15). The modal \nabundances of minerals calculated by using the RIR method affirms the \npresence of kaolinite in all the samples analysed (Table 2, Note that the \nRIR method is not always very accurate and percentages may have an \nerror of + 5 %). The quantitative results of the clay fraction indicate the \npercentages following minerals; kaolinite, quartz, mica, feldspar, gibbsite, \ngoethite and smectite (Table 3). \n  \n\n\n\n\n\n\n\nFigure 6: X-ray diffractogram of Clay Sample 4 From Origo. \n \n\n\n\n \nFigure 7: X-ray diffractogram of clay fample 8 from Origo. \n\n\n\n \nFigure 8: X-ray diffractogram of clay sample 9 from Origo \n\n\n\n\n\n\n\n \nFigure 9: X-ray diffractogram of clay sample 10 from Origo. \n\n\n\n\n\n\n\n \nFigure 10: X-ray diffractogram of clay sample 11 from Origo. \n\n\n\n\n\n\n\n \nFigure 11: X-ray diffractogram of clay sample 14 from Origo \n\n\n\n\n\n\n\nAnode material Cu \n\n\n\nK-Alpha1 wavelength 1.540598 \u00c5 \n\n\n\nK-Alpha2 wavelength 1.544426 \u00c5 \n  \n\n\n\nRatio K-Alpha2/K-Alpha1 0.5 \n  \n\n\n\nGenerator voltage 45 kV \n\n\n\nTube current 40 mA \n\n\n\nScan axis Gonio \n\n\n\nScan range 2.499999996o - 74. 99348o \n  \n\n\n\nScan step size 0.0167113o \n\n\n\nNo. of points 4338 \n\n\n\nScan type Continuous \n  \n\n\n\nTime per step 50.8 s \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 52-58 \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Oziegbe Ehitua Julius, Olarewaju Victor Ola, Ocan Ojouk Onesmus (2019) Characterization And Utilization Of Clays From Origo And Awo Southwestern \nNigeria. Malaysian Journal Of Geosciences, 3(2): 52-58. \n\n\n\n\n\n\n\n \nFigure 12: X-ray diffractogram of clay sample 16 from Awo \n\n\n\n \nFigure 13: X-ray diffractogram of clay sample 17 from Awo. \n\n\n\n \nFigure 14: X-ray diffractogram of clay sample 18 from Awo \n\n\n\n\n\n\n\n \nFigure 15: X-ray diffractogram of clay sample 19 from Origo \n\n\n\n \nTable 2: Calculated modal abundance of minerals \n\n\n\n\n\n\n\nSample No 4 8 9 10 11 14 16 17 18 19            \n\n\n\nAlbite NaAlSi3O8 \n   \n\n\n\n1 \n    \n\n\n\n24 \n \n\n\n\nBeryl  Be3Al2(Si6O18) \n    \n\n\n\n1 2 \n    \n\n\n\nBiotite K2Fe3Mg2(AlSi3O10)[OH,F]2  12 4 \n \n\n\n\n11 5 4 \n \n\n\n\n12 2 8 \n\n\n\nColumbite FeNb2O6 12 \n   \n\n\n\n1 1 \n   \n\n\n\n5 \n\n\n\nDiopside CaMgSi2O6 \n  \n\n\n\n6 5 \n \n\n\n\n12 8 15 7 \n \n\n\n\nDolomite Ca Mg (CO3)2 2 \n \n\n\n\n3 \n \n\n\n\n3 4 8 6 \n  \n\n\n\nDravite NaMg3Al6B3Si6[O,OH]30[OH,F] 13 2 4 \n \n\n\n\n6 13 \n    \n\n\n\nElbaite Na[Li,Al]3Al6B3Si6[O,OH]30[OH,F] \n \n\n\n\n3 4 \n \n\n\n\n2 4 13 \n  \n\n\n\n3 \n\n\n\nGibbsite Al(OH)3 11 4 7 3 3 3 12 13 4 16 \n\n\n\nGoethite FeO.OH \n  \n\n\n\n1 1 1 1 2 3 \n  \n\n\n\nHalloysite Al2Si2O3(OH)8 11 4 \n \n\n\n\n3 10 6 \n    \n\n\n\nHedenbergite CaFeSi2O6 \n  \n\n\n\n3 \n   \n\n\n\n4 12 12 \n \n\n\n\nIlmenite FeTiO3 \n       \n\n\n\n2 \n  \n\n\n\nKaolinite Al4(Si4O10)(OH)8 6 6 47 11 14 24 16 29 2 2 \n\n\n\nLepidolite K[Li, Al]3[Si, Al]4O10 \n \n\n\n\n6 \n \n\n\n\n13 \n     \n\n\n\n29 \n\n\n\nMicrocline KAlSi3O8 \n   \n\n\n\n14 \n  \n\n\n\n16 \n  \n\n\n\n9 \n\n\n\nMuscovite K2Al4(Si6Al2O20) [OH,F]4 10 31 18 19 18 10 14 \n   \n\n\n\nNontronite Na Fe4(Si7Al)O20(OH)4.n H2O \n        \n\n\n\n11 \n \n\n\n\nOrthoclase KAlSi3O8 \n \n\n\n\n2 \n   \n\n\n\n5 \n    \n\n\n\nQuartz SiO2 14 28 19 17 26 10 \n \n\n\n\n5 12 20 \n\n\n\nRutile TiO2 \n         \n\n\n\n1 \nSanidine  KAlSi3O8 6 7 \n\n\n\n \n3 \n\n\n\n  \n18 \n\n\n\n\n\n\n\nSpodumene LiAl (Si2O6 3 3 \n  \n\n\n\n5 2 3 2 7 1 \n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 52-58 \n \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Oziegbe Ehitua Julius, Olarewaju Victor Ola, Ocan Ojouk Onesmus (2019) Characterization And Utilization Of Clays From Origo And Awo Southwestern \nNigeria. Malaysian Journal Of Geosciences, 3(2): 52-58. \n\n\n\n\n\n\n\nTable 3: Minerology of Clay Analysis \n \n\n\n\nSAMPLE                           QTZ                 Kt           Mi          Go            Fs            St           Dt          Gb  \n1 2 3 4 5 6 7 8 \n\n\n\n4 3 76 17 4 0 0 0 0 \n\n\n\n8 16 74 10 0 0 0 0 0 \n\n\n\n9 1 93 3 0 0 3 0 0 \n\n\n\n10 9 82 4 0 0 5 0 0 \n\n\n\n11 14 77 9 0 0 0 0 0 \n\n\n\n14 1 94 3 0 0 2 0 0 \n\n\n\n16 10 68 0 10 8 0 4 0 \n\n\n\n17 10 76 7 0 0 0 0 7 \n\n\n\n18 0 74 0 0 0 0 0 26 \n\n\n\n19 20 51 10 0 19 0 0 0 \n\n\n\nKEY NOTES: \n 1 Qz Quartz 6 St Smectite \n 2 Kt Kaolinite 7 Dt Dolomite \n 3 Mi Mica 8 Gb Gibbsite \n 4 Go Goethite \n 5 Fs Feldspa\n\n\n\n\n\n\n\nTable 4: Major element analysis by raw clay samples XRF, Rh Tube \n             \n\n\n\nSample name Al2O3 CaO Cr2O3 Fe2O3T K2O MgO MnO Na2O P2O5 SiO2 TiO2 LOI H2O- Sum \n\n\n\nUnit \n \n\n\n\n     (wt%)        \n\n\n\n2 24.59 0.04 0.021 12.43 2.59 0.13 0.03 0.27 0.08 43.39 1.35 12.25 4.51 101.68 \n\n\n\n4 21.38 0.02 0.016 9.98 2.95 0.14 0.03 0.32 0.06 51.70 1.04 10.06 3.85 101.55 \n\n\n\n6 20.17 0.01 0.007 5.13 2.19 0.10 0.02 0.18 0.04 59.65 0.51 9.86 3.84 101.70 \n\n\n\n8 18.60 0.01 BD 1.11 2.08 0.08 0.02 0.13 0.02 68.92 0.12 7.10 1.81 100.00 \n\n\n\n9 26.33 0.02 0.004 0.42 0.75 BD 0.01 BD 0.07 53.80 0.02 14.07 4.91 100.40 \n\n\n\n10 19.57 0.01 0.001 0.78 1.09 0.03 0.01 BD 0.02 67.43 0.04 9.69 3.28 101.93 \n\n\n\n11 21.42 0.01 0.004 1.78 0.98 0.14 0.01 BD 0.02 65.07 0.23 8.87 1.38 99.91 \n\n\n\n12 16.06 0.01 BD 0.71 4.03 0.06 0.01 0.10 0.01 71.56 0.04 5.97 1.69 100.25 \n\n\n\n13 14.71 0.03 0.003 0.69 2.99 0.03 0.10 0.17 0.02 68.00 0.02 9.17 4.76 100.69 \n\n\n\n14 24.35 0.01 0.002 0.99 2.57 BD 0.34 0.06 0.04 60.32 0.03 10.11 2.26 101.07 \n\n\n\n15 33.35 0.04 BD 0.18 4.05 BD 0.00 0.11 0.15 47.89 0.01 13.12 2.26 101.15 \n\n\n\n16 28.86 0.03 BD 0.25 7.24 BD 0.01 0.22 0.14 52.32 0.02 9.09 1.21 99.39 \n\n\n\n17 36.73 0.14 0.009 3.22 0.45 0.03 0.01 BD 0.05 42.45 0.33 15.96 2.36 101.73 \n\n\n\n18 29.54 0.01 BD 0.36 0.23 BD 0.01 1.56 0.07 46.82 0.01 16.80 6.50 101.91 \n\n\n\n19 14.00 0.02 BD 2.08 4.02 0.13 0.00 0.09 0.07 69.97 0.25 7.24 3.51 101.38 \n\n\n\nTable 5: Trace element in raw samples of clay \n \n\n\n\nSample name Unit 2 4 6 8 9 10 11 12 13 14 15 16 17 18 19 \n\n\n\nV (ppm) 257 187 77 17 1 3 23 4 3 1 2 4 34 1 31 \n\n\n\nCr (ppm) 182 114 65 21 7 22 23 8 70 9 10 9 41 9 50 \n\n\n\nCo (ppm) 113 108 106 79 141 80 61 82 100 66 19 16 19 83 123 \n\n\n\nNi (ppm) 36 21 28 30 33 16 13 11 20 15 10 14 22 13 12 \n\n\n\nCu (ppm) 39 31 16 6 2 1 2 10 8 4 2 11 3 BD 13 \n\n\n\nZn (ppm) 28 21 19 19 14 19 8 16 12 10 8 10 37 24 25 \n\n\n\nGa (ppm) 42 36 25 17 41 36 20 22 23 38 52 45 61 81 21 \n\n\n\nRb (ppm) 86 95 64 44 89 95 52 210 155 138 1225 3467 153 106 162 \n\n\n\nSr (ppm) 69 66 58 74 13 7 19 17 15 10 45 27 28 11 206 \n\n\n\nY (ppm) 39 42 20 9 4 18 8 84 85 21 28 64 10 6 44 \n\n\n\nZr (ppm) 312 267 154 80 22 27 133 21 42 42 16 17 80 16 274 \n\n\n\nNb (ppm) 27 28 18 5 157 38 6 17 12 19 BD BD 23 12 7 \n\n\n\nBa (ppm) 887 732 698 1145 16 6 205 24 99 259 54 25 33 82 651 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 52-58 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Oziegbe Ehitua Julius, Olarewaju Victor Ola, Ocan Ojouk Onesmus (2019) Characterization And Utilization Of Clays From Origo And Awo Southwestern \nNigeria. Malaysian Journal Of Geosciences, 3(2): 52-58. \n\n\n\n\n\n\n\nLa (ppm) 51 81 43 51 249 26 51 39 108 28 128 48 29 10 312 \n\n\n\nCe (ppm) 173 260 112 136 421 94 228 15 28 86 170 40 31 14 310 \n\n\n\nNd (ppm) 68 98 44 50 159 35 84 12 19 33 69 18 13 7 130 \n\n\n\nPb (ppm) 42 41 32 62 253 33 29 62 219 188 68 47 40 34 59 \n\n\n\nTh (ppm) 27 27 11 BD 1 9 4 7 5 11 BD BD 9 0 79 \n\n\n\nU (ppm) 11 18 8 5 4 6 8 5 7 6 4 4 8 9 7 \n\n\n\n\n\n\n\n3.2 Geochemistry \n \n\n\n\nResults of the major element analysis indicate high amount of SiO2, Al2O3, \nFe2O3, K2O and H2O-, a relatively low value of Na2O, CaO, MgO, MnO, Cr2O3 \nand P2O5 (Table 4). Trace analysis show values of zircon to range from 16 \n-312 ppm (Table 5). \n \n4. DISCUSSION \n\n\n\n\n\n\n\nKaolinite is the dominant mineral for all the samples followed by quartz. \nIn some samples, the percentage of quartz is as low as 2 % (Table 3). The \nresults of both the raw samples and the clay fraction indicate that all the \nsamples contain Kaolinite. The quartz content of the clay fraction is lower \nthan that of the raw samples. For the raw samples, it ranges from 5 to 28 \n% and from 0 to 20 % for the clay fraction.  \n \n\n\n\nThe Kaolinite content of the clay fraction is higher than that of raw \nsamples. For the raw samples it ranges from 2 to 47 % (Table 2) and from \n74 to 94 % for the clay fraction (Table 3). The decrease in the amount of \nquartz and increase in the amount of Kaolinite in the clay fraction is as \nresult of the beneficiation process which has reduced the non-clay \nfractions. The beneficiation process has also succeeded in removing some \nof the minerals totally, most of which are in small amount in the raw \nsamples. The presence of smectite in the oriented samples indicates the \npresence of swelling clay mineral (montmorillonite). The three samples \nwhere smectite was confirmed are all from Origo deposit. The high \nconcentrations of SiO2 ranges from 42.45 % to 71.56 %, Al2O3 ranges from \n14.00 % to 36.73 %, Fe2O3 from 0.18 % to 12.43 %, K2O from 0.23 % to \n7.24 % and H2O from 1.21 % to 6.5 %, which is a direct reflection of high \nabundance of Kaolinite, while the lower concentrations of K2O (0.23  % to \n7.24%), CaO (0.1 % to 0.14 %), MgO (0.3 % to 0.14 %) is in consonance \nwith the relative chemical mobility of the elements during the process of \nchemical weathering which in turn account for the high percentage of \nKaolinite. \n \n\n\n\nSamples 2,4,6, and 8 which were taken from the same pit at intervals of 1 \nm show an increase in the amount of SiO2 and a decrease in the amount of \nAl2O3, Fe2O3T, TiO2, H2O, CaO as well as the LOI from the top to the bottom \nof the pit. The decrease in the amount of Fe2O3T from top to bottom can be \nrelated to the variation in colour from red to white, which is attributable \nto the effect of direct precipitation of Fe-Oxides. The whitest of the clays \nwhich was taken from Awo has the least amount of SiO2 (42.45%) and has \nthe highest values for Al2O3, with Na2O below detection limit. The total K \nand Na contents of the clays can be attributed largely to the mica and \nfeldspar component of the residual clay which are visible in the \nunweathered pegmatite (Figures 1, 2, 3 and 4). The samples from Origo \nhave high Zr compared to those from Awo and there is a relatively high Ba \ncontent in most of the clay samples (Table 5) \n \n\n\n\n5. CONCLUSION \n \n\n\n\nThe results of mineralogical analyses of whole rock samples and clay \nfractions show that Kaolinite is the dominant clay mineral while smectite \noccurs in trace amounts. Non-clay mineral identified in the clay fraction \ninclude quartz, mica, feldspar, goethite, gibbsite, dolomite. The Kaolinite \ncontent of the clay fraction was greatly enhanced by the beneficiation \nprocess; thus, this process can be recommended to upgrade the \nmineralogical quality thereby increasing its application for different \nindustrial purposes. Comparison with standards show that Origo and Awo \nclays if properly beneficiated can be utilized by several clay-based \nindustries. The presence of significant proportion of Kaolinite in clays of \nOrigo and Awo reflects a warm, wet terrestrial climate. The residual clay \nof Origo can be used as Fire clay. The fire clay with its high percentage of \nalumina and silica is used as refractory clay. There is a match between the \nX-ray diffraction patterns, calculated modal abundance and the \nquantitative result of the pure clay fractions. \n \nRECOMMENDATION \n \n\n\n\nThe clay samples can further be characterized by means of transmission \nelectron microscopy (TEM) in order to determine the morphology, crystal \nhabit, and element distribution of the different minerals present in the \nclay. \n\n\n\n \nACKNOWLEDGMENT  \n \nMy sincere gratitude goes to Dr. Wasiu Sonibare for facilitating the \nanalysis of my samples while at Stellenbosch University. Special thanks to \nDr. O. M. Oyebanjo of the Natural History Museum, Obafemi Awolowo \nUniversity, Ile-Ife for his assistance during the field work.  \n \nREFERENCES \n \n[1] Guggenheim, S., Martin, R.T. 1995. Definition of Clay and Clay Mineral: \nJoint report of the Aipea Nomenclature and CMS Nomenclature \nCommittees. Clays and Clay Minerals, 43 (2), 255-256. \n \n[2] Bergaya, F., Lagaly, G. 2006. General introduction: clays, clay minerals, \nand clay science. In: Bergaya, F., Theng, B.K.G., Lagaly, G. (Eds.). \nDevelopments in Clay Science, first ed., 1. Elsevier, Amsterdam, 1-18. \n \n [3] Rautureau, M., de Sousa Figueiredo Gomes, C., Liewig, N., Katouzian-\nSafadi, M. 2017. Clay and clay mineral definition, in: Clays and Health: \nProperties and Therapeutic Uses. Springer International Publishing, \nCham, Switzerland, pp. 5\u201331. \n \n[4] Elueze, A.A., Ntekini, E.E., Ekwere, J.J. 1999. Compositional and \nindustrial assessment of clay bodies in Itu area, South Eastern Nigeria. \nJournal of Mining and Geology, 35 (2), 117-124. \n \n[5] Borode, J.O., Onyemaobi, O.O., Omotoyinbo, J.A. 2000. Suitability of \nsome Nigerian clay as refractory raw materials. Nigerian Journal of \nEngineering Management, 1, 14-18. \n \n[6] Fasuba, O.A., Egunlae, O., Jimoh, B. 2001. Metallurgical analysis of Orin-\nEkiti alumina clay deposit for use as a refractory. Journal of engineering \ntechnology and industrial applications, 1(4), 67-71. \n \n[7] Omowunmi, O.J. 2001. Characterization of some Nigerian clays as \nrefractory materials for furnace lining. Nigerian Journal of Engineering \nManagement, 2 (3), 1-4. \n \n[8] Irabor, P.S.A. 2002. Physical and chemical investigation on some \nNigerian kaolinite clays for use in the ceramics and allied industries. \nNigerian journal of engineering research and development, 1 (1), 54-59. \n \n[9] Gbadebo, A.M. 2002. Evaluation of Engineering and Industrial \nPotentials of Tidal Flat clays in parts of Niger Delter, Nigeria. Nigerian \nJournal of Engineering Research and Development, 1 (3), 20-27. \n \n[10] Igbokwe, P.K., Ogbuagu, J.O. 2003. Effects of process parameters on \nthe extraction of alumina from indigenous kaolinitic clay deposit. Nigerian \njournal of engineering research and development, 2 (2), 23-26. \n \n[11] Odo, J.U., Nwajagu, C.O. 2003.  Possible application of Eha-Alumona \nclay deposit in the production of refractories and ceramic ware. \nProceedings of the Nigerian materials congress and meeting of the \nNigerian Materials Research Society at Conference Hall, Engineering \nmaterials development institute, Akure, 109-111. \n \n[12] Lori, J.A., Lawal, A.O., Ekanem, E.J. 2007. Characterisation and \nOptimisation of Deferration of Kankara Clay. ARPN Journal of Engineering \nand Applied Sciences, 2 (5), 60 \u2013 74. \n \n[13] Grim, R.E. 1962. Applied Clay Mineralogy, McGraw-Hill Book Co., New \nYork. \n \n [14] Obaje, S.O., Omada, J.I., Dambatta, U.A. 2013. Clays and their \nIndustrial Applications: Synoptic Review. International Journal of Science \nand Technology, 3(5), 264 \u2013 270. \n \n[15] Wesley, L.M. 2014. Clays and Clay Minerals: Geological Origin, \nMechanical Properties and Industrial Applications. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 52-58 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Oziegbe Ehitua Julius, Olarewaju Victor Ola, Ocan Ojouk Onesmus (2019) Characterization And Utilization Of Clays From Origo And Awo Southwestern \nNigeria. Malaysian Journal Of Geosciences, 3(2): 52-58. \n\n\n\n\n\n\n\n[16] Murray, H.H. 1999. Applied Clay Mineralogy today and tomorrow, \nClay minerals, 39 \u2013 49. \n \n[17] Velde, B., Meunier, A. 2008. The origin of clay minerals in soils and \nweathered Rocks. Springer-Verlag, Berlin, Heidelberg. \n \n[18] Ross, C.S., Kerr, P.F. 1931. The clay minerals and their identity. J. \nSediment. Petrol. 1, 55-65. \n \n[19] Gruner, J.W. 1934. The structures of vermiculite and their collapse by \ndehydration. Am. Mineral., 19, 557-575. \n \n[20] Clark, G.L., Reynolds, D.H. 1936. Quantitative analysis of mine Dusts. \nInd. Eng. Chem. Anal., Ed 8, 36-40. \n \n[21] Nagelschmidt, G. 1938. On the atomic arrangement and variability of \nthe members of montmorillonite group. Mineral. Mag., 25, 140-155. \n \n[22] Bradley, W.F. 1940. The structure scheme of attapulgite. Am. Mineral., \n25, 405-410. \n \n[23] Alexander, L., Klug, H.P. 1948. Basic aspects of X-ray absorption in \nquantitative diffraction analysis of powder mixtures. Anal. Chem., 20, 886-\n889. \n \n[24] Taylor, J.C., Rui, Z. 1992. Simultaneous use of observed and calculated \nstandard profiles in quantitative XRD analysis of minerals by the \nmultiphase Rietveld method: the determination of pseudorutile in mineral \nsands products. Powder Diffr., 7, 152-161. \n \n[25] Bish, D. L., 1994. Quantitative X-ray diffraction analysis of soils. In: \nAmonette, J.E., Zelazny, L.W. (Eds.), Quantitative Methods in Soil \nMineralogy: Soil Science Society of America. Madisen, 267-295.  \n \n[26] Srodon, J. 2002. Quantitative mineralogy of sedimentary rocks with \nemphasis on clays and with applications to K-Ar dating. Mineral. Mag., 66, \n677-687. \n \n\n\n\n[27] Chipera, S.J., Bish, D.L. 2013. Fitting full x-ray diffraction patterns for \nquantitative analysis: a method for readily quantifying crystalline and \ndisordered phases. Adv. Mater. Phys. Chem., 03, 47-53. \n \n[28] Couture, R.A., Dymek, R.F. 1996. A reexamination of absorption and \nenhancement effects in X-ray fluorescence trace element analysis. Am. \nMineral., 81, 639 - 650. \n \n[29] Nayak, P.G., Singh, B.K. 2007. Instrumental Characterization of Clay by \nXRF, XRD and FTIR. Bull. Mater. Sc., 30 (3) 235-238. \n \n[30] Ahmad, S., Zaini, H., Zaharidah, A. 2009. XRF Determination of Major \nElemental Contents of Clay Samples from North-West Peninsular Malaysia. \nJournal of Nuclear and Related Technologies, 6 (1), 230 \u2013 236. \n \n[31] Elueze, A.A., Bakare, C.A., Bolarinwa, A.T. 2004. Mineralogical, \nchemical and industrial characteristics of residual clay occurrences in Iwo \nand Ijebu districts, southwestern Nigeria. Journal of Mining and \nGeology, 40 (2), 119-126. \n \n[32] Aramide, F.O., Alaneme, K.K., Olubambi, P.A., Borode, J.O. 2014. \nCharacterization of some clay deposits in South West Nigeria. Leonardo \nElectronic Journal of Practices and Technologies, 25, 46 -47. \n \n[33] Rahaman, M. A. 1976. Review of the basement geology of \nSouthwestern Nigeria. In: C. A. Kogbe (ed), Geology of Nigeria. Elizabethan \npublishing co., Lagos., 41 - 58. \n \n[34] Rahaman, M.A. 1988. Recent advances in the study of the basement \ncomplex of Nigeria. Pecambrian Geology of Nigeria. Geol. Surv. Nig., 11- 41. \n[35] Smith, J.V. 1974. Chemical and textural properties. Feldspar Minerals, \nSpringer-Verlag Berlin Heidelberg New York.  \n \n[36] B\u00fchmann, C., Rapp, I., Laker, M.C. 1996. Differences in mineral ratios \nbetween disaggregated and original clay fractions in some South African \nsoils as affected by amendments. Australian Journal of Soil Research, 34, \n909\u2013923. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2023.109.134 \n\n\n\n\n\n\n\n Cite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n\n\n\n \nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2023.109.134 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFORWARD MODELING THE GROUP AND PHASE VELOCITIES OF RAYLEIGH AND \nLOVE SURFACE WAVES BENEATH THE CENTRAL ANATOLIA: FIFTH PARAMETER \nFOR TRANSVERSE ISOTROPY \n\n\n\n\u00d6zcan \u00c7ak\u0131ra, Yusuf Arif Kutlub \n\n\n\nS\u00fcleyman Demirel University, Department of Geophysics, Isparta, T\u00fcrkiye. \n\u00c7anakkale Onsekiz Mart University, Department of Geophysics, \u00c7anakkale, T\u00fcrkiye. \n*Corresponding Author Email: ozcancakir@sdu.edu.tr \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 06 June 2023 \nRevised 01 September 2023 \nAccepted 03 October 2023 \nAvailable online 16 October 2023 \n\n\n\n The complex dynamic processes such as the magma movements in the crust and uppermost mantle result in \nthe anisotropic wave propagation. In case of the Rayleigh and Love surface waves, this anisotropy is known \nas the Rayleigh-Love wave discrepancy. The surface wave propagation beneath the Central Anatolia shows \nthis discrepancy for which we utilize the vertical transverse isotropy in the form of forward solutions. We use \nsingle-station and two-station methods to attain the observed surface wave dispersion curves in the period \nrange 7-40 s and then apply the two-dimensional (2-D) tomography to convert these curves into the velocity \nmaps defined through a 0.05o x 0.05o \u2013 sized grid. The damped least-squares technique is used to invert the \nindividual group and phase velocity curves for the one-dimensional (1-D) structure. The latter 1-D inversion \nprovides the depth profiles for the Voigt isotropic average shear-wave velocity (\ud835\udc49\ud835\udc46) and the radial anisotropy \n(\ud835\udf09). The \ud835\udc49\ud835\udc46 and \ud835\udf09 depth profiles are employed to construct the vertical transverse isotropic (VTI) velocity \nstructure beneath a grid point. Through the forward modeling, the VTI velocity structure is revised to jointly \nfit the observed Rayleigh and Love group and phase velocities. In the forward modeling, the fifth VTI \nparameter (\ud835\udf02\ud835\udc3e) measuring the departure from the elliptic condition (\ud835\udf02\ud835\udc3e = 1.0) and the anisotropic velocity \nperturbations (\ud835\udc4e\ud835\udc43 2\u2044  and \ud835\udc4e\ud835\udc46 2\u2044 ) are primarily adjusted to achieve the fit between the observed and theoretical \ndispersion curves. In the depth range ~20-30 km, the \ud835\udf02\ud835\udc3e is generally found to be \ud835\udf02\ud835\udc3e < 1.0 changing in the \nrange 0.94 \u2264 \ud835\udf02\ud835\udc3e \u2264 0.95, which is consistent with the PREM and the expression \ud835\udc4e\ud835\udc43 2\u2044 = 0.5 \ud835\udc4e\ud835\udc46 2\u2044  appears to \nbe valid for the mid-to-lower crust beneath the studied region. In particular, the Rayleigh group velocities \nrequire the setting \ud835\udf02\ud835\udc3e < 1.0 in the mid-to-lower crustal depth range. \n\n\n\nKEYWORDS \n\n\n\nAnisotropy, Central Anatolia, Crust, Forward Modeling, Inversion, Surface Wave, Tomography \n\n\n\n1. INTRODUCTION  \n\n\n\nThe convergence tectonics between Africa-Arabia and Eurasia followed by \nthe continental collision marks the beginning of the neo-tectonic regime in \nthe Anatolian plate and the surrounding area (i.e., ~12 Ma \u2013 Dewey et al., \n1986). More recently the northward subduction of the African plate \nbeneath Anatolia and the westward extrusion along the North and East \nAnatolian Fault Zones (NAFZ and EAFZ in Figure 1) mainly shapes the \nregional tectonics (e.g., Barka and Reilinger, 1997; Bozkurt, 2001; Dhont \net al., 2006). The crust and the uppermost mantle structure beneath the \nAnatolian plate is extensively deformed by many forms of tectonic activity \nsuch as magmatism, exhumation and faults (e.g., see Bartol and Govers, \n2014). For the last 20 or more years, there exists a rich repository of \nearthquake waves recorded by permanent seismic stations spread \nthroughout the Anatolian plate. In addition, there are several temporary \nstations deployed in association with certain seismic projects having \nrecording period around 2\u20133 years. We consider seismic surface wave \nrecordings at these permanent and temporary stations to study the \nvelocity structure beneath the Central Anatolia. Herein we primarily focus \non the forward modeling of the observed dispersion curves (both phase \nand group velocities) for the determination of radial anisotropic structure \n\n\n\nof the crust and uppermost mantle depth section beneath some selected \nlocations in the studied area. \n\n\n\nDepending on the crustal deformation styles (e.g., sills and dykes created \nby plutonic magmatism, stack of thin layers, fracture and crack systems, \nsheet-like melts, and oriented melt pockets), the subsurface velocity \nstructure may show anisotropic effects. This effect may be realized as \ndiscrepancy in the observed dispersion curves of Rayleigh and Love \nsurface waves (e.g., see Mordret et al., 2015; Li et al., 2016; Lee et al., 2021; \nGranados-Chavarr\u00eda et al., 2022; Zhang et al., 2022). For the present \nsurface wave data representing the Central Anatolia, we observe this \ndiscrepancy. To model the corresponding velocity structure, we utilize the \nradial anisotropy (or vertical transverse isotropy). \n\n\n\nIn fact, the crust structure beneath the Central Anatolia has attracted many \ngeophysical studies. For instance, a group researchers have examined the \ngeothermal potential of the Nev\u015fehir region (Cappadocia) cooperatively \nutilizing several geophysical methods (i.e., vertical electrical sounding, \nself-potential, magnetotelluric, and gravity) (K\u0131yak et al., 2015). In another \nwork, authors have considered the geoelectrical structure beneath the \nKarada\u011f (37.40oN, 33.14oE) stratovolcano using the magnetotelluric \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\nimaging. In the depth range 3\u20137 km, they have found low resistivities (<10 \n\u03a9m), which are interpreted as corresponding to a magma reservoir \n(Ba\u015fokur et al., 2022). Some researchers have inverted P wave arrival \ntimes of local crustal earthquakes to study the crust and upper mantle P \nwave velocity structure beneath Turkey (Wang et al., 2020). Their results \nspecific to the region beneath the Central Anatolia have shown mantle \nupwelling associated with low P wave velocities in the upper mantle.  \n\n\n\nConsidering the teleseismic earthquakes, a group researcher have \nemployed the receiver functions to analyze the crust and upper mantle S \nwave velocity structure beneath the Central Anatolia (Abgarmi et al., \n2017). They have concluded that there exists good correlation between \ncrustal thickness and elevation, which may correspond to a crust \nisostatically compensated. Ogden and Bastow have studied the crustal \nstructure of the Anatolian Plate from receiver functions where they have \nfound \ud835\udc49\ud835\udc5d \ud835\udc49\ud835\udc46\u2044  ratio greater than 1.8, which is interpreted as resulting from \n\n\n\nthe widespread mafic volcanism and ophiolite belts (Ogden and Bastow, \n2021). A group researchers have utilized results from broadband seismic \nexperiment in CD-CAT project (Continental Dynamics \u2013 Central Anatolia \nTectonics), 40Ar/39Ar geochronology, and basalt geochemistry to \nexamine the development of melting circumstances and upper mantle \n\n\n\nstructure in the Central Anatolia (Reid et al., 2017). They have interpreted \nthat mantle upwelling, lithospheric delamination, and tectonic escape \nresulted extensive mafic volcanism, high crustal temperatures, and \nplateau-type topography and that slow seismic speeds in the upper mantle \nmay indicate mostly absent or thin mantle lithosphere intruded by melts.  \n\n\n\nWe apply both single-station and two-station methods to the observed \nwaveforms to acquire the fundamental mode Rayleigh and Love phase and \ngroup velocity dispersion curves. The two-dimensional (2-D) tomographic \nimages of the observed dispersion curves are obtained at discrete points \ndefined by a 0.05o x 0.05o \u2013 sized grid. The shear-wave velocities \nrepresenting the one-dimensional crust and uppermost mantle structure \nunderneath a grid point are obtained by jointly considering the phase and \ngroup velocity dispersion curves. Herein we focus on the radial anisotropic \nsolutions. The forward modeling is employed to simultaneously fit the \nobserved Rayleigh and Love (group and phase velocity) dispersion curves. \nThe effect of the fifth parameter (i.e., \ud835\udf02\ud835\udf05 \u2013 see Kawakatsu et al., 2015) on \nthe radial anisotropic solution is particularly studied. In addition, the \nrelationship between P and S wave anisotropy parameters (i.e., \ud835\udc4e\ud835\udc43 and \ud835\udc4e\ud835\udc46) \nused to define the perturbations to the isotropic reference medium are \nadditionally explored (Nagaya et al., 2008). \n\n\n\n\n\n\n\nFigure 1: Regional geological map is presented. The map displays the major tectonic features on the Anatolian plate and the surrounding.  Turquoise lines \nrepresent Tethyan suture zones adapted from Okay and T\u00fcys\u00fcz (1999). IAES stands for Izmir-Ankara-Erzincan Suture and ITS for Inner-Tauride Suture. \nThe relative plate motions are shown by large arrows. The fault zones are shown by blue lines (Duman et al., 2018; Styron and Pagani, 2020). BZS stands \nfor Bitlis-Zagros Suture; DSFZ for Dead Sea Fault Zone; EAFZ for East Anatolia Fault Zone; KTJ for Karl\u0131ova Triple Junction; NAFZ for North Anatolia Fault \n\n\n\nZone; KM for K\u0131r\u015fehir Massif; TGFZ for Tuzg\u00f6l\u00fc Fault Zone. The studied region is shown shaded. \n\n\n\n2. REGIONAL GEOLOGY \n\n\n\nThe northern portion of the African plate northerly subducts beneath the \nAnatolian plate. For the African subduction initiation there exist two \nsubstantially different theories (e.g., see Catlos and Cemen, 2021). One \ntheory claims subduction zone initiation in the Late Cenozoic (Eocene-\nPliocene, i.e., short-lived) based on the information related to the \ntopography, and several timing data, i.e., slab age, paleomagnetism, \nmetamorphism, sedimentation, and magmatism. The other theory \nproposes earlier initiation in the Late Cretaceous-Jurassic (long-lived) \nbased on the information obtained from the tomographic images of the \nsubducted slab and ages of obducted ophiolite segments.   \n\n\n\nStarting from the early Miocene the southern branch of the Neo-Tethys \nOcean evolved in more complex fashion, i.e., collision between Eurasia and \nArabia along the Bitlis-Zagros Suture (BSZ) zone and northward \nsubduction of the African plate along the Aegean and Cyprian trenches \n(e.g., Dewey et al., 1989; Bozkurt, 2001). The Inner Tauride Suture \u2013 ITS \n(Figure 1) is considered as remnant of the Inner Tauride Ocean (part of \nthe Neo-Tethys Ocean) closed between the Anatolian and Taurus blocks \n(e.g., Parlak et al., 2013). Generally, the Anatolian plate shows high heat \nflow and has thin lithosphere (i.e., delamination). Both conditions may \nindicate viscous coupling between the weakened lower crust and the \nvertically flowing asthenosphere (e.g., see Jolivet et al., 2018).  \n\n\n\nRegarding the removal of the continental lithosphere and the associated \nasthenospheric upwelling along with elevated topography, a group \nresearcher have presented some numerical geodynamic models showing \nthe time dependent growth of lithospheric drips (G\u00f6\u011f\u00fc\u015f et al., 2017). They \nhave used this hypothesis, which involves initially thicker lithosphere, to \nexplain eventually missing or thin lithosphere beneath the Central \n\n\n\nAnatolian region. The slab deformation of the subducting African \nlithosphere is considered as transition from subduction to collision (Biryol \net al., 2011; Kounoudis et al., 2020; Lynner et al., 2021). Overall, the mantle \nprocesses related to the subducting African slab beneath the Anatolian \nplate involve subduction dynamics of slab rollback, breakoff, lateral and \nvertical tearing, fragmentation, and delamination (Artemieva and Shulgin, \n2019). \n\n\n\nIn Figure 2, superimposed on the relief map, these Neogene-to-Quaternary \nvolcanic areas (blue color triangles) show that the Anatolian plate and the \nsurrounding area were extensively intruded by magmatic rocks. There \nexist several mechanisms effective in the mantle to cause these volcanic \nactivities (Dilek and Altunkaynak, 2007; \u00d6\u011fretmen et al., 2018; Reid et al., \n2019; Catlos and Cemen, 2021). The mantle hydration brought by ancient \nslabs following the closure of the northern and southern Neo-Tethyan \nOceans and more recently by the African slab is heterogeneous beneath \nthe Anatolian plate causing flux (or hydrous) melting in the mantle (Urann \net al., 2022). The latter mechanism is particularly effective for the \ngeneration of southern Aegean arc volcanism. Extensive convergent \ntectonism across Anatolia resulted in the lithospheric shortening and \nthickening and then gravitational collapse of the excess mass accompanied \nby lateral extension along detachment faults and eventually uplift (Jolivet \net al., 2018). Decompression melting occurred in response to delamination \nand/or drip tectonics, which removed the lithospheric bottom. Upwelling \n(hot) asthenosphere to the base of the lithosphere along with partial \nmelting driven by slab tearing cause convective removal of the \nlithosphere, which is another mechanism to provide magma source for the \nvolcanism observed in Anatolia. The widespread magmatism mentioned \nabove cause crustal deformations, which may be explored by using the \nsurface wave tomography. \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 2: The subducting African lithosphere beneath Anatolia is illustrated on a three-dimensional slab model (adapted from Biryol et al., 2011; \nSchildgen et al., 2012). Asthenospheric upwelling through the slab window is assumed. The relief map, on which the studied area is shaded, shows some \n\n\n\nimportant tectonic features (see Fig. 1). The Neogene-to-Quaternary volcanic areas are indicated on the relief map using the blue color triangles (e.g., \n\u00c7oban, 2007; Reid et al., 2019; Furman et al., 2021). AP stands for Anatolian Plate, AS for Aegean Sea, MS for Mediterranean Sea, BZS for Bitlis-Zagros \n\n\n\nSuture. \n\n\n\n3. RELATED WORK \n\n\n\nIn an anisotropic solid, the propagation velocity is dependent on direction. \nThe signs of anisotropy should be carefully analyzed and should not be \nconfused with horizontal and vertical inhomogeneities such as layering or \ngradients in the Earth. There exist several methods used to detect \nsubsurface anisotropy. The most known methods are shear-wave splitting \n(or birefringence) via SKS waves, azimuthal anisotropy of body waves (Pn \nwaves) and Rayleigh-Love wave discrepancy (Anderson, 1989). In \naddition, the conversion phases (i.e., P-to-S or S-to-P) in the receiver \nfunction observations are also employed for the anisotropy analysis in the \nshallower parts of the Earth (Eckhardt and Rabbel, 2011). \n\n\n\nHerein we consider the radial anisotropy in the crust and upper mantle \nstructure, which is observed elsewhere globally and is recognized as \nRayleigh-Love wave discrepancy in the case of surface wave propagation. \nFor instance, have considered the Qinghai-Tibet Plateau and surrounding \nregions in terms of the radial anisotropy where they have reported more \nthan 6% radial anisotropy in the crust and upper mantle and have \nattributed this anisotropy to the existence of horizontal compressive \nforces due to the convergent Himalayan\u2013Tibetan system as well as the \nlithospheric rheology and the differential movements (Chen et al., 2009). \nSome researchers have evaluated the Rayleigh and Love surface wave \nrecordings after the 2008 Sichuan earthquake and have found evidence of \npositive radial anisotropy (i.e., \ud835\udc49\ud835\udc46\ud835\udc3b > \ud835\udc49\ud835\udc46\ud835\udc49) in the middle crust of Tibet, \nwhich was attributed to the mid-crustal flow and thinning of the crust \n\n\n\n(Duret et al., 2010).  \n\n\n\nAfter the great earthquakes, the radial anisotropy observed in the crust \nmay show temporal changes. In other study, author have reported that \nafter the 2004 Mw 9.2 Sumatra earthquake, there were temporal velocity \nchanges monitored from Rayleigh and Love surface wave propagations \n(Yu et al., 2021). The velocity changes were attributed to the damages in \nthe near-surface sediments created by this large earthquake and is found \nto be corresponding to ~6% radial anisotropy where the velocity change \n(i.e., reduction) in the Rayleigh surface waves was greater than that in the \nLove surface waves, i.e., an increase in radial anisotropy in the near \nsurface. There exist several studies in which the observed Rayleigh-Love \nwave discrepancy is interpreted in terms of the radial anisotropy (Mordret \net al., 2015; Li et al., 2016; Lee et al., 2021; Granados-Chavarr\u00eda et al., 2022; \nZhang et al., 2022). \n\n\n\n4. DATA AND METHOD \n\n\n\nWe employ modern seismic waveform data and data processing \ntechniques. All the details regarding the distribution of seismic stations \nand earthquakes, surface wave data and the respective data processing \nprocedures are defined in (\u00c7ak\u0131r and Kutlu, 2023). Herein we omit these \ndetails and start the analysis with the observed dispersion curves utilized \nin the 2-D tomographic inversions. Figure 3 shows the observed phase and \ngroup velocities. We refer the reader to \u00c7ak\u0131r and Kutlu for more details of \nthe data selection procedures (\u00c7ak\u0131r and Kutlu, 2023). \n\n\n\n\n\n\n\nFigure 3: Selected dispersion curves are shown. First two panels to the left give dispersion curves (green color \u2013 phase velocity and purple color \u2013 group \nvelocity) corresponding to the two-station data. Last two panels to the right presents single-station (black color) and two-station (red color) group \nvelocities superimposed on each other. Phase and group velocity averages are indicated by different color solid circles. Different color solid squares \n\n\n\nindicate group and phase velocities used as initial velocity values in the tomographic inversions. \n\n\n\nIn addition to the shear-wave velocities, we try to model the radial \nanisotropy in the studied region. There exists a fault system in the region \n(Figure 1) made of mostly strike slip motions that may cause azimuthal \n\n\n\nanisotropy that we do not currently discuss. Therefore, we must consider \nbasic measures to control the effect of the azimuthal anisotropy, which \nmay be subdued by averaging the observed dispersion curves over many \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\nray-paths approaching from a wide range of back-azimuths (Montagner, \n2007). The latter process requires a well-developed ray-path coverage, \n\n\n\notherwise the computed radial anisotropy may include some bias from the \nazimuthal anisotropy not well averaged out. \n\n\n\n\n\n\n\nFigure 4: The ray-path coverages are shown for the Love group velocities (upper left panel), for the Love phase velocities (upper right panel), for the \nRayleigh group velocities (lower left panel), and for the Rayleigh phase velocities (lower right panel). The red color represents the two-station data and \nthe black color stands for the single-station data. On the group velocity ray-path coverages, these two data sets are shown superimposed on each other. \n\n\n\nThe white color lines in each panel encircle an area in which the ray-path coverage is higher. \n\n\n\nFigure 4 shows these ray-path coverages corresponding to the observed \nRayleigh and Love surface wave group and phase velocities. We prepare \nthe ray-path coverage map by subdividing the studied region into 16 \u00d7 13 \n= 208 circular areas. A small line segment pointing outward from the \ncenter of the corresponding circle is used to represent the applicable ray-\npath azimuth. The number of line segments on each circular area gives the \nfrequency of earthquake paths approaching from varying azimuths. For all \nfour cases illustrated in Figure 4 the ray-path coverages generally develop \nwell within the area indicated by the encircling white lines. The phase \nvelocity ray-path coverages around the point (39.5oN, 36.5oE) are \nsomewhat weaker, but the latter case is alleviated by additional ray-path \ncoverages provided by the observed group velocities. \n\n\n\n4.1   Two-Dimensional Velocity Maps \n\n\n\nThe two-dimensional (2-D) velocity maps for the current surface wave \n(group and phase) arrival times are obtained by employing the fast-\nmarching method (FMM \u2013 Sethian and Popovici, 1999; Rawlinson and \nSambridge, 2003). The corresponding coordinate system is 2-D (latitudes, \nlongitudes), which is essentially a spherical shell coordinate system. The \nsoftware package provided by Rawlinson has several routines for both \ncheckerboard tests (i.e., forward travel times) and 2-D velocity maps (i.e., \ninverse travel times) (Rawlinson, 2005). The relationship between \nvelocity and travel time is non-linear and is regularized by smoothing and \ndamping methods. The averages of the observed group and phase \nvelocities (see Figure 3) are used to define the background (or initial) \nmodel for which a grid of nodes with bi-cubic B-spline interpolation is \nimplemented. In the FMM, ray-path-bending caused by heterogeneities in \nthe wave propagating medium is considered, which differs from the \nraytracing. The latter is due to the travel times computed for all grid points \nof the propagating medium. The observed travel times are iteratively fit by \nthe predicted travel times for which the objective function \ud835\udf19(\ud835\udc5a) is \nemployed as follows (Tarantola, 1987). \n\n\n\n\ud835\udf19(\ud835\udc5a) = [\ud835\udc54(\ud835\udc5a) \u2212 \ud835\udc51]\ud835\udc47\ud835\udc36\ud835\udc51\n\u22121[\ud835\udc54(\ud835\udc5a) \u2212 \ud835\udc51] + \uf065 [\ud835\udc5a \u2212 \ud835\udc5a\ud835\udc5c]\ud835\udc47\ud835\udc36\ud835\udc5a\n\n\n\n\u22121[\ud835\udc5a \u2212 \ud835\udc5a\ud835\udc5c] +\n\ud835\udefe\ud835\udc5a\ud835\udc47\ud835\udc37\ud835\udc47\ud835\udc37\ud835\udc5a    \n\n\n\n(1) \n\n\n\nwhere \ud835\udc51 and \ud835\udc54(\ud835\udc5a) show the observed and predicted travel times, \nrespectively. \ud835\udc36\ud835\udc5a\n\n\n\n\u22121 and \ud835\udc36\ud835\udc51\n\u22121 define the model and data covariance matrices, \n\n\n\nrespectively. The inverted model (\ud835\udc5a) is prevented from diverging too far \nfrom the initial (or background) model (\ud835\udc5a\ud835\udc5c) by employing the damping \n(\ud835\udf00 = 0.75) and smoothing (\ud835\udefe = 1.50) factors. The matrix \ud835\udc37 is used to \nimplement the inverted model smoothness. Eq 1 is solved twice to obtain \nthe group and phase velocity maps, and this is repeated for each surface \n\n\n\nwave period. On a mesh with size given by 0.05o x 0.05o in latitude and \nlongitude, the 2-D tomographic velocity maps are constructed. \n\n\n\nBy the application of the tomography source code, the individual \ndispersion curves are converted into the 2-D velocity maps. In Figure 5, we \nshow one example of the surface wave tomography result obtained for the \nRayleigh group phase velocities with 12-s period in which checkerboards \n(Figure 5a), velocity maps (Figure 5b), ray-path coverage (Figure 5c), and \ntravel time residuals (Figure 5d) are shown. The ray-path coverage \nsuperimposed on the checkerboard tests exhibits how effectively the \ncurrent surface wave data resolves the studied region. The checkerboard \npatterns degrade near the boundaries indicating poorer resolution \ntherein. For the remaining area away from the edges, the studied region is \ngenerally well resolved by the Rayleigh surface waves. Figure 5d indicates \nthat 63% of the observed Rayleigh travel times are fit by the theoretical 2-\nD model within \u00b12 s. In the same panel, 18% of the travel times are fit in \nthe range \u22126 \u2264 \ud835\udc47\ud835\udc5f\ud835\udc52\ud835\udc60 \u2264 \u22122 s and the other 16 % have the residuals in the \nrange 2 \u2264 \ud835\udc47\ud835\udc5f\ud835\udc52\ud835\udc60 \u2264 6 s. The remaining 3% have greater travel time residuals \n(s). The color scale (Figure 5b) shows that the 12-s Rayleigh group \nvelocities change from ~2.6 km/s to ~3.0 km/s in the studied region. The \nRayleigh group velocities around the point (39.5oN, 37.0oE) are relatively \nslower. Generally, the lower velocities show up in the south while the \nhigher velocities are evident in the north. \n\n\n\nWe show the surface wave tomography result for the 12-s Love group \nvelocities in Figure 6 where the presentation is the same as in Figure 5; i.e., \ncheckerboard tests (Figure 6a), 2-D group velocity map (Figure 6b), ray-\npath coverage (Figure 6c) and travel time residuals (Figure 6d). The \ntheoretical model fits 65% of the observed Love group arrival times within \nresiduals corresponding to \u00b12 s (Figure 6d). In the same panel, the fit in \nthe range \u22126 \u2264 \ud835\udc47\ud835\udc5f\ud835\udc52\ud835\udc60 \u2264 \u22122 s occurs with the travel time residuals \ncorresponding to 14% while the fit in the range 2 \u2264 \ud835\udc47\ud835\udc5f\ud835\udc52\ud835\udc60 \u2264 6 s has the \nproportion of 17%. The remaining 4% corresponds to greater travel time \nresiduals (s). The 12-s Love group velocities change from ~2.8 km/s to \n~3.3 km/s in the studied region (see color scale in Figure 6b). In a similar \nfashion to the group velocities in Figure 5b, the 2-D Love group velocity \nmap (Figure 6b) shows that the low velocities are generally dominant in \nthe south and that the high velocities show up in the north. The Love group \nvelocities around the point (39.5oN, 37.0oE) like the Rayleigh waves are \nrelatively slower. More surface tomography results for the Rayleigh and \nLove phase velocities at 12-s period are presented in Appendix A (Figures \nA1 and A2). \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Group velocity tomography result for 12-s Rayleigh surface waves is shown. The illustration includes 2-D checkerboards (upper left), 2-D \nvelocity maps (upper right), 2-D ray-path coverage (lower left), and travel time residuals (lower right). \n\n\n\n\n\n\n\nFigure 6: Group velocity tomography result for 12-s Love surface waves is shown. The panel arrangement is the same as in Figure 5. \n\n\n\n4.2   Inverted Velocity-Depth Profiles \n\n\n\nFollowing the 2-D tomography for the Rayleigh and Love surface waves, \nthe group and phase velocity dispersion curves with period dependence \nare constructed at these geographic locations where the studied area is \nwell resolved as indicated by the ray-path coverages (see Figure 4) and the \ncheckerboard tests (see Figures 5 and 6). The one-dimensional (1-D) \ninversion result corresponding to one of these locations (i.e., 38.0oN, \n34.0oE) is illustrated in Figure 7 where the 1-D shear-wave velocity-depth \nprofiles are acquired from the joint inversion of group (U) and phase (C) \nvelocity curves. The Love (L) surface wave inversion is given in Figure 7a \nwhile the inversion result for the Rayleigh (R) surface waves is shown in \nFigure 7b. The damped least-squares inversion technique along with the \ninitial model (blue color dashed line) was employed in the inversions \npresented in Figure 7. \n\n\n\nThe average Rayleigh phase and group velocity curves (filled circles \u2013 see \nFigure 3) are inverted for which the half-space model with 4.0 km/s shear-\nwave velocity (\ud835\udc49\ud835\udc60) is utilized. The resultant 1-D \ud835\udc49\ud835\udc60 model (i.e., blue color \ndashed line) is employed as the initial model for the remaining 1-D \ninversions (for details e.g., see \u00c7ak\u0131r, 2018 and 2019). In Figure 7, Love \n(Figure 7a) and Rayleigh (Figure 7b) surface wave phase and group \nvelocities are inverted independently. The inverted models (red color \nvelocity-depth profiles) fit well to the observed dispersion curves (black \ncolor symbols). Note that the Love and Rayleigh solutions differ in the \nshear-wave velocity, which implies anisotropic material in the subsurface. \nFigure 7c shows that the Rayleigh and Love surface waves are inverted \njointly, which corresponds to the green color velocity-depth profile in \nFigure 7d that cannot model all four dispersion curves concurrently, i.e., \nRayleigh-Love wave discrepancy is inherent.  \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 7: Inverted shear-wave velocity-depth profiles are shown for the geographic location (38.0oN, 34.0oE). Group (U) and Phase (C) velocity \ndispersion curves are jointly inverted. Love (l) surface wave inversion (a), Rayleigh (r) surface wave inversion (b), and Love/Rayleigh surface waves \n\n\n\ninverted jointly (c) are illustrated. The lower right panel (d) displays all three velocity-depth profiles together.  \n\n\n\nThe transverse isotropy with vertical symmetry axis is commonly utilized \nto model the Rayleigh-Love wave discrepancy (i.e., radial anisotropy \u2013 see \n\u00c7ak\u0131r, 2018 and 2019; Lee et al., 2021; \u00c7ak\u0131r, 2021; Granados-Chavarr\u00eda et \nal., 2022). The related expressions are as follows. \n\n\n\n\ud835\udc49\ud835\udc46\n2 = (\n\n\n\n2\ud835\udc49\ud835\udc46\ud835\udc49\n2 +\ud835\udc49\ud835\udc46\ud835\udc3b\n\n\n\n2\n\n\n\n3\n)                                                                                         (2) \n\n\n\n\n\n\n\n\ud835\udf09 = (\n\ud835\udc49\ud835\udc46\ud835\udc3b\u2212\ud835\udc49\ud835\udc46\ud835\udc49\n\n\n\n\ud835\udc49\ud835\udc46\n)                                                                                                (3) \n\n\n\nThe Voigt isotropic average shear-wave velocity (km/s) is given by \ud835\udc49\ud835\udc46 in \neq 2 and \ud835\udf09 specifies the radial anisotropy (%) in eq 3. The radial anisotropy \n(\ud835\udf09) is positive (negative) when \ud835\udc49\ud835\udc46\ud835\udc3b > \ud835\udc49\ud835\udc46\ud835\udc49  (\ud835\udc49\ud835\udc46\ud835\udc3b < \ud835\udc49\ud835\udc46\ud835\udc49). When the symmetry \naxis is slow (fast), the phase velocity surface looks like pumpkin \n(watermelon). The \ud835\udc49\ud835\udc46\ud835\udc3b  (km/s) and \ud835\udc49\ud835\udc46\ud835\udc49 (km/s) describe horizontally \npropagating horizontally (SH) and vertically (SV) polarized shear-wave \nvelocities, respectively (for more details, e.g., see \u00c7ak\u0131r, 2018 and 2019). \nWe utilize the Rayleigh (Love) surface waves to obtain the \ud835\udc49\ud835\udc46\ud835\udc49 (\ud835\udc49\ud835\udc46\ud835\udc3b) \nvelocities. \n\n\n\n4.3   Synthetic Tests for Rayleigh-Love Wave Discrepancy \n\n\n\nWe perform numerical calculations to test how well the inversion strategy \ndescribed in Figure 7 can resolve the 1-D velocity structure beneath a \nlocation under consideration. Herein the observed dispersion curves \n(both group and phase velocities) show the Rayleigh-Love wave \ndiscrepancy. We think that periodic structures in the wave propagating \nmedium such as sills and dykes along with mineral alignment driven by \nmagma flow within the conduits cause this discrepancy. The vertical \ntransverse isotropy \u2013 VTI (i.e., hexagonal symmetry with a vertical \nsymmetry axis) is effective to study such dispersion discrepancy (e.g., see \n\u00c7ak\u0131r, 2018 and 2019; Lee et al., 2021; \u00c7ak\u0131r, 2021; Granados-Chavarr\u00eda et \nal., 2022). The VTI medium is described by five elastic parameters (i.e., \n\ud835\udc34, \ud835\udc36, \ud835\udc39, \ud835\udc41 and \ud835\udc3f), which are measured from speeds of vertically and \nhorizontally propagating P and S waves (Love, 1927; Anderson, 1961; \nAnderson, 1989).  \n\n\n\nHorizontally (H) and vertically (V) spreading P waves are used to find the \n\ud835\udc34 and \ud835\udc36 parameters, i.e., \ud835\udc34 = \ud835\udf0c\ud835\udc49\ud835\udc43\ud835\udc3b\n\n\n\n2  and \ud835\udc36 = \ud835\udf0c\ud835\udc49\ud835\udc43\ud835\udc49\n2 . Vertically (SV) and \n\n\n\nhorizontally (SH) polarized horizontally transmitting S waves are utilized \nto determine the \ud835\udc41 and \ud835\udc3f parameters, i.e., \ud835\udc41 = \ud835\udf0c\ud835\udc49\ud835\udc46\ud835\udc3b\n\n\n\n2  and \ud835\udc3f = \ud835\udf0c\ud835\udc49\ud835\udc46\ud835\udc49\n2  where \ud835\udf0c \n\n\n\nis density. The body wave velocity is dependent on the incidence angle \n\n\n\nbetween the symmetry axis and the ray path, which is established by \ud835\udf02 =\n\ud835\udc39 (\ud835\udc34 \u2212 2\ud835\udc3f)\u2044 . In this configuration, the phase velocity surface is either \nconcave or convex for which the anisotropic S and P wave velocities could \nbe described as follows (e.g., Kawakatsu, 2016; Nagaya et al., 2008; \nKawakatsu, 2018). \n\n\n\n\ud835\udc49\ud835\udc46\ud835\udc3b,\ud835\udc46\ud835\udc49 = \ud835\udc49\ud835\udc46\n(0)(1 \u00b1 \ud835\udc4e\ud835\udc46 2\u2044 )      (4) \n\n\n\n\n\n\n\n\ud835\udc49\ud835\udc43\ud835\udc3b,\ud835\udc43\ud835\udc49 = \ud835\udc49\ud835\udc43\n(0)(1 \u00b1 \ud835\udc4e\ud835\udc43 2\u2044 )  (5) \n\n\n\nwhere \ud835\udc49\ud835\udc46\n(0)\n\n\n\n and \ud835\udc49\ud835\udc43\n(0)\n\n\n\n stand for the isotropic velocities describing the \nbackground structure, which are perturbed by anisotropic velocity \nchanges given by \ud835\udc4e\ud835\udc60 2\u2044  and  \ud835\udc4e\ud835\udc5d 2\u2044  for S and P waves, respectively. We utilize \n\n\n\nthe new variable (\ud835\udf02\ud835\udc3e) given to characterize the incidence angle \ndependency as follows (Kawakatsu et al., 2015): \n\n\n\n\ud835\udf02\ud835\udc3e =\n(\ud835\udc39+\ud835\udc3f)\n\n\n\n(\ud835\udc34\u2212\ud835\udc3f)1 2\u2044 (\ud835\udc36\u2212\ud835\udc3f)1 2\u2044           (6) \n\n\n\nIn addition, we employ the simplifying elliptic condition (\ud835\udf02\ud835\udc3e = 1), which \nresults in the phase slowness surfaces of body waves either elliptic (P and \nSH) or circular (SV, e.g., Kawakatsu, 2016).  \n\n\n\nThe multi-layered theoretical earth model (crust and uppermost mantle) \nis already introduced in Table 1 where the layers are assumed to be either \nisotropic (0%) or \u00b15% radial anisotropic (both P and S waves \u2013 last two \ncolumns in Table 1). The uppermost 10-km (upper crust) is considered to \nshow negative radial anisotropy (\ud835\udc49\ud835\udc46\ud835\udc3b < \ud835\udc49\ud835\udc46\ud835\udc49) characterized by \ud835\udc4e\ud835\udc43 2\u2044 =\n\ud835\udc4e\ud835\udc46 2\u2044 = 0.05 (i.e., fast symmetry axis). The underlying 10-km (upper-to-\nmiddle-crust) is assumed to be isotropic (i.e., \ud835\udc4e\ud835\udc43 2\u2044 = \ud835\udc4e\ud835\udc46 2\u2044 = 0). Starting \nfrom 20-km depth, the middle-to-lower-crust with 10-km thickness shows \npositive radial anisotropy (\ud835\udc49\ud835\udc46\ud835\udc3b > \ud835\udc49\ud835\udc46\ud835\udc49) described by \ud835\udc4e\ud835\udc43 2\u2044 = \ud835\udc4e\ud835\udc46 2\u2044 = \u22120.05 \n(i.e., slow symmetry axis). Part of the lower crust in the depth range 30-35 \nkm and part of the sub-Moho in the depth range 35-40 km are assumed to \nshow negative radial anisotropy (i.e., \ud835\udc4e\ud835\udc43 2\u2044 = \ud835\udc4e\ud835\udc46 2\u2044 = 0.05).  The \nuppermost mantle below the 40-km depth characterizing the half-space is \nsupposed to have an isotropic velocity structure (i.e., \ud835\udc4e\ud835\udc43 2\u2044 = \ud835\udc4e\ud835\udc46 2\u2044 = 0). \nSince the earth structure beneath the studied region (Anatolia) is \ngenerally hot, we model the crust and uppermost mantle with attenuation \nrepresented by \ud835\udc44\ud835\udc60 = 200 (sixth column in Table 1), which is more \nattenuative than the standard earth model (\u0130lk\u0131\u015f\u0131k, 1995; Ozer et al., 2018; \nPREM \u2013 Dziewonski and Anderson, 1981). \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\nTable 1: The layer parameters for the multi-layered crust and uppermost mantle model structure are listed. The compressional-wave quality factor is \n\n\n\nset to \ud835\udc44\ud835\udc43 = 2\ud835\udc44\ud835\udc46. Poisson\u2019s ratio is 0.25 and density is computed from \ud835\udf0c = 0.32\ud835\udc49\ud835\udc43\n(0)\n\n\n\n+ 0.77 (e.g., Berteussen, 1977). The parameters \ud835\udc4e\ud835\udc5d 2\u2044  and \ud835\udc4e\ud835\udc60 2\u2044  used \n\n\n\nin eqs 4 and 5 define the anisotropic speed changes for the P and S waves, respectively. \n\n\n\nLayer number \n\ud835\udc89 \n\n\n\n(km) \n\ud835\udc7d\ud835\udc77\n\n\n\n(\ud835\udfce)\n \n\n\n\n(km/s) \n\n\n\n\ud835\udc7d\ud835\udc7a\n(\ud835\udfce)\n\n\n\n\n\n\n\n(km/s) \n\n\n\n\ud835\udf46 \n(gr/cm3) \n\n\n\n\ud835\udc78\ud835\udc7a \n\ud835\udc82\ud835\udc77 \ud835\udfd0\u2044  \n(%) \n\n\n\n\ud835\udc82\ud835\udc7a \ud835\udfd0\u2044  \n(%) \n\n\n\n1 5 4.58 2.65 2.24 200 5 5 \n\n\n\n2 5 5.36 3.10 2.49 200 5 5 \n\n\n\n3 5 6.31 3.65 2.79 200 0 0 \n\n\n\n4 5 6.23 3.60 2.76 200 0 0 \n\n\n\n5 5 5.88 3.40 2.65 200 -5 -5 \n\n\n\n6 5 6.06 3.50 2.71 200 -5 -5 \n\n\n\n7 5 6.66 3.85 2.90 200 5 5 \n\n\n\n8 5 7.70 4.45 3.23 400 5 5 \n\n\n\n9 5 7.79 4.50 3.26 400 0 0 \n\n\n\n10 \u221e 7.87 4.55 3.29 400 0 0 \n\n\n\nWe compute theoretical Rayleigh and Love dispersion curves (both phase \nand group velocities) corresponding to the model structure in Table 1. The \nisotropic dispersion curves representing the background velocity \n\n\n\nstructure (\ud835\udc49\ud835\udc43\n(0)\n\n\n\n and \ud835\udc49\ud835\udc46\n(0)\n\n\n\n) are computed based on the normal mode \nalgorithm provided in the literature (i.e., Abo-Zena, 1979; Cakir 1989; \nChen, 1993; Cakir 1993). The radial anisotropic phase velocity curves are \ncomputed via the perturbation method presented by Montagner where \nthe relevant energy integrals based on the perturbation of the isotropic \ndispersion curves are adapted from (Montagner, 2007; Aki and Richards, \n1980). The radial anisotropic group velocity (\ud835\udc62) is computed via the \nexpression \ud835\udc62 = \ud835\udc50 (1 + (\ud835\udc47 \ud835\udc50\u2044 ) \ud835\udc51\ud835\udc50 \ud835\udc51\ud835\udc47\u2044 )\u2044  where \ud835\udc47 is period (s) and \ud835\udc50 is phase \nvelocity (km/s).  \n\n\n\nA sixth-order approximation \ud835\udc42(\ud835\udc596) is utilized for the derivative (\ud835\udc51\ud835\udc50 \ud835\udc51\ud835\udc47\u2044 ) \nalong with the phase velocity curve discretized at \ud835\udc59 = \u2206\ud835\udc47 = 0.005 s. The \n\n\n\nlatter theoretical dispersion curves are inverted using the inversion \nstrategy like the one outlined in Figure 7. The \ud835\udc49\ud835\udc46\ud835\udc49 velocities are obtained \nfrom the inversion of Rayleigh dispersion curves while the Love surface \nwaves are utilized for the \ud835\udc49\ud835\udc46\ud835\udc3b  velocities. The resolved radial anisotropy (\ud835\udf09) \nis computed using the expressions in eqs 2 and 3. The theoretical inversion \nresults are summarized in Figure 8 where the dispersion curves in Figures \n8b and 8c reflect the noisy conditions. The noise-added surface wave \nvelocities (\ud835\udc63) are emulated by utilizing the relation \ud835\udc63 = \ufffd\u0305\ufffd + \ud835\udf0e(\ud835\udc5f \u2212 0.5) \nwhere \ufffd\u0305\ufffd stands for the unperturbed surface wave velocity (either group or \nphase velocity), which is randomly (0 < \ud835\udc5f < 1) perturbed within error \nbounds specified by two different values, i.e., \ud835\udf0e = 0.1 km/s and \ud835\udf0e = 0.2 \nkm/s. The noise free condition is implemented in Figure 8a. Note that the \nperiod ranges for the phase and group velocity curves, which are selected \nin accordance with the observed dispersion curves (i.e., see Figure 3), are \ndifferent from each other. \n\n\n\n\n\n\n\nFigure 8: The theoretical [circles \u2013 Love (L) and squares \u2013 Rayleigh (R)] and the inverted (lines) group (U) and phase (C) velocities (upper row), the \nmodel structure (purple) and the inverted (blue \u2013 Rayleigh and red \u2013 Love) structures (middle row) and the radial anisotropy \u2013 VTI (lower row) are \n\n\n\npresented. The starting (initial) model is a constant velocity (4.0 km/s) half-space. \n\n\n\nIn Figure 8, the upper row shows the fit between the theoretical (depicted \nby symbols) and inverted (depicted by lines) dispersion curves while the \nmiddle row displays the inverted 1-D velocity-depth profiles \ncorresponding to the Rayleigh (R) and Love (L) surface waves. The \nresolved radial anisotropy value computed from eqs 2 and 3 for each layer \nin the model is shown in the lower row where the anisotropic depth \nsections are highlighted using different color shadings along with the \nmodel radial anisotropy values (see Table 1). The inversion results in \nFigure 8 are obtained by first inverting the Rayleigh dispersion curves for \nwhich a half-space model with 4.0 km/s shear-wave velocity is employed \nas an initial model. The resulting velocity-depth profile (\ud835\udc49\ud835\udc46\ud835\udc49) is used as an \ninitial model for the inversion of the Love surface waves (\ud835\udc49\ud835\udc46\ud835\udc3b). The middle \nrow in Figure 8 shows that the velocity inversion satisfactorily resolves \nthe sub-surface velocity features (i.e., upper crustal low velocities, \nconstant velocity middle crust and velocity reversal in lower crust), even \nin the case of noisy conditions. However, due to the resolution limits, the \n\n\n\nvelocity jumps are only resolved as velocity gradients, especially the Moho \ndiscontinuity taking place at 35-km depth.  \n\n\n\nThe case with the resolved radial anisotropy shown in the lower row \n(Figure 8) is somewhat different. The resolution power near the surface is \nacceptable, but degenerates with increasing depth. The circled numbers \nfrom 1 to 5 indicate these 10-km thick depth sections in the model having \nisotropic (0%) or anisotropic (\u00b15%) features. The first depth section \n(\u22125%) centered at 5-km depth is best resolved although the noise \ncondition slightly degrades the resolution power (e.g., see Figure 8c \ncorresponding to \ud835\udf0e = 0.2). The second depth section centered at 15-km \ndepth is modelled isotropic (0%), but the resolved radial anisotropy (\ud835\udf09) \nfalsely shows a gradient change between \u22122.5% and 2.5% running from \ntop (10-km depth) to bottom (20-km depth). In fact, the mentioned \ngradient structure acts like a transition from the first depth section (\u22125%) \nto the third depth section (5%) and the true zero value of the isotropic \nlayer is attained around the central depth point (i.e., 15 km).  \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\nThe third depth section is evident with approximately 3.6% radial \nanisotropy versus the actual 5% peaked around 25-km depth (Figure 8a). \nThe peak radial anisotropy for the third depth section occurs at relatively \nshallower depth when the noise conditions get effective (i.e., see \ud835\udf0e = 0.1 \nin Figure 8b and \ud835\udf0e = 0.2 in Figure 8c). The fourth depth section with -5% \nradial anisotropy is the one very poorly resolved. The corresponding peak \nanisotropy value is attained around 42-km depth, which is 7-km deeper \nthan the actual central depth point at 35-km indicated by the orange color \narrow in the lower row in Figure 8. The resolved radial anisotropy value \n(\ud835\udf09) for this depth section is around -1% versus the actual -5%, which also \nshows some fluctuations under the noise conditions, i.e., around -0.8% in \nFigure 8b and around -2% in Figure 8c. The deepest (fifth) depth section \nstarting at 40-km depth is modeled isotropic (0%), but because of poor \nresolution in the inversion this depth range is mixed with the upper depth \nsection while slightly anisotropic subsurface replacing the isotropic \nmaterial is implied. \n\n\n\nThe surface waves have the skin depth (\ud835\udc3b) increasing with increasing \nwavelength (\ud835\udf06) where \ud835\udc3b is proportional to the one-half wavelength of the \npropagation \u2013 \ud835\udf06 2\u2044  (Xia et al., 1999). On the other hand, the vertical \nresolution power of surface waves decreases with increasing wavelength, \ni.e., the near surface structures are resolved better compared to the deeper \nstructures. Therefore, when the seismic wavelength is longer than the \nlayer thickness, the corresponding layer is poorly resolved in the \ninversion. For instance, the group velocity dispersion curves in the upper \nrow in Figure 8 shows that the Rayleigh and Love surface waves around \n30-s period travel at ~3.15 km/s velocity, which corresponds to ~94-km \n\n\n\nwavelength (\ud835\udf06) along with ~47-km skin depth (\ud835\udc3b). Such longer period \nsurface waves have the skin depth (\ud835\udc3b) to detect the deeper structures (i.e., \nfourth and fifth depth sections in Figure 8). If we assume vertical \nresolution achieved by quarter-wavelength, then these depth sections in \nthe deeper subsurface with thickness thinner than ~23-km (i.e., \ud835\udf06 4\u2044 ) is \npoorly resolved by the corresponding surface waves (e.g., Sheriff, 1977). \nIn fact, this is what happens in Figure 8 regarding the resolution of the \nradial anisotropy deeper than 30-km. The resolution loss for the inverted \nshear-wave velocities (i.e., \ud835\udc49\ud835\udc46\ud835\udc3b  and \ud835\udc49\ud835\udc46\ud835\udc49) is evident as shown by the middle \nrow in Figure 8 but gets severe for the resolved radial anisotropy (\ud835\udf09) in \nespecially deeper depth sections (lower row in Figure 8). \n\n\n\n4.4   Forward Modeling the Observed Dispersion Curves \n\n\n\nIn Figure 9, we present the dispersion curve fits between the observed \n(colored symbols) and theoretical (colored lines) in which the theoretical \ndispersion curve is re-computed using the parameters in eqs 4 and 5 along \n\n\n\nwith \ud835\udf02\ud835\udc3e = 1. The isotropic shear-wave velocity (\ud835\udc49\ud835\udc46\n(0)\n\n\n\n \u2013 eq 4) is estimated \nfrom the Voigt isotropic average shear-wave velocity (\ud835\udc49\ud835\udc46) in eq 2. The \n\n\n\nisotropic compressional-wave velocity (\ud835\udc49\ud835\udc5d\n(0)\n\n\n\n \u2013 eq 5) is calculated from the \n\n\n\n\ud835\udc49\ud835\udc46\n(0)\n\n\n\n using a Poisson\u2019s ratio of 0.25 while the density is estimated from the \n\n\n\n\ud835\udc49\ud835\udc5d\n(0)\n\n\n\n using the empirical relation \ud835\udf0c = 0.32\ud835\udc49\ud835\udc43\n(0)\n\n\n\n+ 0.77 (e.g., Berteussen, \n\n\n\n1977). The S wave perturbation parameter (\ud835\udc4e\ud835\udc60 2\u2044 ) in eq 4 is estimated \nfrom \ud835\udf09 in eq 3 where the P wave perturbation parameter (\ud835\udc4e\ud835\udc5d 2\u2044 ) in eq 5 is \n\n\n\nset equal to \ud835\udc4e\ud835\udc60 2\u2044 . The layer thicknesses and attenuation parameters are \nkept like these given in Table 1. \n\n\n\n\n\n\n\nFigure 9: The forward modeling results for the radial anisotropic structure underneath the point (38.0oN, 34.0oE) is shown step by step. (a) isotropic \nmodeling with \ud835\udc4e\ud835\udc43 2\u2044 = \ud835\udc4e\ud835\udc46 2\u2044 = 0, (b) radial anisotropic modeling with \ud835\udc4e\ud835\udc43 2\u2044 = \ud835\udc4e\ud835\udc46 2\u2044 = \ud835\udf09 and \ud835\udf02\ud835\udc3e = 1.0, (c) radial anisotropic modeling with \ud835\udc4e\ud835\udc43 2\u2044 =\n\n\n\n\ud835\udc4e\ud835\udc46 2\u2044 = \ud835\udc50\ud835\udc56\ud835\udf09, \ud835\udc56 = 1,2,3,4 and \ud835\udf02\ud835\udc3e = 1.0, and (d) radial anisotropic modeling with \ud835\udc4e\ud835\udc46 2\u2044 = \ud835\udc50\ud835\udc56\ud835\udf09,  \ud835\udc4e\ud835\udc43 2\u2044 = \ud835\udc51\ud835\udc56 \ud835\udc4e\ud835\udc46 2\u2044  and \ud835\udf02\ud835\udc3e = 0.94. For this location, \ud835\udc501 = 0.55, \ud835\udc502 =\n0.60, \ud835\udc503 = 0.70, \ud835\udc504 = 1.00 and \ud835\udc511 = 1.0, \ud835\udc512 = 0.5, \ud835\udc513 = 0.5, \ud835\udc514 = 1.0. \n\n\n\nThe dispersion curve fits in Figure 9 (location 38o N, 34o E) are obtained \nvia the forward modelling in four steps. The modelling parameters printed \nin the lower right (Figure 9a) are valid for all panels in Figure 9 while these \nmodelling parameters progressively evolving after forward modelling \nsteps are printed in the upper left in each panel. Figure 9a corresponds to \nthe case of isotropic modelling with \ud835\udc4e\ud835\udc43 2\u2044 = \ud835\udc4e\ud835\udc46 2\u2044 = 0 (various color thin \nlines), which cannot fit all four dispersion curves, simultaneously. In Fig. \n9b, we introduce the radial anisotropy to the forward modelling by setting \n\ud835\udc4e\ud835\udc43 2\u2044 = \ud835\udc4e\ud835\udc46 2\u2044 = \ud835\udf09 (various color thick lines) that significantly increases the \ndispersion curve fitting.  However, as indicated by the black color arrow, \nthere is some poor fitting to the Rayleigh group velocities in the period \nrange ~15-30 s, which is adjusted by defining some new parameters. The \ndepth range above the 30-km depth is divided into three depth sections \neach with 10 km thickness and then newly defined \ud835\udc50\ud835\udc56 and \ud835\udc51\ud835\udc56 parameters \nare assigned to these depth ranges. Since the magnitude of the radial \nanisotropy (\ud835\udf09) is sometimes overestimated, we utilize the \ud835\udc50\ud835\udc56 parameter to \nmodify its value in the corresponding depth range as required by the \ndispersion fitting, i.e., \ud835\udc4e\ud835\udc46 2\u2044 = \ud835\udc50\ud835\udc56\ud835\udf09.  \n\n\n\nOn the other hand, the \ud835\udc51\ud835\udc56 parameter is used to coordinate the relationship \nbetween the P and S wave anisotropy, i.e., \ud835\udc4e\ud835\udc43 2\u2044 = \ud835\udc51\ud835\udc56 \ud835\udc4e\ud835\udc46 2\u2044 . The various \ncolor dashed lines in Figure 9c shows the dispersion curve fitting achieved \nby the \ud835\udc50\ud835\udc56 values listed in the figure caption where the \ud835\udc51\ud835\udc56 values are set to \nunity, i.e., the P and S wave anisotropies are equal. In Figure 9d, we further \n\n\n\nchange the \ud835\udf02\ud835\udc3e and \ud835\udc51\ud835\udc56 values to obtain the dispersion curve fitting shown \nby the various color thick lines. The \ud835\udf02\ud835\udc3e parameter, which is set to 0.94 in \nthe depth range ~20-30 km (i.e., lower crust), and the \ud835\udc51\ud835\udc56 parameter, which \nis set to 0.5 in the depth range ~10-30 km (i.e., mid-to-lower crust), \nconsiderably alleviate the latter fitting problem as indicated by the \ndifference between the group velocity curves shown by the thick and thin \nred color lines in Figure 9d. Note that in Figure 9d, for the other depth \nsections, the \ud835\udf02\ud835\udc3e and \ud835\udc51\ud835\udc56 parameters are set to unity. \n\n\n\nTable 2: Model parameters utilized to attain the theoretical \ndispersion curves in Figures 9-11 and Figures B1 and B2 in Appendix \n\n\n\nB (various color thin and thick straight lines) are listed.  \n\n\n\nRadial Anisotropy Modified Radial Anisotropy \n\n\n\n\ud835\udc49\ud835\udc46\n(0)\n\n\n\n= \ud835\udc49\ud835\udc46 \n\n\n\n\ud835\udc49\ud835\udc43\n(0)\n\n\n\n= \u221a3\ud835\udc49\ud835\udc46\n(0)\n\n\n\n\n\n\n\n\ud835\udf0c = 0.32\ud835\udc49\ud835\udc43\n(0)\n\n\n\n+ 0.77 \n\n\n\n\ud835\udc4e\ud835\udc46 2 = \ud835\udc4e\ud835\udc43 2\u2044\u2044 = \ud835\udf09 \n\n\n\n\ud835\udf02\ud835\udf05 = 1.0 \n\n\n\n\ud835\udc49\ud835\udc46\n(0)\n\n\n\n= \ud835\udc49\ud835\udc46 \n\n\n\n\ud835\udc49\ud835\udc43\n(0)\n\n\n\n= \u221a3\ud835\udc49\ud835\udc46\n(0)\n\n\n\n\n\n\n\n\ud835\udf0c = 0.32\ud835\udc49\ud835\udc43\n(0)\n\n\n\n+ 0.77 \n\n\n\n\ud835\udc4e\ud835\udc46 2\u2044 = \ud835\udc50\ud835\udc56\ud835\udf09  , \ud835\udc56 = 1,2,3,4 \n\n\n\n\ud835\udc4e\ud835\udc43 2 = \ud835\udc51\ud835\udc56 (\ud835\udc4e\ud835\udc46 2\u2044\u2044 ) \n\n\n\n\ud835\udf02\ud835\udf05 = 0.94 \u2212 0.95  , \ud835\udc57 = 2 \n\n\n\n\ud835\udf02\ud835\udf05 = 1  , \ud835\udc57 = 1,3 \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\nTable 2 summarizes the parameter relationships utilized in the forward \nmodeling of the observed dispersion curves. In fact, to achieve the results \nin Figure 9, we have followed a hybrid method where the observed \ndispersion curves are first inverted as explained in  Figure 7  and  then  the \nforward solution (Figure 9) is performed based on the information \nacquired from the latter inversion (Figure 7). In Figure 10, for better \nvisualization we display the dispersion curve fitting (middle panel) \nachieved in the last step shown in Figure 9d.  Figure 10 includes additional \ninformation where the left panel shows the Voigt isotropic average \ud835\udc49\ud835\udc46 and \nthe right panel gives the change of the radial anisotropy (\ud835\udf09) with depth. \n\n\n\nModel 2 (various color thick lines) is modified from Model 1 (various color \nthin lines) to attain the desired dispersion curve fitting. Model 1 employs \nthese \ud835\udc50\ud835\udc56, \ud835\udc51\ud835\udc56 and \ud835\udf02\ud835\udc3e parameters set to unity while Model 2 utilizes the \nmentioned parameters in accordance with the changes listed in Figures 9 \nand 10. Note that there exists a significant enhancement in the group \nvelocity dispersion curve fitting of the Rayleigh surface waves from Model \n1 to Model 2. The Rayleigh surface wave dispersion is very sensitive to the \n\ud835\udf02\ud835\udc3e parameter (Dziewonski and Anderson, 1981). The sensitivity to the \ud835\udc50\ud835\udc56 \nparameter is also high as indicated by the differences between Model 1 \nand Model 2 (see right panel in Figure 10). However, the sensitivity to the \n\ud835\udc51\ud835\udc56 parameter is subtle. We have noticed its effect in a way to change the \n\n\n\nphase velocity dispersion curve of the Rayleigh surface waves in the period \nrange ~10-20 s as indicated by the cyan color arrow in Figure 9c, i.e., due \nto \ud835\udc4e\ud835\udc43 2\u2044 = 0.5 \ud835\udc4e\ud835\udc46 2\u2044 . \n\n\n\nFor a different location (38.2o N, 35.3o E), we present an additional hybrid \nsolution in Figure 11. The 1-D shear-wave velocity-depth profiles obtained \nby the joint inversion of phase (C) and group (U) velocity curves are \nconsidered. In Figure 11, the upper left panel (a) gives the Love surface \nwave inversion (\ud835\udc49\ud835\udc46\ud835\udc3b) while the upper right panel (b) corresponds to the \nRayleigh surface wave inversion (\ud835\udc49\ud835\udc46\ud835\udc49). The observed dispersion curves \nparticularly group velocities show some signs of influence by noise, but \nstill Rayleigh and Love surface wave phase and group velocities are fit well \nby the corresponding theoretical models (red color velocity-depth \nprofiles). Note that these theoretical models not fitting the Rayleigh and \nLove surface wave data simultaneously imply the existence of anisotropic \nstructure in the subsurface. The middle-left panel (c) in Figure 11 shows \nthe inversion result related to the Rayleigh and Love surface wave data \njointly inverted. The three velocity-depth profiles obtained after the \ninversion of Rayleigh (red color), Love (blue color) and Rayleigh plus Love \n(green color) surface waves are shown in the middle-right panel (d) in \nFigure 11.  \n\n\n\n\n\n\n\nFigure 10: The forward modeling results for the radial anisotropic structure underneath the point (38.0oN, 34.0oE) is shown at the final step. The left \npanel displays the Voigt isotropic average S wave velocity, the middle panel shows the dispersion curve fits between the observed and theoretical and the \n\n\n\nright panel gives the distribution of the radial anisotropy with depth. Here \ud835\udc50\ud835\udc56 and \ud835\udc51\ud835\udc56 values are listed in Fig. 9. \n\n\n\n\n\n\n\nFigure 11: Inverted shear-wave velocity-depth profiles are shown for the geographic location (38.2oN, 35.3oE). The panel arrangement for the first four \npanels (a, b, c and d) is the same as in Fig. 7. The last panel (e) shows the result of the radial anisotropic forward modeling according to the parameters \n\n\n\nlisted in Table 2.  For this location, \ud835\udc501 = 0.80, \ud835\udc502 = 0.80, \ud835\udc503 = 0.85, \ud835\udc504 = 1.00 and \ud835\udc511 = 1.0, \ud835\udc512 = 0.5, \ud835\udc513 = 0.5, \ud835\udc514 = 1.0. \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\nThe lower panel (e) in Figure 11 displays the results corresponding to the \nforward modelling, which has the same display arrangement as in Figure \n10. The theoretical dispersion curves depicted by various color thin and \nthick lines are obtained after setting the model parameters in a forward \nmodelling fashion. Table 2 lists these model parameters separately \nutilized to attain the thin and thick line dispersion curves. For the thin line \ndispersion curves (left column in Table 2) the essential model parameters \n(i.e., \ud835\udc49\ud835\udc46 \u2013 Voigt isotropic average S wave velocity and \u03be \u2013 radial anisotropy) \nare estimated from eqs 4 and 5. In addition, the elliptic condition (\ud835\udf02\ud835\udc3e =\n1.0), Poisson ratio of 0.25 and an empirical relation between the density \n\n\n\n(\ud835\udf0c) and P wave velocity are assumed. Herein \ud835\udc49\ud835\udc46\n(0)\n\n\n\n and \ud835\udc49\ud835\udc5d\n(0)\n\n\n\n represent the \n\n\n\nisotropic (or reference) S and P wave velocities, respectively. To enhance \nthe dispersion curve fitting between the observed (various color symbols) \nand theoretical (various color thick lines) dispersion curves we modify the \nmodel parameters given in the left column of Table 2 with these given in \nthe right column. The modifications aimed to get better fitting theoretical \n(thick lines) dispersion curves include changing the P wave anisotropy (\ud835\udc51\ud835\udc56 \nparameter), departure from the elliptic condition (\ud835\udf02\ud835\udc3e parameter) and \ncorrection to the radial anisotropy (\ud835\udc50\ud835\udc56 parameter). The \ud835\udc50\ud835\udc56 and \ud835\udc51\ud835\udc56 parameter \nvalues are listed in the figure caption (see Figure 11) and the \ud835\udf02\ud835\udc3e parameter \nis depicted in the right column in Figure 11e. The modifications regarding \nthe P wave anisotropy are effective for the depth range ~10-30 km (i.e., \nmid-to-lower crust) and for the elliptic condition in the depth range ~20-\n30 km (i.e., lower crust). The model parameters (i.e., 0.94 \u2264 \ud835\udf02\ud835\udc3e \u2264 0.95 and \n\ud835\udc4e\ud835\udc43 2\u2044 = 0.5 \ud835\udc4e\ud835\udc46 2\u2044 ) appear persistent throughout the studied region.  \n\n\n\nThe forward modelling results like in Figure 11 are presented for two \nmore locations in Appendix B (see Figures B1 and B2). The Voigt isotropic \naverage shear-wave velocity (\ud835\udc49\ud835\udc46) and the radial anisotropy (\ud835\udf09) utilized in \nthe forward modeling of different geographic locations (indicated as \nlatitude, longitude) are shown in the lowermost panel in each illustration, \ni.e., left panel for the \ud835\udc49\ud835\udc46, middle panel for the dispersion curve fitting and \nthe right panel for the \ud835\udf09. In the depth range ~10-35 km, the radial \nanisotropy (%) is typically positive (\ud835\udc49\ud835\udc46\ud835\udc3b > \ud835\udc49\ud835\udc46\ud835\udc49) where the anisotropy \nvalue is modelled to be around 2-4%. In the lower half of this depth range \n(i.e., ~20-30 km), \ud835\udf02\ud835\udc3e < 1.0. For these different geographic locations, the \ndepth profiles for the \ud835\udc49\ud835\udc46 (left panel) and the \ud835\udf09 (right panel) are shown \ntogether in Appendix C (Figure C1). Therein the \ud835\udf09 values are given as \ncomputed from eq 3, i.e., before the forward modeling. Note that in Figure \nC1, the radial anisotropy (\ud835\udf09) for particularly the deeper depth section (>30 \nkm) gets somewhat dispersed from one location to another, which is \nprobably due to the decrease in the resolution power with the increasing \ndepth (see Figure 8). \n\n\n\n5. DISCUSSIONS AND CONCLUSION \n\n\n\nThe 2-D tomographic inversions described above have provided us with \nRayleigh-and-Love group and phase velocities (i.e., four sets of dispersion \ncurves) at these geographical points defined by a 0.05o x 0.05o \u2013 sized grid. \nAt each grid point, the Rayleigh group and phase velocity curves are jointly \ninverted via the damped least-squares to obtain the pertinent 1-D shear-\nwave velocity-depth profile, which gives the \ud835\udc49\ud835\udc46\ud835\udc49. The same is repeated for \nthe Love group and phase velocity curves to attain the \ud835\udc49\ud835\udc46\ud835\udc3b  and then eqs 2 \nand 3 are used to compute the relevant depth-dependent radial anisotropy \n(\ud835\udf09) and Voigt isotropic average shear-wave velocity (\ud835\udc49\ud835\udc46). The \ud835\udf09 and \ud835\udc49\ud835\udc46 \nalong with \ud835\udf02\ud835\udc3e set to a specific value are utilized in eqs 4 and 5 to model the \nsubsurface in terms of the radial anisotropy corresponding to a grid point. \nEventually a fit between the observed and the theoretical dispersion curve \ncomputed from the earth model is sought. \n\n\n\nThe seismic surface waves are primarily sensitive to the S wave velocity \nwhile the effect of the P wave velocity and density is secondary. In the \nanisotropic case, the P wave anisotropy may primarily affect the wave \npropagation, particularly the Rayleigh phase velocities. For the vertical \ntransverse isotropy \u2013 VTI or radial anisotropy, the change from the elliptic \ncondition (\ud835\udf02\ud835\udc3e = 1.0), which results concave or convex phase velocity \nsurfaces, is mainly controlled by the fifth parameter \ud835\udf02\ud835\udc3e besides the A, C, N \nand L parameters. The P wave phase velocity surface is convex (concave) \nwhen \ud835\udf02\ud835\udc3e > 1.0 (\ud835\udf02\ud835\udc3e < 1.0) and the effect of \ud835\udf02\ud835\udc3e on the phase velocity surface \nis opposite for the SV-waves (e.g., Kawakatsu et al., 2015). Currently, the \ndeparture from the elliptic condition was relatively simple, i.e., only \ncertain depth range and periods along with the group and phase velocities \nwere sufficient to attain the desired curve fitting in the forward modelling \n(Figures 9-11 and Figures B1 and B2 in Appendix B). The \ud835\udf02\ud835\udc3e parameter \nmay change in multiple depth ranges, the relation between \ud835\udc4e\ud835\udc46 2\u2044  and \ud835\udc4e\ud835\udc43 2\u2044  \nmay be more complex or Poisson\u2019s ratio may have a wider range (e.g., 0.20-\n0.30) in the wave propagating medium. Then one may need to consider a \nsophisticated algorithm capable of multi-parameter inversion. In this \nrespect, the genetic or neighborhood algorithm may be more beneficial \n(e.g., Lawrence and Wiens, 2004; Yao et al., 2008). Perhaps, a combination \nof deterministic (current approach) and stochastic (e.g., genetic or \n\n\n\nneighborhood) methods should be preferred over one of these approaches \napplied alone. Such combinations of different algorithms require forward \ncomputations of thousands of dispersion curves, which is beyond our \ncurrent computational capacity. Therefore, we stay with the deterministic \napproach. \n\n\n\nThe simultaneous fits between the observed and theoretical dispersion \ncurves (Figures 9-11 and Figures B1 and B2 in Appendix B) indicate that \nthe inversion strategy currently adapted (i.e., vertical transverse isotropy \nthrough eqs 4 and 5) suitably resolves the radial anisotropic structure \nbeneath an inversion location. Note that despite careful analysis in the \ndata selection processes, there could be errors persistent in the present \nsurface wave observations due to the scattering, multi-pathing and higher \nmode interference causing some fitting problems in the observed \ndispersion curves, particularly group velocities (for instance, location \n38.2o N, 35.3o E in Figure 11) (\u00c7ak\u0131r and Kutlu, 2023). In addition, \ndifferences in resolution between Rayleigh and Love surface waves and \ndifferences in skin depth between group and phase velocities may yield \nsome inconsistencies in the inverted velocity-depth profiles. We employ a \ndamping parameter in the inversion to suppress such inconsistencies so \nthat velocity-depth profiles geologically simple yet reasonably well \nexplaining the observed data are primarily aimed. \n\n\n\nThe studied region (Central Anatolia) taking place above the northward \nsubducting African slab has complex tectonic history manifested by broad \nfault and suture zones, volcanic intrusions and eruptions, exhumed \nmassifs and wide sedimentary basins (Figure 1 and 2). The region is \ndensely covered by broadband seismograph stations. We utilize the local \nand regional Rayleigh and Love surface waves recorded at these seismic \nstations for tomographic investigation of the crust and uppermost mantle \nvelocity structure beneath this remarkable region (\u00c7ak\u0131r and Kutlu, 2023). \nThe observed Rayleigh and Love surface wave dispersion curves (group \nand phase velocities) show discrepancy, i.e., an isotropic model cannot fit \nall four dispersion curves simultaneously. The vertical transverse isotropy \n\u2013 VTI is successfully applied to resolve the issue of dispersion curve \ndiscrepancy, i.e., all four dispersion curves are concurrently fit by a VTI \nmodel (Figures 9-11 and Figures B1 and B2 in Appendix B). The case of \n\ud835\udf02\ud835\udc3e < 1.0 (i.e., 0.94 \u2264 \ud835\udf02\ud835\udc3e \u2264 0.95) in the lower crust is consistent with the \nPREM and may indicate a horizontal layering of sills developed after the \nNeogene to Quaternary magmatism in the region see Figure 2) (Di \nGiuseppe et al., 2018; Kawakatsu, 2016; Dziewonski and Anderson, 1981; \nde Wit et al., 2014). \n\n\n\nACKNOWLEDGEMENTS  \n\n\n\nWe would like to thank the anonymous reviewers for critically reviewing \nthe manuscript. KOERI (Kandilli Observatory and Earthquake Research \nInstitute) is thankfully recognized for providing the seismograms. We \ngratefully acknowledge the use of Generic Mapping Tool (GMT \u2013) in \nseveral figure illustrations.  \n\n\n\nREFERENCES \n\n\n\nAbgarmi, B., Delph, J.R., Ozacar, A.A., Beck, S.L., Zandt, G., Sandvol, E., \nTurkelli, N., and Biryol, C.B., 2017. Structure of the crust and African \nslab beneath the central Anatolian plateau from receiver functions: \nNew insights on isostatic compensation and slab dynamics, \nGeosphere, 13, Pp. 1774-1787. doi: 10.1130/GES01509.1 \n\n\n\nAbo-Zena, A., 1979. Dispersion function computations for unlimited \nfrequency values, Geophysical Journal International, 58, 91\u2013105. \ndoi: 10.1111/j.1365-246X.1979.tb01011.x \n\n\n\nAki, K., Richards, P.G., 1980. Quantitative Seismology: Theory and \nMethods, pp. 286\u2013298, W. H. Freeman, San Francisco, CA. \n\n\n\nAnderson, D.L., 1961. Elastic wave propagation in layered anisotropic \nmedia, Journal of Geophysical Research, 66, Pp. 2953\u20132963. \n\n\n\nAnderson, D.L., 1989. Theory of the Earth. Boston, MA: Blackwell Scientific \nPublications. \n\n\n\nArtemieva, I.M., and Shulgin, A., 2019. Geodynamics of Anatolia: \nlithosphere thermal structure and thickness, Tectonics, 38, Pp. \n4465-4487. doi: 10.1029/2019TC005594 \n\n\n\nBarka, A.A., and Reilinger, R., 1997. Active tectonics of the Mediterranean \nregion: deduced from GPS, neotectonic and seismicity data, Annali \nDi Geofisica, XL, Pp. 587\u2013610. doi: 10.4401/ag-3892 \n\n\n\nBartol, J., and Govers, R., 2014. A single cause for uplift of the Central and \nEastern Anatolian plateau? Tectonophysics, 637, Pp. 116-136. doi: \n10.1016/j.tecto.2014.10.002 \n\n\n\nBa\u015fokur, A.T., Ko\u00e7yi\u011fit, A., Hac\u0131o\u011flu, \u00d6., Arslan, H.I., and Meqbel, N., 2022. \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\nMagnetotelluric imaging of the shallow-seated magma reservoir \nbeneath the Karada\u011f stratovolcano, Central Anatolia, Turkey, \nJournal of Volcanology and Geothermal Research, 427, Pp. 107567. \ndoi: 10.1016/j.jvolgeores.2022.107567 \n\n\n\nBerteussen, K.A., 1977. Moho depth determinations based on spectral \nanalysis of NORSAR long period P waves, Physics of the Earth and \nPlanetary Interiors, 134, Pp. 13\u201327. doi: 10.1016/0031-\n9201(77)90006-1 \n\n\n\nBiryol, C. B., Beck, S. L., Zandt, G., and \u00d6zacar, A. A., 2011. Segmented \nAfrican lithosphere beneath the Anatolian region inferred from \nteleseismic P-wave tomography, Geophysical Journal International, \n184, Pp. 1037-1057. doi: 10.1111/j.1365-246X.2010.04910.x \n\n\n\nBozkurt, E., 2001. Neotectonics of Turkey-a synthesis, Geodinamica Acta, \n14, Pp. 3\u201330. doi: 10.1016/S0985-3111(01)01066-X \n\n\n\nCakir, O., 1989. High frequency Rayleigh and Love waves, MS Thesis, Texas \nTech University, Lubbock Texas, United States. \n\n\n\nCakir, O., 1993. Propagation of high frequency P and S waves under oceanic \nstructures, PhD Dissertation, Texas Tech University, Lubbock Texas, \nUnited States. \n\n\n\n\u00c7ak\u0131r, \u00d6., 2018. Seismic crust structure beneath the Aegean region in \nsouthwest Turkey from radial anisotropic inversion of Rayleigh and \nLove surface waves, Acta Geophysica, 66, Pp. 1303\u20131340. doi: \n10.1007/s11600-018-0223-1 \n\n\n\n\u00c7ak\u0131r, \u00d6., 2019. Love and Rayleigh waves inverted for vertical transverse \nisotropic crust structure beneath the Biga Peninsula and the \nsurrounding area in NW Turkey, Geophysical Journal International, \n216, Pp. 2081\u20132105. doi: 10.1093/gji/ggy538. \n\n\n\n\u00c7ak\u0131r, \u00d6., 2021. Transverse Isotropic Crust Structure Beneath the \nNorthwest and Central North Anatolia Revealed by Seismic Surface \nWaves Propagation, Malaysian Journal of Geosciences, 5, Pp. 41\u201350. \ndoi: 10.26480/mjg.02.2021.41.50. \n\n\n\n\u00c7ak\u0131r, \u00d6., and Kutlu, Y.A., 2023. A New Method for Selecting the Phase and \nGroup Velocity Dispersion Curves of Rayleigh and Love Surface \nWaves: Real Data Case of Central Anatolia, Turkey (T\u00fcrkiye), \nIndonesian Journal of Earth Sciences, 3, Pp. 795. doi: \n10.52562/injoes.2023.795 \n\n\n\nCatlos, E.J., and Cemen, I., 2021. A Review of the Dynamics of Subduction \nZone Initiation in the Aegean Region, AGU Books. doi: \n10.1002/essoar.10508919.1 \n\n\n\nChen, X., 1993. A systematic and efficient method of computing normal \nmodes for a multilayered half-space, Geophysical Journal \nInternational, 115, Pp. 391\u2013409. doi: 10.1111/j.1365-\n246X.1993.tb01194.x \n\n\n\nChen, Y., Badal, J., and Zhang, Z., 2009. Radial anisotropy in the crust and \nupper mantle beneath the Qinghai-Tibet Plateau and surrounding \nregions, Journal of Asian Earth Sciences, 36, Pp. 289\u2013302. doi: \n10.1016/j.jseaes.2009.06.011 \n\n\n\n\u00c7oban, H., 2007. Basalt magma genesis and fractionation in collision- and \nextension-related provinces: A comparison between eastern, \ncentral and western Anatolia, Earth-Science Reviews, 80, Pp. 219-\n238. doi: 10.1016/j.earscirev.2006.08.006 \n\n\n\nDewey, J.F., Hempton, M.R., Kidd, W.S.F., \u015earo\u011flu, F., and \u015eeng\u00f6r, A.M.C., \n1986. Shortening of continental lithosphere: the neotectonics of \nEastern Anatolia \u2013 a young collision zone, Collision Tectonics, \nGeological Society Special Publication, 19, Pp. 3-36. \ndoi:10.1144/gsl.sp.1986.019.01.01 \n\n\n\nDewey, J.F., Helman, M.L., Turco, E., Hutton, D.H.W., and Knott, S.D., 1989. \nKinematics of the western Mediterranean, in: Coward M.P., Dietrich \nD., Park R.G.  (Eds.), Alpine Tectonics, Geological Society Special \nPublication no. 45, Geological Society, London, Pp. 265- 283. \n\n\n\nde Wit, R.W.L., K\u00e4ufl, P.J., Valentine, A.P., and Trampert, J., 2014. Bayesian \ninversion of free oscillations for Earth's radial (an)elastic structure, \nPhysics of the Earth and Planetary Interiors, 237, Pp. 1\u201317. doi: \n10.1016/j.pepi.2014.09.004 \n\n\n\nDhont, D., Chorowicz, J., and Luxey, P., 2006. Anatolian escape tectonics \ndriven by Eocene crustal thickening and Neogene\u2013Quaternary \nextensional collapse in the eastern Mediterranean region Y. Dilet, S. \nPavilides (Eds.), Postcollisional tectonics and magmatism in the \nMediterranean region and Asia, Geol. Soc. Spec. Pap., 409, Pp. 331-\n\n\n\n462. doi: 10.1130/2006.2409(21) \n\n\n\nDi Giuseppe, P., Agostini, S., Manetti, P., Savas\u00e7in, M.Y., and Conticelli, S., \n2018. Sub-lithospheric origin of Na-alkaline and calc-alkaline \nmagmas in a post-collisional tectonic regime: Sr-Nd-Pb isotopes in \nrecent monogenetic volcanism of Cappadocia, Central Turkey, \nLithos, 316, Pp. 304-322. doi: 10.1016/j.lithos.2018.07.018 \n\n\n\nDilek, Y., and Altunkaynak, \u015e., 2007. Cenozoic crustal evolution and mantle \ndynamics of post collisional magmatism in Western Anatolia, \nInternational Geology Review, 49, Pp. 431-453. doi: 10.2747/0020-\n6814.49.5.431  \n\n\n\nDuman, T.Y., \u00c7an, T., Emre, \u00d6., Kadirio\u011flu, F.T., Ba\u015far\u0131r-Ba\u015ft\u00fcrk, N., K\u0131l\u0131\u00e7, T., \nArslan, S., \u00d6zalp, S., Kartal, R.F., Kalafat, D., Karakaya, F., Ero\u011flu-Azak, \nT., \u00d6zel, N.M., Ergintav, S., Akkar, S., Alt\u0131nok, Y., Tekin, S., Cing\u00f6z, A., \nand Kurt, A.I., 2018. Seismotectonics database of Turkey, Bulletin of \nEarthquake Engineering, 16, Pp. 3277-3316. doi: 10.1007/s10518-\n016-9965-9 \n\n\n\nDuret, F., Shapiro, N.M., Cao, Z., Leshin, V., Molnar, P., and Roecker, S., 2010. \nSurface wave dispersion across Tibet: Direct evidence for radial \nanisotropy in the crust, Geophysical Research Letter, 37, Pp. \nL16306. doi:10.1029/2010GL043811 \n\n\n\nDziewonski, A. M., and Anderson, D. L., 1981. Preliminary reference Earth \nmodel, Physics of the Earth and Planetary Interiors, 25, Pp. 297\u2013356. \n\n\n\nEckhardt, C., and Rabbel, W., 2011. P-receiver functions of anisotropic \ncontinental crust: a hierarchic catalogue of crustal models and \nazimuthal waveform patterns, Geophysical Journal International, \n187, Pp. 439\u2013479. doi: 10.1111/j.1365-246X.2011.05159.x \n\n\n\nG\u00f6\u011f\u00fc\u015f, O.H., Pysklywec, R.N., \u015eeng\u00f6r, A.M.C., and G\u00fcn, E., 2017. Drip \ntectonics and the enigmatic uplift of the Central Anatolian Plateau, \nNature Communications, 8, Pp. 1538. doi: 10.1038/s41467-017-\n01611-3 \n\n\n\nGranados-Chavarr\u00eda, I., Cal\u00f2, M., Figueroa-Soto, \u00c1., and Jousset, P., 2022. \nSeismic imaging of the magmatic plumbing system and geothermal \nreservoir of the Los Humeros caldera (Mexico) using anisotropic \nshear wave models, Journal of Volcanology and Geothermal \nResearch, 421, Pp. 107441. doi: 10.1016/j.jvolgeores.2021.107441 \n\n\n\n\u0130lk\u0131\u015f\u0131k, O.M., 1995. Regional heat flow in western Anatolia using silica \ntemperature estimates from thermal springs, Tectonophysics, 244, \nPp. 175\u2013184. doi: 10.1016/0040-1951(94)00226-Y \n\n\n\nJolivet, L., Menant, A., Clerc, C., Sternai, P., Bellahsen, N., Leroy, S., Pik, R., \nStab, M., Faccenna, C., and Gorini, G., 2018. Extensional crustal \ntectonics and crust-mantle coupling, a view from the geological \nrecord, Earth Science Reviews, 185, Pp. 1187-1209. doi: \n10.1016/j.earscirev.2018.09.010 \n\n\n\nKawakatsu, H., 2016. A new fifth parameter for transverse isotropy, \nGeophysical Journal International, 204, Pp. 682\u2013685. doi: \n10.1093/gji/ggv479 \n\n\n\nKawakatsu, H., Montagner, J.-P., and Song, T.-R.A., 2015. On DLA\u2019s \u03b7, in the \ninterdisciplinary Earth: a volume in Honor of Don L. Anderson, GSA \nAGU, 514, Pp. 33\u201338. doi:10.1130/2015.2514(03) \n\n\n\nKawakatsu, H., 2018. A new fifth parameter for transverse isotropy III: \nReflection and transmission coefficients, Geophysical Journal \nInternational, 213, Pp. 426\u2013433. doi: 10.1093/gji/ggy003 \n\n\n\nK\u0131yak, A., Karavul, C., G\u00fclen, L., Pek\u015fen, E., and K\u0131l\u0131\u00e7, A.R., 2015. Assessment \nof geothermal energy potential by geophysical methods: Nev\u015fehir \nRegion, Central Anatolia, Journal of Volcanology and Geothermal \nResearch, 295, Pp. 55-64. doi: 10.1016/j.jvolgeores.2015.03.002 \n\n\n\nKounoudis, R., Bastow, I.D., Ogden, C.S., Goes, S., Jenkins, J., Grant, B., and \nBraham, C., 2020. Seismic tomographic imaging of the Eastern \nMediterranean mantle: implications for terminal-stage subduction, \nthe uplift of Anatolia, and the development of the North Anatolian \nFault, Geochemistry, Geophysics, Geosystems, 21. doi: \n10.1029/2020GC009009 \n\n\n\nLawrence, J.F., and Wiens, D.A., 2004. Combined Receiver-Function and \nSurface Wave Phase-Velocity Inversion Using a Niching Genetic \nAlgorithm: Application to Patagonia, Bulletin of the Seismological \nSociety of America, 94, Pp. 977-987. doi: 10.1785/0120030172 \n\n\n\nLee, S.J., Kim, S., Rhie, J., Kang, T.S., and Kim, Y.H., 2021. Upper crustal shear \nwave velocity and radial anisotropy beneath Jeju Island volcanoes \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\nfrom ambient noise tomography, Geophysical Journal International, \n225, Pp. 1332-1348. doi: 10.1093/gji/ggab026 \n\n\n\nLi, L., Li, A., Murphy, M.A., and Fu, Y.V., 2016. Radial anisotropy beneath \nnortheast Tibet, implications for lithosphere deformation at a \nrestraining bend in the Kunlun fault and its vicinity, Geochemistry, \nGeophysics, Geosystems, 17, Pp. 3674-3690. doi: \n10.1002/2016GC006366 \n\n\n\nLove, A. E. H., 1927. A Treatise on the Mathematical Theory of Elasticity, \n4th edn, Cambridge Univ. Press, Cambridge. \n\n\n\nLynner, C., Delph, J.R., Portner, D.E., Beck, S. L., Sandvol, E., and Ozacar, A.A., \n2021. Slab induced mantle upwelling beneath the Anatolian plateau, \nGeophysical Research Letters, e2021GL097451. doi: \n10.1029/2021GL097451 \n\n\n\nMontagner, J.P., 2007. Deep Earth structure\u2014upper mantle structure: \nglobal isotropic and anisotropic elastic tomography, in Seismology \nand Structure of the Earth, Treatise on Geophysics, 1, Pp. 559\u2013590, \neds Dziewonski, A.M. & Romanowicz, B., Elsevier. \n\n\n\nMordret, A., Rivet, D., Land\u00e8s, M., and Shapiro, N., 2015. Three-dimensional \nshear velocity anisotropic model of Piton de la Fournaise Volcano \n(La R\u00e9union Island) from ambient seismic noise, Journal of \nGeophysical Research, 120, Pp. 406-427. doi: \n10.1002/2014JB011654 \n\n\n\nNagaya, M., Oda, H., Akazawa, H., and Ishise, M., 2008. Receiver Functions \nof Seismic Waves in Layered Anisotropic Media: Application to the \nEstimate of Seismic Anisotropy, Bulletin of the Seismological Society \nof America, 98, Pp. 2990-3006. doi: 10.1785/0120080130 \n\n\n\nOgden, C.S., and Bastow, I.D., 2021. The crustal structure of the Anatolian \nPlate from receiver functions and implications for the uplift of the \ncentral and eastern Anatolian plateaus, Geophysical Journal \nInternational, 229, 1041\u20131062. doi: 10.1093/gji/ggab513. \n\n\n\n\u00d6\u011fretmen, N., Cipollari, P., Frezza, V., Faranda, C., Karanika, K., Gliozzi, E., \nGiuditta, R., and Cosentino, D., 2018. Evidence for 1.5 km of uplift of \nthe Central Anatolian Plateau\u2019s southern margin in the last 450 kyr \nand implications for its multiphased uplift history, Tectonics, 37, Pp. \n359\u2013390. doi: 10.1002/2017TC004805 \n\n\n\nOkay, A.I., and T\u00fcys\u00fcz, O., 1999. Tethyan sutures of northern Turkey. In: \nDurand, B., Jolivet, L., Horv\u00e1th, D., S\u00e9rranne, M. (eds), The \nMediterranean basins: tertiary extension within the Alpine Orogen, \nvol 156, Geological Society of London (Special Publication), London, \n475\u2013515. doi: 10. 1144/ GSL. SP. 1999. 156. 01. 22 \n\n\n\nOzer, C., Gok, E., and Polat, O., 2018. Three-dimensional seismic velocity \nstructure of the Aegean Region of Turkey from local earthquake \ntomography, Annals of Geophysics, 61, 1, SE111. doi: 10.4401/ag-\n7543 \n\n\n\nParlak, O., Karao\u011flan, F., R\u0131zao\u011flu, T., Kl\u00f6tzli, U., Koller, F., and Billor, Z., \n2013. U\u2013Pb and 40Ar\u201339Ar geochronology of the ophiolites and \ngranitoids from the Tauride belt: implications for the evolution of \nthe Inner Tauride suture, Journal of Geodynamics, 65, Pp. 22-37. doi: \n10.1016/j.jog.2012.06.012 \n\n\n\nRawlinson, N., and Sambridge, M., 2003. Seismic traveltime tomography of \nthe crust and lithosphere. Advances in Geophysics, 46, Pp. 81\u2013198. \ndoi: 10.1016/S0065-2687(03)46002-0 \n\n\n\nRawlinson, N., 2005. FMST: Fast marching surface tomography package, \nResearch School of Earth Science, Australian National University, \nCanberra. \n\n\n\nReid, M.R., Schleiffarth, W.K., Cosca, M.A., Delph, J.R., Blichert-Toft, J., and \n\n\n\nCooper, K.M., 2017. Shallow melting of MORB-like mantle under hot \ncontinental lithosphere, Central Anatolia, Geochemistry, \nGeophysics, Geosystems, 18, Pp. 1866-1888. doi: \n10.1002/2016GC006772 \n\n\n\nReid, M.R., Delph, J.R., Cosca, M.A., Schleiffarth, W.K., and Ku\u015fcu, G.G., 2019. \nMelt equilibration depths as sensors of lithospheric thickness \nduring Eurasia-Arabia collision and the uplift of the Anatolian \nPlateau, Geology, 47, Pp. 943-947. doi: 10.1130/G46420.1 \n\n\n\nSchildgen, T.F., Cosentino, D., Caruso, A., Buchwaldt, R., Y\u0131ld\u0131r\u0131m, C., \nBowring, S.A., Rojay, B., Echtler, H., and Strecker, M.R., 2012. Surface \nexpression of eastern Mediterranean slab dynamics: neogene \ntopographic and structural evolution of the southwest margin of the \nCentral Anatolian Plateau, Turkey, Tectonics, 31, Pp. TC2005. doi: \n10.1029/2011TC003021 \n\n\n\nSethian, J.A., and Popovici, A.M., 1999. 3-D traveltime computation using \nthe fast marching method, Geophysics, 64, Pp. 516-523. doi: \n10.1190/1.1444558 \n\n\n\nSheriff, R.E., 1977. Limitations on resolution of seismic reflections and \ngeologic detail derivable from them C.E Payton (Ed.), Seismic \nStratigraphy-Applications to Hydrocarbon Exploration, AAPG Mem., \n16, Pp. 3-14. doi: 10.1306/M26490C1 \n\n\n\nStyron, R., and Pagani, M., 2020. The GEM Global Active Faults Database, \nEarthquake Spectra, 36(S1), Pp. 160\u2013180. doi: \n10.1177/8755293020944182 \n\n\n\nTarantola, A., 1987. Inverse problem theory. Elsevier Science Company \nInc., Amsterdam, pp 187\u2013255 \n\n\n\nUrann, B.M., Le Roux, V., Jagoutz, O., M\u00fcntener, O., Behn, M. D., and Chin, E. \nJ., 2022. High water content of arc magmas recorded in cumulates \nfrom subduction zone lower crust, Nature Geoscience, Pp. 1-8. doi: \n10.1038/s41561-022-00947-w \n\n\n\nWang, H., Huang, Z., Eken, T., Kele\u015f, D., Kaya-Eken, T., Confal, J.M., Erman, \nC., Yolsal-\u00c7evikbilen, S., Zhao, D., and Taymaz, T., 2020. Isotropic and \nanisotropic P wave velocity structures of the crust and uppermost \nmantle beneath Turkey, Journal of Geophysical Research, 125, Pp. \ne2020JB019566. doi: 10.1029/2020JB019566 \n\n\n\nWessel, P., Smith, W.H.F., Scharroo, R., Luis, J.F., and Wobbe, F., 2013. \nGeneric mapping tools: improved version released, EOS, Trans. \nAmerican Geophysical Union, 94, Pp. 409\u2013410. doi: \n10.1002/2013EO450001 \n\n\n\nXia, J., Miller, R.D., and Park, C.B., 1999. Estimation of near-surface shear-\nwave velocity by inversion of Rayleigh waves, Geophysics, 64, Pp. \n691\u2013700. doi: 10.1190/1.1444578 \n\n\n\nYao, H., Beghein, C., and Van Der Hilst, R.D., 2008. Surface wave array \ntomography in SE Tibet from ambient seismic noise and two-station \nanalysis - II. Crustal and upper-mantle structure, Geophysical \nJournal International, 173, Pp. 205\u2013219. doi: 10.1111/j.1365-\n246X.2007.03696.x \n\n\n\nYu, W., Song, T.R.A., Su, J., and Lin, J.T., 2021. Rayleigh-Love Discrepancy \nHighlights Temporal Changes in Near-Surface Radial Anisotropy \nAfter the 2004 Great Sumatra Earthquake, Journal of Geophysical \nResearch, 126, Pp. e2021JB022896.  \n\n\n\nZhang, Y., Yao, H., Xu, M., and Liu, B., 2022. Radial anisotropy in the crust \nbeneath Fujian and the Taiwan strait from direct surface-wave \ntomography, Tectonophysics, 827, Pp. 229270. doi: \n10.1016/j.tecto.2022.229270 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\nAppendix A: More examples regarding the two-dimensional (2-D) Rayleigh and Love phase velocity maps \n\n\n\n\n\n\n\nFigure A1. Phase velocity tomography result for 12-s Rayleigh surface waves is shown. The panel arrangement is the same as in Fig. 5. \n\n\n\n\n\n\n\nFigure A2. Phase velocity tomography result for 12-s Love surface waves is shown. The panel arrangement is the same as in Fig. 5. \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\nAppendix B: More examples regarding the hybrid (first inverse and then forward) modeling of the radial anisotropy \n\n\n\n\n\n\n\nFigure B1. Inverted shear-wave velocity-depth profiles are shown for the geographic location (37.7oN, 36.7oE). The panel arrangement for the first four \npanels (a, b, c and d) is the same as in Fig. 7. The last panel (e) shows the result of the radial anisotropic forward modeling according to the parameters \n\n\n\nlisted in Table 2. For this location, \ud835\udc501 = 0.60, \ud835\udc502 = 0.55, \ud835\udc503 = 0.80, \ud835\udc504 = 1.00 and \ud835\udc511 = 1.0, \ud835\udc512 = 0.5, \ud835\udc513 = 0.5, \ud835\udc514 = 1.0. \n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure B2:  Inverted shear-wave velocity-depth profiles are shown for the geographic location (39.7oN, 36.7oE). The panel arrangement is the same as in \nFig. B1. For this location, \ud835\udc501 = 0.65, \ud835\udc502 = 0.90, \ud835\udc503 = 0.90, \ud835\udc504 = 1.00 and \ud835\udc511 = 1.0, \ud835\udc512 = 0.5, \ud835\udc513 = 0.5, \ud835\udc514 = 1.0. \n\n\n\n\n\n\n\n\n\n\n\n\n\nCite the Article: \u00d6zcan \u00c7ak\u0131r, Yusuf Arif Kutlu (2023).  Forward Modeling the Group and Phase Velocities of Rayleigh and Love Surface  \nWaves Beneath the Central Anatolia: Fifth Parameter for Transverse Isotropy. Malaysian Journal of Geosciences, 7(2): 109-134. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 109-134 \n\n\n\n\n\n\n\n\n\n\n\nAppendix C: The Voigt isotropic average shear-wave velocity and the radial anisotropy for \ndifferent locations in the forward modeling  \n\n\n\n\n\n\n\nFigure C1. The Voigt isotropic average shear-wave velocity (\ud835\udc49\ud835\udc46 \u2013 left panel) and the radial anisotropy (\ud835\udf09 \u2013 right panel) underneath different geographical \nlocations (latitude, longitude) are shown in one figure. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal Geosciences (MJG) 1(1) (2017) 38-42 \n\n\n\nContents List available at RAZI Publishing\n\n\n\nMalaysian Journal of Geosciences\nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/\n\n\n\nSLOPE STABILITY STUDY AROUND KAMPUNG KUALA ABAI, KOTA BELUD, SABAH, MALAYSIA\nIsmail Abd Rahim & Mohd Noor Raffee Usli\nNatural Disasters Research Centre, Faculty of Science and Natural Resources,Universiti Malaysia Sabah, Jalan UMS,88400 Kota Kinabalu, \nSabah, Malaysia,Phone: +6 088 320000 (5743)\n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle history:\n\n\n\nReceived 22 January 2017  \nAccepted 03 February 2017  \nAvailable online 05 February 2017\n\n\n\nKeywords:\n\n\n\nKota Belud, Crocker forma-tion, factor \nof safety (FOS), kinematic analysis, \nkinetic analysis, slope design\n\n\n\nThe study area is located in the northwestern part of Kota Belud, Sabah and underlain by Late Eocene-late Early \nMiocene of the Crocker formation. The objectives of this study are to determine the mode of failures, factors of \nsafety and to propose slope designs. Engineering geological mapping, kinematic analysis, new approach of \nadjustment factor, dry density analysis, stereographic measurement, kinetic analysis and prescriptive measures \nwere used to produce geological map and described rock mass characteristics, to determine the mode of failure \nand optimum slope angle, the most critical mode of failure, unit weight of the rock, wedge angle, factor of safety and \nslope protection and stabilization measures, respectively. Results of this study shows that the mode of failures are \nwedge failure, the factors of safety ranges from 1.93 to 4.43 which generally considered stable and the proposed \nslope design are flattening the slope angle between 31o-45o, installation of the wire mesh, rock trap ditch and spot \nrock bolting.\n\n\n\nINTRODUCTION\nSlope failure is a main issue in lives and property loss all around the world. \nModification or creating of artificial slope by improper slope cutting and \ndesign are sometimes contributed to the formation of slope failures. In \norder to design a stable slope, engineering geological mapping and stability \nanalysis are vital to be conducted. \n\n\n\nRock mass classification system is one of a design tool in determining \nearly design for the slopes. Another universal slope stability analysis used \nfor slope designs are stereographic and kinematic method, kinetic (limit \nequilibrium analysis), numerical methods [continuum modeling (e.g. \nfinite element, finite difference) and discontinuum modeling (e.g. distinct \nelement, discrete     element)], hybrid/coupled modeling, rockfall simulation \nand probabilistic approach.\n\n\n\nOccurrences of the more than a cubic meter (m3) falling rock block at the \nbottom of rock cut slope in Kampung Kuala Abai road have become an issue \nfor this study. These rock blocks are large enough in damaging engineering \nstructures or properties, causing injury and even death. Structure and \nroad impended by even small spills of rock material are in addition an \ninconvenience for the public\u2019s and motorists (Maerz, 2000). \nThe study area is underlain by Crocker Formation of Late Eocene \u2013 late \nEarly Miocene ages which have been experienced complex tectonic history \nand unique geological structures (Figure 1). This formation is a turbidite \ndeposits that consists of interbedded sandstone, siltstone and shale units. \nThe Bouma sequence can be identified in some beds and sandstone to shale \nratios vary between outcrop. \n\n\n\nCite this article as: Slope Stability Study Around Kampung Kuala Abai, Kota Belud, Sabah, Malaysia Ismail Abd Rahim & Mohd Noor Raffee Usli / Mal. J. Geo \n1(1)  (2017)  38-42\n\n\n\nISSN:2521-0920 (Print) \nISSN:2521-0602 (Online)\n\n\n\nhttps://doi.org/10.26480/mjg.01.2017.38.42\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\n http://www.razipublishing.com/journals/galeri-warisan-sains-gws/ \n\n\nhttp://doi.org/10.26480/mjg.01.2017.38.42\n\n\nhttps://doi.org/10.26480/mjg.01.2017.38.42\n\n\n\n\n\n\nIsmail Abd Rahim & Mohd Noor Raffee Usli / Malaysian Journal of Geosciences 1(1) (2017) 38-42 39\n\n\n\nCite this article as: Slope Stability Study Around Kampung Kuala Abai, Kota Belud, Sabah, Malaysia Ismail Abd Rahim & Mohd Noor Raffee Usli / Mal. J. Geo \n1(1)  (2017)  38-42\n\n\n\n  The sandstone is grey to brown, moderately well sorted, very fine to \nmedium grained lithic wacke. It is very thin to thick-bedded.  The beds of \nfine sandstone and very fine sandstone are rich in sole marks, load cast, \nflute cast, graded bedding, parallel, cross and convolute lamination and \ntrace fossil. The siltstone is grey to greenish grey, predominantly of silt \nmaterial, hard when fresh but soft and buff when weathered in thin bedded \n(less than 5 cm). \nShale unit can be divided into grey and red shales. The grey shale unit \nhas represented by rhythmic interbedded of thick grey shale and thin \nvery fine sandstone, parallel to wavy laminated, less well developed sole \nmarks, occasional trace fossil (Nereites association) and common slumps \ndeposition. The red shale is interbeds with very thin siltstone, parallel to \nwavy laminated, occasional slumps deposit and patched of grey or green \nshales. \nThe objectives of this study are to determine the mode of failures, factors of \nsafety and to propose slope designs.\n\n\n\nMETHODOLOGY\n \nEngineering geological mapping, kinematic analysis, new approach of \nadjustment factor, dry density analysis, stereographic measurement, kinetic \nanalysis and prescriptive measures have been used in this study. Engineering \n\n\n\ngeological mapping includes lithological, slope and slope failure mapping \nand random discontinuity survey (ISRM, 1981). \n\n\n\nMarkland test (Markland, 1972) has been used in kinematic analysis to \ndetermine the mode of failure and optimum slope angle. The most potential \nmode of failure for more than one failure in a slope has been determined by \nthe new approach of adjustment factor, NAAF method (Ismail Abd Rahim, \net al., 2012). Dry density analysis (ISRM, 2007) has been conducted to \ndetermine unit weight of the rock. The wedge angle i.e. the angle between \nwedge plane, sliding plane, slope face or upper slope face, line of intersection \nfor wedge geometry and normal plane in wedge model have been measured \nby stereographic method.\n\n\n\nFactor of safety (F.O.S.) has been determined by kinetic analysis (Kliche, \n1999). In conducting the kinetic analysis, the model of the wedge is assumed \nwithout tension crack and water pressure. The steps in calculating F.O.S. are \nas follows;\n\n\n\n\n\n\n\n\nIsmail Abd Rahim & Mohd Noor Raffee Usli / Malaysian Journal of Geosciences 1(1) (2017) 38-42 40\n\n\n\nCite this article as: Slope Stability Study Around Kampung Kuala Abai, Kota Belud, Sabah, Malaysia Ismail Abd Rahim & Mohd Noor Raffee Usli / Mal. J. Geo \n1(1)  (2017)  38-42\n\n\n\nFigure 5 Location on the stereonet of wedge weight, W; the normal Ni, NA \nand NB and angle to the normal, \u03b2i (\u03b4i), \u03b4A and \u03b4B (Kliche, 1999).\n\n\n\nThe resisting force on plane A and B according to Mohr-Coulomb criterion \nare as follows;\nCA + NAtan A\nCB + NBtan B\nThe driving force parallel to the line of intersection of planes A and B \ncan be express as Wsini Therefore, the wedge force can be resolved into \ncomponents normal to and parallel to the line of intersection of two planes.  \nThe location of W, NA, NB and Ni as well as the measured angle \u03b2i, \u03b4A and \n\n\n\n\u03b4B must be measures with stereonet or calculated as shown in Figure 5. \n\n\n\n5. Determination of force polygons to determine the magnitude of N \n(Figure 6).\n\n\n\nForce polygons, along with measured angles, used to determine the \nmagnitude of Nj, NA and NB are shown in Figure 6. Then, the factor of safety \ncan be calculated by Equation 7.\n\n\n\n\n\n\n\n\nIsmail Abd Rahim & Mohd Noor Raffee Usli / Malaysian Journal of Geosciences 1(1) (2017) 38-42 41\n\n\n\nCite this article as: Slope Stability Study Around Kampung Kuala Abai, Kota Belud, Sabah, Malaysia Ismail Abd Rahim & Mohd Noor Raffee Usli / Mal. J. Geo \n1(1)  (2017)  38-42\n\n\n\nCombination of prescriptive measures (Yu et al., 2005) and Markland test \n(Markland. 1972) have been used to propose slope design in the study area.\n\n\n\nRESULT AND DISCUSSION\nThe results of the Markland test are shown in Figure 7 and Table 3. The \nmode of failure is wedge failure. The wedge failures are potential in slope 2 \nand 3 but only possible in slope 1 and 4. The wedge failure in slope 1, 2, 3 \nand 4 have been kinematically formed by the intersection of planes B and \nK3, K1 and K2, B and K2 and K1 and K3, respectively. \n\n\n\nThe stability of selected slopes or factors of safety are shown in Table 4. \nThe limit equilibrium analysis (kinetic analysis) method shows that all \nof the slopes are generally stable. Most of the slopes (slope 1 to slope 4) \nare analysis for wedge failure. There are two (2) potential wedge failures \nin slope 2 but intersection of K1 and K2 have been selected as the most \npotential after evaluated by using NAAF method.\n\n\n\nThe main factors that control the stability of the slopes are lithological unit \nand slope height. Most of the slopes are dominated by sandstone beds i.e. \n70%-95% (Table 3). The sandstone beds are stronger than shale then the \nslope become more stable. Most of the slope in the study area are less than \n10m and considered as lowered slopes. Theoretically, the lower slope is \n\n\n\nmore stable compare to higher slope.\n\n\n\nThe proposed slope design for the selected slopes are based on Markland \ntest and prescriptive measure and shown in Table 5. Most of the selected \nslopes are potential and possible to fail then flattening based on optimum \nslope angle by Markland test is recommended. The range of optimum slope \nangle varies from 31o to 51o.\n\n\n\nThe potential and possible wedge failures in slope 1 to slope 4 are proposed \nfor the installation of wire mesh. This wire mesh is useful to trap, reduce \nand protect the road from falling wedge blocks. Constructions of rock trap \nditch at the bottom of the slope are also proposed for slope 1, 2 and 4 which \nare higher than 5m. This is because higher slope producing high energy for \nfalling rock block. The block is potentially moving or rolling on the road and \nendangered users. The potential wedge failure in slopes 2 and 3 are needed \nfor installation of spot rock bolting to protect falling wedge block from slope \nface.\n\n\n\n\n\n\n\n\nIsmail Abd Rahim & Mohd Noor Raffee Usli / Malaysian Journal of Geosciences 1(1) (2017) 38-42 42\n\n\n\nCite this article as: Slope Stability Study Around Kampung Kuala Abai, Kota Belud, Sabah, Malaysia Ismail Abd Rahim & Mohd Noor Raffee Usli / Mal. J. Geo \n1(1)  (2017)  38-42\n\n\n\nCONCLUSIONS\n\n\n\nConclusions of this study are:\n1. The modes of failures are wedge and planar failure.\n2. The factors of safety ranges from 1.93 to 4.43 by calculation and \ngenerally stable..\n3. The proposed slope design are flattening based on 31o-45o \noptimum slope angles, installation of the wire mesh, rock trap ditch and \nspot rock bolting.\n\n\n\nREFERENCES \n\n\n\nIsmail Abd Rahim, Sanudin Hj. Tahir, Baba Musta and Shariff A. K. Omang. \n2010. Slope stability evaluation of selected rock cut slope of the Crocker \nFormation in Kota Kinabalu, Sabah. Proceeding of the 3rd Southeast Asian \nNatural Resources and Environmental Management (SANREM 2010), 3-5 \nAugust 2010, Promenade Hotel, Kota Kinabalu, Sabah.\nIsmail Abd Rahim, Sanudin Hj. Tahir, Baba Musta, & Shariff A. K. Omang. \n2012. Adjustment factor for Slope Mass Rating (SMR) system: Revisited. \nProsiding Persidangan Geosains Kebangsaan 2012 (NGC2012), 22-23 Jun \n2012, Hotel Pullman, Kuching, Sarawak.\nISRM, 1981. Rock Characterization, Testing and Monitoring. In: Brown, E.T. \n(Ed.). International Society for Rock Mechanics (ISRM) Suggested Methods. \nPergamon, Oxford. 211 pp.\nISRM, 2007. The Complete ISRM Suggested Mothods for Rock \nCharacterization, Testing and Monitoring: 1974-2006. In: Ulusay, R. & \nHudson. J. A. (Ed.). Commission on Testing Methods International Society \nfor Rock Mechanics (ISRM). Elsevier. 627 pp.\nKliche, C.A., 1999. Rock slope stability. Society for Mining, Metallurgy and \nExploration, Inc.\nMaerz, N. H. 2000. Highway rock cut stability assessment in rock masses not \n\n\n\nconducive to stability calculations. Proceeding of the 51st Annual Highway \nSymposium, Seattle, Washington, 29 August-1 September 2000: 249-259.\nMarkland, J. T. (1972). A Useful technique for  estimating the stability of rock \nslopes when the rigid wedge slide type of failure  is  expected.   Imperial  \nCollege  Rock  Mechanics Research reprint, no. 19.\nSanudin Tahir & Baba Musta. 2007. Pengenalan Kepada Stratigrafi. Universiti \nMalaysia Sabah, Kota Kinabalu, Sabah. \nYu, Y. F., Siu, C. K. & Pun, W. K. 2005. Guidelines on the use of prescriptive \nmeasures for rock cut slopes. GEO Report No. 161, Hong Kong Geotechnical \nEngineering Office, 31p.\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 97-100 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2022.97.100 \n\n\n\nCite The Article: Sufiyan I., Alkali M., Sagir I.M, (2022). 3D Modeling and Assessment of Flood Risk Zones Using Gis and  \nRemote Sensing in Catchment Area Terengganu, Malaysia. Malaysian Journal of Geosciences, 6(2): 97-100. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2022.97.100 \n\n\n\n3D MODELING AND ASSESSMENT OF FLOOD RISK ZONES USING GIS AND REMOTE \nSENSING IN CATCHMENT AREA TERENGGANU, MALAYSIA \n\n\n\nSufiyan I., Alkali M., Sagir I.M, \n\n\n\nDepartment of Environmental Management, Nasarawa State University, Keffi, Nigeria. \n*Corresponding Author Email: ibrahimsufiyan0@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 28 August 2022 \nRevised 11 September 2022 \nAccepted 31 October 2022 \nPublished 02 November 2022\n\n\n\nThe application of 3D GIS has enabled better representation and visualization of flood events than previous \n\n\n\n2D maps. Flooding is common in the Terengganu basin. Flash floods occur most of the year during the \n\n\n\nmonsoon season, which lasts from November to January. Flooding along riverbanks is mainly affected by \n\n\n\nheavy rainfalls of 2500mm to well over 3500mm per year. This has significant impacts on environmental \n\n\n\nresources such as land use/land cover, local soil types and slopes. The study area of Kuala Terengganu was \n\n\n\nparticularly affected by heavy rains and flash floods during the monsoon season. ASTER DEM resolution 5m \n\n\n\nconverted to ArcScene 10.3 using ArcGIS 10.3 and 3D software. In recent years, flood monitoring methods \n\n\n\nhave been developed that can predict water flow and associated risks and hazards. 3D visualization \n\n\n\ntechniques include remote sensing such as satellite imagery and geographic information systems \"GIS\". and \n\n\n\nLiDAR modeling \n\n\n\nKEYWORDS \n\n\n\nRemote Sensing, GIS, Modelling, 3D, Watershed, Hydrology \n\n\n\n1. INTRODUCTION \n\n\n\nFlooding can be defined as natural or man-made flooding from a riverbank \n\n\n\nthat dominates the surrounding area and causes overflow (Lin et al., \n\n\n\n2013). Large amounts of water can spread across flood plains and become \n\n\n\ndangerous to society. In situations like flooding, climate change becomes \n\n\n\nthe dominant factor. Flooding can pose significant risks to life, property \n\n\n\nand the environment. Flooding is one of the most devastating hazards and \n\n\n\ndisasters Malaysia has experienced in decades. However, there are about \n\n\n\n189 river basins, most of which drain into the North China Sea, of which \n\n\n\nthe total number of rivers prone to flooding is 85.  \n\n\n\nThe estimated flood prone and high-risk area is about 29,800 km2, or \n\n\n\nabout 9% of Malaysia's total land area, which directly affects about 4.82 \n\n\n\npeople, or about 22% of Malaysia's total population influence. Previous \n\n\n\nstudies have shown promising results using SWAT as a hydrological \n\n\n\nmodel. SWAT was used in Taxes to calculate soil moisture in large river \n\n\n\nbasins. (Narasimhan et al., 2005). SWAT was also used by used to model \n\n\n\nthe effects of soil erosion and sediment loss (Betrie et al., 2011). In India, \n\n\n\nSWAT was used to simulate daily precipitation from 1951 to 2014 (Singh \n\n\n\net al., 2014). Rain is a boon for Peninsular Malaysia with high forest cover \n\n\n\n(Sufiyan et al., 2018). \n\n\n\n2. METHODOLOGY \n\n\n\nThe study area in Kuala Terengganu experienced heavy rains and flash \n\n\n\nfloods, especially during the monsoon season. This study sought to \n\n\n\naddress the issue of flood impacts based on digital image processing and \n\n\n\nthe high multispectral resolution of satellite imagery and his GIS. The 5 m \n\n\n\nresolution of the ASTER DEM was obtained using the ArcGIS10.3 3D \n\n\n\nsoftware Converted to ArcScene 10.3 (Pirasthe et al., 2018). In recent \n\n\n\nyears, people have developed flood level monitoring methods that can \n\n\n\npredict water flow and associated risks and hazards. 3D visualization \n\n\n\ntechniques include remote sensing such as satellite imagery and \n\n\n\ngeographic information systems \"GIS\". LiDAR modelling. The recent \n\n\n\napplication of 3D GIS has made it possible to better represent and visualize \n\n\n\nflood hazards than previous 2D maps (Wang and Xu, 2008).  \n\n\n\n3D computer graphics models allow users to simulate reality from a \n\n\n\nvisualized perspective. ArcGIS techniques tend to create realistic \n\n\n\nanimations of liquids, smoke, water, or pollutants in the environment. \n\n\n\nState of the art in remote sensing and geographic information systems \n\n\n\n(GIS) (Wylie et al., 2019). and developed a watershed depiction of the \n\n\n\nKuala Terengganu River Basin. Flood mitigation measures require \n\n\n\nanalytical watershed management combined with technological \n\n\n\napproaches to control flood risk and environmental hazards. The use of 3D \n\n\n\nin flood simulation development is of paramount importance, especially \n\n\n\nfor rapid flood warnings and emergency assistance to flood victims \n\n\n\n(Winchell et al., 2007). \n\n\n\n2.1   Study Area \n\n\n\nThe study area is located at upper left corner 50 30/.40// N, 1020 23/ 15// E \n\n\n\nand the lower right corner is 40 39/ 25// N, 1030 11/ 62// E respectively. The \n\n\n\nbottom has a gentle slope gradually deepening toward the open of the \n\n\n\nSouth China Sea. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 97-100\n\n\n\nCite The Article: Sufiyan I., Alkali M., Sagir I.M, (2022). 3D Modeling and Assessment of Flood Risk Zones Using Gis and  \nRemote Sensing in Catchment Area Terengganu, Malaysia. Malaysian Journal of Geosciences, 6(2): 97-100. \n\n\n\nFigure 1:  Map of the Study Area \n\n\n\n3. RESULT AND DISCUSSION \n\n\n\nFigure 2: Terengganu River catchment Area sub-basins stream links and \nthe main Rivers. Source: (ArcSWAT analysis 2022) \n\n\n\nThe temperature ranges from 23 \u00b0C to 32 \u00b0C on average, and the annual \nrainfall is approximately 3300 millimetres. During the northeast monsoon, \nwhich lasts from October to December, the catchment gets the most rain, \nwhile the southwest monsoon typically lasts from May to late September \n(Marganey at el., 2002). There are very few grasslands and a lot of \nevergreen forests in Malaysia. Rubber, palm trees, and timber can thrive \nin dense forests. The monsoon season is typically when floods occur \n(Tehrany et al., 2015). \n\n\n\nThe Hydrologic Response Units (HRU) consists of the land use, soil types, \nand the catchment slope. They are characterized by unique performance \nand distributions of the individual report within the catchment area. In \nthis study, the following result is shown in Table 1, 2 and 3. \n\n\n\n3.1   Land Use/Land Cover Analysis \n\n\n\nTable 1 shows the SWAT output from one of the HRU results. Land cover \nplays an important role in controlling climate and flood-causing water \nflows. For example, forest cover is the predominant land cover in the study \narea. When part of the forest is cut down, floods inundate other lower \nelevation areas. \n\n\n\nTable 1: Land Use/ Land cover Result from ArcSWAT Software \n\n\n\nLand use Abbreviation Area [ha] Area[acres] %wat. Area \n\n\n\nWater Body WATR 42,685 105,476 14.90 \n\n\n\nResidential-\nHigh Density \n\n\n\nURHD 3,347 8,270 1.17 \n\n\n\nOrchard ORCD 46.8465 115.7601 0.02 \n\n\n\nRubber Trees RUBR 11,981 29,606 4.18 \n\n\n\nResidential \u2013\nLow Density \n\n\n\nURLD 167.2060 413.1745 0.06 \n\n\n\nOil Palm OILP 13,251 32,744. 4.63 \n\n\n\nPaddy PADD 3,209 7,930 1.12 \n\n\n\nGrassland GRSS 10.9008 26.9365 0.00 \n\n\n\nForest-\nEvergreen \n\n\n\nFRSE 211,809 523,391 73.93 \n\n\n\nFigure 3: Land Cover classification of Terengganu River Catchment Area \nSource: (ArcSWAT analysis 2022) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 97-100\n\n\n\nCite The Article: Sufiyan I., Alkali M., Sagir I.M, (2022). 3D Modeling and Assessment of Flood Risk Zones Using Gis and  \nRemote Sensing in Catchment Area Terengganu, Malaysia. Malaysian Journal of Geosciences, 6(2): 97-100. \n\n\n\nFigure 3 View the land cover map of the Kuala Terengganu River Basin. \nThe following legends represent different land cover patterns such as \nforest, water, urban land use, rubber, rice, orchard, oil palm, and grassland. \nThe Terengganu River basin was completely occupied by evergreen \nforests where most forest products are found. The map shows that \nevergreen forest is the predominant land cover throughout the study area. \n\n\n\n3.2   Local Soil Types Classification Results \n\n\n\nSoil classification is based on USGS using standard SWAT and can update \nlocal soil databases. Local soils for the study area are created from existing \nsoils around the world based on SWAT updates. Table 2 shows the soil \nclassification results by total area (hectares, acres and total percentage) \nobtained during the analysis.  \n\n\n\nTable 2: Soil types result of Terengganu River Catchment Area \n\n\n\nSoils Area [ha] Area[acres] % wat. Area \n\n\n\nKuala Brang 35,605 87,981 12.43 \n\n\n\nMarang 26,763 66,132 9.34 \n\n\n\nPeat 47,32 11,694 1.65 \n\n\n\nRudua 1,358 3,355 0.47 \n\n\n\nSteepland 200,118 494,501 69.85 \n\n\n\nTelemong 10,250 25,328 3.58 \n\n\n\nTok Yong 7,682 18,983 2.68 \n\n\n\nFigure 4 shows a digitized soil map of the Terengganu River basin. Soil \nabsorbs moisture and cools or heats up quickly. Depending on \ntemperature, water retention capacity varies from humid equatorial \nclimates to monsoons to arid and semi-arid environments. The \npredominant local soils in the Terengganu River Basin are steep and at the \nhighest altitudes. Most of these areas around the escarpment are flood-\nfree zones. \n\n\n\nFigure 4: Soil Map of Terengganu River Catchment Area (Source: \nArcSWAT analysis 2022) \n\n\n\n3.3   Slope Analysis  \n\n\n\nSlope data obtained from the SWAT database were intrinsic evolution \nfrom 10/10/10 percent thresholds selected from HRU. Table 3 shows the \ntotal area results for each slope category in hectares and acres, explaining \nthe slope percentages from 0-10 to 40 meters. \n\n\n\nFrom Table 3 below, we can conclude that the area occupied by steep land \ncover of 66,130.4348 ha has the highest elevation and has the most \nsignificant percentage of about 23.08%. This justifies the results of the soil \nclassification model in Figure 5, where steep land represents the most \nimportant space in the Terengganu River basin. \n\n\n\nTable 3: Slope Results of Terengganu River Catchment Area \n\n\n\nSlope Area [ha] Area[acres] %wat. Area \n\n\n\n0-10 62,168 153,620 21.699 \n\n\n\n10-20 59,974 148,199 20.933 \n\n\n\n20-30 543,93 134,407 18.985 \n\n\n\n30-40 43,843 108,337 15.302 \n\n\n\n40-9999 66,130 163,412 23.082 \n\n\n\n3.4   3D Analysis in ArcScene10.3 \n\n\n\nA 3D simulation was performed and the results were obtained in ArcScene \n10.3 using a moving Z-value to indicate the elevation of the Terengganu \nriver basin. Figure 5 is his 3D setup in the ArcScene10.3 environment to \nproperly visualize the flood analysis scenario. A 3D flood model simulation \nwith an underlying floating water mask was performed using a digital \nelevation model (DEM). Simulation is possible according to the altitude \nfrom low altitude to high altitude. ASTER Digital Elevation Models (DEMs) \nhave the resolution required to display them in a 3D analysis mask. \n\n\n\n3.5   Final 3D Flood Model of Terengganu Watershed \n\n\n\nA digital elevation model (DEM) of the study area was overlaid with a mask \nand the Terengganu River was taken as the reference elevation. Figure 5 \nshows a 3D model created from ArcScene10.3. At this point, the Z values \nare calculated differently, and the simulation is created. \n\n\n\nFigure 5: 3D flood Simulation of Terengganu Watershed \n\n\n\n4. CONCLUSION \n\n\n\nThe 3D model in Figure 5 represents a real-time 3D simulation, with the \nblue color representing water running down a high slope. The most \nfrequent type of flooding in these areas of Peninsular Malaysia is flash \nflooding. It occurs during the wettest period (monsoon) from November \nto late January, causing flood events reaching slopes of 1-6 m, flooding \nwith increasing rainfall intensity, and lasting perhaps 2-3 days. Persistent \nshowers will follow. \n\n\n\nRECOMMENDATIONS \n\n\n\nThere are problems with inadequate drainage systems and clogged \nwaterways. Draining debris from the drainage system is paramount to \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 97-100\n\n\n\nCite The Article: Sufiyan I., Alkali M., Sagir I.M, (2022). 3D Modeling and Assessment of Flood Risk Zones Using Gis and  \nRemote Sensing in Catchment Area Terengganu, Malaysia. Malaysian Journal of Geosciences, 6(2): 97-100. \n\n\n\nensure that running water can easily drain into the main stream. \nGeographic Information System \u201cGIS\u201d, Flood Maps are useful to some \nextent in flood risk assessment and management, flood protection, flood \ninsurance estimation and coverage. Flood maps help provide flood alerts \nand warnings when flood levels reach certain hazard levels, especially \nfreeboard. The positive aftermath of the flood in a river may form features \nsignificance to human activities land use/cover dynamics accumulated \nsediments improve fertile soils suitable for agriculture. \n\n\n\nREFERENCES \n\n\n\nBaker, T.J., and Miller, S.N., 2013. Using the Soil and Water Assessment \nTool (SWAT) to assess land use impact on water resources in an East \nAfrican watershed. Journal of Hydrology, 486, Pp. 100\u2013111. \n\n\n\nBetrie, G.D., Mohamed, Y.A., van Griensven, A., Srinivasan, R., 2011. \nSediment management modelling in the Blue Nile Basin using SWAT \nmodel. Hydrology and Earth System Sciences, 15 (3), Pp. 807. \n\n\n\nGalv\u00e1n, L., Ol\u00edas, M., de Villar\u00e1n, R.F., Santos, J.M.D., Nieto, J.M., Sarmiento, \nA.M., C\u00e1novas, C.R., 2009. Application of the SWAT model to an AMD-\naffected river (Meca River, SW Spain). Estimation of transported \npollutant load. Journal of Hydrology, 377 (3), Pp. 445\u2013454. \n\n\n\nLin, G.F., Chou, Y.C., Wu, M.C., 2013. Typhoon flood forecasting using \nintegrated two-stage support vector machine approach. Journal of \nHydrology, 486, Pp. 334\u2013342. \n\n\n\nMarghany, M., Ibrahim, Z., and Van Genderen, J., 2002. Azimuth cut-off \nmodel for significant wave height investigation along coastal water \nof Kuala Terengganu, Malaysia. International Journal of Applied \nEarth Observation and Geoinformation, 4 (2), Pp. 147\u2013160. \n\n\n\nNarasimhan, B., Srinivasan, R., Arnold, J.G., Di Luzio, M., 2005. Estimation \nof long-term soil moisture using a distributed parameter hydrologic \nmodel and verification using remotely sensed data. Transactions of \nthe ASAE, 48 (3), Pp. 1101\u20131113. \n\n\n\nOnu\u015fluel G\u00fcl, G., and Rosbjerg, D., 2010. Modelling of hydrologic processes \nand potential response to climate change through the use of a \nmultisite SWAT. Water and Environment Journal, 24 (1), Pp. 21\u201331. \n\n\n\nPirasteh, S., Li, J., and Chapman, M., 2018. Use of LiDAR-derived DEM and \na stream length-gradient index approach to investigation of \nlandslides in Zagros Mountains, Iran. Geocarto International, 33 (9), \nPp. 912\u2013926. \n\n\n\nSingh, A., Imtiyaz, M., Isaac, R.K., and Denis, D.M., 2014. Assessing the \nperformance and uncertainty analysis of the SWAT and RBNN \nmodels for simulation of sediment yield in the Nagwa watershed, \nIndia. Hydrological Sciences Journal, 59 (2), Pp. 351\u2013364. \n\n\n\nSufiyan, I., Zakariya, R., Yacoob, R., Idris, M.S., and Idris, N.M., 2018. SWAT \nSubbasins Parameters and Flood Risk Simulations Using 3d In \nTerengganu Watershed. Earth Sciences Malaysia, 2 (2), Pp. 10\u201315. \n\n\n\nTehrany, M.S., Pradhan, B., Mansor, S., and Ahmad, N., 2015. Flood \nsusceptibility assessment using GIS-based support vector machine \nmodel with different kernel types. Catena, 125, Pp. 91\u2013101. \n\n\n\nThampi, S.G., Raneesh, K.Y., Surya, T.V., 2010. Influence of scale on SWAT \nmodel calibration for streamflow in a river basin in the humid \ntropics. Water Resources Management, 24 (15), Pp. 4567\u20134578. \n\n\n\nWang, F., and Xu, Y.J., 2008. Development and application of a remote \nsensing-based salinity prediction model for a large estuarine lake in \nthe US Gulf of Mexico coast. Journal of Hydrology, 360 (1\u20134), Pp. 184\u2013\n194. https://doi.org/10.1016/j.jhydrol.2008.07.036\n\n\n\nWinchell, M., Srinivasan, R., Di Luzio, M., and Arnold, J., 2007. ArcSWAT \ninterface for SWAT 2005. User\u2019s Guide, Pp. 1\u2013436. \n\n\n\nWylie, B.K., Pastick, N.J., Picotte, J.J., and Deering, C.A., 2019. Geospatial \ndata mining for digital raster mapping. GIScience & Remote Sensing, \n56 (3), Pp. 406\u2013429. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2023.81.93 \n\n\n\n\n\n\n\n Cite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2023.81.93 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCHARACTERIZATION AND DISTRIBUTION OF POLYNUCLEAR AROMATIC \nHYDROCARBONS IN WET PRECIPITATIONS, SURFACE WATERS AND SOILS FROM \nSOUTH-EASTERN NIGERIA \n\n\n\nUsoro M. Etesina*, Ibanga O. Isaacb, Udo J. Ibokc, Aniefiok E. Ited  \n\n\n\naAnalytical Chemistry Unit, Chemistry Department, Akwa Ibom State University, Ikot Akpaden, Mkpat Enin, Nigeria   \n\n\n\nbOrganic Chemistry Unit, Chemistry Department, Akwa Ibom State University, Ikot Akpaden, Mkpat Enin,   Nigeria \ncApplied Chemistry Unit, Chemistry Department, Akwa Ibom State University, Ikot Akpaden, Mkpat Enin, Nigeriad \ndEnvironmental Unit, Chemistry Department, Akwa Ibom State University, Ikot Akpaden, Mkpat Enin, Nigeria \n*Corresponding Author Emsail:  usoroetesin@aksu.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 06 June 2023 \nRevised 09 July 2023 \nAccepted 13 August 2023 \nAvailable online 18 August 2023 \n\n\n\n Polynuclear aromatic hydrocarbons (PAHs), are known as persistent organic pollutants (POP) which exist in \ndifferent media as contaminants in parts of the environment\u2019s atmosphere,   seawater, sediments, soils, and \nvegetation. PAHs have been known for their mutagenic, carcinogenic, and toxic properties to humans through \nthe food chain. This study is aimed to characterize and investigate the distribution of PAHs in wet \nprecipitations, surface waters, and soils from Ikot Abasi, Ibeno, and Eastern Obolo Local Government Areas \nof Akwa Ibom State, Nigeria. PAHs were determined in the environmental components by gas \nchromatography-mass spectrometry technique. Two soil and two surface water samples were taken from \nselected locations and wet precipitation samples were harvested on event basis from the study areas .The \nresults reveal the presence of the following priority PAHs at different concentrations: naphthalene, (0.02-\n0.09 mg/L);benzo[k]fluoranthrene (0.001 -0.09 mg/L); benzo[e]pyrene (0.001-0.08 mg/L); benzo[a]pyrene \n(0.001 -0.09 mg/L) ; 3-methylcholanthrene (1.27-7.21 mg/L) during the rainy and dry seasons in Ikot Abasi. \nThe concentrations of naphthalene ranges (0.02-0.06 mg/L), 3-methylcholanthrene ranges (2.40-3.65 mg/L), \nwhile other PAHs levels were below detection limits in rainwater from Ibeno in both seasons. The \nconcentrations of naphthalene ranges  (0.02-0.05 mg/L); 3-methylcholanthrene (2.30 \u2013 5.65 mg/L), while \nother PAHs levels were below detection limits in rainwater from Eastern Obolo in both seasons The PAHs \nlevels indicated were higher than the World Health Organization (WHO) acceptable limit of PAHs (0.0007 \nmg/L) in drinking water. The PAHs detected in the surface waters were in the order: Ikot Abasi (11.09 \u00b10.02 \nmg/L) > Eastern Obolo (3.87\u00b1 0.002 mg/L) > Ibeno (1.94 \u00b1 0.004 mg/L), during the rainy season, while the \nPAHs detected in the surface waters were in the order: Ikot Abasi (13.79 \u00b1 0.03 mg/L) > Eastern Obolo (3.95\u00b1 \n0.008 mg/L) > Ibeno (2.45\u00b10.011 mg/L), during the dry season. The PAHs obtained for the soils in the three \nstudy areas during the rainy season were in the order: Eastern Obolo (776 \u00b1 5.92 ug/kg) > Ibeno (732 \u00b1 8.33 \nug/kg) > Ikot Abasi (8 \u00b1 0.07 ug/kg), while, during the dry season, the results were in the order: Ibeno \n(872\u00b111.05 ug/kg) > Eastern Obolo (105\u00b19.03 ug/kg) > Ikot Abasi (20 \u00b1 1.95 ug/kg). Soils from Eastern \nObolo and Ibeno have PAHs levels greater than 700 ug/kg, which is categorized as slightly polluted, with toxic \nequivalent concentrations (TEQ) higher than permissible limit (33 ug/kg).This may cause ecological risk and \nraises public health concern that should attract more attention. Molecular diagnostic ratio analyses show that \nthe sources of PAHs in the three study area are mainly pyrogenic. \n\n\n\nKEYWORDS \n\n\n\nPetrogenic; pyrogenic; carcinogenic; mutagenic; organic pollutant, toxicity \n\n\n\n1. INTRODUCTION  \n\n\n\nDevelopment and technology have negative impacts of contamination of \nthe ecosystem and damage to human health. Consequently, polynuclear \naromatic hydrocarbons (PAHs) are a group of environmental pollutants \nclassified as persistent organic pollutants (POPs), being resistant to \ndegradation and can remain in the ecosystem for long periods, with he \npotential to cause adverse environmental effects (Liang et al.2022; Wang \net al., 2010).  \n\n\n\nDue to the fact that PAHs are resistant to environmental degradation, thus \n\n\n\nleading to ubiquitous distribution (V. Wanjeri et al, 2013). PAHs have been \nwidely found everywhere on earth, such as water, soil, air and food (Y. \nYang et al. 2014; W. Wilcke, 2000 ; E. Christopher,2017). \n\n\n\nSome of the PAHs are capable of being dispersed on a global scale and, in \naddition to being persistent in the environment, are semi-volatile, moving \nbetween the atmosphere and the earth\u2019s surface in repeated temperature-\ndriven cycles of deposition and volatilization (Gray and Hall, 2014; \nOthman et al., 2012).  \n\n\n\nPolynuclear aromatic hydrocarbons (as persistent organic pollutants) are \n\n\n\n\n\n\n\n\nCite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\ntruly multimedia contaminants that occur in parts of the environment\u2019s \natmosphere, in land and seawater, sediments, soils, and vegetation, \nalthough they are mainly anthropogenic in origin with negligible natural \nsources (Kim et al., 2011; Bihanic et al., 2015; WHO, 2003; Anderson, \n2009) \n\n\n\nPAHs have been known for their mutagenic, carcinogenic, and toxic \nproperties (Wang et al., 2011; Igwe et al., 2020; Bolden et al., 2017).  \n\n\n\nPAHs have mainly carbon and hydrogen atoms as constituents of fused \nbenzene rings of various structural linear, cluster, and angular \narrangements (Abdel-Shafy and Mansour, 2016; Andrade et al., 2017). \n\n\n\n So far, sixteen listed polynuclear aromatic hydrocarbons have been \npractically characterized (Fig. 1), with benzo [\u03b1] pyrene (Bap) rated an \nindicator contaminant due to frequent occurrence in a mixture of PAHs \n(Garc\u00eda-S\u00e1nchez et al., 2018; Mojiri et al., 2019). \n\n\n\n\n\n\n\nFigure 1:Structure of USEPAs Sixteen  priority PAHs ( Bojes and Pope, 2007 ) \n\n\n\nThe major sources of PAHs  are from burning of coal, oil, gas, wood, \ntobacco, coal tars, crude oil, and petroleum products such as creosote and \nasphalt. However, some natural sources such as forest fires and volcanoes, \nhave been well documented (Gupte et al., 2016; Chaloupka et al., 1993; Liu \net al., 2007; Sahoo et al., 2020). The USEPA and the European Commission \nhave implicated and listed PAHs as priority pollutants (Tong et al., 2020; \nIARC, 2004). PAHs released into the atmosphere go over long distances \nwith attendant deposition with atmospheric precipitation on soils, \nvegetation, the sea, and inland waters (Diggs et al., 2011; IARC, 2010). \n\n\n\nIn addition, these agencies namely, International Agency for Research on \nCancer (IARC),  Agency of Toxic Substances and Diseases Register (ATS-\nDR), the Environmental Protection Agency (EPA), and the European Union \nhave enlisted PAHs as the priority pollutants on account of exhibiting \ncarcinogenic and mutagenic (genotoxicity) properties (Patel et al.,2014). \nPAHs in the atmosphere may undergo photochemical and chemical \nreactions with oxides of nitrogen, sulphur (IV) oxide, oxygen, and \nhydroxides, which may likely produce more toxic compounds (Bojes and \nPope, 2007). \n\n\n\nA major study indicated that inhalation accounts for two to twelve percent \nof PAHs, while dietary exposure contributes 88 to 98 percent of PAHs \nintake mostly, among non-smoking populations (Chuang et al.2003). Also \nstudies indicated that benzo[a]pyrene (BaP) dietary exposure is in the \nrange of  2\u2013500 ng/day. On a global scale, the estimated average intake of \nPAHs is 0.02\u20133.6 ug/person/day, while other nations such as India, \nNigeria, and China have 11 ug/person/day, 6 ug/person/day, and 3.56 \nug/person/day, respectively (Diggs et al., 2011). \n\n\n\nThere are three major classes of PAHs which differ mainly in their origin \nto have petrogenic, biogenic, and pyrogenic (Dahle et al., 2003). PAHs can \nfurther be categorized into low molecular weight (LMW) PAHs\u2019 that \nconsist of two or three fused benzene rings and higher molecular weight \n(HMW) PAHs, consisting of four or more fused benzene rings, and having \nhigher tendency for stability and persistence for long in the environment \nthan the LMW PAHs (Dahle et al., 2003). The ratio of low molecular weight \nPAHs (LMW-PAHs) to high molecular weight PAHs (HMW-PAHs) has been \nused to characterize the origin of PAHs in the environment. According to \nRocher et al. (2004), PAHs from petrogenic source show a \ncharacteristically higher proportion of LMW-PAHs such as naphthalene \nand acenaphthenes, while pyrolytic PAHs have a characteristically higher \nproportion of HMW-PAHs such as pyrene and benzo[a]pyrene. \n\n\n\nThus, PAHs from  petrogenic source exhibit LMW/HMW ratios greater \nthan one, whereas PAHs from pyrogenic source exhibit LMW/HMW ratios \nless than one. As an additional tool, PAH molecular diagnostic ratios used \n\n\n\nfor sources of PAHs are as follows : \n\n\n\n\u2022 BaA/(BaA + Chrysene) < 0.2 indicates a petrogenic source, 2\u20130.35 \nindicates mixed sources (petrogenic and pyrogenic), and > 0.35 \nindicates a pyrogenic source (Unwin et al., 2006). \n\n\n\n\u2022 Ant / (Ant + Phe) < 0.1 indicates a petrogenic source; > 0.1 indicates \na pyrogenic source (Tobiszewski and Namie\u015bnik, 2012; Pies et al., \n2008). \n\n\n\n\u2022 BaA is benzo[a]anthrene, Ant is anthracene, and Phe is \nphenanthrene. \n\n\n\nThe pollution of soil by PAHs is virtually classified into three categories: \nunpolluted (\u2211PAH < 200 ug/kg), weakly polluted ( \u2211PAH 200\u2013600 ug/kg), \nand heavily polluted (\u2211PAH > 1,000 ug/kg), in accordance with Wu et al. \n(2019) classification . \n\n\n\nIn general, PAHs have low water solubility and high hydrophobicity,  have \na high affinity for the organic fraction of the sample, and in solution, are \nadsorbed on particulate matter, which can be deposited as sediments \n(Vione et al.,2004). In addition, PAHs are accumulated in the fat tissue of \nfiltrating organisms such as mussels, oysters, clams, etc., which for a long \ntime have been used as bio-indicators (Nendza et al., 1997).  \n\n\n\nThere has not been any reported study of polyaromatic hydrocarbons in \nenvironmental components in the study areas; therefore, to fill the \nknowledge and information gap, this study is designed to investigate the \npresence of sixteen priority PAHs in wet precipitations, surface waters, \nand soils from the three local government areas of Akwa Ibom State, Niger \nDelta, South-eastern Nigeria. Our objectives are to (1) identify the  \npresence of priority PAHs in the study area, (2) determine PAH molecular \ndiagnostic ratios, and (3) determine the concentrations of the pollutant \nspecies in the environmental components in comparison with the set \nstandards by international regulated agencies. We hypothesize that there \nis no variation in the levels of the pollutants in the study areas and no \nseasonal variation of the pollutants compared to the previously published \ndata in the Niger Delta, Region, Nigeria. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1   Study Area Description  \n\n\n\nThe study areas which consist of Ikot Abasi, Eastern Obolo and Ibeno Local \nGovernment Areas of Akwa Ibom State , Nigeria are located in \n\n\n\n\n\n\n\n\nCite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\nthe mangrove swamp forest, with occurrence of rain throughout the year, \nwhich peaked between May and September (Figure 2). The climatic \nconditions prevailing in the study area are favourable throughout the year \nfor fishing and farming. The landscape of the area consists generally of \nlow-lying plains and riverine areas, with no portion exceeding 175 m \nabove mean sea level (Amadi, 2014). \n\n\n\nIkot Abasi Local Government Area located between latitude 4.3111 o to \n4.4512 o North, and between longitude 7.5213 o to 8.0219 o East , plays \nhost to major multinational companies such as the Aluminium Smelter \nCompany of Nigeria (ALSCON), Septa Energy Company Limited, Ibom \nPower Company, and Sterling Global Oil Drilling Company. Ikot Abasi is \nalso a coastal area bounded by Imo River and the Bight of Bonny estuary, \nwhere the major economic activities include farming, artisanal fishing, \ntrading (Etesin and Inim, 2021; Ikot Abasi: The Aluminium Town, 1997). \n\n\n\nIbeno is located in South-eastern Nigeria and lies within the coordinates: \n4.57o N and 7.98 o E (Figure 2) . It lies on the eastern side of the Qua Iboe \nRiver, which is about 3 kilometers from the Atlantic, and is one of the \nlargest fishing settlements on the Nigerian coast (Description of Fisheries, \n2011) . In the west, Ibeno is located along the Mangrove Forest Belt of \nthe Niger Delta region of Nigeria and bounded by the Eastern Obolo Local \nGovernment Area. To the north, it is bounded by Onna, Esit Eket, and Eket \n\n\n\nLGAs and to the south by the Atlantic Ocean. It occupies the largest \nAtlantic coastline, more than 129 km, in Akwa Ibom State. It also occupies \na vast coastal area of over 1,200 km2 (Description of Fisheries, 2011).   \n\n\n\nEastern Obolo is a local government area (LGA) in Akwa Ibom State of  \nSouth-eastern Nigeria, with headquarters at Okoroete  and lies between \nlatitudes 4\u00b0 28' and 4\u00b0 53' and longitudes 7\u00b0 50' and 7\u00b0 55' East (Figure 2) \n. It is a coastal area under great tidal influence from the Bight of Bonny. \nEastern Obolo is located in the Niger Delta fringe between the \nImo and Qua Iboe River estuaries. It is bounded to the north by Mkpat \nEnin Local Government Area, northeast by Onna LGA, west, by Ikot Abasi \nLGA, Southeast by Ibeno Local Government Area, and in the south by \nthe Atlantic Ocean (Etesin et al. 2013).  \n\n\n\nThese areas are underlain by sedimentary formations of late Tertiary and \nHolocene ages (Magnus et al., 2014; Amadi, 2014). Deposits of recent \nalluvium and beach ridge sand occur along the coast and the estuaries of \nthe Imo and Qua Iboe Rivers, as well as along flood plains of creeks. The \nstudy areas are also characterized by coastal plain sands. The sands are \nmature, coarse, and moderately sorted. The Coastal Plain sands, otherwise \nknown as the Benin formation, overlie the Bende-Ameki formation and dip \nsouth-westward (Mbonu and Ebeniro, 1991; Akankpo and Igbokwe, \n2011). \n\n\n\n \nFigure 2: Map showing Nigeria (a) and Akwa Ibom state describing the study areas, sampling locations and   Atlantic Ocean \n\n\n\n2.2   Collection and Preparation of Samples \n\n\n\n2.2.1   Rainwater Samples \n\n\n\nRainwater samples were collected manually using a clean aluminium plate \non a tripod iron stand about 1.5 meters above the ground in an open field, \nas depicted in Figure 3. In order to avoid the effects of fine particle \n\n\n\ndeposition in the atmosphere in the absence of rainfall, the aluminium \nplate was removed and returned before precipitation. The harvested \nrainwater was decanted into glass bottles and stored in a refrigerator in \nthe laboratory until analysis . Rainwater samples were collected for each \nseason in the three study areas to cover January, February, and March \n2022 during the dry season and May, June, and July 2022 during the rainy \nseason, according to the method of APHA (2005). \n\n\n\n\n\n\n\nFigure 3: Rainwater collection stand\n\n\n\n2.2.2   Surface Water Sample  \n\n\n\nTwelve (12) surface water samples were collected from streams in the \n\n\n\nthree local government areas using glass bottles, consisting of two water \nsamples from each area, to cover January, February, and March 2022 \nduring the dry season and May, June, and July 2023 during the rainy \n\n\n\n\nhttps://en.wikipedia.org/wiki/Niger_Delta\n\n\nhttps://en.wikipedia.org/wiki/Eastern_Obolo\n\n\nhttps://en.wikipedia.org/wiki/Onna\n\n\nhttps://en.wikipedia.org/wiki/Esit_Eket\n\n\nhttps://en.wikipedia.org/wiki/Eket\n\n\nhttps://en.wikipedia.org/wiki/Atlantic_Ocean\n\n\nhttps://en.wikipedia.org/wiki/Coastline\n\n\nhttps://en.wikipedia.org/wiki/Local_Government_Areas_of_Nigeria\n\n\nhttps://en.wikipedia.org/wiki/Niger_Delta\n\n\nhttps://en.wikipedia.org/wiki/Imo_State\n\n\nhttps://en.wikipedia.org/wiki/Qua_Iboe_River\n\n\nhttps://en.wikipedia.org/wiki/Mkpat-Enin\n\n\nhttps://en.wikipedia.org/wiki/Mkpat-Enin\n\n\nhttps://en.wikipedia.org/wiki/Points_of_the_compass\n\n\nhttps://en.wikipedia.org/wiki/Onna\n\n\nhttps://en.wikipedia.org/wiki/Ikot-Abasi\n\n\nhttps://en.wikipedia.org/wiki/Atlantic_Ocean\n\n\n\n\n\n\nCite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\nseason. No preservative was added to any of these samples. The water \nsamples were stored in ice-cooled coolers while in transit to the laboratory \nfor physico-chemical and PAH analysis according to the method of APHA \n(2005). \n\n\n\n2.2.3   Soil Samples \n\n\n\nTwelve (12) soil samples were collected for each season in the three study \nareas to cover January, February, and March 2022 during the dry season \nand May, June, and July 2022 during the rainy season. Soil samples were \ncollected using an auger, stored in black polyethylene bags, air-dried, and \nsieved to 63 micron sizes and stored in polyethylene bags until analysis , \naccording to the method of APHA (2005). \n\n\n\n2.3   Digestion of Samples And Determination of Pahs \n\n\n\n2.3.1   Digestion and Determination of PAHs in Rainwater  \n\n\n\nThe gas chromatograph used was an Agilent 5977 GC-MSD (method code: \nMTH010). Prior to calibration, the MS was auto-tuned to \nperfluorotributylamine (PFTBA) using already established criteria to \ncheck the abundance of m/z 69, 219, 502, and other instrument optimal \nand sensitivity conditions. \n\n\n\nThe levels of PAHs in rainwater samples were determined using GC-MS \nwith MSD operating in selective ion monitoring (SIM) and scan mode to \nensure low-level detection of the target constituents (Ehsan et al., 2022). \n\n\n\nAn Agilent 8860A gas chromatograph coupled to a 5977C inert mass \nspectrometer (with a triple-axis detector) with an electron-impact source \n(Agilent Technologies) was used. The stationary phase of separation of the \ncompounds was a HP-5 capillary column coated with 5% phenylmethyl \nsiloxane (30m length x 0.25mm diameter x 0.25 \u00b5m film thickness) \n(Agilent Technologies). \n\n\n\nThe carrier gas was helium, used at a constant flow rate of 1.2 mL/min at \nan initial nominal pressure of 026 psi and an average velocity of 40.00 \ncm/sec. 1\u00b5L of each sample was injected in a split less mode at an injection \ntemperature of 250 \u00b0C. The purge flow to spilt vent was 30.0 mL/min at \n0.35 min with a total flow of 31.24 mL/min; the gas saver mode was \nswitched off. The oven was initially programmed at 50 \u00b0C (2 min) and then \nramped at 10 \u00b0C/min to 300 \u00b0C (5 min). The run time was 32 minutes with \na 3-minute solvent delay (Andrianova and Quimby, 2019). \n\n\n\nThe mass spectrometer was operated in electron-impact ionization mode \nat 70 eV with an ion source temperature of 230 \u00b0C, a quadrupole \ntemperature of 150 \u00b0C, and a transfer line temperature of 300 \u00b0C. \nAcquisition of ions was via scan mode (scanning from m/z 50 to 500 amu \nat 2.0 s/scan rate) and selective ion mode (SIM). After calibration, the \nsamples were analyzed and corresponding PAH concentrations obtained \n(Quimby et al., 2013). \n\n\n\nCalibration Procedure \n\n\n\nPAHs standard, 1000 ppm (Catalog Number: H-QME-01), containing 23 \nenvironmental PAH components, was purchased from AccuStandard. Four \n(4) point serial dilution calibration standards (0.1, 0.05, and 1.5 mg/L) \nwere prepared from the stock and used to calibrate the GC-MS. \n\n\n\n2.3.2   Digestion And Determination of Pahs in Surface Waters \n\n\n\nSurface waters were digested and PAHs determined according to the \nmethod in Section 2.3.1. \n\n\n\n2.3.3   Digestion And Determination of Pahs in Soil Samples \n\n\n\nSample preparation was carried out by ultrasonic extraction (Method \nCode: MTH, 004). The instruments used were ultrasonic baths: CLEAN \n120-HD (China). Rotary Evaporator: BUCHI Rotavapor R-215 \n(Switzerland); Analytical Balance: ADAM AAA250LE Weighing Balance \n(UK); \n\n\n\nThe reagents used were of analytical grade: Acetone: GC Ultratrace \nScharlau (Spain); n-Hexane: 96% GC Ultratrace Scharlau (Spain); Silica \nGel: Loba Chemie (India); Anhydrous Sodium Sulphate: Merck (US). \n\n\n\nExtraction Procedure \n\n\n\nProperly homogenized samples (5 g) were weighed into beakers and \nmixed with 10 ml of n-hexaneacetone (1:1). The beakers were then placed \nin an ultrasonic bath and sonicated for 20 minutes. The mixture was \n\n\n\nallowed to settle, and the solvent layer was decanted and concentrated   to \n2 ml using a rotary evaporator. \n\n\n\nClean-Up Procedure \n\n\n\nGranular silica gel (Mesh Size 60\u2013200A) was activated by heating at 1300 \n\n\n\nC for 16 hours and stored in a desiccator. A glass column was packed with \n5g of silica gel, and 1g of anhydrous Na2S04 was added. 20 ml of n-hexane \nwas added to the column and eluted into a beaker. The 2 ml sample extract \nwas quantitatively added to the top of the column. Another 10 ml of n-\nhexane was added to the column and eluted into waste. Before the column \nhead dried, 10 ml (1+1) of dichloromethane and hexane were added, and \nthe eluent was collected. The eluent was then concentrated to 2 mL using \na rotary evaporator and analyzed for PAHs. \n\n\n\n2.4   Ecological Risk Assessment of Pahs in Soil \n\n\n\nBenzo[a]pyrene is an indicator PAH and has the highest carcinogenicity \namong the sixteen (16) PAHs. Therefore, toxic equivalent concentrations \nof Benzo[a]pyrene (TEQB[a]P) are utilised toassess the ecological risk of \nPAHs in the soil (G.-C. Fang et al, 2004).  \n\n\n\nThe formula for calculating the total toxic equivalent concentration of \nPAHs in the soil is: \n \n\n\n\nTEQB[a]P  = \u03a3\ud835\udc56\uff08\ud835\udc64\ud835\udc56 \u00d7 TEF\ud835\udc56\uff09 \n\n\n\nWi is the concentration of the \u201ci\u201d PAH component,  \n\n\n\nTEFi is the toxic equivalent factor of the \u201ci\u201d PAHs.  \n\n\n\nTEQB[a]P is the toxic equivalent concentration of total PAHs.  \n\n\n\nTEQB[A]P is a standard reference chemical since it has the highest \ncarcinogenicity among the  \n\n\n\nsixteen PAHs. Its lethal equivalent factor is set at 1. The TEF values for the \nPAHs utilised are  \n\n\n\nlisted in Appendix 1. The TEF value can show the toxicity and \nenvironmental risks of PAHs.  \n\n\n\nThere were no standard threshold values of PAHs in the soil quality of \nNigeria, even Africa, so  \n \n\n\n\nthe allowable values of PAHs in quality from the Netherlands were also \napplied to analyse the \n\n\n\npollution level of PAHs in soils from the study areas (Appendix 1). \n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\n3.1   Results of Rainwater Samples  \n\n\n\nRainwater results during the rainy season in Ikot Abasi revealed the \npresence of the following PAH compounds: viz,   naphthalene, \nbenzo[k]fluoranthrene, benzo[e]pyrene, benzo[a]pyrene, and 3-\nmethylcholanthrene. \n\n\n\nThe levels of PAHs were in the following order: Conveyor Belt (7.24 \nmg/kg) > ALSCON (3.503 mg/L) > Ibom Power (1.295 mg/L) > Ikpetim \n(0.39 mg/L), these are depicted in Table 1. \n\n\n\nThe results for the rainwaters  during the rainy season in Eastern Obolo \nLGA reveal the presence of the following PAHs: naphthalene and 3-\nmethylcholanthrene only. The PAHs were in the order: Iko (5.65 mg/L) > \nOkoroete (2.34 mg/L), these are   depicted in Table 2. \n\n\n\nThe results for the rainwater during the rainy season in Ibeno showed the \npresence of naphthalene, benzo[e]pyrene, 3-methylcholanthrene, and \nbenzo[a]pyrene only. The PAHs were in the order: Ibeno Hall (3.67 mg/L) \n> Ukpeneikang (2.42 mg/L), as depicted in Table 2. \n\n\n\nThe results obtained from the analyses of the rainwater during the dry \nseason in Ikot Abasi reveal the presence of the following PAH \ncompounds:viz, naphthalene, benzo[k]fluoranthrene, benzo[e]pyrene, \nbenzo[a]pyrene, and 3-methylcholanthrene. The PAHs were in the \nfollowing order: Conveyor Belt (6.64 mg/kg) > ALSCON (3.464 mg/L) > \nIbom Power (1.278 mg/L) > Ikpetim (0.38 mg/L) , and these results are  \npresented in Table 3. \n\n\n\n\n\n\n\n\nCite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\nTable 1:  Concentrations of PAHs in Rainwaters from Ikot Abasi During Rainy Season \n\n\n\nPAH Ikpetim ALSCON Ibom Power Conveyor Belt \n\n\n\n Ikot Abasi Ikot Abasi Ikot Abasi Ikot Abasi \n\n\n\n Conc. (mg/L ) Conc. (mg/L ) Conc.  (mg/L ) Conc. (mg/L ) \n\n\n\nNaphthalene 0.05 0.09 0.02 0.02 \n\n\n\nAcenaphthylene ND* ND ND ND \n\n\n\nAcenaphthene ND ND ND ND \n\n\n\nFluorene ND <0.001 ND ND \n\n\n\nPhenanthrene ND ND ND ND \n\n\n\nAnthracene ND < 0.001 ND ND \n\n\n\nFluoranthene ND < 0.001 0.001 ND \n\n\n\nPyrene ND < 0.001 0.001 ND \n\n\n\nBenzo[c]phenanthrene ND ND ND ND \n\n\n\nBenz[a]anthracene ND ND ND ND \n\n\n\nChrysene ND ND ND ND \n\n\n\nBenzo[k]fluoranthene 0.09 < 0.001 0.001 < 0.001 \n\n\n\nBenzo [e]pyrene 0.09 0.002 0.001 0.01 \n\n\n\nBenzo [a]pyrene 0.09 0.001 0.001 < 0.001 \n\n\n\n3-Methylcholanthrene ND 3.41 1.27 7.21 \n\n\n\nIndeno (1,2,3-cd)pyrene ND < 0.001 ND < 0.001 \n\n\n\nBenzo[ghi]perylene 0.07 < 0.001 ND < 0.001 \n\n\n\nSUM(  PAHs) 0.39 3.503 1.295 7.24 \n\n\n\nND , below detection limit \n\n\n\nThe results of the analyses of rainwater during the dry season from \nEastern Obolo show the presence of naphthalene, 3-methylcholanthrene, \nand benz[a]anthracene only. The PAHs were in the following order: Iko \n(5.46 mg/L) > Okoroete (2.37 mg/L). These results are presented in Table \n4. \n\n\n\nThe results of rainwater analyses during the dry season in Ibeno reveal the \npresence of naphthalene, benzo[e]pyrene, 3-methylcholanthrene, and \nbenzo[a]pyrene only. The PAHs were in the order: Ukpeneikang, Ibeno \n(3.67 mg/L) > Ibeno, Hall (3.61 mg/L), as presented in Table 4. \n\n\n\nTable 2: Concentrations of PAHs in rainwaters from Eastern Obolo and Ibeno during rainy season \n\n\n\nPAH Eastern Obolo Eastern Obolo Ibeno, Hall Ibeno, \n\n\n\n ( Iko ) ( Okoroete )  Ukpeneikang \n\n\n\n Conc. (mg/L) Conc. (mg/L) Conc. (mg/L) Conc. (mg/L) \n\n\n\nNaphthalene 0.02 0.02 0.09 0.02 \n\n\n\nAcenaphthylene ND ND ND ND \n\n\n\nAcenaphthene ND ND ND ND \n\n\n\nFluorene ND ND ND ND \n\n\n\nPhenanthrene ND ND ND ND \n\n\n\nAnthracene ND ND ND ND \n\n\n\nFluoranthene ND < 0.001 ND < 0.001 \n\n\n\nPyrene ND ND ND < 0.001 \n\n\n\nBenzo[c]phenanthrene ND ND ND ND \n\n\n\nBenz[a]anthracene ND ND ND ND \n\n\n\nChrysene ND ND ND ND \n\n\n\nBenzo[k]fluoranthene < 0.001 < 0.001 < 0.001 < 0.001 \n\n\n\nBenzo [e]pyrene < 0.001 0.01 0.01 < 0.001 \n\n\n\nBenzo [a]pyrene < 0.001 0.01 0.01 < 0.001 \n\n\n\n3-Methylcholanthrene 5.65 2.30 3.56 2.40 \n\n\n\nIndeno (1,2,3-cd)pyrene ND < 0.001 < 0.001 ND \n\n\n\nBenzo[ghi]perylene ND < 0.001 < 0.001 ND \n\n\n\nSUM(  PAHs) 5.65 2.34 3.67 2.42 \n\n\n\nThe levels of PAHs in the rainwater in the two seasons were higher than \nthe WHO acceptable limit for PAHs (0.0007 mg/L) in drinking water \n(Skupinska et al., 2004). Though, there is paucity of data and background \n\n\n\nlevels of PAHs in rainwater in Nigeria for comparison , opined that gas \nflaring is the major factor that precipitates acid rain in the Niger Delta \nregion, of which these study areas are part of (Alakpodia, 2000; Efe, 2006). \n\n\n\n\n\n\n\n\nCite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\n3.2   Results of Surface Water Samples Analyses \n\n\n\nThe surface waters from the three study areas during the rainy season \nhave been shown to have the presence of naphthalene and 3-\n\n\n\nmethylcholanthrene only; all other priority PAHs were below the \ndetection limit . The PAHs levels  in the surface waters were in the \nfollowing order: Ikot Abasi (11.09 mg/L) > Eastern Obolo (3.87 mg/L) > \nIbeno (1.94 mg/L), and presented in Table 5. \n\n\n\nTable 3: Concentrations of PAHs in Rainwaters from Ikot Abasi During the Dry Season \n\n\n\nPAH Ikpetim ALSCON Ibom Power Conveyor Belt , \n Ikot Abasi Ikot Abasi Ikot Abasi Ikot Abasi \n Conc. (mg/L) Conc. (mg/L) Conc.  (mg/L) Conc. (mg/L) \n\n\n\nNaphthalene < 0.001 0.07 0.05 0.02 \n\n\n\nAcenaphthylene < 0.001 ND ND ND \n\n\n\nAcenaphthene ND ND ND ND \n\n\n\nFluorene ND <0.001 ND ND \n\n\n\nPhenanthrene 0.06 ND ND ND \n\n\n\nAnthracene 0.07 < 0.001 ND ND \n\n\n\nFluoranthene ND < 0.001 0.002 ND \n\n\n\nPyrene ND < 0.001 0.002 ND \n\n\n\nBenzo[c]phenanthrene ND ND ND ND \n\n\n\nBenz[a]anthracene ND ND ND ND \n\n\n\nChrysene ND ND ND ND \n\n\n\nBenzo[k]fluoranthene ND < 0.001 0.001 < 0.001 \n\n\n\nBenzo [e]pyrene 0.09 0.001 0.001 0.01 \n\n\n\nBenzo [a]pyrene 0.09 0.003 0.002 < 0.001 \n\n\n\n3-Methylcholanthrene ND 3.39 1.22 6.39 \n\n\n\nIndeno (1,2,3-cd)pyrene ND < 0.001 ND < 0.001 \n\n\n\nBenzo[ghi]perylene 0.07 < 0.001 ND < 0.001 \n\n\n\nSUM(PAHs) 0.38 3.464 1.278 6.42 \n\n\n\nSurface waters from the three study areas during the dry season reveal the \npresence of naphthalene, 3-methylcholanthrene, and benzo[a]pyrene \nonly; all other priority PAHs were below detection limit. The PAHs \ndetected in the surface waters were in the following order: Ikot Abasi \n(13.79 mg/L) > Eastern Obolo (3.95 mg/L) > Ibeno (2.45 mg/L), and \npresented in Table 6. \n\n\n\nPAHs concentrations in drinking and surface waters vary between 1 ug/L \nand 11 \u03bcg/L, with the highest permissible concentration of \nbenzo[a]pyrene (0.7 \u03bcg/L) by WHO (Skupinska et al., 2004). The PAHs in \nthe surface waters of the three study areas were found to be higher than \nthe acceptable limit of 0.7 ug/L set by WHO.  The results obtained for the \nconcentration of PAHs in surface waters are higher than those obtained   \nby who determined the concentrations of PAHs in drinking water samples \nand found that the PAH compounds in all samples were below the limits \nproposed by the Portuguese legislation limiting the total concentrations of \nfour PAHs (IcdP, BghiP, BkF, and BbF) to 0.10 \u03bcg/L and BaP limited to the \nmaximum level of 0.010 \u03bcg/L. In comparison,this study has higher PAH \nvalues (Cardoso et al., 2008).  To evaluated the concentration of PAHs in \nwater in Brazil and reported that the mean total concentrations of PAHs \nwere 51.20\u2013162.37 \u03bcg/L, which are lower than the results obtained in this \nstudy, thus raising a serious public health concern (Froehner et al., 2018). \n\n\n\nThe predominance of PAHs of higher molecular weight congeners has \ndemonstrated that petroleum and the combustion products of gas flaring, \n\n\n\nas well as other pyrogenic sources, may have contributed to the main input \nof PAHs in the surface waters in the study areas. \n\n\n\nOn the overall, the levels of PAHs determined in soils of the study areas are \nlower than those reported in Kenya and  China (Guo,2007; Liang, 2022)  \n\n\n\n3.3   Results of Soil Samples Analyses \n\n\n\nThe results for the analyses of soil samples from Ikot Abasi, Ibeno, and \nEastern Obolo  LGAs during the rainy season are presented in Table 7. \n\n\n\nSoils from Ibeno LGA showed the presence of naphthalene (920 ug/kg), \nfluorene (4 ug/kg), phenanthrene (52 ug/kg), fluoranthrene (64 ug/kg), \npyrene (196 ug/kg), benzo[c]phenanthrene (20 ug/kg), \nbenzo[a]anthracene (24 ug/kg), chrysene (24 ug/kg), \nbenzo[k]fluoranthrene (40 ug/kg), benzo[e]pyrene (40 ug/kg), \nbenzo[a]pyrene (44 ug/kg), indeno (1,2,3-cd)pyrene (52 ug/kg), and 3-\nMethylcholanthrene (72 ug/kg), with only indeno (1,2,3-cd)pyrene \n(780  ug/kg) recorded in Eastern Obolo and only naphthalene (8 ug/kg) \nrecorded in Ikot Abasi. The PAHs levels obtained for the soils in the three \nstudy areas during the rainy season followed the following order: Eastern \nObolo (776 ug/kg) >Ibeno (732 ug/kg) > Ikot Abasi (8 ug/kg). \n\n\n\nThe results obtained for soil samples from Ikot Abasi, Ibeno, and Eastern \nObolo LGAs during the dry season are presented in Table 8. \n\n\n\nTable 4: Concentrations of PAHs in Rainwaters from Eastern Obolo During Dry Season \n\n\n\nPAH Eastern Obolo Eastern Obolo Ibeno Ibeno \n Iko Okoroete Hall Ukpeneikang \n Conc. (mg/L) Conc. (mg/L) Conc. (mg/L) Conc. (mg/L) \n\n\n\nNaphthalene 0.03 0.01 0.07 0.04 \n\n\n\nAcenaphthylene < 0.001 < 0.001 < 0.001 < 0.001 \n\n\n\nAcenaphthene ND ND 0.01 < 0.02 \n\n\n\nFluorene ND ND ND ND \n\n\n\nPhenanthrene ND ND ND ND \n\n\n\nAnthracene ND ND ND ND \n\n\n\nFluoranthene < 0.001 < 0.001 < 0.001 < 0.001 \n\n\n\nPyrene ND ND < 0.001 < 0.001 \n\n\n\nBenzo[c]phenanthrene ND ND < 0.001 <0.001 \n\n\n\nBenz[a]anthracene 0.01 0.01 ND ND \n\n\n\nChrysene ND ND ND ND \n\n\n\nBenzo[k]fluoranthene < 0.001 < 0.001 < 0.001 < 0.001 \n\n\n\nBenzo [e]pyrene < 0.001 0.01 0.01 < 0.001 \n\n\n\nBenzo [a]pyrene < 0.001 0.01 0.01 < 0.001 \n\n\n\n3-Methylcholanthrene 5.42 2.33 3.51 3.63 \n\n\n\nIndeno (1,2,3-cd)pyrene ND < 0.001 < 0.001 < 0.001 \n\n\n\nBenzo[ghi]perylene ND < 0.001 < 0.001 < 0.001 \n\n\n\nSUM( PAHs) 5.46 2.37 3.61 3.67 \n\n\n\n\n\n\n\n\nCite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\nTable 5: Concentrations of PAHs in Surface Waters During Rainy Season \n\n\n\nPAH  Eastern Obolo Ibeno Ikot Abasi \n\n\n\n Okoroete  Nkpanak  Ikpetim  \n\n\n\n Conc. (mg/L)    Conc. (mg/L)   Conc. (mg/L) \n\n\n\n Naphthalene   0.02 ND 0.02 \n\n\n\nAcenaphthylene  ND ND ND \n\n\n\nAcenaphthene  ND ND ND \n\n\n\nFluorene  ND ND ND \n\n\n\n Phenanthrene ND ND ND \n\n\n\n Anthracene  ND ND ND \n\n\n\n Fluoranthene   < 0.001 ND ND \n\n\n\n Pyrene  < 0.001 ND ND \n\n\n\n Benzo[c]phenanthrene  ND ND ND \n\n\n\nBenz[a]anthracene  ND ND ND \n\n\n\n Chrysene  ND ND ND \n\n\n\n Benzo[k]fluoranthene  < 0.001 < 0.001 < 0.00 \n\n\n\n Benzo [e]pyrene   < 0.001 < 0.001 < 0.001 \n\n\n\n Benzo [a]pyrene < 0.001 < 0.001 0.01 \n\n\n\n 3-Methylcholanthrene   3.85 1.94 11.06 \n\n\n\n Indeno (1,2,3-cd)pyrene  < 0.001 ND < 0.001 \n\n\n\nBenzo[ghi]perylene  < 0.001 ND < 0.001 \n\n\n\nSUM(PAHs)   3.87 1.94 11.09 \n\n\n\nThe concentrations of PAHs in soils from Ibeno were as follows:  \nnaphthalene (92 ug/kg), fluorene (14 ug/kg), phenanthrene (72 ug/kg), \nfluoranthrene (52 ug/kg), pyrene (172 ug/kg), benzo[c]phenanthrene (36 \nug/kg), benzo[a]anthracene (32 ug/kg), chrysene (36 ug/kg), \nbenzo[k]fluoranthrene (48 ug/kg), benzo[k]fluoranthrene (48 ug/kg), \nbenzo[e]pyrene]pyrene (68 ug/kg), benzo[a]pyrene (60 ug/kg), indeno \n(1,2,3-cd) pyrene (68 ug/kg), 3-Methylcholanthrene (60 ug/kg), \nfluoranthrene (52 ug/kg), pyrene (172 ug/kg), benzo[c]phenanthrene (36 \nug/kg), benzo[a]anthracene (32 ug/kg), chrysene (36 ug/kg), \nbenzo[k]fluoranthrene (48 ug/kg), benzo[e]pyrene (68 ug/kg), \nbenzo[a]pyrene (60 ug/kg), indeno (1,2,3-cd) pyrene (68 ug/kg), 3-\nMethylcholanthrene (60 ug/kg), and benzo[ghi]perylene (52 ug/kg), with \nonly indeno (1,2,3-cd) pyrene (105 ug/kg) recorded in Eastern Obolo and \nonly naphthalene (20 ug/kg) recorded in Ikot Abasi. The PAHs levels in the \nsoils in the three study areas during the dry season followed the order: \n\n\n\nIbeno (872 ug/kg) >Eastern Obolo (105 ug/kg) > Ikot Abasi (20 ug/kg). \n\n\n\nFrom the results of PAHs in the soil samples, soils from Eastern Obolo and \nIbono could be slightly polluted (> 700 ug/kg) during the rainy season; \nhowever, during the dry season, soils from Ibeno LGA could be slightly \npolluted (> 700 ug/kg), while soil from Ikot Abasi is said to be unpolluted \n(< 200 ug/kg) in both seasons, based on the classification by (Wu et \nal.2019). \n\n\n\n3.4   PAH Molecular Diagnostic Ratio Analyses  \n\n\n\nThe PAH molecular diagnostic ratio analyses using LMW/HMW for \nrainwater during the rainy season are presented in Table 9. The results \nwere in the following order : Ikot Abasi (0.05) > Ibeno (0.02) > Eastern \nObolo (0.01) \n\n\n\nTable 6:  Concentrations of PAHs in Surface Waters During Dry Season \n\n\n\nPAH Eastern Obolo Ibeno Ikot Abasi \n Okoroete Nkpanak Ikpetim \n Conc. (mg/L) Conc. (mg/L) Conc. (mg/L) \n\n\n\nNaphthalene 0.04 < 0.001 0.02 \n\n\n\nAcenaphthylene < 0.001 ND ND \n\n\n\nAcenaphthene < 0.001 ND ND \n\n\n\nFluorene ND ND ND \n\n\n\nPhenanthrene ND ND ND \n\n\n\nAnthracene ND ND ND \n\n\n\nFluoranthene < 0.001 ND ND \n\n\n\nPyrene < 0.001 ND ND \n\n\n\nBenzo[c]phenanthrene ND ND ND \n\n\n\nBenz[a]anthracene ND ND ND \n\n\n\nChrysene ND ND ND \n\n\n\nBenzo[k]fluoranthene < 0.001 < 0.001 < 0.001 \n\n\n\nBenzo [e]pyrene < 0.001 < 0.001 < 0.001 \n\n\n\nBenzo [a]pyrene < 0.001 < 0.001 0.04 \n\n\n\n3-Methylcholanthrene 3.91 2.45 13.73 \n\n\n\nIndeno (1,2,3-cd)pyrene < 0.001 < 0.001 < 0.001 \n\n\n\nBenzo[ghi]perylene < 0.001 < 0.001 < 0.001 \n\n\n\nSUM(PAHs) 3.95 2.45 13.79 \n\n\n\n\n\n\n\n\nCite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\nTable 7:  Concentrations of PAHs in Soils During Rainy Season \n\n\n\nPAH Ibeno Eastern Obolo Ikot Abasi \n\n\n\n Ukpeneikang Iko Ikpetim \n\n\n\n ( \u00b5g/kg ) ( \u00b5g/kg ) ( \u00b5g/kg ) \n\n\n\nNaphthalene 920 ND 8.00 \n\n\n\nAcenaphthylene < 0.001 ND < 0.001 \n\n\n\nAcenaphthene < 0.001 ND < 0.001 \n\n\n\nFluorene 4.00 ND < 0.001 \n\n\n\nPhenanthrene 520 ND < 0.001 \n\n\n\nAnthracene 8.00 ND < 0.001 \n\n\n\nFluoranthene 640 ND < 0.001 \n\n\n\nPyrene 196 ND < 0.001 \n\n\n\nBenzo[c]phenanthrene 20.00 ND ND \n\n\n\nBenz[a]anthracene 240 ND ND \n\n\n\nChrysene 240 ND ND \n\n\n\nBenzo[k]fluoranthene 40.0 < 0.001 ND \n\n\n\nBenzo [e]pyrene 40.0 < 0.001 ND \n\n\n\nBenzo [a]pyrene 440 < 0.001 ND \n\n\n\nIndeno (1,2,3-cd)pyrene 520 776.0 ND \n\n\n\n3-Methylcholanthrene 720 ND ND \n\n\n\nBenzo[ghi]perylene < 0.001 ND ND \n\n\n\nSUM(PAHs) 4548 776 8.00 \n\n\n\n\n\n\n\nTable 8:  Concentrations of PAHs in Soils During Dry Season \n\n\n\nPAH Ibeno Eastern Obolo Ikot Abasi \n\n\n\n Ukpeneikang, Iko , Ikpetim , SS 3 \n\n\n\n Conc. (\u00b5g/kg ) Conc. (\u00b5g/kg ) Conc. (\u00b5g/kg ) \n\n\n\nNaphthalene 92.00 ND 20.00 \n\n\n\nAcenaphthylene < 0.001 ND < 0.001 \n\n\n\nAcenaphthene < 0.001 ND < 0.001 \n\n\n\nFluorene 4.00 ND < 0.001 \n\n\n\nPhenanthrene 72.0 ND < 0.001 \n\n\n\nAnthracene 20.0 ND < 0.001 \n\n\n\nFluoranthene 52.0 ND < 0.001 \n\n\n\nPyrene 172.0 ND < 0.001 \n\n\n\nBenzo[c]phenanthrene 36.0 ND ND \n\n\n\nBenz[a]anthracene 32.0 ND ND \n\n\n\nChrysene 36.0 ND ND \n\n\n\nBenzo[k]fluoranthene 48.0 < 0.001 ND \n\n\n\nBenzo [e]pyrene 68.0 < 0.001 ND \n\n\n\nBenzo [a]pyrene 60.0 < 0.001 ND \n\n\n\nIndeno (1,2,3-cd)pyrene 68.0 105.00 ND \n\n\n\n3-Methylcholanthrene 60.0 ND ND \n\n\n\nBenzo[ghi]perylene 52.0 ND ND \n\n\n\nSUM(  PAHs) 872.0 105.0 20.0 \n\n\n\n \nThe LMW/HMW PAH ratios for rainwater in the three study areas during \nthe rainy season are less than one, indicating a petrogenic source, \naccording to Rocher et al. (2004). The BaA/(BaA + Chrysene) ratios \ncalculated for rainwater for all study areas during the rainy season was \nless than 0.2, indicating a petrogenic source of PAHs. The Ant/(Ant + Phe) \nratios calculated for rainwater for all the study areas during the rainy \nseason were  less than 0.1, indicating a petrogenic source of PAHs. \n\n\n\nSurface water samples from the three study areas during the rainy season \ngave an LMW/HMW ratios of less than 1 (Table 9), indicating a pyrogenic \nsource of PAHs. BaA/(BaA + Chrysene) ratios were less than 0.2, and \nAnt/(Ant + Phe) were less than 0.1, indicating a petrogenic source of PAH. \n\n\n\nPAH molecular diagnostic ratio using LMW/HMW for soil samples during \nthe rainy season for Ikot Abasi  was 0.001, Ibeno  0.47 and Eastern Obolo \n8.0, as presented in Table 9. According to LMW/HMW ratios for soils in \nIkot Abasi and Ibeno were less than one, indicating a pyrogenic source \nfrom incomplete combustion of organic substances,  while Eastern Obolo \nsoils had values greater than one, indicating  petrogenic source from \npetroleum leakages (Rocher et al., 2004). This also corroborates the \nobservation by S. Liang et al. 2022 , in their studies  of PAHs in Kenya. \n\n\n\nPAH molecular diagnostic ratios using LMW/HMW for rainwater during \nthe dry season in Ikot Abasi  was 0.05,  Ibeno 0.02  and Eastern Obolo 0.01, \nand are  presented in Table 10. \n\n\n\n\n\n\n\n\nCite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\nTable 9: Molecular Ratio of PAHs During the Rainy Season \n\n\n\nMolecular Ratio Sample Ikot Abasi Ibeno Eastern Obolo Standard /  indication \n\n\n\n\n\n\n\nLMW /HMW  0.05 0.02 0.01 <  1,    Pyrogenic \n\n\n\nBaA/(BaA + Chrysene) Rainwater < 0.01 < 0.01 < 0.01 < 0.2,  Petrogenic \n\n\n\n     0.2  -  0.35 , Petrogenic \n\n\n\n     and pyrolytic \n\n\n\nAnt /(Ant + Phe )  < 0.001 < 0.01 < 0.01 < 0.1 ,  Petrogenic \n\n\n\n\n\n\n\nLMW /HMW Surface water 0.01 < 0.01 < 0.01 < 1,Petrogenic \n\n\n\nBaA/(BaA + Chrysene)  < 0.01 < 0.01 < 0.01 Petrogenic \n\n\n\n\n\n\n\nAnt /(Ant + Phe )  < 0.01 < 0.01 < 0.01 Petrogenic \n\n\n\n\n\n\n\nLMW /HMW Soil 0.469 < 0.001 > 8 < 1 , Pyrogenic \n\n\n\nBaA/(BaA + Chrysene)  < 0.001 0.5 < 0.001  \n\n\n\n\n\n\n\nAnt /(Ant + Phe )  < 0.1 < 0.1 < 0.1 Petrogenic \n \n\n\n\n\n\n\n\nTable 10: Molecular Ratio of Pahs During the Dry Season \n\n\n\nMolecular Ratio Sample Ikot Abasi Ibeno Eastern Obolo Standard /  indication \n\n\n\n\n\n\n\nLMW /HMW Rainwater 0.05 0.02 0.01 <  1,    Petrogenic \n\n\n\n\n\n\n\nBaA/(BaA + \nChrysene) \n\n\n\n < 0.01 < 0.01 < 0.01 < 0.2  ,  Petrogenic \n\n\n\n     0.2  -  0.35 , Petrogenic \n\n\n\n     and pyrolytic \n\n\n\nAnt /(Ant + Phe )  < 0.001 < 0.01 < 0.01 < 0.1 ,  Petrogenic \n\n\n\n\n\n\n\n Surface water 0.01 < 0.01 < 0.01 pyrogenic \n\n\n\nLMW /HMW      \n\n\n\n\n\n\n\nBaA/(BaA + \nChrysene) \n\n\n\n < 0.01 < 0.01 < 0.01 Petrogenic \n\n\n\n\n\n\n\nAnt /(Ant + Phe )  < 0.01 < 0.01 < 0.01 Petrogenic \n\n\n\n\n\n\n\nLMW /HMW Soil 20 0.27 <    0  \n\n\n\n  pyrogenic petrogenic petrogenic  \n\n\n\nBaA/(BaA + \nChrysene) \n\n\n\n < 0 0.47 < 0  \n\n\n\n  petrogenic pyrogenic petrogenic  \n\n\n\nAnt /(Ant + Phe )  < 0 0.22 < 0  \n\n\n\n  petrogenic pyrogenic petrogenic  \n \n\n\n\nThe PAH molecular ratios for the three study areas are less than one, \nindicating a pyrogenic source, according to Rocher et al. (2004). The \nBaA/(BaA + Chrysene) ratio calculated for rainwater during the dry \nseason was all less than 0.2, indicating a petrogenic source of PAHs. The \nant/(Ant + Phe) ratio calculated for rainwater during the dry season was \nless than 0.1, indicating a petrogenic source of PAHs. \n\n\n\nSurface water samples from the three study areas during the dry season, \nhad an LMW/HMW ratio of less than 1 (Table 10), indicating a petrogenic \nsource of PAHs. BaA/(BaA + Chrysene) ratio was less than 0.2, indicating \na petrogenic source and Ant/(Ant + Phe) ratios were less than 0.1, \nindicating a petrogenic source of PAH. \n\n\n\nPAH molecular diagnostic ratio analyses by LMW/HMW  for soil samples \nduring  the  dry  season  were  Ikot  Abasi (> 20), Ibeno (0.27),  and  Eastern \n\n\n\nObolo (< 0), as presented in Table 10. The ratios for Eastern Obolo and \nIbeno were less than one, indicating a petrogenic source, while the ratio \nfor Ikot Abasi was greater than one, indicating a pyrogenic source \nprobably from incomplete combustion of organic substances (Rocher et al. \n2004). \n\n\n\n3.5   Physicochemical Parameters of Rainwater \n\n\n\nThe physicochemical parameters of rainwater from the three study areas \nin the dry and rainy seasons are presented in Table 11. During the rainy \nseason, the pH values were in the following order: Ibeno (6.76 \u00b1 0.05) > \nIkot Abasi (6.38 \u00b1 0.02) > Eastern Obolo (5.87\u00b1 0.07), while the pH values \nduring the dry season were in the following order: Ikot Abasi (6.58 \u00b1 0.06) \n> Eastern Obolo (6.55 \u00b1 0.07) > Ibeno (6.49 \u00b1 0.09). \n\n\n\n\n\n\n\n\nCite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\nTable 11: Physicochemical Parameters of Rainwater \n Wet Season Dry Season \n\n\n\nParameter Ikot Abasi Ibeno Eastern Obolo Ikot Abasi Ibeno Eastern Obolo \n\n\n\npH 6.38 \u00b1 0.02 6.76   \u00b1 0.05 5.87   \u00b1 0.07 6.58   \u00b1 0.06 6.49   \u00b1 0.08 6.55   \u00b1  0.07 \n\n\n\nConductivity \n\n\n\n(\u00b5 S/cm ) \n7.99  \u00b1 0.03 187.1 \u00b1 2. 85 102.5   \u00b1 3.06 8.63   \u00b1 0.14 202   \u00b1 4.95 82.4   \u00b1 3.95 \n\n\n\nSalinity (o% ) 0.00 0.02   \u00b1 0.001 0.00 0.00 0.01   \u00b1 0.003 0.00 \n\n\n\nTotal dissolved solids ( mg /L) 6.05  \u00b1 0.02 125.2  \u00b12.76 53.09   \u00b1 2.84 4.37  \u00b1 0.03 101.2   \u00b1 2.92 41.5   \u00b1 2.66 \n\n\n\nTemperature( o C ) 24.72 \u00b11.09 24.75  \u00b10.74 25.62   \u00b1 1.52 25.92   \u00b10.34 26.4   \u00b1 0.84 26.5   \u00b1  0.67 \n\n\n\nDuring the rainy season, conductivity was in order: Ibeno ( 187.1\u00b1 2.85 \nuS/cm) > Eastern Obolo (102.5 \u00b1 3.06 uS/cm) > Ikot Abasi (7.99  \u00b1 0.03 \nuS/cm), while during the dry season, conductivity was in the order: Ibeno \n(202  \u00b1 4.95 uS/cm) > Eastern Obolo (82.4 \u00b1 3.95 \u00b5S/cm) > Ikot Abasi (8.63 \n\u00b1 0.14 \u00b5S/cm). \n\n\n\nThe salinity determined in the rainwater from the three study areas was \nIkot Abasi (0.0 %o) , Ibeno (0.02 \u00b1 0.001%o), and Eastern Obolo (0.0%o), \nwhereas during the dry season,the salinity was Ikot Abasi (0.0 %o), Ibeno \n(0.01 \u00b1 0.003%o), and Eastern Obolo (0.0%o), indicating less impact of \nsalt water evaporation. \n\n\n\nThe total dissolved solids in the rainwater during the rainy season were in \nthe order:  Ibeno (125.2 \u00b1 2.76 mg/L) > Eastern Obolo (53.09 \u00b1 2.84 mg/L) \n> Ikot Abasi (6.05  \u00b1 0.02 mg/L) , while  the total dissolved solids in the \nrainwater during the dry season were in the order Ibeno (101.2 \u00b1 2.92 \nmg/L) > Eastern Obolo (41.5 \u00b1 2.66 mg/L) > Ikot Abasi (4.37 \u00b1 0.03 mg/L), \nas presented in Table 11. The variation in the results in the two seasons \nwas not significant ( P < 0.01) in the three locations. \n\n\n\n3.6   Ecological Risk Assessment of Pahs In Soil \n\n\n\nThe method used to calculate the benzo[a]pyrene toxic equivalent \nconcentration (TEQB[a]P) was as adopted by S. Liang et al.(2022), to \nevaluate the risk of PAHs in the soil. The total TEQB[A]P determined \nduring the rainy season were in the order (Table 12) ; Ibeno (747\u00b5g/kg ) \n>  Eastern Obolo ( 77.6 \u00b5g/kg ) >  Ikot Abasi ( 0.008 \u00b5g/kg ) , while the \nTEQB[A]P values for the dry season were in the order : Ibeno ( 114 \u00b5g/kg \n)  >  Eastern Obolo ( 10.6 \u00b5g/kg ) >  Ikot Abasi ( 0.02 \u00b5g/kg).   \n\n\n\nThese values were lower than the total TEQB[A]P of sixteen PAHs in \nsemiarid soils of India (650 \u00b5g/kg, A. Masih and A. Taneja, (2006).), except \nIbeno, during the rainy season. However, the values were higher than the \ntotal TEQB[A]P of Visevu (24 \u00b5g/kg) and Ribens soil of Portugal (S. \nRodrigues, 2006). \n\n\n\nPresently, there is no evaluation standard for soil PAHs in Nigeria, hence \nthe threshold value of TEQB[a]P (33.0 \u00b5g/kg) from Dutch soil \nmanagement regulations was applied to test the potential risk of PAHs in \nsoils from the study areas. The TEQB[a]P values of soils in this study are \nhigher than 33.0 \u00b5g/kg, indicating that the soil PAHs in these locations has \npotential  \n\n\n\ncarcinogenic risks to the human body. \n\n\n\nTable 12: TEQ of PAHs in Soil \n\n\n\nRainy Season   \n\n\n\nIbeno  ( \u00b5g/kg ) Eastern Obolo (\u00b5g/kg ) Ikot Abasi (\u00b5g/kg ) \n\n\n\n747 77.6 0.008 \n\n\n\nDry Season   \n\n\n\nIbeno (\u00b5g/kg ) Eastern Obolo (\u00b5g/kg ) Ikot Abasi (\u00b5g/kg ) \n\n\n\n114. 10.5 0.02 \n \n\n\n\n4. CONCLUSION \n\n\n\nThe study reveals the following polyaromatic hydrocarbons in rainwater \nand surface waters in Ikot Abasi, Ibeno, and Eastern Obolo LGAs during \nthe rainy and dry seasons: naphthalene, benzo[k]fluoranthrene, \nbenzo[e]pyrene, benzo[a]pyrene, and 3-methylcholanthrene. Most of the \nother sixteen priority PAHs, like acenaphthylene, acenaphthene, fluorene, \nphenanthrene, anthracene, fluoranthene, and pyrene, were not present in \nthe rainwater and surface waters. The levels of the PAHs in the rainwater \nand surface waters from the three study areas of Ikot Abasi, Eastern Obolo, \nand Ibeno were higher than the WHO permissible limits for PAHs (0.7 \n\u00b5g/L) in drinking water. \n\n\n\nFrom the results obtained, the levels of the PAHs in the soil samples, from \n\n\n\nEastern Obolo and Ibono are slightly polluted (> 700 ug/kg) during the \nrainy season, whereas during the dry season, soils from Ibeno are slightly \npolluted (>700 ug/kg). In comparison, soils from Ikot Abasi are most likely \nto be unpolluted (< 200 ug/kg) in both seasons, based on the classification \nby (Wu et al., 2019). \n\n\n\nPAH molecular diagnostic ratios using LMW/HMW, BaA/(BaA + Chrysene) \nratio, and Ant/(Ant + Phe) ratios for rainwater and surface waters from \nthe three study areas revealed  a petrogenic source of PAHs during the \nrainy and dry seasons.  \n\n\n\nPAH molecular diagnostic ratios using LMW/HMW for soil samples during \nthe rainy season in Ikot Abasi and Ibeno show that the PAHs are from a \npetrogenic source, while the Eastern Obolo ratios show a pyrogenic source \nof PAHs from incomplete combustion of organic substances from \nanthropogenic activities. However, PAH molecular diagnostic ratios using \nLMW/HMW for soil samples during the dry season in Eastern Obolo and \nIbeno show a petrogenic source, while Ikot Abasi PAH molecular ratios \nshow a pyrogenic source from incomplete combustion of organic \nsubstances from anthropogenic activities as a result of the power plant \nlocated in the area. \n\n\n\nThe variation in the physicochemical parameters of the rainwaters from \nthe three study areas in the two seasons was not significant (P< 0.05); \nhowever, the total dissolved solids of the rainwater from Ibeno were \nsignificantly higher than those from Ikot Abasi and Eastern Obolo, \nprobably because of the intense anthropogenic activities in the area. \n\n\n\nGenerally, there is no evaluation standard for PAHs in Nigeria, hence the \nthreshold value of TEQB[a]P (33.0 \u00b5g/kg ) from  Dutch soil management \nregulations was applied to test the potential risk of PAHs in soils from the \nstudy areas. The TEQB[a]P values of soils in this study are higher than 33.0 \n\u00b5g/kg, indicating that the soil PAHs in these locations has potential \ncarcinogenic risks to the human body.  \n\n\n\nTherefore, there is need for close monitoring of PAHs levels in the study \nareas and the need to minimise gas flaring activities by appropriate \nlegislation in Nigeria. \n\n\n\nCONFLICT OF INTEREST  \n\n\n\nThe authors express no conflict of interest in the study \n\n\n\nSPONSORSHIP  \n\n\n\nThis research work was funded by TETFUND Institution \u2013 Based \nResearch Grant  \n\n\n\nACKNOWLEDGEMENT  \n\n\n\nThe authors wish to express a sincere gratitude to TETFUND, Nigeria for \nthe sponsorship of the research, management of AKSU, some staff of AKSU \nfor helping in preparing the manuscript and staff of CION Laboratories, \nLagos for handling of laboratory analyses. \n\n\n\nREFRENCES \n\n\n\nAbdel-Shafy, H. I., Mansour, M. S., 2016. A review on polycyclic aromatic \nhydrocarbons: source, environmental impact, effect on human \nhealth and remediation. Egypt. J. Pet. 25, Pp. 107\u2013123. doi: \n10.1016/j.ejpe.2015.03.011 \n\n\n\nAkankpo A.O.,  Igbokwe M. U., 2011. Monitoring Ground-water \ncontamination using surface electrical resistivity and geochemical \nmethods. Journals of water resources proceedings. 3 (5); Pp. 318-\n324. \n\n\n\nAky\u00fcz, M., \u00c7abuk, H., 2010. Gas\u2013particle partitioning and seasonal \nvariation of polycyclic aromatic hydrocarbons in the atmosphere of \n\n\n\n\n\n\n\n\nCite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\nZonguldak, Turkey, Sci. Total Environ. 408, Pp. 5550 - 5558. \nhttps://doi.org/10.1016/j.scitotenv.2010.07.063.566  \n\n\n\nAlakpodia, J.I., 2000.The environment of the Niger Delta. In B.C. Uweru and \nJ.O. Ubrurhe (Eds), Readings in General Studies, Nigerian People and \nCulture. (pp190-203) About Research Pub. \n\n\n\nAmadi, N., Akobundu, 2014. Impact of Gas-Flaring on the Quality of Rain \nWater, Groundwater and Surface Water in Parts of Eastern Niger \nDelta, Nigeria.\" Journal of Geosciences and Geomatics 2.3: Pp. 114-\n119. \n\n\n\nAnderson, L. G., 2009. Ethanol fuel use in Brazil: air quality impacts. Energy \nand  Environmental Science 2, Pp. 1015 - 1037. \n\n\n\nAndrade, M. F., Kumar, P., Freitas, E. D., Ynoue, R. Y., Martins, J. A., Martins, \nL. D., Nogueira, T., Perez-Martinez, P., de Miranda, R. M., \nAlbuquerque, T., Gon\u00e7alves, F. L. T., Oyama, B., Zhang, Y.,  2017. Air \nquality in the megacity of S\u00e3o Paulo: Evolution over the last 30 years \nand future perspectives. Atmospheric Environment 159,Pp. 66 - 82. \n\n\n\nAndrianova, A. A., Quimby, B. D., 2019. Optimized GC/MS/MS Analysis for \nPAHs in Challenging Matrices, Agilent Technologies Application \nNote, publication number 5994\u20110498EN. \n\n\n\nAPHA, AWWA, WEF., 2005.  Standard Methods  for the Examination of \nWater and Wastewaters. 21st Edition. American  Public Health \nAssociation. USA.  \n\n\n\naromatic hydrocarbons (PAHs) in tank bottom solids and associated \ncontaminated soils at oil exploration and production sites in \nTexas.Regul., Toxicol. Pharmacol., 47(3), Pp. 288-295. \nhttp/doi.org// 10.1016/j.yrtph.2006.11.007 \n\n\n\nAtainyang, Joseph, 2021. Calabar, Obolo States creation feature at House \nof Reps public hearing. National News Track. Retrieved 2022 -04-11 \n\n\n\nBihanic, F.L., Sommard, V., Perrine, D.L., Pichon, A., Grasset, J., Berrada, S., \nBudzinski, H., Cousin, X., Morin, B., Cachot, J., 2015. Environmental \nconcentrations ofbenz[a]anthracene induce developmental defects \nand DNA damage and impair photomotor response in Japanese \nmedaka larvae. Ecotox. Environ. Safe. 113, Pp. 321-328. http/doi. \norg//10.1016/j.ecoenv.2014.12.011 \n\n\n\nBojes, K.,  Pope, G., 2007. Characterization of EPA\u2019s 16 priority pollutant \npolycyclic  \n\n\n\nBolden, A. L., Rochester, J. R., Schultz, K., Kwiatkowski, C. F., 2017. \nPolycyclic aromatic hydrocarbons and female reproductive health: \na scoping review. Reproductive Toxicology. 73, Pp. 61\u201374. doi: \n10.1016/j.reprotox.2017.07.012 \n\n\n\nCardoso, A., Feliciano, S., Rebelo, M., Jos\u00e9, S.,  Reis C., 2008. Optimization \nand validation of a chromatographic methodology for the \nquantification of PAHs in drinking water samples. WIT Trans Ecol \nEnviron 110: Pp. 271\u2013280. https://doi.org/10.1211/jpp.60.1.0014 \n\n\n\nChaloupka, K., 1993. Synergistic Activity of Polynuclear Aromatic \nHydrocarbon Mixtures as Aryl Hydrocarbon ( AH) Receptor \nAgonists . Chemico-Biological Interactions . 89 : Pp. 141 \u2013 158. \n\n\n\n Chemosphere. 69 ,Pp. 458\u2013468. \nhttps://doi.org/10.1016/j.chemosphere.2007.04.050. \n\n\n\nChristopher Onyemaechi, E.,  2017. Assessment of Polycyclic Aromatic \nHydrocarbons (Pahs) in Hardwood, Palmwood and Softwood - \nSmoked Fish, Int. J. Ecotoxicol. Ecobiol. 2 ,Pp. 178 \nhttps://doi.org/10.11648/j.ijee.20170204.17. \n\n\n\nChuang, C.Y., Lee, C.C., Chang, Y.K., Sung F.C., 2003. Oxidative DNA damage \nestimated by urinary 8-hydroxydeoxyguanosine; influence of taxi \ndriving, smoking and areca chewing. Chemosphere;52(7): Pp. 1163\u2013\n71.  \n\n\n\nDahle, S., Savinov, V., Matishov, G., Evenset, A., Neas, K., 2003. Polycyclic \naromatic hydrocarbons (PAHs) in bottom sediments of the Kara Sea \nshelf, Gulf of Ob and Yenisei Bay. Sci Total Environ 306: Pp. 57\u201371. \n\n\n\nDescription of Fisheries, 2011. Marine fishery resources of Nigeria: A \nreview of exploited fish stocks. Fisheries and Aquaculture \nDepartment. Retrieved September 9,  \n\n\n\nDiggs, D.L., Huderson, A.C., Harris, K.L., Myers, J.N., Banks, L.D., Rekhadevi,  \n\n\n\nP.V., 2011. Polycyclic Aromatic Hydrocarbons and Digestive Tract \nCancers: A Perspective. Journal of Environmental Science and \nHealth, Part C.;29(4): Pp. 324-57. \n\n\n\n Efe, S. I., 2010. Spatial variation in acid and some heavy metal composition \nof rainwater harvesting in the oil producing region of Nigeria. \nNatural Hazard ,5:307-319, DOI10.1007/s11069-010-9526-2 \n\n\n\nEfe, S.I., 2006. Quality of rainwater harvesting for rural communities of \nDelta State, Nigeria. Environmentalist 26; Pp 175-181. \n\n\n\nEhsan Ashouri ; Ahmad Jonidi Jafari , Mitra Gholami , Majid Kermani,  \nMahdi     Farzadkia, and Abbas Shahsavani., 2022.  Characterization, \nspatial distribution, and health risk assessment of polycyclic \naromatic hydrocarbons and heavy metals bounded PM2.5 in urban \nair of Tabriz, Iran. Journal of Air Pollution and Health (Spring ); 7(2): \nPp. 173-196 \n\n\n\nEtesin,  Usoro, M.,  Inim, Iniemem J., 2021.  Hydrochemical study of shallow \nground water in Ikot Abasi Coastal Aquifer . Communication in \nPhysical Science, 7(3): Pp. 218-228  \n\n\n\nFroehner, S., Rizzi, J., Vieira, L. M.,  Sanez, J., 2018. PAHs in water, sediment \nand biota in an area with port activities. Arch Environ Contam \nToxicol 75: Pp. 236\u2013246. https://doi.org/10.1007/s00244-018-\n0538-6 \n\n\n\nG.-C. Fang, K.-F. Chang, C. Lu, H. Bai, 2004. Estimation of PAHs dry \ndeposition and BaP toxic equivalency factors (TEFs) study at Urban, \nIndustry Park and rural sampling sites in central Taiwan, Taichung, \nChemosphere. 55, Pp. 787\u2013\n796.https://doi.org/10.1016/j.chemosphere.2003.12.012. \n\n\n\nGarc\u00eda-S\u00e1nchez, M., Ko\u0161n\u00e1\u00f8, Z., Mercl, F., Aranda, E., and Tlusto\u0161, P., 2018. \nA comparative study to evaluate natural attenuation, \nmycoaugmentation, phytoremediation, and microbial-assisted \nphytoremediation strategies for the bioremediation of an aged PAH-\npolluted soil. Ecotoxicol. Environ. Saf. 147, Pp. 165\u2013174. doi: \n10.1016/j.ecoenv..08.012  \n\n\n\nGray, J.P., Hall, G.J., 2014. Benz[a]anthracene, in book: Encyclopedia of \nToxicology, 3rdedition,. http/doi.org//: 10.1016/B978-0-12-\n386454-3.00247-5  \n\n\n\nGuo, W.,  He, M.-C., Yang, Z.-F.,  Lin, C.-Y., Quan, X.-C.,  Wang, H.-Z.,  2007. \nContamination characters of polycyclic aromatic hydrocarbons in \nDaliao River system of China, Ying Yong Sheng Tai Xue Bao J. Appl. \nEcol. 18, Pp. 1534\u20131538. \n\n\n\nGupte, A., Tripathi, A., Patel, H., Rudakiya, D., Gupte, S., 2016. \nBioremediation of polycyclic aromatic hydrocarbon (PAHs):  A \nperspective. Open.Biotechnol.J. 10, Pp. 363\u2013378. \n\n\n\nIgboekwe, M. U.,  Ezinna, E. L.,  Okon, A. A., 2014. Determination of aquifer  \ncharacteristics in Eket, Akwa Ibom State, Nigeria, using the vertical \nelectrical sounding method.  International journal of water  \nresources and engineering. 4(1), Pp. 1-7, \n\n\n\nIgwe, Jude.C., Onwuka,Kelechi E., Nneke,Chinyere G.,Wogu, Chidi, Aghalibe, \nChristopher U, Ezeudu, Emeka, Joseph, Emeka E., 2020. Polycyclic \nAromatic Hydrocarbons (PAHs) and Heavy Metals in Selected dried \nSea Foods from Portharcourt City Nigeria, Internal journal of \nanalytical and applied chemistry. ISSN: Pp. 2582-5922 vol 6, Issue 1, \nwww.journalspub.com \n\n\n\nIkot Abasi, The Aluminium Town, 1997. A Socio-Economic Transformation \nof a Nigerian Community. An ALSCON Publication.  \n\n\n\nInternational Agency for Research on Cancer (IARC), 2004). Overall \nevaluations of carcinogenicity: an updating of IARC monographs, \nvols. Pp. 1\u201342, suppl. 7. Available at: http://www-\ncie.IARC.fr/htdocs/ indexes/Suppl7index.html. Accessed 13 \nDecember . \n\n\n\nInternational Agency for Research on Cancer (IARC), 2010.  Working \nGroup on the Evaluation of Carcinogenic Risks to Humans. Some \nnon-heterocyclic polycyclic aromatic hydrocarbons and some \nrelated exposures. IARC Monographs on the evaluation of \ncarcinogenic risks to humans . ;92:1. \n\n\n\nKim, A., Park, M., Yoon, T.K., Lee, W.S., Ko, J.J., Lee, K., Bae, J., 2011. Maternal \nexposure to benzo[b]fluoranthene disturbs reproductive \nperformance in male offspring mice. Toxicol.Lett. 203(1),Pp.  54-61. \n\n\n\n\n\n\n\n\nCite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\nhttp/doi.org//: 10.1016/j.toxlet.2011.03.003 \n\n\n\nLiang, S., Yan, X., An, B., Yang, Y.,  Liu, M., 2022. Distribution and ecological \nrisk assessment of polycyclic aromatic hydrocarbons in different \ncompartments in Kenya: A review, Emerging Contaminants, doi: \nhttps://doi.org/10.1016/j.emcon.2022.08.001 \n\n\n\nLiu, L-b., Liu, Y., Lin,  J.M., Tang, N., Hayakawa, K., Maeda, T., 2007. \nDevelopment of analytical methods for polycyclic aromatic \nhydrocarbons (PAHs) in airborne particulates: A review. Journal of \nEnvironmental Sciences.;19(1): Pp. 1-11. \n\n\n\nMasih, A.,  Taneja, A.,  2006. Polycyclic aromatic hydrocarbons (PAHs) \nconcentrations and relate carcinogenic potencies in soil at a semi-\narid region of India, Chemosphere. 65 (2006) Pp. 449\u2013456. \nhttps://doi.org/10.1016/j.chemosphere..01.062. \n\n\n\nMbonu, P.D.C.,  Ebeniro J.O., 1991. Geoelectric Sounding for the \nDetermination of Aquifer Characteristics in Parts of  Umuahia Area \nof Nigeria. Geophys.,56: Pp. 284-291.. \n\n\n\nMojiri, A., Zhou, J. L., Ohashi, A., Ozaki, N., and Kindaichi, T., 2019. \nComprehensive review of polycyclic aromatic hydrocarbons in \nwater   sources,  their   effects  and   treatments.   Sci.   Total  Environ.  \n\n\n\n\n\n\n\n:133971. doi: 10.1016/j.scitotenv..133971 \n\n\n\nNendza, T., Herbst, C.,  Kussatz, A., 1997. Chemosphere 1 35 875. \n\n\n\nOthman, H.B., Leboulanger, C., Le Floc H, E., Mabrouk, H.H.and Hlaili, A.S., \n2012. Toxicity of benz(a)anthracene and fluoranthene to marine \nphytoplankton in culture: Does cell size really matter?. J. Hazard. \nMater.243, 204-2011.doi: 10.1016/j.jhazmat.10.020 \n\n\n\nPatel, A.B, Shaikh, S, Jain, K.R., Desai, C., Madamwar, D., 2020. Polycyclic \nAromatic Hydrocarbons: Sources, Toxicity, and Remediation \nApproaches. Front. Microbiol. 11:562813. doi: \n10.3389/fmicb.2020.562813 \n\n\n\nPies, C., Hoffmann, B., Petrowsky, J., Yang, Y.,  Ternes, T.A., Hofmann, T., \n2008. Characterization and  source identification of polycyclic \naromatic hydrocarbons (PAHs) in river bank soils, Chemosphere. 72 \n,Pp. 1594\u20131601. \nhttps://doi.org/10.1016/j.chemosphere.2008.04.021. \n\n\n\nQuimby, B. D.,  Prest, H. F.,  Szelewski, M. J.,  Freed, M. K.,  2013. In-situ \nconditioning in mass spectrometer systems, US Patent 8, Pp. \n378,293 Feb 1. \n\n\n\n \n\n\n\n\n\n\n\n\nCite the Article: Usoro M. Etesin, Ibanga O. Isaac, Udo J. Ibok, Aniefiok E. Ite (2023).  Characterization and Distribution of Polynuclear Aromatic  \nHydrocarbons in Wet Precipitations, Surface Waters and Soils from South-Eastern Nigeria. Malaysian Journal of Geosciences, 7(2): 81-93. \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 81-93 \n\n\n\n\n\n\n\n\n\n\n\nAppendix 1: TEF Values of Pahs and Allowable Values of Soil Quality in the Netherlands (A. Srivastava and Som, 2007) \n\n\n\nPollutant Nap Acy Ace Fle Phe Ant Flu Pyr \n\n\n\nTEF Value 0.001 0.001 0.001 0.001 0.001 0.01 0.001 0.001 \n\n\n\nStandard(ng/g) 15    50 50 15  \n\n\n\nPollutant BaA Chr B[b]F B[k]F B[a]P Dib B[ghi]P Ind \n\n\n\nTEF Value 1 0.01 0.1 0.1 1 1 0.01 0.1 \n\n\n\nStandard(ng/g) \n\n\n\n \n20 20 25 25   25  \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\n\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN : MJGAAN \n\n\n\n    ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 31 September 2018  \nAccepted 29 November 2018 \nAvailable online 3 January 2019 \n\n\n\nABSTRACT \n\n\n\nDespite existing literature suggesting that Dhaka City, Bangladesh is undergoing subsidence, few researches have \n\n\n\nbeen carried out to actually measure the subsidence rate. Previously conducted studies either do not provide \n\n\n\nsufficiently accurate subsidence results, or the study period is not long enough. In this research, we have tried to \n\n\n\naddress that gap by performing time series subsidence analysis of Dhaka City utilizing Interferometric Synthetic \n\n\n\nAperture Radar (InSAR) technique for a study period of 20 years.  Synthetic Aperture Radar (SAR) C-band images \n\n\n\nfrom ERS, ENVISAT and Sentinel-1A were used to obtain the results. We had to use C-band SAR data from multiple \n\n\n\nsensors considering data availability issue of the period of investigation (i.e. 1992 to 1999(using ERS); 2003 to \n\n\n\n2010(using ENVISAT); 2014 to 2017(using Sentinel 1A)). Most parts of the city is found to be subsiding. Mirpur and \n\n\n\nUttara have subsided by over 221mm and 232mm respectively over the 20 years. Ramna and Cantonment subsided \n\n\n\naround 205mm compared to their level in 1992, whereas both Gulshan and Tejgaon have subsided by about 200mm. \n\n\n\nDemra and Lalbagh show similar subsidence to the Ramna area, whereas Dhanmondi and Mohammadpur show \n\n\n\nsubsidence rates similar to Tejgaon. We have also assessed the parameter sensitivity to perform this time series \n\n\n\nsubsidence analysis. The parameter selection of coregistration, filtering and unwrapping was found to greatly \n\n\n\ninfluence the results. The result is being calibrated with the available GPS observation. \n\n\n\nKEYWORDS \n\n\n\nSubsidence, InSAR, Time Series Analysis \n\n\n\nThe Methodological Framework below gives an overview of the entire process through which the data has been \n\n\n\nanalyzed. The Framework on the left shows the individual operations that are performed to obtain a single \n\n\n\nDisplacement Map (in LOS direction) for a period of y minus x years. This is repeated multiple times for each image \n\n\n\npair so as to cover the entire study period years. The resulting maps are then stacked for Time Series Analysis which \n\n\n\nis represented using the framework on the Right. \n\n\n\n1. INTRODUCTION \n\n\n\nDeltas, by their very nature, are dynamic. Therefore, it is not surprising to \n\n\n\nfind 24 out of the 33 major deltas worldwide are subsiding [1]. The \n\n\n\nGanges\u2013Brahmaputra\u2013Meghna Delta, covering a large portion of  \n\n\n\nBangladesh, is one amongst them. \n\n\n\nThe City of Dhaka (Figure 1), located at the heart of Bangladesh, is also \n\n\n\nexperiencing subsidence, at a slow but continuous manner [2,3]. The \n\n\n\npotential impacts of subsidence can range from damage to engineering \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : http://doi.org/10.26480/mjg.01.2019.32.44\n\n\n\n  RESEARCH ARTICLE \n\n\n\nTIME SERIES ANALYSIS OF SUBSIDENCE IN DHAKA CITY, BANGLADESH USING \nINSAR \n\n\n\nDewan Mohammad Enamul Haque*, Tanzim Hayat, Samanin Tasnim \n\n\n\nDepartment of Disaster Science and Management, University of Dhaka, Dhaka-1000, Bangladesh \n\n\n\n*Corresponding Author Email: dewan.dsm@du.ac.bd \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited \n\n\n\n\nmailto:dewan.dsm@du.ac.bd\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\nstructures to even increased waterlogging as evident in Jakarta, Indonesia \n\n\n\nand in San Joaquin Valley, California [4-7]. Therefore, it is deemed crucial \n\n\n\nto know the pattern of subsidence, in order to anticipate and mitigate \n\n\n\nthose impacts. Moreover, subsidence rates estimation will certainly \n\n\n\ncontribute to a better understanding of the process of subsidence and its \n\n\n\ncontributing factors. \n\n\n\nAlthough it is well established that Dhaka is subsiding, only a handful of \n\n\n\nresearch has been conducted to actually measure the subsidence rates. \n\n\n\nPreviously, researches only attempted to infer the rates. Such \n\n\n\nmethodologies mainly utilized in situ techniques such as magneto-\n\n\n\nstratigraphic dating, carbon dating, bore/well-logs and geomorphic \n\n\n\nsurveys [8\u201310]. The authors note that the findings have high uncertainties \n\n\n\nand limited spatial extent. More recently, a scholar aimed at unraveling the \n\n\n\nsubsidence rates through a time series analysis from 2003 to 2008 with \n\n\n\nGPS velocity measurements [11]. However, only 1 GPS station observation \n\n\n\nwas available for Dhaka city. The results, therefore, do not provide any \n\n\n\nidea of the spatial variability of subsidence rates that may be present \n\n\n\nwithin the city. Subsidence can also be measured by the earth observation \n\n\n\ntechnique of Interferometric Synthetic Aperture Radar (InSAR). InSAR \n\n\n\npermits remote detection of ground deformation to a high degree of \n\n\n\nprecision [12-14]. Moreover, it eliminates the need for expensive and \n\n\n\ntime-consuming field measurements while at the same time enabling large \n\n\n\nspatial coverage (unlike the rest of the in situ techniques). A study by \n\n\n\nanother scholar used L-band InSAR to investigate subsidence rates in \n\n\n\nBangladesh and unlike the previous studies, this was able to show any \n\n\n\nspatial variability that was present. However, the study period was \n\n\n\nshortened to only 4 years (2008 to 2011). The average subsidence rate \n\n\n\nmeasured by GPS at Dhaka was 12.4 mm/year but the InSAR derived \n\n\n\naverage found it to be only 3.8 mm/year. Consequently, the authors \n\n\n\nrecommended that time series analysis for a larger study period will \n\n\n\nreduce the anomalies. \n\n\n\nFortunately, the European Remote Sensing (ERS) satellite\u2019s and Envisat\u2019s \n\n\n\n(C-band Synthetic Aperture Radar) data was recently made freely \n\n\n\navailable for the period 1992 to 2010 [15]. In addition, Sentinel-1 SAR data \n\n\n\nis also available for free from 2014 for research purpose. In this study, we \n\n\n\nhave performed the time series subsidence analysis for Dhaka city utilizing \n\n\n\nthese SAR data from different sensors using conventional InSAR \n\n\n\ntechnique. Synthetic Aperture Radar (SAR) C-band images from ERS, \n\n\n\nENVISAT and Sentinel-1A were used to obtain the results. Although \n\n\n\nPersistent Scatter Interferometry and Small Baseline have certain \n\n\n\nadvantages over Conventional InSAR, i.e., they are less affected by phase \n\n\n\nnoise but conventional InSAR technique has been chosen in this study due \n\n\n\nto the lack of a large number of images (with small temporal gaps) [16-18]. \n\n\n\nSpecifically, we have addressed two research questions through this study \n\n\n\nin the context of subsidence rate estimation of Dhaka city using InSAR time \n\n\n\nseries analysis. These are (1) what are the results of time series analysis \n\n\n\nof subsidence in Dhaka city? (2) Which parameters are found to be highly \n\n\n\nsensitive for this displacement mapping. \n\n\n\n2. STUDY AREA \n\n\n\nIt is well established that the city of Dhaka has been subsiding for quite \n\n\n\nsome time, driven by the combined influence of factors like expansive \n\n\n\nseasonal flooding, tectonics, and compaction of sedimentary layers. Given \n\n\n\nthat the individual contribution of those factors still remains unexplored, \n\n\n\nit denotes that more reliable, long-term data is missing. InSAR is expected \n\n\n\nto solve that problem. In order for C-band InSAR to work, it requires \n\n\n\nground targets that can produce strong backscatter of the radio waves and \n\n\n\nfor them to remain coherent (i.e. relatively unchanged). The presence of \n\n\n\nurban clusters within the city, many of which have changed little (between \n\n\n\nthe acquisition times of satellite images), and, the area is mostly devoid of \n\n\n\nlarge-scale vegetation-cover, ensure that the technique can be applied \n\n\n\nwithin the Dhaka City Corporation area. Furthermore, to calibrate the \n\n\n\nresults, few surface displacement data must be known and the green star \n\n\n\n(in Figure 1) marks the location of the GPS station which was used for the \n\n\n\ncalibration purpose. These formed the basis of our justification for \n\n\n\nselecting Dhaka City Corporation (DCC) as our study area. \n\n\n\nFigure 1: The map of the Study Area, Dhaka City Corporation \n\n\n\n3. MATERIALS AND METHODS \n\n\n\n3.1 Materials \n\n\n\nThe process of InSAR requires SAR (Synthetic Aperture Radar) images. \n\n\n\nSAR Level1 images from ERS-1 & -2 and ENVISAT-1 & -2 and Sentinel-1A \n\n\n\nhave been used for the analysis. When selecting the images (listed in Table \n\n\n\n1) several factors that reduce the signal to noise ratio, i.e., induce \n\n\n\ndecorrelation, were taken into account. Amongst them are atmospheric \n\n\n\nnoises, perpendicular baseline, and seasonality. To reduce noise from \n\n\n\natmospheric sources (mainly water vapor), scenes from dry seasons have \n\n\n\nbeen preferred [19]. When images from the dry period are not available in \n\n\n\nthe archive only then images from the rest of the year are considered. \n\n\n\nFurthermore, in order to ensure that atmospheric noises are not too high, \n\n\n\nscenes that were imaged when the water vapor content was high has been \n\n\n\ndisregarded. Moderate Resolution Imaging Spectroradiometer (MODIS) \n\n\n\nLevel 2 Water Vapour Product gives a good idea of this phenomenon. This \n\n\n\nproduct is derived from the ratio of reflectance value of different bands \n\n\n\n(ranging from 11 to 12 \u00b5m) that are highly sensitive to moisture [20]. \n\n\n\nHowever, it is necessary to point out that this derived product only exists \n\n\n\nfor years after 2000. \n\n\n\nThe other factor, Spatial Baseline, refers to the distance between satellite \n\n\n\npositions when the images are acquired in repeat-pass orbits. These orbits \n\n\n\nare quite close together but do not overlap (resulting in slightly different \n\n\n\nviewing geometries). The perpendicular component of that distance is \n\n\n\ncalled the perpendicular baseline. The smaller this component is the lesser \n\n\n\nis the geometric decorrelation. All perpendicular baseline (for InSAR \n\n\n\npairs) have been kept below the critical baseline limit of about 400m [21]. \n\n\n\nSeasonality means the time gap between image acquisitions, has been \n\n\n\nattempted to be kept constant. This means (when possible) the images \n\n\n\nwere taken from the same month of each year. When images could not be \n\n\n\ntaken from the same month, it is to be understood that the image was not \n\n\n\navailable in the archive. \n\n\n\nThe images (listed in Table 1) were processed in SNAP (Sentinel \n\n\n\nApplication Platform by European Space Agency) and unwrapping was \n\n\n\ndone in SNAPHU [22]. Both software is freely available over the internet. \n\n\n\nSRTM 1arcsecond Digital Elevation Models (DEM) was used for \n\n\n\ntopographic corrections [23]. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\nTable 1: Characteristics of different images that were used for analysis. All the images were sensed in descending orbits. \n\n\n\nSatellite Sensing Date Perpendicular Baseline Track \nAtmospheric \nCondition \n\n\n\nInSAR Pair \n(#number) \n\n\n\nSENTINEL 1A 2017 01 08 46.3 150 Good 2017 12 22(1) \n\n\n\nSENTINEL 1A 2015 11 27 41.2 150 Good 2017 01 08(2) \n\n\n\nSENTINEL 1A 2014 12 02 38.12 150 Good 2015 11 27(3) \n\n\n\nENVISAT 2009 10 08 -113 133 Good 2010 08 19(4) \n\n\n\nENVISAT 2008 10 08 -149 133 Good 2009 10 08(5) \n\n\n\nENVISAT 2007 12 13 -170 133 Good 2008 10 08(6) \n\n\n\nENVISAT 2005 08 25 -178 133 Poor 2007 12 13(7) \n\n\n\nENVISAT 2004 11 18 -167 133 Moderate 2005 08 25(8) \n\n\n\nENVISAT 2003 06 12 -329 133 Poor 2004 11 18(9) \n\n\n\nERS1 1996 04 03 -341 133 Unknown 1999 11 25(10) \n\n\n\nERS1 1993 03 22 -135 133 Unknown 1996 04 03(11) \n\n\n\nERS1 1992 05 11 -150 133 Unknown 1993 03 22(12) \n\n\n\n3.2 Methods \n\n\n\nThe phenomenon of extracting the displacement values from the phase \n\n\n\ndifference information (i.e. an interferogram) is the core concept of \n\n\n\nInterferometric SAR. Each SAR image consists of an amplitude band and a \n\n\n\nphase band. InSAR only utilizes the phase band. This band contains the \n\n\n\nnumber of complete cycles that the radio waves underwent during its \n\n\n\ntravel time (i.e., it represents a sinusoidal function). This phase \n\n\n\ninformation, in turn, signifies how much distance the wave has traveled. \n\n\n\nWhen another SAR image\u2019s phase band is subtracted from the preceding \n\n\n\nimage (i.e. the older master image is subtracted by the recent slave image) \n\n\n\nit gives the phase difference, which can then be used to find the ground \n\n\n\ndisplacement between the duration of the two image acquisitions (after \n\n\n\ncompensating for Geometric decorrelation, Atmospheric Noise etc.).[21] \n\n\n\nThe difference in phase for a given pixel, in an interferogram, is the sum \n\n\n\nof- land deformation, topography, atmospheric noise, and satellite \n\n\n\ngeometry-induced errors: \n\n\n\n\u03a6Total Phase Difference = \u03a6Deformation + \u03a6Geometry + \u03a6Atmosphere + \n\n\n\n\u03a6Topography[21] \n\n\n\nThe aim is to find Deformation. Therefore, in order to make the phase \n\n\n\ndifference value representative of deformation only, the contribution of all \n\n\n\nother factors must be compensated. Reduction of Geometric decorrelation \n\n\n\nis done during the Coregistration process and by choosing images with low \n\n\n\nbaseline values. \n\n\n\nFirstly, one image pair is being subset and subsequently coregistered (to \n\n\n\nensure that the  SAR images overlie the same area). For coarse \n\n\n\ncoregistration 2500 to 3000 GCPs were selected, whereas the fine \n\n\n\ncoregistration window was kept as 8 pixels in order to ensure that only \n\n\n\ncorresponding pixels\u2019 phase is subtracted in the interferogram formation \n\n\n\nstage. \n\n\n\nTo account for Topographic contributions a DEM has to be subtracted \n\n\n\nwhich results in a Differential Interferogram. Furthermore, to account for \n\n\n\nchanges in the surface that is not due to subsidence (like changes in water \n\n\n\nlevel, building construction etc.) a coherence map is created. It is used in \n\n\n\nlater steps to mask out the areas of low coherence. \n\n\n\nPhase filtering is also performed for the reduction of remaining noise. This \n\n\n\nstep utilizes an adaptive filtering algorithm, namely Goldstein [24]. Such \n\n\n\nan algorithm is self-learning, meaning it will change the filtering \n\n\n\ncoefficients automatically based on the values of neighboring pixels in \n\n\n\norder to select the most suitable coefficients for a given pixel [25]. \n\n\n\nThe phase difference value of each pixel in an Interferogram ranges from \n\n\n\n\u2013pi to +pi (i.e. within -180 to +180 degrees). Phase Unwrapping converts \n\n\n\nthe phase difference to the absolute phase, by adding the appropriate \n\n\n\ninteger number of cycles (i.e. multiples of 360 degrees) [26]. \n\n\n\nThese steps ensure that deformation (due to subsidence) is the only \n\n\n\ncontributing factor remaining in the phase difference of the \n\n\n\nInterferograms and that they are ready for Phase Unwrapping. \n\n\n\nThe result of SNAPHU is not an image but tiles of unwrapped phase. Those \n\n\n\ntiles are stitched together to form a continuous image in SNAP. As the \n\n\n\nabsolute phase is proportional to the travel distance, thus the \n\n\n\ndisplacement can be obtained from this unwrapped image (in the Line-of-\n\n\n\nsight direction). \n\n\n\nThe subsequent generation of Vertical Displacement maps has been done \n\n\n\nthrough the \u201cPhase to Displacement\u201d option in SNAP. These maps \n\n\n\nillustrate the (spatially varying) deformation values of the terrain between \n\n\n\nthe times of acquisitions of two images used in the Interferogram. \n\n\n\nHowever, there will be the inevitable presence of noise in the \n\n\n\ninterferogram and these will, therefore, be transferred into the \n\n\n\ndisplacement maps. Hence, the low coherence areas (i.e. areas with high \n\n\n\nnoise) are masked out using a threshold of the coherence band. The \n\n\n\npresence of ambiguity in displacement readings necessitate that they are \n\n\n\ncalibrated. Fortunately, there is the presence of GPS data from the \u201cDHAK\u201d \n\n\n\nstation (marked with a green star in Figure 1) is situated in the Ramna \n\n\n\narea. The long-term average subsidence value adjusted for the line-of-\n\n\n\nsight component (12.4.004 mm/ year) has been used for calibration \n\n\n\n(Figure 1). \n\n\n\nThe resulting masked-out and calibrated displacement-maps were then \n\n\n\nGeocoded. This means a transformation is performed from Range-\n\n\n\nDoppler-Coordinates (RDC) to Geographic Coordinates which is also \n\n\n\nreferred to as WGS84 ellipsoid (WGS84). \n\n\n\nThe steps described above are for a single pair of images which would \n\n\n\neventually show the displacement that occurred within the time of their \n\n\n\nacquisition. These steps repeated for every Interferogram (i.e. each image \n\n\n\npair). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\nTime Series Analysis helped determine the spatially and temporally \n\n\n\nvarying nature of subsidence for the entire study period. When all \n\n\n\nCalibrated and Geocoded maps had been prepared, the time series analysis \n\n\n\nwas performed. All the maps were stacked in a chronological order. Then \n\n\n\n\u201cPixel Info\u201d of SNAP window reveals the displacement values for each pair \n\n\n\nof images at the location where the cursor is pointed at. \n\n\n\nSince the coherence threshold for each image is slightly different, the same \n\n\n\nlocations across all the images are not displayed in the displacement maps. \n\n\n\nMeaning, the same location in one image may have passed the coherence \n\n\n\nthreshold but in another image, it did not. Not passing the threshold \n\n\n\nresults in that location being masked out and so no subsidence values are \n\n\n\nfound at that location. When this is the case, subsidence value from the \n\n\n\nnearest coherent location has been taken. \n\n\n\n4. RESULTS AND DISCUSSIONS \n\n\n\n4.1 Result Interpretation \n\n\n\nThrough visual inspection of the subsidence maps, 2 profiles were \n\n\n\ndemarcated; one is from south to north, from Ramna (via Tejgaon and \n\n\n\nCantonment) to Uttara. The second profile is from Gulshan towards \n\n\n\nMirpur and Pallabi. The points of interest (from the two profiles) were \n\n\n\nmarked (Figure 7) and their displacement values from each map were \n\n\n\ncollected and tabulated to show the cumulative subsidence (Table 2). In \n\n\n\ngeneral, increasing subsidence trends along both the profiles were \n\n\n\nnoticed. Mirpur and Uttara had subsidence rates of 10mm/year and \n\n\n\n10.1mm/year respectively in 1992. By the end of the year 2017, the rates \n\n\n\nhad increased to 14.5 and 16.6 mm/year respectively. Consequently, these \n\n\n\nareas were found to have subsided the most, by around 221mm and \n\n\n\n232mm in 20 years (i.e. 1992 to 1999(using ERS); 2003 to 2010(using \n\n\n\nENVISAT); 2014 to 2017(using Sentinel 1A)). \n\n\n\nFigure 2: The violet triangles demarcate track 1 and the other track is shown with orange squares \n\n\n\nTable 2: The cumulative subsidence value of the marked points in every displacement map throughout the study period. \n\n\n\nSlave Year Ramna 1 Tejgaon 2 Cantonment 3 \nUttara \n\n\n\nGulshan 5 Mirpur 6 \n4 \n\n\n\n2017 01 08 2017 12 22 205.5 201.6 205.9 232.6 200.3 221.0 \n\n\n\n2015 11 27 2017 01 08 195.1 190.6 193.7 218.1 188.3 207.4 \n\n\n\n2014 12 02 2015 11 27 182.7 178.4 182.6 203.1 177.1 193.5 \n\n\n\n2009 10 08 2010 08 19 170.2 165.7 170.4 190.0 164.1 179.5 \n\n\n\n2008 10 08 2009 10 08 158.1 153.7 156.1 173.1 150.2 163.2 \n\n\n\n2007 12 13 2008 10 08 146.8 140.5 142.8 157.0 137.1 148.3 \n\n\n\n2005 08 25 2007 12 13 134.9 127.9 129.7 143.1 125.1 133.3 \n\n\n\n2004 11 18 2005 08 25 107.8 102.7 106.2 116.7 102.0 105.5 \n\n\n\n2003 06 12 2004 11 18 94.8 90.4 92.4 101.6 91.5 91.6 \n\n\n\n1996 04 03 1999 11 25 82.8 77.9 78.5 86.6 78.4 76.4 \n\n\n\n1993 03 22 1996 04 03 48.7 45.0 45.0 47.8 44.9 42.3 \n\n\n\n1992 05 11 1993 03 22 11.2 10.9 10.9 10.0 9.9 10.1 \n\n\n\nTable 2 reveals that different areas across both the profiles are subsiding \n\n\n\nwith time. Profile 1 starts with Ramna thana (containing the GPS station) \n\n\n\nat the south which shows total subsidence of 207.5mm, followed by \n\n\n\nTejgaon (which subsided by 201.6mm) and Cantonment (205.9 mm). \n\n\n\nFinally, the profile ends in Uttara which has subsided 232.6mm during the \n\n\n\nstudy period of 20 years. The second profile starts at Gulshan and moves \n\n\n\nwest, towards Mirpur. These areas have subsided 200mm and 221mm \n\n\n\nrespectively. It is obvious that the cumulative subsidence has a rising trend \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\nalong the profiles. Not only is cumulative subsidence rising along the \n\n\n\nprofile but also with the passage of time. This notion is supported by \n\n\n\nFigure 3 which shows that the cumulative subsidence in the early years of \n\n\n\nthe study period (1992 to 1996) was more or less similar. The drastic rise \n\n\n\nwas only noticed after 1996. \n\n\n\nFigure 3: The plots show Cumulative Subsidence against the individual locations in Profile 1 (on the left) and Profile 2 (on the right). \n\n\n\nCumulative values only provide an overall scenario of subsidence along \n\n\n\nthe profiles. However, it is also necessary to study how the marked \n\n\n\nlocations have subsided with time. In order to investigate that, the \n\n\n\nindividual subsidence rates for those locations were noted (Table 3). The \n\n\n\ngeneral trend shows a rise in subsidence over time. For areas like Uttara \n\n\n\nand Mirpur subsidence rates have increased by half their original values \n\n\n\nduring the 20 years study period, and rates for Tejgaon, Cantonment and \n\n\n\nGulshan have increased by around a quarter times their rates measured in \n\n\n\n1992. Demra and Lalbagh were found to have similar subsidence pattern \n\n\n\nto the Ramna area, whereas Dhanmondi and Mohammadpur displayed \n\n\n\nrates similar to Tejgaon and that is why their subsidence rates were not \n\n\n\nexplicitly stated. \n\n\n\nTable 3: The subsidence rates in mm at each marked location. The years marked with * means that the quoted subsidence values are yearly averages \n\n\n\nMaster Year Slave Year Ramna 1 Tejgaon 2 Cantonment 3 Uttara 4 Gulshan 5 Mirpur 6 \n\n\n\n2017 01 08 2017 12 22 12.4 11 12.2 14.5 12 13.6 \n\n\n\n2015 11 27 2017 01 08 12.4 12.2 11.1 15 11.2 13.9 \n\n\n\n2014 12 02 2015 11 27 12.5 12.7 12.2 13.1 13 14 \n\n\n\n2009 10 08 2010 08 19 12.1 12 14.3 16.9 13.9 16.3 \n\n\n\n2008 10 08 2009 10 08 11.3 13.2 13.3 16.1 13.1 14.9 \n\n\n\n2007 12 13 2008 10 08 11.9 12.6 13.1 13.9 12 12.9 \n\n\n\n2005 08 25 2007 12 13 12.9 12 11.2 12.6 11 13.2 \n\n\n\n2004 11 18 2005 08 25 13 12.3 13.8 15.1 10.5 13.9 \n\n\n\n2003 06 12 2004 11 18 12 12.5 13.9 15 13.1 15.2 \n\n\n\n1996 04 03 1999 11 25 11 10.6 10.8 12.5 10.8 11 \n\n\n\n1993 03 22 1996 04 03 12 11 11 12.2 11.3 10.4 \n\n\n\n1992 05 11 1993 03 22 11.5 10.9 10.9 10 9.9 10.1 \n\n\n\nSince the subsidence rates are changing by a fraction of millimeters, the  \n\n\n\ndata will be better visualized through a graph (Figure 4). Upon closer \n\n\n\ninspection of the graph, it was noticed that the subsidence rates were not \n\n\n\nconstantly rising. Starting from 1992 there is a gradual rise up till 1996. \n\n\n\nFrom then onwards till 2004 a sharper rising trend is witnessed. During \n\n\n\nthis time Uttara (which started with one of the lowest rates in 1992) is \n\n\n\nundergoing a rapid increase in subsidence. This is followed by a period of \n\n\n\nreduced subsidence rates from 2005 to 2007 (where Gulshan is \n\n\n\nexperiencing the lowest subsidence rate). From 2007 to 2010 there is \n\n\n\nagain a rise in subsidence. It is in this phase where Mirpur and Uttara are \n\n\n\nboth subsiding at a much higher rate than their neighbors. \n\n\n\nAlthough the identification of the exact cause of the changes in subsidence \n\n\n\nis well beyond the scope of this study, some changes may be explained \n\n\n\nlogically. For example, the time period (from 2005 to 2007) that is marked \n\n\n\nby reduced subsidence is actually derived from a very noisy interferogram \n\n\n\n(A.4). This resulted in a poor quality displacement map (Figure 7 Bottom \n\n\n\nRight) which in turn manifested as low subsidence rates in the graph \n\n\n\n(Figure 4). The change in rates from 2015 may be attributed to the change \n\n\n\nin sensor characteristics. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\nFigure 4: The graph shows the yearly subsidence rates at each marked location. \n\n\n\nThe results discussed above were obtained by using the value of the \n\n\n\nparameters mentioned in Section 3.2. Amongst them, Coregistration has \n\n\n\nproven to be one of the most sensitive steps in this study. Using a greater \n\n\n\nnumber of Ground Control Points (GCPs) (i.e. increasing them to 3000, \n\n\n\nfrom 2000) proved to be effective. In order to understand why it was \n\n\n\neffective, it is necessary to first understand how GCPs are used in the \n\n\n\nCoregistration process. A GCP is a distinguishable feature in an image that \n\n\n\nis identified automatically by the software and has a known coordinate \n\n\n\n[27]. Initially, GCPs are identified in the master image by automated \n\n\n\nalgorithms. Using their geographical position information, the algorithm \n\n\n\nsearches for those GCPs in the slave image (within the user-defined \n\n\n\nwindow size). \n\n\n\nOnce GCPs in both images are identified, the information is used to find \n\n\n\ncoefficients for transformation equations. These equations, in turn, are \n\n\n\nused to establish the relationship between master and the slave image.  So \n\n\n\nthe improvement of results with a greater number of GCPs is logical [28]. \n\n\n\nMoreover, a good number of GCPs in urban areas results in better \n\n\n\nregistration [29]. Upon inspecting the image footprints, it was clear that \n\n\n\nthey did not have a spatial shift of more than 600m in either azimuth or \n\n\n\nslant range direction. So, a window size of 128 pixels was deemed to be \n\n\n\nsufficient (ERS and ENVISAT SAR pixels had a resolution of 9.5 x 5m). \n\n\n\nHowever, keeping larger window size will not render the results \n\n\n\ninaccurate, but will take a greater amount of time to produce more or less \n\n\n\nthe same results. \n\n\n\nThis step is followed by fine coregistration. The process attempts to align \n\n\n\nthe images to sub-pixel accuracy which is not possible through coarse \n\n\n\ncoregistration. This is a crucial step since the results directly determine \n\n\n\nthe accuracy of displacement readings. The algorithm is based on a cross-\n\n\n\ncorrelation technique that creates an alignment between the master and \n\n\n\nthe slave by automatically matching similar pixels through the use of a \n\n\n\ndistributed correlation optimization window [30]. The aim is to improve \n\n\n\nthe coherence between the two images. Multiple sub-pixel shifts of the \n\n\n\nslave GCPs is computed and the one that produces the highest coherence \n\n\n\nis selected and applied. Having high coherence means that there is little \n\n\n\nnoise in the images. The noise source can be from satellite viewing \n\n\n\ngeometry (influenced by the size of the perpendicular baseline), \n\n\n\natmosphere (water vapor) or even a change in the reflective properties of \n\n\n\nground targets. This information is helpful in recognizing that InSAR pairs, \n\n\n\nin which the same parameters have been applied, fail to produce similar \n\n\n\nresults. \n\n\n\nSince the results of coregistration cannot be directly visualized, the \n\n\n\ncoherence maps (Figure A.1) are intended to illustrate them. The \n\n\n\ncoherence map on the left is for InSAR pair #4 (see the InSAR pair column \n\n\n\nin Table 1). This pair has one of the lowest perpendicular baselines of all \n\n\n\nthe pairs that have been used. The time difference is restricted to one year \n\n\n\nmeaning there is relatively less change in the land cover and the \n\n\n\natmospheric condition during acquisitions was also good. The map shows \n\n\n\nthat using a fine correlation window of 2 pixels, have resulted in good \n\n\n\ncorrelation in the majority of the pixels representing the urban areas. \n\n\n\nHowever, for pairs with larger baselines, such as for pair #7, larger fine \n\n\n\ncoregistration windows (like 4, 8 pixels) needed to be used. This was \n\n\n\nnecessary since the large baseline adds mathematical constraints to the \n\n\n\nfine coregistration process. Moreover, the large temporal gaps between \n\n\n\nthe image acquisitions and the significant presence of moisture in the \n\n\n\natmosphere also add to the noise in the image. The extent of noise is so  \n\n\n\nmuch that using 2 by 2-pixel windows cause coregistration to fail \n\n\n\ncompletely in these specific cases. Even using larger windows does not \n\n\n\nproduce good coherence (Figure A.2). \n\n\n\nCompletion of coregistration is followed by generation of a flattened \n\n\n\ninterferogram. The subtraction of slave phase images from their \n\n\n\nrespective masters is a relatively simple procedure. This is done in \n\n\n\nconjunction with the topographic phase removal. Since the contribution of \n\n\n\nSatellite viewing geometry was compensated in the coregistration phase, \n\n\n\nnow the topographic contribution is removed from the interferogram \n\n\n\nusing SRTM DEMs. However, as already mentioned the interferogram still \n\n\n\nconsists of atmospheric noise. Therefore, Goldstein filtering is necessary. \n\n\n\nThis ensures that the fine changes (due to deformation) are preserved but \n\n\n\nthe larger changes (due to noise) gradually becomes less significant. This \n\n\n\nis illustrated in Figure A.3. However, the presence of too much noise \n\n\n\ncannot be effectively removed by filtering as is shown in Figure A.4. \n\n\n\nWhen all the Interferograms are made representative of deformation only \n\n\n\n(by compensating the noise contributions as much as possible) they are \n\n\n\nphase unwrapped in SNAPHU. The products of unwrapping are then \n\n\n\nconverted to displacement, masked, geocoded and calibrated resulting in \n\n\n\nthe following Subsidence maps (Figure 5 through 10): \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\nFigure 5: Geocoded and calibrated displacement maps for InSAR pairs #1 (top left), # 2(top right) and #3 (bottom left). All the 3 maps are derived from \n\n\n\nSentinel 1 images. It can be clearly seen that Uttara region is undergoing the greatest amount of subsidence compared to its neighbouring regions. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\nFigure 6: Geocoded and calibrated displacement maps for InSAR pairs #4 (left), and # 5(right). \n\n\n\nFigure 7: Geocoded and calibrated displacement maps for InSAR pairs #6 (Bottom left), # 7(bottom right). It needs to be pointed out that the high \n\n\n\npresence of noise in pair #4 is why the map has so few coherent readings and that too all in the same range of around 13 mm. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\nFigure 8: Geocoded and calibrated displacement maps for InSAR pairs #8 (top left), # 9(top right). For pair #9 the apparent higher values are due to the \n\n\n\nmap displaying subsidence for one and a half years \n\n\n\nFigure 9: Geocoded and calibrated displacement maps for InSAR pairs #10 (bottom left), # 11(bottom right). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\nFigure 10: Geocoded and calibrated displacement maps for InSAR pairs #12 \n\n\n\n4.2 Limitations \n\n\n\nThe availability of SAR images was a major limitation. Although ERS and \n\n\n\nENVISAT are supposed to have a revisit time of 35 days, the image archive \n\n\n\nfor Dhaka\u2019s location tells a different tale. The lack of images in the dry \n\n\n\nseason and those with small baselines has caused some Interferograms to \n\n\n\nbe very noisy. \n\n\n\nSince 3 different Satellite data were used to cover the non-continuous 20 \n\n\n\nyear study period, it cannot be said with certainty whether the change in \n\n\n\nsubsidence shown is representative to reality or simply due to the \n\n\n\ndifferent sensor characteristics. \n\n\n\nWhilst, the results were indeed calibrated, there was no way to validate \n\n\n\nthe results since a second GPS station was not available in or near Dhaka \n\n\n\nCity. Moreover, the GPS station used for calibration only had data from \n\n\n\n2003 to 2015. \n\n\n\nC band SAR is known to cause decorrelation in vegetated areas. This can \n\n\n\nbe seen quite clearly in Figure A.1 So areas with dense vegetation cover \n\n\n\nwere always masked out and no idea of displacements in those areas could \n\n\n\nbe found. As a matter of fact, we applied the same process to map mining-\n\n\n\ninduced subsidence of Boropukuria Mine, Dinajpur, which is a heavily \n\n\n\nvegetated area (Figure A.5). The results were not acceptable owing to the \n\n\n\nhigh presence of noise in the images. \n\n\n\n5. CONCLUSION\n\n\n\nThe overall objective of the study was to perform time series analysis in \n\n\n\norder to find the subsidence pattern in Dhaka city for the last 20 years. \n\n\n\nWith the passage of time subsidence increases from south to north, from \n\n\n\neast to west. Time series analysis reveals that Mirpur and Uttara have \n\n\n\nsubsided by over 221mm and 232mm over the 20 years. Ramna and \n\n\n\nCantonment are around 205mm subsided compared to their level in 1992, \n\n\n\nwhereas both Gulshan and Tejgaon have subsided by about 200mm. \n\n\n\nDemra and Lalbagh show similar subsidence to the Ramna area, whereas \n\n\n\nDhanmondi and Mohammadpur show rates similar to Tejgaon. In general, \n\n\n\nit can be stated that subsidence rates have risen with time across the study \n\n\n\narea. The highest rise is experienced in Mirpur and Uttara (almost one and \n\n\n\na half times increase since 1992). \n\n\n\nIn the calculation of those results, minute change in the parameters of \n\n\n\ncoregistration, filter and unwrapping were found to greatly influence the \n\n\n\nresults. Coregistration proved to be one of the most sensitive steps in the \n\n\n\nanalysis process. For images with small baselines and low atmospheric \n\n\n\nnoise, good subpixel coregistration (using the fine window of 2 pixels) was \n\n\n\nachieved, which resulted in good coherence (Figure A.1). Fortunately, all \n\n\n\nexcept for 2 image pairs (#7 and 9 from Table 1) exhibit low noise. Using \n\n\n\nthe Goldstein filtering also aided in the removal of most of the residual \n\n\n\nnoise (Figure A.3). As for, images with high noise levels the problem was \n\n\n\novercome by using larger coregistration window (4 or 8 pixels) and a \n\n\n\nsmaller (masking) threshold of coherence. However, the noise content in \n\n\n\npair #7 was so high that even after filtering the result was not satisfactory \n\n\n\n(Figure 7 (bottom right)). \n\n\n\nThe presence of noise can never be completely eliminated, and this means \n\n\n\nwhen the displacement maps are created there is still an extent of \n\n\n\nambiguity in the displacement values. Thus, those maps are required to be \n\n\n\ncalibrated with the readings from the GPS station. The results of \n\n\n\ncalibration are shown as Geocoded and Calibrated Displacement Maps in \n\n\n\nFigure 5 through 10. These maps display the subsidence values in different \n\n\n\nplaces of the city during specific portions of the study period. It is also \n\n\n\nworth to note that we have used C-band SAR imagery from multiple \n\n\n\nsensors to cover non-continuous 20 year study period which has made the \n\n\n\nanalysis quite challenging. Still, the subsidence result is realistic \n\n\n\nconsidering the image acquisition temporality, data quality and sensor \n\n\n\ncharacteristics. \n\n\n\nACKNOWLEDGMENT \n\n\n\nThe Department of Disaster Science and Management supported us by \n\n\n\nproviding available technical assistance and lab facilities for performing \n\n\n\ncomputationally expansive processing steps, without which the research \n\n\n\nwould be difficult to complete. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\nThe study was possible due to the availability of freely available \n\n\n\nCopernicus Sentinel 1A, ERS & ENVISAT SAR data; and processing \n\n\n\nsoftware (SNAP - ESA Sentinel Application Platform v2.0.2, \n\n\n\nhttp://step.esa.int) which were provided by the European Space Agency. \n\n\n\nSpecial thanks go to the STEP Forum, SAREDU.com and to the patient \n\n\n\nresearchers in this field whose feedback and research work served as the \n\n\n\nmotivation for doing this research. \n\n\n\nAPPENDIX  \n\n\n\nFigure A.1: The map on the left shows pixels with good correlation in white. Comparing these white pixels with the Google Earth image on the right, it is \n\n\n\nclearly visible that the urban areas have good correlation and little noise, whereas the vegetated areas have very high noise (and so less coherence). \n\n\n\nFigure A.2: When the coherence map for pair #4 is compared to Figure A1(left) it becomes clear that the presence of noise sources has taken a toll on the \n\n\n\nfine coregistration process. There are significantly less number of coherent (white) pixels. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\nFigure A.3: The interferograms above represents InSAR pair #4. The \n\n\n\ninterferogram on the right where the fringe pattern is clearly visible is the \n\n\n\nresult of filtering the original interferogram on the left \n\n\n\nFigure A.4: The interferograms above represents InSAR pair #7, which \n\n\n\nhad significant noise. The interferogram on the right is the result of \n\n\n\nfiltering the original interferogram on the left. However, it is clear that the \n\n\n\npresence of noise is rendering this interferogram less accurate since the \n\n\n\nfringes are not clearly distinguishable (compared to the results from pair \n\n\n\n#4). \n\n\n\nFigure A.5: The subsidence map of Boropukuria Coal Mine had very few \n\n\n\ncoherent pixels. Even the one that were coherent did not produce \n\n\n\nacceptable results, owing to the heavily vegetated nature of the area \n\n\n\nFigure A.6: Shows GPS data from Dhaka station. The long-term average \n\n\n\nsubsidence rate was found to be 12.4 mm/year from the trendline of this \n\n\n\ngraph[3] \n\n\n\nREFERENCES \n\n\n\n[1] Syvitski, J.P.M. 2009. Sinking deltas due to human activities, Nature \n\n\n\nGeoscience, 2(10), 681-686. \n\n\n\n[2] Brown, S., Nicholls, R.J. 2015. Subsidence and human influences in \n\n\n\nmega deltas: The case of the Ganges\u2013Brahmaputra\u2013Meghna, Science of the \n\n\n\nTotal Environment, 527-528, 362-374. \n\n\n\n[3] Higgins, S.A., Overeem, I., Steckler, M.S., Syvitski, J.P.M., Seeber, L., \n\n\n\nAkhter, S.H. 2014. InSAR measurements of compaction and subsidence in \n\n\n\nthe Ganges-Brahmaputra Delta, Bangladesh, Journal of Geophysical \n\n\n\nResearch: Earth Surface, 119(8), 1768\u20131781. \n\n\n\n[4] Michelle, S., Justin, B., Mike, S. 2013. Land Subsidence along the Delta-\n\n\n\nMendota Canal in the Northern Part of the San Joaquin Valley, California, \n\n\n\n2003-10. \n\n\n\n[5] Du, Z., Ge, L., Li, X., Ng, A. 2016. Subsidence Monitoring over the \n\n\n\nSouthern Coalfield, Australia Using both L-Band and C-Band SAR Time \n\n\n\nSeries Analysis, Remote sensing, 8(7), 543. \n\n\n\n[6] Chaussard, E., Amelung, F., Abidin, H., Hong, S.H. 2013. Sinking cities \n\n\n\nin Indonesia: ALOS PALSAR detects rapid subsidence due to groundwater \n\n\n\nand gas extraction, Remote Sensing of Environment, 128, 150-161. \n\n\n\n[7] Galloway, D.L. 1998. Detection of aquifer system compaction and land \n\n\n\nsubsidence using interferometric synthetic aperture radar, Antelope \n\n\n\nValley, Mojave Desert, California, Water Resources Research, 34(10), \n\n\n\n2573\u20132585. \n\n\n\n[8] Hoque, M., Alam, M. 1997. Subsidence in the lower deltaic areas of \n\n\n\nBangladesh, Marine Geodesy, 20(1), 105\u2013120. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 32-44 \n\n\n\nCite The Article: Dewan Mohammad Enamul Haque, Tanzim Hayat, Samanin Tasnim (2019). Time Series Analysis Of Subsidence In Dhaka City, Bangladesh Using Insar. \nMalaysian Journal of Geosciences, 3(1): 32-44. \n\n\n\n[9] Goodbred, S.L., Kuehl, S.A. 2000. The significance of large sediment -\n\n\n\nsupply, active tectonism, and eustasy on margin sequence development: \n\n\n\nLate Quaternary stratigraphy and evolution of the Ganges\u2013Brahmaputra \n\n\n\ndelta, Sedimentary Geology, 133(3), 227\u2013248. \n\n\n\n[10] Hanebuth, T.J.J., Kudrass, H.R., Linstadter, J., Islam, B., Zander, A.M. \n\n\n\n2013. Rapid coastal subsidence in the central Ganges-Brahmaputra Delta \n\n\n\n(Bangladesh) since the 17th century deduced from submerged salt-\n\n\n\nproducing kilns, Geology, 41(9), 987\u2013990. \n\n\n\n[11] Steckler, M.S. 2013. GPS Velocity Field in Bangladesh: Delta \n\n\n\nSubsidence, Seasonal Water Loading and Shortening Across the Burma \n\n\n\nAccretionary Prism and Shillong Massif, American Geophysical Union, Fall \n\n\n\nMeeting, Abstract ID: T13D-2566. \n\n\n\n[12] Pepe, A., Cal\u00f2, F. 2017. A Review of Interferometric Synthetic \n\n\n\nAperture RADAR (InSAR) Multi-Track Approaches for the Retrieval of \n\n\n\nEarth\u2019s Surface Displacements, Applied Sciences. \n\n\n\n[13] Lanari, R., Lundgren, P., Manzo, M., Casu, F. 2004. Satellite radar \n\n\n\ninterferometry time series analysis of surface deformation for Los \n\n\n\nAngeles, California, Geophysical Research Letters, 31(23). \n\n\n\n[14] Zhou, X., Chang, N.B., Li, S. 2009. Applications of SAR Interferometry \n\n\n\nin Earth and Environmental Science Research, Sensors, 9(3), 1876\u20131912. \n\n\n\n[15] ERS Free and Open. 2018. Alaska Satellite Facility. [Online] \n\n\n\nAvailable: https://www.asf.alaska.edu/news-notes/ers-free-open/. \n\n\n\n[Accessed: 22-Jan-2018]. \n\n\n\n[16] Crosetto, M., Monserrat, O., Cuevas-Gonz\u00e1lez, M., Devanth\u00e9ry, N., \n\n\n\nCrippa, B. 2016. Persistent Scatterer Interferometry: A review, ISPRS \n\n\n\nJournal of Photogrammetry and Remote Sensing, 115, 78\u201389. \n\n\n\n[17] Ferretti, A., Prati, C., Rocca, F. 2001. Permanent scatterers in SAR \n\n\n\ninterferometry, IEEE Transactions on Geoscience and Remote Sensing, \n\n\n\n39(1), 8\u201320. \n\n\n\n[18] Hooper, A., Bekaert, D., Spaans, K., Ar\u0131kan, M. Recent advances in \n\n\n\nSAR interferometry time series analysis for measuring crustal \n\n\n\ndeformation, Tectonophysics, 514\u2013517, 1\u201313. \n\n\n\n[19] Foster, J., Brooks, B., Cherubini, T., Shacat, C., Businger, S., Werner, \n\n\n\nC.L. 2006. Mitigating atmospheric noise for InSAR using a high-resolution \n\n\n\nweather model, Geophysical Research Letters, 33(16), L16304. \n\n\n\n[20] NASA GODDARD. Water Vapor MODIS product. [Online]. Available: \n\n\n\nhttps://modis-atmos.gsfc.nasa.gov/products/water-vapor. [Accessed: \n\n\n\n21-Jun-2018]. \n\n\n\n[21] Hanseen. 2002. Radar system theory and interferometric \n\n\n\nprocessing, 9\u201360. \n\n\n\n[22] Chen, C.W., Zebker, H.A. 2002. Phase unwrapping for large SAR \n\n\n\ninterferograms: Statistical segmentation and generalized network models, \n\n\n\nIEEE Transactions on Geoscience and Remote Sensing, 40(8), 1709\u20131719. \n\n\n\n[23] Rabus, B., Eineder, M., Roth, A., Bamler, R. 2003. The shuttle radar \n\n\n\ntopography mission\u2014a new class of digital elevation models acquired by \n\n\n\nspaceborne radar, ISPRS Journal of Photogrammetry and Remote Sensing, \n\n\n\n57(4), 241\u2013262. \n\n\n\n[24] Goldstein, R.M., Werner, C.L. 1982. Radar interferogram filtering for \n\n\n\ngeophysical applications, Geophysical Research Letters, 25(21), 4035\u2013\n\n\n\n4038. \n\n\n\n[25] Mathworks, Overview of Adaptive Filters and Applications - \n\n\n\nMATLAB &amp; Simulink. [Online]. Available: \n\n\n\nhttps://www.mathworks.com/help/dsp/ug/overview-of-adaptive-\n\n\n\nfilters-and-applications.html?requestedDomain=true. [Accessed: 04-Feb-\n\n\n\n2018]. \n\n\n\n[26] Zuo, C., Huang, L., Zhang, M., Chen, Q., Asundi, A. 2016. Temporal \n\n\n\nphase unwrapping algorithms for fringe projection profilometry: A \n\n\n\ncomparative review, Optics and Lasers in Engineering, 85, 84\u2013103. \n\n\n\n[27] Spyropoulos, A., Komodakis, N., Mordohai, P. 2014. Learning to \n\n\n\ndetect ground control points for improving the accuracy of stereo \n\n\n\nmatching,\u201d in Proceedings of the IEEE Conference on Computer Vision and \n\n\n\nPattern Recognition, 1621\u20131628. \n\n\n\n[28] Wangfei, Z., Jianguo, G., Tianshu, X., Yanru, H. 2009. The Selection of \n\n\n\nGround Control Points in a Remote Sensing Image Correction Based on \n\n\n\nWeighted Voronoi Diagram, in 2009 International Conference on \n\n\n\nInformation Technology and Computer Science, 326\u2013329. \n\n\n\n[29] Wang, J., Ge, Y., Heuvelink, G.B.M., Zhou, C., Brus, D. 2012. Effect of \n\n\n\nthe sampling design of ground control points on the geometric correction \n\n\n\nof remotely sensed imagery, International Journal of Applied Earth \n\n\n\nObservation, 18, 91\u2013100. \n\n\n\n[30] Leprince, S., Barbot, S., Ayoub, F., Avouac, J.P. 2007. Automatic and \n\n\n\nprecise orthorectification, coregistration, and subpixel correlation of \n\n\n\nsatellite images, application to ground deformation measurements, IEEE \n\n\n\nTransactions on Geoscience and Remote Sensing, 45(6), 1529\u20131558 \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 13-18 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2020.13.18 \n\n\n\nCite the Article: E. J. Oziegbe, V. O. Olarewaju, O. O. Ocan (2020). Mineral Chemistry And Geochemistry Of Hypersthene-Bearing Diorite From Erusu Akoko, \nSouthwestern Nigeria. Malaysian Journal of Geosciences, 4(1): 13-18. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences \n\n\n\n(MJG) DOI: http://doi.org/10.26480/mjg.01.2020.13.18 \n\n\n\nMINERAL CHEMISTRY AND GEOCHEMISTRY OF HYPERSTHENE-BEARING \n\n\n\nDIORITE FROM ERUSU AKOKO, SOUTHWESTERN NIGERIA \n\n\n\nE. J. Oziegbea, V. O. Olarewajub, O. O. Ocanc  \n\n\n\na Department of Geosciences, Faculty of Science, University of Lagos, Nigeria. \nb Department of Geology, Faculty of Science, Obafemi Awolowo University, Nigeria \ncDepartment of Geological Sciences, College of Science, Engineering and Technology, Osun     State University, Nigeria \n*Corresponding Author Email: eoziegbe@unilag.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 05 December 2019 \nAccepted 12 January 2020 \nAvailable online 07 February 2020\n\n\n\nSamples of mafic intrusive rock were analyzed for their mineralogical and chemical properties. The textural \nrelationship was studied using the petrographic microscope, elemental composition of minerals was \ndetermined using the Electron Microprobe and the whole rock chemical analysis was done using the XRF and \nICP-MS. The following minerals were observed in order of abundance; pyroxene, amphibole, plagioclase, \nbiotite, opaque minerals, quartz and chlorite, with apatite and zircon occurring as accessory mineral. Two \ntypes of pyroxenes were observed; orthopyroxene (hypersthene) and clinopyroxene. Texturally, amphiboles \nhave inclusions of plagioclase and pyroxene. The plagioclase has undergone sericitization. The chemical \ncomposition of the pyroxene is En51.95Fs44.53Wo3.52, biotite has Fe/(Fe+Mg):0.42, Mg/(Fe+Mg):0.59, and \nplagioclase is Ab63.5An34.55Or1.95. Whole rock chemistry shows a chemical composition; SiO2: 45.15 %, Al2O3: \n\n\n\n14.04 %, Fe2O3: 16.01 %, MgO: 5.65 %, CaO: 7.58 % and TiO2: 3.59 %. There is an enrichment of LREE and a \ndepletion of HREE. Based on the minerals, mineral chemistry and the geochemistry of the studied rock, the \nrock is mafic and hydrous minerals formed by hydration recrystallization of pyroxene. The rock has \nextensively retrogressed but has not been affected by any form of deformation.  \n\n\n\nKEYWORDS \n\n\n\nElectron Microprobe, retrogressed, sericitization, inclusions.\n\n\n\n1. INTRODUCTION \n\n\n\nThe Basement complex of Nigeria comprises some components of mafic to \nultramafic in addition to other rock units which some believed to be \nremnants of mantle diapirs. The mafic intrusive rock (Hypersthene-\nBearing diorite) under study is dark coloured, fine-medium grained and \nnon-foliated (Figure 1). The other rock units in this area include: \nmigmatite gneiss; which has both the grey and granitic gneisses. Erusu is \na town in Akoko North West of Ondo State, which is part of the Basement \nComplex of Southwestern Nigeria. The Basement Complex of Nigeria is \nrocks of Precambrian age which underlay about half of the landmass of \nNigeria (Haruna, 2017). The Basement Complex of Nigeria is divided into \nthe Western and Eastern Provinces (Ajibade et al., 1979). The Basement \nComplex of Southwestern Nigeria is believed to have formed in a back-arc \nbasin (Oyinloye and Odeyemi, 2001). Mafic intrusive were first mentioned \nin this area in a study, in which it was described as one of the rock units of \nIkare area (Rahaman and Ocan, 1988). The petrography, mineral \nchemistry, and geochemistry (major, trace and REE elements) of samples \ncollected from the mafic intrusive rock at Erusu Akoko is presented in this \narticle. \n\n\n\n2. MATERIALS AND METHOD \n\n\n\nPetrographic studies were carried out both at the Department of Geology, \nObafemi Awolowo University and Rhodes University, South Africa using \n\n\n\nPetrographic Microscopes. Data acquisition for Electron Probe Micro-\nAnalysis (BSE and Cathodo-luminescence imaging, major, minor and trace \nelement analysis) was carried out using a JEOL JXA 8230 Superprobe, with \n4 WD spectrometers at Rhodes University, South Africa. The Probe \nMachine was used under the following operating conditions: 15 kV \nacceleration voltage, 20 nA probe current 20 nA, beam size of ~1 micron, \ncounting time 10 sec on peak and 5 sec. on each lower and upper \nbackground, respectively. The minerals grains selected were analyzed \nboth at the core and at the rim.  \n\n\n\nFigure 1: Field photograph of a mafic intrusive rock at Erusu Akoko. \n\n\n\nMajor elemental analysis was carried out by XRF spectrometry on a \nPANalytical Axios Wave length Dispersive spectrometer at the Central \nAnalytical Facilities (CAF), Stellenbosch University, South Africa. Laser \nAblation ICPMS was used for the determination of trace and rare elements \nalso at CAF Stellenbosch University. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 13-18 \n\n\n\nCite the Article: E. J. Oziegbe, V. O. Olarewaju, O. O. Ocan (2020). Mineral Chemistry And Geochemistry Of Hypersthene-Bearing Diorite From Erusu Akoko, \nSouthwestern Nigeria. Malaysian Journal of Geosciences, 4(1): 13-18. \n\n\n\n3. RESULTS \n\n\n\n3.1 Petrography \n\n\n\nUnder the microscope the mafic rock is generally granoblastic in texture \nand consists of the following minerals: pyroxene, amphiboles, plagioclase, \nbiotite, chlorite, opaque minerals, and quartz with zircon and apatite \noccurring as accessory mineral. The two types of pyroxenes were \nobserved, the orthopyroxene and the clinopyroxene (Figures 2 & 3). The \northopyroxene is highly pleochroic. The pyroxene is surrounded by \namphiboles and with some occurring as inclusions in amphiboles. The \npyroxene has opaque minerals occurring along its rims, especially where \nit (orthopyroxene) is in contact with amphiboles (Figures 4, 5, 6, 7 & 8). \nThere is a recrystallization of the pyroxene to biotite (Figure 9).   \n\n\n\nThe amphiboles are greenish brown in colour (Figures 2, 4, 6 & 10) and \nstrongly pleochroic, with inclusions of plagioclase and pyroxene (Figures \n10 & 11). Crystals of biotite show form of alteration to a greenish mineral \nwhich is chlorite (Figure 12). The biotite crystals are brownish in color and \ncuts across amphiboles and plagioclase (Figure 13 & 14). The plagioclase \nexhibit both albite and Carlsbad form of twinning and has inclusions of \namphiboles. There is the alteration of some of the plagioclase crystals to \nsericite (Figure 15 & 16). Opaque minerals (Ilmenite) occur in close \nassociation with plagioclase.  The Backscattered Electron (BSE) image \nshow that there is no elemental zonation in any of the minerals (Figures \n17 & 18). \n\n\n\nFigure 2: Photomicrograph of diorite from Erusu Akoko showing \n\n\n\nOrthopyroxene and Clinopyroxene. PPL (Cpx, clinopyroxene; Opx, \n\n\n\northopyroxene; and PPL, plane polarized light) \n\n\n\nFigure 3: Photomicrograph of diorite from Erusu Akoko showing \n\n\n\northopyroxene and clinopyroxene. XPL (Cpx, clinopyroxene; Opx, \n\n\n\northopyroxene; and XPL, cross-polarized light) \n\n\n\nFigure 4: Photomicrograph of diorite from Erusu Akoko showing opaque \n\n\n\nmineral (dark mineral) surrounding orthopyroxene (Opx). PPL \n\n\n\nFigure 5: Photomicrograph of diorite from Erusu Akoko showing opaque \n\n\n\nmineral (dark mineral) surrounding orthopyroxene (Opx). PPL \n\n\n\nFigure 6: Photomicrograph of diorite from Erusu Akoko showing opaque \n\n\n\nmineral surrounding orthopyroxene (Opx). PPL \n\n\n\nFigure 7: Photomicrograph of diorite from Erusu Akoko showing opaque \n\n\n\nmineral surrounding orthopyroxene (Opx). PPL \n\n\n\nFigure 8: Photomicrograph of diorite from Erusu Akoko showing opaque \n\n\n\nmineral surrounding orthopyroxene (Opx). XPL \n\n\n\nFigure 9: Photomicrograph of diorite from Erusu Akoko showing an \n\n\n\nalteration of orthopyroxene to (Opx) amphibole (Am). PPL \n\n\n\nOpx \n\n\n\nAm \n\n\n\nCpx \n\n\n\nAm \n\n\n\nCpx Opx \n\n\n\nOpx \n\n\n\nAm \n\n\n\nOPx \n\n\n\nPl \n\n\n\nCpx \n\n\n\nPl \n\n\n\nOpx \n\n\n\nBt \n\n\n\nAm \n\n\n\nAm \n\n\n\nAm \n\n\n\nAm \n\n\n\nOpx \n\n\n\nOpx \n\n\n\nBt Opx \n\n\n\nOpx Am \n\n\n\nAm \n\n\n\nAm \n\n\n\nBt \n\n\n\nOpx \n\n\n\nAm Pl \n\n\n\nPl \n\n\n\nOpx \n\n\n\nOpx \n\n\n\nBt \n\n\n\nBt \n\n\n\nAm \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 13-18 \n\n\n\nCite the Article: E. J. Oziegbe, V. O. Olarewaju, O. O. Ocan (2020). Mineral Chemistry And Geochemistry Of Hypersthene-Bearing Diorite From Erusu Akoko, \nSouthwestern Nigeria. Malaysian Journal of Geosciences, 4(1): 13-18. \n\n\n\nFigure 10: Photomicrograph of diorite from Erusu Akoko showing \n\n\n\ninclusions of plagioclase (Pl) in amphiboles (Am) and pyroxene (Px) in \n\n\n\namphiboles. PPL \n\n\n\nFigure 11: Photomicrograph of diorite from Erusu Akoko rock showing \ninclusions of plagioclase (Pl) in amphiboles (Am). XPL \n\n\n\nFigure 12: Photomicrograph of diorite showing biotite (Bt) surrounded \n\n\n\nby chlorite. The blue arrows pointing towards chlorite. Chloritization of \n\n\n\nbiotite. PPL \n\n\n\nFigure 13: Photomicrograph of diorite from Erusu Akoko showing \n\n\n\nbiotite (Bt) cutting across amphibole (Am). Opaque mineral (Opq) PPL \n\n\n\nFigure 14: Photomicrograph of diorite from Erusu Akoko showing \n\n\n\nbiotite (Bt) cutting across amphibole (Am) and plagioclase (Pl). XPL \n\n\n\nFigure 15: Photomicrograph of diorite from Erusu Akoko showing \n\n\n\nalteration of feldspar to sericite. Biotite (Bt) occurring as inclusion in \n\n\n\nplagioclase (Pl). The blue arrow pointing towards sericite (XPL) \n\n\n\nFigure 16: Photomicrograph of diorite from Erusu Akoko showing \n\n\n\nalteration of plagioclase feldspar (Pl) to sericite (XPL) \n\n\n\nFigure 17: EPMA BSE image of diorite showing biotite, pyroxene, \n\n\n\namphibole and plagioclase. \n\n\n\nFigure 18: EPMA BSE image of diorite showing plagioclase, amphibole \n\n\n\nand pyroxene. \n\n\n\n3.2 Mineral Chemistry  \n\n\n\nBiotite: The mineral chemistry of biotite for the mafic rock show the \nfollowing: SiO2 ;36. 64 wt %, Al2O3;14.02 %, FeO;16.76 %, MgO;13.23 %, \nK2O;8.35 % while the total oxide is 92.53 wt % (Table 1).  \n\n\n\nPyroxene: The mineral composition of pyroxene has the composition: \nSiO2; 50.79 %, FeO;27.13 %, MgO;17.59 % and CaO;1.08 % (Table 2). The \naverage of 52.48 % enstatite and 45.09 % ferrosilite. This composition \nconfirms that the pyroxene present is hypersthene. The formula of the \npyroxene from this result can be written as MgFeSi2O6 (Table 2). \n\n\n\nPlagioclase: The mineral composition of plagioclase show; albite - 63.5 %, \nanorthite - 34.55 % and orthoclase -1.95 % (Table 3).  \n\n\n\nAmphiboles: The mineral composition of amphiboles shows that it is \nedenite-hornblende (Table 4). \n\n\n\nAm \n\n\n\nAm \n\n\n\nAm Am \n\n\n\nPx \n\n\n\nPx \n\n\n\nPl \n\n\n\nPl \n\n\n\nPx \n\n\n\nPl \n\n\n\nAm \n\n\n\nPx \n\n\n\nAm \n\n\n\nPx \n\n\n\nBt \n\n\n\nPl \n\n\n\nPl \n\n\n\nPx \n\n\n\nAm \n\n\n\nAm \n\n\n\nPx \n\n\n\nPl \n\n\n\nAm \n\n\n\nIl \n\n\n\nPx \n\n\n\nPl \n\n\n\nAm \n\n\n\nAm \n\n\n\nAm \n\n\n\nPl \n\n\n\nPx \n\n\n\nAm \n\n\n\nBt\n\n\n\nPl \n\n\n\nPl \n\n\n\nAm \n\n\n\nAm \n\n\n\nPx \n\n\n\nPl Pl \n\n\n\nBt \n\n\n\nAm \n\n\n\nAm \n\n\n\nBt \n\n\n\nAm \nAm \n\n\n\nOpq Am \n\n\n\nPl \n\n\n\nPl \n\n\n\nAm \n\n\n\nBt \n\n\n\nBt \n\n\n\nPl Bt \n\n\n\nPl \n\n\n\nPl \n\n\n\nOpx \n\n\n\nPl \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 13-18 \n\n\n\nCite the Article: E. J. Oziegbe, V. O. Olarewaju, O. O. Ocan (2020). Mineral Chemistry And Geochemistry Of Hypersthene-Bearing Diorite From Erusu Akoko, \nSouthwestern Nigeria. Malaysian Journal of Geosciences, 4(1): 13-18. \n\n\n\nIlmenite: the mineral composition of ilmenite in the mafic rock has a \ncomposition of, SiO2: 1.01 %, TiO2: 52.52 % and FeO: 33.62 % (Table 5). \n\n\n\n3.3 Major Element Chemistry of Diorite  \n\n\n\nThe whole rock chemistry (Table 6) show compositions of the oxides as: \nSiO2 (45.15 wt %), Al2O3 (14.04 wt %), CaO (7.58 wt %), Fe2O3 (16.01 wt \n%), MgO (5.65 wt %), K2O (1.94 wt %), MnO (0.21 wt %), Na2O (2.45 wt \n%), TiO2 (3.59 wt %), P2O5 (0.51 wt %) and Cr2O3 (0.02 wt %).  \n\n\n\n3.4 Trace Element Composition of Diorite \n\n\n\nThe trace elements composition of the mafic rock show high level of \ncomposition of V, Cr, Zn, Cu, Co, Ni and Ba as, V: 312.6 ppm, Cr: 89.86 ppm, \nZn: 136.5 ppm, Cu: 39.92 ppm, Co: 74.7 ppm, Ni: 71.59 ppm and Ba: 483.1 \nppm, respectively (Table 7).   \n\n\n\nTable 1: Chemical Composition of Biotite in Diorite \n\n\n\nSample 17 \n\n\n\nSiO2 36.64 \n\n\n\nTiO2 3.18 \n\n\n\nAl2O3 14.02 \n\n\n\nCr2O3 0.09 \n\n\n\nFeO 16.76 \n\n\n\nMnO 0.08 \n\n\n\nMgO 13.23 \n\n\n\nCaO 0.02 \n\n\n\nNa2O 0.09 \n\n\n\nK2O 8.35 \n\n\n\nCl 0.1 \n\n\n\nH2+O 1.83 \n\n\n\nTotal 92.53 \n\n\n\nSi 5.93 \n\n\n\nAlIV 2.07 \n\n\n\nAlVI 0.60 \n\n\n\nTi 0.39 \n\n\n\nFe2+ 2.27 \n\n\n\nCr 0.01 \n\n\n\nMn 0.01 \n\n\n\nMg 3.19 \n\n\n\nCa 0.00 \n\n\n\nNa 0.03 \n\n\n\nK 1.72 \n\n\n\nCations 16.22 \n\n\n\nCCl 0.06 \n\n\n\nOH 1.97 \n\n\n\nO 24 \n\n\n\nFe/(Fe+Mg) 0.42 \n\n\n\nMg/(Fe+Mg) 0.59 \n\n\n\n(AlIV: aluminium in tetrahedral site, AlVI: aluminium in octahedral site) \n\n\n\nTable 2: Mineral Composition of Pyroxene in Diorite \n\n\n\nSample 17c 17r mean \n\n\n\nSiO2 50.809 50.772 50.7905 \n\n\n\nTiO2 0.026 0.04 0.033 \n\n\n\nAl2O3 0.406 0.441 0.4235 \n\n\n\nFeO 26.073 28.189 27.131 \n\n\n\nCr2O3 0.065 0.043 0.054 \n\n\n\nMnO 0.537 0.499 0.518 \n\n\n\nMgO 17.418 17.754 17.586 \n\n\n\nCaO 1.642 0.517 1.0795 \n\n\n\nNa2O 0.002 0 0.001 \n\n\n\nK2O 0.034 0.041 0.0375 \n\n\n\nTotal 97.01 98.3 97.655 \n\n\n\nTSi 2.004 1.983 1.9935 \n\n\n\nTAl 0 0.017 0.0085 \n\n\n\nTFe3+ 0 0 0 \n\n\n\nM1Al 0.019 0.003 0.011 \n\n\n\nM1Ti 0.001 0.001 0.001 \n\n\n\nM1Fe3+ 0 0.012 0.006 \n\n\n\nM1Fe2+ 0 0 0 \n\n\n\nM1Cr 0.002 0.001 0.0015 \n\n\n\nM1Mg 0.978 0.982 0.98 \n\n\n\nM2Mg 0.046 0.052 0.049 \n\n\n\nM2Fe2 0.86 0.908 0.884 \n\n\n\nM2Mn 0.018 0.017 0.0175 \n\n\n\nM2Ca 0.069 0.022 0.0455 \n\n\n\nM2Na 0 0 0 \n\n\n\nM2K 0.002 0.002 0.002 \n\n\n\nSum_cat 3.998 3.998 3.998 \n\n\n\nCa 3.52 1.093 2.3065 \n\n\n\nMg 51.948 52.204 52.076 \n\n\n\nFe2_Mn 44.533 46.703 45.618 \n\n\n\nJD1 0.095 0.104 0.0995 \n\n\n\nAE1 0 0 0 \n\n\n\nCFTS1 0.104 0.701 0.4025 \n\n\n\nCTTS1 0 0.06 0.03 \n\n\n\nCATS1 0 0.052 0.026 \n\n\n\nWO1 3.445 0.288 1.8665 \n\n\n\nEN1 52.375 52.588 52.4815 \n\n\n\nFS1 43.981 46.207 45.094 \n\n\n\nQ 1.954 1.963 1.9585 \n\n\n\nJ 0 0 0 \n\n\n\nWO 3.52 1.086 2.303 \n\n\n\nEN 51.948 51.878 51.913 \n\n\n\nFS 44.533 47.036 45.7845 \n\n\n\nWEF 99.984 100 99.992 \n\n\n\nJD 0.016 0 0.008 \n\n\n\nAE 0 0 0 \n\n\n\n(M1: Y octahedral site, M2: X octahedral site, JD: jadeite, AE; aegirine, WO: \n\n\n\nwollastonite, EN: enstatite, FS: ferrosilite, CAT: cation) \n\n\n\nStrontium is high and Rb is very high in sample. Barium is high (483.06 \nppm), Zr is also high but Hf and Th are both low in values. \n\n\n\n3.5 Rare Earth Element of Diorite \n\n\n\nThe REE abundances are given in Table 8 and the chondrite-normalized \nREE plot display a negative Eu anomaly. In calculated terms, the europium \nanomaly is 0.832 while the cerium anomaly is 1.087 (Table 8). The ratio of \nlanthanum to ytterbium is 5.344 (Table 8), which shows an enrichment of \nlight rare earth elements (LREE) and a depletion of heavy rare earth \nelements (HREE) in these rocks.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 13-18 \n\n\n\nCite the Article: E. J. Oziegbe, V. O. Olarewaju, O. O. Ocan (2020). Mineral Chemistry And Geochemistry Of Hypersthene-Bearing Diorite From Erusu Akoko, \nSouthwestern Nigeria. Malaysian Journal of Geosciences, 4(1): 13-18. \n\n\n\n4. DISCUSSION\n\n\n\nBased on the petrographic studies the mafic intrusive rock can be given \nthe name two-pyroxene diorite because of the presence of orthopyroxene \nand clinopyroxene. The strong pleochroic nature of the pyroxene suggests \nhypersthene which has been confirmed by the mineral chemistry (Table \n2). The relationship between the pyroxene and the amphibole suggests \nthat the pyroxene (anhydrous mineral) reacts with the hydrous melt in the \npresence of plagioclase to give amphiboles (hydrous mineral), this can be \nrepresented as ; Opx + Plagioclase 1 + H2O = Amphibole + Plagioclase 2. \nPlagioclase 1 will be Ca-rich (anorthite) while plagioclase 2 will be Ca-\npoor. The mineral chemistry confirms the Plagioclase 2 as Ca-poor (Table \n3).The presence of opaque minerals (ilmenite) around pyroxene in close \nassociation with amphiboles an indication that these reaction gives off \ntitanium (Ti4+). The pyroxene-biotite relationship can be due to the \nreaction of orthopyroxene with alumina in the presence of potassium ions \nto give biotite and can be represented as; Opx + plagioclase + K+ + H2O = \nBiotite. The biotite is rimmed by a greenish mineral and indication that it \nis reacting to form chlorite.  \n\n\n\nTable 3: Chemical Composition of Plagioclase Feldspar in Diorite \n\n\n\nSample 17 \n\n\n\nSiO2 59.15 \n\n\n\nTiO2 0.035 \n\n\n\nAl2O3 24.775 \n\n\n\nFeO 0.04 \n\n\n\nMnO 0.015 \n\n\n\nMgO 0.005 \n\n\n\nCaO 7.195 \n\n\n\nNa2O 7.31 \n\n\n\nK2O 0.345 \n\n\n\nTotal 98.9 \n\n\n\nSi 2.67 \n\n\n\nAl 1.317 \n\n\n\nFe3+ 0 \n\n\n\nTi 0.0015 \n\n\n\nFe2+ 0.0015 \n\n\n\nMn 0.0005 \n\n\n\nMg 0.0005 \n\n\n\nBa 0.0005 \n\n\n\nCa 0.348 \n\n\n\nNa 0.64 \n\n\n\nK 0.0195 \n\n\n\nCations 4.9995 \n\n\n\nX 3.9885 \n\n\n\n (Ab: Albite, An: anorthite, Or: orthoclase) \n\n\n\nTable 4: Chemical Composition of Amphibole in Diorite \n\n\n\nSample 17c 17r Average \n\n\n\nSiO2 43.81 44.28 44.05 \n\n\n\nTiO2 1.096 1.010 1.053 \n\n\n\nAl2O3 9.825 10.02 9.920 \n\n\n\nFeO 15.41 15.02 15.21 \n\n\n\nCr2O3 0.057 0.204 0.130 \n\n\n\nMnO 0.150 0.084 0.117 \n\n\n\nMgO 11.76 11.70 11.73 \n\n\n\nCaO 11.74 11.43 11.58 \n\n\n\nNa2O 1.459 1.334 1.396 \n\n\n\nK2O 1.278 1.211 1.245 \n\n\n\nCl 0.078 0.074 0.076 \n\n\n\nTotal 96.61 96.16 96.39 \n\n\n\nO_Cl 0.020 0.020 0.020 \n\n\n\nTSi 6.613 6.689 6.651 \n\n\n\nTAl 1.387 1.311 1.349 \n\n\n\nSum(T)    8.000    8.000   8.000 \n\n\n\nCAl 0.359 0.471 0.415 \n\n\n\nCCr 0.007 0.024 0.016 \n\n\n\nCFe3+ 0.099     0.00 0.050 \n\n\n\nCTi 0.124 0.115 0.120 \n\n\n\nCMg 2.647 2.633 2.640 \n\n\n\nCFe2+ 1.763 1.757 1.760 \n\n\n\nSum(C)    5.000    5.000   5.000 \n\n\n\nBFe2+ 0.083 0.140 0.1115 \n\n\n\nBMn 0.019 0.011 0.015 \n\n\n\nBCa 1.898 1.850 1.874 \n\n\n\nSum(B)    2.000    2.000   2.000 \n\n\n\nANa 0.427 0.391 0.409 \n\n\n\nAK 0.246 0.233 0.240 \n\n\n\nSum(A) 0.673 0.624 0.649 \n\n\n\nSum_cat 15.67 15.624 15.65 \n\n\n\nCCl 0.020 0.019 0.019 \n\n\n\nSum_oxy 23.00 23.02 23.01 \n\n\n\nTable 5: Chemical Composition of Ilmenite in Diorite \n\n\n\nSample 17 \n\n\n\nSiO2 1.012 \n\n\n\nTiO2 52.52 \n\n\n\nAl2O3 0.162 \n\n\n\nFeO 33.62 \n\n\n\nCr2O3 0.010 \n\n\n\nMnO 9.079 \n\n\n\nMgO 0.001 \n\n\n\nCaO 0.885 \n\n\n\nNa2O 0.000 \n\n\n\nTotal 97.29 \n\n\n\nSi 0.026 \n\n\n\nAl 0.005 \n\n\n\nTi 1.004 \n\n\n\nFe2+ 0.714 \n\n\n\nCr 0.000 \n\n\n\nMn 0.195 \n\n\n\nMg 00.00 \n\n\n\nCa 0.024 \n\n\n\nNa 00.00 \n\n\n\nCations 1.968 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 13-18 \n\n\n\nCite the Article: E. J. Oziegbe, V. O. Olarewaju, O. O. Ocan (2020). Mineral Chemistry And Geochemistry Of Hypersthene-Bearing Diorite From Erusu Akoko, \nSouthwestern Nigeria. Malaysian Journal of Geosciences, 4(1): 13-18. \n\n\n\nTable 7: Trace Element Composition of Diorite (ppm) \n\n\n\nSample V Cr Co Ni Cu Zn Rb Sr Zr Nb Mo Cs Ba Hf Ta Pb Th U \n\n\n\n17 312.6 89.86 74.7 71.59 39.92 136.5 100.5 326 281.4 24.72 1.86 4.02 483.1 7.18 1.52 10.5 3.38 0.7 \n\n\n\nTable 8: Rare Earth Element of Diorite (ppm) \n\n\n\nSample Sc Y La Ce Pr Nd Sm Eu Gd Tb Dy Ho Er Tm Yb Lu \n\n\n\n17 27.45 43.46 32.38 73.01 9.5 41.22 8.97 2.495 9.38 1.399 8.32 1.675 4.745 0.6 4.35 0.676 \n\n\n\nLaN/YbN GdN/YbN EuN/EuN* CeN/CeN* \n\n\n\n5.344 1.79 0.832 1.087 \n\n\n\nEu/Eu* = EuN/(SmN x GdN)0.5 ; Ce/Ce* = CeN/(LaN x NdN)0.5. Subscript N denotes Chondrite normalzed values. Normalized factors are from (Sun and \n\n\n\nMcDonough, 1989). \n\n\n\nTherefore, amphiboles and biotite can be said to have formed by hydration \ncrystallization, the evidence of which is the resorbed pyroxene and oxide \nmineral (ilmenite) mantled by amphiboles and biotite (Figures 4-7) \n(Beard et al., 2004; Beard et al., 2005). The association of these minerals \nsuggest that pyroxene is the earliest, because it occurs as inclusions in \namphiboles and biotite. The amphiboles is quite earlier than biotite \nbecause the biotite cuts across the amphibole and plagioclase (Figures 13 \nand 14), this represents the normal crystallization of minerals in igneous \nrocks (Bowen, 1928).The plagioclase feldspar exhibit both albite and \nCarlsbad twinning an indication that it is of igneous origin. The \npetrographic study show the plagioclase alteration to sericite through a \nprocess called sericitization, common phenomenon in igneous rocks.  \n\n\n\nThis could have resulted from the hydrothermal fluids reacting with the \npores in the plagioclase a process which is usually associated with \nchloritization of biotite (Figure 12) (Que and Allen, 1996). The \nphotomicrographs, BSE images and result from the mineral chemistry \nshows no zoning in any of the minerals. The Al2O3 in the pyroxene is \nextremely low (0.4 %). The aluminium content in the amphibole is high \nwhile TiO2 is very low. The amphibole shows an Edenite-hornblende type \nwhich is calcium rich. The plagioclase in the mafic intrusive is oligoclase, \nan indication that it was formed at lower temperature in the later stage of \ncrystallization and thus have inclusions of earlier crystallized minerals \nsuch as pyroxene.  \n\n\n\nThe percentage of silica present (45.15 %) in the whole rock is an \nindication that the rock is basic or mafic in composition (Hafferen and \nO\u2019Brien, 2010; Haldar, 2013). It is an indication that the rock evolved from \nMgO - FeO rich component which is typical of tholeiitic cumulates. The low \nNd and high Sr isotopic ratios is characteristic of continental crust, \nwhereas the upper mantle has high Nd and low Sr isotopic ratios (Best and \nChristiansen, 2001). The photomicrographs and BSE images of the \nminerals show no form of zoning an indication that the minerals were in \nequilibrium with the melt during crystallization. \n\n\n\n5. CONCLUSION \n\n\n\nBased on the mineral chemistry and the geochemistry of the studied rock, \nthis mafic intrusive rock can be named as hypersthene-diorite. The \npresence of amphibole is an indication that the magma from which the \nrock was formed was hydrous in nature. The rock has not been affected by \nany form metamorphism and deformation based on texture. There is an \nenrichment of LREE and a depletion of HREE. The fact that the rock \ncontains hypersthene, which is a high temperature mineral with \namphibole and biotite forming from hydration crystallization, the \nhypersthene-bearing diorite can be said to have extensively retrogressed. \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe use of JEOL JXA 8230 Superprobe, instrument sponsored by NRF/NEP \ngrant 40113 (UID 74464) at Rhodes University, Grahamstown South \n\n\n\nAfrica is kindly acknowledged and the financial support from Tertiary \nEducation Trust Fund (TETFUND) is recognized.       \n\n\n\nREFERENCES \n\n\n\nAjibade, A.C., Fitches, W. R., Wright, J. B., 1979. The Zungeru Mylonites, \n\n\n\nNigeria recognition of a major tectonic unit. Rev. de Geol. Pbys., 21 (5), \n\n\n\npp. 359-363. \n\n\n\nBeard, J. S., Ragland, P.C., Crawford, M.L., 2005. Using Incongruent \n\n\n\nEquilibrium Hydration Reactions to Model Latter-Stage Crystallization \n\n\n\nin Plutons: Examples from the Bell Island Tonalite, Alaska. Journal of \n\n\n\nGeology. 113, pp. 589-599. \n\n\n\nBeard, J.S., Ragland, P.C., Rushmer, T. 2004. Hydration Crystallization \n\n\n\nReactions between Anhydrous Minerals and Hydrous Melt to Yield \n\n\n\nAmphibole and Biotite in Igneous Rocks: Description and Implications. \n\n\n\nThe Journal of Geology, 112 (5), pp. 617-621. \n\n\n\nBest, M. G., Christiansen, E. H. 2001. Igneous petrology. Blackwell, London. \n\n\n\nBowen, N.L., 1928. The evolution of the igneous rocks. Princeton, N.J., \n\n\n\nPrinceton University Press, Pp. 332. \n\n\n\nHaldar, S. K., 2013. Introduction to Mineralogy and Petrology, Elsevier. \n\n\n\nHaruna, I.V., 2017. Review of the Basement Geology and Mineral Belts of \n\n\n\nNigeria. Journal of Applied Geology and Geophysics, 5 (1), pp 37 \u2013 45. \n\n\n\nHefferen, K., O\u2019Brien, J. 2010. Earth Materials. Hoboken, NJ: Wiley-\n\n\n\nBlackwell. \n\n\n\nOyinloye, A.O., Odeyemi, S. B., 2001. The geochemistry, tectonic setting and \n\n\n\norigin of the Massive melanocratic amphiboles in Ilesha schist belt \n\n\n\nSouthwestern Nigeria. Global Journal, Pure and Appl. Sci. (7), pp. 55-66. \n\n\n\nQue, M., Allen, A.R., 1996. Sericitization of plagioclase in the Rosses Granite \n\n\n\nComplex, Co. Donegal, Ireland Mineralogical Magazine, 60, pp. 927-936. \n\n\n\nRahaman, M. A., Ocan, O. 1988. The Nature of Granulite of Granulite Facies \n\n\n\nMetamorphism in Ikare Area, Southwestern Nigeria. In: Precamb. Geol. \n\n\n\nof Nigeria. GSN pub. Pp. 157-163. \n\n\n\nSun, S.S., McDonough, W.F. 1989. Chemical and isotopic systematics of \noceanic basalts: implications for mantle composition and processes. In: \nSaunders, A. D., Norry, M. J. (Eds.), Magmatism in the Ocean Basins. Geol. \nSoc. London, London, pp. 313\u2013345. \n\n\n\nTable 6: Bulk Rock Composition of Diorite (%) \n\n\n\nSample SiO2 Al2O3 CaO Fe2O3 MgO K2O MnO Na2O TiO2 P2O5 Cr2O3 LOI Total \n\n\n\n17 45.15 14.04 7.58 16.01 5.65 1.94 0.21 2.45 3.59 0.51 0.02 2.05 99.2 \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 23-32 \n\n\n\nCite The Article: M. O. Eyankware, O. O. Omo-Irabor (2019). An Integrated Approach To Groundwater Quality Assessment In Determining Factors That Influence The \nGeochemistry And Origin Of Sandstone Aquifers Southern Niger Delta Region Of Nigeria. Malaysian Journal Of Geosciences, 3(2): 23-32. \n\n\n\n\n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 01 March 2019 \nAccepted 29 April 2019 \nAvailable online 2 May 2019\n\n\n\nABSTRACT\n\n\n\nRapid industrialization and oil exploration activities are believed to have influence on groundwater quality globally, \nand Niger Delta Region of Nigeria is no exception.  Hence, this research is conducted to evaluate factors that affect \ngroundwater origin and its geochemistry. For the purpose of this study, 20 groundwater samples were collected (4 \nfrom borehole and 16 from hand-dug wells). The parameters used in the assessment include physical parameters; pH, \ntotal dissolved solid and electrical conductivity and chemical parameters such as; major cations and anions.  From the \nfindings it was observed that pH values fell within the slightly acidic range with the exception of sample location \nHG/08 with value of 7.01 which can be considered as neutral.  The dominant factors that influence groundwater origin \nand geochemistry within the study area are mainly precipitation and weathering. From Gibb\u2019s plot ninety percent (90 \n%) of groundwater chemistry is influenced by precipitation. Soltan classification showed that 98 % of groundwater \nbelongs to (Na+ \u2013 SO4\n\n\n\n2\u00af), hence it can be classified as deep meteoric (precipitation influenced), while the remaining \ntwo percent (2%) is of (Na+ - HCO3\n\n\n\n\u00af) and can be classified as shallow meteoric type. Lastly from relationship between \nCl\u00af/HCO3\u00af groundwater was slightly moderately affected by saline water intrusion.  \n\n\n\n KEYWORDS \n\n\n\nPrecipitation, Groundwater, Interaction, Weathering and Source \n\n\n\n1. INTRODUCTION \n\n\n\nThe study area is richly endowed with natural resources such as crude oil, \nwater and silica sand among others. The presence of crude oil has resulted \nin the rapid urbanization and industrialization, which is believed to \nincrease human activities within the area and likewise the generation of \nvarious forms of wastes.  These wastes are mostly disposed of close to \nrivers and streams, while the others infiltrate into groundwater which \nabout seventy six percent of the inhabitants of the study area rely on for \ndomestic and other uses. Over 80% of the water abstracted from aquifers \nfinds its way back to the underlying aquifer from septic tanks. [1], further \nstated that the construction of an onsite sewage system by individuals \noften lack institutional control and thus quality is compromised, thereby \nleading to failure. Hence, there is need for constant assessment of water \nfor domestic, irrigation, industrial use and even having prior information \nas regards groundwater origin, if necessary [2].  Although research has \nbeen carried out on hydro geochemistry of groundwater in recent decades, \nthis poses a challenge to the scientific researchers based on hydrology and \nlithology [2-5, 5-11] due to the complexity of groundwater movement and \nthe fact that it is considered free from pollution compared to surface water \n[11]. However, that is no longer the case, as anthropogenic activities have \nalso been considered to greatly influence the quality of groundwater for \ndomestic, irrigation and industrial use [5,13]. Human activities such as oil \nspillage, wrongful disposal of waste and leakage from septic tanks have \nbeen major sources of groundwater pollution within the Niger Delta \nRegion [14]. The authors further stated that acid rain and gas flaring \nwithin the region, pose threat to the quality of water [13]. Acid rain and its \nenvironmental effects within this region have been confirmed by various \nresearchers although no mention was made on the effects of gas flaring on \nwater quality [15-18].  Influence of human activities on water chemistry \n\n\n\nin the region has also been studied [19]. However, factors that influence \nthe origin of groundwater have not been discussed within the area. \nAlthough studies from previous literature has proven that source rock \ndeduction (SRD) and Soltan approach have been used in determining \nfactors that influences groundwater origin and water type [2, 20-12]. SRD \nis analytical check used in determining origin of groundwater that is not \nyet known [22].  SRD is derived from simplistic mass balance approach \nfrom water quality data. While in some situations source rock minerals \nmay be deduced from the groundwater composition. It is an approach \nused for explaining groundwater chemistry, as groundwater chemistry \nplays major role in determining its quality and origin. Therefore, there is \nthe need to evaluate factors that influence the origin of groundwater \nwithin the study area. \n\n\n\n2. STUDY AREA \n\n\n\n2.1 Climate and Topography  \n\n\n\nThe study area lies between longitudes 5\u00ba58'E to 6\u00ba 14'E and latitudes \n5\u00ba24'N to 5\u00ba46'N and covers five local government areas (LGAs), namely \n(Okpe, Ethiope East, Udu, Ughelli North and Ughelli South) in Delta State \nwith Ughelli being the major town (Figure 1).  The area experiences sub-\nequatorial climate that is now influenced by gas flaring with mean \ntemperature that ranging between 25 \u00baC and 37 \u00baC. The total amount of \nannual rainfall is over 300 cm without a distinctive dry season, with \nmonthly rainfall (January \u2013 December) averaging 20.5 cm. The rainwater \naccording to  [17] is acidic in nature and the quality is being threatened \nbecause of high level of impurities [18]. \nThe study area is mainly low-lying devoid of hills, with the relief varying \nfrom 10 m in the south-western section to 22 m in the northern part. \n\n\n\nMalaysian Journal of Geosciences (MJG) \nDOI : http://doi.org/10.26480/mjg.02.2019.23.32 \n\n\n\nRESEARCH ARTICLE \n\n\n\nAN INTEGRATED APPROACH TO GROUNDWATER QUALITY ASSESSMENT IN \nDETERMINING FACTORS THAT INFLUENCE THE GEOCHEMISTRY AND ORIGIN OF \nSANDSTONE AQUIFERS, SOUTHERN NIGER DELTA REGION OF NIGERIA \n\n\n\nM. O. Eyankware*1, O. O. Omo-Irabor2 \n\n\n\n1Department of Geology, Faculty of Science Ebonyi State University, Abakaliki. Nigeria. \n2Department of Earth Sciences, Federal University of Petroleum Resources, Effurun Delta State.Nigeria \nCorresponding author: geomoses203@gmail.com  \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\n\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\n\nmailto:geomoses203@gmail.com\n\n\n\n\n\n\n  \nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 23-32 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCite The Article: M. O. Eyankware, O. O. Omo-Irabor (2019). An Integrated Approach To Groundwater Quality Assessment In Determining Factors That Influence The \nGeochemistry And Origin Of Sandstone Aquifers Southern Niger Delta Region Of Nigeria. Malaysian Journal Of Geosciences, 3(2): 23-32. \n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Accessibility map of the study area with groundwater sampling points. \n \n\n\n\n \n2.2 Hydrogeology \n \nFrom the hydrogeological point of view, the aquifer types occurring in the \nstudy area are; unconfined, confined and semi-confined aquifers.  The \naquifers are high yielding and characterized by high specific capacities, as \nwell as high recharge rates [1]. The water-table in the study area is very \nclose to the ground surface, ranging from 0 to 9 m below ground level. The \naquifers in the area obtain steady recharge through direct precipitation \nand from major rivers. Rainfall in the Niger Delta is heavy, varying from \nabout 2400 mm a year inland to 4800 mm near the coast [23]. [1], further \nstated that the static water level of water bearing unit is on the high side; \nwith value ranging from less than 0.2 to 4m. Fluctuation of water level is \ninfluenced by seasonal fluctuation of the wet and dry seasons within the \nyear.  The water level is almost at the ground surface during the wet season \nand decreases during the dry season. The study area is drained by one \nmajor river (Ughelli River) with other tributaries that flows throughout \nthe season and, many streams that are perennial in nature. These streams \nare part of the wetlands and sometimes contribute to the recharge of the \naquifers. The aquifers are characterized by grain sizes that range from fine \nthrough medium to coarse grained sand. \n\n\n\n \n2.3 Geology \n \nThe geology of study area indicates that it is underlain by the Niger Delta \nFormations (Figure 2). The formations from top to the base are \nSomebreiro-Warri Deltaic Plain sands (Figure 2). The Benin Formation, \nAgbada Formation and the Akata Formation have been described in detail \n[24-27]. According to [27] the Somebreiro-Warri Deltaic Plain sand is \nabout 120 m thick and it is Quaternary to Recent in age. Texturally, the \nunconsolidated sediments range from fine plastic clay through medium to \ncoarse grain sands and are rarely gravelly. The Benin Formation \n(Oligocene to Pleistocene) consists predominantly of unconsolidated sand, \ngravel and occasionally intercalation of shales and with an average \nthickness about 2000 m. The Agbada Formation (Eocene to Oligocene) is \nthe oil-bearing formation of the Niger Delta sedimentary basin. It consists \nof shale and alternate sand sequence and is about 3000 m thick. The Akata \nFormation is the basal units of the Niger Delta sedimentary basin and \noverlies the basement complex. It is made up of open marine facies and is \nhighly pressured with 1000 km thickness and is of the Ecocene to \nOligocene in age.\n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Geological map of parts of the western Niger Delta (NGSA, 2006). \n \n\n\n\n3.  METHODOLOGY \n \nTwenty water samples were randomly collected from six boreholes (BH) \n\n\n\nand fourteen hand dug wells (HDW) (Figure 1).  Sampling was carried out \nduring the dry season when there was a decrease in the water level and \nthe concentration of cations and anions were more stable. Precautionary \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 23-32 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: M. O. Eyankware, O. O. Omo-Irabor (2019). An Integrated Approach To Groundwater Quality Assessment In Determining Factors That Influence The \nGeochemistry And Origin Of Sandstone Aquifer Southern Niger Delta Region Of Nigeria. Malaysian Journal Of Geosciences, 3(2): 23-32. \n\n\n\n\n\n\n\nmeasures were taken by washing the bottles with clean water, followed by \ncleaning reagents and finally thoroughly rinsing with distilled, de-ionized \nwater prior to collection of water samples from the site.  The samples were \n\n\n\nanalyzed for eleven parameters, namely pH, electrical conductivity, total \ndissolved solids, magnesium, calcium, chloride, nitrate, sulphate, \npotassium, sodium and bicarbonate following standard [29] (Table 1). \n\n\n\n \nTable 1: Methods used to analyze physicochemical parameters \n\n\n\n\n\n\n\nS/No Parameters Units Analytical Method \n1 pH  pH meter Hach sensION + PH1 portable pH meter and Hach sensION + 5050 T \n\n\n\nPortable Combination pH Electrode \n\n\n\n2 Electrical Conductivity (EC) \u00b5S/cm HACH Conductivity \n\n\n\n3 Total dissolved solids (TDS) mg/L TDS meters (model HQ14D53000000, USA). \n\n\n\n4 Magnesium (Mg2+) meq/L EDTA titrimetric method \n\n\n\n5 Calcium (Ca2+) meq/L Titrimetric method \n\n\n\n6 Chloride (Cl\u00af) meq/L Titrimetric method \n\n\n\n7 Nitrate (NO3\u00af) meq/L Ion-selective electrode (Orion 4 star) \n\n\n\n8 Sulphate (SO42\u00af) meq/L Turbidimetric method using a UV-Vis spectrometer \n\n\n\n9 Potassium (K+) meq/L Jenway clinical flame photometer (PFP7 model) \n\n\n\n10 Sodium (Na+) meq/L Jenway clinical flame photometer (PFP7 model) \n\n\n\n11 Bicarbonate (HCO3\n\u00af) meq/L Titrimetric method \n\n\n\n \n3.1 Rock Source Deduction \n\n\n\n \nSource rock deduction attempts to determine the possible origin of water \nsamples analyzed. Equations 1 to 7 present a summary of the source rock \ndeduction process on the basis of elemental ratios as proposed by [22]. \n \n\n\n\n\ud835\udc41\ud835\udc4e++\ud835\udc3e+ \u2212\ud835\udc36\ud835\udc59\u2212\n\n\n\nNa++ K+  \u2212 Cl\u2212+ Ca2+     \n\n\n\n   (1) \n\ud835\udc41\ud835\udc4e+\n\n\n\n\ud835\udc41\ud835\udc4e+  +  \ud835\udc36\ud835\udc59\u2212      \n\n\n\n   (2) \n\ud835\udc40\ud835\udc542+\n\n\n\n\ud835\udc36\ud835\udc4e2+  +\ud835\udc40\ud835\udc542+     \n\n\n\n    (3) \n\n\n\nCa2+\n\n\n\n\ud835\udc36\ud835\udc4e2+ +\ud835\udc46\ud835\udc424\n2\u2212     \n\n\n\n    (4) \nCa2++\ud835\udc40\ud835\udc542+\n\n\n\n\ud835\udc46\ud835\udc424\n2\u2212      \n\n\n\n    (5) \nCl\u2212\n\n\n\n\ud835\udc46\ud835\udc62\ud835\udc5a \ud835\udc5c\ud835\udc53 \ud835\udc34\ud835\udc5b\ud835\udc56\ud835\udc5c\ud835\udc5b\ud835\udc60\n     \n\n\n\n    (6) \n\ud835\udc3b\ud835\udc36\ud835\udc423\n\n\n\n\u2212\n\n\n\n\ud835\udc46\ud835\udc62\ud835\udc5a \ud835\udc5c\ud835\udc53  \ud835\udc34\ud835\udc5b\ud835\udc56\ud835\udc5c\ud835\udc5b\ud835\udc60\n     \n\n\n\n    (7) \n \n \nThe composition and quality of groundwater was used to deduce source \nrock as shown in Table 2.\n\n\n\n \nTable 2: Source-Rock Deduction Summary of Reasoning \n\n\n\n \nParameters Value           Conclusion \n\n\n\n\ud835\udc41\ud835\udc4e+ + \ud835\udc3e+ \u2212 \ud835\udc36\ud835\udc59\u2212\n\n\n\nNa+ +  K+  \u2212  Cl\u2212 +  Ca2+    \n>0.2 and <0.8 \n<0.2 or >0.8 \n\n\n\nPWP \nPWU \n\n\n\n \n\ud835\udc41\ud835\udc4e+\n\n\n\n\ud835\udc41\ud835\udc4e+  +   \ud835\udc36\ud835\udc59\u2212   \n \n\n\n\n>0.5 \n= 0 \n\n\n\n<0.5 TDS >500 \n<0.5 TDS <500> 50 \n\n\n\n<0.5 TDS < 50 \n\n\n\nSodium source other than halite \nHS \n\n\n\nReverse softening, sea water \nAE \n\n\n\nRain water. \n\ud835\udc40\ud835\udc542+ \n\n\n\n\ud835\udc36\ud835\udc4e2+  + \ud835\udc40\ud835\udc542+       \n \n\n\n\n= 0.5 \n< 0.5 \n>0.5 \n\n\n\nDW \nLD \n\n\n\nDD, calcite precipitation or seawater. \n\n\n\n  Ca2+\n\n\n\n\ud835\udc36\ud835\udc4e2+ + \ud835\udc46\ud835\udc424\n2\u2212     \n\n\n\n= 0.5 \n< 0.5 PH < 5.5 \n<0.5 neutral \n\n\n\n>0.5 \n\n\n\nGD \nPO \n\n\n\nCalcium  remove-ion exchange \nCalcium source other than gypsum \n\n\n\n  Ca2+ + \ud835\udc40\ud835\udc542+\n\n\n\n\ud835\udc46\ud835\udc424\n2\u2212  \n\n\n\n>0.8 and <1.2 D \n\n\n\nTDS >500 \n<500 \n\n\n\nCW or brine, seawater \nSW \n\n\n\n\n\n\n\n            \n  Cl\u2212\n\n\n\n\ud835\udc46\ud835\udc62\ud835\udc5a \ud835\udc5c\ud835\udc53 \ud835\udc34\ud835\udc5b\ud835\udc56\ud835\udc5c\ud835\udc5b\ud835\udc60\n   \n\n\n\n>0.8 TDS >500 \n>0.8 TDS <100 \n\n\n\n<0.8 \n\n\n\nSea water, or brine or evaporate \nRW \nRW \n\n\n\n\n\n\n\n         \n\ud835\udc3b\ud835\udc36\ud835\udc423\n\n\n\n\u2212 \n\n\n\n\ud835\udc46\ud835\udc62\ud835\udc5a \ud835\udc5c\ud835\udc53  \ud835\udc34\ud835\udc5b\ud835\udc56\ud835\udc5c\ud835\udc5b\ud835\udc60\n \n\n\n\n>0.8 \n<0.8 Sulphate High \n<0.8 Sulphate low \n\n\n\nSilicate or carbonate weathering \nGW \n\n\n\nSWB \n\n\n\nConclusion Aquifer mineralogy  \n\n\n\nConclusion Reactions  \n\n\n\n\n\n\n\n\n  \nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 23-32 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCite The Article: M. O. Eyankware, O. O. Omo-Irabor (2019). An Integrated Approach To Groundwater Quality Assessment In Determining Factors That Influence The \nGeochemistry And Origin Of Sandstone Aquifers Southern Niger Delta Region Of Nigeria. Malaysian Journal Of Geosciences, 3(2): 23-32. \n\n\n\nWhere :DW= Dolomite weathering, LD =Limestone Dolomoite, DD = \nDolomite Dissolution, GD = Gyspum Dissolution, PO = Pyrite Oxidation, \nD= Dedolomitiztion, CW = Carbonate weathering, GW = Gypsum \nweathering,  RW = Rock weathering, PWP = Plagioclase weathering \npossible, PWU= Plagioclase weathering unlikely, SW = Silicate \nweathering, SWB = Sea water brine, HS = Halite solution, AE = Analysis \nerror and RW = Rain water. \n \n3.2 Gibb\u2019s Plot \n\n\n\n \nGibb\u2019s plot was attempted to obtain knowledge of the separate influences \nof precipitation, rock-water interaction and evaporation on the \ngroundwater. The formula for cations and anions derived [30-32] by are \ndisplayed in equations 8a and 8b. \n \nFor Cations \n \n\n\n\n\ud835\udc41\ud835\udc4e+/(\ud835\udc41\ud835\udc4e+ + \ud835\udc36\ud835\udc4e2+)     (8a)\n   \n\n\n\n \nFor Anions \n\n\n\n \n\ud835\udc36\ud835\udc59\u2212/(\ud835\udc36\ud835\udc59\u2212 + \ud835\udc3b\ud835\udc36\ud835\udc423\n\n\n\n\u2212)     (8b)\n   \n\n\n\n \n3.3 Soltan Classification \n \nThe sources of groundwater have been classified into two types by [33].  \nThe classifications are based on base-exchange indices (r1) and meteoric \ngenesis indices (r2) as presented in equations 9a and 9b. \n \n\n\n\n\ud835\udc5f1 = (\ud835\udc41\ud835\udc4e+ \u2212 \ud835\udc36\ud835\udc59\u2212)/\ud835\udc46\ud835\udc424\n2_    (9a)\n\n\n\n\n\n\n\n\ud835\udc5f2 = [( \ud835\udc41\ud835\udc4e+ + \ud835\udc3e+   ) \u2212 \ud835\udc36\ud835\udc59\u2212 \ud835\udc46\ud835\udc424\n2_]\u2044  )                                 (9b) \n\n\n\n \n \n4. RESULTS AND DISCUSSION \n \nThe physicochemical parameters analyzed for both hand-dug wells and \nborehole samples are shown in Tables 3a and 3b. \n \n\n\n\n \nTable 3a: Physicochemical result of hand-dug wells samples \n\n\n\n \nSample Code TDS Electrical Conductivity pH Cl- Na+ Mg2+ NO3- SO4\n\n\n\n2- Ca2+ HCO3\n- K+ \n\n\n\n   mg/L \u00b5S/cm   meq/L meq/L meq/L meq/L meq/L meq/L meq/L meq/L \n\n\n\nHG/02 20 39.6 6.58 0.29 0.08 0 0 0.01 0.03 0.1 0 \n\n\n\nHG/03 68 136.6 6.05 1.09 0.28 0.09 0 0.03 0.47 0.24 0 \n\n\n\nHG/04 4 8.9 6.73 0.07 0.04 0 BDL 0 0.01 0.01 0 \n\n\n\nHG/06 18 34.9 6.98 0.25 0.09 0.05 BDL 0.03 0.1 0.07 0 \n\n\n\nHG/07 51 101.2 6.22 0.56 0.33 0.16 0 0.04 0.55 0.16 0 \n\n\n\nHG/08 136 271.01 7.01 1.48 0.48 0.18 0 0.09 0.91 0.5 0 \n\n\n\nHG/09 13 24.77 6.18 0.14 0.05 0.02 BDL 0.01 0.15 0.03 0 \n\n\n\nHG/10 10 18.96 6.65 0.07 0 BDL 0 0 0.14 0.02 0 \n\n\n\nHG/12 27 51.33 6.36 0.44 0.27 0 BDL 0.01 0.15 0.15 0 \n\n\n\nHG/13 23 43.72 6.13 0.31 0.19 0 0 0.01 0.14 0.12 0 \n\n\n\nHG/15 68 104.6 6.83 0.62 0.42 0.29 0 0.06 0.7 0.2 0 \n\n\n\nHG/16 7 13.5 6.72 0.08 0 0 BDL 0 0.02 0.04 0 \n\n\n\nHG/17 24 47.7 6.4 0.3 0.16 0.73 0 0.01 0.19 0.09 0 \n\n\n\nHG/19 10 20.7 6.45 0.11 0.02 0 0 0 0.03 0.05 0 \n\n\n\nHG/20 22 44.1 6.53 0.36 0.21 0.1 0 0.02 0.25 0.1 0 \n\n\n\nMin 4 8.9 6.05 0.07 0 0 0 0 0.01 0.01 0 \n\n\n\nMax 136 271.01 7.01 1.48 0.48 0.73 0 0.09 0.91 0.5 0 \n\n\n\nAverage 37.7 73.02 6.52 0.45 0.18 0.14 0 0.02 0.28 0.14 0 \n \nWhere HG = Hand-dug well, Min = Minimum, Max= Maximum and BDL = Below Detective Limit    \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 23-32 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: M. O. Eyankware, O. O. Omo-Irabor (2019). An Integrated Approach To Groundwater Quality Assessment In Determining Factors That Influence The \nGeochemistry And Origin Of Sandstone Aquifer Southern Niger Delta Region Of Nigeria. Malaysian Journal Of Geosciences, 3(2): 23-32. \n\n\n\n\n\n\n\n \nTable 3b: Physicochemical result of boreholes samples \n\n\n\n\n\n\n\nSample \nCode \n\n\n\nTDS Electrical \nConductivity \n\n\n\npH Cl- Na+ Mg2+ NO3- SO4\n2- Ca2+ HCO3\n\n\n\n- K+ \n\n\n\n \nmeq/L meq/L meq/L meq/L meq/L meq/L meq/L meq/L \n\n\n\nHBG/01 7 14.01 6.41 0.08 0 0 BDL 0 0.25 0.03 0 \n\n\n\nHBG/05 87 122.8 6.91 1.04 0.41 0.11 0 0.06 0.79 0.21 0 \n\n\n\nHBG/11 63 119.74 6.17 0.79 0.55 0.07 0 0.03 0.44 0.19 0 \n\n\n\nHBG/14 142 284.3 6.55 1.85 0.71 0.5 0 0.14 1.19 0.39 0 \n\n\n\nHBG/18 5 10.6 6.64 0.08 0 0.01 BDL 0 0.01 0.02 0 \n\n\n\nMin \n5 10.6 6.17 0.08 0 0 0 0 0.01 0.02 0 \n\n\n\nMax \n142 284.3 6.91 1.85 0.71 0.5 0 0.14 1.19 0.39 0 \n\n\n\nAverage \n64.42 120.9 6.53 0.82 0.34 0.17 0 0.05 0.55 0.17 0 \n\n\n\nWhere HBG = Borehole, Min = Minimum, Max= Maximum and BDL = Below Detective Limit     \n\n\n\n4.1 Source Rock Deduction \n\n\n\n \nIt can be understood that there are several possibilities of deciphering the \n\n\n\nsource rock as shown in Table 2 during the weathering and dissolution \nprocesses.  The results obtained for the application of Source Rock \nDeduction is displayed in Table 4. \n \n\n\n\n \nTable 4: Results obtained from application of Source Rock Deduction within the Study Area \n\n\n\n \nSample   \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTDS \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCode \n\n\n\nHBG/1 0.24 0 0 1 0 7 0.72 0.27 \n\n\n\nHG/02 0.87 0.21 1 0.75 3 20 0.72 0.25 \n\n\n\nHG/03 -0.63 0.2 0.16 0.94 18.0\n6 \n\n\n\n68 0.8 0.17 \n\n\n\nHG/04 -0.75 0.36 0 1 0 4 0.87 0.12 \n\n\n\nHBG/0\n5 \n\n\n\n-0.44 0.28 0.12 0.92 15 87 0.79 0.16 \n\n\n\nHG/06 0.61 0.26 0.33 0.76 5 18 0.71 0.2 \n\n\n\nHG/07 0.29 0.37 0.22 0.93 17.7\n5 \n\n\n\n51 0.65 0.21 \n\n\n\nHG/08 0.52 0.24 0.16 0.91 12.1\n1 \n\n\n\n136 0.71 0.24 \n\n\n\nHG/09 0.37 0.26 0.11 0.93 17 13 0.77 0.16 \n\n\n\nHG/10 0.08 0 0.14 0.91 0 10 0.77 0.22 \n\n\n\nHBG/1\n1 \n\n\n\n0.35 0.41 0.31 0.93 17 63 0.78 0.18 \n\n\n\nHG/12 0.53 0.38 0 1 15 27 0.73 0.25 \n\n\n\nHG/13 0.46 0.38 0 0.93 14 23 0.7 0.27 \n\n\n\nHBG/1\n4 \n\n\n\n0.48 0.27 0.29 0.93 12.0\n7 \n\n\n\n142 0.77 0.16 \n\n\n\nHG/15 0.22 2.47 0.29 0.93 16.5 68 0.7 0.22 \n\n\n\nHG/16 0.8 0 0 0.89 0 7 0.66 0.33 \n\n\n\nHG/17 0.42 0.34 0.79 0.92 92 24 0.75 0.22 \n\n\n\nHBG/1\n8 \n\n\n\n8.88 0 0.5 1 0 5 8 0.2 \n\n\n\nHG/19 0.75 0.15 1 1 0 10 0.68 0.31 \n\n\n\nHG/20 0.37 2.71 0.28 0.92 0 22 0.75 0.2 \n\n\n\nDisintegration of magnesium ions and calcium ion in groundwater play a major role in the understanding of source rock for dolomite under average TDS \nconditions, however, the magnesium ion to calcium ratio approaches one, it is very likely that calcium could be removed from the solution and this process \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 23-32 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: M. O. Eyankware, O. O. Omo-Irabor (2019). An Integrated Approach To Groundwater Quality Assessment In Determining Factors That Influence The \nGeochemistry And Origin Of Sandstone Aquifer Southern Niger Delta Region Of Nigeria. Malaysian Journal Of Geosciences, 3(2): 23-32. \n\n\n\n\n\n\n\nis referred to as dedolomitization as shown in Figure 4 [21, 22]. \n \n \n\n\n\n\n\n\n\nFigure 4: Plot of Mg2+ versus (Ca2+ + Mg2+). \n \nFrom Figure 5, it can be observed that ten sampling points fall above the equiline while the remaining ten falls below the equiline. This indicates that \ncarbonate and silicate weathering undergo physical action as the source of calcium ion in the groundwater. \n \n\n\n\n\n\n\n\nFigure 5: Plot of Ca2+ + Mg2+ (meq/L) versus HCO3- + SO42- (meq/L). \n \n\n\n\n \nIt was observed that most of the samples have a Na+/Cl\u00af ratio value around or above 1, this implies that ion exchange process is more dominant in the study \narea (Figure 6). \n\n\n\n\n\n\n\n\n\n\n\nFigure 6: Plot of Na+ versus Cl\u00af. \n \n\n\n\nFigure 7 shows the ion exchange reactions, where Na+ is plotted against Ca2+ in which Ca2+ levels  are ranges from 0.01 to 1.19 (meq/L), while Na+ levels are \nranges from 0.00 to 0.71 (meq/L).  \n\n\n\n\n\n\n\n-0.1\n\n\n\n0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0.3\n\n\n\n0.4\n\n\n\n0.5\n\n\n\n0.6\n\n\n\n0.7\n\n\n\n0.8\n\n\n\n0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8\n\n\n\nM\ng2+\n\n\n\n (m\neq\n\n\n\n/L\n)\n\n\n\nCa2+ + Mg2+ (meq/L)\n\n\n\n0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0.3\n\n\n\n0.4\n\n\n\n0.5\n\n\n\n0.6\n\n\n\n0.7\n\n\n\n0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8\n\n\n\nH\nC\n\n\n\nO\n3-\n\n\n\n+ \nSO\n\n\n\n42\n-\n(m\n\n\n\neq\n/L\n\n\n\n)\n\n\n\nCa2+ + Mg2+ (meq/L)\n\n\n\n0\n\n\n\n0.1\n\n\n\n0.2\n\n\n\n0.3\n\n\n\n0.4\n\n\n\n0.5\n\n\n\n0.6\n\n\n\n0.7\n\n\n\n0.8\n\n\n\n0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2\n\n\n\nN\na+\n\n\n\n(m\neq\n\n\n\n/L\n)\n\n\n\nCl\u00af(meq/L)\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 23-32 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: M. O. Eyankware, O. O. Omo-Irabor (2019). An Integrated Approach To Groundwater Quality Assessment In Determining Factors That Influence The \nGeochemistry And Origin Of Sandstone Aquifer Southern Niger Delta Region Of Nigeria. Malaysian Journal Of Geosciences, 3(2): 23-32. \n\n\n\n\n\n\n\n\n\n\n\nFigure 7: Plot of Na+ versus Ca2+. \n \n4.2 Relationship of Cl\u00af/HCO3\u00af ratio \n\n\n\n \nThe Cl\u00af/HCO3\u00af ionic ratios were also studied in order to characterize the \norigin of groundwater salinity in the study area. Ionic ratio is an approach \n\n\n\nused to evaluate seawater intrusion especially in coastal regions [34, 35].  \nThe ratios of Cl\u00af/HCO3\u00af in the study area ranged between 1.13 and 2.84 \n(Table 5) and have strong positive and direct relationship with Cl\u00af \nconcentrations (Figure 8).  \n \n\n\n\n \nTable 5: Result of Cl\u00af and HCO3\u00af in mg/L \n\n\n\n\n\n\n\nSample No Cl\u00af HCO3\u00af Cl\u00af/HCO3\u00af \nHBG/1 3.06 1.93 .53 \nHG/02 10.55 6.49 1.62 \nHG/03 38.76 15.25 2.54 \nHG/04 2.78 1.17 2.37 \n\n\n\nHBG/05 37.04 13.4 2.84 \nHG/06 9.13 4.52 2.01 \nHG/07 20.05 10.18 1.97 \nHG/08 52.56 30.71 1.71 \nHG/09 5.06 2.15 2.35 \nHG/10 2.56 1.68 1.52 \n\n\n\nHBG/11 28.34 11.82 2.39 \nHG/12 15.6 9.4 1.65 \nHG/13 11.3 7.51 1.50 \n\n\n\nHBG/14 65.82 24.12 2.72 \nHG/15 22.09 12.41 1.78 \nHG/16 3.12 2.75 1.13 \nHG/17 10.81 5.5 1.96 \n\n\n\nHBG/18 2.95 1.54 1.91 \nHG/19 4.13 3.42 1.20 \nHG/20 12.78 6.15 2.07 \n\n\n\nMin 2.56 1.17 1.13 \nMax 65.82 30.17 2.84 \n\n\n\n \nWhere parameters are in mg/L, Min =Minimum, Max = Maximum\n\n\n\n.  \n\n\n\n\n\n\n\nFigure 8: Relationship between Cl\u00af/HCO3\u00af vs Cl\u00af concentration in the study area. \n\n\n\n0\n\n\n\n0.2\n\n\n\n0.4\n\n\n\n0.6\n\n\n\n0.8\n\n\n\n0 0.2 0.4 0.6 0.8 1 1.2 1.4\n\n\n\nN\na+ \n\n\n\n(m\neq\n\n\n\n/L\n)\n\n\n\nCa2+ (meq/L)\n\n\n\n\n\n\n\n\n  \nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 23-32 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCite The Article: M. O. Eyankware, O. O. Omo-Irabor (2019). An Integrated Approach To Groundwater Quality Assessment In Determining Factors That Influence The \nGeochemistry And Origin Of Sandstone Aquifers Southern Niger Delta Region Of Nigeria. Malaysian Journal Of Geosciences, 3(2): 23-32. \n\n\n\nThe relationship between Cl\u00af/HCO3\u00af and Cl\u00af shows the mixing of fresh \ngroundwater with saline water. Based on Cl\u00af/HCO3\u00af ratios groundwater \ncan be classified into unaffected (< 0.2), slightly or moderately affected \n(0.4 \u2013 4.1), and strongly affected (> 4.1) by salinization process. [36, 37]. \nOn the basis of the ratio of Cl\u00af/HCO3\u00af, groundwater was moderately \naffected by seawater intrusion probably because the study area is distant \nfrom the sea and groundwater exists in sandstone aquifer at great depths.  \nA combined effect of seawater and urban wastewaters may also be \nattributed to the poor quality of groundwater, which is influenced by low \nrelief close to the coastal area [38]. It was also interesting to note that 6.2% \nof the samples unaffected by salinization were located near Perumal Lake \nin the study area, which may be due to the dilution effect from the surface \nwater recharged into the aquifer or lesser extraction of groundwater due \nto the increased usage of surface water. \n \n\n\n\n4.3 Soltan classification \n\n\n\n \n[20] classified groundwater into two types, which are base-exchange \nindices (r1) and meteoric genesis indices (r2) as shown in equations 9a \nand 9b, where Na+, K+, Cl\u00af and SO4\n\n\n\n2\u00af concentrations are expressed in meq/L \n(Table. 2a and 2b). If r1<1 and r2<1, the groundwater sources are of Na+\u2013\nSO42\u00af and deep meteoric type, respectively, while r1>1 and r2>1 indicates \nthe sources are of Na+\u2013 HCO3\u00af  and shallow meteoric type, respectively \n[20], and also presented in Table 6. Based on Soltan classification 98 % of \ngroundwater belongs to Na+ \u2013 SO42\u00af and are classified as meteoric type.  \nThis implies that groundwater is greatly influenced by precipitation \nprocess, as shown in Table 5, with the exception of samples HGB/05 and \nHG/06 that belong to shallow meteoric type. \n \n\n\n\nTable 6: Groundwater classification according to base-exchange (r1) and meteoric genesis index (r2) criteria modified after [20] \n \n\n\n\nSample code r1 Water Type r2 Water Type \n\n\n\nHBG/1 0 Na+ - SO42-(DM) 0 Na+ - SO42-(DM) \nHG/02 -21 Na+ - SO42-(DM) -21 Na+ - SO42-(DM) \nHG/03 -27 Na+ - SO42-(DM) -27 Na+ - SO42-(DM) \nHG/04 0 Na+ - SO42-(DM) 0 Na+ - SO42-(DM) \n\n\n\nHBG/05 10.5 Na+ - HCO3-(SM) 10.5 Na+ - HCO3-(SM) \nHG/06 5.33 Na+ - HCO3-(SM) 5.33 Na+ - HCO3-(SM) \nHG/07 -5.75 Na+ - SO42-(DM) -5.75 Na+ - SO42-(DM) \nHG/08 -11.11 Na+ - SO42-(DM) -11.11 Na+ - SO42-(DM) \nHG/09 -0.1 Na+ - SO42-(DM) -9 Na+ - SO42-(DM) \nHG/10 0 Na+ - SO42-(DM) 0 Na+ - SO42-(DM) \n\n\n\nHBG/11 -8 Na+ - SO42-(DM) -8 Na+ - SO42-(DM) \nHG/12 -17 Na+ - SO42-(DM) -17 Na+ - SO42-(DM) \nHG/13 -12 Na+ - SO42-(DM) -12 Na+ - SO42-(DM) \n\n\n\nHBG/14 -8.14 Na+ - SO42-(DM) -8.14 Na+ - SO42-(DM) \nHG/15 -3.33 Na+ - SO42-(DM) 3.33 Na+ - HCO3-(SM) \nHG/16 0 Na+ - SO42-(DM) 0 Na+ - SO42-(DM) \nHG/17 -14 Na+ - SO42-(DM) -4 Na+ - SO42-(DM) \n\n\n\nHBG/18 0 Na+ - SO42-(DM) 0 Na+ - SO42-(DM) \nHG/19 0 Na+ - SO42-(DM) 0 Na+ - SO42-(DM) \nHG/20 0 Na+ - SO42-(DM) 0 Na+ - SO42-(DM) \n\n\n\nWhere; SM = Shallow Meteoric Type and DM = Deep meteoric. \n\n\n\n4.4 Hydrogeochemical Analysis \n\n\n\n \nThe attribute of anions and cations in groundwater denotes the aspect of \nphysicochemical quality caused by the groundwaters interaction with soil \nand rock, while flowing in the aquifer [29]. Water bearing formation show \nthe attributes of water bodies with various chemical compositions; \ntherefore, such attributes can be referred to hydrochemical facies of \ngroundwater. Most times hydrochemical facies is usually affected by the \nrocks of the water bearing formation and the flow patterns of \ngroundwater. The distribution of anions (Cl\u00af , HCO3\u00af) and cations (Na+, Ca2+) \nas well as the TDS value as shown in (Eqn, 8a and 8b) were used to plot \nthe Gibbs diagram in other to show the dominant processes that have \n\n\n\neffect on groundwater within the study area such as; evaporation \ndominance, rock dominance, precipitation dominance. Gibbs diagram \nhelps in interpreting the effect of hydrogeochemical processes such as; \nrock-water interaction mechanism, precipitation and evaporation on \ngroundwater geochemistry. The chemical data of groundwater samples \nwere plotted in Gibbs diagram (Figure 8). From Figure 8, ninety percent \n90% of groundwater fell within the precipitation dominance region \nindicating that groundwater chemistry in the study area is highly \ncontrolled by precipitation. [39] stated that calcium-chloride type facies \ncould be attributed to a combination of atmospheric precipitation charged \nwith chloride ion, leachates from surrounding dumpsites.  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 8: Gibb\u2019s Plot of Water Sampled Location of the Study Area \n\n\n\n\nhttps://en.wikipedia.org/wiki/Precipitation_(meteorology)\n\n\n\n\n\n\n  \nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 23-32 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCite The Article: M. O. Eyankware, O. O. Omo-Irabor (2019). An Integrated Approach To Groundwater Quality Assessment In Determining Factors That Influence The \nGeochemistry And Origin Of Sandstone Aquifers Southern Niger Delta Region Of Nigeria. Malaysian Journal Of Geosciences, 3(2): 23-32. \n\n\n\n5. SUMMARY AND CONCLUSION \n \nFrom detailed studies, pH value within the study area fell within the \nslightly acidic range with the exception of sample location HG/08 with \nvalue of 7.01. It was observed that from source rock deduction \ngroundwater chemistry is influenced by the following; dolomite type \nweathering, gypsum type weathering, alkaline and alkaline type \nweathering. It was also observed that weathering has great influence on \ngroundwater within the study area. From Gibb\u2019s plot ninety percent (90%) \nof groundwater quality chemistry is influenced by precipitation. From \nSoltan classification, 98 % of groundwater belongs to (Na+ \u2013 SO4\n\n\n\n2-), hence \nit can be classified under deep meteoric, while the remaining (2 %) is of \n(Na+ - HCO3-) can be classified under shallow meteoric type. Groundwater \nwas slightly affected by seawater intrusion from relationship of Cl\u00af/HCO3\u00af. \n \nREFERENCES \n \n[1] Ohwoghere\u2013Asuma, O, Adaikpoh, E. O. 2013. Assessment of Shallow Aquifers \nContamination by Failure of on Site Sewage Disposal System in Ughelli, Western \nNiger Delta, Nigeria. Journal of Environment and Earth Science. 5 (9), 208-216. \n \n[2]  Eyankware, M.O, Nnajieze, V. S., Aleke, C. G. 2018. Geochemical \nAssessment of Water Quality for Irrigation in Abandoned Limestone \nQuarry Pits at Nkalagu Area, Southern Benue Trough Nigeria. \nEnvironment Earth Science. 77, 66 https://doi.org/101007/s12665-\n0187232-x. \n \n[3] Gurugnanam, B. 2017. Groundwater Quality Assessment Using \nGeospatial and Statistical Tools in Salem District, Tamil Nadu, India. \nApplied Water Science. 7, 2737\u20132751. DOI 10.1007/s13201-016-0501-5. \n \n[4] Eyankware, M. O.  2016. Hydrochemical Apprasial of Groundwater for \nIrrigation Purpose; a Case Study of Ekaeru Inyimagu and Its Adjoining \nArea, Ebonyi State, Nigeria. Indian Journal of Science. 23(88), 924-943. \n \n[5] Eyankware, M. O., Akudinobi, B. E. B., Akakuru, O. C. 2017. Quality \nAssessment and Hydrochemical Characteristics of Groundwater in Ughelli \nand its adjoining area for Irrigation purpose, Niger delta region, Nigeria. \nScience and Technology.  3(10), 120-134. \n \n[6] Eyankware, M.O., Selemo, A. O. I., Omo-Irabor, O. O. 2017. \nHydrogeochemical Evaluation and Suitability study of Groundwater for \nDomestic and Irrigation Purpose. A Case Study of Eruemukohwarien \nCommunity, Niger Delta Region, Nigeria. Science and Technology. 3(10), \n91-108. \n \n[7] Eyankware, M. O., Ogwah, C., Okeke, G. 2018. Geochemical Evaluation \nof Groundwater Origin using Source Rock Deduction and Hydrochemical \nFacies at Umuoghara Mining Area, Lower Benue Trough. SE. Nigeria. \nInternational Research Journal of Earth Sciences.  6(10), 1-8. \n \n[8] Ede, C.O , Nnabo, P.N. 2015. Hydrogeochemical Evaluation of \nGroundwater Quality of Abakaliki and its Environments, South Eastern \nNigeria. International Journal of Innovation and Science Research. 13 (2), \n689-696. \n \n[9] Ezeh, V. O., Eyankware, M. O., Irabor, O. O. Nnabo. P. N. 2016. \nHydrochemical Evaluation of Water Resources in Umuoghara and its \nenvirons, near Abakaliki, South Eastern Nigeria. International Journal of \nScience and Health care Research.  1 (2), 23-31. \n \n[10] Obasi, P. N., Eyankware, M. O., Akudinobi, B. B. E. and Nweke, M. O. \n2015. Hydrochemical Investigation of Water Resources around Mkpuma \nEkwaoku Mining District, Ebonyi State Southeastern Nigeria. African \nJournal of Geo-Science Research. 3 (3), 01-07. \n \n[11] Ojobo, J.R., Nnabo, P.N. 2015. Levels of Heavy Metals on \nGroundwater in Abakaliki and its Environs, Southeastern Nigeria.  \nInternational Journal of Innovation and Science Research. 12 (2), 444-452. \n \n[12] Rilwanu, T. Y. 2014. Assessment of Groundwater Potential for Rural \nWater Supply in Parts of Kano State, Northern Nigeria. Unpublished Ph.D \nThesis Submitted to the Department of Geography Ahmadu Bello \nUniversity, Zaria.  \n \n[13] Eyankware, M. O., Nnabo, P. N., Omo-Irabor, O. O., Selemo, O. I. \n(2016). Assessment of the Effect of Anthropogenic Activities on \nHydrogeochemical Quality of Water Resources of  Ekaeru Inyimagu and Its \nEnvirons,  Southeastern, Nigeria. Sky Journal of Soil Science and \nEnvironmental Management. Nigeria. 5(5), 33 \u2013 43. \n\n\n\n[14] Efe, S.I., Mogborukor, J. O. A. 2012. Acid Rain in Niger Delta Region: \nImplication on Water Resources Quality and Crisis. International Journal \nof Science and Technology. 1 (1), 17-46. \n \n[15] Oguejiofor, G.C. 1998. Gas flaring in Nigeria: converting fuel gas \npollutant into revenue \u2013 Learning fertilizer by low cost retrofit of flare \nstations. Environments Reviews. 2(2), 91-100. \n \n[16] Okecha, S.A. 2000. Pollution and conservation of Nigeria\u2019s \nenvironment. Ekpoma T. Afriqu International Assesment. W.A. \n \n[17]  Alakpodia, J. I. 2000.The environment of the Niger Delta. In B.C. \nUweru and J.O. Ubrurhe (Eds), Readings in General Studies, Nigerian \nPeople and Culture. 190-203. \n \n[18] Efe, S.I. 2006. Quality of rainwater harvesting for rural communities \nof Delta State, Nigeria. Environmentalist. 26, 175-181. \n \n[19] Omo-Irabor, O. O., Olobaniyi, S, B., Oduyemi, K., and Akunna, J. 2008. \nSurface and groundwater water quality assessment using multivariate \nanalytical methods: A case study of the Western Niger Delta, Nigeria. \nPhysics and Chemistry of the Earth. 33, 666-673. \n \n[20] Narsimha, A., Sudarshan, V. 2018. Geochemical characterization and \nevaluation of groundwater suitability for domestic and agricultural utility \nin semi-arid region of Basara, Telangana State, South India.  Applied Water \nScience. \n \n[21] Jong, Y. Hwang, S. P., Hyun. K. K., Moon, S, K., Hun, J. J., Gyeong, M. L.,  \nK. S., Tae, S. K. 2017. Hydrochemistry for the Assessment of Groundwater \nQuality in Korea. Journal of Agricultural Chemistry and Environment. 6,1-\n29. \n \n[22] Hounslaw W. 1995. Water Quality Data: Analysis and \nInterpretation.CRC Press, Boca Raton, 71-127. \n \n[23] Adelana, S.M.A., Olasehinde, P.I., Bale, R.B.(2008). An Overview of \nthe Geology and Hydrogeology of Nigeria. 185-186. \n \n[24] Allen, J. R. L. 1965. Late Quaternary Niger Delta and adjacent areas: \nSedimentary environments and lithofacies. Bulletin AAPG.  49, 547-600. \n \n[25] Reyment, R.A. 1965. Aspects of the Geology of Nigeria. University \n\n\n\nPress: Ibadan, Nigeria \n\n\n\n \n[26] Short, K.C.; Stauble, A. J. 1967. Outline of Geology of Niger Delta, \nBulletin AAPG 51(5), 761- 779.  \n \n[27] Weber, K. Daukoru, J., E.  1975. Petroleum geology of the Niger Delta: \n9th World Petroleum Congress Proceedings. 2, 209-221. \n \n[28] Wigwe, G.A. 1975. The Niger Delta: Physical. In G.E.K. Ofomata . \nNigeria in maps: Eastern States. Ethiope Publication House, Benin City. \n380 - 400. \n \n[29] America Public Health Association (APHA). 2012. Standards \nmethods for the examination of water and wastewater, 22nd EDN America \nPublic Health Association (APHA), America Water Works Association \n(AWWA) and Environmental Federation (WEF), New York.1360.   \n \n[30] Omo-Irabor, O.O., Eyankware, M.O., Ogwah, C. 2018. Integration of \nHydrogeochemical Analytical Methods and Irrigation Parameters in the \nEvaluation of Groundwater Quality at Ibinta, Southern Benue Trough \nNigeria. FUPRE Journal of Scientific and Industrial Research. 2(1), 38-49. \n \n[31] Eyankware, M. O.  Ogwah, C. 2019. An Integration of Groundwater \nModelling and Hydrogeochemical Facies in evaluating Fractured Shale \nAquifer for irrigation purpose; a case study of Oju, Lower Benue Trough \nNigeria. Pakistan Journal of Geology. 3(1):1-12. \n \n[32] Gibbs, R. J. 1970. Mechanisms Controlling World Water Chemistry. \nScience, 17, 1088\u20131090. \n \n[33] Soltan, M. E. 1999. Evaluation of Groundwater Quality in Dakhla \nOasis (Egyptian Western Desert). Environmental Monitoring Assessment. \n57, 157\u2013168. \n \n[34] Sanchez Martos, F., Pulido Bosch, A., Calaforra, J. M.  1999. \nHydrogeochemical Processes In An Arid Region Of Europe (Almeria, SE \nSpain). Applied Geochemical. 14, 735\u2013745. \n \n\n\n\n\nhttps://doi.org/101007/s12665-0187232-x\n\n\nhttps://doi.org/101007/s12665-0187232-x\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 23-32 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: M. O. Eyankware, O. O. Omo-Irabor (2019). An Integrated Approach To Groundwater Quality Assessment In Determining Factors That Influence The \nGeochemistry And Origin Of Sandstone Aquifer Southern Niger Delta Region Of Nigeria. Malaysian Journal Of Geosciences, 3(2): 23-32. \n\n\n\n\n\n\n\n[35] Kim, Y., Lee, K., Koh, D., Lee, D., Lee, S., Park, W., Koh G, Woo N 2003. \nHydrogeochemical And Isotopic Evidence Of Groundwater Salinization In \nA Coastal Aquifer: A Case Study In Jeju Volcanic Island, Korea. Journal \nHydrolics. 270, 282\u2013294. \n \n[36] Revelle R (1941) Criteria For Recognition Of Sea Water In \nGroundwaters. Trans Am Geophysics Union. 22,593\u2013597. \n \n[37] Todd, D. K. 1959. Groundwater Hydrology. Wiley, New York. \n \n\n\n\n[38] Subba Rao, N., Prakasa, Rao J., John, Devadas, D., Srinivasa Rao, K, \nKrishna, C. 2001. Multivariate analysis for identifying the governing \nfactors of groundwater quality. Journal Environments Hydrolics. 9(16),1\u2013\n9. \n \n[39] Akpoborie, A.I., K.E. Aweto, O. Ohwoghene-Asuma . 2014. \nUrbanization and Major Ion Hydro Chemistry of Shallow Aquifers at \nEffurun-Warri Metropolis, Nigeria.  Environment and Pollution. 4(1), \n1927-0917. \n \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 22-30 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \nwww.myjgeosc.com \n\n\n\nDOI: \n10.26480/mjg.01.2021.22.30 \n\n\n\nCite the Article: Charles A. Oyelami, Tesleem O. Kolawole and Gabriel S. Ojo (2021). Assessment of Vadose Zone Characteristics For Environmental \nImpact Audit of Selected Cemeteries Around Osun State, South-Western Part of Nigeria. Malaysian Journal of Geosciences, 5(1): 22-30. \n\n\n\n\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nREVIEW ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \nDOI: http://doi.org/10.26480/mjg.01.2021.22.30 \n\n\n\nASSESSMENT OF VADOSE ZONE CHARACTERISTICS FOR ENVIRONMENTAL \nIMPACT AUDIT OF SELECTED CEMETERIES AROUND OSUN STATE, SOUTH-\nWESTERN PART OF NIGERIA \nCharles A. Oyelami*, Tesleem O. Kolawole and Gabriel S. Ojo \n\n\n\nOsun State University, Osogbo, Osun State Nigeria \n*Corresponding Author E-Mail: adebayo.oyelami@uniosun.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 18 February 2021 \nAccepted 16 March 2021 \nAvailable online 01 April 2021\n\n\n\nCemeteries have been identified as a possible source of groundwater and environmental pollution. This may \nbe due to wrong siting of cemeteries, poor soil selection and very thin vadose zone. Over the years, most \ncommunities around Osun State experience a number of communicable diseases like dysentery, diarrhea and \ntyphoid, especially after episodes of flash floods. Therefore, this study was carried out to audit the impact of \nburial practices on the immediate environment viz-a-viz their interaction with both surface and groundwater \nwhich forms the main source of potable water for the communities.  Method of approach include; assessing \npossible water contamination, studying the vadose hydrological characteristics as well as studying the \ngeotechnical properties of soils within the vadose zone.  Water samples from the vicinity of both cemeteries \nshows probable contamination with an average pH of 6.19 for Ede and 6.57 for Iragbiji,  EC with an average \n480\u00b5S for Ede and 1210\u00b5S for Iragbiji. Biological constituents found within the area include; \nenterobacteriaceae (salmonella spp., serratia spp., proteus spp., shigella spp.), suggesting likely contamination \nof both surface and groundwater around the vicinity of the cemeteries. Most cations and anions analysed for \n(Mg2+, NO3-, SO42-, and PO43-) comply with the WHO standards based on their maximum permissible limits \n(MPL). Geotechnical investigations revealed that soils within the study areas are largely unsuitable for a \nstandard cemetery due to their high moisture content, poor grading characteristics, low compaction value, \npoor hydraulic characteristics and shallow water level. The study concluded that cemeteries from both towns \nhave a negative impact on their immediate environment due to poor selection of soil materials (porous and \npermeable sandy soil) as reflected in the quality of surface and groundwater.  \n\n\n\nKEYWORDS \n\n\n\ncemetery, decomposition, surface water, groundwater contamination, soils. \n\n\n\n1. INTRODUCTION\n\n\n\nBurial is a basic social need and a moral practice depending on belief and \nculture. There are several ways of disposing human corpse; the most \nwidely practised in Africa is the earth burial. Siting of cemeteries and grave \nsites oftentimes tends to lose focus of the environmental implications at \nthe expense of these cultural needs (Dippenaar, 2014). All in all, the issue \nis not burial per se but rather the neglect of proper hydrogeological and \nengineering geological investigations for this activity. Little attention has \nbeen given to cemeteries as a potential source of contamination to \ngroundwater because cemeteries are believed to be sacred places where \ninvestigations are seldom carried out which makes this study important. \nThe focus of this paper will be on the careful selection of soil materials for \ncemetery purposes and establishing a proper protocol for ground \ninvestigations prior to the development of cemeteries and the effect of \ntheir proximity to human settlements. \n\n\n\nThe cemetery or burial ground\u2019s potential to pollute the environment, and \ntheir risk management, has been the subject of research  (Hall and \nHanbury 1990; Pacheco et al. 1991; Janaway 1997; \u00dc\u00e7isik and Rushbrook \n\n\n\n1998; Dent and Knight 1998; Young et al. 1999; Spongberg and Becks \n2000a, b; Hart 2005; Dippenaar, 2014). Groundwater have been identified \nas the principal receptor of cemetery pollutant which are principally NO3\u2212 \nand NH4+  ](Pacheco et al. 1991; Knight and Dent 1998; Lelliot 2002; Buss \net al. 2003; Dent 2005; Pollard et al. 2008). \n\n\n\nPollard et al., (2008) proposed a risk based decision making frameworks \nwhich has been widely used in UK and other parts of Europe. This present \nresearch adopts this framework following the conventional method as \nproposed by Willam et al. (2009) shown in Fig 1 below. The overall aim is \nto assess the risk factor of burial ground and cemeteries on their \nimmediate environment. This is expected to be achieved using a trilateral \nmethod of studying the source, the properties of the pathway and the \nreceptor. Among other things, the study will involve identifying the \nsuitability of the medium to contain the source contaminant, this is done \nby assessing the geological and hydrological properties of the pathway \nwith a view to breaking or intercepting the movement of contaminant and \nfinally recommend ways of protecting receptors from contamination. \n\n\n\n\nmailto:adebayo.oyelami@uniosun.edu.ng\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 22-30 \n\n\n\nCite the Article: Charles A. Oyelami, Tesleem O. Kolawole and Gabriel S. Ojo (2021). Assessment of Vadose Zone Characteristics For Environmental \nImpact Audit of Selected Cemeteries Around Osun State, South-Western Part of Nigeria. Malaysian Journal of Geosciences, 5(1): 22-30. \n\n\n\n\n\n\n\nFigure 1: Conventional Approach to Environmental Risk Assessment. \n(Source; William et al. 2009) \n\n\n\nThe products of decomposition from human burials can be divided into \ntwo broad categories: natural and synthetic. Willam et al. (2009). A \ndecomposing body mostly contains contaminants from different sources. \nThese sources could include; chemical substances (arsenic, formaldehyde \nand methanol) during chemotherapy and embalming procedures, make-\nup (cosmetics, pigments and chemical compounds), other sources are \nmaterials such as fillings, cardiac pacemakers, paints, varnishes, metal \nhardware elements, iron nails, (Silva & Filho, 2011; Fiedler et al., 2012). \nMicroorganisms that may pollute substrates, surface water and \ngroundwater are also found in these leachates. Bacteria, bacteria, \nintestinal fungi and protozoa are primarily microorganisms (Trick et al, \n2001). Rodriguez and Pachecho, (2003) found higher bacteria \nconcentrations in groundwater samples collected beneath cemeteries \nthan those collected hundreds of metres away. \n\n\n\nMore recently, Turajo et.al. (2019) in the research on the impact of \ncemeteries on groundwater around Maiduguri metropolis concluded that; \nusing only water quality parameters as index of an environmental impact \nof a cemetery, ample evidences of environmental pollution abound, \nespecially from cemeteries around Maiduguri metropolis. This was \nattributed to site overload and therefore recommended an enactment of \nlegislation to review the sitting methodology, management issues such as; \nlongevity of remains, grave re-use, funeral artifacts and buffer zone, and \nplanning policies as a way of reducing the soil and water resource risk and \nenvironmental hazards associated with cemetery operation. \n\n\n\nBased on these earlier literatures, cemeteries are undoubtedly a source of \nconcern especially when they are poorly sited. Beyond siting, the main \nmaterial for cemetery construction is soil and, in most cases, the \nproperties of these soil will either make or mar the functions of cemetery. \nAttention must be given to proper soil selection which is the main pathway \nof interaction between cadavers and the groundwater. \n\n\n\nConsequently, emphasis will however be placed on the pathway which is \nthe medium by which contaminant flow between the source and the \nreceptor. Therefore, this paper focuses on assessment of the geotechnical \nand hydrological properties of vadose zone around burial sites in Ede and \nIragbiji areas of Osun State, Nigeria. It considers the index and engineering \nproperties of the soil within the vicinity viz-a-viz their suitability for the \npurpose and impacts of the decay of the human corpse on the physico-\nchemical, chemical and biological properties of groundwater within the \nstudy areas. \n\n\n\n2. STUDY AREA \n\n\n\nInvestigations were carried out in two localities, Ede and Iragbiji areas of \nOsun State, Nigeria. Ede lies between Lat 7\u00ba42'N and 7\u00ba47'N and \nLong.4\u00ba21' and 4\u00ba27' East of Greenwich Meridian. It has two local \ngovernment areas namely Ede North and Ede South Local Governments. It \nis a town with a population of 304,738 persons following the 2006 census \nfigures (Nigerian Populations Commission, 2006). Iragbiji is the \nheadquarters of Boripe Local Government located in Osun State of Nigeria \nand lies between Lat 7\u00ba48'N and 7\u00b053'N and between Long 4\u00ba36'E and \n4\u00b042'E. Both locations are generally accessible by road Fig. 2.  The first \ncemetery named Alharam Islamic foundation Muslim burial ground, Ede \nis located off the road linking Abere in Osogbo to Ede. The second cemetery \nwhich is in Iragbiji can be accessed via a footpath off the road linking \nOsogbo to Iragbiji. Hence, both locations have access road  link with \nOsogbo making them busy throughout the year. \n\n\n\nThe land surface is generally undulating and the drainage system is mainly \ndendritic and ranges from open water bodies (dams, reservoirs and lakes) \nto rivers, streams, springs, wells, run-off waters and swamp/wetlands \n(Audu et al., 2015). Many rivers, including the Osun River from which the \nstate derives its name, have their source in the northern part of the state. \nErinle River and Awon River which are tributaries to the Osun River flows \nacross the Ede town. The climate is of the lowland tropical rain forest type \nwith distinct wet and dry seasons. Rainfall pattern of the area is generally \n\n\n\ncharacterized by long and short rainy season with short and long dry \nseason (Bamiji, 2012). It is however pertinent to note that the recent \nrainfall in the area has been of high intensity with a long time range which \nmay be due to the effects of climate change. \n\n\n\nThe main sources of water supply in the area are hand-dug shallow wells \nand boreholes fitted with pump power by generator or hand pump. Some \nof the people depend on stream and rivers for sources of drinking water \nwhich are not protected. There is a dam at Ede which serves as source of \nwater to Ede water works and provides water for the municipality. The \nwater works served less than 35% of the urban centre (FMWR, 2004). \n\n\n\nFigure 2: Topographical Map of the Study Area Showing Relief and \nSampling Locations \n\n\n\n2.1 Geology of the Study Area \n\n\n\nRocks in both locations forms part of the south-western basement \ncomplex rocks. Ede is underlined by two main rock types which include \npegmatite and Schist. The pegmatites, which occur as near vertical dykes, \nstrike primarily in the direction of NNE-SSW intrudes the older banded \ngneiss lithology. Banded gneiss occurs as a massive rock consisting of \nalternating felsic mineral bands, especially plagioclase feldspars and \nquartz, and dark biotite and hornblende bands. Two main lithologies \nunderlie Iragbiji area which includes granite-gneiss and schist. The \ngeology of the area is presented in Fig. 3 modified after Oyelami and Van \nRooy (2016b). \n\n\n\nThe soils within the study areas belong to the highly ferruginous tropical \nred soils (lateritic) associated with basement complex rocks. As a result of \nthe dense humid forest cover in the area and the distinct wet and dry \nseasons prevalent in the area. The soils are generally deep and of two \ntypes, namely, deep clayey soils formed on low smooth hill crests and \nupper slopes; and the sandy hill wash soils on the lower slopes (Bamiji, \n2012). \n\n\n\nThe well drained red soils of the hill crest and slopes are very important, \nbecause they provide the best soils for agriculture in the area. Soil \ndegradation and soil erosion are generally not serious in the area, but \nconsiderable hill wash is recorded along the slopes of the hills. \n\n\n\nFigure 3: Geological Map of the Study Area (Source; Oyelami and \nVanRooy, 2018) \n\n\n\nDecomposing \nCadavers\n\n\n\nSOURCE PATHWAY RECEPTOR\n\n\n\nVadose Zone\nPhreatic Zone\n\n\n\nSurface & \nGroundwater\n\n\n\nStudy Area\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 22-30 \n\n\n\nCite the Article: Charles A. Oyelami, Tesleem O. Kolawole and Gabriel S. Ojo (2021). Assessment of Vadose Zone Characteristics For Environmental \nImpact Audit of Selected Cemeteries Around Osun State, South-Western Part of Nigeria. Malaysian Journal of Geosciences, 5(1): 22-30. \n\n\n\n\n\n\n\n3. METHODOLOGY \n\n\n\nFollowing a preliminary visit, 14 (fourteen) water samples were collected \nfrom both locations. Each of the study area provides 7 (seven) water \nsamples. 6 (six) of the samples were the main focus of study while the \nremaining one served as a control. The water samples were collected in \nsitu from streams, hand-dug wells of 5-12m deep, rivers and artisan wells \nusing appropriate water sampler with the location coordinates recorded. \nThe control samples were taken from wells at a distance of about 1.5 km \nfrom the location of the cemetery. At each sampling point, the water \nsamples were collected in triplicate in a plastic bottle and labelled as A, B \nand C. These were subsequently transported to the laboratory within 24 \nhours of collection for cation and anion determination at FATLAB Nigeria \nLtd, Ibadan. The A samples were acidified using concentrated nitric acid \n(HNO3) and taken to the laboratory to determine the concentration of \ncations. The B samples were used for anions (HNO3-, SO42-, PO43-) while the \nC samples are subjected to biological analysis at the microbiology \nlaboratory of Osun State University, Osogbo. \n\n\n\nThe physico-chemical parameters of the water samples were measured in-\nsitu using the appropriate instruments such as pH, temperature, TDS and \nEC meters. \n\n\n\nSoil investigation involved the collection of 12 bulk soil samples from 4 \nhand dug pits in both locations. The soil samples were recovered at depths \nranging between 2-4 metres which provides 3 soil samples from each pit. \nThe physical as well as textural properties of the soil were studied in-situ. \nSoil samples were later taken to the laboratory with samples for moisture \ncontent determination kept in polythene bags to prevent loss of moisture \non exposure to air. Geotechnical investigations of the soil were carried out \nat the Geology Laboratory of the Federal University of Technology Akure, \n(FUTA). The tests include; grain size analysis, specific gravity, Atterberg \nlimits, compaction and permeability tests. All tests were carried out \naccording to the British Standard BS 1377 (1990) procedures with small \nmodifications where necessary. \n\n\n\n4. RESULTS\n\n\n\n4.1 pH, Temperature and Electrical Conductivity \n\n\n\nThe geographical location and the physical properties of the water \nsampled around the vicinity of both cemeteries are presented in Table 1 \n\n\n\nwhile their physic-chemical properties are presented in Table 2. The pH of \nthe water samples collected from the vicinity of the cemetery at Ede ranges \n5.57- 6.53 with an average of 6.19. It was observed that the water bodies \nthat surrounds the cemetery are acidic in nature as they fall below the \nneutral point. Silva (1995), concluded that acid leachates from nitrogen \n(N), phosphorous (P), chlorine (Cl), and bicarbonate (HCO3-) are produced \nfrom the decomposition of the human body include ions, hence, the \npossibility of acidic nature of water around the present study area may be \ndue to the decomposition of the human body. However, control well \nrecords a pH of 5.57 (most acidic of all the samples taken) and is located \nabout 1.5 km from the cemetery. This suggests that the acidic nature of the \nwater bodies may be from other sources apart from the decomposition of \nthe human body. Such sources may be from the use of pesticides, fertilizers \nfor agricultural practices, bed rock geology of the area. Iragbiji cemetery \non the other hand has a pH range of 5.51 to 8.16 with an average of 6.57. \nAccording to WHO (2006) and Standards Organisation of Nigeria, (SON) \non drinking water quality standard (NSDWQ, 2007 and 2015) (pH;6.5-\n8.5), most water samples within the vicinity of Ede cemetery is not safe for \ndomestic use with the exception of well 4 (pH of 6.53). Iragbiji on the other \nhand may be useful in terms of pH except wells 7 and 9 with  high degree \nof acidity. \n\n\n\nTemperatures of the water samples are as presented in the Table 2. Most \nbacteria and viruses according to WHO (2006) can survive in soils and \nwater at temperatures of between 5\u00b0C and 30\u00b0C and die off at rapidly \nincreasing rates of about 10\u00b0C.  Temperatures in both cemeteries are less \nthan 30 \u00b0C, which implies a conducive environment for bacteria and \nviruses to thrive. \n\n\n\nEde has an average electrical Conductivity (EC) of 480\u00b5S while Iragbiji has \n1210\u00b5S, The EC indicates the amount of dissolved solute which invariably \nis an indication of the extent of possible contamination within an \nenvironment. There is more potentials of contamination around Iragbiji \ncemetery than Ede, this is reflected in the result of the EC. However, based \non the findings of Adedeji and Ajibade (2005), that lower conductance in \nwater indicates the abundance of Ca2+ while higher conductance indicates \nthe abundance of Na+ and K+, the high conductivity observed around \nIragbiji cemetery might be linked to the farming activities around the \ncemetery. The control well reflects a high EC as well suggesting high levels \nof Na+ and K+ which might be as a result of decomposition of human body \ncombined with nutrients from the fertilisers used in crops around the area. \n\n\n\nTable 1: Water samples collected around the cemetery at Ede. \n\n\n\nLocality Well no Coordinates Description \n\n\n\nED\nE \n\n\n\n1 N 7\u00b0 44.537\u2019 E 4\u00ba 27.420\u2019 Clear, Pumping well \n\n\n\n2 N 7\u00ba 44.553\u2019 E 4\u00ba 27.452\u2019 Stream channel, muddy \n\n\n\n3 N 7\u00ba 44.503\u2019 E 4\u00ba 27.418\u2019 Stream, Clear water \n\n\n\n4 N 7\u00ba 44.500\u2019 E 4\u00ba 27.450\u2019 Surface water \n\n\n\n5 N 7\u00ba 44.393\u2019 E 4\u00ba 27.396\u2019 Gaining stream \n\n\n\n6 N 7\u00ba 44.494\u2019 E 4\u00ba 27.414\u2019 Stream channel \n\n\n\nControl well N 7\u00ba 44.684\u2019 E 4\u00ba 27.218\u2019 Hand-dug well \n\n\n\nIR\nAG\n\n\n\nBI\nJI\n\n\n\n\n\n\n\n7 N 7\u00ba 48.456\u2019 E 4\u00ba 36.297\u2019 Artisan well, turbid \n\n\n\n8 N 7\u00ba 48.457\u2019 E 4\u00ba 36.292\u2019 Swampy, lake \n\n\n\n9 N 7\u00ba 48.369\u2019 E 4\u00ba 36.299\u2019 Hand-dug well \n\n\n\n10 N 7\u00ba 48.426\u2019 E 4\u00ba 36.263\u2019 Stream channel \n\n\n\n11 N 7\u00ba 48.433\u2019 E 4\u00ba 36.232\u2019 Hand-dug well, Milky \n\n\n\n12 N 7\u00ba 48.121\u2019 E 4\u00ba 36.221\u2019 River water, muddy \n\n\n\nControl well N 7\u00ba 48.442\u2019 E 4\u00ba 36.200\u2019 Hand-dug well, clean \n\n\n\n4.2 Cations (Fe2+, K+, Mg2+ and Na+) \n\n\n\nThe observed concentrations of cations within the study areas are as \npresented in Table 2. Most cation appears to be within limits as \nrecommended by WHO (2017) and Nigerian Standards of Drinking Water \nQuality (2015) except Fe2+. Emphasis here will be on the high \nconcentration of Iron (Fe). Generally speaking, high concentration of Fe is \nexpected within this environment, being a tropical environment where \n\n\n\nleaching of mobile elements are very common due to alternating dry and \nwet seasons. The question is, why is Fe occurring as high as 7.99mg/L in \nareas close to a cemetery, that might be a possible indicator to a near \nsurface aquifer that is very rich in organic materials. Organic \ndecomposition will lead to depletion of oxygen which creates a reducing \nenvironment enriching the Fe2+ ion. This indicates a possibility of \ncontamination around the cemetery areas. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 22-30 \n\n\n\nCite the Article: Charles A. Oyelami, Tesleem O. Kolawole and Gabriel S. Ojo (2021). Assessment of Vadose Zone Characteristics For Environmental \nImpact Audit of Selected Cemeteries Around Osun State, South-Western Part of Nigeria. Malaysian Journal of Geosciences, 5(1): 22-30. \n\n\n\n\n\n\n\nTable 2: Physio-chemical results of the water samples collected around cemetery at Ede and Iragbiji \nLocation Well no pH T(\u00baC) EC(\u00b5S) K+ Na+ Fe2+ Mg2+ NO3- Cl PO43- HCO3 \n\n\n\nED\nE \n\n\n\n1 6.42 30.6 370 0.41 8.21 0.301 5.796 5.776 90 1.722 91.5 \n2 6.4 28 410 2.11 19.34 0.803 7.102 3.325 90 0.061 91.5 \n3 6.31 33.4 650 2 21.05 0.046 9.083 4.959 198 0.009 61 \n4 6.53 28.9 400 2.1 20.03 0.987 6.387 1.283 90 0.032 122 \n5 6.45 29.7 630 2.23 23.54 0.413 7.894 2.263 90 0.003 122 \n6 5.66 30.4 530 1.62 23.01 0.904 8.083 0.057 126 0.027 152.5 \n\n\n\nControl \nwell 1 5.57 27.1 390 2.32 24.56 0.304 3.303 34.289 108 0.05 152.5 \n\n\n\nIR\nAG\n\n\n\nBI\nJI\n\n\n\n\n\n\n\n7 5.51 29.4 670 24.01 23.65 7.996 8.125 5.858 126 1.463 122 \n8 6.52 28.2 1,430 1.93 8.8 0.151 14.442 24.485 90 0.078 61 \n9 5.46 26.9 300 6.29 22.07 0.802 12.842 0 108 0.05 152.5 \n\n\n\n10 6.75 26.9 650 43.42 125.02 1.335 8.947 1.201 198 0.153 183 \n11 8.16 28.4 2,770 40.11 100.5 1.979 9.254 28.815 126 0.788 183 \n12 6.72 28.6 660 42.13 126.04 0.138 8.201 2.018 54 0.211 61 \n\n\n\nControl \nwell 2 6.87 28 1,970 2.8 25 0,000 12.374 0 108 0.113 274.5 \n\n\n\nWHO \n[29] 6.5-9.5 1000 250 200 0.3 - 45 250 3 - \n\n\n\nSON \n[30] 6.5-8.5 1000 250 200 0.3 20 50 250 - 250 \n\n\n\n4.3 Anions (NO3-, Cl-, PO42-, HCO32-) \n\n\n\nMost anions tested are within the limits as specified by WHO (2017) and \nNSDWQ (2015)  except for Bi-carbonate in the control well around Iragbiji \ncemetery. HCO3 occurs naturally in groundwater from the reaction of rain \nwater and soil pH. Bi-carbonate is equally a function of pH and salinity, \nwhich anthropogenic activities within an area could influence greatly, \ntherefore, the high concentration of HCO3 in the control well is related to \nthe anthropogenic sources around the well. Equally, concentration of \nNitrate within the study area though within limits but varies considerably \nwithin locations. Presence of Nitrate is within the study area is linked more \nwith fertilised agricultural land and waste disposal rather than \ncontamination from the cemetery. \n\n\n\n4.4 Biological Organisms \n\n\n\nThis involves the direct culturing of the water samples to identify the \nprobable micro-organisms present in the samples. The probable micro-\norganisms in the wells are presented in Table 3 below. They include the \nfollowing micro-organisms; Proteus spp., Enterobacteriaceae spp., Shigella \nspp., Salmonella spp., Chryseomonas cloacae, Citrobacteria, Serratia spp., \nEdwardsiella spp. \n\n\n\nSome of these micro-organisms are used as indicators for contamination \n\n\n\nin water and can affect human health when such water is taken into the \nbody. Salmonella typhi is known for typhoid fever when present in water \nbeyond the standard limit. Other organisms such as shigella, serratia can \ncause dysentery. However, the most prominent indicator organisms for \ncontamination are the coli form bacteria such as E.coli. E.coli is a \nprominent species of a larger family of Enterobacteriaceae. According to \nWHO (2006), the coli form bacteria must not be detected in any 100ml of \nwater intended for drinking. Salmonella spp., Shigella spp., Proteus spp., \nSerratia spp., Edwardsiella spp., all belongs to the family of \nEnterobacteriaceae. Salmonella spp. is widely distributed in the \nenvironment. According to WHO (2006), the pathogens gain entry into \nwater systems through faecal contamination from sewage discharges, \nlivestock and wild animals. The most common infection is typhoid fever \nand can be fatal. Shigella spp. can cause serious intestinal diseases \nincluding bacillary dysentery. Humans and other primates appear to be \nthe only natural host for the shigellae. According to WHO (2006), the \nbacteria remain localized in the intestinal epithelial cells of their host and \nis predominantly transmitted by the faecal-oral routes. Escherichia coli. is \na generally reliable index for salmonella spp. and shigella spp. in drinking \nwater samples. Proteus spp. are commonly found in the human intestinal \ntract as part of the normal intestinal flora, along with Escherichia coli of \nwhich E.coli is the predominant resident. They are commonly responsible \nfor urinary and septic infections (Guz, 2018). \n\n\n\nTable 3: Probable micro-organisms present in the water samples \nEde Iragbiji \n\n\n\nWell Isolates Prob. Organisms Well Isolates Prob. Organisms \n\n\n\n1 3 Proteus spp.(3) 7 5 Proteus spp. (3), Salmonella spp. (1), \nSerratia spp. (1)  \n\n\n\n2 2 Proteus spp.(2) 8 3 Proteus spp.(2), Salmonella spp.(1)  \n\n\n\n3 4 Proteus spp.(1), Enterobacteriaceae spp. (1), \nShigella spp.(1), Salmonella spp. (1) 9 2 Shigella spp. (1), Edwardsella spp. (1) \n\n\n\n4 3 Proteus spp. (2), Enterobacteriaceae spp.(1) 10 1 Shigella spp. (1) \n5 2 Proteus spp. (2) 11 2 Proteus spp. (1), Serratia spp. (1)  \n\n\n\n6 3 Chryseomona, Cloacae (1), Proteus spp(1), \nCitrobacteria (1) 12 2 Proteus spp. (1), Enterobacteriacaea spp. \n\n\n\n(1) \nControl 1 Proteus spp. (1) Control 2 Proteus spp. (2) \n\n\n\n4.5 Geotechnical Properties \n\n\n\n4.5.1 Index Properties of Soil \n\n\n\nThe results of the index properties of the sample soil are as presented in \nTable 4. The natural moisture contents of the soil reveal the impact of the \ntopography as it slopes towards Pit 2 around Ede cemetery and towards \n\n\n\nPit 3 around Iragbiji cemetery. The conceptual model illustrates the \nsituation in both study areas, using Pits 1 and 2 as a model, while Pit 3 can \nsubstitute for Pit 2 in the model and Pit 4 for Pit 1. The ground surface \nslopes downward towards Pit 2 and Pit 3 in Ede and Iragbiji respectively \nand following the principle that water level is a replica of the ground \nsurface, both pits have a shallow water table. This ranked Pits 2 and 3 as \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 22-30 \n\n\n\nCite the Article: Charles A. Oyelami, Tesleem O. Kolawole and Gabriel S. Ojo (2021). Assessment of Vadose Zone Characteristics For Environmental \nImpact Audit of Selected Cemeteries Around Osun State, South-Western Part of Nigeria. Malaysian Journal of Geosciences, 5(1): 22-30. \n\n\n\n\n\n\n\nundesirable for cemetery constructions because of direct interaction \nbetween leachates and groundwater.  Hence, soils with high moisture \ncontent having a shallow water depth and are not suitable for a standard \ncemetery.  It can thus be said that only the soils that occur at Pit 1(Ede) \nand Pit 4 (Iragbiji) are suitable for a standard cemetery based on the \nmoisture content. \n\n\n\nThe cemetery soils were further classified using Unified Soil Classification \nSystem (USCS). The results correlated with that of the natural moisture \ncontent. The soils from both areas are well graded, mainly clayey sands \nwith few inorganic clays of high plasticity (See fig 4). They are expected to \nhave considerable amount of porosity and permeability due to the \npresence of sandy particles. This suggests that such soils may not be \ndesirable for a standard cemetery. However, soils of pit 3 at Iragbiji are \nmainly of the fat clay type with high plasticity based on the USCS \nclassification. The amount of fine (%) is a measure of the clay and silt \ncontent in the soils. Thus, soils of pit 3 are expected to be higher in clay as \nthey show above average % of fines (57.8% on the average). Clays are \ngenerally desirable in geotechnics for a standard cemetery due to their low \npermeability and because they can act as natural filters helping to \nattenuate leachates. Although their suitability may be questioned as it \nrelates to aeration and natural process of decomposition of human \nremains. In terms of plasticity index, soil samples from the study areas \nranges from low plasticity (Pit 1) to medium (Pits 2 and 4) and high \nplasticity (Pit 3). \n\n\n\nFigure 4: Grain Size Distribution Curves of the Soil Samples \n\n\n\nSoils with shrinkage limits of less than 5 are of believed to be of good \nresistance to shrinkage, between 5 and 10% are of medium resistance, 10 \nand 15% are poor while less than 15% are of very poor resistance to \nshrinkage. This shows that in terms of resistance to shrinkage, Pit 1, Pit 2 \nand Pit 4 soils are of poor resistance to shrinkage while soils of Pit 3 \n(average, 8.46) is of medium resistance to shrinkage. The lower the \nshrinkage limit, the better the soil as material for cemetery siting. \n\n\n\nThe average specific gravity of the soils around both cemeteries are 2.66, \n2.65, 2.66 and 2.66 for Pits 1 to 4 respectively. The specific gravity of \nclayey soils is 2.6-2.9 while that of sands is 2.65-2.67. This shows that most \nof the soils around both cemeteries are of clayey sands. The high specific \n\n\n\ngravity of the soil also suggests that the soils are rich in mineral matter and \nmay have originated from the parent rock of the study areas.   Higher \nspecific gravity implied higher soil strength and more stable walling for \ncemetery holes. \n\n\n\n4.6 Engineering Properties of Soils \n\n\n\nIn order to fully understand the behaviour of soils used in cemetery \nconstruction, the strength parameters and permeability characteristics \nare very critical. Hence, Table 5 presents the summary of the engineering \nproperties of the soils within the vicinity of the cemeteries. The maximum \ndry density (MDD) is mainly of interest from the compaction test. Ede has \na MDD of 2037kg/m3 and 1933kg/m3 for Pits 1 and 2 respectively while \nIragbiji has 1616kg/m3 and 1937kg/m3 for Pits 3 and 4 respectively. \nAccording to Hall and Hanbury (1990) cited in   Dippenaar (2014), \nsuitability of soils for cemetery, based on their MDD are good to excellent \nif MDD is greater than 1800kg/m3, less than 1800kg/m3 are ranked fair, \nless than 1700kg/m3 are poor and less than 1500kg/m3 are ranked very \npoor. In light of this, it can be suggested based on MDD that Pit 1 contain \nexcellent soil, Pits 2 and 4 contain good soils while Pit 3 contain poor soils \nfor use as a standard cemetery. \n\n\n\nPermeability of soils is function of the grain size and textural \ncharacteristics. The average coefficient of permeability (KT) of the soils \naround the cemetery at are as follow; Ede has 8.86 X 10-6 m/s for Pit 1 and \n1.01 X 10-5m/s) for pit 2. Iragbiji with 6.94 X 10-9m/s for Pit 3 and 1.35 X \n10-8m/s for pit 4. Ramamurthy and Sitharam (2015) classified soils based \non their coefficient of permeability (KT). Soils of permeability of between \n0.01 to 1m/s are gravels, between 10^-5 and 0.01m/s are sands, between \n10^-8 to 10^-5 are silts, and < 10^-8 are clays. This shows that pit 1 and \npit 2 contain mainly silts, pit 3 contain mainly clays and pit 4 contain silty \nclays. Correspondingly, soils of KT values < 10-7 are rated Impermeable \nand are good soils for cemeteries, between 10-7 and 10-6 are rated \nrelatively impermeable and are excellent soils, between 10-6 and 10-5 are \nrated relatively permeable and are fair soils, while soils of KT values < 10-\n\n\n\n5 are rated permeable and are poor soils for cemeteries (Dippenaar, 2014). \nIn light of this, it can be said that pit 1 and pit 2 contains fair soils, while \npit 3 and pit 4 contain good soils for use as cemeteries in terms of their \npermeability. \n\n\n\n5. DISCUSSIONS \n\n\n\nThe pathway serves as the most important bridge between the \ncontaminant source and receptor. This in most cases has to with siting, \nengineering construction and geo materials. Assessment of pathway as it \nrelates with burial practices and siting of cemeteries should begin with the \nlocation of the cemetery. European Union (1998) recommended a 250m \ndistance away from any well or borehole in the case of human or animal \nburial, a place of interment should be at least 30m away from spring or any \nwatercourse and a minimum of 1m bottom clearance of burial pits above \nthe highest natural water table. Following this recommendation, within \nthe context of the study areas, the cemetery in Ede lacks in every standard. \nIt is built within a residential community (see fig. 5) and it slopes directly \ninto a river. Iragbiji cemetery on the other hand is a bit fair as it relates to \ndistance to residential area and sources of potable water supply. \nApparently, the salient feature of a pathway within an environment such \nas Ede cemetery has been obliterated. The pathway serves as the most \nimportant bridge between the contaminant source and receptor. This in \nmost cases has to with siting, engineering construction and geo materials. \nThe implication of these as a pathway is discussed below. \n\n\n\nTable 4: Index Properties of Cemetery Soils \nLocation Sample \n\n\n\nNo\nMC \n(%)\n\n\n\nGravel \n(%)\n\n\n\nSand \n(%)\n\n\n\nSilt \n(%)  \n\n\n\nClay \n(%)\n\n\n\n% \nFine\n\n\n\n% \nCoarse\n\n\n\nLL PL PI USCS SL SG \n(%)\n\n\n\nED\nE \n\n\n\n1A 8.7 1.5 70 19.5 9 28.5 71.5 30 22.5 7.55 SC 12 2.655\n1B 6.8 2.4 70.6 13.3 13.7 27 73 34 23.5 10.55 SC 12 2.672\n1C 8.2 8.1 73.4 12.7 5.8 18.5 81.5 23.8 19.9 3.9 SM 14.4 2.656\n2A 10.1 18.1 67.2 11.8 2.9 14.7 85.3 33.8 23.5 10.35 SC 12 2.648\n2B 20.9 20.6 65.5 8.8 5 13.9 86.2 32.2 21.6 10.6 SC 12.5 2.657\n2C 18.9 0.6 31.2 19.7 48.5 68.2 31.8 54.8 24.3 34.15 CH 7.7 2.668\n\n\n\nIr\nag\n\n\n\nbi\nji \n\n\n\n3A 9.3 1.3 56.3 17 25.4 42.4 57.6 35.2 19.5 15.7 SC 11 2.649\n3B 19.3 0 34.1 20.6 45.3 65.9 34.1 62.4 25.5 36.9 CH 7.2 2.666\n3C 17.9 1 33.8 19 46.2 65.2 34.8 65.2 25.2 40.05 CH 7.2 2.674\n4A 9.4 0.8 53.8 17.6 27.8 45.4 54.6 34.6 21.3 13.35 SC 11.5 2.662\n4B 7.4 0 52.5 16.5 30.9 47.5 52.5 36.3 22.6 13.7 SC 10.6 2.67\n4C 8.8 3.2 51.4 22.7 22.7 45.4 54.6 35.4 22.6 12.8 SC 10.6 2.677\n\n\n\n0.1 1 10\n\n\n\n10\n\n\n\n20\n\n\n\n30\n\n\n\n40\n\n\n\n50\n\n\n\n60\n\n\n\n70\n\n\n\n80\n\n\n\n90\n\n\n\n100\n\n\n\n110\n\n\n\nPE\nRC\n\n\n\nEN\nTA\n\n\n\nGE\n P\n\n\n\nAS\nSI\n\n\n\nNG\n\n\n\nSIEVE SIZE\n\n\n\n P1a\n P1b\n P1c\n P2a\n P2b\n P2c\n P3a\n P3b\n P3c\n P4a\n P4b\n P4c\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 22-30 \n\n\n\nCite the Article: Charles A. Oyelami, Tesleem O. Kolawole and Gabriel S. Ojo (2021). Assessment of Vadose Zone Characteristics For Environmental \nImpact Audit of Selected Cemeteries Around Osun State, South-Western Part of Nigeria. Malaysian Journal of Geosciences, 5(1): 22-30. \n\n\n\n\n\n\n\nSC; Clayey Sand, SM; Silty Sand, CH; Inorganic Clay with high plasticity, MC; \nMoisture Content, LL; Liquid Limits, PL; Plastic Limits, PI; Plasticity Index, \nUSCS; Unified Soil Classification System, SL; Shrinkage Limit and SG; Specific \nGravity. \n\n\n\nTable 5: Engineering Properties of Cemetery Soils \n\n\n\nSample \nNo \n\n\n\nMDD \n(kg/\ud835\udc8e\ud835\udc8e\ud835\udfd1\ud835\udfd1) \n\n\n\nOMC \n(%) KT (m/s) K20 (m/s) Dippenaar   \n\n\n\n(2014) \n\n\n\n1A 2011 14.6 2\u00d710-6 1.65\u00d710-6 Relatively \nimpermeable \n\n\n\n1B 2017 14.4 2.79\u00d710-6 2.32\u00d710-6 Relatively \nimpermeable \n\n\n\n1C 2082 12.2 2.18\u00d710-6 1.80\u00d710-6 Relatively \npermeable \n\n\n\n2A 2098 11.7 1.35\u00d710-6 1.12\u00d710-5 Relatively \npermeable \n\n\n\n2B 2107 11.4 1.67\u00d710-5 1.38\u00d710-5 Relatively \npermeable \n\n\n\n2C 1595 28.1 6.75\u00d710-10 5.59\u00d710-10 Impermeable \n\n\n\n3A 1893 18.4 1.97\u00d710-8 1.63\u00d710-8 Impermeable \n\n\n\n3B 1490 31.6 5.28\u00d710-10 4.38\u00d710-10 Impermeable \n\n\n\n3C 1465 32.4 5.96\u00d710-10 4.94\u00d710-10 Impermeable \n\n\n\n4A 1921 17.5 1.78\u00d710-8 1.48\u00d710-8 Impermeable \n\n\n\n4B 1936 17.0 1.08\u00d710-8 8.98\u00d710-9 Impermeable \n\n\n\n4C 1955 16.4 1.19\u00d710-8 9.89\u00d710-9 Impermeable \n\n\n\nFigure 5: Illustrative Layout of Cemeteries in Ede and Iragbiji \n\n\n\nIn siting a cemetery, index properties of soil in terms of its grain size, \npacking density, permeability and degree of porosity dictates to a large \nextent the rate and ease of fluid movement within a medium. For instance, \ncoarse grained soil present in most of the study areas would allow rapid \ninfiltration of fluid which invariably transmits decomposition fluid to the \nunderlying formation or increase the potentials of groundwater \ncontamination within the subsurface. This could conceivably be a cause of \nlocal epidemics from waterborne diseases where the surface or \ngroundwater is used as a water source. Characteristic of soils primarily \ncontrol the potentials of unsaturated zone in acting as a filter of wastes and \ncadavers buried within it. The prevailing particle size distribution of the \nsoil influences the hydrogeological characteristics of the formations. It \ncontrols the porosity and permeability which are the key parameter for \npercolation and movement of fluids within the unsaturated vadose zone. \nOther important factors lacking in both study areas, are the slope and \ndistance of cemeteries to water abstraction points. Watershed areas \nshould be considered before siting cemeteries. In other words, areas of \nhigh topography which may likely slope into streams and rivers should be \navoided completely. Figure 6 illustrates the conceptual model of potential \nflow of contaminants within a typical cemetery close to rivers or streams. \nPit 2 as observed in the field has a shallow water level of less than 3m \nwhich makes that section of the cemetery water-logged. Not only water-\nlogged but a source of visible leachates from the cemetery. The proximity \nof shallow aquifers to cemeteries pose a great danger to the environment, \nas this reduces the time needed for mobile waste production to degrade \ncompletely and for the geological subsurface material to purify the \npotential pathogens. Evidence of this is presented in figure 6 showing a \nleachate flow right from inside the cemetery flowing to a water body (see \ntraces in arrow according to figure 7) at about 20m away from the \n\n\n\ncemetery. This clearly portends danger for inhabitant of that environment. \nAdditionally, contamination can be increased where corpses are buried in \ndirect contact with the groundwater, causing reduction in the time taken \nfor mobile degradation to reach the subsurface, or with an increase in the \nnumber of burials (Engelbrecht, 2000). Closely related to this is another \nimportant factor of weather and climate which play an important role in \nthe risk of water contamination by cemeteries. Cemeteries sited in places \nwith high rainfall intensity, shallow water tables, fractured rocks, and any \nother high permeability are highly susceptible to contamination. It was \nreported earlier that the current study area fall within the tropical \nevergreen rain forest area of south-western Nigeria, which makes it more \nvulnerable to contamination. The question here is; what will be the fate of \ninhabitants in an unfortunate event of flooding? \n\n\n\nFigure 6: Conceptual Model Flow of Contaminants within a Typical \nCemetery Close to a Flowing River. \n\n\n\nFigure 7: Field Pictures of Flow of Leachate from Cemetery to A Body of \nWater. \n\n\n\nSafety of cemeteries against transmission of infectious diseases have been \nquestioned recently, published data supports the fact that most infectious \ndiseases would not survive burial below certain depth. However, few \nresearchers supports the idea that various organisms including anthrax, \nsmallpox, infective Clostridia spp. and HIV are capable of surviving burial, \npossibly in anaerobic, environments for some time (Yates et al., 1985, \nTurnbull, 1990; 1996b; Haagsma, 1991). The situation may be more \nprecarious within the present context based on the topography and \nhydrogeological setting of the cemetery (See fig 6).  In this kind of instance, \ngiven the uncertainties of the cemetery environment and burial practices, \nthese organisms may be brought to the surface from the grave due to \noverland runoff or flood. Closely related to this is the case of natural \nenteric and thoracic bacteria of human, which, when exposed to \nfavourable conditions may multiply and spread in groundwater. In line \nwith Rudolfs et al. (1950), Salmonella typhi can survive up to 100 days \nwhile E. coli up to 5 years in soil, this is evident in the number of isolates \nfound in wells sampled around cemeteries. \n\n\n\nFinally, in a bid to assess the physical and sanitary aspects of the \ncemeteries, results of the present study were ranked according to \nconditions for safe cemetery siting as compiled by Hall and Hanbury \n(1990) in Dippenaar (2014). Table 6 shows the assessment carried out on \nthe soils that surround the cemeteries. The rankings are based on \ngeotechnical parameters. The assessment ranked Pit 4 in Iragbiji \u2018poor \nwith precautions needed\u2019 (67 out of 100), Pit 1 Ede was ranked \u2018poor with \nprecautions needed\u2019 (65 out of 100), Pits 2 and 3 were \u2018unacceptable\u2019   based \non ranking of 57 and 47 respectively. Geotechnical parameters shows that \nthe soils in Pits 1 and 4 are mainly sandy clay, with low to medium \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 22-30 \n\n\n\nCite the Article: Charles A. Oyelami, Tesleem O. Kolawole and Gabriel S. Ojo (2021). Assessment of Vadose Zone Characteristics For Environmental \nImpact Audit of Selected Cemeteries Around Osun State, South-Western Part of Nigeria. Malaysian Journal of Geosciences, 5(1): 22-30. \n\n\n\n\n\n\n\nplasticity, medium to high compaction value, and medium to low \npermeability.  Based on the ranking and geotechnical parameters, it could \n\n\n\nbe concluded that clayey soil with low permeability and porosity are most \nsuitable for cemetery siting. \n\n\n\nTable 6: Physical and Sanitary Aspects for a Cemetery Site Investigation, (After, [1 \u2013 2]) \n\n\n\nAssessment Rating Score \n\n\n\nPit 1 Pit 2 Pit 3 Pit 4 \nExcavatability \nPick and Spade \n\n\n\nGeological pick causes slight indentation. 10 10 10 10 \n\n\n\nStability \nOver break \n\n\n\nOverbreak between 1.3 and 1.8m 15 \n15 \n\n\n\n15 15 \n\n\n\nWorkability USCS MOD ASSHTO(kg/m^3) Pit 1 Pit 2 Pit 3 Pit 4 \nExcellent to good \n\n\n\nSC \nCH \n\n\n\n>1800 \n>1700 \n\n\n\n10 \n- \n\n\n\n10 \n- \n\n\n\n- \n2 \n\n\n\n10 \n- \n\n\n\nWater table Water table depth(m) Pit 1 Pit 2 Pit 3 Pit 4 \n\n\n\nPossibly perched water table \nWaterlogged soil \n\n\n\n0-4 \n0-4 \n\n\n\n5 \n- \n\n\n\n- \n0 \n\n\n\n- \n0 \n\n\n\n5 \n- \n\n\n\nSubsoil permeability Approx. Permeability(m/s) Pit 1 Pit 2 Pit 3 Pit 4 \n\n\n\nImpermeable \nRelatively impermeable \n\n\n\n<10^-7 \n10^-6 \u2013 10^-7 \n\n\n\n- \n15 \n\n\n\n- \n15 \n\n\n\n15 \n- \n\n\n\n- \n20 \n\n\n\nBackfill permeability Unified class Pit 1 Pit 2 Pit 3 Pit 4 \nImpermeable \nRelatively permeable \n\n\n\nOH,CL,CH \nGC,SC,MH \n\n\n\n10 \n- \n\n\n\n- \n07 \n\n\n\n5 \n- \n\n\n\n- \n07 \n\n\n\nFinal ranking 65 57 47 67 \n\n\n\nFinal ranking Suitability \n>90 \n75-90 \n60-75 \n<60 \n\n\n\nVery good \nSatisfactory \nPoor- precautions required \nUnacceptable \n\n\n\n6. CONCLUSIONS AND RECOMMENDATIONS \n\n\n\nIn a bid to establish the suitability of two Muslim cemeteries in Ede and \nIragbiji, a detailed geotechnical and hydrological assessment were carried \nout at both cemeteries with a view to establishing the critical role a \npathway plays in attenuating potentials of contamination and pollution. \nResults from the study revealed the very critical role of soil characteristics \nand concludes clayey sand with good compaction characteristics coupled \nwith low permeability are best suitable for siting a cemetery. The study \nidentified a public health challenge around the vicinity of Ede cemetery \nwith every indicator pointing to negative in terms of location, topography, \nshallow water level, nearness to drainage channels and a flowing river. \nAlthough it has been established that human corpses may not cause \ngroundwater pollution due to any specific toxicity but rather pollution \ncomes through concentration of any naturally organic and inorganic \nsubstance to a level sufficient to render groundwater unusable. Pollution \nis a function of time and mostly due to negligence and poor choice of \nlocation and geo-materials. Results revealed a few cases of probable \ncontamination, evident in the mainly in the concentration of Fe2+ and the \nnature of leachates from Ede cemetery. This contamination may be \naccelerated with time leading to full scale pollution which is adverse to \nhuman health. Electrical conductivity seems to be higher in the wells \naround Iragbiji than that of Ede suggesting a greater extent of \ncontamination. Chemical analysis of the wells show natural levels of Mg+, \nNO3, and PO4 ions compared with the WHO standards. High EC values in \nIragbiji may be partly associated with decomposition of human corpse but \nother anthropogenic activities such as use of fertilizers and pesticides in \nagricultural land near the cemetery are suspected as responsible. \nTemperature conditions are generally favourable for the survival of \nbacteria and other micro-organisms in the water samples taken from both \ncemeteries. The water samples shows probable occurrence of the families \nof enterobacteriaceae (Salmonella spp., Proteus spp., Serratia spp., Shigella \nspp,) which are naturally present in the human body and may be released \ninto the ground water by decomposition of the human corpse and other \n\n\n\nsources such as feacal contamination from sewage discharge, livestock \nand wild animals. Such bacteria are responsible for infections such as \ntyphoid, dysentery, urinary infections. These findings coupled with the \nprevailing weather condition and high intensity of rainfall may portend a \ngreat danger to public health. According to Pacheco et al. (1991) in William \net al.(2009) \u201cCemeteries are a potential risk that can become a real risk if \nprevious geological and hydrogeological studies are not consulted\u201d. \n\n\n\n6.1 Recommendations \n\n\n\nFindings from the present study have strengthened the fact that geological \ncharacteristics of potential cemeteries are assessed critically before siting. \nBased on the prevailing conditions in the two (2) cemeteries, that is, being \na Muslim cemetery, where coffins are not used, a condition leading faster \ndecomposition and putrefaction of the human remains. The following \nspecific and general recommendations are made to protect the \ngroundwater and safeguard the public health especially of the residents \nliving in the vicinity of cemeteries, as well as to preserve the natural \nenvironment for future generations.  \n\n\n\n\u2022 Government should enact regulation guiding the establishment or\nsiting of public cemetery. \n\n\n\n\u2022 Existing cemeteries should be subjected to constant monitoring in\nterms of water and soil quality assessment to ascertain level of\ncontamination and/or pollution.\n\n\n\n\u2022 Efforts should be made to adhere to the EU (1998) recommended\nguidelines in terms of minimum distance of cemeteries from well\n(250m), springs or watercourse (30m) and at least 1m clearance\nfrom the grave bottom to the highest water level within the\npotential cemetery. A higher clearance distance if the substrate has \na permeability ranging from 10-5 to 10-7 cm/s.\n\n\n\n\u2022 Extra regulation should be established to cater more for the\npeculiarities of Muslim cemeteries where interred remain are in\ndirect interaction with grave soils. For instance, careful selection of \nsites rich in swelling clay minerals like smectite. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 22-30 \n\n\n\nCite the Article: Charles A. Oyelami, Tesleem O. Kolawole and Gabriel S. Ojo (2021). Assessment of Vadose Zone Characteristics For Environmental \nImpact Audit of Selected Cemeteries Around Osun State, South-Western Part of Nigeria. Malaysian Journal of Geosciences, 5(1): 22-30. \n\n\n\n\n\n\n\n\u2022 Cemeteries should be located on gentle slopes or flat terrains as\nhigher slope gradients creates favourable conditions for surface\nflow, flooding of graves, leaching and migration of decomposition\nproducts. \n\n\n\n\u2022 Cemeteries should be located where clay mineral content ranges\nbetween 20 and 40%. \n\n\n\n\u2022 Cemeteries should not be located in areas where: \na) The groundwater level is shallow \nb) Seasonal or ephemeral floods occur.\nc) The substrate is very permeable (e.g. sands and gravels,\n\n\n\nfractured rocks, karst structures).\n\u2022 Cemeteries should be surrounded by buffer zones composed of\n\n\n\ntrees with deep root systems. \n\u2022 In the case of high permeability of the soils, clay lining should be \n\n\n\nadopted as precaution. \n\n\n\nREFERENCES \n\n\n\nAdedeji A. and Ajibade L.T. (2005). Quality of Well water in Ede, \nSouthwestern Nigeria. Jour. Hum. Ecol., 17(3): pp 223-228. \n\n\n\nAudu, S.O., Ejenma, E., Emehute,V.C., Okoroafor, I.B., Oyediran, A.G., Njoku, \nI.O. and Nebo, A.I. (2015). Assessment of status of wetlands in Ede, \nOsun State, Nigeria. Journal of Environmental and Earth Science. Vol. 5, \nno 10, 2015. \n\n\n\nBamiji Zachariah A. (2002). Assessment of groundwater potential for rural \nwater supply scheme using geographic information system in Ede \nNorth Local Government Area of Osun State. A dissertation submitted \nto the department of post-graduate studies, Ahmadu Bello Universitiy, \nABU. Pp 1-114. \n\n\n\nBuss S, Herbert A, Morgan P and Thornton S (2003). Review of ammonium \nattenuation in soil and groundwater. Quarterly Journal of Engineering \nGeology and Hydrogeology 37:347\u2013359. \n\n\n\nDent BB (2005). Vulnerability and the unsaturated zone \u2013 the case for \ncemeteries. In: Proceedings \u201cWhere Waters Meet\u201d, Joint Conference of \nthe New Zealand Hydrological Society, International Association of \nHydrogeologists Australian Chapter and New Zealand Soil Science \nSociety, Auckland, 30 November\u20132 December, paper A13. \n\n\n\nDent, B. B. and Knight, M. J. (1998). Cemeteries: a Special Kind of Landfill. \nThe Context of their Sustainable Management. Proceedings: \nGroundwater: Sustainable Solutions (International Association of \nHydrogeologists), Melbourne, February 1998):451-456. \n\n\n\nDippenaar, M.A., (2014). Towards a multifaceted Vadoze zone \nassessement protocol: Cemetery guidelines and application to a burial \nsite located near a season wetland. Bull. Eng. Geol. Environ. Vol.73 pp \n1105-1115. \n\n\n\nEngelbrecht, P. Ground Water Pollution from Cemeteries\u2014A Case Study. \nIn Proceedings of Environmental 2010: Situation and Perspectives for \nthe European Union, Porto, Portugal, 6\u201310 May 2003. \n\n\n\nFederal Ministry of Water Resources FMWR,. (2004). National Rural Water \nSupply and Sanitation Programme A Strategic Framework-Draft Final; \nDepartment of Water Supply and Quality Control, Abuja. \n\n\n\nFiedler, S., Breuer, J., Pusch, C. M., Holley, S., Wahl, J., Ingwersen, J. & Graw, \nM. (2012) Graveyards \u2013 special landfills. Sci. Total Environ. 419, 90\u2013\n97. \n\n\n\nGuz Gonzalez (2018). Proteus Infections. Available at \nwww.emedicine.medscape.com. An article written on 13th Sept, 2018.  \nAccessed 12 December, 2019. \n\n\n\nHaagsma, J. (1991).  Pathogenic anaerobic bacteria and the environment. \nRev. Sci. Tech. Off. Int. Epiz. 10 (3), 749\u2013764. \n\n\n\nHall, B. H. and Hanbury, R., (1990). Some Geotechnical Considerations in \nthe Selection of Cemetery Sites. IMIESA: 2125Pacheco, A., Mendes, \nJ.M.B., Martins, T., Hassuda, S., Kimmelmann, A.A., (1991). Cemeteries \n\u2013 A Potential Risk to Groundwater.Water Science & Technology 24(11):\n97\u2013104. \n\n\n\nHart A (2005). Ammonia shadow of my former self. Land Contamination \nand Reclamation 13(3):239\u2013245. \n\n\n\nJanaway, R.C., (1997). The decay of buried human remains and their \nassociated materials, in, Studies in Crime: An Introduction to Forensic \n\n\n\nArchaeology, Hunter, J., Roberts, C. and Martin, A. (eds), Routledge, \nLondon. \n\n\n\nLelliott, M., (2001). Investigation of the Pollution Potential of Cemeteries, \nM.Sc. Proj. Rept., University of Reading, unpub. \n\n\n\nNational Standard of Drinking Water Quality, NSDWQ (2007). Nigerian \nStandard for drinking water quality. Standard Organisation of Nigeria \n(SON).  NIS:2007, ICS 13.060.20. \n\n\n\nNational Standard of Drinking Water Quality, NSDWQ (2015). Nigerian \nStandard for drinking water quality. Standard Organisation of Nigeria \n(SON).  NIS-554-2015, ICS 13.060.20. \n\n\n\nNigerian Populations Commission, (2006) Osun State Population. \nhttps://nigeria.opendataforafrica.org/ifpbxbd/state-population-\n2006. Accessed 12 December, 2019. \n\n\n\nOyelami CA, Van Rooy JL (2016b) Geotechnical characterisation of lateritic \nsoils from south-western Nigeria as materials for cost-effective and \nenergy-efficient building bricks. Environ Earth Sci 75:1475. https \n://doi.org/10.1007/s1266 5-016-6274-1. \n\n\n\nPollard SJT, Hickman GAW, Irving P, Hough RL, Gauntlett DM, Howson S, \nHart A, Gayford P and Gent N (2008). Exposure assessment of carcass \ndisposal options in the event of a notifiable exotic animal disease \u2013 \nmethodology and application to avian influenza virus. Environmental \nScience Technology 42(9):3145\u20133154. \n\n\n\nRamamurthy T.N and Sitharam T.G. (2015). Geotechnical Engineering. 4th \nedition. S.Chand Publishers. \n\n\n\nRodrigues, L. & Pacheco, A., (2003). Groundwater contaminationfrom \ncemeteries cases of study. In: International Symposium: Environment \n2010: Situation and Perspectives for theEuropean Union, Abstract \nBook CD-Rom Full Paper C01,University of Porto, Porto, pp. 1\u20136. \n\n\n\nRudolfs, W., Falk, L. L. & Ragotskie, R. A. (1950).  Literature review on the \noccurrence and survival of enteric, pathogenic and relative organisms \nin soil, water, sewage and sludges, and on vegetation. Bacterial and \nvirus diseases. Sewage Indust. Waste. 22 (10), 1261\u20131277. \n\n\n\nSilva C. and Filho W. M. (2011). Geoelectrical mapping of contamination in \nthe cemeteries:  the case study in Piracicaba, S\u00e3o Paulo/Brazil; \nEnvironmental Earth Sciences; doi;10.1007/s12-665-011-1347-7. \n\n\n\nSilva L.M. (1998). Cemeteries: Potential source of contamination of \nsuperficial aquifers. In IV Latin American Congress of the hydrology of \nthe groundwater. ALHSUD, Montevideo, pp 667- 681. \n\n\n\nSpongberg, A.L., Becks, P.M. (2000b). Inorganic Soil Contamination from \nCemetery Leachate. Water, Air, & Soil Pollution 117, 313\u2013327, \nhttps://doi.org/10.1023/A:1005186919370. \n\n\n\nSpongberg, A.L., Becks, P.M., (2000). Organic Contamination in Soils \nAssociated with Cemeteries. J. Soil Contam. 9, 87\u201397. \ndoi:10.1080/10588330008984177. \n\n\n\nTrick, J. K., Williams, G. M., Noy, D. J., Moore, Y. & Reeder, S. (1999) Pollution \nPotential of Cemeteries: Impact of the 19th century Carter Gate \nCemetery, Nottingham. Technical Report WE/99/4. British Geological \nSurvey, Keyworth, Nottingham, pp. 1\u201334. \n\n\n\nTurajo, K.A., Abubakar, B.S.U.I., Dammo, M.N.  and Sangodoyin A.Y. \n(2019) Burial practice and its effect on groundwater pollution in \nMaiduguri, Nigeria. Environ Sci Pollut Res 26, 23372\u201323385. \nhttps://doi.org/10.1007/s11356-019-05572-6 \n\n\n\nTurnbull, P., 1996b, Stubborn contamination with anthrax spores, \nEnvironmental Health, 104 (June), 171 \u2013 173. \n\n\n\nTurnbull, P.C.B., 1990, Anthrax, in, Smith, G.R. and Easman, C.S.F., Eds, \nTopley & Wilson\u2019s Principles of Bacteriology, Virology and Immunity, 8th \ned, Vol. 3, 365 \u2013 379, Edward Arnold, London. \n\n\n\n\u00dc\u00e7isik Ahmet S. and Philip Rushbrook, (1998). The Impact of Cemeteries \nOn the environment and Public health: An Introductory briefing: \nWaste managementWHO Regional Office for Europe European Centre \nfor Environment and Health Nancy Project Office. \n\n\n\nWHO (World Health Organisation), 1998, The Impact of Cemeteries on the \nEnvironment and Public Health, An Introductory Briefing, prepared by \nUcisik, A. S. and Rushbrook, P., WHO Regional Office for Europe, Rept. \n\n\n\n\nhttp://www.emedicine.medscape.com/\n\n\nhttps://nigeria.opendataforafrica.org/ifpbxbd/state-population-2006\n\n\nhttps://nigeria.opendataforafrica.org/ifpbxbd/state-population-2006\n\n\nhttps://doi.org/10.1007/s12665-011-1347-7\n\n\nhttps://doi.org/10.1023/A:1005186919370\n\n\nhttps://doi.org/10.1007/s11356-019-05572-6\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 22-30 \n\n\n\nCite the Article: Charles A. Oyelami, Tesleem O. Kolawole and Gabriel S. Ojo (2021). Assessment of Vadose Zone Characteristics For Environmental \nImpact Audit of Selected Cemeteries Around Osun State, South-Western Part of Nigeria. Malaysian Journal of Geosciences, 5(1): 22-30. \n\n\n\n\n\n\n\nEUR/ICP/EHNA 01 04 01 (A) \n\n\n\nWilliams A., Temple T., Pollard S.J., Jones R.J.A., Ritz K. (2009) \nEnvironmental Considerations for Common Burial Site Selection After \nPandemic Events. In: Ritz K., Dawson L., Miller D. (eds) Criminal and \nEnvironmental Soil Forensics. Springer, Dordrecht, p87 \u2013 101. \n\n\n\nWorld Health Organization (WHO) (2008). Guidelines for Drinking-water \nQuality. 3rd ed., vol. 1. Geneva: World Health Organization. \n\n\n\nWorld Health Organization (WHO) (2017). Guidelines for Drinking-water \n\n\n\nQuality. 4th ed., incorporating first addendum. Geneva: World Health \nOrganization. \n\n\n\nYates, M. V., Gerba, C. P. & Kelley, L. M., (1985).  Virus persistence in ground \nwater. Appl. Environ. Microbiology. 49, 778\u2013781. \n\n\n\nYoung, C. P., Blackmore, K. M., Reynolds, P. J., and Leavens, A. (2002). \nPollution Potential of Cemeteries. Bristol, UK: Environment Agency. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 84-87 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2022.84.87 \n\n\n\nCite The Article: Temitope Oni, Ayodele Falade, Olumuyiwa Oso (2022). Application of Electrical Resistivity Tomography in Engineering Site \nCharacterization: A Case Study of Igarra, Akoko Edo, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 84-87. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2022.84.87\n\n\n\nAPPLICATION OF ELECTRICAL RESISTIVITY TOMOGRAPHY IN ENGINEERING SITE \nCHARACTERIZATION: A CASE STUDY OF IGARRA, AKOKO EDO, SOUTHWESTERN \nNIGERIA \n\n\n\nTemitope Onia, Ayodele Faladeb*, Olumuyiwa Osoa \n\n\n\na Department of Mineral and Petroleum Resources Engineering, Federal Polytechnic Ado-Ekiti, Ekiti State \nb Department of Geological Sciences, Achievers University Owo, Ondo State \n*Corresponding Author\u2019s Email: ayouseh2003@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 10 June 2022 \nAccepted 30 August 2022 \nAvailable online 08 September 2022\n\n\n\nTo better understand the subsurface geological composition (relief, fault, fracture, discontinuities, \norientation of structures) of the surrounding environment that anticipates infrastructural development in \nthe near future due to population explosion, an Electrical Resistivity Tomography (ERT) survey was \nconducted in Igarra, Akoko Edo area of Edo state, Nigeria. The depth to bedrock, possible geologic structures \n(faults, fractures, voids) were some of the properties investigated. To determine the orientation and \ncontinuity of the underlying geological features, 2D resistivity surveys were conducted along three parallel \ntraverses (S-N orientation) with a total length of 205m and an inter-traverse spacing of 50m. The ERT results \ndemonstrate that there are constant areas of low resistivity along the three traverses. Stations between 70 m \n- 90 m and 110 m - 150 m revealed low resistivity values, indicating possible geological structure. As seen in \nthe resistivity pseudo-section, competent beds can be found at around 5 m and about 10 m in some stations \n(70 \u2013 90 m and 110 \u2013 150 m). It is generally accepted that geological features (fault, fracture) that pose a risk \nto geotechnical and engineering projects can be found in the regions with low resistivity. According to the \nresearch, pervasive underground geological structures are to blame for most road failures. Since electrical \nresistivity tomography is useful in describing an engineering site, further geophysical investigation for \nhydrogeological objectives should be undertaken on the identified faulted and fractured zones to establish \nits hydrologic importance and reserved for such. \n\n\n\nKEYWORDS \n\n\n\nElectrical Resistivity Tomography, Resistivity, Site Characterization, Geologic Structure, Dipole-Dipole \n\n\n\n1. INTRODUCTION \n\n\n\nIn the last three decades, the application of geophysics in engineering site \ncharacterization has gained more prominence. Although in the early days \nof construction up to recent years the fundamentals of engineering site \ncharacterization or evaluation only adopts the general geotechnical test \napproach. Geotechnical engineering as a branch of civil engineering \nevaluates the behavior of earth under stress by adopting the principles of \nsoil and rock mechanics, while it thrives on the knowledge of geology, \nhydrology and geophysics. As a ground truth, reliable and proven method, \nthe application of geo-techniques in engineering site characterization \nextends to the military, mining engineering, petroleum engineering, \ncoastal engineering and offshore construction. Some of the methods; \nCalifornia bearing ratio (CBR), cone penetration test (CPT), standard \npenetration test (SPT), atterberg limit test, liquid limit test, plasticity index \ntest, American Association of State Highway and Transportation Officials \ntest (AASHO) basically provide point information which does not provide \nquantitative information about a large subsurface environment. Such \npoint information based on the density or interval/frequency of sampling \ncould miss out on vital subsurface structures such as presence of cavity, \nfaults, fractures and other buried artifacts which could negatively impact \n\n\n\nthe engineering structure erected on such area of land leading to several \ndegrees of social and economic losses including loss of lives. \n\n\n\nThe main purpose in the design of geotechnical engineering is to define the \nshear strength and settlement of the soil through methods of analysis for \ndeformed soil, as well as the flow that fluids follow in structures that are \neither supported or made of soil, or even structures buried into the soil \n(Philotheos et al., 2021). Due to the varying moisture levels, the soil's \ncharacteristics can change from season to season. All loads from structures \nmust be securely transferred to the ground. Therefore, it is important to \naccurately assess the soil's safe bearing capacity. Geotechnical engineering \nincludes various studies required for the development of pavements, \ntunnels, earthen dams, canals, and earth retaining structures in addition \nto determining safe bearing capacity for building foundations. It includes \ninvestigating ground-improvement techniques as well. This area of civil \nengineering is crucial because the stability of every structure depends on \nhow safely loads are transferred to the ground. However, despite all these \nrelevance of geotechnical engineering, the method is highly expensive, \nlimited to obtaining point information and could not account for large area \nat once, hence a need for geophysical method in electrical resistivity \ntomography.  \n\n\n\n\nmailto:ayouseh2003@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 84-87 \n\n\n\nCite The Article: Temitope Oni, Ayodele Falade, Olumuyiwa Oso (2022). Application of Electrical Resistivity Tomography in Engineering Site \nCharacterization: A Case Study of Igarra, Akoko Edo, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 84-87. \n\n\n\n7\n\n\n\nGeophysical methods including ERT are rapid, less expensive, and non-\n\n\n\ninvasive methods that provides insight to subsurface geology and \n\n\n\nconfiguration once the methodology is rightly chosen (Falade et al., 2019). \n\n\n\nAccording to Asem et al., (2018), the presence of natural and man-made \n\n\n\nstructures on engineering construction sites, such as pipes and sinkholes, \n\n\n\ncauses serious geotechnical and geo-environmental issues. Tomography \n\n\n\ninvestigation of the earth\u2019s subsurface at sites designated for construction \n\n\n\nengineering has drawn the attention of experts because, near-surface \n\n\n\nstructures, cavities, sink holes, voids, fractures, faults among others and/ \n\n\n\nor in-homogeneities in the foundation geo-materials pose major risk to \n\n\n\ncivil engineering structures. Geophysics, therefore, had been used \n\n\n\nextensively including prediction of earthquakes, also geophysical \n\n\n\ntechniques have been applied to structural engineering problems and they \n\n\n\nnow form a significant component of non-destructive testing (NDT) \n\n\n\n(McDowell et al., 2002).  \n\n\n\nThe purpose of the resistivity method therefore is to detect underground \n\n\n\ninhomogeneities and interpret them as modifications in underground \n\n\n\nmaterials or structures (Amin and Hamidreza, 2016). Sirwa et al., (2013), \n\n\n\nintegrated 2D (ERT) method and boreholes data to determine the \n\n\n\nsubsurface geological deficiencies that could endanger engineering \n\n\n\nconstruction. Some researchers used Electrical resistivity method (ERT) \n\n\n\nin mapping subsurface geologic sequence and concealed geological \n\n\n\nstructures (Amosun et al., 2018; Olorunfemi et al., 2015). Features \n\n\n\ncorresponding to major and minor linear fractures within the basement \n\n\n\nrocks were identified as the root cause of incessant road failure using \n\n\n\ncombined electromagnetics and electrical resistivity method (Osinowo et \n\n\n\nal., 2011). Based on the assumption of electrical resistivity contrast, the 2D \n\n\n\nelectrical resistivity tomography had been used to locate subsurface cavity \n\n\n\nunderlying an engineering site in Thailand, conducted (Rungroj and Mark, \n\n\n\n2014). Further studies, by on a site for prospective Pishva hospital in Iran \n\n\n\nhad detected resistivity anomaly corresponding to Qanat tunnel and \n\n\n\naqueduct shaft underlining the proposed site that constitutes weak zones \n\n\n\nunderneath (Amin and Hamidreza, 2016).  \n\n\n\nThe electrical resistivity tomography (ERT) is a proven insightful tool that \n\n\n\nprovides reliable information on internal structure of altered/disrupted \n\n\n\nsubsurface (Ayolabi et al., 2012; Syed et al., 2020; Al-Awsi and \n\n\n\nAbdulrazzaq, 2022). In UK, the 2D and 3D electrical resistivity tomography \n\n\n\n(ERT) were successfully applied in determining the geometry of a buried \n\n\n\nquarry within an abandoned dolerite quarry and landfill (Jonathan et al., \n\n\n\n2006). Some researchers successfully adopt ERT to determine seepage \n\n\n\nzones through earthen embankments of wastewater treatment pond \n\n\n\nsystems (Rungroj and Mark, 2015). An investigation into the causes of \n\n\n\nlandslide in Western Nepal identified cracked, weathered, sheared, and \n\n\n\nfractured bedrock as root cause of landslide in the area (Ashok and Radha, \n\n\n\n2020). According to a study, identified subsidence hazard zone due to \n\n\n\ncavity limestone, conforms to the N-S elongated fracture pattern found in \n\n\n\nthe research area (Wilopo et al., 2022). \n\n\n\nAs a vast method of engineering site investigation, the electrical resistivity \n\n\n\nmethods of geophysics can be used as a pre-construction site investigation \n\n\n\ntool, or post construction investigation tool. The aim of this study is to \n\n\n\nprovide insight into the subsurface geologic environment that is \n\n\n\nresponsible for incessant road failure along Igarra-Auchi road and map \n\n\n\nsubsurface geological features underlying the adjoining sites that \n\n\n\nanticipate engineering construction in the study area. By understanding \n\n\n\nmore about the lateral and vertical subsurface geologic variation, the \n\n\n\nstudy conducts geophysical investigation to identify areas that could \n\n\n\npotentially threaten engineering construction while also suggesting \n\n\n\nappropriate remedies in accordance with their engineering competence. \n\n\n\n2. GEOLOGY OF THE STUDY AREA\n\n\n\nThe Igarra schist belt is located in the southwestern segment of the \n\n\n\nPrecambrian Basement Complex of Nigeria (Figure 1). The study area \n\n\n\n(about 230 km2) is delimited by latitudes 07\u00b0 15' to 07\u00b0 20' 12''N and \n\n\n\nlongitudes 06\u00b0 00' to 06\u00b0 12'E. The Older Granite suite is well exposed in \n\n\n\nscenic hills, while the metasediments occur in plains and low-lying areas \n\n\n\nespecially stream channels such as river Onyami. Secondary structural \n\n\n\nfeatures such as fractures and folds are often ubiquitous (Ogbe et al., \n\n\n\n2018). \n\n\n\nFigure 1: Geological map of Igarra and surrounding areas \n\n\n\n3. METHODOLOGY\n\n\n\nThe Electrical Resistivity method involving dipole-dipole array was used \nto investigate the possibility of subsurface geological structures like \ncavities, faults, fractures and to produce a subsurface image in order to \nidentify the locations of these geological structures that may impair \npresent and future structural development. Three parallel traverses of \n205m length and 50m inter-traverse separation were established (Figure \n2). The station-to-station interval of 10m along each traverse was adopted \nfor the survey. Dipro - Win software was used to process the electrical \nresistivity (dipole-dipole) data. \n\n\n\nFigure 2: Data acquisition Map of the study area \n\n\n\n4. RESULTS AND DISCUSSION\n\n\n\n4.1  Electrical Resistivity Tomography \n\n\n\nThe apparent resistivity along traverse 1 is presented in pseudo-section \n(Figure 3) and it proved to be the onset of geological structures observed \nin the study area. At offsets 80 m, 120 \u2013 150 m low resistivity value of 27.7 \n\u2126-m, 122 \u2126-m, 17.67 \u2126-m, 15.27 \u2126-m and 14.47 \u2126-m which typically \nsuggests the presence of geological structures was interpreted to be faults, \nor fractures. However, competent formation was encountered at about 7m \ndepth at different offsets while onset of structures encountered at 10m \ndeep. The pseudo-section (Figure 4) embodies apparent resistivity along \ntraverse 2 and continuation of geological structure (fault) as observed \nbetween stations 20 m -60 m, 80 m \u2013 90 m and 120 m \u2013 130 m which \nexhibits low resistivity values 21.9 \u2126-m - 46.7 \u2126-m, 41.3 \u2126-m - 29.1 \u2126-m \nand 56.9 \u2126-m - 39.8 \u2126-m respectively. The values are suggestive of the \npresence of conductive body, typical of cavities, faults, fractures etc. The \nsubsurface images reveal the presence of fault, or fractures indicated by \nthe obvious depression zones. The pseudo-section in figure 5 along \ntraverse 3 demonstrates the continuation of the intercepted geological \nstructure. Low resistivity values at stations 70m to 90m and 110m to \n150m show low resistivity values of 26.4 \u2126-m - 9.8 \u2126-m, and 46.7 \u2126-m - \n24.8 \u2126-m respectively, which are suggestive of the presence of cavities, \nfaults and fractures. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 84-87 \n\n\n\nCite The Article: Temitope Oni, Ayodele Falade, Olumuyiwa Oso (2022). Application of Electrical Resistivity Tomography in Engineering Site \nCharacterization: A Case Study of Igarra, Akoko Edo, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 84-87. \n\n\n\n7\n\n\n\nFigure 3: Dipole-dipole pseudo-section for Traverse 1 \n\n\n\nFigure 4: Dipole-dipole pseudo-section for Traverse 2 \n\n\n\nFigure 5: Dipole-dipole pseudo-section for Traverse 3 \n\n\n\nFault 1 Fault 2 \n\n\n\nFault 1 Fault 2 \n\n\n\nFault 1 Fault 2 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 84-87 \n\n\n\nCite The Article: Temitope Oni, Ayodele Falade, Olumuyiwa Oso (2022). Application of Electrical Resistivity Tomography in Engineering Site \nCharacterization: A Case Study of Igarra, Akoko Edo, Southwestern Nigeria. Malaysian Journal of Geosciences, 6(2): 84-87. \n\n\n\n7\n\n\n\n5. CONCLUSION\n\n\n\nThe investigation conducted across the three established traverses \nintercepted the geological structures and revealed the range of topsoil \ndepth between 0 \u2013 5 m and occasionally up to 10m along the traverses. \nAlong traverse 1, slight depression is observed between stations 70 m and \n90 m considered as the onset of the geological structure, while stations \nbetween 110 m and 150 m revealed a larger geological structure. In \ntraverse 2, depression is observed between station 70 m and 90 m, and \nstation 110 m \u2013 140 m to deeper depths. In traverse 3, depression \nsuggestive of geological structure is observed between station 70 m and \n90 m, and stations 110 m \u2013 150 m. The information along the three \ntraverses shows consistency of the identified geological structures in the \nstudy area. Depth 0 \u2013 5 m depicts the topsoil and subsequently competent \nformation suitable for engineering and constructions purposes except for \nstations 70 m to 90 m and stations 110 m to 150 m where the structures \nare obvious and clearly pronounced.  \n\n\n\nSiting structures on faulted/fractured zones could lead to degrees of \ndistress, ranging from multiple cracks, sinking of building, and partial or \ncomplete differential settlement. The faulted zones between stations 70 m \n\u2013 90 m and stations 110 m \u2013 150 m could be considered weak zones usually\nsuitable for groundwater development. The resulting subsurface image of \nthe investigation helps to relate the subsurface with respect to its \ngeotechnical and environmental engineering relevance. The area \nidentified competent for engineering purpose should be marked out by \nfurther confirming the depth of the competent formation and given the \nappropriate treatment for such purpose. The identified geological \nstructures (faults and fractures) could be dedicated to groundwater \ndevelopment due to it hydrogeological significance. Neither test borings \nnor geophysical methods alone provide all the information needed in \nsubsurface investigation. Non-destructive geophysical methods prior to a \ntest boring program assist in the proper location of borings and reduce the \nnumber of boreholes. The investigation demonstrates the use of Electrical \nResistivity Tomography method as a supplemental, cost-effective \ntechnique in subsurface investigations. \n\n\n\nAUTHORS CONTRIBUTION \n\n\n\nTemitope Oni: Conceptualization, Methodology, Software, Visualization, \nInvestigation, Supervision. Ayodele Falade: Visualization, Investigation, \nSoftware, Validation, Writing- Reviewing and Editing. Olumuyiwa \nOso: Writing- Reviewing and Editing \n\n\n\nFUNDING \n\n\n\nNo funding was made available for this research. It was funded by the \nauthors. \n\n\n\nREFERENCES \n\n\n\nAmin, A., and Hamidreza, R., 2016. Application of Electrical Resistivity \nImaging for Engineering Site Investigation. A Case Study on \nProspective Hospital Site, Varamin, Iran. Acta Geophysica, 64 (4), Pp. \n2200-2213. \n\n\n\nAmosun, J.O., Olayanju, G.M., Sanuade, O.A., Fagbemigun, T., 2018. \nPreliminary geophysical investigation for road construction using \nintegrated methods. Materials and Geo-environment, 65 (4), Pp. 199 \u2013 \n206. \n\n\n\nAsem, A.H., Munther, D., AL-Awsi, M.M., Al-Obaidi, 2018. Application of 2D \nElectrical Resistivity Imaging Technique for Engineering Site \nInvestigation. Journal of University of Babylon, Pure and Applied \nSciences, 26 (5), 74-85. \n\n\n\nAshok, S., and Radha, K.A., 2020. Application of electrical resistivity \ntomography (ERT) survey for investigation of the landslide: a case \n\n\n\nstudy from Taprang landslide, Kaski district west-central Nepal. \nJournal of Nepal Geological Society, 60, Pp. 103-115. \n\n\n\nAyolabi, E.A., Ibok, J.E.E., and Adetayo, F.F., 2012. Engineering Site \nCharacterisation Using 2-D and 3-D Electrical Resistivity Tomography. \nEarth Science Research, 2 (1), Pp. 133-142.  \n\n\n\nFalade, A.O., Amigun, J.O., Kafisanwo, O.O., 2019. Application of electrical \nresistivity and very low frequency electromagnetic induction methods \nin groundwater investigation in ilara-mokin, Akure Southwestern \nNigeria. Environ Earth Sci. Res. J., 6 (3), Pp. 125-135. \n\n\n\nJonathan, E.C., Oliver, K., Philip, I.M., Richard, D.O., and Jonathan, H., 2006. \nElectrical Resistivity Tomography Applied to Geological, \nHydrogeological and Engineering Investigations at a former waste \ndisposal site. Geophysics, 71, Pp. B231-B239. \n\n\n\nMcDowell, P.W., Barker, R.D., Butcher, A.P., Culshaw, M.G., Jackson, P.D., \nMcCann, D.M., Skipp, B.O, Matthews, S.L., and Arthur, J.C.R., 2002. \nGeophysics in Engineering Investigations, Geological Society, London, \nEngineering Geology Special Publications, 19, Pp. 19-22. \n\n\n\nMunther, D.A., Zaidoon, T.A., 2022. Implementing Electrical Resistivity \nTomography to delineate soil contamination zone, Southern Baqubah \nCity, Iraq. Kuwait journal of science, 49 (2), Pp. 1-15. \n\n\n\nOgbe, O.B., Olobaniyi, S.B., Ejeh, O.I., Omo-Irabor, O.O., Osokpor, J., Ocheli, \nA., Overare, B., 2018. Petrological and Structural Investigation of \nRocks around Igarra, Southwestern Nigeria. Ife Journal of Science, \n20(3), DOI: 10.4314/ijs.v20i3.19. \n\n\n\nOlorunfemi, M.O., Okiyi, I.M., Ojo, J.S., Akintorinwa, O.J. and Bayode, S., \n2015. Geoelectric Investigation of a Site for a Proposed Airport in \nEbonyi State, Southeast Nigeria. Ife Journal of Science, 17 (2), 313-322. \n\n\n\nOsinowo, O.O., Akanji, A.O., Akinmosin, A., 2011. Integrated geophysical \nand geotechnical investigation of the failed portion of a road in \nbasement complex Terrain, Southwest Nigeria. Materials and \nGeoenvironment, 58 (2), Pp. 143-162. \n\n\n\nPhilotheos, L., Ioannis, C., Theodoros, C., Dimitrios, C., Emmanouil, P., and \nEvangelos, P., 2021. Historical background and evolution of Soil \nMechanics. Wseas Transactions on Advances In Engineering \nEducation, 18 (10), 96-113. \n\n\n\nRungroj and Mark, 2015. Application of 2D electrical resistivity \ntomography to engineering projects: Three case studies. \nSongklanakarin J. Sci. Technol., 37 (6), Pp. 675-681. \n\n\n\nRungroj, A., and Mark, E.E., 2014. Application of 2D Electrical Resistivity \nTomography to Engineering projects: Three case studies. \nSongklanakarin Journal of Science and Technology, 37 (6), Pp. 675-\n681 \n\n\n\nSirwa, Q.S., Gardi, Ahmed, J.R., Al-Heety, Rizkr, Z.M., 2015. 2D Electrical \nResistivity Tomography for the Investigation of the Subsurface \nStructures at the Shaqlawa Proposed Dam Site at Erbil Governorate, \nNE Iraq. International Journal of Science and Research (IJSR) \n\n\n\nSyed, A.A., Muhammad, S., Mukhtiar, G., and Taseer, A., 2020. Subsurface \nCavity Detection Using Electrical Resistivity Tomography (Ert); a Case \nStudy from Southern Quetta, Pakistan.  Pakistan Journal of Geology, 4 \n(2),  \n\n\n\nWilopo, W., Putra, D.P.E., Fathani, T.F., Widodo, S., Pratama, G.N.I.P., \nNugroho, M.S., Prihadi, W.R., 2022. Identification of subsidence hazard \nzone by integrating engineering geological mapping and electrical \nresistivity tomography in Gunung Kidul karst area, Indonesia. Journal \nof Degraded and Mining Lands Management, 9 (2), Pp. 3281-3291. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 31-34 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2021.31.34 \n\n\n\nCite the Article: Johnson C. Ibout, Mfoniso U. Aka, Amarachukwu A. Ibe, Bethrand E. Oguama, Azuanamibebi D. Osu (2021). Delineation of Faults and Cavities Using \nGravity Techniques: An Implication for Road Construction, South-South Nigeria. Malaysian Journal of Geosciences, 5(1): 31-34. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2021.31.34 \n\n\n\nDELINEATION OF FAULTS AND CAVITIES USING GRAVITY TECHNIQUES: AN \nIMPLICATION FOR ROAD CONSTRUCTION, SOUTH-SOUTH NIGERIA  \n\n\n\nJohnson C. Ibouta*, Mfoniso U. Akaa, Amarachukwu A. Ibeb, Bethrand E. Oguamac, Azuanamibebi D. Osud \n\n\n\na University of Nigeria, Nsukka \nb Nigeria Maritime University \nc Enugu State College of Education (Technical), Enugu State. \nd Federal College of Education (Technical) Omoku \n*Corresponding Author email: ibuot2000@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 19 March 2021 \nAccepted 22 April 2021 \nAvailable online 11 May 2021\n\n\n\nThis study was carried out using five digitized aerogravity data to delineate near-surface structural faults, \n\n\n\ncavities, low-density zones and estimate the mass balance unit in foundations. Qualitative and quantitative \n\n\n\nanalysis were performed in order to examines the depths to anomalous bodies, density/mass and \n\n\n\nstratigraphic features such as faults and cavities. The techniques employed were: Source parameter imaging \n\n\n\n(SPI), 3D Euler deconvolution, forward and inverse modeling. The results of the SPI shallow values range \n\n\n\nfrom -5.62 to -53.74 m and deep values range from 3.33 to 120 m. The 3D Euler deconvolution results range \n\n\n\nfrom -1892.2 to -1278.3 m for obscure and -12264 to 644.6 m for superficial formations. The forward and \n\n\n\ninverse modeling result shows the values of depth ranging from 2.5 to 4.8 km, density/mass range from (0.7 \n\n\n\nto 2.4) x 10-3 kg/m3 and  (27 to 133) x 1010 kg of three profiles which is the parameter contrast of the gravity \n\n\n\nsurveys. This shows sequential depths and density/mass contrast between the body of interest and the \n\n\n\nsurrounding material which depicts the presents of faults, sedimentary basins and rock bearing minerals of \n\n\n\nshale/marble which comprises of air, water and sediment-filled formations. The information from this study \n\n\n\nhas revealed the true nature of the subsurface and this will serve as a guide during road construction.  \n\n\n\nKEYWORDS \n\n\n\nmicrogravity, aerogravity, SPI, Euler deconvolution, sedimentary basin.\n\n\n\n1. INTRODUCTION\n\n\n\nRoads are essential channels of transportation that need to be build and \nsustained in order to fast track mineral and economic development, such \nas roads in the south-south region of the country where heavy rainfall \ndominates throughout the year. There is need for proper knowledge of \nsubsurface information before undergoing road constructions. However, \nit is necessary to employ geophysical techniques that would provide \ninformation about the lithology and stratigraphy of the subsurface \nformation (Obiora et al., 2016). Numerous construction and rehabilitation \nprojects such as; infrastructural development of new and expansion of the \nexisting infrastructural projects are ongoing. It is essential to be very \ncareful in the planning of roads in order to avoid direct impacts such as \nexcessive costs, energy and time, or indirect costs such as environmental \nand ecological impacts.  \n\n\n\nFurthermore, in most cases the main reasons for project delays is due to \nunsatisfactory project planning with regards to conditions in subsurface \nmaterial, bedrock and groundwater levels (Ezekiel et al., 2013; Sharma, \n2013). Different subsurface materials can cause local disturbances in the \nEarth\u2019s natural fields which can be detected with sensitive instruments \n(Obiora et al., 2015; Oha et al., 2016; Ekpa et al., 2020). Some geological \n\n\n\nfactors such as clayey subgrade soil below the road pavement, lateral \ninhomogeneity, near surface geological structures and changes in \nelevations due to fluctuation in the saturated zone are responsible for the \nsusceptibility of the roads to failure (Adesola et al., 2017). Geophysical \nmethods can be classified as passive and active methods, passive method \nmeasures the variations in natural fields of earth such as; magnetic and \ngravitational fields whereas active method makes use of artificially \ngenerated fields such as electrical and seismic signal.  \n\n\n\nActive methods have been used by some researchers within the study area \nto investigate the subsurface, in which artificial signals (energy) were \ngenerated, and the results help in determining the soil water and \ncompaction state (Reynold, 2011; Mandal et al., 2015, Aka et al., 2018 and \n2013; Okiwelu et al., 2013). However active methods used did not give \nclearer information due to anomalies such as; high levels of electrical and \nacoustic noise encountered from public facilities leading to errors in the \nresults, volume of material being sampled and resolution desired. Gravity \ntechnique investigates changes in the subsurface density/mass variations \nin the earth\u2019s gravitational field. It is distinctly capable of delineating the \nsubsurface faults, cavities and mapping of geological boundaries, \nlineaments, dykes and layered complexes which may have influence on the \noverlying sediments (Ekpa et al., 2020). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 31-34 \n\n\n\nCite the Article: Johnson C. Ibout, Mfoniso U. Aka, Amarachukwu A. Ibe, Bethrand E. Oguama, Azuanamibebi D. Osu (2021). Delineation of Faults and Cavities Using \nGravity Techniques: An Implication for Road Construction, South-South Nigeria. Malaysian Journal of Geosciences, 5(1): 31-34. \n\n\n\nHence, the current research therefore employed aerogravity (a \nreconnaissance survey) which can be use in high level noise terrains with \nvaluable data for large area with greater depths. These delineate cavities \nand faults; identify areas with varying density/mass bedrock type and \nminimize stress and manpower (Milsom, 2003). The aerogravity data are \nacquired with sufficient resolution which contributes towards resource-\nscale projects which can be used to characterize salt domes for petroleum \nexploration, monitoring of geothermal reservoirs under exploitation, \ninferring location of faults, and permeable areas for hydrothermal \nmovement. This method can be used for shallow engineering applications \nsuch as; road design, constructions and maintenance phases. The unit of \nmeasurement used in the gravity method is the Gal (in honor of Galileo), \ntypical gravity surveys for environmental and engineering applications \nrequire measurements with an accuracy of a few \u03bcGals (10-6 Gals), which \nis referred to as microgravity surveys (Hunt et al., 1995).  \n\n\n\nOn the other hand, it is necessary to collect data about the area before \nconstruction in order to calculate the amount of cut and fill required, \nmaterials available and materials which need to be transported to the site. \nBy doing this, we can obtain information regarding the surface and \nsubsurface condition of an area such as; cavities, structural anomalies \n(faults) and bedrock types using geophysical methods (Benson, 2000; \nBiswas et al., 2017). The objectives of this study are to delineate near-\nsurface structural faults, cavities, low-density zones and determination of \nmaterials balance in foundations. \n\n\n\n2. LOCATION AND GEOLOGY OF THE STUDY AREA \n\n\n\nThe study area is located on the continental margin of the Gulf of Guinea, \notherwise called Niger Delta formed in the Paleocene (Figure 1). The \nregion is fed with sediments from the Niger River with an extensional rift \nbasin, where rifting occurred from the Late Jurassic to the Late Cretaceous \n(Okiwelu et al., 2013; Aka et al., 2018). Cretaceous fracture zones, \nexpressed as trenches and ridges, control the tectonic framework of the \ndelta and separate the margin into individual sub-basins, which form the \nboundary faults of the Cretaceous Benue to Abakaliki trough. As the delta \nprogrades, growth to fault bounded depobelts are created such as: the \nnorthern delta, central delta and the distal delta province. Three large \nscale lithostratigraphic units extend across the tertiary delta, each range \nin age from early Tertiary to Recent and distinguished from one another \non the basis of their sand to shale ratios. These units are related to \noutcrops and environmental deposition (Mickus, 2004). It comprises of; \nupper sandy Benin formation, intervening unit of alternating \nsandstone/shale Agbada formation and a lower shaly Akata formation.  \n\n\n\nAkata formation underlies the entire base of the delta. It is composed of \nmarine pro-delta shales and turbidite sands with minor amounts of clay \nand silt depositions of about 7,000 m thick since the Paleocene.  Agbada \nformation overlies the Akata formation, it contains paralic siliciclastics \nand is considered to be the main deltaic sequence with most economically \nexploitable hydrocarbon in the region. The formation lies approximately \n930 m below sea level at the base of freshwater sands (Lehmann et al., \n2009; Telford, 1990). Also, the base of the formation lies 2480 m below sea \nlevel thickens towards the offshore. Benin Formation overlies the Agbada \nformation, composed of fluvial and upper coastal plain facies that has been \ndeposited since the Oligocene and extends across the entire delta. It is \nconsisting of sands that are up to 2000 m thick in the delta (Okiwelu et al., \n2013). The delta subsurface structures are described as resulting from \nmovement under the influence of gravity and their distribution is related \nto growth stages of the delta.  \n\n\n\nFigure 1: Map of Nigeria showing the region of the study area \n\n\n\n2.1 Data Acquisition \n\n\n\nFugro airborne surveys carried out the airborne geophysical surveys \noverseen by the Nigerian Geological Survey Agency (NGSA). Five digitized \n\n\n\naerogravity sheets were obtained from Nigerian Geological Survey Agency \nin XYZ Geosoft format. X and Y were the distance in meters and Z is the \nBouguer anomaly measured in miligal. The five sheets were gridded into \ntwenty-five blocks for proper delineation of structural trends such as \nfaults and depth.  \n\n\n\n2.2 Data processing \n\n\n\nDigitized airborne gravity data was uploaded into Oasis Montaj software \nusing database tools menu button in the software. The first step was the \ngridding of the data set to enhance better resolution and imaging. By \ngridding, it means interpolating data into equally spaced cells co-ordinate \nsystem. These frameworks generate robust applications avail to produce \n3D Bouguer gravity of the study area by employing minimum curvature \nalgorithm method. The second step was the interpretation of bouguer \ngravity map; qualitatively and quantitatively in order to delineate faults, \ncavities and depths of gravity anomalous bodies. In qualitative \ninterpretation, subsurface structures examine the grid of gravity \nanomalies, contours maps and gravity profiles to determine the lateral \nlocation of spatial gravity variations.  \n\n\n\nThis depicts fragmented fractures, cavities and faults patterns as well as \ngroundwater level fluctuation. The quantitative interpretation quantified \nthe anomaly of interest from the remaining background anomaly and \nmodeled to determine the depth, density and geometry of the anomaly\u2019s \nsource. These was done using three techniques namely; source parameter \nimaging (SPI), 3D Euler Deconvolution, forward and inverse modeling to \nenhance a better understanding of the subsurface. (Biswas et al., 2017). \nSPI is an image formed from potential field\u2019s depth and local wavenumber \nof the observed field at any gridded data point through horizontal and \nvertical derivations (Eletta and Udensi, 2012). It analyses the properties \nfrom the local wavenumber; first and second orders deriving analytic \nsignal responses. It is expressed as shown in Eqns. 1 - 4 respectively. \n\n\n\n\ud835\udc341(\ud835\udc65, \ud835\udc67) =\n\ud835\udf15\ud835\udc40(\ud835\udc65,\ud835\udc67)\n\n\n\n\ud835\udf15\ud835\udc65\n\u2212 \ud835\udc57\n\n\n\n\ud835\udf15\ud835\udc40(\ud835\udc65,\ud835\udc67)\n\n\n\n\ud835\udf15\ud835\udc67\n   (1) \n\n\n\n\ud835\udc3e1      =  \n\ud835\udf15\n\n\n\n\ud835\udf15\ud835\udc65\n\ud835\udc61\ud835\udc4e\ud835\udc5b\u22121[  \n\n\n\n\ud835\udf15\ud835\udc40\n\n\n\n \ud835\udf15\ud835\udc4d\n/ \n\n\n\n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc4b  \n  ]   (2) \n\n\n\nA2(x, z) =\n\u22022M(x,z)\n\n\n\n\u2202z \u2202x\n\u2212 j\n\n\n\n\u22022M(x,z)\n\n\n\n\u22022z\n   (3) \n\n\n\n\ud835\udc3e2      =  \n\ud835\udf15\n\n\n\n\ud835\udf15\ud835\udc65\n\ud835\udc61\ud835\udc4e\ud835\udc5b\u22121[  \n\n\n\n\ud835\udf152\ud835\udc40\n\n\n\n \ud835\udf152\ud835\udc4d\n /  \n\n\n\n\ud835\udf152\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc4b\ud835\udf15\ud835\udc4b  \n  ]    (4) \n\n\n\nWhere M(x,z) is the magnitude of gravitational field in x and z Cartesian \ncoordinates for vertical and horizontal directions.  J is the imaginary \nnumber, A1(x,z)  and A2(x,z) is the first and second order analytical signals. \nK1 and K2 is the first and second local wavenumber model and depth \nestimations (Biswas et al., 2017). Euler deconvolution is an inversion \ntechnique that reflects the source locations and depth boundary of \nunderground anomalous sources (Maden, 2010). 3D Euler equations \nrelate gravity field and gravity gradient tensor with structure index. It is \nexpressed as shown in equation 5 and 6. \n\n\n\n\ud835\udc49\ud835\udc65 (\ud835\udc65\u2212\ud835\udc650)  +  \ud835\udc49\ud835\udc66 (\ud835\udc66\u2212\ud835\udc660)  + \ud835\udc49\ud835\udc67 (\ud835\udc67\u2212\ud835\udc670)    =   -N [V(x,y,z) -  B]     (5) \n\n\n\n\ud835\udf15\ud835\udc49\u221d\n\n\n\n\ud835\udf15\ud835\udc65\n(\ud835\udc65 \u2212 \ud835\udc650) + \n\n\n\n\ud835\udf15\ud835\udc49\u221d\n\n\n\n\ud835\udf15\ud835\udc66\n(\ud835\udc66 \u2212 \ud835\udc660) +  \n\n\n\n\ud835\udf15\ud835\udc49\u221d\n\n\n\n\ud835\udf15\ud835\udc67\n(\ud835\udc67 \u2212 \ud835\udc670) = - (N+1) (\ud835\udc49\u221d \u2212  \ud835\udc35\u221d)    (6) \n\n\n\nWhere x, y, z are gravity anomaly of point x0, y0 and z0., V is the gravity field, \nB is the base level of observed field and N is the structure index, and V\u03b1 is \nthe gravity gradient tensor. The forward and reverse modeling is the final \nstep in processing potential field data in order to determine the density, \ndepth and geometry of the anomalous bodies. In forward and reverse \nmodeling, iterative methods were employed using potent 3D tools of Oasis \nMontai software, where the calculated and observed field data were \ncompared (Nwankwo et al., 2011). In order to have a deemed close match \nand improve fitting between the calculated and observed anomalies. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nSource Parameter Imaging (SPI), Euler Deconvolution, forward and \nreverse modeling were adopted for delineating structural trends and \ncavities. In qualitative analysis, 25 gridded data blocks of SPI were merged \ntogether, contour maps and gravity profile were done to determine the \nspatial variations in earth\u2019s gravitational field. Quantitative analysis \nexamines; depths and thicknesses of the layers, density and mass, \nstructural trends and stratigraphic features such as faults, syncline and \nanticlines and unconformities that causes the gravity field variations. \nFigure 2 shows 25 gridded data block of aerogravity SPI map. The analysis \npresents offshoots parallel/ perpendicular flow banding from shallowest \n\n\n\n\nhttps://en.wikipedia.org/wiki/Gulf_of_Guinea\n\n\nhttps://en.wikipedia.org/wiki/Paleogene\n\n\nhttps://en.wikipedia.org/wiki/Niger_River\n\n\nhttps://en.wikipedia.org/wiki/Rift\n\n\nhttps://en.wikipedia.org/wiki/Rift\n\n\nhttps://en.wikipedia.org/wiki/Jurassic\n\n\nhttps://en.wikipedia.org/wiki/Cretaceous\n\n\nhttps://en.wikipedia.org/wiki/Fracture_zone\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 31-34 \n\n\n\nCite the Article: Johnson C. Ibout, Mfoniso U. Aka, Amarachukwu A. Ibe, Bethrand E. Oguama, Azuanamibebi D. Osu (2021). Delineation of Faults and Cavities Using \nGravity Techniques: An Implication for Road Construction, South-South Nigeria. Malaysian Journal of Geosciences, 5(1): 31-34. \n\n\n\nto deepest anomalous gravity bodies. The shallowest values range from -\n5.62 to -53.74 m, the negative value delineates invasive. The deepest \nvalues range from 3.33 to 120 m respectively. Low depth values portray \nloose sediment formation, whereas, high depth portray dense sediment \nformation indicating hydrocarbon mass (Obiora eta al., 2016).  \n\n\n\nOn the other hand, bouguer gravity anomaly values range from -15.5 to \n35.2 m, in which the negative values indicate low compactness disparity of \ngravity. Thus, the positive values indicate areas with high density/mass of \nthe underlying rocks. Figure 3 shows 3D surface map with deepest \nsedimentary thickness at north-eastern region, shallowest sedimentary \nthickness and depression at the south-eastern part. Fig. 4 shows the \nstructural trend map derived from the bouguer gravity data. The map was \noffshoot perpendicular and parallel which depict the presence of faults \nlines on the lineament map showing the minerals and rocks bearing zones \nof the region. It is necessary to separate the anomaly of interest; that is the \nresidual anomaly, from the remaining background anomaly which is the \nregional anomaly by involving the 3D tools mathematical operations of \nOasis montaj. Therefore, the residual anomaly was modeled to determine \nthe depth, density and geometry of the anomaly source using 3D Euler \nDeconvolution in x, y and z vertical offshoot of three index numbers; 0,1 \nand 2.  \n\n\n\nThe index numbers imaging reflects the geological structure of the region \nand the Euler map as shown in Fig.5 with different colour formations. The \nlight green to pink colors shows areas with superficial gravity anomalous \nbodies while the aqua to blue colors shows obscure anomalous gravity \nbodies. The results range from -1892.2 to -1278.3 m for obscure and -\n12264 to 644.6 m for superficial formations of basic indication. The final \nstep involves the forward and inverse modeling to determine the \ndensity/mass of the subsurface using residual gravity anomaly. This \nshows sequential dimensions of the density contrast between the body of \ninterest and the surrounding material. The techniques involve was \niterative modeling, where the earth\u2019s gravitational field due to the models \nis calculated and compared to the observed gravity anomalies. This was \ndone in three profiles for easily matching between the calculated and the \nobserved anomalies.  \n\n\n\nHowever, Fig. 6 shows a contour map with highest value of 3.4 m at the \nnorthern region and lowest value of 1.8 at the southern region with three \nprofiling of; P1, P2 and P3.  The profiling depicts three colors (blue, pink \nand gray) with cylindrical and ellipsoidal shapes, indicating accumulations \nof shale, petroleum and kaolinite as possible cause of gravity anomalies as \nshown in Fig.7. The modeling descriptions of cylindrical and ellipsoidal \nwere used to deduce the structural pattern in the region as shown in Table \n1. The table shows low gravity values attributed to anticline structural \ntrends, high gravity values attributed to syncline structural trends. The \nforward and inverse modeling depth values range from 2.5 to 4.8 km with \ncorresponding density and mass values ranging from (0.7 to 2.4) x 10-\n\n\n\n3kg/m3 and (27 to 133) x 1010 kg respectively. These density and mass \nvalues depicts the presents of strike-slip and normal faults with natural \ncavities of air, water and sediments-filled formations (Hunt et al., 1995). \nThese sediments filled formations with poor engineering geo-materials \nhas to be removed in order to avoid engineering problem and refilled with \ngood competent and rock quality formations which could enhance \nplanning and construction of good roads.   \n\n\n\n21 22 23 24 25\n\n\n\n16 17 18 19 20\n\n\n\n11 12 13 14 15\n\n\n\n6 7 8 9 10\n\n\n\n1 2 3 4 5\n\n\n\nFigure 2: 3D Gridded data point for SPI analysis \n\n\n\nFigure 3: 3D map of depth gravity sources showing sedimentary \n\n\n\ntopography \n\n\n\nFigure 4: Structural trends map showing the lines of faults in the study \n\n\n\narea \n\n\n\nA ER O G R AVIT Y\nEu le r 3 D  S I =  0\n     m e te rs\n\n\n\nFigure 5: 3D Aerogravity Euler depth map \n\n\n\nFigure 6: Contour map showing gravity profiles \n\n\n\nFigure 7: Profile trends of gravity anomaly \n\n\n\nTable 1: Structural stratigraphy trends, depths and density of modeling \n\n\n\nProfiles \nModel \n\n\n\nPatterns \nStructural \nPatterns \n\n\n\nDepths \n(km) \n\n\n\nDensity Contrast \n(10-3 kg/m3) \n\n\n\nFaults Cavities \nGravity anomaly \n\n\n\nCauses \nP1 Cylindrical Syncline 2.5 2.4 Strike-slip Natural Kaoline \nP2 Cylindrical Anticline 3.9 1.8 Strike- slip Natural Shale \nP3 Ellipsoidal Anticline 4.8 0.7 Normal Natural Petroleum \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 31-34 \n\n\n\nCite the Article: Johnson C. Ibout, Mfoniso U. Aka, Amarachukwu A. Ibe, Bethrand E. Oguama, Azuanamibebi D. Osu (2021). Delineation of Faults and Cavities Using \nGravity Techniques: An Implication for Road Construction, South-South Nigeria. Malaysian Journal of Geosciences, 5(1): 31-34. \n\n\n\nFigure 8: 3D forward and inverse modeling profiles \n\n\n\n4. CONCLUSION \n\n\n\nAerogravity data has been used to delineate faults and cavities using \ngravity techniques in order to aid in better roads construction. Gravity \ntechniques depict clearer information with regards to the presence of \nsubsurface faults and cavities and identify possible gravity sources. \nHowever, the presence of negative anomalies indicates natural cavities \nthat pose menace to ground bearing capacity in engineering constructions, \ndue to decreased density contrast caused by rock fracture with possible \ncalculation of depths of anomalies. From the results, bouguer gravity \nanomaly values, both shallow and deep depths were delineated in which \nthe negative values indicate low compactness of gravity. Also, low depth \nportrays loose sediments formation whereas high depth portray dense \nsediment formation indicating hydrocarbon mass. Three profile were \nmade showing cylindrical and ellipsoidal model patterns with syncline and \nanticline structure trends. These density/mass assess depicts the presents \nof strike-slip and normal faults with natural cavities of air, water and \nsediments-filled formations, indicating accumulations of shale, petroleum \nand kaolinite as possible cause of gravity anomalies within the study area. \n\n\n\nACKNOWLEDGEMENT  \n\n\n\nThe authors thank and acknowledge Nigeria Geological Survey Agency \n\n\n\n(NGSA) for the data used in this study.  \n\n\n\nREFERENCES \n\n\n\nAdesola, A.M., Ayokunle, A.A., Adebowale, A.O., 2017. Integrated \n\n\n\ngeophysical investigation for pavement failure along a dual carriage \n\n\n\nway, Southwestern Nigeria: a case study. Kuwait Journal of Science, 44 \n\n\n\n(4), Pp. 135-149. \n\n\n\nAka, M.U., Okeke, F.N., Ibuot, J.C., Obiora, D.N., 2018. Geotechnical \n\n\n\nInvestigation of near-surface structures using seismic refraction \n\n\n\ntechniques in parts of Akwa Ibom State, Southern, Nigeria. Modeling \n\n\n\nEarth systems and Enivronment, 4, Pp. 451-459. \n\n\n\nhttps:/doi.org/10.1007/s40808-018-0440-2 \n\n\n\nBenson, R.C., 2000. An Overview of Geophysical and Non-Destructive \n\n\n\nMethods for Characterization of Roads and Bridges,\u201d in Use of \n\n\n\nGeophysical Methods in Construction, Pp. 1\u201314.  \n\n\n\nBiswas, A., Parija, M.P., Kumar, S., 2017. Global nonlinear optimization for \n\n\n\nthe interpretation of source parameters from total gradient of gravity \n\n\n\nand magnetic anomalies caused by thin dyke. Ann. Geophys., 60 (2), Pp. \n\n\n\n218, 1-17. \n\n\n\nEkpa, M.M.M., Ibuot, J.C., Okeke, F.N., Obiora, D.N., 2020. Investigation of \n\n\n\nSubsurface Structures within Parts of Niger Delta, Nigeria, Via \n\n\n\nAeromagnetic Data. Geological Behavior, 4 (2), Pp. 66 - 71. \n\n\n\nEletta, B.E., Udensi, E.E., 2012. Investigation of the curie point Isotherm \n\n\n\nfrom the magnetic fields of Eastern sector of central Nigeria. \n\n\n\nGeosciences, 2 (4), Pp. 101-106. \n\n\n\nEzekiel, J.C., Onu, N.N., Akaolisa, C.Z., Opara, A.I., 2013. Preliminary \n\n\n\nInterpretation of gravity mapping over the Njaba sub-basin of \n\n\n\nSoutheastern Nigeria. An implication to petroleum potential. J. Geol. \n\n\n\nMin. Res., 5 (3), Pp. 75-87. \n\n\n\nHunt, C.P., Moskowitz, B.M., Banerjee, S.K., 1995. Magnetic properties of \n\n\n\nrocks and minerals, in Ahrens, T.J., ed., Rock Physics and Phase \n\n\n\nRelations: A Handbook of Physical Constants. Am. Geophys. Union, 3, Pp. \n\n\n\n189\u2013204. \n\n\n\nLehmann, B.D., Orlowsky, and Misiek, R., 2009. Exploration of Tunnel \n\n\n\nAlignment using Geophysical Methods to Increase Safety for Planning \n\n\n\nand Minimizing Risk. Rock Mechanics and Rock Engineering, 43 (1), Pp. \n\n\n\n105\u2013116. \n\n\n\nMaden, N., 2010. Curie-point depth from spectral analysis of magnetic data \n\n\n\nin Erciyes stratovolcano (central Turkey). Pure and Applied Geophysics, \n\n\n\n167 (3), Pp. 349-358. \n\n\n\nMandal, A., Biswas, A., Mittal, S., Mohanty, W.K., Sharma, S.P., Sengupta, D., \n\n\n\nSen, J., Bhatt, A.K., 2013. Geophysical anomalies associated with \n\n\n\nuranium mineralization from Beldih mine, South Purulia Shear Zone, \n\n\n\nIndia. J. Geol. Soc. Ind., 82 (6), Pp. 601-606. \n\n\n\nMandal, A., Mohanty, W.K., Sharma, S.P., Biswas, A., Sen, J., Bhatt, A.K., 2015. \n\n\n\nGeophysical signatures of uranium mineralization and its subsurface \n\n\n\nvalidation at Beldih, Purulia District, West Bengal, India: A case study. \n\n\n\nGeophys. Prospect., 63, Pp. 713-726. \n\n\n\nMickus, K., 2004. The gravity method in engineering and environmental \n\n\n\napplications, In: Geophysics 2003: Federal Highway Administration and \n\n\n\nFlorida Department of Transportation special publication. \n\n\n\nMilsom, J., 2003. Field Geophysics, The geological field guide series, 3rd ed. \n\n\n\nWiltshire (UK): Wile, Pp. 321. \n\n\n\nNwankwo, L.I., Olasehinde, P.I., Akoshile, C.O., 2011. Heat flow anomalies \n\n\n\nfrom the spectral analysis of Airborne Magnetic data of Nupe Basin, \n\n\n\nNigeria. Asian Journal of Earth Sciences, 1 (1), Pp. 1-6. \n\n\n\nObiora, D.N., Ossai, M.N. and Okwoli, E., 2016. A case study of aeromagnetic \n\n\n\ndata interpretation of Nsukka area, Enugu State, Nigeria for \n\n\n\nhydrocarbon exploration. International Journal of Physical Sciences, 10 \n\n\n\n(17), Pp. 503-519. \n\n\n\nObiora, D.N., Ossai, M.N., Okwoli, E., 2015. A case study of aeromagnetic \n\n\n\ndata interpretation of Nsukka area, Enugu state, Nigeria, for \n\n\n\nhydrocarbon exploration. International Journal of Physical Sciences, 10 \n\n\n\n(17), Pp. 503519. \n\n\n\nOha, I.A., Onuoha, K.M., Nwegbu, A.N., Abba, A.U., 2016. Interpretation of \n\n\n\nhigh-resolution aeromagnetic data over southern Benue Trough, \n\n\n\nsoutheastern Nigeria. Journal of Earth System Science, 125 (2), Pp. 369\u2013 \n\n\n\n385. \n\n\n\nOkiwelu, A.A. ofrey-Kulo, O. and Ude, I.A., 2013. Interpretation of regional \n\n\n\nmagnetic data offshore Niger Delta reveals relationship between deep \n\n\n\nbasement architecture and hydrocarbon target. Earth Science Research, \n\n\n\n2 (1), Pp. 13-32.  \n\n\n\nReynolds, J., 2011.  An introduction to applied and environmental \n\n\n\ngeophysics, 2nd ed. Oxford: wiley blackwell, Pp. 695 \n\n\n\nSharma, S.P., Biswas, A., 2013. Interpretation of self-potential anomaly \n\n\n\nover 2D inclined structure using very fast simulated annealing global \n\n\n\noptimization\u2013An insight about ambiguity. Geophysics, 78 (3), Pp. WB3\u2013\n\n\n\n15. \n\n\n\nTelford, W.M., Geldart, L.P., 1990. Sheriff Applied geophysics. New York: \n\n\n\nCambridge University Press. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 33-38  \n\n\n\nCite The Article: Isfarita Ismail, Wan Salihin Wong Abdullah,  Aidy @ Mohamed Shawal M. Muslim Rozaimi Zakaria (2018). Physical Impact Of Sea Level Rise To The \nCoastal Zone Along The East Coast Of Peninsular Malaysia.  Malaysian Journal of Geosciences, 2(2) : 33-38. \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 26 June 2018  \nAccepted 2 July 2018  \nAvailable online 1 August  2018 \n\n\n\nABSTRACT\n\n\n\nABSTRACT \n\n\n\nSea level rise around the world caused by global warming since decade and effects on coastal especially country \nbelow mean sea level and country island. The prediction of sea level rise by 2100 is over 3m. Sea level rise increases \ncaused by melting ice and thermal expansion. The impact of sea level rise concentrated along the coastal area. This \npaper studied the impact of sea level rise to physical parameters along the East Coast of Peninsular Malaysia. Seven \nphysical variables such as geomorphology, shoreline change rate, coastal slope, lithology, maximum wave height, \nmean tidal range and sea level change were chosen to find an physical vulnerability index. The index also was \nmapped using ArcGIS software to picture the vulnerability. The worst area for physical vulnerability index is along \nthe Pahang coastline especially Kuantan district. The prevention and adaptation from government and non-\ngovernment agencies should be taken to reduce the effects of sea level rise.  \n\n\n\n KEYWORDS \n\n\n\nSea level rise, GIS, Physical Vulnerability Index, Simulation sea level rise.\n\n\n\n1. INTRODUCTION \n\n\n\nSea level rise is the rising of sea level caused by several factors such as \nthermal expansion and melting ice. This sea level rise are recorded using \ngeological data, tide gauge records and satellite altimeter [1-7]. There are \ntwo types of a sea level rise: eustatic and isostatic. Eustatic refers to the \nrise in global sea levels due to global warming. It changes as a result in an \nalteration to the global sea levels, such as changes in the volume of water \nin the world oceans or changes in the volume of an ocean basin. While an \nisostatic level rise refers to the increase in the water level of local \nreference areas only.  \n\n\n\nOver 140,000 years ago, sea levels were changing at an average rate of \nabout 10 mm per year (1 m per century) during the end of the last ice age. \nSea level stabilized in a few thousand years and there was little change \nbetween 1AD and 1800AD. Sea level rose much more slowly over the past \n7,000 years. In the 19th century, sea level rose again and rose more rapidly \nin the 20th century. IPCC (2007) report that \u201cThe average rate was 1.7\u00b10.5 \nmm/year for the 20th century, 1.8\u00b10.5 mm/year for 1961-2003, and \n3.1\u00b10.7 mm/year for 1993-2003\u201d clearly showing the increase of global \nmean of sea level rise between mid-19th and 20th century\u201d. \n\n\n\nThe impacts of a sea level rise will vary according to the local water levels \ndue to local variations in vertical crustal movements, topography, wave \nclimatology, long shore currents, and storm frequencies. The sea level rise \nwill impact the coastal zone on physical, socioeconomic, ecology, biological \nand chemical aspect. Besides the increasing flood level risk, sea level rise \nalso causes erosion, sedimentation deficits, inundation of low-lying areas, \nsaltwater intrusion and biological effects. \n\n\n\nFor this study, the physical impact of sea level rise was concentrated. \nPhysical impacts of sea level rise is vulnerability towards impact on the \n\n\n\ncoastal in terms of the physical aspects. Physical vulnerability concerns \nthe ultimate impacts of a hazard event, and is often viewed in terms of the \namount of damage experienced by a system as a result of an encounter \nwith a hazard [8]. \n\n\n\nTo determine the impact of rising water levels, the physical vulnerability \nindex should be calculated. There are various terms used to designate \nphysical vulnerability index such as CVI, CSI [9-16]. The variables can \neffectively measure the both erosion and inundation risk factors [17,18]. \nThe erosion risk could be determined based on the factors related to \ngeology, geomorphology, tidal ranges and wave heights whereas the \ninundation risk could be estimated by sea-level and elevation data. \n\n\n\n2. METHODOLOGY\n\n\n\nEast Coast of Peninsular Malaysia extends over 675 km facing the South \nChina Sea and is shared by four coastal states i.e. Kelantan, Terengganu, \nPahang and Johor. These states cover fifteen districts along the coastline \nstarting from Tumpat in Kelantan to Kota Tinggi in Johor. The study sites \ndo not include the islands along the East Coast because of the lack of \nphysical data.  \n\n\n\nThe coastline along the East Coast of Peninsular Malaysia is well known \nfor its sandy beach, aquaculture ponds, agriculture area, recreational area, \nand major and minor industries. Environmental Sensitivity Index reported \nthat there is not much coastal development along the coastal areas of the \neast coast of Peninsular Malaysia [19]. Meanwhile major town for each \nstate located near the coastal area such as Kota Bharu, Kuala Terengganu \nand Kuantan. \n\n\n\nFor this study, seven variables were considered to be included in the \ndevelopment of physical vulnerability index. They are geomorphology, \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: https://doi.org/10.26480/mjg.02.2018.33.38 \n\n\n\nPHYSICAL IMPACT OF SEA LEVEL RISE TO THE COASTAL ZONE ALONG THE EAST \nCOAST OF PENINSULAR MALAYSIA \n\n\n\nIsfarita Ismail1*, Wan Salihin Wong Abdullah3,  Aidy @ Mohamed Shawal M. Muslim2 Rozaimi Zakaria4 \n\n\n\n1Borneo Marine Research Institute, Universiti Malaysia Sabah, Jalan UMS, 88400, Malaysia. \n2Institute of Oceanography and Environment, Universiti Malaysia Terengganu, 21030 Kuala Terengganu, Malaysia  \n3Center of Quality Assurance and Accrediation, Universiti Malaysia Kelantan, Locked bag 36, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, \nMalaysia  \n4Mathematics, Graphics and Visualization Research Group (M-GRAVS), Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Jalan \nUMS, 88400, Malaysia. \n*Corresponding author email: isfarita@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\n\n\n\n\nPrint ISSN : 2521-0920 \nOnline ISSN : 2521-0602  \n\n\n\nCODEN: MJGAAN \n\n\n\n\nhttps://doi.org/10.26480/mjg.02.2018.33.38\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 33-38 \n\n\n\nCite The Article: Isfarita Ismail, Wan Salihin Wong Abdullah,  Aidy @ Mohamed Shawal M. Muslim Rozaimi Zakaria (2018). Physical Impact Of Sea Level Rise To The \nCoastal Zone Along The East Coast Of Peninsular Malaysia.  Malaysian Journal of Geosciences, 2(2) : 33-38. \n\n\n\nshoreline change rate, coastal slope, lithology, maximum wave height, \nmean tidal range and sea level change. \n\n\n\n2.1 Geomorphology/Landform \n\n\n\nGeomorphology is the scientific study of landforms and the processes that \nshape them. The geomorphology variable that are of concern the relative \nerodibility of the different landform types. A summary of the landforms is \npresented in Table 1 below.  \n\n\n\nTable 1: Geomorphology of each district along the East Coast of \n\n\n\nPeninsular Malaysia [19]. \n\n\n\nState District Geomorphology \n\n\n\nKelantan \n\n\n\nTumpat Deltas, sandy beaches \n\n\n\nKota Bharu Sandy beaches \n\n\n\nBachok Sandy beaches \n\n\n\nPasir Puteh Sandy beaches \n\n\n\nTerengganu \n\n\n\nBesut Sandy beaches \n\n\n\nSetiu Mudflats, sandy beaches \n\n\n\nKuala \n\n\n\nTerengganu \n\n\n\nBarrier beaches, sandy \n\n\n\nbeaches \n\n\n\nMarang Barrier beaches, sandy \n\n\n\nbeaches \n\n\n\nDungun Sandy beaches \n\n\n\nKemaman Mudflats, barrier beaches, \n\n\n\nsandy beaches \n\n\n\nPahang \n\n\n\nKuantan Mudflats, sandy beaches \n\n\n\nPekan Sandy beaches \n\n\n\nRompin Sandy beaches \n\n\n\nJohor Mersing Mudflats, sandy beaches \n\n\n\nKota Tinggi Estuary, beaches, lagoon \n\n\n\n2.2 Shoreline Change Rate (Erosion and Accretion) \n\n\n\nCoastal erosion is due to natural causes as a result of shoreline response \nto natural shoreline conditions driven by meteorological ocean conditions \nof wind, waves, tides and currents. Coastal erosion is pronounced in \nmorphological active areas and areas immediately adjacent to river \nmouths which are subject to considerable short-term morphological \nchanges. Table 2 below shows shoreline change rate along the East Coast \nof Peninsular Malaysia.  \n\n\n\nTable 2: Shoreline change rate (erosion and accretion) along the East \n\n\n\nCoast of Peninsular Malaysia [20]. \n\n\n\nState District Shoreline Change \n\n\n\nRate \n\n\n\nKelantan \n\n\n\nTumpat Stable \n\n\n\nKota Baharu -5 \n\n\n\nBachok -5 \n\n\n\nPasir Puteh -4 \n\n\n\nTerengganu \n\n\n\nBesut Stable \n\n\n\nSetiu -3 \n\n\n\nKuala Terengganu -5 \n\n\n\nMarang -4 \n\n\n\nDungun -10 \n\n\n\nKemaman Stable \n\n\n\nPahang Kuantan Stable \n\n\n\nPekan Stable \n\n\n\nRompin Stable \n\n\n\nJohor Mersing Stable \n\n\n\nKota Tinggi Stable \n\n\n\nThe shoreline change rate along the Malaysia coastline was monitored by \nDepartment of Irrigation and Drainage since last few decades. The data of \nthis study also provided by DID (1985). This data was carried out in 1985 \nfrom National Coastal Erosion Malaysia Study (NCES). They mentioned \nthat about 29% of shoreline are eroding. They identified three categories \nof erosion: category I (critical), category II (significant)and category III \n(acceptable). Natural causes of coastal erosion are tides and currents, \nstorm waves and sea level rise. \n\n\n\n2.3 Coastal Slope \n\n\n\nThe slope of the immediate hinterland is one of the most important factors \nto be considered in estimating the impact of sea level rise on a given coast \n[12]. Steep slopes experience less flooding compared to gentle to \nmoderately sloping coasts where any rise in sea level will inundate larger \nextents of land [16]. The coastal slope is the main factor of area inundation. \nSloping areas flood faster than the steep areas. The coastal slope data was \nprovided by ESI which covers slope along the East Coast of Peninsular \nMalaysia [19]. Table 3 below shows coastal slope along the East Coast of \nPeninsular Malaysia \n\n\n\nTable 3: Coastal slope for each district along the East Coast of Peninsular \n\n\n\nMalaysia [19]. \n\n\n\nState District Coastal Slope \n\n\n\nKelantan Tumpat 0.573 - 4.093 \n\n\n\nKota Bharu 1.05 - 4.17 \n\n\n\nBachok 1.9 - 4.5 \n\n\n\nPasir Puteh 1.99 - 4.36 \n\n\n\nTerengganu \n\n\n\nBesut 3.74 - 8.19 \n\n\n\nSetiu 2.7 - 10.7 \n\n\n\nKuala Terengganu 1.67 - 10.25 \n\n\n\nMarang 5.62 - 12.82 \n\n\n\nDungun 4.3 - 10.89 \n\n\n\nKemaman 3.595 - 9.598 \n\n\n\nPahang Kuantan 0.91 - 7.52 \n\n\n\nPekan 1.38 - 7.64 \n\n\n\nRompin 1.53 - 8.14 \n\n\n\nJohor Mersing 0.11 - 9.94 \n\n\n\nKota Tinggi 0.94 - 9.65 \n\n\n\n2.4 Wave Height  \n\n\n\nMaximum wave height is used as a proxy for wave energy which drives \ncoastal sediment transport. Wave data are obtain from the Malaysian \nMeteorological Department. The data are derived from marine surface \nobservations reported by ship that participated in the World \nMeteorological Organization (WMO) Voluntary Observing Ships Scheme, \noilrigs/oil platforms and light houses that are located in the Malaysian \nwaters [21]. Table 4 below shows maximum wave height for 12 months \nalong the East Coast of Peninsular Malaysia \n\n\n\nTable 4: Wave height along the East Coast of Peninsular Malaysia [21]. \n\n\n\nMonth \n\n\n\nState \n\n\n\nKelantan Terengganu Pahang Johor \n\n\n\nJan - 1 3 2 \n\n\n\nFeb 1 1.5 2 3 \n\n\n\nMar - 1 1 2.5 \n\n\n\nApr - 2 2 1 \n\n\n\nMay - - 1 1 \n\n\n\nJun 1.5 1.5 4.5 1 \n\n\n\nJul 2.5 1.5 7 2 \n\n\n\nAug - 2 2 1.5 \n\n\n\nSep - - 2 1.5 \n\n\n\nOct - 1 4 1 \n\n\n\nNov - 1.5 2 1.5 \n\n\n\nDec - 2.5 2.5 2.5 \n\n\n\nThe wave height along the coastal area in range 1 to 7 m. All of districts for \nKelantan and Terengganu in rank 1 (very low) while Johor in ranking 2 \n(low). Pahang have the highest ranking which is no 5 (very high). \n\n\n\n2.5 Tidal Range \n\n\n\nThe tide is rising and lowering of sea levels caused by the combined effects \nof gravitational forces due to the Moon, Sun and Earth\u2019s rotation. Tides are \nnot constant but vary depending on the position of the Moon and the Sun. \nMalaysia\u2019s coasts are influenced by diurnal, semi diurnal and mixed tides.  \nThe data used for tide in the calculation of physical vulnerability index \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 33-38 \n\n\n\nCite The Article: Isfarita Ismail, Wan Salihin Wong Abdullah,  Aidy @ Mohamed Shawal M. Muslim Rozaimi Zakaria (2018). Physical Impact Of Sea Level Rise To The \nCoastal Zone Along The East Coast Of Peninsular Malaysia.  Malaysian Journal of Geosciences, 2(2) : 33-38. \n\n\n\n(PVI) is obtained from the Mike C Map software. Mike C Map is an efficient \ntool for extracting depth data and predicted tidal elevation from the world \nwide Electronic Chart Database CM-93 Edition 3.0, named C-Map \nprofessional +, manufactured by Jeppesen Marine AS,Norway. Mean tidal \nrange is linked to both permanent and episodic inundation hazards. Mean \ntidal range below 1.9 is classified as low and very low, mean tidal range in \nbetween 2.0 to 4.0 is moderate and more than 4.1 is classified as high and \nvery high vulnerability. Table 5 below shows tidal range along the East \nCoast of Peninsular Malaysia. \n\n\n\nTable 5: Tidal range along the East Coast of Peninsular Malaysia. \n\n\n\nState District Tidal Range \n\n\n\nKelantan \n\n\n\nTumpat 1.788 \n\n\n\nKota Bharu 1.788 \n\n\n\nBachok 1.788 \n\n\n\nPasir Puteh 1.788 \n\n\n\nTerengganu \n\n\n\nBesut 2.21 \n\n\n\nSetiu 2.114 \n\n\n\nKuala Terengganu 2.118 \n\n\n\nMarang 2.582 \n\n\n\nDungun 2.605 \n\n\n\nKemaman 2.394 \n\n\n\nPahang \n\n\n\nKuantan 3.03 \n\n\n\nPekan 2.999 \n\n\n\nRompin 2.783 \n\n\n\nJohor Mersing 2.845 \n\n\n\nKota Tinggi 2.563 \n\n\n\n2.6 Sea Level Rise Change \n\n\n\nSea level rise is the changes in sea level datum calculated on average 15 \n\n\n\nyears. For the purpose of this study data for sea level rise change is \n\n\n\nadopted from a study [7]. The author based on their findings from analysis \n\n\n\nof 12 tidal gauges from the coastline of Peninsular Malaysia.  \n\n\n\nThis tidal network was operational since 1984. Sea level rise change \n\n\n\nvariable is derived from the change in annual mean water elevation over \n\n\n\ntime as measured at tide gauge stations along the coast such as Tanjung \n\n\n\nSedili, Pulau Tioman, Tanjung Gelang, Chendering and Geting. Table 6 \n\n\n\nshows the sea level rise change along the East Coast of Peninsular \n\n\n\nMalaysia. \n\n\n\nTable 6: Sea level rise change along the East Coast of Peninsular \n\n\n\nMalaysia. \n\n\n\nState District Sea level rise change \n\n\n\n(mm/year) \n\n\n\nTumpat \n\n\n\nKelantan Kota Bharu 1.73 \n\n\n\nBachok \n\n\n\nPasir Puteh \n\n\n\nTerengganu \n\n\n\nBesut \n\n\n\nSetiu \n\n\n\nKuala Terengganu 3.2 \n\n\n\nMarang \n\n\n\nDungun \n\n\n\nKemaman \n\n\n\nPahang \n\n\n\nKuantan \n\n\n\nPekan 2.64 \n\n\n\nRompin \n\n\n\nJohor Mersing 1.83 \n\n\n\nKota Tinggi \n\n\n\n2.7 Lithology (Rock Type) \n\n\n\nRock type variable represents the bedrock occurring at, or underlying the \nshoreline [16]. According to Dictionary of Environmental Science (2003), \n\u201csediment is defined as the organic and inorganic materials or solid \nfragments derived from the weathering processes of sand, pebbles, silt, \nmud and loess (fine-grained soil). These are then carried by wind, ice or \nother naturally occurring agents. Sediments can also be defined as the \nmaterial deposited at the bottom of rivers, which are silt and deposits\u201d. \n\n\n\nThere are many factors affecting the separation of settleable solids from \nwater. The common types of factors to consider are particle size, water \ntemperature and currents. Lithology is categorized into five ranking; 1) \nvery low, low, medium, high and very high. Very low rank is high medium, \ngrade and metamorphic, low rank is low grade and metamor, medium is \nsedimentary rock, high is coarse and/or poorly sorted and unconsolidated \nsediment and very high ranking is fine, and unconsolidated sediment.  \n\n\n\nBeaches in Malaysia constituted of easily erodible. The east coast shoreline \nconsists of sand along 860km whereas west coast consist of silt and clay \nalong the 110 km length. Table 7 below shows lithology along the East \nCoast of Peninsular Malaysia. \n\n\n\nTable 7: Rock type along the East Coast of Peninsular Malaysia. \n\n\n\nState District Lithology \n\n\n\nKelantan \n\n\n\nTumpat Fine- unconsolidated sediment \n\n\n\nKota Bharu Coarse and/or poorly sorted, \n\n\n\nUnconsolidated sediment \n\n\n\nBachok Coarse and/or poorly sorted, \n\n\n\nUnconsolidated sediment \n\n\n\nPasir Puteh Coarse and/or poorly sorted, \n\n\n\nUnconsolidated sediment \n\n\n\nTerengganu \n\n\n\nBesut Coarse and/or poorly sorted, \n\n\n\nUnconsolidated sediment \n\n\n\nSetiu Coarse and/or poorly sorted, \n\n\n\nUnconsolidated sediment \n\n\n\nKuala \n\n\n\nTerengganu \n\n\n\nCoarse and/or poorly sorted, \n\n\n\nUnconsolidated sediment \n\n\n\nMarang Coarse and/or poorly sorted, \n\n\n\nUnconsolidated sediment \n\n\n\nDungun Coarse and/or poorly sorted, \n\n\n\nUnconsolidated sediment \n\n\n\nKemaman Coarse and/or poorly sorted, \n\n\n\nUnconsolidated sediment \n\n\n\nPahang \n\n\n\nKuantan Fine- unconsolidated sediment \n\n\n\nPekan Coarse and/or poorly sorted, \n\n\n\nUnconsolidated sediment \n\n\n\nRompin Coarse and/or poorly sorted, \n\n\n\nUnconsolidated sediment \n\n\n\nJohor Mersing Coarse and/or poorly sorted, \n\n\n\nUnconsolidated sediment \n\n\n\nKota Tinggi Most sedimentary rock \n\n\n\nAfter identified the physical variables, then the variables were ranked into \n5 ranking (Table 8). Rank 5 means very high risk and rank 1 means very \nlow risk. Then variables are calculated using the physical vulnerability \nindex equation created by Gornitz to assess the risk of rising sea levels on \nthe east coast of Peninsular Malaysia [17]. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 33-38 \n\n\n\nCite The Article: Isfarita Ismail, Wan Salihin Wong Abdullah,  Aidy @ Mohamed Shawal M. Muslim Rozaimi Zakaria (2018). Physical Impact Of Sea Level Rise To The \nCoastal Zone Along The East Coast Of Peninsular Malaysia.  Malaysian Journal of Geosciences, 2(2) : 33-38. \n\n\n\nTable 8: Ranking of coastal risk classes for physical variables [17]. \n\n\n\nVariable Rank \n\n\n\nVery Low \n\n\n\n1 \n\n\n\nLow \n\n\n\n2 \n\n\n\nModerate \n\n\n\n3 \n\n\n\nHigh \n\n\n\n4 \n\n\n\nVery High \n\n\n\n5 \n\n\n\n1. Slope \u226530.1 20.1 \u2013 30.0 10.1 - 20.0 5.1 \u2013 10.0 0 \u2013 5.0 \n\n\n\n2. Rock Type \n\n\n\n(Lithology) \n\n\n\nHigh medium \n\n\n\ngrade \n\n\n\nmetamorphics \n\n\n\n-Low grade \n\n\n\nmetamor \n\n\n\n-Sandstone and \n\n\n\nconglomerate \n\n\n\nMost \n\n\n\nsedimentary \n\n\n\nrocks \n\n\n\n-Coarse and/or \n\n\n\npoorly-sorted \n\n\n\n-\n\n\n\nUnconsolidate\n\n\n\nd sediments \n\n\n\nFine-\n\n\n\nunconsolidated \n\n\n\nsediment \n\n\n\n3. \n\n\n\nGeomorphology \n\n\n\n/ Landform \n\n\n\n-Rocky, cliffed \n\n\n\n-Coasts \n\n\n\n-Fiords \n\n\n\n-Fiards \n\n\n\n-Medium cliffs \n\n\n\n-Indented coasts \n\n\n\n-Low cliffs \n\n\n\n-Glacial drift \n\n\n\n-Salt marsh \n\n\n\n-Coral reef \n\n\n\n-Mangrove \n\n\n\n-Beaches \n\n\n\n-Estuary \n\n\n\n-Lagoon \n\n\n\n-Alluvial plains \n\n\n\n-Barrier beaches \n\n\n\n-Beaches (sand) \n\n\n\n-Mudflats \n\n\n\n-Deltas \n\n\n\n4. Sea Level Rise \n\n\n\nChange (vertical \n\n\n\nmovement) \n\n\n\n\u2264-1.1 \n\n\n\nLand rising \n\n\n\n-1.0 \u2013 0.99 \n\n\n\nLand rising\n\n\n\n1.0 \u2013 2.0 \n\n\n\nWithin range of \n\n\n\neustatic rise \n\n\n\n2.1 \u2013 4.0 \n\n\n\nLand sinking \n\n\n\n\u22654.1 \n\n\n\nLand sinking \n\n\n\n5. Shoreline \n\n\n\nChange Rate \n\n\n\n(erosion \n\n\n\n@accretion) \n\n\n\n\u22652.1 \n\n\n\nAccretion \n\n\n\n1.0 \u2013 2.0 \n\n\n\nAccretion \n\n\n\n-1.0 - +1.0 \n\n\n\nStable \n\n\n\n-1.1 - -2.0 \n\n\n\nErosion \n\n\n\n\u2264-2.0 \n\n\n\nErosion \n\n\n\n6. Tidal Range \n\n\n\n(Mean) \n\n\n\n\u22640.99 \n\n\n\nMicrotidal \n\n\n\n1.0 \u2013 1.9 \n\n\n\nMicrotidal \n\n\n\n2.0 \u2013 4.0 \n\n\n\nMesotidal \n\n\n\n4.1 \u2013 6.0 \n\n\n\nMacrotidal \n\n\n\n\u22656.1 \n\n\n\nMacrotidal \n\n\n\n7. Wave Height \n\n\n\n(Max) \n\n\n\n0 \u2013 2.9 3.0 \u2013 4.9 5.0 \u2013 5.9 6.0 \u2013 6.9 \u22657.0 \n\n\n\nAfter identified variables for physical parameter, an index for each \nvariable were calculated. According to some study, the physical \nvulnerability index (PVI) is calculated as the square root of the product of \nthe ranked variables divided by the total number of variables [9, 22-24]; \n\n\n\nPVI = \u221a(\ud835\udc4e \u2217 \ud835\udc4f \u2217 \ud835\udc50 \u2217 \ud835\udc51 \u2217 \ud835\udc52 \u2217 \ud835\udc53 \u2217 \ud835\udc54)/7 \n\n\n\nWhere a = geomorphology, b = shoreline change rate, c = coastal slope, d = \nmax wave height, e = mean tidal range, f = sea level rise change and g = \nrock type.   \n\n\n\nThe variables of physical vulnerability index then were mapped using \n\n\n\nArcGIS software. The map has five ranks from very high, high, moderate, \nlow to very low. Very high ranking means the area is most vulnerable and \nvery low vulnerability means least vulnerable, to sea level rise. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nSeven variables were selected for the physical parameters along the East \nCoast of Peninsular Malaysia. These variables are qualitative data and \nclassified using the coastal risk classes used (Refer Table 8) [9]. Then the \nvalue will be calculated to find an index. Table 9 below shows the physical \nrisk classes for each district along the east coast of Peninsular Malaysia. \n\n\n\nTable 9: Physical risk classes for each variable. \n\n\n\nState District \nVariable \n\n\n\nCoastal \nSlope Geomorphology \n\n\n\nSLR \nChange \n\n\n\nShoreline \nChange \nRate \n\n\n\nWave \nHeight \n\n\n\nRock \nType \n\n\n\nTidal \nRange \n\n\n\nKelantan \nTumpat 5 5 3 3 1 5 2 \n\n\n\nKota Bharu 5 5 3 5 1 4 2 \n\n\n\nBachok 5 5 3 5 1 4 2 \n\n\n\nPasir Puteh 5 5 3 5 1 4 2 \nTerengganu \n\n\n\nBesut 5 5 4 3 1 4 3 \n\n\n\nSetiu 4 5 4 5 1 4 3 \nKuala \nTerengganu 4 5 4 5 1 4 3 \n\n\n\nMarang 4 5 4 5 1 4 3 \n\n\n\nDungun 4 5 4 5 1 4 3 \n\n\n\nKemaman 5 5 4 3 1 4 3 \nPahang \n\n\n\nKuantan 5 5 4 3 5 5 3 \n\n\n\nPekan 5 5 4 3 5 4 3 \n\n\n\nRompin 5 5 4 3 5 4 3 \nJohor \n\n\n\nMersing 5 5 3 3 2 4 3 \n\n\n\nKota Tinggi 5 4 3 3 2 3 3 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 33-38 \n\n\n\nCite The Article: Isfarita Ismail, Wan Salihin Wong Abdullah,  Aidy @ Mohamed Shawal M. Muslim Rozaimi Zakaria (2018). Physical Impact Of Sea Level Rise To The \nCoastal Zone Along The East Coast Of Peninsular Malaysia.  Malaysian Journal of Geosciences, 2(2) : 33-38. \n\n\n\nAfter giving rank to each variable, physical vulnerability index is \ncalculated as the square root of the product of the ranked variables divided \n\n\n\nby the total number of variables. Table 10 below shows the physical \nvulnerability index for the districts. \n\n\n\nTable 10: Physical vulnerability index along the East Coast of Peninsular Malaysia. \n\n\n\nState District PVI Characterization \n\n\n\nKelantan Tumpat 17.93 Very low \n\n\n\nKota Bharu 20.7 Very low \n\n\n\nBachok 20.7 Very low \n\n\n\nPasir Puteh 20.7 Very low \n\n\n\nTerengganu Besut 22.68 Low \n\n\n\nSetiu 26.19 Low \n\n\n\nKuala Terengganu 26.19 Low \n\n\n\nMarang 26.19 Low \n\n\n\nDungun 26.19 Low \n\n\n\nKemaman 22.68 Low \n\n\n\nPahang Kuantan 56.69 Moderate \n\n\n\nPekan 50.71 Moderate \n\n\n\nRompin 50.71 Moderate \n\n\n\nJohor Mersing 27.77 Low \n\n\n\nKota Tinggi 21.51 Low \n\n\n\nThe areas with the highest vulnerability value are Kuantan followed by \nPekan and Rompin while the lowest values at Tumpat district. Value of \nvulnerability is influenced by physical factors in the districts such as \ngeomorphology, shoreline change rate, coastal slope, wave height, tidal \nrange, sea level rise change and rock type.  \n\n\n\nThe values of a vulnerable area were mapped using ArcGIS to get a picture \nof the vulnerability. The vulnerability level is ranked into five categories \nsuch as very low, low, moderate, high and very high. Very high category \nmeans the most vulnerable level. This study only focuses on the districts \nnear the coast. As a recollect there are fifteen districts along the coast of \nthe east coast of Peninsular Malaysia.  \n\n\n\nThe vulnerability indices are mapped according to the lowest (0) to \npossible maximum score. The possible maximum score for physical \nvulnerability index is 105.6. For this study, there are no district will very \nhigh and high category of the physical vulnerability index. The moderate \nranks consist Kuantan, Pekan and Rompin, low ranks are at the districts of \nBesut, Setiu, Kuala Terengganu, Marang, Dungun, Kemaman, Mersing and \nKota Tinggi while very low rank included the districts of Tumpat, Kota \nBharu, Bachok, and Pasir Puteh (Figure 1). \n\n\n\nFigure 1: Physical Vulnerability Index map. The red color shows the \n\n\n\nmost vulnerable followed by blue (high), green (moderate), pink (low) \n\n\n\nand yellow (very low). The districts along the coastal are colored by dark \n\n\n\nbrown. \n\n\n\n4. CONCLUSION \n\n\n\nGlobal warming is very serious effect on the whole world and in Malaysia \nin particular. Global warming is caused by greenhouse gases resulting \nfrom human activity. As the Earth continues to warm, there is a growing \nrisk that the climate will change in ways that will disrupt our lives. The \nimpacts caused by global warming is climate change such as rising sea \nlevels, droughts and wildfires, hurricanes, acid rain, warmer oceans, \nchanges in plant life cycles and more extreme weather events. Among the \ngases that contribute to the greenhouse effect are carbon dioxide (CO2), \nmethane (CH4), nitrogen dioxide (N2O) and CFCs.  \n\n\n\nThere are seven variables for physical parameters namely \ngeomorphology, shoreline change rate, coastal slope, mean wave height, \nmean tidal range, sea level rise change and rock type. Each of the seven \nvariables, relating to coastal inundation and erosion hazards, has been \nassigned a rank, from 1 to 5, based on the relative risk factor. These risk \nfactors are then combined into an overall physical vulnerability index, PVI, \nhere taken as the square root of the geometric mean of the risk classes. \nThe worst physical vulnerable district along the east coast of Peninsular \nMalaysia is Kuantan, Pekan and Rompin while the least vulnerable area is \nTumpat. The main objective of finding the coastal indices is the \nclassification coastal beaches in the units for displaying the properties or \ncharacteristics of the same.  \n\n\n\nThe worst physical vulnerability indices are interconnected with each \nother, which consists of the geomorphology area of sandy beaches, \nmudflats and deltas, most of this beach area is exposed to erosion with \nsloping coastal slope which <5.0, rock types are fine unconsolidated \nsediment, while sea level rise change \u2265 2.1. Tidal range along the coast is \nbetween 2.0 \u2013 4.0 m which mesotidal type and wave height is between 7 \nm and are ranked in category 5. \n\n\n\nPhysical vulnerability index was mapped using GIS software to picture of \nvulnerable areas. They are visualized in ArcGIS 10. The map will be \nclassified into five categories such as very low, low, moderate, high and \nvery high. Very high showed the most vulnerable and very low means least \nvulnerable. The most vulnerable is colored by red color while highly \nvulnerable is blue color, moderately vulnerable is green color, low \nvulnerable is pink color and very low vulnerable is yellow color. \n\n\n\nNevertheless, it is prudent for policy makers to develop policies to lessen \nthe risk of sea level rise especially to sensitive areas and shift new \ndevelopment and infrastructure to areas less prone to sea level rise.  \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThis research was supported from MOSTI for the E-Science fund (Vot. \nNumber 52061) and also MyBrain Scholarship (under MyPhD) for \ngranting a scholarship for three years. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 33-38 \n\n\n\nCite The Article: Isfarita Ismail, Wan Salihin Wong Abdullah,  Aidy @ Mohamed Shawal M. Muslim Rozaimi Zakaria (2018). Physical Impact Of Sea Level Rise To The \nCoastal Zone Along The East Coast Of Peninsular Malaysia.  Malaysian Journal of Geosciences, 2(2) : 33-38. \n\n\n\nREFERENCES \n\n\n\n[1] Donnelly, J.P., Cleary, P., Newby, P., Ettinger, R. 2004. Coupling \n\n\n\ninstrumental and geological records of sea\u2010level change: Evidence from \n\n\n\nsouthern New England of an increase in the rate of sea\u2010level rise in the late \n\n\n\n19th century. Geophysical Research Letters, 31 (5). \n\n\n\n[2] Church, J., Gregory, J., Huybrechts, P., Kuhn, M., Lambeck, K., Nhuan, M. \n\n\n\n2001. Changes in sea level. , in: JT Houghton, Y. Ding, DJ Griggs, M. Noguer, \n\n\n\nPJ Van der Linden, X. Dai, K. Maskell, and CA Johnson (eds.): Climate \n\n\n\nChange 2001: The Scientific Basis: Contribution of Working Group I to the\n\n\n\nThird Assessment Report of the Intergovernmental Panel, 639-694. \n\n\n\n[3] Church, J.A., White, N.J. 2006. A 20th century acceleration in global sea-\n\n\n\nlevel rise. Geophys. Res. Lett., 33 (1), L01602. \n\n\n\n[4] Woodworth, P. 1990. A search for accelerations in records of European\n\n\n\nmean sea level. International Journal of Climatology, 10 (2), 129-143. \n\n\n\n[5] Woodworth, P.L. 1999. High waters at Liverpool since 1768: the UK's \n\n\n\nlongest sea level record. Geophysical Research Letters, 26 (11), 1589-\n\n\n\n1592. \n\n\n\n[6] Maul, G.A., Martin, D.M. 1993. Sea level rise at Key West, Florida, 1846\u2010\n\n\n\n1992: America's longest instrument record? Geophysical Research Letters, \n\n\n\n20 (18), 1955-1958. \n\n\n\n[7] Din, A.H.M., Omar, K.M., Naeije, M., Ses, S. 2012. Long-term sea level \n\n\n\nchange in the Malaysian seas from multi-mission altimetry data. \n\n\n\nInternational Journal of Physical Sciences, 7 (10), 1694-1712. \n\n\n\n[8] Brooks, N. 2003. Vulnerability, risk and adaptation: A conceptual \n\n\n\nframework. Tyndall Centre for Climate Change Research Working Paper, \n\n\n\n38, 1-16. \n\n\n\n[9] Gornitz, V. 1991. Global coastal hazards from future sea level rise.\n\n\n\nPalaeogeography, Palaeoclimatology, Palaeoecology, 89 (4), 379-398. \n\n\n\n[10] Gornitz, V.M., Beaty, T.W., Daniels, R.C. 1997. A coastal hazards data \n\n\n\nbase for the US West Coast: Oak Ridge National Laboratory Oak Ridge, TN.\n\n\n\n[11] Doukakis, E. 2005. Coastal vulnerability and risk parameters. \n\n\n\nEuropean water, 11 (12), 3-7. \n\n\n\n[12] Nageswara Rao, K., Subraelu, P., Venkateswara Rao, T., Hema Malini, \n\n\n\nB., Ratheesh, R., Bhattacharya, S. 2008. Sea-level rise and coastal \n\n\n\nvulnerability: an assessment of Andhra Pradesh coast, India through \n\n\n\nremote sensing and GIS. Journal of Coastal Conservation, 12 (4), 195-207. \n\n\n\n[13] Pendleton, E.A., Barras, J., Williams, S., Twichell, D. 2010. Coastal \n\n\n\nvulnerability assessment of the Northern Gulf of Mexico to sea-level rise \n\n\n\nand coastal change. \n\n\n\n[14] Abuodha, P.A., Woodroffe, C.D. 2006. Assessing vulnerability of coasts \n\n\n\nto climate change: A review of approaches and their application to the \n\n\n\nAustralian coast. In Woodroffe CD, Bruce, E, Puotinen, M and Furness RA \n\n\n\n(Eds), GIS for the Coastal Zone: A selection of Papers from CoastGIS 2006, \n\n\n\nAustralian National Centre for Ocean Resources and Security University of \n\n\n\nWollongong, Wollongong, Australia, 458. \n\n\n\n[15] Dwarakish, G.S., Vinay, S.A., Natesan, U., Asano, T., Kakinuma, T., \n\n\n\nVenkataramana, K. 2009. Coastal vulnerability assessment of the future \n\n\n\nsea level rise in Udupi coastal zone of Karnataka state, west coast of India. \n\n\n\nOcean &amp; Coastal Management, 52 (9), 467-478. \n\n\n\n[16] Abuodha, P.A., Woodroffe, C.D. 2010. Assessing vulnerability to sea-\n\n\n\nlevel rise using a coastal sensitivity index: a case study from southeast \n\n\n\nAustralia. Journal of Coastal Conservation, 14 (3), 189-205. \n\n\n\n[17] Gornitz, V., White, T.W., Cushman, R.M. 1991. Vulnerability of the US\n\n\n\nto future sea level rise. In Proceedings of Seventh Symposium on Coastal \n\n\n\nand Ocean Management. ASCE, Coastal Zone '91, 2354-2368. \n\n\n\n[18] Bryan, B., Harvey, N., Belperio, T., Bourman, B. 2001. Distributed \n\n\n\nprocess modeling for regional assessment of coastal vulnerability to sea-\n\n\n\nlevel rise. Environmental Modeling and Assessment, 6 (1), 57-65. \n\n\n\n[19] ESI. 2010. Environmental Sensitivity Index 2010 for Kelantan,\n\n\n\nTerengganu, Pahang and Johor. Universiti Malaysia Terengganu \n\n\n\n[20] DID. 1985. Department of Irrigation and Drainage. \n\n\n\n[21] MET. 2010. Malaysian Meteorological Department.\n\n\n\n[22] Thieler, E.R., Hammar-Klose, E.S. 2000. National assessment of coastal \n\n\n\nvulnerability to sea-level rise, preliminary results for the US Atlantic Coast: \n\n\n\nThe Survey. \n\n\n\n[23] Gornitz, V.M., Daniels, R.C., White, T.W., Birdwell, K.R. 1994. The \n\n\n\nDevelopment of a Coastal Risk Assessment Database: Vulnerability to Sea-\n\n\n\nLevel Rise in the U.S. Southeast. Journal of coastal research(ArticleType: \n\n\n\nresearch-article / Issue Title: Special Issue No. 12. Coastal Hazards: \n\n\n\nperception, susceptibility and mitigation / Full publication date: 1994 / \n\n\n\nCopyright \u00a9 1994 Coastal Education & Research Foundation, Inc.), 327-\n\n\n\n338. \n\n\n\n[24] Shaw, J., Taylor, R.B., Forbes, D.L., Ruz, M.H., Solomon, S. 1998. \n\n\n\nSensitivity of the coasts of Canada to sea-level rise. Geological Survey of \n\n\n\nCanada, Bulletin, 505. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 73-83 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2022.73.83 \n\n\n\nCite the Article: Matthew Coffie Wilson, Geoffrey Chiri Amedjoe, Simon Kafui Yao Gawu (2022). Petrographic and Geochemical \n Constraints on Tectonic Settings of the Birimian Supergroup Volcanic Rocks, Evidence from New Drobo Environs  \n\n\n\nSouth of Jaman District in the Bono Region of Ghana. Malaysian Journal of Geosciences, 6(2): 73-83. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2022.73.83 \n\n\n\nPETROGRAPHIC AND GEOCHEMICAL CONSTRAINTS ON TECTONIC SETTINGS OF \nTHE BIRIMIAN SUPERGROUP VOLCANIC ROCKS, EVIDENCE FROM NEW DROBO \nENVIRONS SOUTH OF JAMAN DISTRICT IN THE BONO REGION OF GHANA \n\n\n\nMatthew Coffie Wilson*, Geoffrey Chiri Amedjoe, Simon Kafui Yao Gawu \n\n\n\nDepartment of Geological Engineering, Faculty of Civil-Geo, College of Engineering, Kwame Nkrumah University of Science and Technology, \nKumasi \u2013 Ghana. \n*Corresponding author\u2019s Email: regimatt2003@yahoo.co.uk, mcwilson.coe@knust.edu.gh \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 24 June 2022 \nAccepted 26 July 2022 \nAvailable online 01 August 2022\n\n\n\nThe petrographic and geochemical studies of Birimian Supergroup meta-volcanic rocks in the New Drobo \nenvirons in Ghana help to decipher the tectonic settings at the study area. Twenty thin sections were prepared \nwith rock samples from the field at the KNUST Geological Engineering Laboratory and petrographic \nmicroscope used to determine the different types of minerals in the samples and also the rock type. Whole \nrock geochemical analysis was done, using both X-Ray Fluorescence and Inductively Coupled Plasma Mass \nSpectrometer methods at Australian Laboratory Services in Canada. The main volcanic rock types at the study \narea comprise of basalt, andesite, dacite and rhyo-dacite with porphyritic mineralogical composition such as \nplagioclase feldspar, augite, olivine, hornblende, biotite, quartz, rutile, chlorite. The average concentrations \nof Zr < 150 ppm, TiO2 < 1.5 % and P2O5 < 0.25 % and the ratios of Nb/Y < 1.2 and Y/Nb > 1 reveal the magma \ntype in the study area to be continental tholeiitic basalt in nature. The mafic volcanic basalt is from a plate \nmargin tectonic setting and thus of normal MORB and volcanic arc basalts. Also, the low basaltic values of the \nratios of Ti/Y and Nb/Y confirm the tectonic setting of the area to be of plate margin. Zr is highly incompatible \nwith respect to Ti and renders the ratio of Zr/TiO2 to be influenced by partial melting and causes \nheterogeneity in the mantle. The heterogeneity in the source of the mantle can be confirmed with the high \nvalue of the ratio of Zr/Y. \n\n\n\nKEYWORDS \n\n\n\nTectonic settings, geochemistry, petrography, Birimian volcanic rocks, plate margin \n\n\n\n1. INTRODUCTION\n\n\n\nGeology of Ghana is divided into two metallogenic zones, namely \nPaleoproterozoic and Phanerozoic, of which the study area falls in the \nPaleoproterozoic units. The Paleoproterozoic units are made up of a \npackage of the Birimian metavolcanic rocks and metasedimentary rocks. \nThe granitoids intrude this Birimian rock package. Overlying the Birimian \nmetavolcanic rocks is the Tarkwaian supracrustal rocks and granitoids \n(Kesse, 1985). The Paleoproterozoic Birimian Supergroup units in New \nDrobo environs, the study area basically consists of varying metavolcanic \nand metasedimentary rocks. Overlying the Birimian Supergroup are also \nvarying supracrustal rocks of the Tarkwaian Supergroup (Kesse, 1985). \nThe Birimian and Tarkwaian rocks and the comagmatic granitoids were \naffected at least by lower greenschist-facies metamorphism (generally in \nthe epidote-chloritoid subfacies) during the Eburnean event (Zitzmann et \nal., 1997).  \n\n\n\nThe tectonic setting of the Birimian terrane in the West Africa Craton \n(WAC) has been a subject of debate for researchers hitherto. Some early \nresearchers argue that the Birimian terrane was deposited in intra-\noceanic tectonic setting due to the presence of pillow lavas, marine \nsediments that lack older crustal rocks (Abouchami et al., 1990). Again, the \nBirimian terrane and the Archean in the WAC appear to have many \n\n\n\nsimilarities structurally, compositionally, in terms of juvenile magmatism \nand alternating greenstone belts and metasedimentary basins \n(Abouchami et al., 1990). Thus, the Birimian terrane may represent \ntransitional tectonic setting between Archean and proterozoic activities \ndue to unevenly cooling mantle of higher Archean geotherm into the \nproterozoic (Sylvester and Attoh, 1992). This implies that several aspects \nof the geodynamic evolution of the Birimian terrane remains unanswered.  \n\n\n\nAn oceanic plateau of flood basalts associated with plume activity origin of \nthe Birimian terrane has been proposed while others like hold on to \nformation through accretion of island arcs (Abouchami et al., 1990; \nSylvester and Attoh, 1992). Five (5) of the gold belts (Kibi-Winneba, \nAshanti, Sehwi-Bibiani, Bole-Nangodi, Wa-Laura) out of the six (6) in \nGhana have been extensively investigated using various methods such as \nisotopic dating, deformation mechanisms, petrological research, etc. \nHowever, the study area along the Bui-Banda Belt, even though have been \nresearched on, it is not so extensive as compared to the others. The \ntectonic environment at the study area is thus to be investigated. \nConsidering the work on \u201cgeology of the Bui Belt\u201d and that of on \u201cmineral \noccurences, geochemical investigation and gold potential in the Bui Belt in \nGhana\u201d, formation of the tectonic environment could not be addressed \n(Zitzmann et al., 1997; Kiessling, 1997).  \n\n\n\n\nmailto:mcwilson.coe@knust.edu.gh\n\n\n\n\n\n\nCite the Article: Matthew Coffie Wilson, Geoffrey Chiri Amedjoe, Simon Kafui Yao Gawu (2022). Petrographic and Geochemical Constraints on  \nTectonic Settings of the Birimian Supergroup Volcanic Rocks, Evidence from New Drobo Environs South of Jaman District  \n\n\n\nin the Bono Region of Ghana. Malaysian Journal of Geosciences, 6(2): 73-83. \n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 73-83 \n\n\n\nThere is thus the need to investigate to understand the different types of \ntectonic environments. In petrographic studies, alterations are sometimes \ndifficult to be identified and understood thus whole rock geochemistry of \nthe samples become useful to highlight and understand the alterations to \nassist unravelling tectonic setting of terranes. Since different \ndeformational events are linked with availability of the lithologies in the \narea, the study of petrology plays a key role in this research work. \nObserving the alterations in the minerals and rocks in the area, sometimes \npetrology through micro tectonics study is not enough. Hence, analyzing \nthe minerals through geochemistry studies help to identify the minerals \nand the rocks. Knowledge on the type and nature of tectonism can as well \nbe determined by geochemical analysis.   \n\n\n\nThe purpose of this research work is to apply an integrated approach of \npetrological and geochemical studies to establish the tectonic settings of \nthe Birimian rocks in the study area. This paper focuses on the field \nstructural and metamorphic observations integrated with petrographic \nand geochemical studies to investigate the tectonic settings of Birimian \nvolcanic rocks in New Drobo environs of Jaman District in the Bono Region \nof Ghana. Sometimes alterations cannot all be seen in petrography. \nHowever, Whole Rock geochemistry is useful to unravel some tectonic \nenvironment. So, where petrography would not be helpful to see the things \nvery well, geochemistry helps to clearly identify the alterations and \ntectonism of the rocks.  \n\n\n\n2. GEOLOGIC SETTINGS\n\n\n\nThe volcanic belts of the West African Craton (WAC) alternate with \n\n\n\nsedimentary basins which consist of the Birimian and Tarkwaian \nSupergroups with granitoid intrusions (Kesse, 1985). The granitoid \nintruded the Birimian Supergroup at a later stage and thus post-dates the \nformation of the Tarkwaian Supergroup (Griffis et al., 2002). The Lower \nBirimian stratigraphy consists of predominantly metasedimentary rocks. \nThe Upper Birimian stratigraphic column defines metavolcanics such as \nlava flows, basaltic and andesitic dykes which mostly have been \nmetamorphosed to hornblende actinolite-schists, calcareous chlorite \nschists and amphibolites (the greenstones) and the felsic units include \ndacitic pyroclastic rocks, minor andesitic and rhyolite flows, and \nundifferentiated volcaniclastic rocks (Junner, 1940).  \n\n\n\n2.1   The Study Area \n\n\n\nThe NW New Drobo is located in the southwestern part of Ghana, in the \nsouth of Jaman District in the Bono Region of Ghana and covers the \nsouthwest part of the Bui Belt and lies between 7\u00b030' and 8\u00b0 N and from \n2\u00b030' W (Figure 1) up to the Cote d\u2019Ivoire boundary (Zitzmann et al., \n1997). The lithologies of the study area consists of both the Birimian and \nTarkwaian Supergroups striking on NE-SW. The Birimian Supergroup \nconsists of a metavolcanic and metasedimentary rocks comprising of \nargillite-argillite/wacke-wacke-volcaniclastic-argillite/volcaniclastic and \nchert (Zitzmann et al., 1997). The fine-grained clastic facies of mostly \nsericite phyllite is typical for the sedimentary basins, while the coarser \nwacke facies accumulated closer to the volcanic belts. The Birimian \nmetavolcanic rocks are basalt-andesite-rhyodacite lavas, with elevated \nMg-Ca-Na contents, and volcaniclastics (Zitzmann et al., 1997). \n\n\n\nFigure 1: Geological Map of Ghana showing the study area (Agyei Duodu et al., 2009) \n\n\n\n3. MATERIALS AND METHODOLOGY\n\n\n\n3.1   Petrographic Analysis \n\n\n\nTwenty (20) representative rocks were used to prepare thin sections at \nthe KNUST Geological Engineering Laboratory. Thin section of rocks was \nprepared to be used for mineralogical and geological analysis using \npolarizing microscope. Rocks were first cut using the Hillquist Rock \nCutting machine. A particular face of the rock sample to be analyzed was \nselected and smoothened. Examination of the rock samples was done both \nmacroscopically and microscopically for textures, composition of the \nminerals, fabric, structures and micro-structures. The hand specimens \n(macro-analysis) and thin sections (micro-analysis) were examined to \nidentify and explain the alterations of the minerals and deformation of the \ngrains and any other inclusions and also to determine its significance to \nengineering properties of rocks ascending to the ASTM (C295-08) \nstandard. Samples were described macroscopically with the aid of a hand \nlens. Thin section was prepared to a thickness of 30\ud835\udfb5m for analysis using \na Leica DM 750P light transmitted petrographic microscope, with mainly \n5x magnification. The minerals present, their textural characteristics and \n\n\n\nvisual estimations of relative abundances were used to appraise the rock \ntype and alteration. \n\n\n\n3.2   Geochemical Analysis \n\n\n\nTwenty (20) Birimian rock samples comprising of basaltic flow and \nvolcaniclastic rock samples were selected and sent to the Australian \nLaboratory Systems (ALS) in Canada through the Kumasi Branch of the \nCompany for geochemical analysis. These rock samples were analyzed by \nX-Ray Fluorescence (XRF) method for major-, trace- and rare earth \nelements and also analyzed through Inductively Coupled Plasma Mass \nSpectroscopy (ICP-MS) for whole rock geochemistry.  \n\n\n\n4. RESULTS AND DISCUSSIONS\n\n\n\n4.1   Petrography \n\n\n\nThe meta-basalts (Figure 2A) portray porphyritic and intergranular \nigneous textures, however, porphyric textures are mostly observed. These \nporphyritic textures usually consist of phenocrysts of feldspars \n\n\n\n\n\n\n\n\nCite the Article: Matthew Coffie Wilson, Geoffrey Chiri Amedjoe, Simon Kafui Yao Gawu (2022). Petrographic and Geochemical Constraints on  \nTectonic Settings of the Birimian Supergroup Volcanic Rocks, Evidence from New Drobo Environs South of Jaman District  \n\n\n\nin the Bono Region of Ghana. Malaysian Journal of Geosciences, 6(2): 73-83. \n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 73-83 \n\n\n\n(plagioclase and kalifeldspar), quartz, clino-pyroxene (augite) and \nhornblende. Occasionally, olivine crystals were observed in the thin \nsections. These meta-basalts consist mainly of fine- to medium-grained \nmineral assemblages with granoblastic textures suggestive of greenschist \nfacies range of metamorphic grade. The mineralogical composition of the \nmatrix comprises of plagioclase feldspar, clino-pyroxene and Fe-Ti oxides. \nMineral alterations produced are typical of greenschist facies index \nmineral assemblage such as epidote, albite, chlorite, calcite, quartz. There \nare also intermediate and felsic-volcaniclastic rocks, namely, andesites \n(Figure 2B) and dacites (Figures 2 C and D).  \n\n\n\nThe thin sections consist of irregularly crystallized (non-directional \ntextures) minerals and are porphyritic in nature (Figure 2). The aphanitic \ngroundmass minerals are somewhat dark green to greenish in both plane \npolarized light (PPL) and cross polarized lights (XPL) to almost opaque in \nappearance and thus cannot immediately be identified. However, \nphenocrysts of hornblende and plagioclase feldspars, and quartz, though \nof varying sizes are euhedral to subhedral and angular, suggest these \nsamples are volcanic rocks. The pattern of non-directional texture of the \naphanitic groundmass as observed in the framework (Figure 2A), shows \ninterlocking arrangement of the mineral crystals arising from \nsolidification of molten rock material. Thus, the general porphyritic \ntexture of the rocks, is an affirmation of its igneous origin and the fineness \nof the groundmass implies that they are volcanic.  \n\n\n\nThe quartz crystals are monocrystalline however, the fractured crystals \nare polycrystalline. With the feldspars, showing plagioclase appear to \ndominate over the K-feldspars which seem to be altering relatively faster. \nPlagioclase feldspars with granulated edges signify alteration. The \nalterations have produced sericites that occurred as granulated patches in \nthe framework. The subhedral hornblende crystals in the framework show \nfracturing in some of the crystals. The mineralogical composition in the \nrock samples may be classified as volcanic rocks. Volcanic and \nvolcaniclastic rocks such as basalt (Figure 2A), andesite (Figure 2B), and \ndacite (Figures 2 C, 2D), etc. may be identified in the study area. Also, \nsubhedral hornblende crystals appeared somewhat rotated and \nbrecciated with blurred edges between platy minerals may be responsible \nfor the wavy foliation observed in the framework.  \n\n\n\nThe apparent rotation of the subhedral hornblende crystals signifies \nshearing. The quartz crystals within the framework are subhedral to \nanhedral, though some crystals show rounded edges and they exhibit \nundulose extinction. The plagioclase feldspars show albite twining though \nidentified with some difficulties due to poorly preserved twining property. \nSericitization in some portions of the thin section probably result from \nalteration of some feldspars. It may be inferred that the rocks are volcanic \nrocks and may be coming from either weakly sheared zone or near a \n\n\n\nsheared zone that is mildly metamorphosed. The plagioclase crystals \nshowing elongated habit penetrates the pyroxene crystals and exhibit \nophitic microstructure locally (Figures 2 A, 2B). \n\n\n\nDark greenish mineral, possibly amphibole, greenish pyroxene and \nchlorite (Figure 2C) and brownish rutile (Figure 2D) are seen to have a \nsharp contact and intrusion in the rock. These minerals are said to be post \nkinematic as well as post tectonic formed minerals. These flake and \nangular minerals are microscopically identified as large and fresh \nphenocrysts which took some time to crystallize after the normal fast \ncrystallization of the volcanic rock. The biotite mineral appeared altered \nwhilst the plagioclase feldspar displays a chequerboard pattern and \ntexture within the chlorite and pyroxene. The dark greenish post mineral \nmay be intruded with quartz veinlet (Figure 2C).  \n\n\n\nLarge pyroxene crystal showing substitution by hornblende in an \nexsolution-like structure that may be mistaken for passive inclusions \n(Passchier and Trouw 2005). Needles of rutile in biotite (Figure 2D) depict \nalteration of biotite to rutile or exsolution structures along \ncrystallographic controlled planes such that these may also be difficult to \ndistinguish from inclusion patterns. Minerals that are solid solutions of \ntwo or more phases can show exsolution when metamorphic conditions \nchange (Passchier and Trouw 2005). This is especially common for \nminerals that crystallized at high temperature. During retrogression, small \ngrains of the minor phase may form in the host crystal (Passchier and \nTrouw 2005). \n\n\n\n4.2   Geochemistry \n\n\n\n4.2.1   Classification Schemes of the Rock Types in the Study Area \n\n\n\nTo be able to determine the tectonic setting of some of the rocks in the \nstudy area, the major and trace elements data (Table 1) were used to help \nidentify different volcanic rock types. According to Rollinson, bivariate \noxide-oxide major element plots (Figure 3) is a very recognizable way to \nclassify igneous rocks (Rollinson, 2013). These elements may be used to \nclassify rocks on the basis of their chemical composition in the \nconstruction of variation diagrams and thus yield volcanic rock types such \nas basalts, andesites, dacites and rhyo-dacites. On the basis of the \npotassium (K) and silicon (Si) oxide concentration, the subalkaline or the \ntholeiitic series of the volcanic rocks division of the subalkaline rocks into \nlow-K, medium-K and high-K types were proposed (Peccerillo and Taylor, \n1976; Le Maitre, 1989). The Figure 4b defines the study area to have low-\nK and medium-K and partially high-K volcanic rocks (Rickwood, 1989). \nWith respect to Cox et al. (1979) definition, the volcanic rocks in the study \narea classified as low-K subalkalic basalt (tholeiite), and sub-alkalic basalt \n(calc-alkaline) based on K concentrations (Figure 4b). \n\n\n\n(a) (b) \n\n\n\n\n\n\n\n\nCite the Article: Matthew Coffie Wilson, Geoffrey Chiri Amedjoe, Simon Kafui Yao Gawu (2022). Petrographic and Geochemical Constraints on  \nTectonic Settings of the Birimian Supergroup Volcanic Rocks, Evidence from New Drobo Environs South of Jaman District  \n\n\n\nin the Bono Region of Ghana. Malaysian Journal of Geosciences, 6(2): 73-83. \n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 73-83 \n\n\n\n(c) (d) \n\n\n\nFigure 2: Photomicrographs showing minerals of: (a) mafic basalt; (b) andesite; (c & d) dacite \n\n\n\nTable 1: Characteristic Magma Series Associated with Specific Tectonic Settings (Wilson, 1989) \n\n\n\nTectonic \nSetting \n\n\n\nPlate Margin Within Plate \n\n\n\nConvergent (Destructive) Divergent (Constructive) Intra-Oceanic Intra-Continental \n\n\n\nVolcanic Feature \nIsland arcs, Active continental \n\n\n\nmargins \n\n\n\nMid-oceanic ridges \n\n\n\nBack-arc spreading centres \nOceanic islands \n\n\n\nContinental rift zones, \n\n\n\nContinental flood-basalt provinces \n\n\n\nCharacteristic \nMagma Series \n\n\n\nTholeiitic \n\n\n\nCalc-alkaline \n\n\n\nAlkaline \n\n\n\nTholeiitic \n\n\n\n- \n\n\n\n- \n\n\n\nTholeiitic \n\n\n\n- \n\n\n\nAlkaline \n\n\n\nTholeiitic \n\n\n\n- \n\n\n\nAlkaline \n\n\n\nSiO2 Range Basalts and differentiates Basalts \nBasalts and \n\n\n\ndifferentiates \nBasalts and differentiates \n\n\n\nFigure 3: Discrimination Diagrams of: (a) Total Alkalis (Na2O+K2O) Versus Silica (SiO2) (TAS) of Volcanic Rocks in the Study Area (Cox et al., 1979)\n\n\n\n\n\n\n\n\nCite the Article: Matthew Coffie Wilson, Geoffrey Chiri Amedjoe, Simon Kafui Yao Gawu (2022). Petrographic and Geochemical Constraints on  \nTectonic Settings of the Birimian Supergroup Volcanic Rocks, Evidence from New Drobo Environs South of Jaman District  \n\n\n\nin the Bono Region of Ghana. Malaysian Journal of Geosciences, 6(2): 73-83. \n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 73-83 \n\n\n\nTable 2: Concentrations of Some Major Oxides, Immobile High Field Strength Elements (HFSE) and their Ratios as Well as Alteration-Resistant Elements and Elemental Ratios to Define Tectonic Settings of Volcanic Rocks in \nthe Study Area (RD = Rhyo-Dacite) \n\n\n\nAnalyte Basalts Basaltic Andesite Andesite Dacite RD \n\n\n\nr10 r11 r13 r17 r18 r22 r33 r34 r12 r21 r37 r15 r27 r30 r16 r19 r20 r29 r31 r25 \n\n\n\nSiO2 50.80 46.80 49.30 48.70 47.60 48.40 42.60 48.70 54.80 52.10 54.30 59.80 59.10 62.20 66.50 68.60 70.20 64.80 67.70 85.40 \n\n\n\nTiO2 0.85 0.74 0.85 1.56 0.86 0.86 1.08 0.92 1.52 0.85 0.51 1.10 0.37 0.82 0.45 0.50 0.49 1.01 0.76 0.03 \n\n\n\nCaO 6.86 9.93 10.55 12.55 9.87 8.96 4.30 7.62 3.61 11.90 5.09 0.17 0.07 3.30 0.04 2.74 2.30 2.69 1.38 0.40 \n\n\n\nMgO 6.95 8.32 5.66 1.29 6.54 7.79 7.62 8.28 5.93 5.48 3.34 1.05 0.04 2.32 0.36 1.44 1.48 2.17 2.21 0.43 \n\n\n\nK2O 0.17 0.53 0.14 0.09 0.53 0.52 0.01 0.04 0.20 0.04 0.02 2.59 0.06 1.40 2.56 1.58 0.87 1.48 0.79 0.01 \n\n\n\nP2O5 0.04 0.05 0.08 0.16 0.06 0.08 0.11 0.08 0.10 0.06 0.12 0.10 0.14 0.20 0.06 0.10 0.07 0.24 0.15 0.02 \n\n\n\nCaO+MgO 13.81 18.25 16.21 13.84 16.41 16.75 11.92 15.90 9.54 17.38 8.43 1.22 0.11 5.62 0.40 4.18 3.78 4.86 3.59 0.83 \n\n\n\nLa 2.70 1.90 2.60 6.20 2.40 2.90 3.90 2.90 13.10 2.40 0.60 4.90 1.50 20.40 21.20 10.30 10.80 26.10 18.60 0.40 \n\n\n\nCe 7.04 5.19 7.19 17.60 6.59 7.18 9.78 8.44 36.10 6.46 1.65 11.10 3.39 46.30 42.20 21.70 21.50 50.10 40.80 0.65 \n\n\n\nNb 2.00 1.70 2.20 5.90 2.00 2.10 2.80 2.50 9.70 2.10 0.80 6.70 1.10 10.50 8.90 3.90 3.40 11.20 8.90 0.40 \n\n\n\nNi 79.90 1 98.00 91.90 38.40 99.00 79.10 102.5 114.00 82.20 79.90 63.90 109.50 39.00 53.10 14.30 37.80 47.80 57.80 50.40 18.00 \n\n\n\nTa 0.13 0.11 0.14 0.35 0.13 0.14 0.18 0.15 0.65 0.13 0.04 0.40 0.06 0.69 0.60 0.33 0.28 0.70 0.61 0.04 \n\n\n\nTh 0.23 0.12 0.21 0.50 0.18 0.18 0.20 0.17 1.97 0.19 0.07 1.60 0.08 2.90 3.74 1.90 1.52 2.05 2.31 0.02 \n\n\n\nTi 0.52 0.43 0.49 0.97 0.51 0.51 0.64 0.55 0.92 0.50 0.26 0.46 0.22 0.49 0.23 0.30 0.29 0.62 0.45 0.02 \n\n\n\nHf 1.10 1.10 1.10 3.00 1.20 1.20 1.60 1.50 4.60 1.20 0.40 3.70 0.60 4.30 4.90 1.90 1.70 4.40 3.80 0.10 \n\n\n\nZr 34.40 35.20 35.20 106.0 40.9 39.6 56.5 45.8 179.0 40.3 14.5 150.5 22.1 162.0 193.5 70.10 66.5 170.5 146.5 2.30 \n\n\n\nSm 1.76 1.54 1.83 4.12 1.75 1.93 2.50 2.45 5.18 1.71 0.78 1.80 2.23 4.96 3.58 2.06 1.91 6.00 4.32 0.11 \n\n\n\nSr 84.8 223.0 90.7 19.6 215.0 75.6 44.6 115.0 20.8 20.0 35.8 412.0 3.80 210.0 243.0 243.0 225.0 903.0 156.0 6.30 \n\n\n\nCr   74.00  17.00 216.0 352.0 145.0 143.0 124.0 121.0 91.0 179.0 292.0 208.0 335.0 117.0 127.0 228.0 334.0 144.0 207.0 541.0 \n\n\n\nY 17.70 15.90 18.20 36.60 17.50 20.20 13.10 21.60 31.20 16.00 5.10 17.50 17.30 23.20 24.90 11.70 10.10 34.10 21.10 0.80 \n\n\n\nYb 1.86 1.67 1.87 3.91 1.85 2.15 1.68 2.38 3.90 1.68 0.70 2.36 2.35 2.32 2.59 1.24 1.03 3.09 2.23 0.11 \n\n\n\nLu 0.29 0.26 0.30 0.59 0.28 0.34 0.28 0.37 0.61 0.26 0.11 0.39 0.33 0.34 0.41 0.19 0.16 0.45 0.32 0.02 \n\n\n\nY/Nb 8.85 9.35 8.27 6.20 8.75 9.62 4.68 8.64 3.22 7.62 6.38 2.61 15.73 2.21 2.80 3.00 2.97 3.04 2.37 2.00 \n\n\n\nTi/Y 0.03 0.03 0.03 0.03 0.03 0.03 0.05 0.03 0.03 0.03 0.05 0.03 0.01 0.02 0.01 0.03 0.03 0.02 0.02 0.03 \n\n\n\nNb/Y 0.11 0.11 0.12 0.16 0.11 0.10 0.21 0.12 0.31 0.13 0.16 0.38 0.06 0.45 0.36 0.33 0.34 0.33 0.42 0.50 \n\n\n\nZr/Y 1.94 2.21 1.93 2.90 2.34 1.96 4.31 2.12 5.74 2.52 2.84 8.60 1.28 6.98 7.77 5.99 6.58 5.00 6.94 2.88 \n\n\n\nZr/Nb 17.20 20.71 16.00 17.97 20.45 18.86 20.18 18.32 18.45 19.19 18.13 22.46 20.09 15.43 21.74 17.97 19.56 15.22 16.46 5.75 \n\n\n\nHf/Ta 8.46 10.00 7.86 8.57 9.23 8.57 8.89 10.00 7.08 9.23 10.00 9.25 10.00 6.23 8.17 5.76 6.07 6.29 6.23 2.50 \n\n\n\nHf/Th 4.78 9.17 5.24 6.00 6.67 6.67 8.00 8.82 2.34 6.32 5.71 2.31 7.50 1.48 1.31 1.00 1.12 2.15 1.65 5.00 \n\n\n\nNb/Ta 15.38 15.45 15.71 16.86 15.38 15.00 15.56 16.67 14.92 16.15 20.00 16.75 18.33 15.22 14.83 11.82 12.14 16.00 14.59 10.00 \n\n\n\nZr/Hf 31.27 32.00 32.00 35.33 34.08 33.00 35.31 30.53 38.91 33.58 36.25 40.68 36.83 37.67 39.49 36.89 39.12 38.75 38.55 23.00 \n\n\n\nTa/Yb 0.07 0.07 0.07 0.09 0.07 0.07 0.11 0.06 0.17 0.08 0.06 0.17 0.03 0.30 0.23 0.27 0.27 0.23 0.27 0.36 \n\n\n\nLa/Ta 20.77 17.27 18.57 17.71 18.46 20.71 21.67 19.33 20.15 18.46 15.00 12.25 25.00 29.57 35.33 31.21 38.57 37.29 30.49 10.00 \n\n\n\n\n\n\n\n\nCite the Article: Matthew Coffie Wilson, Geoffrey Chiri Amedjoe, Simon Kafui Yao Gawu (2022). Petrographic and Geochemical Constraints on  \nTectonic Settings of the Birimian Supergroup Volcanic Rocks, Evidence from New Drobo Environs South of Jaman District  \n\n\n\nin the Bono Region of Ghana. Malaysian Journal of Geosciences, 6(2): 73-83. \n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 73-83 \n\n\n\nFigure 4: (a) Plot of Tectonic Plates of the Plate Margin and Within Plate at NW New Drobo; (b) A SiO2-K2O Diagram Showing Tholeiitic and Calc-Alkaline \nSeries; (c) Discrimination Diagram of Nb-Yb-TiO2 (Pearce, 2008); (d) Discrimination Diagram of MnO-TiO2-P2O5 ( Mullen, 1983). \n\n\n\nFigure 5: Discrimination Diagram of Zr-Nb-Y (Meschede, 1986). The Symbols Include A = Within- Plate Basalts; B = E-MORB; C = Within-Plate and \nVolcanic-Arc Basalts; D = N-MORB and Volcanic-Arc Basalts \n\n\n\n\n\n\n\n\nCite the Article: Matthew Coffie Wilson, Geoffrey Chiri Amedjoe, Simon Kafui Yao Gawu (2022). Petrographic and Geochemical Constraints on  \nTectonic Settings of the Birimian Supergroup Volcanic Rocks, Evidence from New Drobo Environs South of Jaman District  \n\n\n\nin the Bono Region of Ghana. Malaysian Journal of Geosciences, 6(2): 73-83. \n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 73-83 \n\n\n\nThe TiO2-Nb-Yb discrimination diagram portrays basalts and basaltic \nandesites to be of normal mid-oceanic ridge basalts (NMORB) whilst the \nintermediate (andesites) rocks and acidic (dacites and rhyo-dacites) rocks \nmay be identified to be of evolved mid-oceanic ridge basalts (EMORB) \n(Figure 4a, 4c). Tholeiitic basalts are generated at mid-oceanic ridges but \nalso in back-arc basins, oceanic islands, island arcs, active continental \nmargins and continental flood basalt provinces (Wilson, 1989). The \naddition of the concentration of CaO and MgO (CaO+MgO) of basalt in the \nstudy area lies between 12 and 20 (Table 3) and thus confirms the rock to \nbe a basalt. \n\n\n\n4.2.3   Discrimination Diagrams for Basalts Based Upon Minor \nElements \n\n\n\nAn incompatible element such as Nb is important in interpreting mantle \ndifferentiation processes, since differences in the content of Nb cannot be \neasily ascribed to fractional crystallization processes (Erlank and Kable, \n1976). The Zr/Nb ratio is potentially more useful measure of mantle-\nrelated depletion or enrichment processes than ratios involving K and Rb, \nwhich are highly sensitive to seawater alteration. The immobile element \nNb is a good discriminator for different types of MORB (Meschede, 1986). \nThe tectonic setting of the geochemical mafic volcanic rocks (basalts and \nbasaltic andesites) using the Zr-Nb-Y discrimination diagram fall in the \nplate margin and thus Normal Mid-Ocean Ridge Basalt (NMORB) and \nvolcanic-arc basalts field (Meschede, 1986). Since the intermediate and \nfelsic volcanic rocks (andesite, dacite, rhyo-dacite) are evolved from the \nmafic volcanic rocks (basalts, basaltic andesites), it may be concluded that \nthe area of study is from a plate margin and volcanic-arc basalts and \nnormal mid-oceanic ridge basalts (NMORB) field (Figure 5). \n\n\n\nThe ratios of the concentration of light rare-earth elements (such as La or \n\n\n\nCe) to that of the concentration of heavy rare-earth elements (such as Yb \nor Y) help to explain or deduce the degree of fractionation of rare-earth \nelements (REE). The ratio (La/Yb)N is often plotted against either CeN or \nYbN on a bivariate graph and is a measure of the degree of REE \nfractionation with changing REE content. Similar diagrams may be \nconstructed to measure the degree of LREE fractionation [(La/Sm)N versus \n(Eu/Eu*)] in individual REE patterns (Rollinson, 2013). The Nb/Ta versus \nZr/Hf plot (Figure 6a) the basalt and andesite samples show a slightly \npositive correlation that constrains the upper part of the terrestrial silicate \nfractionation trend (Muenker et al., 2003). Basalts, basaltic andesites, \ndacites and rhyo-dacites depict a negative correlation in the following \nplots: Nb/Ta versus Zr/Nb (Figure 6b), Zr/Hf versus MgO (Figure 6e), \nNb/Ta versus MgO (Figure 6f), Nb/Ta versus Ni (Figure 6d), and Zr/Hf \nversus Ni (Figure 6c) to reflect different degrees of partial melting in the \nrocks. \n\n\n\nDecreasing Nb/Ta values coupled with decreasing MgO and Ni \nconcentrations (Figures 6d, 6f) suggest crystallization processes having \nlow-Mg amphibole and dominated by olivine and possibly clino-pyroxene \nin the fractionation assemblage because DNb/Ta > 1 (Tiepolo et al., 2000). \nThis is confirmed with the porphyritic mineralogical composition of \nolivine, amphibole and pyroxene (augite) of basalt and basaltic andesite in \nthe study area. The basaltic volcanic rocks display Nb/Ta values in the \nrange 15.00-16.86 which lies below the chondritic value of 19.9 +/-0.6 but \nwithin values for Ocean-Island basalts (15-16) (Muenker et al., 2003; \nPfaender et al., 2007). These Nb/Ta basaltic values in the study area are \nhigher than the Nb/Ta values (12.00-13.00) in the continental crust \naccording to (Pfaender et al., 2007). This therefore implies that the \nsubcontinental lithospheric mantle has high Nb/Ta values and potentially \nbalance the Nb deficit observed in most terrestrial silicate reservoirs \n(Pfaender et al., 2007).\n\n\n\n(a) (b) \n\n\n\n(c) (d) \n\n\n\n\n\n\n\n\nCite the Article: Matthew Coffie Wilson, Geoffrey Chiri Amedjoe, Simon Kafui Yao Gawu (2022). Petrographic and Geochemical Constraints on  \nTectonic Settings of the Birimian Supergroup Volcanic Rocks, Evidence from New Drobo Environs South of Jaman District  \n\n\n\nin the Bono Region of Ghana. Malaysian Journal of Geosciences, 6(2): 73-83. \n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 73-83 \n\n\n\n(e) (f) \n\n\n\nFigure 6: Discrimination Diagrams of Concentrations of Elements of Different Ratios \n\n\n\n4.2.4   Tholeiitic Basalts and Basaltic Andesites \n\n\n\nThe tholeiitic volcanic rocks which are primarily the basalts and basaltic \nandesites at the study area span a range of 42.60 % -59.18 % in \ncomposition in SiO2. The high Al2O3 values (4.87-15.9 %) in the rocks \nreflect high plagioclase phenocryst abundance. There is also high variation \nin the iron (Fe2O3) content which leads to the functioning of crystal \nfractionation (Leybourne et al., 1997). The tholeiitic basalts show strong \nnegative K and Ti anomalies and are thus depleted. However, the Nb and \nTa troughs do not portray strong negative anomalies on the spider \ndiagrams (Figure 7). These non-strong negative Nb and Ta anomalies with \nrespect to the spider diagrams are characteristics of island arc volcanic \nrocks (Figure 7) (Ryerson and Watson, 1987). \n\n\n\n4.2.5   Discrimination Diagrams \n\n\n\n4.2.5.1   TiO2-Y/Nb Diagram \n\n\n\nThe ternary plot of Ti-Zr-Y developed by Pearce and Cann has proven very \nuseful in specifying the type of tectonic environment from different \ntectonic settings (Pearce and Cann, 1973). According to Rollinson, \nchemistry of basalts, granites or both can be used to distinguish between \ndifferent types of ocean ridge, characterize intraplate setting basalts, \n\n\n\nwhereas as well as recognizing the volcanic-arcs using three types of \ndiscriminant analysis (Rollinson, 2013). According to some researchers, \nthe variations in the immobile trace elements Ti-Zr-Nb-Y may be used to \ndifferentiate between basalt suites (Pearce and Cann, 1973; Pearce and \nNorry, 1979). Low value of the ratios of Ti/Y and Nb/Y render the tectonic \nsetting to be plate margin. The high value of the ratio Zr/Y defines the \nmantle source to be heterogeneous. The basaltic rocks in the study area \nreveal the ratios of Ti/Y and Nb/Y (Table 2) to be low and thus define the \nenvironment to be plate margin tectonic setting. Also, the high ratio of \nZr/Y causes the mantle to have different sources.  \n\n\n\nThe following parameters Y/Nb > 1, TiO2 < 1.5 %, P2O5 < 0.25 %, Nb/Y < \n1.2, Zr/P2O5*104 > 0.04, and Zr < 150 ppm provide the basalt rock to be \ntholeiitic basalt magma type, not alkali basalt (Pearce and Cann, 1973; \nFloyd and Winchester, 1975). In Table 5 with the same parameters, the \nbasaltic rocks of the studied volcanic rocks are tholeiitic. The oceanic \ntholeiites exhibit a wide scatter of Y/Nb ratios and a \u201chorizontal\u201d trend, \nwhilst the continental tholeiites plot show a steep negative trend due to a \nmarginal increase in the Y/Nb ratio. The Figure 8b shows both positive \nand negative slope of the plot. However, the steep negative slope \ndominates as a result of a marginal increase in the Y/Nb ratio and a \nprogressive differentiation. Thus, the tholeiites become more continental \nthan oceanic. \n\n\n\nFigure 7: Plots showing MORB tectonic environments of the tholeiitic rocks in the study area (Sun and McDonough, 1989) \n\n\n\n\n\n\n\n\nCite the Article: Matthew Coffie Wilson, Geoffrey Chiri Amedjoe, Simon Kafui Yao Gawu (2022). Petrographic and Geochemical Constraints on  \nTectonic Settings of the Birimian Supergroup Volcanic Rocks, Evidence from New Drobo Environs South of Jaman District  \n\n\n\nin the Bono Region of Ghana. Malaysian Journal of Geosciences, 6(2): 73-83. \n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 73-83 \n\n\n\n(a) (b) \n\n\n\nFigure 8: Discrimination diagram of: (a) Zr versus TiO2 and, (b) Y/Nb versus TiO2 of basalts \n\n\n\n(b) (c) \n\n\n\n(d) (e) \n\n\n\nFigure 9: The plots of Nb versus Zr and Nb versus Th demonstrate an immobile behaviour with two separate trends, in both tholeiitic and calc-\nalkaline volcanic rocks. \n\n\n\n\n\n\n\n\nCite the Article: Matthew Coffie Wilson, Geoffrey Chiri Amedjoe, Simon Kafui Yao Gawu (2022). Petrographic and Geochemical Constraints on  \nTectonic Settings of the Birimian Supergroup Volcanic Rocks, Evidence from New Drobo Environs South of Jaman District  \n\n\n\nin the Bono Region of Ghana. Malaysian Journal of Geosciences, 6(2): 73-83. \n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 73-83 \n\n\n\nWith exception of rare earth elements (REEs) like Eu, Th, La, Ga and Sc, all \nother REEs (e.g. Zr, Hf, Y, Cr, Ti, Ta, Nb) are relatively found to be immobile, \nand is typically true even at greenschist-grade metamorphism (Hastie et \nal., 2007). One of the most useful immobile elements is the Nb which \nplotted against other immobile elements explain some different \ndifferentiation formations. A plot of Nb versus Zr (Figure 9a) gives a linear \ntrend with a slope of unity for the calc-alkaline basalts and tholeiitic \nbasalts, tholeiitic volcanic rocks, etc. This indicates that both Zr and Nb \nelements are immobile and that their variations are a result of intra-\nformation differentiation (Hastie et al., 2007). Same intra or within \ndifferentiation formation are noticed in the plot of Nb versus Sm and La \n(Figure 9). \n\n\n\nIn Figure 9, plots of Nb versus La (a light REE) and Sm (middle REE) \nindicate inter-formation correlations consistent with immobility, Sm \n\n\n\nbeing more immobile than La (Hastie et al., 2007). Plots of Nb versus Ba \nand K2O explains that Ba and K which are elements of low ionic potential \nand are mobile in most settings, exhibit a large scatter with no evidence of \nthe expected and pre-alteration slope of unity. It may thus be clearly stated \nthat, K has been variably added and/or subtracted during hydrothermal \nalteration and weathering and is not useful for classifying these rocks \n(Hastie et al., 2007). In the spider plots of Boynton (1984), there is a slight \ninitial increase in LREE concentrations of the chondrite normalized REE \nplots of the study area (Figure 10) with respect to the basalts and basaltic \nandesites of the volcanic rock types in the area. However, there is a \ndecrease in concentration from the LREE through the MREE to the HREE \nwith respect to increase in SiO2 from dacites to rhyo-dacites. A small Eu \nanomaly developed in higher silica (SiO2) are identified in the dacites and \nrhyo-dacites. \n\n\n\nFigure 10: Rare Earth Element (REE) Concentrations of the Chondrite Normalized REE Plots of the Study Area (Boynton, 1984) \n\n\n\n5. CONCLUSION \n\n\n\nThe porphyritic rock textures in the study area define the rocks to be of \nigneous origin, whilst the fine-grained matrix and the mineralogical \ncomposition in the rock samples confirm the rocks to be volcanic. The \ngeochemical plots and the combined concentration of CaO and MgO (i.e. \nCaO + MgO) confirm and conclude on the different volcanic rock types \n(from the petrography) in the study area.  The tectonic setting of the \ngeochemical mafic volcanic rocks (basalts and basaltic andesites) using \nthe Zr-Nb-Y discrimination diagram (after Meschede, 1986) fall in the \nPlate Margin and thus Normal Mid-Ocean Ridge Basalt (NMORB) and \nvolcanic-arc basalts field. Since the intermediate and felsic volcanic rocks \n(andesite, dacite, rhyo-dacite) are evolved from the mafic volcanic rocks \n(basalts, basaltic andesites), it may be concluded that the area of study is \nfrom a plate margin and volcanic-arc basalts and normal mid-oceanic \nridge basalts (NMORB) field. The basaltic rocks in the study area reveal the \nratios of Ti/Y and Nb/Y to be low and thus confirms the environment to \nbe plate margin tectonic setting. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThanks go to my co-authors for their immense contributions toward the \nfruitfulness of this paper.  \n\n\n\nREFERENCES \n\n\n\nAbouchami, W., Boher, M., Michard, A., Albarede, F., 1990. A major 2.1 Ga \nevent of mantle magmatism in West Africa: An early stage of crustal \n\n\n\naccretion. Journal of Geophysical Research, 95, Pp. 17605\u201317629. \n\n\n\nAgyei Duodu, J., Loh, G.K., Boamah, K.O., Baba, M., Hirdes, W., Toloczyki, M., \nDavis, D.W., 2009. Geological Map of Ghana 1:1,000,000, Geological \nSurvey of Ghana, Map 1:1M. \n\n\n\nBoynton, W.V., 1984. Geochemistry of the rare earth elements: meteorite \nstudies. In: Henderson, P. (Ed.), Rare Earth Element Geochemistry. \nElsevier, Amsterdam, Pp. 63\u2013114. \n\n\n\nCox, K.G., Bell, J.D., Pankhurst, R.J., 1979. The interpretation of igneous \nrocks. Chapman & Hall in 1979. DOI 10.1007/978-94-017-3373-1. \n\n\n\nErlank, A.J., Kable, E.J.D., 1976. The significance of incompatible elements \nin Mid-Atlantic Ridge basalts from 45 ~ N with particular reference \nto Zr/Nb. Contributions to Mineralogy and Petrology, 54, Pp. 281-\n291. \n\n\n\nFloyd, P.A., Winchester, J.A., 1975. Magma type and tectonic setting \ndiscrimination using immobile elements. Earth and planetary \nscience letters, 27, Pp. 211-218. \n\n\n\nGriffis, R.J., Barning, K., Agezo, F.L., Akosah, F.K., 2002. Gold deposits of \nGhana. Minerals Commission Report. \n\n\n\nHastie, A.R., Kerr, A.C., Pearce, J.A., Mitchell, S.F., 2007. Classification of \naltered Volcanic Island Arc Rocks using immobile trace elements: \nDevelopment of the Th-Co discrimination diagram. Journal of \nPetrology, 48 (12), Pp. 2341-2357. \n\n\n\n\n\n\n\n\nCite the Article: Matthew Coffie Wilson, Geoffrey Chiri Amedjoe, Simon Kafui Yao Gawu (2022). Petrographic and Geochemical Constraints on  \nTectonic Settings of the Birimian Supergroup Volcanic Rocks, Evidence from New Drobo Environs South of Jaman District  \n\n\n\nin the Bono Region of Ghana. Malaysian Journal of Geosciences, 6(2): 73-83. \n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 73-83 \n\n\n\nJunner, N.R., 1940. Geology of the Gold Coast and Western Togoland with \nRevised Geological Map, Gold Coast Geological Survey Bulletin, 11, \nPp. 40. \n\n\n\nKesse, G.O., 1985. The Mineral and Rock Resources of Ghana. A.A. Balkema, \nRotterdam. \n\n\n\nKiessling, R., 1997. Sedimentation and structure in the Tarkwaian Group \nof the Bui Basin in West Ghana. Geologisches Jahrbuch Reihe B. Heft, \n83, Pp. 269.  \n\n\n\nLe Maitre, R.W., 1989. A classification of igneous rocks and a glossary of \nterms. Recommendations of the International Union of Geological \nSciences Sub commission on the systematics of igneous rocks. \nOxford: Blackwell. \n\n\n\nLeybourne, M.I., Van Wagoner, N., Ayres, L.D., 1997. Chemical Stratigraphy \nand Petrogenesis of the Early Proterozoic Amisk Lake Volcanic \nSequence, Flin Flon-Snow Lake Greenstone Belt, Canada. Journal of \nPetrology, 38 (11), Pp. 1541-1564. \n\n\n\nMeschede, M., 1986. A method of discriminating between different types \nof mid-ocean ridge basalts and continental tholeiites with Nb\u2013Zr\u2013Y \ndiagram. Chemical Geology, 56, Pp. 207\u2013218. \n\n\n\nMuenker, C., Pfaender, J.A., Weyer, S., Buechl, A., Kleine, T., Mezger, K., \n2003. Evolution of planetary cores and the Earth\u2013 Moon system \nfrom Nb/Ta systematics. Science, 301, Pp. 84\u201387. \n\n\n\nMullen, E.D., 1983. MnO--TiO2--P20~: a minor element discrimination for \nbasaltic rocks of oceanic environments and its implications for \npetrogenesis. Earth Planet. Sci. Lett., 62, Pp. 53-62. \n\n\n\nPasschier, C.W., Trouw, R.A.J., 2005. Micro-tectonics. Springer Berlin \nHeidelberg New York. \n\n\n\nPearce, J.A., 2008. Geochemical fingerprinting of oceanic basalts with \napplications to ophiolite classification and the search for Archean \noceanic crust. Lithos, 100 (1\u20134), Pp. 14-48. \n\n\n\nPearce, J.A., Cann, J.R., 1973. Tectonic setting of basic volcanic rocks \ndetermined using trace element analyses. Earth Planetary Science \nLetters, 19, Pp. 290-300. \n\n\n\nPearce, J.A., Norry, M.J., 1979. Petrogenetic implication of Ti, Zr, Y, and Nb \n\n\n\nvariations in volcanic rocks. Contribution to Mineralogy and \nPetrology, 69, Pp. 33-47. \n\n\n\nPeccerillo, A., Taylor, S.R., 1976. Geochemistry of Eocene calc-alkaline \nvolcanic rocks in the Kastamonu area, northern Turkey. \nContributions to Mineralogy and Petrology, 58, Pp. 63-81. \n\n\n\nPfaender, J.A., Muenker, C., Stracke, A., Mezger, K., 2007. Nb/Ta and Zr/Hf \nin ocean island basalts\u2014implications for crust\u2013mantle \ndifferentiation and the fate of Niobium. Earth Planet Sci Lett., 254, \nPp. 158\u2013172. \n\n\n\nRickwood, P.C., 1989. Boundary lines within petrologic diagrams which \nuse oxides of major and minor elements. Lithos., 22, Pp. 247-263. \n\n\n\nRollinson, H.R., 2013. Using Geochemical Data: Evaluation, Presentation, \nInterpretation. Longman Group, UK, Pp. 1\u2013315. \n\n\n\nRyerson, F.J., Watson, E.B., 1987. Rutile saturation in magmas: implications \nfor Ti\u2013Nb\u2013Ta depletion in island-arc basalts. Earth and Planetary \nScience Letters, 86, Pp. 225\u2013239. \n\n\n\nSun, S.S., McDonough, W.F., 1989. Chemical and Isotopic Systematics of \nOceanic Basalts: Implications for Mantle Composition and \nProcesses. Geological Society of London Special Publication, 42, Pp. \n313-345. https://doi.org/10.1144/GSL.SP.1989.042.01.19. \n\n\n\nSylvester, P.J., Attoh, K., 1992. Lithostratigraphy and composition of 2.1 Ga \ngreenstone belts of the West African craton and their bearing on \ncrustal evolution and Archean\u2013Proterozoic boundary. Journal of \nGeology, 100, Pp. 377\u2013393. \n\n\n\nTiepolo, M., Vannucci, R., Oberti, R., Foley, S., Bottazzi, P., Zanetti, A., 2000. \nNb and Ta incorporation and fractionation in titanian pargasite and \nkaersutite: crystal\u2013chemical constraints and implications for \nnatural systems. Earth Planetary Science Letters, 176, Pp. 185\u2013201. \n\n\n\nWilson, M., 1989. Igneous Petrogenesis. London: Chapman and Hall, Pp. \n450. \n\n\n\nZitzmann, A., Kiessling, R., Loh, G., 1997. Geology of the Bui Belt area in \nGhana. In A. Zitzmann (ed), Geological, Geophysical and \nGeochemical Investigation in the Bui Belt Area in Ghana., page 269. \nGeologisches Jahrbuch Reihe B, Heft 88 \n\n\n\n\nhttps://www.sciencedirect.com/journal/lithos/vol/100/issue/1\n\n\nhttps://doi.org/10.1144/GSL.SP.1989.042.01.19\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal Geosciences (MJG) 1(1) (2017) 32-33\n\n\n\nContents List available at RAZI Publishing\n\n\n\nMalaysian Journal of Geosciences\nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/\n\n\n\nPsychological Debriefing Intervention: From the Lens of Disaster Volunteers\nSiti Rozaina Kamsani,Nabisah Ibrahim,Noor Azniza Ishak\nUniversiti Utara Malaysia, Kedah Correspondence: rozaina@uum.edu.my\nThis study is funded by FRGS-Flood Disaster 2015\n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle history:\n\n\n\nReceived 22 January 2017  \nAccepted 03 February 2017  \nAvailable online 05 February 2017\n\n\n\nKeywords:\nvolunteers, psychological debriefing, \nvolunteers\u2019 attributes, post disaster \nmanagement\n\n\n\nThe experience of posttraumatic stress is a sign of discomfort feeling and miserable situation especially for \nflood survivors. Being a first responder to the victims, the disaster volunteers are not only support providers \nfor moral and psychological services, but also agents to reduce disaster-related-stress. Thus, the purpose of this \nstudy was to identify the key experiences of volunteers as the first responder in using psychological debriefing \nintervention with disaster victims. There were 20 volunteers from different agencies involved in this study. The \nsemi-structured interview sessions were utilized for data collection. Based on the thematic analysis process, the \nfindings indicated that the volunteer\u2019s resilience skill, emotional stability, and social altruism have been found to \nbe the major volunteers\u2019 attributes in conducting psychological debriefing intervention. Volunteers\u2019 suggestions on \nimplementation the psychological debriefing intervention for Malaysian context was also provided\n\n\n\n1.0 INTRODUCTION\nFlood disaster has emotionally impact on the mental health and stability of \nthe primary victims.  Numerous researches have stated that people who are \nexposed to this traumatic event mostly experience intense fear, helplessness, \nor horror (Talbott, 2009).  Caught off guard and \u201cnumb\u201d from the impact of \na critical incident, individuals, and communities are often ill-equipped to \nhandle the chaos of such a catastrophic situation.  Providing a support and an \nassistance for survivors to deal with undesirable situation are significantly \nimportant among disaster volunteers as to help the survivors to continue \nwith their lives.  There are various supports that might be useful for the \nsurvivors such as psychological aspects, emotional stability, products, and \nmonetary donations.  Indeed, the psychological support has been found to \nbe an important factor to help the survivors in overcoming and dealing with \nthe traumatic experiences rather than other aspects. \nThe psychological debriefing was initially described as a critical incident \nstress debriefing (CISD) by Jeffery Mitchell in 1983.  Bisson, Jenkins, \nAlexander, and Bannister (1997) defined that psychological debriefing \n(PD) as a set of procedures, which include counselling and information \ngiven, which aim at preventing psychological morbidity and aiding recovery \nafter a traumatic event.  The purpose of PD is to inhibit the development of \npost-traumatic stress disorder and other negative sequel (Cooper, 2003).  \nConducting the PD is not just a simple procedure as individual or group \ntherapy, but it needs more attention and sometimes it is considered as a \ncomplex process of early intervention given by the volunteers during or \nafter the incidents.  Previous research found that the PD intervention has \nsignificantly reduced the negative effect of traumatic event (Mitchel & \nEverly, 1996).  However, the focus on the implementation is based on the \nlens\u2019 of emergency responders from North America (Dyregrov, 1997).  Thus, \nthe volunteers\u2019 responses from different cultural background are necessary \nand needed to be highlighted in order to identify the significant elements for \nconducting the PD in other cultures.\nLiterature Review\nIndividuals who are involved in the volunteer service can widely benefit \nnot only to the community but also to the individuals who involve in it.  \nSurprisingly, little attention has been paid to the actual consequences of \nvolunteer service for individuals\u2019 physical and/or psychological well-being \n(Thoits & Hewitt, 2001).   The volunteers are the first responders to deal \nwith the survivors, they need to be aware about their physical, psychological, \nand emotional stability while working with survivors in any traumatic and \nstressful events.  Sometimes the critical incidents may produce a stressful \nimpact on the survivors, in turn, it is sufficient to overwhelm an individual\u2019s \nsense of control, connection, and meaning in his/her life (Pietrantoni & \nPrati, 2008).  In fact, each volunteer is required to be conscious on what \nkinds of attributes or skills that are desirable to provide in his or her \nservice assistance.  In this regard, the most important element for disaster \nvolunteers is to prepare themselves with the basic personal preparation and \nreadiness in dealing with the vulnerable people and situations. \nThe disaster volunteers are eager and excited to demonstrate their social \ninvolvements along with their physical or emotional aspects, which might be \n\n\n\nimportant to the volunteer work.  The researchers argued that individuals\u2019 \npersonal resources and well-being may facilitate volunteers\u2019 involvements \nin the volunteer work and subsequently enhance their commitments while \nworking with any critical incidents and events.  As mentioned by Allen and \nRushton (1983), the volunteer\u2019s participation is highly demonstrated in \nindividual who has a higher level of internal locus of control, self-esteem, \nand greater emotional stability.  Their findings also showed that people who \nare generally have greater personal coping resources (e.g., high self-esteem \nor an internal locus of control) and who are in better mental health might be \nmore likely to be involved in any volunteer services. Therefore, the purpose \nof this paper was to identify some personal attributes and skills through the \nlens of Malaysian disaster volunteers\nMethodology\nThis study employed a qualitative research design, conducted on 20 \nvolunteers from different agencies such as counsellors, social workers, \nNGOs officers, and welfare officers.  All of these volunteers had experienced \nworking with Kelantan\u2019s flood survivors at least for a period of one month.  \nBased on the Crisis Intervention Theory (Lindermann, 1944), the semi-\nstructured interview questions were designed according to volunteers\u2019 \npersonal background, flood involvement experiences, psychological \ndebriefing exposures, and personal reflections while working with \nsurvivors.  \nAll volunteers were gathered in a large group and requested to respond \nto the informed consent as to allow the researchers to record the sessions \nconducted with them.  Then, the volunteers were divided into two (2) groups \nin which each group was facilitated by one researcher.  The interview session \nlasted for about 60 to 90 minutes. The questions were designed on six sub-\ntopics related to volunteers\u2019 flood disaster experiences while conducting a \npsychological debriefing intervention.  Data were analyzed by using NVivo \nsoftware and there were some themes that emerged from the data collected, \nwhich related to the  preparation of pyschological debrieifng intervention.\nFindings and Discussions\nSeveral studies found that there are some of criteria such as  \u0336  values, \nreligiosity, value of altruism, and resilience   \u0336  are needed by the volunteers \nin providing their services to the community (Dury, De Donder, De Witte, \nBuffel, Jacquet, & Verte, 2015; Pietrantoni & Prati, 2008).  In line with the \nprevious study, the researchers have identified several themes that were \narisen from the present study.  Volunteers\u2019 resilience skills, emotional \nstability, and social altruism were emerged as some personal attributes \nand skills that are necessary for disaster volunteers in conducting the PD \nintervention among survivors.\nResilience Skills\nBeing the first responder for disaster survivors, the resilience skill is one \nof the required skills needed by the volunteers.  It reflects the ability to \nresponse positively with circumstances, which in turn challenges their \nstabilities and functioning.  The resilience can be defined as an individual\u2019s \nability to cope and deal with highly disruptive situations, such as death, \ndisaster, vulnerable, and traumatic events (Bonanno, 2004).  \n\n\n\nCite this article as: Psychological Debriefing Intervention: From the Lens of Disaster Volunteers Siti Rozaina Kamsani,Nabisah IbrahimNoor Azniza Ishak\n/ Mal. J. Geo 1(1)  (2017) 17-18\n\n\n\nISSN:2521-0920 (Print) \nISSN: 2521-0602 (Online)\n\n\n\nhttps://doi.org/10.26480/mjg.01.2017.32.33\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\n http://www.razipublishing.com/journals/galeri-warisan-sains-gws/ \n\n\nhttp://doi.org/10.26480/mjg.01.2017.32.33\n\n\nhttps://doi.org/10.26480/mjg.01.2017.32.33\n\n\n\n\n\n\nSiti Rozaina Kamsani,Nabisah Ibrahim,Noor Azniza Ishak / Malaysian Journal of Geosciences 1(1) (2017) 32\u201333 33\n\n\n\nCite this article as: Psychological Debriefing Intervention: From the Lens of Disaster Volunteers Siti Rozaina Kamsani,Nabisah IbrahimNoor Azniza Ishak\n/ Mal. J. Geo 1(1)  (2017) 17-18\n\n\n\n).  In this study, the volunteers described that they were experienced with a \ngood level of satisfaction while conducting the PD intervention in which they \nwere not affected by traumatic stress and experiences from the survivors.  \nDealing with the same experience with the other victims, it would be helpful \nfor the volunteers to develop their resilience skills.  Consequently, it helps \nthem to feel more empathy towards the survivors.  For example, Volunteer A \nand B mentioned about their experiences as flood survivors, which helped \nthem to understand the survivors\u2019 feelings and conditions. \n\u201cbeing a flood survivor in 2000, and now I\u2019m a volunteer and work with \nflood survivors, so it helps me to understand their feelings\u2026\u201d (Excerpt \nVolunteer A)\nThis is supported by the excerpt from Volunteer B, who said that:\n\u201cDuring that time, my family was also in the same situation, and I couldn\u2019t \nhelp \nthem and keep thinking of their situations\u2026my worry was very high \u2013 I tried \nto \nfocus on the task given and get more food for the survivors\u2026\u201d \nIn fact, the resilience skill is found as an important element among the \ndisaster volunteers due to their personal and social resources that could \nprotect their healthy lives.  Their abilities to cope with the traumatic and \nstressful events would be helpful for them in controlling their unstable \nfeelings and physical conditions.  Working with the disaster survivors is \nhighly exposed to depression, it sometimes challenges the volunteers\u2019 \npatience and burn-out.  In addition, previous research found out that the \nvolunteers\u2019 sense of belonging to the community where they live and work \nat is an important factor to help them to be strong in helping the disaster \nsurvivors (Pietrantoni & Prati, 2008).  It is considered as a positive adaption \nin facing any critical incidents, especially, a natural disaster. \nEmotional Stability\nExperiencing the disaster or any traumatic events will leave some emotional \neffects not only on the survivors, but also on the volunteers or whoever \nare dealing with such situations.  Given a specific period of time working \nwith the flood survivors, most of the volunteers mentioned that their \nemotions were stable even though it was very hard for them to express their \nreal feelings.  They were trying to control and express their frustrations, \ntiredness, give up, and anger, while conducting the PD intervention with the \nsurvivors.  The volunteers also explained that they have tried to show their \nempathy instead of sympathy, in order to understand the real feeling of the \nsurvivors. \nThe group process was involved in PD intervention whereby the volunteers \naimed at instilling the value of hope on the survivors.  In this case, condemn \nand judgmental on survivors\u2019 situations and conditions were applicable.  \nBesides, they were able to control themselves from being negative towards \nsurvivors\u2019 behaviors and responses, and always be patient with them during \nassistance, as like what the Volunteer D mentioned that: \n\u201cI was scolded and insulted by the survivors when my rescue boat had \nflipped over and threw out one of the kid into the flood during evacuation. At \nthat time I just jumped into the flood and saved the kid. After that incident, \nI met again with that survivor and he came to me and hugged me. He said \nthank you for your help\u2026\u201d\nIn addition, the Volunteer C also explained on what he felt when he saw \nthe water.  It was a hard time for him to choose either to fulfill his duty as \nvolunteer or go back and evacuated his family members.  He described that \nhis emotion was not stable during that time, but he was able to control his \nemotion and focus on his at that particular time.  This scenario reflected \nthat the emotional stability of disaster volunteer significantly influenced \ntheir services especially while working with the survivors.  The stability \nof emotions and feelings would be helpful for disaster volunteers while \nworking with survivors because it could reduce their pains and frustrations \nparticularly on the incidents.  Besides, the volunteers could be more patient \nand calmer in facing any vulnerable situations in future. \nSocial Altruism\nHelping behavior is a value which relates to individual\u2019s personal attribute.  \nIn fact, the volunteering activity can be considered as one of the social \ncontribution in leading to psychological well-being and it is referred to as \nother-oriented views of the self and significant involvement in the world \n(Kahana, Bhatta, Lovegreen, Kahana, & Midlarsky, 2013).  In fact, the social \naltruism can be defined as a concern about others\u2019 welfare, behavior, and \ncommitments to help, often at a personal cost (Hartenian & Lily, 2009).  In \nthis study, the researchers found that most of the volunteers were able to \ncommit themselves in giving an aid to the survivors and they were scarified \ntheir time to stand-by whenever they were needed.  \nAs a first responder, the attribute of social altruism might be necessary \nin assisting the volunteers working with the survivors.  The values of \nsocial and protection were incorporating with the sense of volunteering \ninvolvement among the volunteers in this study.  The result found that most \n\n\n\nof the volunteers were able to strengthen their social roles, not only as a \nwelfare co-worker, but also as a disaster volunteers.  In fact, these different \nroles would be helpful for them to alleviate the feeling of guilt especially \nabout the circumstances of others.  As mentioned by two volunteers:\n\u201cI tried to be patient with myself and believe if I keep thinking with what\u2019s \ngoing on, it will distract my attention on what am I doing \u2013 to help people \nin here\u2026\u201d \n(Excerpt Volunteer E)\n\u201cwe worked as a team and helped each other\u2026need to find a safe location \nfor the survivors and somehow we broke the hospital gates and few other \nplaces \u2013 work hard to provide enough food to survivors and safe place for \nthem\u2026\u201d \n(Excerpt Volunteer F)\nThus, having a sense of helping others, it would enhance a personal \nmotivation in enhancing the desire to continue the volunteer participation \nas to provide services towards survivors.  In fact, the social altruism is a vital \nelement in promoting a desired motivation to engage in the volunteer work \nin community setting. \nConclusion\nConducting a psychological debriefing intervention is one of the contributions \nof the disaster volunteers to help the survivors.  The disaster volunteers \ndeveloped their resilience skills while working with the survivors.  In fact, \nthe emotional stability had made the disaster volunteers to understand the \nfeelings of the survivors in relation to painful experiences.  In addition, the \nsocial altruism is a sense of helping other, which is necessary to motivate \nthe volunteers\u2019 participation in any volunteer activities.  Therefore, with \nthese three elements, the service of the disaster volunteers will be improved \nsincerely and willingly.  \nReferences\nAllen, N., & Rushton, J. P. (1983). Personality characteristics of community \nmental health \nvolunteers: a review. Journal of Voluntary Action Research, 12, 36\u201349\nBisson, J. I., Jenkins, P. L., Alexander, J., & Bannister, C. (1997). Randomised  \ncontrolled trial of psychological debriefing for victims of acute burn trauma. \nThe British Journal of Psychiatry, 171(1), 78-81.\nBonanno, G. A. (2004). Loss, trauma, and human resilience: Have we \nunderestimated the human \ncapacity to thrive after extremely aversive events? American Psychologist, \n59, 20-28. \nCooper, A. (2003). Psychological Debriefing: Theory, Practice and Evidence. \nEdited by \nBeverley Raphael and John P. Wilson. New York, Cambridge University Press \n2000, 376 \npp, ISBN 0-521-64700-2. Australasian Journal of Paramedicine, 1(1). \nRetrieved from \nhttp://ro.ecu.edu.au/jephc/vol1/iss1/17\nDury, S., De Donder, L., De Witte, N., Buffel, T., Jacquet, W., & Verte, W. (2015). \nTo volunteer \nor not: The influence of individual characteristics, resources, and social \nfactors on the \nlikelihood of volunteering by older adults. Nonprofit Voluntary Sector \nQuarterly, 44(6), 1107-1128. \nDyregrov, A. (1997). The process in Psychological Debriefings. Journal of \nTraumatic Stress, \n10(4), 589-685. \nHartenian, L. S., & Lily, B. (2009). Egoism and commitment: A \nmultidimensional approacj to \nunderstanding sustained volunteering. Journal of Managerial Issues, 21(1), \n97-118. \nKahana, E., Bhatta, T., Lovegreen, L. D., Kahana B., & Midlarsky, E. (2013). \nAltruism, \nhelping, and volunteering: Pathways to well-being in late life, Journal Aging \nHealth, 25(1), 159-187. \nMitchell, J. T., & Everly, G. S. (1996). Critical incident stress debriefing. \nJournal of Emergency \nMedical Services, 8, 36-39. \nPietrantoni, L. & Prati, G. (2008). Resilience among first responders. African \nHealth Sciences, 8, \n514-520. \nTalbott, W. R. (2009). Early Mental Health Interventions Following Disasters: \nWhat is the \nStandard of Practice?. Texas Public Health Journal, 612(2), 40-41.\nThoits, P. A, & Hewitt, L. N. (2001). Volunteer work and well-being. Journal \nof Health and \nSocial Behavior, 42(2), 115-131.\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 69-72 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2022.69.72 \n\n\n\nCite The Article: Michael Tomisin, Asubiojo (2022). Characterization of Inchnogenera Trace Fossils in Sedimentary Facies; A \nCase Study of Tomayode Field, Niger Delta Nigeria. Malaysian Journal of Geosciences, 6(2): 69-72. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences \n\n\n\n(MJG) DOI: http://doi.org/10.26480/mjg.02.2022.69.72\n\n\n\nCHARACTERIZATION OF INCHNOGENERA TRACE FOSSILS IN SEDIMENTARY \nFACIES; A CASE STUDY OF TOMAYODE FIELD, NIGER DELTA NIGERIA \n\n\n\nMichael Tomisin, Asubiojo* \n\n\n\nDepartment of Earth Sciences, Adekunle Ajasin University, Akungba Akoko, Nigeria \n*Corresponding Author Email: michael.asubiojo@aaua.edu.ng\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 15 June 2022 \nAccepted 20 July 2022 \nAvailable online 28 July 2022\n\n\n\nTo provide a comprehensive explanation of the properties and relevance of trace fossils ichnogenera in the \nNiger Delta sedimentary facies necessitate this study. The provided core photographs containing identified \ntrace fossils were qualitatively analysed and described using ichnofacies characteristics, appearance, \nlithology, grain size, structures, and depositional environments. Teichichnus and Ophiomorpha burrows have \nbeen identified as ichnogenera. They were found in heterolithic fine-grained sandstone with interbedded \nmudstone that exhibits hummocky and swaley cross \u2013 stratifications, as well as ripples cross \u2013 laminations, \nwhich are frequently encountered in marine shoreface environments. Ophiomorpha and Teichichnus are \nbelieved to have been produced by soft-bodied organisms such as crustaceans. They are deposit-feeding \norganisms with documented evidence of coexistence. The stratigraphic records left by these trace fossils are \nof particular interest to geologists and other scientists because they aid in palaeo-environmental evaluation, \nwhich is a precursor to hydrocarbon generation and accumulation, and subsequent exploration and \nexploitation. \n\n\n\nKEYWORDS \n\n\n\nCore photographs, Ichnogenera, soft-bodied organisms, Paleo-environments, deposit-feeding, Hydrocarbon \ngeneration. \n\n\n\n1. INTRODUCTION \n\n\n\nTrace fossils, or ichnofossils, are imprints left by organism on or in a \nsubstrate that provide indirect evidence of past life. Animals leave behind \nimpressions such as footprints, tracks, burrows, digging, urolites (erosion \ninduced by liquid waste outflow), feeding markings, and excrement, rather \nthan preserved remains of the animal's body. \n\n\n\nTrace fossils are generated when organism leaves imprint in mud or sand \nand records activity, and the sediment later dries and hardens with the \nimprints. \n\n\n\nIchnologic research, as a critical component of the development of trace \nfossil analysis, has influenced the study of a broad variety of other subjects, \nincluding palaeoecology, biostratigraphy, palaeobiology, \npalaeobathymetry, and sedimentology. Trace fossils studies have been \nused as distinct depositional environments indicators in many \nsedimentary basins around the world, hence the need to characterise the \nidentified trace fossils in the Niger delta sedimentary basin for further \nstudies formed the basis of this study.  \n\n\n\nPreviously, numerous authors researched independently on trace fossils. \nA study classified trace fossils according to their habitats, which ranged \nfrom lagoons to offshore bars and shelf environments, with Teichichnus, \nSkolithos, Chondrites, and Trypanites serving as the most prevalent trace \nfossils in each environment (Fillion and Pickerill, 1984). A research used \ntrace fossils analysis alongside other well log data to interpret the paleo-\nenvironmental settings of rock facies in the Niger Delta, the result of the \nstudy revealed that the study area is of shoreface depositional \nenvironment (Asubiojo, 2020). Anderson, (1981) described two forms of \n\n\n\nOphiomorpha nodosa in loose rocks, Southern Sweden. He defined the first \none as consisting of cylindrical to slightly conical with knobby surfaces. \nThey are straight or slightly curved, with some being compressed. They \nare suggested being derivate from beds of ferruginous claystone or \nsandstone due to the few scraps of surrounding rock adhering to their \nsurfaces.  The second form consists of external molds, but with a few \nspecimens exhibiting nearly smooth core preserved. They are straight to \nslightly curve with one specimen being branching. He concluded by \ndeclaring that there was no bedrock sources known for the two forms of \nOphiomorpha, however he suggested Tertiary for the first form. The \noccurrence of Teichichnus burrows in the Sandbian, Katian and Telychian, \nEstonian was described to have associated with carbonate rocks (Vinn and \nToom, 2018). Two ichnospecies T. rectus and T. patents were identified \nfrom the Lower Palaeozoic of Estonia. He suggested the possibility of \nTeichichnus being more in the Sandbian than in the carbonate sequences \nof lower to middle Ordovician and in the Silurian. This was attributed to \nthe little bathymetric variability and an extremely low sedimentation rate \nin the shallow epicontinental basin.  \n\n\n\nSeilacher, (1964); Frey et al., (1978); Dam, (1990); Bland and Goldring, \n(1995); Boggs, (2001); Mude, (2011) previously suggested that the trace \nfossils Teichichnus and Ophiomorpha are deposit \u2013 feeding burrows \nproduced by soft \u2013 body organisms commonly found in layers of very fine \nto medium grained sand along marine shoreface environments. \n\n\n\nDespite these multiple publications, none of the writers has ever provided \na comprehensive explanation of the properties and relevance of \nOphiomorpha and Teichichnus trace fossils found in the Niger Delta \nsedimentary basin, thus necessitating this study. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 69-72 \n\n\n\nCite The Article: Michael Tomisin, Asubiojo (2022). Characterization of Inchnogenera Trace Fossils in Sedimentary Facies; A \nCase Study of Tomayode Field, Niger Delta Nigeria. Malaysian Journal of Geosciences, 6(2): 69-72. \n\n\n\n2. GEOLOGICAL SETTING \n\n\n\nThe TOMAYODE Field (Figure. 1) is located in the eastern portion of the \ncoastal Niger Delta province in the Gulf of Guinea, which comprises the \nAtlantic session of coastal south-south Nigeria, and extends eastward from \nthe Akassa River to the Cross River area. \n\n\n\nThe Niger Delta is a marginal sag basin located in tropical West Africa on \nthe continental coast of the Gulf of Guinea. It covers an area of around \n75,000km2, with an average thickness of roughly 12km, and is located \nbetween latitudes 3\u00b0 and 6\u00b0N and longitudes 5\u00b0 and 8\u00b0E (Knox and \nOmatsola, 1989). \n\n\n\nFigure 1: Location of the study Area (edited after Corredor et al., 2005) \n\n\n\nThe Tertiary Niger Delta is classified into three primary units based on \ntheir lithostratigraphy: Akata, Agbada, and the Benin Formation. The \nAkata Formation, which created the delta's base sedimentary layer, is \ncomposed of homogeneous dark grey over-pressured marine shales with \nsandy turbidites and channel fills dating from the late Eocene to the Recent \n(Ariere, 2012). The Agbada Formation succeeded the Akata Formation \nwith distinct paralic to marine-coastal and fluvial-marine deposits \ncomposed primarily of sandstone and shale and arranged in coarsening \nupward off-lap cycles (Odumodu, 2011). The Benin Formation, with a \nthickness of approximately 2000m (6600 feet), overlies the Agbada \nFormation, which contains alluvial and upper coastal plain deposits \n(Continental) dating from the Late Eocene to the Recent (Chinazo et al, \n2017). \n\n\n\nThe basin's development has also been linked to Africa's separation from \nSouth America and the subsequent opening of the South Atlantic in the Mid \nCretaceous (Evamy et al., 1978; Doust and Omatsola, 1990). \n\n\n\n3. MATERIALS AND METHOD \n\n\n\nThe total of nine (9) core photographs were provided for this study in the \ndepth range of (13435.0 \u2013 12866.4 ft.) or (4095.0 \u2013 3921.7m) from bottom \nto top. However, five of these core photographs are devoid of trace fossils \ncharacterization, while the other four contained traces of ichnofossils. The \ncore photographs (1 \u2013 4; figures 2 - 5) that contained the identified trace \nfossils were studied and described qualitatively from bottom to top on the \nbasis of trace fossil characteristics, appearance, lithology, grain size, \nsedimentary structures (cross bedding, laminations) and geological \nsuccession of the rock facies. The depth range of the study area from \nbottom to top is 13319.0 to 12945.0ft (4059.63 to 3945.63m), and the data \nset was provided by Shell Petroleum Development Company (SPDC). \n\n\n\n4. RESULT AND DISCUSSION\n\n\n\nTwo ichnogenera; Ophiomorpha and Teichichnus identified in the provided \ncore photographs were studied and described on the basis of their \ncharacteristics in the sedimentary facies.  \n\n\n\nOPHIOMORPHA: (Figure 2, 3 & 4: Cores 1, 2, and 3). \n\n\n\nTheir burrows are elongated horizontally to slightly obliquely. The \nburrows were branching in form and had an uneven outer surface that \n\n\n\nseemed to be lined with faecal pellets. The burrow lining is more or less \nsmooth on the inside and thickly to firmly nodose on the outside, which is \nthought to be the result of the burrow being supported by fecal pellets. \n\n\n\nAccording to previous study, Ophiomorpha's wall is composed of sediment \npellets pressed into the surrounding silt and smoothed internally \n(Hantzscheli, 1952). \n\n\n\nThe lining of Ophiomorpha pellets is often made of strong, spherical \ntubuercles that range in shape from ovoid to irregular polygonal (Frey et \nal., 1978; Bromley, 1991; Miller et al., 1998). \n\n\n\nAs seen in the core photographs, the rock facies with Ophiomorpha traces \nconsists of hummocky and swaley cross \u2013 stratifications, ripples cross \u2013 \nlaminations, heterolithic sandstone with interbedded mudstone, and very \nfine to fine grained finely sorted sandstone. \n\n\n\nO\np\nh\ni\no\nm\n\n\n\no\nr\np\nh\na\n \nb\nu\nr\nr\no\nw\n\n\n\ns\n\n\n\nCore 1\n\n\n\nFigure 2: Core 1 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 69-72 \n\n\n\nCite The Article: Michael Tomisin, Asubiojo (2022). Characterization of Inchnogenera Trace Fossils in Sedimentary Facies; A \nCase Study of Tomayode Field, Niger Delta Nigeria. Malaysian Journal of Geosciences, 6(2): 69-72. \n\n\n\nOphiomorpha burrowsTeichichnus trace\nCore 2\n\n\n\nFigure 3: Core 2 \n\n\n\nThe sedimentary and stratigraphic properties of the Ophiomorpha-\nbearing rock facies in the study field indicated deposition on a shoreface \n(shallow marine) environment with an Ophiomorpha nodosa type. \n\n\n\nOphiomoroha has two primary representatives; O. nodosa is mainly \nassociated with shallow water sediments, whereas O. rudi is more \nassociated with deep sea sediments (Tchoumachenko and Uchman, 2001). \n\n\n\nRecent study defined Ophiomorpha as a trace fossil that is frequently \nfound in layers of extremely fine to medium-grained sands with numerous \ndwelling burrows of decapod crustaceans in marine shoreface settings \nwith brackish water, as well as sandy substrates in estuaries and tidal \nshoals (Anderson, 1981). \n\n\n\nThe Tertiary Niger Delta rock facies studied assigned the Ophiomorpha to \nthe Cenozoic. Ophiomorpha is a trace fossil that is frequently discovered \nin Mesozoic and Cenozoic sedimentary rocks that were deposited in \nshallow marginal environments, while other species were found in deeper \nmarine environments (Frey et al., 1978). \n\n\n\nThe presence of Ophiomorpha in a rock facies indicates a well-oxygenated \nand nutrient-dense setting, which is frequently seen in the shoreface \nenvironment, particularly in the lower \u2013 middle shoreface environment. \n\n\n\nThe coexistence of Ophiomorpha and Teichichnus trace fossils in cores 2 \nand 3 (Figure 3 and 4) indicated that Ophiomorpha may coexist safely with \nother organisms under the same environmental conditions. Ophiomorpha \nis one of the most important environmental markers among trace fossils, \nand by no means a clear entity for facies analysis and palaeoecological \nstudy. Ophiomorpha is interpreted as an organism (particularly a \nCrustacean) that lives in a near shore environment and has a combined \ndwelling and feeding burrow. \n\n\n\nTEICHICHNUS: (Figure 3, 4 & 5: Cores 2, 3, and 4). \n\n\n\nThey exhibit simple horizontally oriented burrows with retrusive spreite \nupwards. They have arcuate forms with homogeneous burrow fill that \nblends in with the host rock macroscopically. Burrows have a smooth \nsurface that runs almost parallel to the bedding. \n\n\n\nThe spreite is considered to have produced in reaction to the causal \nburrow's upward migration and may represent partially the trace maker's \nequilibrium response (Knaust, 2017; Knaust, 2018). The burrow spreiten \nprovided evidence that the producer was a soft-bodied creature. Apart \nfrom crustaceans, vermiform creatures, particularly annelids, have been \nsuggested as potential producers of Teichichnus (Seilacher, 1964; Dam, \n1990; Bland and Goldring, 1995). \n\n\n\nO\np\nh\nio\n\n\n\nm\no\nrp\n\n\n\nh\na b\n\n\n\nu\nrro\n\n\n\nw\n\n\n\nT\neich\n\n\n\nich\nn\nu\ns tra ce\n\n\n\nCore 3\n\n\n\nFigure 4: Core 3 \n\n\n\nT\nei\n\n\n\nch\nic\n\n\n\nhn\nus\n\n\n\n t\nra\n\n\n\nce\n\n\n\nCore 4\n\n\n\nFigure 5: Core 4 \n\n\n\nThe studied rock facies (Figure 3, 4 and 5: Cores 2, 3, and 4) contains \nheterolithic fine-grained sandstone and mudstone beds intercalated with \nvery fine to fine-grained sandstone that exhibits hummocky and swaley \ncross \u2013 stratifications with ripples cross \u2013 laminations. These sedimentary \ncharacteristics, together with the stratigraphic position of the rock facies, \nsuggested that the Teichichnus sediments were deposited on a shoreface. \nTeichichnus sediments are frequently associated with low-energy \ndepositional environments and are frequently found on completely \noxygenated substrates. \n\n\n\nTeichichnus was described by other studies as a trace fossil that occurs in \na variety of environments and is a typical element of estuary, lagoon, and \nlower shoreface to offshore (shelf) deposits (Pemberton et al., 1992; \nKnaust, 2017). \n\n\n\nTeichichnus-like burrows are frequently found in Cambrian to Tertiary \nstrata (such as marine siltstones, sandstones, calcarenites, and chalks). \nThe illustrated closely analogous sections in Cretaceous Chalks and \nClastics deposits (Frey and Howard, 1970), in the Cambrian (Martinsson, \n1965), and in the Carboniferous (Chisholm, 1970b). \n\n\n\nTeichichnus has been classified into eighteen ichnospecies based on the \nsignificant degree of variety in burrow morphology (Knaust, 2018), \nalthough only four of these ichnospecies are currently considered valid: \nTeichichnus rectus, Teichichnus zigzag, Teichichnus patens, and \nTeichichnus duplex (Stanton and Dodd, 1984; Frey and Bromley, 1985; \nSchlirf, 2000; Schlirf and Bromley, 2007; M\u00e1ngano and Buatois, 2011; \nKnaust, 2018; Olev and Ursula, 2018). \n\n\n\nTeichichnus coexisted in low density with Ophiomorpha in the study rock \nfacies (Figures 3 and 4: cores 2 and 3), implying that Teichichnus can \ncoexist with other organisms under similar environmental conditions. \nTeichichnus are frequently found in lower shoreface to offshore habitats \nin combination with cruziana ichnofacies, according to (Pemberton et al., \n2001; Knaust, 2018). It is worth noting, however, that Teichichnus is \nuncommon in deep \u2010 marine habitats and rare instances may imply dysoxic \nbottom conditions. \n\n\n\nThough Teichichnus is typically found on fully oxygenated substrates, it is \nalso found on substrates that exhibit signs of stress, such as energy and \nsalinity changes (Savrda and Nanson, 2003; Gingras et al., 2007). In this \nsituation, specimens produced are typically smaller and have tiny and \nflattened spreiten compared to those produced in non-stressed conditions \n(Pemberton et al., 2001; Buatois et al., 2005). \n\n\n\nTeichichnus burrow's producing animal was characterised as a deposit-\nfeeding worm-like organism that migrated upward in its burrow to keep \nup with sedimentation (Pemberton et al., 1982; Pemberton et al., 1992; \nPemberton et al., 2001). \n\n\n\nTeichichnus is interpreted as a wall-like internally laminated trace maker \nproduced by vertical migration of horizontal cylindrical feeding burrow.  \n\n\n\n5. CONCLUSION \n\n\n\nIchnogenera Ophiomorpha and Teichichnus are burrows made by soft-\nbodied organisms (such as Crustaceans) that are common along marine \nshoreface environments. They coexisted and left behind stratigraphic \nrecords that are extremely relevant for assessing palaeo-environmental \nconditions that were antecedents to hydrocarbon formation and \naccumulation. \n\n\n\nThe study proposes that additional research be conducted on the effect of \nOphiomorpha and Teichichnus ichnogenera on the study field's \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 69-72 \n\n\n\nCite The Article: Michael Tomisin, Asubiojo (2022). Characterization of Inchnogenera Trace Fossils in Sedimentary Facies; A \nCase Study of Tomayode Field, Niger Delta Nigeria. Malaysian Journal of Geosciences, 6(2): 69-72. \n\n\n\npetrophysical parameters in order to gain a better knowledge of the \nreservoir's characteristics and how it\u2019s being affected by the biotubants. \n\n\n\nAKNOWLEDGEMENT \n\n\n\nThe author wishes to express his gratitude to Shell Petroleum \nDevelopment Company (SPDC) for providing the data set for this research \n(field map, base map, and core pictures). Ogunsakin Ebenezer and \nOluwunmi Akinbayo's moral and financial support is greatly appreciated.  \n\n\n\nREFERENCES \n\n\n\nAnderson, K., 1981. Bernhard Lundgren\u2019s (1891) description of \nOphiomorpha. Geologiska F\u00f6reningen I Stockholm F\u00f6rhandlingar, Vol. \n103; 1981. 1. \n\n\n\nAriere, AM., 2012. Application of inorganic geochemical proxies to \nprovenance and paleo tectonic setting of fine grained sediments from \nwell X, Niger delta basin, Nigeira. Federal University of Petroleum \nResources \n\n\n\nAsubiojo, TM., 2020. Facies Architecture Analysis for Paleo-environment \nEvaluation in Tom Oil Field, Eastern Niger Delta, Nigeria. J. Appl. Sci. \nEnviron. Manage. Vol. 24 (2), Pp. 213 - 221 \n\n\n\nBland, BH; Goldring, R., 1995. Teichichnus Seilacher 1955 and other trace \nfossils (Cambrian) from the Charnian of Central England. Neues \nJahrbuch f\u00fcr Geologie und Palaonologie, Abhandlungen. (195): Pp. 5- 23 \n\n\n\nBoggs, S., 2001. Principles of Sedimentary and stratigraphy; Prentice Hall, \nUpper Saddle River, New Jersey, (3rd ed.) Pp. 59-415. \n\n\n\nBromley, RG., 1991. Zoophycos: Strip mine, refuse dump, cache or sewage \nfarm? Lethaia 24, Pp. 460 \u2013 462 \n\n\n\nBuatois, LA; Gingras, MK; Maceachern, JA; M\u00e1gano, MG; Zonneveld, JP; \nPemberton, SG; Netto, RG; Martin, A., 2005. Colonization of brackish \u2013 \nwater systems through time: evidence from the trace \u2013 fossil record. \nPalaios, 20: 321 \u2013 347. Doi.org/10.2110/palo.2004. Pp. 04 \u2013 32. \n\n\n\nChinazo FE; Emmanuel E; Anakwuba IT; Preye OG; Kakayorlme, EU., 2017. \nEvaluation of reservoir compartmentalization and property trends \nusing static modeling and sequence stratigraphy. J. Petrol. explor. Prod. \nTechnol. (7), Pp. 361-377. \n\n\n\nChisholm, JI., 1970b. Teichichnus and related trace fossils in the Lower \nCarboniferous at St. Monance, Scotland. Bull. Geol. Surv. G. B. 32, 21. \n\n\n\nDam, G., 1990. Taxonomy of trace fossils from the shallow marine lower \nJurassic Neill Klinter Formation, East Greenland. Bulletin of Geologic \nsociety Denmark, (38): Pp. 119 \u2013 144. \n\n\n\nDoust, H; Omatsola, E., 1990. Niger Delta In: J. D. Edwards and P.A. \nSantogrossi (Eds.), Divergent/passive Margin Basins, American \nAssociation of Petroleum Geologists Memoir 48, Tulsa, Oklahoma, USA, \nPp. 239-248. \n\n\n\nEvamy, BD; Haremboure, J; Kamarling, P; Knaap, WA; Mollry, FA; Rowland, \nP., 1978. Hydrocarbon habitat of Tertiary Niger Delta, AAPG bull. 63 (1), \nPp. 1-39. \n\n\n\nFillion, D; Pickerill, RK., 1984. Systematic ichnology of the middle \nOrdovician Trenton Group. St. Lawrence Lowland, eastern Canada. \nMaritime Sediments and Atlantic Geology. 20 (1): Pp. 1 \u2013 41. \n\n\n\nFrey, RW; Howard, JD., 1970. Comparison of Upper Cretaceous \nichnofaunas from Silicious sandstones and chalk, Western interior \nRegion, USA. J. in Trace Fossils. T. P. Crimes and J. C. Harper (Eds). \nLiverpool (seel House), 547, Pp. 141 \n\n\n\nFrey, RW; Howard, JD; Pryor, WA., 1978. Ophiomorpha: Its Morphology, \nTaxonomy and Environmental Significance, Paleogeography, \nPaleoclimatology and Paleoecology, Elsevier Scientific Publishing \n\n\n\nCompany, Amsterdam, vol. 23, Pp.199-229. \n\n\n\nGingras, MK; Bann, KL; Maceachern, JA; Pemberton, SG., 2007. A \nconceptual framework for the application of trace fossils. In: J.A. \nMacEachern; L.K. Bann; M.K. Gingras & S.G. Pemberton (eds) Applied \nichnology, SEPM, Short Course Notes 52, Pp. 1 \u2013 26. \n\n\n\nHantzschel, W., 1952. Die Lebensspur Ophiomorpha Lundgren im Mioz\u00e4n \nbei Hamburg, ihre Weltweite Verbreitung und Synonymie. Mitt. Geol. \nStaatsinst. Hamburg. (21): Pp. 142 \u2013 153. \n\n\n\nKnaust, D., 2017. Atlas of trace fossils in well core. Appearance, taxonomy \nand interpretation: Springer, Pp. 209. \n\n\n\nKnaust, D., 2018. The ichnologenus Teichichnus SEILACHER, 1955:- Earth \n\u2013 Science Reviews, Vol. 177, Pp. 386 \u2013 403. \n\n\n\nKnox, GJ; Omatsola, ME., 1989. Development of the Cenozoic Niger Delta in \nterms of the \u201cescalator regression model\u201d and impact on hydrocarbon \ndistribution; proc. KNGMG symposium on Coastal Lowlands, Geology \nand Geotechnology, Dordrecht, Kluwer, Pp. 181-202.  \n\n\n\nMartinsson, A., 1965. Aspects of a Middle Cambrian thanatotope on Oland. \nFor. Stockh. Forh. 87, 181. \n\n\n\nMiller, MF; Curran, HA; Martino, RL., 1998. Ophiomorpha nodosa in tidal \nestuarine sands of the Calvert Formation (Miocene) of Delaware. In R. \nN. Benson (ed): Geology and Paleontology of the Lower Miocene. \nPollack Farm fossil site, Delaware. Delaware Geological Survey Special \nPublication 21. Pp. 41 \u2013 46 \n\n\n\nMude, SN., 2011. Paleoenvironmental significance of ichnofossils from the \nChaya Formation, Porbarder Group, Southwest Coast, India, Greener \nJournal of Physical Sciences,1 (1): Pp. 29-36. \n\n\n\nOdumodu, C., 2011. Geothermal gradients and burial history modelling in \nparts of the Eastern Niger delta, Nigeria. Published by the University of \nNigeria. Pp. 239-248. \n\n\n\nOlev, V; Ursula, T., 2018. First description of rare Teichichnus burrows \nfrom Carbonate rocks of the Lower Palaeozoic of Estonia. Carnets Geol. \n18 (13), Pp. 305-312 \n\n\n\nPemberton, SG; Flach, PD; Mossop, GD., 1982. Trace fossils from the \nAthabasca oil sands, Alberta, Canada. Science 217, Pp. 825-827. \n\n\n\nPemberton, SG; Maceachern, JA; Frey, RW., 1992. Trace fossil facies \nmodels: environmental and allostratigraphic significance. In: R.G. \nWalker & N.P. James (eds) Facies models: response to sea level changes. \nGeological Association of Canada. Pp. 47 \u2013 72. \n\n\n\nPemberton, SG; Spila, M; Pulham, AJ; Saunders, T; Robbins, D; Sinclair, IK., \n2001. Ichnology and sedimentology of Shallow to Marginal Marine \nSystems. Calgary, Geological Association of Canada, Short Course 15, Pp. \n343. \n\n\n\nSavrda, CE; Nanson, LL., 2003. Ichnology of fair \u2013 weather and storm \ndeposits in an upper Cretaceous estuary (Eutaw Formation, Western \nGeorgia, USA). Palaeogeography, Palaeoclimatology, Palaeoecology, \n202: Pp. 67 \u2013 83. Doi. Org/10.1016/S0031 \u2013 0182 (03) 00628 \u2013 X. \n\n\n\nSeilacher, A., 1964. Biogenic sediment structures. In Imbrie, J. & Newell, N. \n(eds): Approaches to paleoecology. Pp. 296 \u2013 316. John Wiley, New \nYork. \n\n\n\nTchoumachenko, P; Uchman, A., 2001. The oldest deep-sea Ophiomorpha \nand Scolicia and associated trace fossils from the Upper Jurassic \u2013 \nLower Cretaceous deep \u2013 water turbidite deposits of SW Bulgaria. \nPalaeogeography, Palaeoclimatology, Palaeoecology. 169, Pp. 85 \u2013 99 \n\n\n\nVinn O. and Toom U., 2018. First description of rare Teichichnus burrows \nfrom carbonate rocks of the lower Palaeozoic of Estonia, Carnets Geol., \nMadrid, Vol. 18, no. 13, Pp. 305-312 \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 22-30 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2023.22.30 \n\n\n\n \nCite The Article: Atef M. Abu Donia (2023). Aeromagnetic investigation of the banded iron formations of  \n\n\n\nUm Nar area, Central Eastern Desert, Egypt. Malaysian Journal of Geosciences, 7(1): 22-30. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2023.22.30 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAEROMAGNETIC INVESTIGATION OF THE BANDED IRON FORMATIONS OF UM \nNAR AREA, CENTRAL EASTERN DESERT, EGYPT \n\n\n\nAtef M. Abu Donia* \n\n\n\nStudies department, Nuclear Materials Authority, P.O. Box (530) El-Maadi, Cairo, Egypt \n*Corresponding Author Email: atef_donia@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 15 January 2022 \nRevised 01 February 2022 \nAccepted 02 March 2023 \nAvailable online 08 March 2023 \n\n\n\n\n\n\n\nWadi Um Nar area acquired its importance since the discovery of banded iron formations (BIFs), and is one \nof the largest iron formation occurrences in the Eastern Desert of Egypt. Therefore, the recorded \naeromagnetic data were processed, using a combination of edge enhancement filters, to identify major \nstructures and recognize the lateral and vertical distribution of BIFs, as well as to determine the locations of \nburied magnetite ore bodies in this area. The obtained results from the application of these techniques \nrevealed that the NW\u2013SE, NE\u2013SW and N\u2013S directions are the common tectonic trends in the region. These \ntrends could be faults or shear zones that have acted as good pathway or channels for hydrothermal fluids.  \nEstimating the basement depth using source parameter imaging (SPI) showed that the BIFs depths varied \nfrom the surface down to about 450 m. Additionally, the orthogonal derivative maps of Wadi Um Nar BIFs \nshowed an E\u2013W trend, which corresponds exactly to the maximum magnetic intensity belt and their \nanomalies are characterized by a distinct dipole nature. These maps also revealed the extent and continuity \nof the main ore body of BIFs and showed that, they were mainly produced from magnetite mineral, formed \nin regions of structurally-controlled fluid flow. \n\n\n\nKeywords \n\n\n\nWadi Um Nar; Aeromagnetic Data; Banded Iron Formations; Edge Enhancement Techniques; Structure.  \n\n\n\n\n\n\n\n1.   INTRODUCTION \n\n\n\nMagnetic survey is widely used in mineral prospecting for regional-scale \nresource mapping and for detailed characterization of mineral ore \ndeposits at site-scale (Telford et al., 1990; Silva, 1999; Nabighian et al., \n2005). Iron ore deposits are well-defined targets for magnetic methods, as \nores rich in magnetite and hematite are easily identified by their high \nmagnetic contrast to host rocks. The banded iron formations (BIFs) are \neconomically essential because they are by far one the most important \nsources of iron ore deposits in the world (Bekker et al., 2010; Nadoll et al., \n2014; Zhu et al., 2014; El Habaak, 2021). BIFs are widely recognized as \nchemical precipitation products for oxides and hydroxides of Fe2+ and Fe3+, \nsilicates rich in Fe, and silica as recorded in the marine environment. They \nare affected by prominent metamorphism and diagenetic processes (Klein \nand Beukes, 1993; M\u00fccke et al., 1996). \n\n\n\nEgyptian BIFs occur in 13 areas, in a space of thirty-thousand km2, in the \nCentral Eastern Desert (CED) of Egypt, as illustrated on Figure 1. These \ndeposits are categorized, according to Sims and James, as Algoma\u2013type, \ndespite the fact that they take place intercalated with volcanosedimentary \nNeoproterozoic units of intermediate composition instead of the typical \nArchean/Paleoproterozoic fundamental volcanic rocks related with most \nAlgoma\u2013type BIFs (Sims and James, 1984; El-Shazly et al., 2019; Gross, \n1996; Klein, 2005; Bekker et al., 2010; El-Shazly and Khalil, 2014; El \nHabaak, 2021). BIF ore deposits are widely distributed in the \nmetamorphic rocks such as greenschist and amphibolite facies during \ncollision stage of Pan-African Orogeny (Loizenbauer et al., 2001; Ali et al., \n2009; El-Shazly and Khalil, 2014; El Habaak, 2021). \n\n\n\nSome authors characterize the formation of the BIF Neoproterozoic units \nto specific tectonic and/or volcanic events, or to big igneous \n\n\n\nprovinces/super plumes, rather than global climatic changes (Isley, 1995; \nIsley and Abbott, 1999; Eyles and Januszczak, 2004; Ohmoto et al., 2006; \nBasta et al., 2011; Freitas et al., 2011; Stern et al., 2013). Every time, the \nemergence of BIFs in a region has worthy implications on the \npaleoenvironment and tectonics, necessitating their study for a good \ntectonic explanation (Stern et al., 2013; El-Shazly et al., 2019). Airborne \nmagnetic methods constitute one of the most widely used geophysical \ntechniques for geological interpretations, playing an important role in \nidentifying geological features including faults, shear zones, folds, \nintrusions, porphyries and other areas preferred for mineralization. These \nstructures are important in the exploration and localization of \nmineralization zones (e.g., Silva, 1999; Holden et al., 2012; Shah et al., \n2013; Abdelrahman and Essa, 2015; Henderson et al., 2015; Abo-Ezz and \nEssa, 2016; Farhi et al., 2016; Essa and Elhussein, 2017; Elhussein and \nShokry, 2020; Shebl et al., 2021; Ekwok et al., 2022; Essa and Diab, 2022; \nBoufkri et al., 2023; Elhusseiny, 2023). \n\n\n\nWadi Um Nar iron ore deposit (Figure 1, # 11) is one of the largest BIFs in \nPan-African rocks in the CED of Egypt (El-Ramly et al., 1963). Wadi Um Nar \nregion is structurally complex, with evidence of several deformation \nphases, as well as, various igneous intrusions that are spatially related to \nthe iron ore (e.g., Akaad et al., 1996; Makroum, 2003; Shalaby et al., 2005, \n2006; Stern et al., 2013). Although, Um Nar BIFs were the focus of many \nauthors, due to their economic potential as the most lucrative sources of \niron, leading to several excellent studies, but it still needs many of \ngeophysical studies (El-Ramly et al., 1963; El Aref et al., 1993a, 1993b; \nAkaad et al., 1996; El Habaak, 2004; Shalaby et al., 2005; El-Shazly and \nKhalil, 2014; El Habaak, 2021). Geophysical methods are able to detect and \nidentify local subsurface features of the ore body that cannot be detected \nby drilling programs. Thus, a geophysical survey can improve exploration \nprograms by maximizing ground coverage rate and reducing drilling \n\n\n\n\nmailto:atef_donia@yahoo.com\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 22-30 \n\n\n\n\n\n\n\n \nCite The Article: Atef M. Abu Donia (2023). Aeromagnetic investigation of the banded iron formations of  \n\n\n\nUm Nar area, Central Eastern Desert, Egypt. Malaysian Journal of Geosciences, 7(1): 22-30. \n \n\n\n\nrequirements. \n\n\n\nThe goals of this study are to supply a clear structural framework model \nof Wadi Um Nar BIFs area (Figure 1, # 11), located in the CED of Egypt. The \nspecific objectives of the present study include: (1) applying several edge \nenhancement techniques to the aeromagnetic data to define the edges of \nmagnetic sources (contacts and/or faults), (2) identification the vertical \nand lateral distributions of the BIFs, and (3) attempting to better \nunderstanding of the mode of occurrence of the BIFs in Um Nar and their \nrelationship to the general structural framework. \n\n\n\n\n\n\n\nFigure 1: A simplified geologic map of the Central Eastern Desert of \nEgypt showing the locations of some banded iron formation (BIF) \n\n\n\noccurrences; 1 \u2013 Hadrabia; 2 \u2013 Abu Merwat; 3 \u2013 Gabal Semna; 4 \u2013 Diwan; \n5 \u2013 Wadi Kareim; 6 \u2013 Wadi El Dabbah; 7 \u2013 Um Shaddad; 8 \u2013 Um Ghamis; 9 \n\n\n\n\u2013 Gabal El-Hadid; 10 \u2013 El Emra; 11 \u2013 Um Nar (study area); 12 \u2013 Wadi \nHammama; 13 \u2013 Um Anab (Khalil et al., 2015). \n\n\n\n2.   GEOLOGICAL SETTING \n\n\n\nSeveral authors have described the geological settings of Wadi Mubarak-\nbelt, which includes Wadi Um Nar (Figure 1, # 11; study area) region (Abu \nEl Ela, 1985; Dardir and El Shimi, 1992; El Aref et al., 1993a, 1993b; El-\nHabaak and Mahmoud, 1994; Akaad et al., 1995, 1996; Makroum, 2003; \nShalaby, 2003; Shalaby et al., 2005, 2006; Kassem, 2011, 2012; Stern et al., \n2013; El-Shazly and Khalil, 2014; El Habaak, 2021). Wadi Mubarak could \nbe described as discrete wedged shape of highly-deformed lower-grade \ngreenschist rocks, bounded by metavolcanics (Akaad et al., 1996; Kassem, \n2011, 2012). The deformed zone is well extended from the Red Sea coast \nin the eastern part to Gabal (G.) El-Hadid (bearing iron ore deposits) in the \nwestern part of the mapped area (Figure 1, # 9) (Akaad et al., 1996). The \nvolcano-sedimentary sequence consists of metavolcanics and biotite \nschists, which are dissected by BIFs. This zone has an extension from G. El-\nHadid northwest into Wadi Um Nar and G. El-Mayet southeast in the \nstudied area (Figure 2), which shows varying topography changing from \n423 m to 896 m above sea level (Figure 3). Rasmy (1968) separated the \nmetasedimentary sequences into graphite\u2013chlorite schist, hornblende \nbiotite schist, biotite schist, pebbly quartz\u2013biotite schist, in addition to \nactinolite\u2013epidote schist.  \n\n\n\nThe BIFs of Wadi Um Nar area are hosted in a metamorphosed clastic and \ncalcareous sedimentary sequence, which represents the upper part of the \nophiolitic m\u00e9lange described by as shelf sedimentary rocks (El Aref et al., \n1993a; 1993b; El Bahariya, 2018; 2021; El Habaak, 2021). The \nmetamorphosed sedimentary succession has undergone several stages of \nbrittle deformation. It constitutes a tight northwest\u2013southeast striking \nand southwest dipping overturned anticline plunging very steeply to the \nsoutheast (El Habaak, 2004). There are two thrust faults bounding the \nmetamorphosed rocks, as shown in Figure 2. The mylonitic gneiss rocks \nwere intruded by granodiorite, post-tectonic granite, gabbro-olivine and \n\n\n\nfelsite dikes. Granodiorite rock is represented by the G. El Umra in the \nnortheastern part, meanwhile, the post-tectonic granite has small, \nscattered masses in the study area, presented in southwestern part \n(Figure 2). The post-tectonic granite is characterized by pink to reddish \npink colour (El Habaak, 2004; 2021). \n\n\n\n\n\n\n\nFigure 2: Geologic map of Wadi Um Nar area, Central Eastern Desert, \nEgypt (Sims and James, 1984; El Habaak, 2004). \n\n\n\n\n\n\n\nFigure 3: Shuttle Radar Topography Mission (SRTM) elevation map of \nWadi Um Nar area, Central Eastern Desert, Egypt. \n\n\n\nGranodiorite is medium grained, from equigranular to porphyritic in \ntextures, and composed of plagioclase and amphibole phenocrysts \nreached up to 0.8 cm in diameter in addition to quartz. It is highly altered, \nwhereas chlorite is the alteration product of amphiboles and biotite, and \nthe sericite and kaolinite replace the plagioclase phenocrysts (El Habaak, \n2004; 2021). BIFs are connected with biotite schist and consists of \nalternating Fe-rich and chert bands. Biotite schists are metamorphosed \npelites and calc-silicate rocks. The intercalations of BIFs with clastic layers \nare good indicators for the deposition in low energy environment, as \nmentioned (El Habaak, 2004; 2021). \n\n\n\nWadi Um Nar was structurally affected by two different deformational \nevents (Neumayr et al., 1998; Loizenbauer et al., 2001; Makroum, 2003; \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 22-30 \n\n\n\n\n\n\n\n \nCite The Article: Atef M. Abu Donia (2023). Aeromagnetic investigation of the banded iron formations of  \n\n\n\nUm Nar area, Central Eastern Desert, Egypt. Malaysian Journal of Geosciences, 7(1): 22-30. \n \n\n\n\nStern et al., 2013; El-Shazly and Khalil, 2014). The first stage identified as \nfolding-thrusting episode resulted in a tight WNW\u2013ESE striking and SW \ndipping overturned anticline plunging very steeply to the SE. BIFs have a \nlateral extension, running along strike of Wadi Um Nar anticline. A series \nof E\u2013W striking and south-dipping thrusts also resulted due to the first \ndeformational phase leading to emplacement of allochthonous sheets, \nwhere BIFs run in the E\u2013W direction, parallel to the serpentinite mass of \nG. El-Mayet (El-Shazly and Khalil, 2014). The second deformational phase \nproduced a synform with a NW\u2013SE trend and southeast-plunging axis. It \nhas been interpreted that the southwestern limb was thrusted onto the \nnortheastern limb. In the southwestern limb, BIFs extend along the \neastern side of Wadi Um Nar. \n\n\n\n3.   DATA ACQUISITION AND METHODOLOGY \n\n\n\nThe aeromagnetic data for the study area were taken from aerial magnetic \nand gamma-ray spectral surveys, carried out by Aero-Service Division of \nWestern Geophysical Company of America. The airborne geophysical \nsurvey was planned to be as a series of parallel flight traverse lines, in the \nNE\u2013SW direction with 1.5 km spacing. The tie lines were perpendicular to \nthe flight traverse lines (NW\u2013SE direction) and spaced about 10 km. \nAeromagnetic measurements were collected using a Varian (V-85) proton \nfree-precession magnetometer sensor, with a sensitivity of 0.1 nT. The \nnominal flight altitude was 120 meters above the ground surface (Aero-\nService, 1984). To make the raw magnetic data appropriate for analysis \nand interpretation, certain corrections were made, such as error removals \nassociated with acquisition system, diurnal variations, parallax/lag \ncorrection, and heading correction. Besides, removal of International \nGeomagnetic Reference Field (IGRF). Aero-Service (1984) made all these \ncorrections.   \n\n\n\n3.1   Reduction to The Magnetic Pole \n\n\n\nReduction to the pole (RTP) is a mathematical approach, that transfers the \ntotal magnetic data anywhere into magnetic anomaly as if measured at the \nmagnetic pole. It was proposed by to eliminate unwanted distortion in the \nshapes, sizes, and locations of the magnetic anomalies, due to the influence \nof inclination and declination of the Earth's magnetic field (Baranov, 1957; \nBaranov and Naudy, 1964). Therefore, the transform of RTP provides a \nmore precise estimation of the position of magnetic sources, and the shape \nis easier to interpret from the perspective of the body. This process was \ndone (Aero-Service, 1984).  \n\n\n\n3.2   Edge Enhancement Techniques \n\n\n\nVertical and horizontal derivatives of the potential field data are used to \nenhance the edges of anomalies and significantly improve the visibility of \nthese features. Many techniques were developed to delineate structural \ngeological features from potential field data. Oasis Montaj 8.4 (Geosoft\u2122) \nsoftware was used to process and enhance the RTP aeromagnetic data. \nEvjen was the first to use the first-order vertical derivative (FVD) to define \nthe boundaries of gravity and magnetic bodies (Evjen, 1936). FVD is \ntypically applied to identify the near-surface (shallower) geological \nstructures, where zero values of the FVD of the RTP magnetic field \ntypically refers to geologic body edges (Oruc and Keskinsezer, 2008). It is \ngiven as follows: \n\n\n\n\ud835\udc39\ud835\udc49\ud835\udc37 =  \n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc67\n                                                                                                                                 (1) \n\n\n\nwhere,  \n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc67\n is the vertical derivative of the magnetic anomaly field (\ud835\udc40). \n\n\n\nIt is commonplace to map the edges of magnetic susceptibility contrasts \nusing the horizontal gradient magnitude (HGM) approach. It makes use of \nthe fact that, when the edges of the anomalous body are vertical and widely \nspaced from one another, the HGM of the RTP magnetic field produced by \na tabular body tends to have maximum values there (Cordell and Grauch, \n1985). HGM is robust in detecting shallow magnetic sources, and is less \nsensitive to noise in the data (Philips, 2002). It has amplitude maxima over \nthe magnetic source edges. According to HGM is formulated as follows \n(Cordell and Grauch, 1985): \n\n\n\n\ud835\udc3b\ud835\udc3a\ud835\udc40 = \u221a(\n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc65\n)\n\n\n\n2\n\n\n\n+ (\n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc66\n)\n\n\n\n2\n\n\n\n                                                                                                         (2) \n\n\n\nwhere,  \n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc65\n and  \n\n\n\n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc66\n are the horizontal derivatives of the magnetic anomaly \n\n\n\nfield (\ud835\udc40) in \ud835\udc65 and \ud835\udc66 directions, respectively. \n\n\n\nIn addition, the analytic signal (AS) method represents also one of the \nmain edge enhancement methods. It reduces the total magnetic field data \nto anomalies whose maxima define the magnetized body edges, when the \nsources are resolvable (Nabighian, 1984; Roest et al., 1992; MacLeod et al., \n\n\n\n1993). However, they appear as a cluster of highs for a group of near-by \nsources, regardless of the regional direction of the magnetic field and the \nmagnetization of the source. The equation of AS is as follows: \n\n\n\n|\ud835\udc34\ud835\udc46(\ud835\udc65, \ud835\udc66)| = \u221a(\n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc65\n)\n\n\n\n2\n\n\n\n+ (\n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc66\n)\n\n\n\n2\n\n\n\n+  (\n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc67\n)\n\n\n\n2\n\n\n\n                                                                                 (3) \n\n\n\nwhere, |\ud835\udc34\ud835\udc46(\ud835\udc65, \ud835\udc66)| is the amplitude of analytic signal at (\ud835\udc65, \ud835\udc66),  \n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc65\n ,  \n\n\n\n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc66\n  and \n\n\n\n\ud835\udf15\ud835\udc40\n\n\n\n\ud835\udf15\ud835\udc67\n are the derivatives of the magnetic anomaly field (\ud835\udc40) in \ud835\udc65, \ud835\udc66 and \ud835\udc67 \n\n\n\ndirections, respectively. \n\n\n\nPredominatingly, the magnetic anomalies due to deep sources are covered \nby shallow ones, therefore, the tilt angle (TDR) was first proposed by \nMiller and Singh (1994) for identifying the boundaries of magnetic sources \nat different depths. It is less affected by the depth of buried magnetic \nsources and can simultaneously enhance the boundaries of sources of \ndifferent anomalous amplitudes. Nevertheless, the localized boundaries \npositions are strongly influenced by the direction of magnetization. TDR is \ngiven as follows: \n\n\n\n\ud835\udc47\ud835\udc37\ud835\udc45 = \ud835\udf03 = \ud835\udc61\ud835\udc4e\ud835\udc5b\u22121 (\n\ud835\udc39\ud835\udc49\ud835\udc37\n\n\n\n\ud835\udc3b\ud835\udc3a\ud835\udc40\n)                                                                                                          (4) \n\n\n\nwhere, \ud835\udc39\ud835\udc49\ud835\udc37 and \ud835\udc3b\ud835\udc3a\ud835\udc40 are the vertical derivative and total horizontal \nderivative of the potential field, respectively. \n\n\n\nSeveral improved methods were developed, based on the aforementioned \nmethods, such as the following: \n\n\n\nTheta (Cos \u04e8) method uses the analytic signal (AS) amplitude to normalize \nthe total horizontal derivative (HGM) in a 2D-image (Wijns et al., 2005). It \nis calculated as follows: \n\n\n\n\ud835\udc47\u210e\ud835\udc52\ud835\udc61\ud835\udc4e = \ud835\udc50\ud835\udc5c\ud835\udc60(\ud835\udf03) = (\n\ud835\udc3b\ud835\udc3a\ud835\udc40\n\n\n\n\ud835\udc34\ud835\udc46\n)                                                                                                             (5) \n\n\n\nDue to the fact that response from deeper sources is more diffuse, the \namplitude of responses from different depths are similar on the theta map \n(Cooper and Cowan, 2006). Edges can be detected with this technique, \nindependent of strike and amplitude. \n\n\n\nCooper and Cowan describe the horizontal tilt angle (TDX) method as the \nnormalization of the HGM by the absolute value of the FVD (Cooper and \nCowan, 2006). It is given as follows: \n\n\n\n\ud835\udc47\ud835\udc37\ud835\udc4b = \ud835\udc61\ud835\udc4e\ud835\udc5b\u22121 (\n\ud835\udc3b\ud835\udc3a\ud835\udc40\n\n\n\n|\ud835\udc39\ud835\udc49\ud835\udc37|\n)                                                                                                                (6) \n\n\n\nTDR is effective when dealing with data from shallow sources, while it is \nregarded relatively inefficient when dealing with data from deep sources. \nTDX is the inverse of the intended TDR, as it works equally well with \nshallow and deep sources. One downside of this method is that it reflects \nedges larger than the actual body size, if there is more than one causative \nbody with different geometries (Cooper and Cowan, 2006). \n\n\n\n3.3   Basement Depth Estimation \n\n\n\nTo determine the depth to the magnetic basement, the source parameter \nimaging (SPI) technique was used to the RTP aeromagnetic anomaly grid, \nas this technique is based on the relationship between source depth and \nthe local wave number (K) of the observed field's analytic signal (Thurston \nand Smith, 1997). It is not affected by remanent magnetization or other \nmagnetic factors such as inclination, declination, dip, and strike. \n\n\n\nDepth = 1/Kmax                                                                                                                          (7) \n\n\n\nwhere, Kmax is the peak value of the local wavenumber over the step source. \n\n\n\n4.   RESULTS AND DISCUSSION \n\n\n\nThe colour-shaded grid of RTP magnetic map (Figure 4) is distinguished \nby the presence of low-and high-amplitude magnetic anomalies scattered \nthroughout the area of study. Generally, the deformation and \nheterogeneity in the basement rocks produce magnetic signatures, that \nare both sharp and strong. A detailed comparison of the RTP aeromagnetic \nmap (Figure 4) and the geologic map of the area (Figure 2) shows that, \nareas of high magnetic anomalies (high-amplitude anomalies), are related \nto different lithologic units, such as mafic volcanic rocks and serpentinite \nrocks at the southwestern, northwestern and southern parts, as well as \ngabbroic rocks in northern and southern parts of the study area. Notably, \nthese anomalies have elongated shapes, which indicate various structural \nprocesses that occurred in the study area and their major trend is in the \nNW\u2013SE direction.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 22-30 \n\n\n\n\n\n\n\n \nCite The Article: Atef M. Abu Donia (2023). Aeromagnetic investigation of the banded iron formations of  \n\n\n\nUm Nar area, Central Eastern Desert, Egypt. Malaysian Journal of Geosciences, 7(1): 22-30. \n \n\n\n\n\n\n\n\nFigure 4: Reduced to the pole (RTP) aeromagnetic map of Wadi Um Nar area, Central Eastern Desert, Egypt (Aero-Service, 1984). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Application of edge enhancement techniques to aeromagnetic data. (a) FVD, (b) HGM, (c) AS, (d) TDR, (e) Theta map and (f) TDX maps of Wadi \nUm Nar area, Central Eastern Desert, Egypt. \n\n\n\n(a) (b) \n\n\n\n(c) \n\n\n\n(e) (f) \n\n\n\n(d) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 22-30 \n\n\n\n\n\n\n\n \nCite The Article: Atef M. Abu Donia (2023). Aeromagnetic investigation of the banded iron formations of  \n\n\n\nUm Nar area, Central Eastern Desert, Egypt. Malaysian Journal of Geosciences, 7(1): 22-30. \n \n\n\n\nBoth Wadi Um Nar and G. El-Hadid BIFs are also located within areas of \nhigh positive magnetic anomalies, with peak intensities of over 43,800 nT. \nBesides, Wadi Um Nar BIFs magnetic anomalies have an E\u2013W trend, while, \na NW\u2013SE trend is associated with the magnetic anomalies of G. El-Hadid \nBIFs. Moreover, a third trend is represented in the NE\u2013SW direction that \nis accompanied by low magnetic intensity response (low-amplitude \nanomalies) in the eastern and northwestern parts. These dominant trends \nsuggest that, the study area was subjected to more than single tectonic \nevent. Both the geological map (Figure 2) and the RTP aeromagnetic map \n(Figure 4) demonstrate substantial connections between the exposed \ngeologic units and the strong magnetic signatures, which may indicate that \nthe magnetic anomalies correlate to the boundaries of geologic structures. \n\n\n\nDerivative methods are considered rapid means of processing grids of \nmagnetic data. They supply precise information on the structural \nframework, tectonic tendencies, and depths. Nevertheless, the structural \nsetting in Wadi Um Nar area is complicated, that it requires an integrated \napproach that uses different methods. The results from applying the edge \nenhancement techniques including FVD, HGM, AS, TDR, Theta (Cos \u04e8) and \nTDX on the RTP magnetic data were shown on Figure 5. According to \ndifferent algorithms, FVD (Figure 5a) and TDR (Figure 5d) use their zero \ncontour lines (black lines in the figures) as the edges of the magnetic \nanomaly (faults/boundaries of the magnetic sources). Meanwhile, HGM \n(Figure 5b), AS (Figure 5c), Theta map (Figure 5e) and TDX (Figure 5f) are \nrepresented by their maxima values (peaks). \n\n\n\nThe resulting maps (Figure 5) show that these edge enhancement \ntechniques are extremely suitable for mapping structures of the basement. \nMany of the detailed anomalies can be noticed and distinguished by these \nderivatives of weakly and strongly magnetized source bodies with \ncomparable solutions. These anomalies do not appear clearly on the RTP \n(Figure 4) magnetic map, especially in the northeastern and southwestern \nparts of the study area. FVD, HGM and TDR maps (Figures 5a, b and d) \nillustrate that the southern to the northwestern parts of the study area are \ndissected by several shallow faults (FVD map), which can extend deep into \nthe basement (HGM and TDR maps). AS map (Figure 5c) highlights the \npresence of high susceptibility iron-bearing units and show clearly the \nmain east\u2013west trending ore zone, which is related to Wadi Um Nar BIF. \nBesides, obvious northwest\u2013southeast trend that extends from Wadi Um \nNar to G. El-Hadid BIF, is noticed in the northwestern part of the area \nunder study. There is a great similarity between FVD, HGM, TDR, Theta \nand TDX maps (Figures 5a, b, d, e and f). In comparison with other \nderivative techniques, Theta and TDX provide more detailed and clear \nresults for more deep magnetized structures as well as a clear and acute \nresponse on boundaries of magnetic sources. \n\n\n\n\n\n\n\nFigure 6: Calculated-depth map to magnetic sources, using source \nparameter imaging (SPI) of Wadi Um Nar area, Central Eastern Desert, \nEgypt. White lines are the banded iron formations (BIFs) boundaries. \n\n\n\nFigure 6 illustrates variation on depth of different magnetic sources inside \nthe area of study. The demonstrated depths estimated from the sensor \nheight of 120 m above ground level. The majority of target anomalies, \nspecified as resulting from BIF mineralization, occur in areas that have \ndepths less than 450 m from the ground surface. Areas of prospecting are \ninferred by delineating the boundaries of intrusive rocks via edge \nenhancement techniques. The structural characteristics that control the \naccumulation of iron ores in the study area are refined in a detailed \nstructural map, generated by the integration of the obtained results \n(Figure 7a). When creating a structural map, only the structural elements \n(faults and contacts) confirmed by various methods were selected. It can \nbe noticed that there is a significant shear zone that extends from the \nsouthern part towards the northwestern part of the study area, and trends \nin a NW\u2013SE direction. Besides, another shear zone is located in the \nsoutheastern corner of the study area and trends in an E\u2013W direction \n(these shear zones are shown in Figure 7a with a grey background colour). \nEach structural lineament (Figure 7a) reflects multiple intersection zones \nacross this direction. The rose diagram (Figure 7b) shows that, the major \ndominant tectonic tendencies in the area are NW\u2013SE (Suez Gulf trend), \nNE\u2013SW (Gulf of Aqaba trend) and N\u2013S trend.\n\n\n\n\n\n\n\n(b) \n\n\n\n\n\n\n\nFigure 7: (a) Interpreted structural lineaments map and (b) Directional analysis (rose diagram) of lineament lengths (%) and lineament frequencies (%) \nof Wadi Um Nar area, Central Eastern Desert, Egypt. \n\n\n\n(a) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 22-30 \n\n\n\n\n\n\n\n \nCite The Article: Atef M. Abu Donia (2023). Aeromagnetic investigation of the banded iron formations of  \n\n\n\nUm Nar area, Central Eastern Desert, Egypt. Malaysian Journal of Geosciences, 7(1): 22-30. \n \n\n\n\n4.1   The Um Nar Iron Ore Deposits \n\n\n\nOasis Montaj 8.4 (Geosoft\u2122) software was used to window a subset grid \n(from a rectangular mask) in the large RTP map (Figure 4). The subset grid \nis shown on Figure 8 and represents the anomaly of Um Nar iron ore. The \nhorizontal magnetic field derivatives, in the X (East) and Y (North) \n\n\n\ndirections, are very useful in resolving composite and complex anomalies \ninto their individual components (Aziz et al., 2013). The vertical magnetic \nfield derivative, in the Z (Down) direction, can enhance and highlight the \nshallow buried features, at the expense of the regional magnetic gradient. \nOasis Montaj software was used to perform the orthogonal derivative \ncalculations, and the results were plotted on the maps (Figures 8b\u2013d). \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 8: (a) Reduced to the pole (RTP) magnetic map and their orthogonal derivatives in the X (East) direction (b), in the Y (North) direction (c), Vertical \nderivative in the Z (Down) direction (d) and RTP map with interpreted magnetic structural lineament patterns (e) of Um Nar iron ore, Central Eastern \n\n\n\nDesert, Egypt. \n\n\n\nThe Y-derivative map (Figure 8c) shows that, the main east-west ore body \nhas a distinguished dipole response with an inflection point, which \nroughly corresponds to the maximum amplitude in the RTP map (Figure \n8a). The X-derivative map (Figure 8b) identifies also the anomalies of BIFs \nas dipoles, consisting of multiple dipolar targets. From the horizontal \n\n\n\nderivatives (X and Y) of the RTP aeromagnetic maps (Figures 8b, c), it can \nbe seen that the structural trends in both x and y directions are improved \nand clearly visible compared to other aeromagnetic maps. The vertical \nmagnetic field derivative map (Figure 8d) shows a similar general \nanomaly pattern to the RTP grid map (Figure 8a) but with enhanced \n\n\n\na) b) \n\n\n\nc) d) \n\n\n\ne) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 22-30 \n\n\n\n\n\n\n\n \nCite The Article: Atef M. Abu Donia (2023). Aeromagnetic investigation of the banded iron formations of  \n\n\n\nUm Nar area, Central Eastern Desert, Egypt. Malaysian Journal of Geosciences, 7(1): 22-30. \n \n\n\n\nmagnetic lineaments, which define the structures affecting the main ore \nbody zone. These interpreted structural lineaments clearly cut the east\u2013\nwest ore body with a lateral displacement and, therefore, can be \ninterpreted as fault or shear zones. This map (Figure 8d) also shows that, \nthe magnetic anomaly over the BIF is dominated by a positive anomaly \nsurrounded by negative anomalies, which may indicate the possibility that \nthe BIFs were remagnetized, since the time of impact. The NW\u2013SE, NE\u2013SW \nand N\u2013S striking interpreted structural lineaments (Figure 8e) cutting the \niron ore zone may be interpreted as brittle faults or shear zones and are \nprobably acted as channels for hydrothermal fluids movement along these \nfault zones, resulting in the transition of magnetite to other minerals, and \nconsequently leading to local decreases in the magnetic susceptibility of \nrocks. \n\n\n\nThe integration of the magnetic data available with the existing geological \nmap (Figure 2) proved to be useful for the litho-structural mapping of the \nUm Nar iron-ore area (Figure 9), where it is classified into three distinctive \nlithologic units. These lithological units are: (1) Weakly-magnetic lithology \n(e.g., granitized terrains; carbonate to low- grade metasediments; felsic- \nand meta-intrusives; and/or down-faulted blocks); (2) Moderately-\nmagnetic lithology (e.g., intermediate intrusives or meta-intrusives; high-\ngrade metasediments); and (3) Highly-magnetic lithology (e.g., mafic \nintrusives or meta-intrusives; sedimentary iron formations). Besides, the \nhighly-magnetic lithological unit was intruded by supra-basement or \nintrasedimentary magnetic bodies (e.g., basic dikes and veins). These \nmagnetic bodies are enriched by the presence of BIFs (as Um Nar zone). \nThey were formed by intruding magma along the structural lineaments \nand fractures, with the intrusions which are characterized by shallow \ndepths (Figure 6). They were dissected by strike-slip faults, trending in \nNE\u2013SW, NW\u2013SE and N\u2013S directions.  \n\n\n\n\n\n\n\nFigure 9: Lithology and structural elements interpreted from magnetic \ndata of Um Nar iron ore zone, Central Eastern Desert, Egypt. \n\n\n\n5.   CONCLUSIONS \n\n\n\nThe aeromagnetic survey data for Wadi Um Nar area was processed using \na set of edge enhancement techniques. The distributions of the BIFs in the \nstudy area are mainly associated with high-response magnetic zones and, \nhence, magnetic structures. The results of edge enhancement techniques \nhelped to identify the inferred magnetic structural lineament map that \naffected the study area. It was found that the geologic features generally \nagree with prominent NW\u2013SE, NE\u2013SW and N\u2013S structural trends. Diverse \ntrends of geological features and structural patterns indicate that the \nstudy area has experienced more than one tectonic event. The basement \ndepth estimation using SPI shows that the depth to the BIFs varied from \nthe surface up to about 450 m deep. The east\u2013west belt of Wadi Um Nar \n\n\n\niron ore corresponds exactly to the zone of highest magnetic intensity. The \npattern of complex dipole anomalies within this zone indicates that the \niron ore deposits are not simple tabular bodies, but characterized by \npodiform magnetite deposits, with a general strike of east-west direction. \nThe structural trends of the NW\u2013SE, NE\u2013SW and N\u2013S pattern, defined by \nmagnetic features within the iron ore zone, indicates the presence of \nfaulting or shearing of Wadi Um Nar zone and suggests a complex and \nmultistage deformational history. This study shows that, the BIFs of the \nstudy area were produced by magnetite formed in structurally controlled \nfluid flow regions. \n\n\n\nACKNOWLEDGMENT \n\n\n\nThe author extends sincere thanks to Prof. Dr. Ahmed A. Ammar and Prof. \nDr. Alaa Aref, Exploration Sector, Nuclear Materials Authority of Egypt, for \ntheir critical and constructive review of the manuscript, and for their \nfruitful discussions. \n\n\n\nREFERENCES \n\n\n\nAbdelrahman, E.M., Essa, K.S., 2015. A new method for depth and shape \ndeterminations from magnetic data. Pure and Applied Geophysics, \n172, Pp. 439\u2013460. \n\n\n\nAbo-Ezz, E.R., Essa, K.S., 2016. A least-squares minimization approach for \nmodel parameters estimated by using a new magnetic anomaly \nformula. Pure Appl Geophys., 173, Pp. 1265\u20131278. \n\n\n\nAbu El Ela, A.M., 1985. Geology of Wadi Mubarak District, Eastern Desert, \nEgypt. Ph.D. Thesis, Tanta University, Tanta, Egypt, Pp. 359. \n\n\n\nAero-Service, 1984. Final Report on airborne magnetic and radiation \nsurvey in Eastern Desert, Egypt. Work Completed for the Egyptian \nGeneral Petroleum Corporation (EGPC), Six Volumes, Aero-Service, \nHouston, Texas, USA. \n\n\n\nAkaad, M.K., Noweir, A.M., Abu El Ela, A.M., 1995. The Volcanosedimentary \nassociation and ophiolites of Wadi Mubarak, Eastern Desert, Egypt. \nIn: Proc. Int. Conf. 30 years Cooper. Geological Survey, Egypt, Spec. \nPubl. 69, Pp. 231\u2013248. \n\n\n\nAkaad, M.K., Noweir, A.M., Abu El Ela, A.M., 1996. Geology of the Pan-\nAfrican Basement Rocks of the Jabal El Hadid\u2013Wadi Mubarak \ndistrict, E.D., Egypt, Covering Parts of Sheets NG 36 G 3, 6 and NG 36 \nH 1, 4. Geological Survey, Egypt, No. 73, Pp. 78. \n\n\n\nAli, K.A., Stern, R.J., Manton, W.I., Kimura J.I., Khamees, H.A., 2009. \nGeochemistry, Nd isotopes and U-Pb SHRIMP zircon dating of \nNeoproterozoic volcanic rocks from the Central Eastern Desert of \nEgypt: New insights into the \u223c750 Ma crust-forming event. \nPrecambr. Res. 171, Pp. 1\u201322. \n\n\n\nAziz, A.M., Sauck, W.A., Shendi, E.H., Rashed, M.A., Abd El-Maksoud, M., \n2013. Application of Analytic Signal and Euler Deconvolution in \nArchaeo-Magnetic Prospection for Buried Ruins at the Ancient City \nof Pelusium, NW Sinai, Egypt: A Case Study. Surv Geophys., 34, Pp. \n395\u2013411. DOI 10.1007/s10712-013-9229-z \n\n\n\nBaranov, V., 1957. A new method for interpretation of aeromagnetic maps \npseudo gravimetric anomalies. Geophysics, 22, Pp. 359\u2013383. \n\n\n\nBaranov, V., Naudy, H., 1964. Numerical calculation of the formula of \nreduction to the magnetic pole. Geophysics, 29, Pp. 67\u201379. \n\n\n\nBasta, F.F., Maurice, A.E., Fontbote, L., Favarger, P.Y., 2011. Petrology and \ngeochemistry of the banded iron formation (BIF) of Wadi Karim and \nUm Anab, Eastern Desert, Egypt: implications for the origin of \nNeoproterozoic BIF. Precambr. Res., 187 (3\u20134), Pp. 277\u2013292. \n\n\n\nBekker, A., Slack, J.F., Planavsky, N., Krapez, B., Hofmann, A., Konhauser, \nK.O., Rouxel, O.J., 2010. Iron formation: the sedimentary product of \na complex interplay amongmantle, tectonic, oceanic, and biospheric \nprocesses. Economic Geology, 105, Pp. 467\u2013508. \n\n\n\nBoufkri, H., El Azzab, D., Miftah, A., 2023. Airborne data processing and 3D \nmodeling of magnetic structures between Nador region and the \nChaffarine Islands: Geological significances. Journal of King Saud \nUniversity \u2013 Science, 35, Pp. 102380. \nhttps://doi.org/10.1016/j.jksus.2022.102380 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 22-30 \n\n\n\n\n\n\n\n \nCite The Article: Atef M. Abu Donia (2023). Aeromagnetic investigation of the banded iron formations of  \n\n\n\nUm Nar area, Central Eastern Desert, Egypt. Malaysian Journal of Geosciences, 7(1): 22-30. \n \n\n\n\nCooper, G.R.J., Cowan, D.R., 2006. Enhancing potential field data using \nfilters based on the local phase. Comput. Geosci., 32, Pp. 1585\u20131591. \n\n\n\nCordell, L., Grauch, V.J.S., 1985. Mapping basement magnetization zones \nfrom aeromagnetic data in the San Juan basin, New Mexico. In The \nUtility of Regional Gravity and Magnetic Anomaly Maps; Hinzc, W.J., \nEd.; Society of Exploration Geophysicists: Tulsa, OK, USA, Pp. 181\u2013\n197. \n\n\n\nDardir, A.A., El Shimi, A.M., 1992. Geology and geochemical exploration for \ngold in the banded iron formation of Um Nar area, Central Eastern \nDesert, Egypt. Ann Geol Survey Egypt XVIII: Pp. 103\u2013111. \n\n\n\nEkwok, S.E., Eldosouky, A.M., Ben, U.C., Alzahrani, H., Abdelrahman, K., \nAchadu, O.I.M., Pham, L.T., Akpan, A.E., G\u00f3mez-Ortiz, D., 2022. \nApplication of High-Precision Filters on Airborne Magnetic Data: A \nCase Study of the Ogoja Region, Southeast Nigeria. Minerals, 12, Pp. \n1227. https://doi.org/10.3390/min12101227 \n\n\n\nEl Aref, M.M., Abdel Wahed, M., El Dougdoug, A., Manawi, A., 1993a. \nGeological setting and deformational history of Umm Nar BIF and \nassociated rocks, Eastern Desert, Egypt. Egyptian Journal of \nGeology, 37, Pp. 205\u2013230. \n\n\n\nEl Aref, M.M., Abdel Wahed, M., El Dougdoug, A., Manawi, A., 1993b. \nDiagenetic and metamorphic history of the Umm Nar BIF, Eastern \nDesert, Egypt. Mineralium Deposita 28, Pp. 264\u2013278. \n\n\n\nEl Bahariya, G.A., 2018. Classification of the Neoproterozoic ophiolites of \nthe Central Eastern Desert, Egypt based on field geological \ncharacteristics and mode of occurrence. Arab J Geosci., 11, Pp. 313. \nhttps://doi.org/10.1007/s12517-018-3677-1 \n\n\n\nEl Bahariya, G.A., 2021. The Ophiolite-Dominated Suprastructure, Eastern \nDesert, Egypt. In: Hamimi, Z., Arai, S., Fowler, AR., El-Bialy, M.Z. (eds) \nThe Geology of the Egyptian Nubian Shield. Regional Geology \nReviews. Springer, Cham. https://doi.org/10.1007/978-3-030-\n49771-2_6 \n\n\n\nEl Habaak, G.H., 2004. Pan-African skarn deposits related to banded iron \nformation, Um Nar area, central Eastern Desert, Egypt. J. Afr. Earth \nSci., 38 (2), Pp. 199\u2013221. \n\n\n\nEl Habaak, G.H., 2021. Banded Iron Formation in the Egyptian Nubian \nShield. In: Hamimi, Z., Arai, S., Fowler, AR., El-Bialy, M.Z. (eds) The \nGeology of the Egyptian Nubian Shield. Regional Geology Reviews. \nCham, Switzerland: Springer Nature Switzerland AG, Pp. 425-486. \nhttps://doi.org/10.1007/978-3-030-49771-2_17 \n\n\n\nEl Habaak, G.H., Mahmoud, M.S., 1994. Carbonaceous bodies of debatable \norganic provenance in the Banded Iron Formation of Wadi Kareim \narea, Eastern Desert, Egypt. J Afr Earth Sci., 19, Pp. 125\u2013133. \n\n\n\nElhussein, M., Shokry, M., 2020. Use of the airborne magnetic data for edge \nbasalt detection in Qaret Had El Bahr area, Northeastern Bahariya \nOasis, Egypt. Bulletin of Engineering Geology and the Environment. \nhttps://doi.org/10.1007/s10064-020-01831-w. \n\n\n\nElhusseiny, A.A., 2023. Integrated Structure and Mineralization Study \nUsing Aero-Magnetic, Aero-Spectrometric and Remote Sensing Data \nat Esh El-Mallaha Area, Eastern Desert, Egypt. Geomaterials, 13, Pp. \n1-22. https://doi.org/10.4236/gm.2023.131001 \n\n\n\nEl-Ramly, M.F., Akaad, M.K., Rasmy, A.H., 1963. Geology and structure of \nUmm Nar iron ore deposit (Eastern Desert of Egypt). Geological \nSurvey and Mineral Research Department Paper No. 28 Egyptian \nGeological Survey, Cairo, Egypt. (United Arab Republic (Egypt), Pp. \n30. \n\n\n\nEl-Shazly, A.K., Khalil, K.I., 2014. Banded iron formations of Um Nar, \nEastern Desert of Egypt: P\u2013T\u2013X conditions of metamorphism and \ntectonic implications. Lithos, Volumes 196\u2013197, Pp. 356\u2013375. \nhttps://doi.org/10.1016/j.lithos.2014.01.006 \n\n\n\nEl-Shazly, A.K., Khalil, K.I., Helba, H.A., 2019. Geochemistry of banded iron \nformations and their host rocks from the Central Eastern Desert of \nEgypt: A working genetic model and tectonic implications. \nPrecambrian Research, 325, Pp. 192\u2013216. \nhttps://doi.org/10.1016/j.precamres.2019.02.011. \n\n\n\nEssa, K.S., Diab, Z.E., 2022. Magnetic data interpretation for 2D dikes by the \nmetaheuristic bat algorithm: sustainable development cases. \nScientific Reports, 12, Pp. 14206. https://doi.org/10.1038/s41598-\n022-18334-1 \n\n\n\nEssa, K.S., Elhussein, M., 2017. A new approach for the interpretation of \nmagnetic data by a 2-D dipping dike. J. Appl. Geophys., 136, Pp. 431\u2013\n443. \n\n\n\nEvjen, H.M., 1936. The place of the vertical gradient in gravitational \ninterpretations. Geophysics, 1, Pp. 127\u2013136. \n\n\n\nEyles, N., Januszczak, N., 2004. \u2018Zipper-rift\u2019: a tectonic model for \nNeoproterozoic glaciations during the breakup of Rodinia after 750 \nMa. Earth Sci. Rev., 65 (1), Pp. 1\u201373. \n\n\n\nFarhi, W., Boudella, A., Saibi, H., Bounif, M.O.A., 2016. Integration of \nmagnetic, gravity, and well data in imaging subsurface geology in \nthe Ksar Hirane region, Laghouat, Algeria. J. Afr. Earth Sci., 124, Pp. \n63\u201374. \n\n\n\nFreitas, B.T., Warren, L.V., Boggiani, P.C., De Almeida, R.P., Piacentini, T., \n2011. Tectono-sedimentary evolution of the Neoproterozoic BIF-\nbearing Jacadigo Group, SW-Brazil. Sediment. Geol., 238 (1), Pp. 48\u2013\n70. \n\n\n\nGross, G.A., 1996. Algoma-type Iron-formation. In: Lefebure, D.V., Hoy, T. \n(Eds.), Selected British Columbia Mineral Deposit Profiles, Volume \n2 \u2014 Metallic Deposits. British Columbia Ministry of Employment \nand Investment, Open File 1996-13, Pp. 25\u201328. \n\n\n\nHenderson, I.H.C., Viola, G., Nasuti, A., 2015. A new tectonic model for the \nKautokeino Greenstone Belt, northern Norway, based on high-\nresolution airborne magnetic data and field structural analysis and \nimplications for mineral potential. Norwegian Journal of Geology, \n95, Pp. 339\u2013363. http://dx.doi.org/10.17850/njg95-3-05. \n\n\n\nHolden, E.J., Wong, J.C., Kovesi, P., Wedge, D., Dentith, M., Bagas, L., 2012. \nIdentifying structural complexity in aeromagnetic data: An image \nanalysis approach to greenfields gold exploration. Ore Geology \nReviews, 46, Pp. 47-59. \nhttps://doi.org/10.1016/j.oregeorev.2011.11.002 \n\n\n\nIsley, A.E., 1995. Hydrothermal plumes and the delivery of iron to banded \niron formation. J. Geol., 103 (2), Pp. 169\u2013185. \n\n\n\nIsley, A.E., Abbott, D.H., 1999. Plume-related mafic volcanism and the \ndeposition of banded iron-formation. J. Geophys. Res., 104, Pp. \n15461\u201315477. \n\n\n\nKassem, O.M.K., 2011. Determining heterogeneous deformation for \ngranitic rocks in the northern thrust in Wadi Mubarak belt, Eastern \nDesert, Egypt. Geotectonics, 45, Pp. 244\u2013254. \nhttps://doi.org/10.1134/S0016852111030046 \n\n\n\nKassem, O.M.K., 2012. Kinematic vorticity technique for porphyroclasts in \nthe metamorphic rocks: an example from the northern thrust in \nWadi Mubarak belt, Eastern Desert, Egypt. Arab J Geosci., 5, Pp. 159\u2013\n167. https://doi.org/10.1007/s12517-010-0229-8  \n\n\n\nKhalil, K.I., El-Shazly, A.E., Lehmann, B., 2015. Late Neoproterozoic banded \niron formation (BIF) in the Central Eastern Desert of Egypt: \nMineralogical and geochemical implications for the origin of the \nGebel El Hadid iron ore deposit. Ore Geol. Rev., 69, Pp. 380\u2013399. \nhttp://dx.doi.org/10.1016/j.oregeorev.2015.02.017. \n\n\n\nKlein, C., 2005. Some Precambrian banded iron formations from around \nthe world: their age, geologic setting, mineralogy, metamorphism, \ngeochemistry, and origin. American Mineralogist, 90, Pp. 1473\u2013\n1499. \n\n\n\nKlein, C., Beukes, N.J., 1993. Proterozoic Iron-formations. In: Condie, K.C. \n(Ed.), Development in Precambrian Geology: Proterozoic crustal, \nevolution, 10, Pp. 383\u2013418. \n\n\n\nLoizenbauer, J., Wallbrecher, E., Fritz, H., Neumayr, P., Khudeir, A., Kloetzli, \nU., 2001. Structural Geology, single zircon ages and fluid inclusion \nstudies of the Meatiq metamorphic core complex: Implications for \nNeoproterozoic tectonics in the Eastern Desert of Egypt. Precambr. \nRes., 110 (1), Pp. 357\u2013383. \n\n\n\nMacLeod, I.N., Jones, K., Dai, T.F., 1993. 3-D analytic signal in the \ninterpretation of total magnetic field data at low magnetic latitudes. \nExploration Geophysics, 24, Pp. 679-688. \n\n\n\nMakroum, F., 2003. Lattice preferred orientation (LPO) study of the \norogeny parallel Wadi Nugrus and Wadi Um Nar shears, Eastern \nDesert, Egypt, using EBSD technique. In: Proceedings of the second \ninternational conference on the geology of Africa: Egypt: Assiut, \nEgypt, Assiut University, 1, Pp. 213\u2013232. \n\n\n\n\nhttps://doi.org/10.1016/j.lithos.2014.01.006\n\n\nhttps://doi.org/10.1016/j.precamres.2019.02.011\n\n\nhttps://doi.org/10.1016/j.oregeorev.2011.11.002\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 22-30 \n\n\n\n\n\n\n\n \nCite The Article: Atef M. Abu Donia (2023). Aeromagnetic investigation of the banded iron formations of  \n\n\n\nUm Nar area, Central Eastern Desert, Egypt. Malaysian Journal of Geosciences, 7(1): 22-30. \n \n\n\n\nMiller, H.G., Singh, V., 1994. Potential Field Tilt\u2014A New Concept for \nLocation of Potential Field Sources. J. Appl. Geophys., 32, Pp. 213\u2013\n217. \n\n\n\nM\u00fccke, A., Annor, A., Neumann, U., 1996. The Algoma-type iron-formations \nof the Nigerian metavolcano-sedimentary schist belts. Mineral. \nDeposita, 31, Pp. 113\u2013122. \n\n\n\nNabighian, M.N., 1984. Toward a three-dimensional automatic \ninterpretation of potential field data via generalized Hilbert \ntransforms: fundamental relations. Geophysics, 49 (6), Pp. 780-786. \n\n\n\nNabighian, M.N., Grauch, V.J.S., Hansen, R.O., LaFehr, T.R., Li, Y., Peirce, J.W., \nRuder, M.E., 2005. The historical development of the magnetic \nmethod in exploration. Geophysics, 70 (6), Pp. 33ND-61ND. \n\n\n\nNadoll, P., Angerer, T., Mauk, J.L., French, D., Walshe, J., 2014. The \nchemistry of hydrothermal magnetite: A review. Ore Geology \nReviews, 61, Pp. 1\u201332. \nhttps://doi.org/10.1016/j.oregeorev.2013.12.013 \n\n\n\nNeumayr, P., Hoinkes, G., Puhl, J., Mogessie, A., Khudeir, A., 1998. The \nMeatiq dome (Eastern Desert, Egypt) a Precambrian metamorphic \ncore complex: petrological and geological evidence. Journal of \nMetamorphic Geology, 16 (2), Pp. 259\u2013279. \n\n\n\nOhmoto, H., Watanabe, Y., Yamaguchi, K.E., Naraoka, H., Haruna, M., \nKakegawa, T., Hayashi, K.I., Kato, Y., 2006. Chemical and biological \nevolution of early Earth: constraints from banded iron formations. \nGeol. Soc. Am. Mem., 198, Pp. 291\u2013331. \n\n\n\nOru\u00e7, B., Keskinsezer, A., 2008. Structural setting of the northeastern Biga \nPeninsula (Turkey) from tilt derivatives of gravity gradient tensors \nand magnitude of horizontal gravity components. Pure Appl. \nGeophys., 165, Pp. 1913\u20131927. \n\n\n\nPhillips, J.D., 2002. Processing and Interpretation of Aeromagnetic Data for \nthe Santa Cruz Basin-Patahonia Mountains Area, South-Central \nArizona; U.S. Geological Survey Open-File Report 02-98; U.S. \nGeological Survey: Reston, VA, USA. \n\n\n\nRasmy, A.H., 1968. Mineralogical and lithological studies of the Umm Nar \niron ore deposit, Eastern Desert, Egypt. M.Sc. thesis, Assiut \nUniversity, Egypt, Pp. 121, unpublished. \n\n\n\nRoest, W.R., Verhoef, J., Pilkington, M., 1992. Magnetic interpretation using \nthe 3-D analytic signal. Geophysics, 57, Pp. 116-125. \n\n\n\nShah, A.K., Bedrosian, P.A., Anderson, E.D., Kelley, K.D., Lang, J., 2013. \nIntegrated geophysical imaging of a concealed mineral deposit: A \ncase study of the world-class Pebble porphyry deposit in \n\n\n\nsouthwestern Alaska: Geophysics, 78 (5), Pp. B317\u2013B328. doi: \n10.1190/geo2013-0046.1. \n\n\n\nShalaby, A., 2003. Structural and tectonic evolution of the Wadi Mubarak \nbelt, Central Eastern Desert, Egypt. Unpublished Ph.D. Thesis, Graz \nUniversity, Pp. 178. \n\n\n\nShalaby, A., St\u00fcwe, K., Fritz, H., Makroum, F., 2006. El Mayah molasses \nbasin, Eastern Desert of Egypt. J. Afr. Earth Sci., 45, Pp. 1\u201315. \n\n\n\nShalaby, A., St\u00fcwe, K., Makroum, F., Fritz, H., Kebede, T., Kl\u00f6tzli, U., 2005. \nThe Wadi Mubarak belt, Eastern Desert of Egypt: a Neoproterozoic \nconjugate shear system in the Arabian\u2013Nubian Shield. Precambrian \nRes., 136, Pp. 27\u201350. \n\n\n\nShebl, A., Abdellatif, M., Elkhateeb, S.O., Cs\u00e1mer, \u00c1., 2021. Multisource Data \nAnalysis for Gold Potentiality Mapping of Atalla Area and Its \nEnvirons, Central Eastern Desert, Egypt. Minerals, 11, Pp. 641. \nhttps://doi.org/10.3390/min11060641 \n\n\n\nSilva, A.M., 1999. Integra\u00e7\u00e3o de dados geol\u00f3gicos e geof\u00edsicos utilizando-se \numa nova t\u00e9cnica estat\u00edstica para sele\u00e7\u00e3o de alvos para explora\u00e7\u00e3o \nmineral, aplicada ao Greenstone Belt Rio das Velhas, Quadril\u00e1tero \nFerr\u00edfero. Instituto de Geoci\u00eancias, Universidade de Bras\u00edlia, \nBras\u00edlia, Tese de Doutoramento, Pp. 195. \n\n\n\nSims, M.A., James, H.L., 1984. Banded iron ore formation of Late \nProterozoic age in the Central Eastern desert, Egypt: geological and \ntectonic setting. Economic Geology, 79, Pp. 1777\u20131784. \n\n\n\nStern, R.J., Mukherjee, S.K., Miller, N.R., Ali, K., Johnson, P.R., 2013. ~750 Ma \nbanded iron formation from the Arabian-Nubian Shield\u2014\nImplications for understanding Neoproterozoic tectonics, \nvolcanism, and climate change. Precamb Res., 239, Pp. 79\u201394. \nhttps://doi.org/10.1016/j.precamres.2013.07.015. \n\n\n\nTelford, W.M., Geldart, L.P., Sheriff, R.E., 1990. Applied geophysics, second \nedition. Cambridge University Press, Cambridge, Pp. 770.  \n\n\n\nThurston, J.B., Smith, R.S., 1997. Automatic conversion of magnetic data to \ndepth, dip, and susceptibility contrast using the SPI (TM) method: \nGeophysics, 62, Pp. 807\u2013813. \n\n\n\nWijns, C., Perez, C., Kowalczyk, P., 2005. Theta Map: Edge Detection in \nMagnetic Data. Geophysics, 70, Pp. 39\u201343. \n\n\n\nZhu, X.Q., Tang, H.S., Sun, X.H., 2014. Genesis of banded iron formations: A \nseries of experimental simulations. Ore Geology Reviews, 63, Pp. \n465\u2013469. https://doi.org/10.1016/j.oregeorev.2014.03.009 \n\n\n\n \n\n\n\n\nhttps://doi.org/10.1016/j.oregeorev.2013.12.013\n\n\nhttps://doi.org/10.1016/j.precamres.2013.07.015\n\n\nhttps://doi.org/10.1016/j.oregeorev.2014.03.009\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 54-60 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2022.54.60 \n\n\n\nCite The Article: Ada Ruth, Rex Ome, Diepiriye C. Okujagu (2022). Integration of Logs and Seismic Data for Delineation of Depositional \nEnvironment of Clastic Sediments in Tomboy Field, Niger Delta. Malaysian Journal of Geosciences, 6(2): 54-60. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2022.54.60 \n\n\n\nINTEGRATION OF LOGS AND SEISMIC DATA FOR DELINEATION OF \nDEPOSITIONAL ENVIRONMENT OF CLASTIC SEDIMENTS IN TOMBOY FIELD, \nNIGER DELTA \n\n\n\nAda Rutha, Rex Omeb, Diepiriye C. Okujagua \n\n\n\na Department of Geology, University of Port-Harcourt (UNIPORT) \nb Center for Petroleum Geosciences, (UNIPORT) \n*Corresponding Author Email: diepiriye.okujagu@uniport.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 17 May 2022 \nAccepted 20 June 2022 \nAvailable online 30 June 2022\n\n\n\nThe depositional environment of Clastic sediments in Tomboy Field Onshore Niger Delta were delineated \nusing 3D Seismic and lithological logs. Well log suites from 5 wells comprising Spontaneous potential, Gamma \nray, Sonic, Resistivity, Density, and Neutron logs were obtained and analyzed. Reverse estimation was done \nfor the Neutron log and Bulk Density log using volumetric method. Gamma-ray log were used to calibrate Log \nmotifs and correlate sand bodies TMB-01, TMB-02, TMB-03, TMB-04, and TMB-06. Prediction of depositional \nenvironment was made through the usage of wireline log shapes of facies combined with result from seismic \ndata. Results of correlation across the five wells showed that. there was continuity at depth interval of 4000-\n5000ft across the wells as well 3 did not have any gamma information to correlate accros it. The seismic \nsection showed two major growth faults and a channel fill on seismic (inline 5940, crossline1660) between \n3.3-3.6ms. According to the paleo-reconstruction from the correlated wells within the tomboy field, the \nreservoir is made up of sands from fluvial and tidal channels, barrier, and barrier bar. These sand bodies are \nlikely to contain hydrocarbons because of the rollover anticline, which is found at the fault's downthrown \nblock. This study has revealed that the Clastic sediments in Tomboy Field Onshore Niger Delta were deposited \nwithin a predominantly deltaic environment (transitional). \n\n\n\nKEYWORDS \n\n\n\nTomboy Field, Clastic sediments, depositional environment, deltaic, Seismic and Log data, \n\n\n\n1. INTRODUCTION \n\n\n\nThe Niger Delta Basin can be found in West Africa's equatorial region, \n\n\n\nbetween latitudes 3 and 6 north and longitudes 5 to 8 east. The Niger Delta \n\n\n\narea The Gulf of Guinea continental shelf is where it is located. Currently, \n\n\n\nit is one of the world's best tertiary deltas for the production of petroleum. \n\n\n\nWhen there was a time (Selly, 1997).  Reservoir rock formation is a long-\n\n\n\nstudied topic among geologists. In addition to those mentioned above \n\n\n\n(Short and Stauble, 1967; Weber, 1971; Weber and Daukoru, 1975; Evamy \n\n\n\net al., 1978; Rider, 1996; and Selly, 1997). Environment of deposition is an \n\n\n\nimportant factor in determining whether sedimentary rock will contain \n\n\n\npetroleum or not. Clastic sediments may be deposited in any of the \n\n\n\nfollowing environments; \n\n\n\nA. Continental Aeolian environment if they are deposited by wind or \nland, the environments types ranges from alluvial, braided stream, \nmeandering stream. \n\n\n\nB. Transitional deltaic environment if they are deposited in the mouth \nof river (delta), they include birdfoot-lobate (fluvial dominated), \ncuspate-arcuate, estuarine. \n\n\n\nC. Coastal interdeltaic environment if they were deposited between \ntwo deltas. \n\n\n\nD. Marine environment if they were deposited in a continuously \nincreasing depth of ocean water. (John O. Etu-Efeotor, 1997). \n\n\n\nLithological logs in combination with other data is an applicable tool that \n\n\n\ncan provide Useful information on the lithology and depositional \n\n\n\nenvironment (Rider, 1996). This study documents the application of \n\n\n\nlithological logs and 3D Seismic data to determine environment of \n\n\n\ndeposition of Tomboy Field, Niger Delta. This study is aimed at delineating \n\n\n\ndepositional environment of Clastic sediments for the field of study using \n\n\n\nan integrated approach of logs and seismic data. The study will involve \n\n\n\nwell correlation with Gamma-ray logs and analysis of the structural trend \n\n\n\nof the faults.  \n\n\n\n1.1  Study Location \n\n\n\nThe Tom Boy Field is located in the OML XYZ within the Onshore Niger \n\n\n\nDelta area of Nigeria see (figure 1). The study area is located in X-field of \n\n\n\nthe Niger Delta basin southern Nigeria which lies approximately within \n\n\n\nlongitude 500 and 800 east, and latitude 400 and 600 north. This basin is \n\n\n\ncharacterized with geological structures (faults and roll over anticlines \n\n\n\netc.) that have attracted much attention in the petroleum industry.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 54-60 \n\n\n\nCite The Article: Ada Ruth, Rex Ome, Diepiriye C. Okujagu (2022). Integration of Logs and Seismic Data for Delineation of Depositional \nEnvironment of Clastic Sediments in Tomboy Field, Niger Delta. Malaysian Journal of Geosciences, 6(2): 54-60. \n\n\n\nFigure 1: The map of Niger delta adapted from expedia 2014. \n\n\n\n2. LITERATURE REVIEW\n\n\n\n2.1  The Geology of the Niger Delta \n\n\n\nBetween latitudes 3 and 6 north and longitudes 5 and 8 east, the Niger \ndelta basin lies in equatorial West Africa. The equator passes through the \nheart of the city. At the present time, approximately 5% of the world's total \noil and gas reserves and 2.5% of its basin areas remain untapped. It's one \nof the most productive tertiary deltas in the world, in other words. 1,200, \n000 km2 is the area covered by people who live near the Niger-current \nBenue. Thus, a delta covering an area of 75,000 square kilometers with a \nClastic fill of about 12,000 meters was formed. It is the largest delta in \nAfrica as well as one of West Africa's most important basins. Many lands \nused to separate Africa from the rest of the world, but that is no longer the \ncase. Clastic wedges form along the failed arm of a triple junction system \nin the Niger Delta. This is where you'll find the Tomboy Field. The plates \nof South America and Africa split apart millions of years ago. It was \ndecided to create the Delta (Burke, 1972; Whiteman, 1982).  West Africa's \npassive continental margin was formed by the two rift arms that ran along \nNigeria and Cameroon's southwest and southeast coasts. Thirdly, the \nBenue Trough was formed under the Gulf of Guinea, off the Nigerian coast. \nRifts began to form during the late Mesozoic period. During the Tertiary, \nas drainage from the African Craton increased and the passive margin \nsank, the Clastic wedge gradually degraded into the Gulf of Guinea. The \nAkata, Agbada, and Benin Formations are three distinct lithostratigraphic \nunits found off the coast of this continent (Short and Stauble, 1967; Doust \nand Omatsola, 1990).  The oldest unit is the Akata Formation. An \nimportant component is Eocene- to Recent-aged marine shales. The \nAgbada Formation and the Akata Formation share a boundary. Sandstone \nand shale are found in alternating layers in the deltaic sedimentary rocks. \nIt is between the Eocene and the present day in age when it was built. Some \nof the Benin Formation is comprised of sandy gritstone, clay-rich \nclaystone, and lignite streaks. As the last in the lithostratigraphic chain, it \nis also the newest member of the family. It dates back to the Oligocene era, \nwhich makes it old enough. However, large-scale synsedimentary features \nsuch as growth faults, rollover anticlines, and diapers have a significant \nimpact on the subsurface of the Niger Delta (Doust and Omatsola, 1990; \nStacher, 1995).  The amount of sediment and the amount of land that is \nsinking can be used to determine the style of a structure. structures with \nmultiple anticline faults and complicated collapse crests are examples of \nstructures that are all types of anticline rollover structures (Evamy et al., \n1978).  Growth faults can also contain k-block structures and structural \nclosures.  \n\n\n\n2.2   Integration of Well Logs and Seismic Data  \n\n\n\nIn clastic reservoir lithofacies analysis, describing and deciphering the \ncore and wireline logs of the clastic reservoir is the most critical step \n(Siemers and Tillman, 1981).  Wireline logs are the only reliable source of \ndata because core samples are rarely available for analysis. In order to \nbetter understand the environment in which sediments were formed, \nlithofacies are used. For each lithofacies, you can identify the physical and \nbiological processes that led to its formation by comparing it to the cored \nsequence or wireline log. This can be accomplished by establishing a link \nbetween the lithofacies and the processes that produced them. A rock's \nshaliness is often used as a gauge of the strength of its radioactive \n\n\n\nelements' gamma radiation, which is produced by the rock's radioactive \nelements. In order to distinguish shale from other sedimentary rocks like \nsandstone, and limestone, a specific radioactive source must be used. The \ngamma ray log fluctuates, revealing changes in the rocks' mineralogy. \nRadiation levels can vary greatly depending on how and where an element \nwas deposited. \n\n\n\nIt is common to use geophysical logs to compare wells and evaluate \nformations in order to determine the type of rock present. Facies analysis \nis currently done using well log (gamma ray) shapes, in order to determine \nsediments depositional environments. The shape of the SP and gamma ray \nlogs are taken into account when classifying sand bodies. Facies and \nsequence stratigraphy are most commonly studied using gamma ray logs. \nThis is due to the fact that gamma ray logs have a greater variety of shapes, \nand characteristics (Dalrymple and Choi, 2007).  Petrophysical modeling \nof a hydrocarbon reservoir in the Niger Delta based on the integration of \nseismic and well log data (Senosy et al., 2020; Nwankwo et al.; 2014). \nBased on previous investigation how the Tomboy field sand bodies formed \nunder various environmental conditions (Nton and Adesina, 2009). \nAccording to their findings, the field is made up of sand bodies that have \nbeen deposited in various locations along normal faults, growth faults, and \nrollover anticlinal structures, among other types of faults. Many scientists \nhave used log shapes to study facies. We can deduce grain size from shifts \nin the log's gamma-ray response, and it is believed that these shifts are \ncaused by abrupt lithological breaks that occur at unconformities and \nsequence boundaries.  \n\n\n\nSeismic data has been used to find vast oil and gas reserves through \nstratigraphic imaging and pore-fluid estimation, lithofacies \ndifferentiation, and other applications. For accurate stratigraphic \ninterpretation, the seismic analysis workflow improves vertical \nresolution, allowing thinner beds to be seen on seismic reflection data \n(Castagna et al., 2003).  Seismic facies, also known as lithofacies, is used to \nunderstand the depositional environment and reservoir as a whole by \ncombining seismic attributes. For reservoir characterization and \nlithofacies analysis, seismic attribute has been the most reliable method of \ninterpreting seismic data since it was first used in the 1970s. This is due to \nthe fact that seismic attributes can be used to predict rock properties, \nwhich are sensitive to changes in the lithology beneath the surface. By \nstudying a rock's seismic properties, one can infer its physical attributes. \nImpedance inversion is a procedure that converts seismograms and \nreflection coefficients into an acoustic impedance time series (Lavergne \nand William, 1977; Lindseth, 1979).  Understanding the meaning and \nrelationships among the properties measured in well logs can be \nsimplified with the aid of impedance traces. A sophisticated method of \ncombining well logs and seismic data in order to distinguish between \nlithofacies and where they were formed can be referred to as inversion.A \nstudy used geophysical well logs to distinguish between the lithofacies in \nhydrocarbon reservoirs (Aigbedion and Iyayi, 2007; Adeoye, 2009; \nEnikanselu, 2009; Opara, 2010). This study will determine the \ndepositional environment of the clastic sediments in Niger Delta's Tomboy \nfield by combining seismic and well log data.  \n\n\n\n3. MATERIALS AND METHOD\n\n\n\nThe different datasets employed in this study include; 3-D Seismic profiles \nin the SEG Y format, composite well logs comprising mainly Spontaneous \npotential, Gamma ray, Sonic, Resistivity, Density, Neutron logs of the 5 \nwells, Checkshot survey data, Header, Deviation data and  interactive \nworkstation with PETREL interpretation software. \n\n\n\n3.1   Well Log Analysis \n\n\n\nReverse estimation was done for the Neutron log and Bulk Density log \nusing volumetric method. This was done to obtain the gas balloon \nstructure and to correspond the shape to reservoir sand on the Gamma-\nray log. \n\n\n\n3.2   Seismic Interpretation \n\n\n\nA well to seismic tie was done using check shot data; this is done to \ncompare synthetic trace generated from density log to the real seismic \ndata. Structural closure and fault planes were mapped and interpreted \nfrom the seismic section. Seismic horizons were mapped from the seismic \nsection. Different colors were used to differentiate each features \ninterpreted within the seismic sections. Two major faults observed from \nthe seismic section were mapped as well (Figure 6). Different Gamma-ray \ntrends such as Bell shape (fining upward), funnel shape (coarsening \nupward), cylindrical shape (reducing clay content) were used to \ndetermine the various depositional environments within the Tomboy \nField. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 54-60 \n\n\n\nCite The Article: Ada Ruth, Rex Ome, Diepiriye C. Okujagu (2022). Integration of Logs and Seismic Data for Delineation of Depositional \nEnvironment of Clastic Sediments in Tomboy Field, Niger Delta. Malaysian Journal of Geosciences, 6(2): 54-60. \n\n\n\n3.3   Well Correlation \n\n\n\nThe major target during this correlation was on the sand bodies for (TMB-\n01, TMB-02, TMB-03, TMB-04, and TMB-06). Gamma-ray log were used \nfor this correlation. During this correlation, top of the formation sand body \ncorrelated were labeled as (name-A) while base of the first sand body \ncorrelated were labeled as (name-B).  \n\n\n\n3.4 Using Gamma Ray Log Shapes to Determine Environment of \nDeposition \n\n\n\nGamma ray logs (GR) measures the radioactivity of a rock layer. Shale \ntends to contain radioactive potassium (K40) and are more radioactive \nthan sandstones containing mostly quartz. On gamma ray logs, increasing \nradioactivity shift the curve to the right. \n\n\n\nIt has been discovered that gamma-ray shapes are very different, show \nmore detail, and are associated with sediment character and depositional \nenvironment. This makes them important. There are many ways to \ndetermine the amount of clay in the shale by looking at the log's shape. On \nthe gamma ray log, grain size patterns can be seen. This is based on the \nsedimentological association. When the gamma ray value decreases, the \ngrain size changes. The grain size decreases as the value increases. \nSedimentological implications of this relationship led to a connection \nbetween log shape and facies type.  \n\n\n\nThere are no thin non-sandy interbeds or only a few if any if the sandstone \nis blocky or cylinder-shaped. Sand that looks like this is used in Tidal \nChannel Barrier Bars. In the outer Deltaic environment, these sands came \nfrom a river.  \n\n\n\nThe presence of more clay in the soil is indicated by a bell-shaped log with \nan increasing gamma ray value or fining up from a low value. This is true \nof channel sand, fluvial channel sand, alluvial bar, and point bar, all of \nwhich are found in the Delta plain environment.  \n\n\n\nThe sandstone gets grittier as the log value decreases, as shown by the \nfunnel shape. This type of sand can be found in deltaic environments in the \nform of beach sand, barrier bar sand, and stream bars. The serrated or \ndigitate log shape indicates rapid alternation of thin bed of sandstone and \nshale. Such beds are typical of Marshy or swampy areas, lagoons and delta \nfronts. When the log shape shows a combination of cylindrical and \nserrated log patterns, it shows that these are deposits of deltaic \nprogradation and river flood plain.  \n\n\n\n3.5   Structural Modelling  \n\n\n\nStructural closure and fault planes were mapped and interpreted from the \nseismic section. The faults mapped in this area are mainly major faults that \ntrend in the SW- NE direction typically of faults in the Niger Delta Province. \n\n\n\nFigure 2: Log shape classifcation. The basic geometrical shapes and \ndescription used to analyze gamma ray response to variation in grain \n\n\n\nsize. \n\n\n\nFigure 3:  Gamma ray and resistivity log shapes suggestive of \ndepositional environment :(a) after Busch, 1975; (b) Schlumberger, 1985 \n\n\n\n4. RESULT AND DISCUSSION\n\n\n\nFigure 4. (a), (b) and (c) show the base map of the wells and the correlated \nstratigraphic cross-section showing formation tops and bases for \nreservoir sands and their stacking pattern within the Tomboy field.  \n\n\n\n4.1  Well Correlation \n\n\n\nThe various wells were correlated using Gamma ray log to identify the \nsand units and resistivity log for detiled correlation work. By comparing \nthe patterns in one log to the patterns in the next, correlation was \nestablished. The distinct surfaces that demonstrated changes in lithologic \ncharacter demonstrated the vertical progression of lithologic units. The \ncorrelation across the five wells showed that there was continuity of the \nsand package across the well at depth interval of 7000-11,000ft.there was \ncontinuity at depth interval of 4000-5000ft across the wells as well 3 did \nnot have any gamma information to correlate accros it. \n\n\n\n4.2  3D- Seismic Interpretation \n\n\n\nEvery tenth inline and tenth cross line was used to examine the earth's \nsurface for fault lines. Two significant holes were discovered and \nhighlighted in the seismic section. Growth has two major flaws. Names, \ncolors, and connections were made between the two major flaws that were \ndiscovered. Because of the way the basin has developed, the field contains \na large number of artificial faults.  \n\n\n\nFigure 4(a): Showing the base map for the wells \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 54-60 \n\n\n\nCite The Article: Ada Ruth, Rex Ome, Diepiriye C. Okujagu (2022). Integration of Logs and Seismic Data for Delineation of Depositional \nEnvironment of Clastic Sediments in Tomboy Field, Niger Delta. Malaysian Journal of Geosciences, 6(2): 54-60. \n\n\n\nFigure 4(b): Showing the well to well correlation for four wells \n\n\n\nFigure 4(c): Showing the well to well correlation across the five wells \n\n\n\nFigure 5: shows the seismic section with structural and stratigraphic \nplay. \n\n\n\nFigure 6: Shows the well to seismic tie using the synthetics and the \nwavelet for    the well 4 with checkshot. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 54-60 \n\n\n\nCite The Article: Ada Ruth, Rex Ome, Diepiriye C. Okujagu (2022). Integration of Logs and Seismic Data for Delineation of Depositional \nEnvironment of Clastic Sediments in Tomboy Field, Niger Delta. Malaysian Journal of Geosciences, 6(2): 54-60. \n\n\n\nFigure 7: shows the seismic to well tie bounded by two growth faults \n(synthetic faults) \n\n\n\n5. DETERMINATION OF DEPOSITIONAL ENVIRONMENT FROM\n\n\n\nLITHO- CORRELATED LOGS AND SEISMIC FOR TOMBOY FIELD \n\n\n\nFigure 8: shows the aggradation (low system tract) for the tomboy field. \n\n\n\nLEGEND FOR DIAGRAMMATIC DISPLAY ON LOGS \n\n\n\nTRANGRESSION (SEA LEVEL RISE) \n\n\n\nREGRESSION (SEA LEVEL FALL) \n\n\n\nTIDAL CHANNEL BARRIER SAND \n\n\n\nDISTRIBUTARY CHANNEL SAND \n\n\n\nBARRIER BAR SAND \n\n\n\nFigure 9: Shows The Relative Fall, Rise and Constant Withdrawal Effect \nOf The Sea Level On Clastic Depositional Package \n\n\n\nFigure 10: Shows the Well to Seismic Tie With A Gamma Ray Motif. \n\n\n\nTCB\nS \n\n\n\nDCS \n\n\n\nBBS \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 54-60 \n\n\n\nCite The Article: Ada Ruth, Rex Ome, Diepiriye C. Okujagu (2022). Integration of Logs and Seismic Data for Delineation of Depositional \nEnvironment of Clastic Sediments in Tomboy Field, Niger Delta. Malaysian Journal of Geosciences, 6(2): 54-60. \n\n\n\nFigure 11: Seismic to Well Tie With Visibble Channel Using Variance \n\n\n\n5.1  Discussion of Findings \n\n\n\nThe paleo-reconstruction indicates that the reservoir sedimentary setting \nis predominantly deltaic environment. Figure 5 showed likely structure of \nthe channel fill on seismic (inline 5940, crossline1660) between 3.3-\n3.6ms. Figure 6 showed a well to seismic tie with synthetics and wavelet \n(inline 5960, cross line 1590). Growth faults as depicted in the Niger delta \nwere observed on seismic section containing well. Fluvial sand channel \ncommon across well correlated in figure 8, aggradation activities depicted \nby thick sand package. \n\n\n\nThe relative rise and fall and constant withdrawal rate shown to be the \nprimary reason for the sand depositional model observed in figure 9. Tidal \nchannel, distributary channel fills and barrier bar sands packages have \nbeen delineated from the correlated wells within the tomboy field. Thus, \nshowing that the environment is a predominantly deltaic environment \n(transitional), a confirmation of channel fills was likely observed in figure \n11 showing a well to seismic tie section. \n\n\n\n6. CONCLUSION \n\n\n\nTomboy Field sand was discovered onshore in the Niger Delta by using 3D \nseismic and logs to find out where it came from. Three reservoir sand \nbodies were correlated for the six wells and were seen to be continuous. \nOn the seismic section, two major growth faults were observed. The fault's \ndownthrown block has a rollover anticline, indicating that the sand bodies \nare likely to contain hydrocarbons. Fluvial channel sand, tidal channel \nbarrier bar sand, and barrier bar sand were all identified as depositional \nenvironments, all of which are typical of deltaic environments. Oil and gas \ncan be stored in these.d. \n\n\n\nREFERENCES \n\n\n\nAdejobi AR, Olayinka AI, 1997. Stratigraphy and hydrocarbon potential of \nthe Opuama channel complex area, western Niger delta. Nig. Ass. of \npetrol. Expl. (NAPE) Bull., 12, Pp. 1-10  \n\n\n\nAdeoye, TO; and Enikanselu, PA, 2009. Hydrocarbon Reservoir Mapping \nand Volumetric Analysis Using Seismic and Borehole Data over \n\u201cExtreme\u201d Field, South western Niger Delta. Ozean Journal of Applied \nSciences 2 (4), Pp. 429-440.  \n\n\n\nAigbedion, I; and Iyayi, SE, 2007. Formation evaluation of Oshioka field \nusing geophysical well logs. Middle-East Journal of Scientific Research, \n2 (4), Pp.107-110.  \n\n\n\nBarde JP, Chambertain P, Gralla P, Harwijanto J, Marsky J, Schroeter T, \n2000. Explaining a complex hydrocarbon system in the Permo- \nTriassic of the precaspian basin by integration of independent models. \nAbstracts, 62nd European Association of Geoscientists and Engineers \nConference and Technical Exhibition, 2, Pp. 4. \n\n\n\nBarde JP, Gralla P, Harwijanto J, Marsky J, 2002. Exploration at the eastern \nedge of the prescapian basin impact of data integration on Upper \nPermian and Trasic prospectivity. Am. Assoc. Petrol. Geol. Bull., 86, Pp. \n\n\n\n399-415.  \n\n\n\nBurke, K.C. and Dewey, F.J., 1972. Orogeny in Africa. In: Dessauvagie, T.F.J. \nand Whiteman, A.J., Ed., African Geology, Ibadan University Press, \nIbadan, Pp. 583-608. \n\n\n\nCastagna, JP; Sun, S; and Siegfried, RW, 2003. Instantaneous spectral \nanalysis: Detection of low-frequencyshadows associated with \nhydrocarbons: The Leading Edge, 22, Pp. 120\u2013127.  \n\n\n\nChukwueke CC, 1997. Factors controlling hydrocarbon distribution in the \ncentral swamp deposit of the Niger Delta. Nig. Assoc. Petrol. Explorat. \nBull., 12, Pp. 41-45. \n\n\n\nDalrymple, R.W. and K. Choi, 2007. Morphologic and facies trends through \nthe fluvial marine transition in tide-dominated depositional systems: \nA schematic framework for environmental and sequence Stratigraphic \ninterpretation. Earth Sci. Rev., 81, Pp. 135-174. DOI: \n10.1016/j.earscirev.2006.10.002 \n\n\n\nDoust, H. and Omatsola, E., 1990. Niger-Delta. In: Edwards, J.D. and \nSantogrossi, P.A., Eds., Divergent/Passive Margin Basins, AAPG \nMemoir 48, American Association of Petroleum Geologists, Tulsa, Pp. \n239-248. \n\n\n\nEtu-Efeotor, J., 1997. Fundamentals of Petroleum Geology. Africana-FEP \nPublishers, Onitsha, Pp. 111-123. \n\n\n\nEvamy, B.D., Haremboure, J., Karmerlimg, P., et al., 1978. Hydrocarbon \nHabitat of Tertiary Niger Delta. American Association of Petroleum \nGeologists Bulletin, 62, Pp. 1-39 \n\n\n\nHosper J, 1971. The geology of the Niger Delta area, in the Geology of the \nEast Atlantic continental margin, Great Britain, Institute of Geological \nScience, Report, 70 (16), Pp. 121- 141  \n\n\n\nKnox, G. J. and Omatsola, M. E., 1989. Development of the Cenozoic Niger \nDelta in terms of the escalator regression model and impact on \nhydrocarbon distribution.  \n\n\n\nLavergne, M; and William, C, 1977. Inversion of seismograms and pseudo-\nvelocity logs: Geophys. Prosp., 25, Pp. 232-250. \n\n\n\nLindseth, RO, 1979. Synthetic sonic logs-a process for stratigraphic \ninterpretation. Geophysics, 44 (1), Pp.3-26 \n\n\n\nMerki PJ, 1972. Structural geology of the Cenozoic Niger Delta: 1st \nConference on African Geology Proceedings, Ibadan University Press, \nPp. 635-646.  \n\n\n\nMurat RC, 1970. Stratigraphy and Paleogeography of the Cretaceous and \nLower Tertiary in Southern Nigerian. 1st Conference on African \nGeology Proceedings, Ibadan University Press, Pp. 251-266.  \n\n\n\nNton, M.E. and Adebambo, B.A., 2009. Petrophysical evaluation and \ndepositional environments of reservoir sands of X- field, offshore \nNiger delta. Mineral Wealth. 150, Pp. 1- 12  \n\n\n\nNton, M.E. and Adesina, A. D., 2009. Aspects of structures and depositional \nenvironment of sand bodies within tomboy field, offshore western \nNiger Delta, Nigeria RMZ \u2013 Materials and Geoenvironment, 56 (3), Pp. \n284\u2013303 \n\n\n\nNwankwo, CN; Anyanwu, J; and Ugwu, SA, 2014. Integration of seismic and \nwell log data for petrophysical modeling of sandstone hydrocarbon \nreservoir in Niger Delta. Sci Afr, 13 (1), Pp. 186-199.  \n\n\n\nOjo AO, 1996. Pre-drill prospect evaluation in deep water Nigeria. Nig. \nAssoc. Petrol. Explo. Bull., 11, Pp. 11-22.  \n\n\n\nOpara A.I., 2010. Prospectivity Evaluation of \u201cUsso\u201d Field, Onshore Niger \nDelta Basin, Using 3-D Seismicand Well Log Data. Petroleum and Coal, \n52 (4), Pp. 307-315. \n\n\n\nReijers, T.J.F., 1996. Selected Chapters on Geology, SPDC of Nigeria, \nCopporate Reprographic Services, Warri, Pp. 197. \n\n\n\nReyment, R.A., 1965. Aspects of the Geology of Nigeria. Ibadan Univ. Press, \nIbadan. \n\n\n\nRider, M., 1996. The Geological Interpretation of Well Logs. 2nd Edition, \nRider-French Consulting Ltd., Sucherland. \n\n\n\nSenosy, A. H., Ewida, H. F., Soliman, H. A., and Ebraheem, M. O., 2020. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 54-60 \n\n\n\nCite The Article: Ada Ruth, Rex Ome, Diepiriye C. Okujagu (2022). Integration of Logs and Seismic Data for Delineation of Depositional \nEnvironment of Clastic Sediments in Tomboy Field, Niger Delta. Malaysian Journal of Geosciences, 6(2): 54-60. \n\n\n\nPetrophysical analysis of well logs data for identification and \ncharacterization of the main reservoir of Al Baraka Oil Field, \nKomombo Basin, Upper Egypt. SN Applied Sciences, 2 (7), Pp. 1-14. \n\n\n\nShort, K.C. and Stauble, A.J., 1967. Outline of Geology of Niger Delta. AAPG \nBulletin, 51, Pp. 761-779. \n\n\n\nSiemers, C.T. and R.W. Tillman, 1981. Recommendations for the Proper \nHandling of Cores and Sedimentological Analysis of Core Sequences. \nIn: Deep-Water Clastic Sediments, Siemers, C.T., R.W. Tillman and C.R. \nWilliamson, (Eds.), SEPM, Pp. 20-44. \n\n\n\nWeber, K.J, 1971. Sedimentological aspects of oil fields in the Niger \nDelta.Environmental Geology, Minbouw, 50 (3), Pp. 559-576 \n\n\n\nWeber, K.J. and Daukoru, E.M., 1975. Petroleum Geology of the Niger Delta. \nProceeding of the Ninth World Petroleum Congress, Tokyo, 2, Pp. 209-\n221. \n\n\n\nWhiteman, A., 1982. Nigeria: Its Petroleum Geology, Resources and \nPotential. I and II. Graham and Trotman Ltd., London. \nhttp://dx.doi.org/10.1007/978-94-009-7361-9 \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 39-43 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2023.39.43 \n\n\n\n \nCite The Article: Akaninyene O. Akankpo, Ubong E. Essien, Magnus U. Igboekwe  (2023). Lithology Discrimination Using Shear and  \n\n\n\nCompressional Waves in Uyo and Its Environ, Southeastern Nigeria. Malaysian Journal of Geosciences, 7(1): 39-43. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nREVIEW ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2023.39.43 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLITHOLOGY DISCRIMINATION USING SHEAR AND COMPRESSIONAL WAVES IN \nUYO AND ITS ENVIRON, SOUTHEASTERN NIGERIA \n\n\n\nAkaninyene O. Akankpoa, Ubong E. Essienb, Magnus U. Igboekwec \n\n\n\na Department of Physics, University of Uyo, Uyo, Nigeria. \nb Department of Science Technology, Akwa Ibom State Polytechnic, Ikot Osurua, Nigeria. \nc Department of Physics, Michael Okpara University of Agriculture, Umudike, Nigeria. \n*Corresponding Author Email: akaninyeneakankpo@uniuyo.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 15 January 2022 \nRevised 04 February 2022 \nAccepted 07 March 2023 \nAvailable online 13 March 2023 \n\n\n\n\n\n\n\nCompressional and shear wave velocities reveal vital information about the physical properties of rocks and \ntheir lithology contrast. In this study, we have applied the seismic refraction method to investigate the \ndynamic elastic modulus of subsurface layers in our study area. Electromagnetic geophones were firmly \ncoupled to the ground to transform generated seismic energy at the source to electrical voltage which is a \nfunction of velocity. A total of twenty-seven P-wave refraction shootings were performed and measured P-\nwave velocity ranged from 218.00 ms-1 to 749.30 ms-1. The S-wave velocity obtained in the field ranged from \n98.30 ms-1 to 340.70 ms-1. These values indicate that both layers 1 and 2 are weathered zones characterized \nby fine and lateritic sands which are capable of withstanding little or no mechanically related load or stress / \npressure applied on the surface. In terms of Vp-Vs ratio, the first layer indicated a range of 2.2071 to 2.2183 \nwith a mean value of 2.2108 for the area while the second layer produced a Vp-Vs ratio ranging from 2.1993 \nto 2.2055 with a mean value of 2.2023. The near constant Vp-Vs ratio of 2.2 obtained in the study shows a \nsimilarity in the formation grains in the shallow layers penetrated by the seismic waves. \n\n\n\nKEYWORDS \n\n\n\nP-waves, S-waves, Refraction, Velocity, Lithology \n\n\n\n\n\n\n\n1.   INTRODUCTION \n\n\n\nSeismic refraction method has broad applications in engineering \nespecially for investigating the strength of foundation materials. The \nmethod has also been applied to ground water prospecting as well as \npetroleum and mineral explorations. In the past two decades, refraction \nseismology has found increasing applications in the evaluation of dynamic \nelastic modulus of the earth\u2019s subsurface in order to obtain valuable \ninformation on the elastic properties of the layers (Essien and Akankpo, \n2013). Measured velocities of compressional (P) and shear (S) waves have \nbeen used to estimate the compressional and shear wave ratio Vp/Vs \nknown as the lithology discriminator. This is because the Vp/Vs ratio is \nsensitive to the fluid pores prevalent in sedimentary rocks. The Vp/Vs ratio \nfor a gas-filled environment is much lower than the liquid-filled \nenvironment (Tathan, 1982; Ogagarue and Asor, 2010). \n\n\n\nSeveral authors have shown that compressional (P) wave velocity Vp is \nbelow 330m/s in the near surface of the earth. A group researchers offered \na quantitative explanation for low P-wave velocities values recorded in the \nnear surface (Bachrach et al., 1998). In a separate study, author observed \nthat P-wave velocities is lower for the near ground surface than the \nvelocity of air (Baker et al., 1999). Pickett in his study of porous media gave \nthe Vp/Vs ratio as 1.9 for Limestone, 1.8 for Dolomite and 1.6 - 1.75 for \nsandstones (Pickett, 1963). As stated by velocity information remains a \nkey petrophysical parameter deployed in oilfield optimization and various \ngeophysical surveys to study, predict and evaluate horizons, geologic \nstructures, faults, stratigraphic boundaries, fluid contents, rock facies and \nunconformities (Tamunobereton-ari, 2010). Based on a detailed \nlaboratory study of velocity relationships using petrographic character in \n\n\n\ncarbonate rocks, successfully used the Vp/Vs ratio to distinguish between \nlimestone and dolomite rocks (Rafavich et al., 1984). Increasing values of \ncompressional and shear velocity have also been used in the \ndiscrimination of pore fluid types in reservoirs (Horsfall et al., 2014). \n\n\n\nOsman noted that by evaluating seismic velocities, Vp and Vs, it is possible \nto delineate alteration zones; investigate cavities; establish the \noccurrence, locations and apertures of structural discontinuities; \ndetermine areas of structural weakness in basements; analyse ground \nstability and more so, determine mechanical properties of rocks (Osman, \n2010). According to the influence of lithology on rocks physical behaviour \nis well recognized and further work has been devoted to the study (Bosch \net al., 2002). In recent times, new theories and approaches for lithologic \ntomography from multiple geophysical data have been proposed and \nmany researchers have developed different inversion methods to estimate \nlithology from seismic data (Bosch et al., 2001; Lortzer and Berkhout, \n1992; Fichtl et al., 1997; Torres-Verdin et al., 1999). In this work, seismic \nrefraction method was employed in determining the elastic properties and \nlithological information as an aid to investigating engineering foundation \nin the study area. \n\n\n\n2.   LOCATION AND GEOLOGY OF THE STUDY AREA \n\n\n\nThe area of this study is Uyo, sandwiched between latitudes 4045' and \n5015' N and between longitudes 7o45' and 8o30' E in the Niger Delta Basin, \nSouthern Nigeria (Figure 1). The area is defined by a total landmass of \napproximately 1110.1km2 and has an equatorial climate characterized by \ntwo major seasons viz: rainy season (March - October) and dry season \n(November - February) (Evans et al., 2010; George et al., 2010 a, b). \nGeologically, our area of study belongs to the Tertiary to Quaternary \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 39-43 \n\n\n\n\n\n\n\n \nCite The Article: Akaninyene O. Akankpo, Ubong E. Essien, Magnus U. Igboekwe  (2023). Lithology Discrimination Using Shear and  \n\n\n\nCompressional Waves in Uyo and Its Environ, Southeastern Nigeria. Malaysian Journal of Geosciences, 7(1): 39-43. \n \n\n\n\nCoastal Plain Sands (CPS) which constitutes the Benin Formation, one of \nthe three major lithostratigraphic units in the Niger Delta Basin and \nalluvial deposits drained from rivers Niger and Benue in Nigeria.  \n\n\n\nThe Benin Formation sits on top of the Agbada Formation which is a \nparalic sequence of siliciclastic sand-shale rocks covering over eighty \npercent of the Niger Delta Basin. Sediments of the Benin Formation are \n\n\n\ncharacteristically poorly sorted and generally consist of intercalated units \nof fine-medium lacustrine and fluvial loose sands, pebbles, gravels, clays \nand lignite streaks of variable sizes while the alluvial units are made up of \nlagoon and tide sediment deposits, beach sands and soils commonly found \nin the southern areas, especially along the riverbanks (Emujakporue and \nEkine, 2009; Reijers et al., 1997; Nganje et al., 2007). However, only the \nBenin Formation is exposed in the study area where our work is centred. \n\n\n\n\n\n\n\nFigure 1: Map showing (a) the location and geology of the study area (b) Map showing the central Uyo district. \n\n\n\n3.    METHODOLOGY \n\n\n\nElectromagnetic geophones were planted and firmly coupled to the \nground to convert generated seismic energy at source to electrical voltage, \nwhich is a function of velocity. Seismic disturbances generated were \npicked up and recorded by a seismograph connected to the geophones. \nHammer was struck vertically on a metal plate to generate P-waves while \nthe S-waves were produced by hitting the ends of a flat-lying wooden \ntimber. A total of twenty-seven P-wave seismic refraction shootings were \nperformed in the area and layers 1 and 2 velocities as well as the depth of \nfirst refractors formed the primary parameters measured in the survey. \n\n\n\nThe Golden Surfer 10 software was employed to analyse measured \ngeologic parameters, dynamic strength of foundation materials and to \nexamine their spread in the study area.    \n\n\n\n4.   RESULTS AND DISCUSSION  \n\n\n\nA summary of layer parameters and elastic properties in the study area \nare shown in Table 1 and Table 2. The 3 D contour maps of layers 1 and 2 \nP-wave velocities are presented in Figure 2 while the 3 D blanked contour \nmaps of layer 1 S-wave velocity (a) and layer 2 S-wave velocity (b) of the \nstudy area are shown in Figures 3. \n\n\n\n\n\n\n\nFigure 2: 3-D blanked contour map of layer 1 P-wave velocity (a) and layer 2 P-wave velocity (b) in the study area\n\n\n\n\n\n\n\n\n\n\n\nFigure 3: 3-D blanked contour map of layer 1 S-wave velocity (a) and layer 2 S-wave velocity (b) in the study area \n\n\n\nBendeAmekie   \n\n\n\nGroup \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nImo \n\n\n\nShale \n\n\n\nAlluviu\n\n\n\nm \n\n\n\nBenin \n\n\n\nFormation \n\n\n\nLegen\n\n\n\nd \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n NIGERIA \n\n\n\n  5\no\n30\n\n\n\n1\nN \n\n\n\n  5\no\n00\n\n\n\n1\nN \n\n\n\n  4\no\n30\n\n\n\n1\nN \n\n\n\n7\no\n30\n\n\n\n1\nE  8\n\n\n\no\n00\n\n\n\n1\nE    8\n\n\n\no\n30\n\n\n\n1\nE \n\n\n\n   8\no\n00\n\n\n\n1\nE  8\n\n\n\no\n30\n\n\n\n1\nE \n\n\n\n4\no\n30\n\n\n\n1\nN \n\n\n\n5\no\n30\n\n\n\n1\nN \n\n\n\n5\no\n00\n\n\n\n1\nN  \n\n\n\n\n\n\n\n \nN \n\n\n\nKw\na \n\n\n\nIbo \n\n\n\nRiver \n\n\n\nCr\nos\ns  \nRi\nve\nr \n\n\n\nGulf      of     \nGuinea \n\n\n\n\n\n\n\n1 2K\nm \n\n\n\nUY\nO \n\n\n\nEte Eket \n\n\n\nIkotEkpene \n\n\n\nIni \n\n\n\nMbo \n\n\n\nObotAka\nra \n\n\n\nIbeno \n\n\n\nREP\nUBLI\nC OF  \nCAM\nERO\nUN \n\n\n\nREPUBLIC OF \nNIGER \n\n\n\nRE\nP\nU\nBL\nIC \nO\nF \nNI\nG\nER \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\na \n\n\n\nIKOT \nABASI \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIBIONO \n\n\n\nIBOM \nITU \n\n\n\nUYO URUAN \n\n\n\nETINAN \n\n\n\nNSIT \n\n\n\nATAI \n\n\n\nNSIT UBIOM \n\n\n\nNSIT IBOM \n\n\n\nIBESIT IKPO \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nST\nU\nDY \nA\nRE\nA \n\n\n\nb \n\n\n\n\n\n\n\n(a) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 39-43 \n\n\n\n\n\n\n\n \nCite The Article: Akaninyene O. Akankpo, Ubong E. Essien, Magnus U. Igboekwe  (2023). Lithology Discrimination Using Shear and  \n\n\n\nCompressional Waves in Uyo and Its Environ, Southeastern Nigeria. Malaysian Journal of Geosciences, 7(1): 39-43. \n \n\n\n\nTable 1: Summary of Layer parameters and elastic properties in the study area. \n\n\n\nLocation Name Number Latitude (0) Longitude (o) Elevation (m) Layer Vp (m/s) Vs (m/s) \n \n\n\n\nEtinan \n\n\n\n1 5.9833 7.8500 67.00 L1 285.0 128.8 2.2120 \n\n\n\n    L2 556.9 252.9 2.2019 \n\n\n\n2 4.9500 7.8333 61.00 L1 327.5 148.2 2.2093 \n\n\n\n    L2 503.4 228.5 2.2030 \n\n\n\n3 4.8333 7.8510 31.00 L1 317.0 143.4 2.2099 \n\n\n\n    L2 495.1 224.7 2.2032 \n\n\n\nNsit Ibom \n\n\n\n1 4.8166 7.8330 36.00 L1 350.0 158.5 2.2081 \n\n\n\n    L2 656.3 298.3 2.2004 \n\n\n\n2 4.8667 7.9167 46.00 L1 350.0 158.5 2.2081 \n\n\n\n    L2 603.7 274.3 2.2011 \n\n\n\n3 4.8510 7.9000 43.00 L1 291.0 131.6 2.2115 \n\n\n\n    L2 745.3 338.9 2.1993 \n\n\n\nNsit Ubium \n\n\n\n\n\n\n\n1 4.7833 7.9000 34.00 L1 285.5 129.1 2.2119 \n\n\n\n    L2 429.2 194.7 2.2051 \n\n\n\n2 4.7833 7.9166 49.00 L1 326.0 147.6 2.2094 \n\n\n\n    L2 519.9 236.0 2.2027 \n\n\n\n3 4.8167 7.9667 37.00 L1 269.0 121.5 2.2132 \n\n\n\n    L2 483.5 219.4 2.2035 \n\n\n\nIbesikpo \n\n\n\n1 4.8500 7.9667 49.00 L1 350.5 158.7 2.2081 \n\n\n\n    L2 621.0 282.1 2.2009 \n\n\n\n2 4.9000 7.9833 133.00 L1 218.0 98.3 2.2183 \n\n\n\n    L2 518.9 235.6 2.2027 \n\n\n\n3 4.9500 7.9667 72.00 L1 325.0 147.1 2.2094 \n\n\n\n    L2 507.5 230.4 2.2030 \n\n\n\nUruan \n\n\n\n1 4.9167 8.0167 52.00 L1 295.0 133.4 2.2113 \n\n\n\n    L2 653.1 296.8 2.2004 \n\n\n\n2 4.9167 8.0333 52.00 L1 334.5 151.4 2.2089 \n\n\n\n    L2 749.3 340.7 2.1993 \n\n\n\n3 4.9500 8.0000 57.00 L1 306.5 138.7 2.2105 \n\n\n\n    L2 509.4 231.2 2.2029 \n\n\n\nNsit Atai \n\n\n\n1 4.8667 8.0500 45.00 L1 313.0 141.6 2.2101 \n\n\n\n    L2 558.4 253.6 2.2019 \n\n\n\n2 4.8000 8.0667 37.00 L1 302.5 136.8 2.2108 \n\n\n\n    L2 501.3 227.5 2.2031 \n\n\n\n3 4..8333 8.0333 31.00 L1 303.0 137.1 2.2107 \n\n\n\n    L2 464.7 210.9 2.2040 \n\n\n\nUyo \n\n\n\n1 4.9833 8.0000 50.00 L1 372.5 168.8 2.2071 \n\n\n\n    L2 558.3 253.6 2.2019 \n\n\n\n2 5.0000 7.9500 82.00 L1 341.0 154.4 2.2086 \n\n\n\n    L2 533.8 242.4 2.2024 \n\n\n\n3 5.0333 7.9167 67.00 L1 319.5 144.6 2.2097 \n\n\n\n    L2 560.8 254.7 2.2019 \n\n\n\nItu \n\n\n\n1 5.0500 7.9167 65.00 L1 272.5 123.1 2.2129 \n\n\n\n    L2 569.9 258.9 2.2017 \n\n\n\n2 5.0667 7.9167 68.00 L1 301.0 136.1 2.2109 \n\n\n\n    L2 538.4 244.5 2.2023 \n\n\n\n3 5.1000 7.9500 57.00 L1 268.5 121.3 2.2132 \n\n\n\n    L2 614.7 279.3 2.2010 \n\n\n\nIbiono Ibom \n\n\n\n1 5.1833 7.9000 66.00 L1 237.5 107.2 2.2161 \n\n\n\n    L2 416.5 188.9 2.2055 \n\n\n\n2 5.1832 7.8667 63.00 L1 278.5 125.9 2.2124 \n\n\n\n    L2 485.2 220.2 2.2035 \n\n\n\n3 5.2000 7.8500 72.00 L1 331.0 149.8 2.2091 \n\n\n\n    L2 492.2 223.4 2.2033 \n\n\n\ns\n\n\n\nP\n\n\n\nV\n\n\n\nV\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 39-43 \n\n\n\n\n\n\n\n \nCite The Article: Akaninyene O. Akankpo, Ubong E. Essien, Magnus U. Igboekwe  (2023). Lithology Discrimination Using Shear and  \n\n\n\nCompressional Waves in Uyo and Its Environ, Southeastern Nigeria. Malaysian Journal of Geosciences, 7(1): 39-43. \n \n\n\n\nTable 2: Minimum, Maximum and Mean Values of The Parameters \n\n\n\nLayers \nVp (m/s) Vs (m/s) \n\n\n\n\ud835\udc7d\ud835\udc91\n\n\n\n\ud835\udc7d\ud835\udc94\n \n\n\n\nMinimum Maximum Mean Minimum Maximum Mean Minimum Maximum Mean \n\n\n\nL1 218.0 372.5 306.3 98.3 168.8 138.6 2.2071 2.2183 2.2108 \n\n\n\nL2 416.5 749.3 549.9 188.9 340.7 249.7 2.1993 2.2055 2.2023 \n\n\n\nFrom our results, the measured P-wave velocity is 218 m/s, far less than \n750 m/s while measures S-wave velocity in the area lies between 98 m/s \nand 340 m/s. Measured P-wave velocity in the area increased northward, \nespecially in parts of Nsit Atai and Etinan as shown in 3D contour \ndistributions of Figures 2. A similar trend is observed for measured S-wave \nvelocity in Uyo. The P- and S-wave velocities measured are relatively \nhigher than layer 1 velocities for both P- and S-waves. Due to bioturbation \nactivities and the artificial nature of the overburden, the surficial \nfoundation layers: 1 and 2 show variations in the mapped area. Measured \nP- and S- waves velocity values obtained indicate that both layers 1 and 2 \nare weathered zones characterized by fine and lateritic sands that can \nwithstand little or no mechanical load or stress applied on the surface. \n\n\n\nSince compressional wave velocity is strongly dependent on effective \nstress, the velocity in unconsolidated near surface soils (weathered layer) \nis affected by high porosity (less than 100 % water saturation), very low \ncementation, low effective pressure, low bulk modulus and therefore yield \nvery low values in weathered layers. For layers with these characteristics, \nVp obtained can be as low as 200 m/s in the unsaturated zone less than \nthe velocity of sound in air. Hence, as shown in the 3D contour maps of \nFigures 2 and 3, most of the heterogeneous weathered layers have \nvelocities less than that of air. The Vp-Vs ratios (lithology discriminator) \nestimated for layers 1 and 2 (Table 1) were used to generate the 3D \ncontour maps of Figure 5. In this study, the Vp-Vs ratio range of between \n2.2071 and 2.2183 for the first layer has been obtained with a mean value \nof 2.2108. For the second layer, the Vp-Vs ratio ranges from 2.1993 to \n2.2055 with a mean value of 2.2023 (Table 2). The near constant Vp-Vs \nratio of 2.2 estimated in the study area indicates a similarity in the \nformation grains in the shallow layers penetrated by seismic waves. \n\n\n\n\n\n\n\n\n\n\n\nFigure 4: The 3-D blanked contour map of layer 1   (a) and layer 2  \n(b) in the study area \n\n\n\n5.   CONCLUSION \n\n\n\nGeophysical measurements have been proven to be a useful and \neconomical tool in probing the subsurface and results obtained represent \nthe geological conditions of area of interest. In this study, seismic \nrefraction measurements have been employed to determine the P and S \nwave velocities of the topsoil of the weathered zone. Two layers were \npenetrated at the maximum spread of geophone. Based on the results and \ninterpretation of our refraction data, the real topsoil is near homogeneous \nand the lateral variations in the P-wave and S-wave velocities measured \ncan be attributed to land filling and bioturbation. The lithology \n\n\n\ndiscriminator ( \n\ud835\udc49\ud835\udc5d\n\n\n\n\ud835\udc49\ud835\udc60\n ) ratio varied from place to place with an average of \n\n\n\n2.7108 for layer 1 and ranged from 2.2071 to 2.2183. For layer 2, it average \nwas 2.202 and a range of 2.199 to 2.205, showing correlation with the \n\n\n\nnature of the topsoil. The value of the \n\ud835\udc49\ud835\udc5d\n\n\n\n\ud835\udc49\ud835\udc60\n ratio varied with the depth of \n\n\n\npenetration i.e the higher the value of \n\ud835\udc49\ud835\udc5d\n\n\n\n\ud835\udc49\ud835\udc60\n ratio, the higher the penetration \n\n\n\ndepth and conversely, the lower the value of \n\ud835\udc49\ud835\udc5d\n\n\n\n\ud835\udc49\ud835\udc60\n ratio the smaller the depth \n\n\n\nof penetration. \n\n\n\n REFERENCES \n\n\n\nBachrach, R., Dvorkin, J., and Nur, A., 1998. High-resolution Shallow-\nseismic Experiments in Sand, Part II: Velocities in shallow \nunconsolidated sand. Geophysics, 63 (4), Pp. 1234-1240. \n\n\n\nBaker, G.S., Steeple, D.W., and Schmissner, C., 1999. In-situ, High Frequency \nP-wave Velocity Measurement within 1m of the earth\u2019s surface. \nGeophysics, 64, Pp. 323-325. \n\n\n\nBosch, M., Guillen, A., and Ledru, P., 2001. Lithologic tomography: An \napplication to geophysical data from the Cadomian belt of northern \nBrittany, France: Tectonophysics, 331, Pp. 197\u2013227. \n\n\n\nBosch, M., Zamora, M., and Utama, W., 2002. Lithology Discrimination from \nPhysical rock Properties. Geophysics, 67 (2), Pp. 573-581. \n\n\n\nEmujakporue, G.O., and Ekine, A.S., 2009. Determination of Rocks Elastic \nConstants from Compressional and Shear Wave Velocities for \nWestern Niger Delta, Nigeria. Journal of Applied Science and \nEnvironmental Management, 13, Pp. 1-5. \n\n\n\nEssien, U.E., and Akankpo, A.O., 2013. Compressional and Shear Wave \nVelocity Measurement in Unconsolidated Topsoil in Eket, South-\neastern Nigeria. Pacific Journal of Science and Technology, 14 (1), \nPp. 476-491. \n\n\n\nEvans, U.F., George, N.J., Akpan, A.E., Obot, I.B. and Ikot, A.N., 2010. A Study \nof Superficial Sediments and Aquifers in Parts of Uyo Local \nGovernment Area, Akwa Ibom State, Southern Nigeria, using \nElectrical Sounding Method. E-Journal of Chemistry, 7 (3), Pp. \n1018\u20131022. \n\n\n\nFichtl, P., Fournier, F., and Royer, J.J., 1997. Cosimulation of lithofacies and \nassociated properties using well and seismic data: 72nd Ann. Tech. \nConf. and Exhib., Soc. Petrol. Eng., Proceedings, 1, Pp. 381\u2013393. \n\n\n\nGeorge, N.J., Akpan, A.E., and Obot, I.B., 2010a. Resistivity Study of Shallow \nAquifer in the Parts of Southern Ukanafun Local Government Area, \nAkwa Ibom State. E-Journal of Chemistry, 7 (3), Pp. 693\u2013700. \n\n\n\nGeorge, N.J., Obianwu, V.I., Akpan, A.E., and Obot, I.B., 2010b. Assessment \nof Shallow Aquiferous Units and Coefficients of Anisotropy in the \nCoastal Plain Sand of Southern Ukanafun Local Government Area, \nAkwa Ibom State, Southern Nigeria. Archives of Physics Research, 1 \n(2), Pp. 118-128. \n\n\n\nHorsfall, O.I., Omubo-Pepple, V.B., and Tamunobereton-ari, I., 2014. \nEstimation of Shear Wave Velocity for Lithological Variation in the \nNorthwestern Part of the Niger Delta Basin of Nigeri. American \nJournal of Scientific and Industrial Research, 5 (1), Pp. 13-22. \n\n\n\nS\n\n\n\nP\n\n\n\nV\n\n\n\nV\n\n\n\nS\n\n\n\nP\n\n\n\nV\n\n\n\nV\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 39-43 \n\n\n\n\n\n\n\n \nCite The Article: Akaninyene O. Akankpo, Ubong E. Essien, Magnus U. Igboekwe  (2023). Lithology Discrimination Using Shear and  \n\n\n\nCompressional Waves in Uyo and Its Environ, Southeastern Nigeria. Malaysian Journal of Geosciences, 7(1): 39-43. \n \n\n\n\nLortzer, G.J.M., and Berkhout, A.J., 1992. An Integrated Approach to \nLithologic Inversion, part I\u2014Theory: Geophysics, 57, Pp. 233\u2013244. \n\n\n\nNganje, T.N., Edet, A.E. and Ekwere, S.J., 2007. Concentrations of Heavy \nMetals and Hydrocarbons in Groundwater near Petrol Stations and \nMechanic Workshops in Calabar Metropolis, South-eastern Nigeria. \nJournal of Environmental Geosciences, 14 (1), Pp. 15-29. doi: \n10.1306/eg.08230505005. \n\n\n\nOgagarue, D.O., and Asor, V.E., 2010. Application of Cross Correlation for \nGlobal Velocity Quantity Control in Seismic Imaging. Australian \nJournal of Basic and Applied Sciences 4 (10), Pp. 4995-4999. \n\n\n\nPickett, G.R., 1963. Acoustic Character Logs and their Applications in \nFoundation Evaluation. Journal of Petroleum. Technology, 15, Pp. \n659-667. \n\n\n\nRafavich, F., Kendall, C.H., St. C., and Todd, T.P., 1984. The Relationship \n\n\n\nbetween Acoustic Properties and the Petrographic Character of \nCarbonate Rock. Geophysics, 49, Pp. 1622 \u2013 1636.  \n\n\n\nReijers, T.J.A., Petters, S.W., and Nwajide, C.S., 1997. The Niger Delta Basin, \nin Selley, R.C., ed., African Basins--Sedimentary Basin of the World \n3: Amsterdam, Elsevier Science, Pp. 151-172. \n\n\n\nTamunobereton-ari, I., Omubo-Pepple, V.B., and Uko, E.D., 2010. The \nInfluence of Lithology and Depth on Acoustic Velocities in South-\nEast Niger Delta, Nigeria. American Journal of Scientific and \nIndustrial Research, 1 (2), Pp. 279 \u2013 292. \n\n\n\nTathan, R.H., 1982. Vp/Vs and Lithology. Geophysics, 47 (3), Pp. 336 \u2013 344. \n\n\n\nTorres-Verdin, C., Victoria, M., Merletti, G., and Pendrel, J., 1999. Trace-\nBased and Geostatistical Inversion of 3-D Seismic Data for thin Sand \nDelineation: An application in San Jorge basin, Argentina. The \nLeading Edge, 18, Pp. 1070\u20131077. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 65-69 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2020.65.69 \n\n\n\nCite the Article: Chaanda M.S.  Alaminiokuma G.I. (2020). Hydrogeophysical Investigation For Groundwater Resource Potential In Masagamu, Magama Area, Fractured \nBasement Complex, North-Central Nigeria. Malaysian Journal of Geosciences, 4(2): 65-69. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2020.65.69\n\n\n\nHYDROGEOPHYSICAL INVESTIGATION FOR GROUNDWATER RESOURCE \n\n\n\nPOTENTIAL IN MASAGAMU, MAGAMA AREA, FRACTURED BASEMENT COMPLEX, \n\n\n\nNORTH-CENTRAL NIGERIA \n\n\n\nChaanda M.S.* Alaminiokuma G.I. \n\n\n\nDepartment of Earth Sciences, Federal University of Petroleum Resources Effurun P.M.B. 1221, Effurun, Nigeria \n\n\n\n*Corresponding Author Email: chaandamohammed@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 25 March 2020 \nAccepted 27 April 2020 \nAvailable online 15 May 2020\n\n\n\nVertical Electrical Sounding (VES) was conducted in Salbi farm in the fractured Basement Complex, North-\nCentral Nigeria to determine the groundwater resource potential to serve for agricultural purposes. Four VES \nstations using Schlumberger electrode configuration with a maximum current electrode spread of 300m were \nemployed for data acquisition. ZHODY software was employed in computing resistivities, depths and \nthicknesses of the various layers and curve types. Results indicate that the area is characterized by 3 distinct \ngeoelectric layers inferred differently at the VES locations. One potential groundwater aquifer zone was \ndelineated at VES 1, 2, and 4 within the fractured/weathered basement columns having depths ranging \nbetween 48.8 \u2013 59.60m and resistivities ranging between 213 - 513 \u2126m. These results suggest that boreholes \nfor sustainable groundwater supply in Salbi farm should be sited either at VES 1, 2 or 4 location and screened \nat a depth \u226560.0m. Wells to develop this resource should be drilled to an effective depth of 40 to 60 m for \noptimum yields. It is recommended that pumping test be done in order to further determine the aquifer \nefficiency and productivity in the area. However, the aquifers at these locations have potentials for \ngroundwater but may be vulnerable to contamination.  \n\n\n\nKEYWORDS \n\n\n\nVertical Electrical Sounding, Aquifer, Salbi Farm, Fractured Basement Complex, Masagamu.\n\n\n\n1. INTRODUCTION \n\n\n\nInvestigating the groundwater resource potential to meet the need of good \n\n\n\nwater supply for farming activities in Masagamu, Magama Local \n\n\n\nGovernment Area, Niger State, North-Central Nigeria is very essential in \n\n\n\nthe absence of natural water supply such as lakes, basins, streams and \n\n\n\nrivers or public water utility system. Masagamu is witnessing rapid \n\n\n\nincrease in farming activities. There is a shift from subsistence to \n\n\n\ncommercial farming in this area leading to high demands for potable water \n\n\n\nto serve the agricultural activities. As at the time of this survey, the source \n\n\n\nof water supply to the area is a non-motorized borehole in Salbi farm \n\n\n\nwhich is of moderate yield and may not meet the rising demand. The aim \n\n\n\nof this research which includes reconnaissance survey followed by \n\n\n\ngeological/hydrogeophysical survey is to locate geological structures that \n\n\n\nare associated with groundwater that could lead to sinking of well or \n\n\n\nborehole. The objective is to determine thicknesses, depths, resistivities \n\n\n\nand lithologies of different rock types that constitute viable aquifers using \n\n\n\nthe VES method.  \n\n\n\nHydrologically, groundwater within the Basement Complex is found within \n\n\n\nfixtures such as cracks, joints and weathered overburden which are \n\n\n\nmajorly dependent on rain water recharge, and other sources such as \n\n\n\nrivers, lakes or basins. These fixtures are necessitated majorly by chemical \n\n\n\nweathering and tectonic activities; which could be deeply buried. But in the \n\n\n\ninstance of a deeper weathered overburden, it provides good condition for \n\n\n\ngroundwater occurrences. Electrical resistivity survey provides a suitable \n\n\n\nmethod to easily access groundwater in these geological structures. \n\n\n\nVertical electrical sounding (VES) which is an electrical resistivity \n\n\n\ntechnique for measuring vertical variations of electrical resistance in the \n\n\n\nground, has proven to be more convenient for hydrogeophysical and \n\n\n\nhydrogeological surveys in the Basement Complex. \n\n\n\nVarious researches have been conducted in search of prolific aquifer in the \n\n\n\nbasement complex of Northern Nigeria. Alaminiokuma and Chaanda, \n\n\n\n2020, employed the vertical electrical sounding technique to explore for \n\n\n\nthe groundwater potential in Mando, located within the Crystalline \n\n\n\nBasement Complex of Nigeria. Results show that the area is characterized \n\n\n\nby four to five geoelectric subsurface layers inferred differently at the VES \n\n\n\ntraverses. An unconfined shallow aquifer zone is delineated. This potential \n\n\n\ngroundwater aquifer zone found at all the VES locations has shallow \n\n\n\noverburden depth ranging between 7.1\u201310.9m with coarse-grained sand \n\n\n\ncolumns having thicknesses ranging between 6.0\u20139.6m. These results \n\n\n\nsuggest that groundwater occurrence in Mando lies within the weathered \n\n\n\noverburden (WO) composed of coarse-grained sands which forms a level \n\n\n\nbelow the loose clayey laterite. These WO consist of sands or gravels \n\n\n\nderived from the weathering of the crystalline rocks. Based on these \n\n\n\nresults, it is suggested that boreholes for sustainable groundwater supply \n\n\n\nin the study area should be drilled to a depth of about 10.0m. Bawallah et. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 65-69 \n\n\n\nCite the Article: Chaanda M.S.  Alaminiokuma G.I. (2020). Hydrogeophysical Investigation For Groundwater Resource Potential In Masagamu, Magama Area, Fractured \nBasement Complex, North-Central Nigeria. Malaysian Journal of Geosciences, 4(2): 65-69. \n\n\n\nal., 2019 investigated aquifer in order to determine its groundwater \n\n\n\npotential in Camic Garden Estate, Ilorin Metropolis, North-Central \n\n\n\nBasement Complex of Nigeria. Very Low Frequency-Electromagnetic \n\n\n\n(VLF-EM) and Ground Magnetics (GM)were used for structural \n\n\n\nevaluation/delineation combined with Electrical Resistivity Method \n\n\n\n(ERM)using the Vertical Electrical Sounding (VES) Technique were \n\n\n\nemployed to produce results which showed predominant three-layer \n\n\n\nearth model: top soil, clayey/weathered layer and fresh basement. The \n\n\n\nclayey/weathered layer constitutes the major auriferous unit in the area, \n\n\n\nand are characterized by moderately low resistivity value which ranged \n\n\n\nbetween 23.00 and 200.00 \u03a9m while the thickness varies from 13.2 to 61.0 \n\n\n\nm. The study reveals that 83% of the study area may be of low water \n\n\n\nbearing/yield owing to the thick clayey column that characterized the \n\n\n\nweathered layer without fractured basement. Markus et. al. 2018 \n\n\n\ninvestigated of groundwater potential of part of Rafin-Yashi, Minna, North \n\n\n\nCentral, Nigeria using Electrical Resistivity Profiling (ERP) and Vertical \n\n\n\nElectrical Sounding (VES). Results revealed about 95% H- type curve and \n\n\n\n5% K- type curve with three distinct geoelectric layers namely: the top soil, \n\n\n\nweathered/fractured basement and the fresh basement. The apparent \n\n\n\nresistivity of the first layer ranged between 18.5 \u2013 706.6\u03a9m with a \n\n\n\ncorresponding thickness of 1.1 m \u2013 4.8m, second layer has apparent \n\n\n\nresistivity values of 16.8 \u2013 591.6\u03a9m with corresponding thickness of 4.8 \u2013 \n\n\n\n15.3m and the third layer has apparent resistivity values of 19.2 \u2013 \n\n\n\n7299.1\u03a9m with an infinite thickness. Bulus et. al., 2017 studied the porous \n\n\n\nzones of Kwal and its environs employing Forty (40) vertical electrical \n\n\n\nsounding data using the Schlumberger configuration in the study area. \n\n\n\nResults revealed three geo-electric layers with varying thickness and \n\n\n\nlevels of weathering. The three geo-electric layers were interpreted to be \n\n\n\nclayey sand, weathered basement with resistivity values ranging between \n\n\n\n18.9 to 498.2\u03a9m indicating porous zones due to secondary porosity or \n\n\n\nwater content while the third layer is interpreted to be fresh basement, \n\n\n\nthis layer revealed high resistivity values that ranges between 521.6 and \n\n\n\n6148.2\u03a9m. \n\n\n\nIn view of the serious demand for water for domestic and agricultural \n\n\n\npurposes in Masagamu, this research provides the basis for determining \n\n\n\nprolific aquifers for siting boreholes. When such boreholes are \n\n\n\nsatisfactorily drilled and completed, they will be greatly utilized for \n\n\n\nfarming purpose. \n\n\n\n2. GEOLOGICAL FRAMEWORK \n\n\n\nNigerian portion of the Precambrian Trans-Saharan Pan-African orogeny \n\n\n\nlie within the mobile belt which separated the West African and Congo \n\n\n\nCratons. It is related to A\u00efr, Hoggar, Cameroun and Borborema Pan-African \n\n\n\n(Brasiliano) regions (Ferre et al., 1998). The total area of Nigeria is \n\n\n\ncovered is nearly of the same ratio by basement and sedimentary \n\n\n\nlithologies (Rahaman, 1988). The Basement rocks are divided into \n\n\n\nBasement Complex, Younger Granites and Tertiary-Recent volcanic rocks \n\n\n\n(Kogbe, 1989). The Nigerian Precambrian Basement Complex is made up \n\n\n\nof the Migmatite - Gneiss complex, Schist belts and Older Granites with the \n\n\n\nlargest area of Basement Complex in north-central Nigeria [Figure 1] \n\n\n\n(Obaje et al., 2006; Obaje 2009, Ajibade et al., 2008). \n\n\n\nFigure 1: Geological Map of Nigeria (Modified from Obaje, 2009) \n\n\n\n3. LOCATION & ACESSIBILITY, PHYSIOGRAPHY, DRAINAGE,\n\n\n\nVEGETATION AND CLIMATE OF THE STUDY AREA \n\n\n\n3.1 Location and Accessibility \n\n\n\nThe area under investigation is the Salbi farm located at Masagamu in \n\n\n\nMagama Local Government Area of Niger State. The farm is accessible by \n\n\n\na tarred road (Kontagora- Kebbi road) (Figure 1). \n\n\n\nFigure 2: Map showing the Location of the Study Area \n\n\n\n3.2 Physiography, Drainage Vegetation and Climate \n\n\n\nThe study area is located on a relatively flat to undulating land. Drainages \n\n\n\nare structurally controlled with some rugged terrain to the east and \n\n\n\nsouthern part of the study area.  Ajibade et al. (2008) noted that these \n\n\n\nwater channels in the area are mostly controlled by lithologic and \n\n\n\nstructural units. The vegetation is of typical Guinea Savannah mosaic zone \n\n\n\nof the West African Sub-region (Ileoje, 1981) which is characterized with \n\n\n\ntall grasses and scattered trees. This comprises shrubs, thorny trees and \n\n\n\nother trees of moderate to high heights as well as different species of tall \n\n\n\nand short grasses. Examples include locust beans/mango trees, \n\n\n\nelephant/carpet grasses among others. The Weather/climate condition of \n\n\n\nthe area is simply considered as steppe climate (NIMET, 2018). The \n\n\n\naverage annual temperature ranged from 28 oC to 31oC classified as BSh \n\n\n\n(Kotten et al., 2006) while the annual average precipitation rages from \n\n\n\n1270mm to 1524mm (Ileoje, 1981), with least amount of rainfall in \n\n\n\nJanuary and highest precipitation of about 230mm in July/August every \n\n\n\nyear.  \n\n\n\n4. METHODOLOGY \n\n\n\n4.1 Field Data Acquisition \n\n\n\nOhmega Digital Terrameter was employed in acquiring the Vertical \n\n\n\nElectrical Sounding data along 4 traverses (Figure 3).  \n\n\n\nFigure 3: Map showing the 4 VES traverses in the study area \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 65-69 \n\n\n\nCite the Article: Chaanda M.S.  Alaminiokuma G.I. (2020). Hydrogeophysical Investigation For Groundwater Resource Potential In Masagamu, Magama Area, Fractured \nBasement Complex, North-Central Nigeria. Malaysian Journal of Geosciences, 4(2): 65-69. \n\n\n\nSchlumberger configuration (Figure 4) with a maximum current \n\n\n\nelectrodes\u2019 separation of 300m was employed to achieve a penetration \n\n\n\ndepth of 120m. Two current electrodes were placed linearly at the same \n\n\n\nmid-point with two potential electrodes but at different distances from \n\n\n\none another. The current electrodes were placed at equal distance, s from \n\n\n\nthe mid-point of the array while the potential electrodes were similarly \n\n\n\nplaced at equal distance but at a/2 < s. Different spreads of current \n\n\n\nelectrodes, AB were achieved, thereby resulting in different probe depths.  \n\n\n\nFigure 4: Schematic of Schlumberger Array for Data Acquisition \n\n\n\n4.2 Computation of Soil Apparent Resistivity, \u03c1a \n\n\n\nApparent resistivities were obtained from field resistance values using the \n\n\n\nequation: \n\n\n\n\ud835\udf0c\ud835\udc4e =\n2\ud835\udf0b\ud835\udc45\n\n\n\n\ud835\udc58\n  (1) \n\n\n\nWhere \u03c1a is apparent resistivity, R is the measured resistance, AB= \n\n\n\nDistance between current electrodes, MN=Distance between potential \n\n\n\nelectrodes and the geometric factor, K is given as: \n\n\n\n  (2) \n\n\n\nTherefore, \u03c1a becomes: \n\n\n\n\ud835\udf0c\ud835\udc4e =\n2\ud835\udf0b\ud835\udc45\n\n\n\n[\n1\n\n\n\n\ud835\udc34\ud835\udc40\n\u2212\n\n\n\n1\n\n\n\n\ud835\udc34\ud835\udc41\n\u2212\n\n\n\n1\n\n\n\n\ud835\udc35\ud835\udc40\n+\n\n\n\n1\n\n\n\n\ud835\udc35\ud835\udc41\n]\n\n\n\n (3) \n\n\n\n5. DATA INTERPRETATION \n\n\n\nThe apparent resistivity, \u03c1a values were fitted against half current \n\n\n\nelectrode spread, AB/2 employing the ZHODY software. The resistivities, \n\n\n\nthicknesses and depth of the various layers were computed and the curve \n\n\n\ntypes were determined. \n\n\n\n6. RESULTS \n\n\n\nFigures 5 - 8 show the geoelectric sections for the four VES stations. \n\n\n\nGenerally, the AK type curve was observed in the study area. Figure 8 \n\n\n\nshows the lithologic cross section for the study area. \n\n\n\nFigure 5: Geoelectric Section for \n\n\n\nVES 1 \n\n\n\nFigure 6: Geoelectric Section for \n\n\n\nVES 2 \n\n\n\nFigure 7: Geoelectric Section for \n\n\n\nVES 3 \n\n\n\nFigure 8: Geoelectric Section for \n\n\n\nVES \n\n\n\nTable 4 is a summary of the interpretation of the results of the Vertical \n\n\n\nElectrical Sounding in the study area while Table 5 is the lithologic cross-\n\n\n\nsection for the study area. The results show that the area is characterized \n\n\n\nby four geoelectric subsurface layers. \n\n\n\nTable 4: VES Data Interpretation Results in the Study Area \n\n\n\nSounding \n\n\n\nLocations \n\n\n\nGeoelectric \n\n\n\nLayers \n\n\n\nResistivity, \n\n\n\n\u03c1(\u2126m) \n\n\n\nDepth, \n\n\n\nD(m) \n\n\n\nThickness, \n\n\n\nh(m) \n\n\n\nInferred \n\n\n\nLithostrata \n\n\n\nCurve \n\n\n\nType \n\n\n\nVES 1 \n\n\n\nLong: 5013\u201921.27\u201d  \n\n\n\nLat: 10027\u201923.83\u201d \n\n\n\nI 18 \u2013 56 0 \u2013 11.56 11.56 Weathered overburden \n\n\n\nAK II 131 -738 \n11.56 \u2013 \n\n\n\n53.64 \n42.08 \n\n\n\nWeathered basement, harden with \n\n\n\ndepth \n\n\n\nIII 513 53.64 - \u221e - Weathered/fractured Basement \n\n\n\nVES 2 \n\n\n\nLong: 5013\u201922.69\u201d  \n\n\n\nLat: 10027\u201923.44\u201d \n\n\n\nI 48 \u2013 164 0 \u2013 5.36 5.36 Weathered overburden \n\n\n\nAK II 188 \u2013 502 \n5.36 \u2013 \n\n\n\n53.64 \n48.28 Weathered/fractured basement \n\n\n\nIII 287 53.64 - \u221e - Weathered/fractured Basement \n\n\n\nVES 3 \n\n\n\nLong: 5013\u201916.79\u201d  \n\n\n\nLat: 10027\u201917.60\u201d \n\n\n\nI 5 \u2013 10 0 \u2013 2.24 2.24 Topsoil \n\n\n\nAK \nII 17 \u2013 266 \n\n\n\n2.24 \u2013 \n\n\n\n48.28 \n46.04 Weathered basement \n\n\n\nIII 800 48.28 - \u221e - \nWeathered Basement, harden with \n\n\n\ndepth \n\n\n\nVES 4 \n\n\n\nLong: 5013\u201923.08\u201d  \n\n\n\nLat: 10027\u201915.72\u201d \n\n\n\nI 22 \u2013 421 0 \u2013 18.85 18.85 Weathered overburden \n\n\n\nAK II 135 \u2013 222 \n18.85 \u2013 \n\n\n\n59.60 \n40.75 Weathered basement \n\n\n\nIII 213 59.60 - \u221e - Weathered/fractured Basement \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 65-69 \n\n\n\nCite the Article: Chaanda M.S.  Alaminiokuma G.I. (2020). Hydrogeophysical Investigation For Groundwater Resource Potential In Masagamu, Magama Area, Fractured \nBasement Complex, North-Central Nigeria. Malaysian Journal of Geosciences, 4(2): 65-69. \n\n\n\nTable 5: Lithologic cross section for the study area \n\n\n\nVES 1 Section \n\n\n\nDepth Thickness Description of lithology Infered Lithostrata \n\n\n\n0 \u2013 11.56 11.56 Weathered overburden \n\n\n\n11.56 \u2013 53.64 42.08 Weathered basement, harden with depth \n\n\n\n53.64 - \u221e - Weathered/fractured Basement \n\n\n\nVES 2 Section \n\n\n\nDepth Thickness Description of lithology Infered Lithostrata \n\n\n\n0 \u2013 5.36 5.36 Weathered overburden \n\n\n\n5.36 \u2013 53.64 48.28 Weathered/fractured basement \n\n\n\n53.64 - \u221e - Weathered/fractured Basement \n\n\n\nVES 3 Section \n\n\n\nDepth Thickness Description of lithology Infered Lithostrata \n\n\n\n0 \u2013 2.24 2.24 Topsoil \n\n\n\n2.24 \u2013 48.28 46.04 Weathered basement \n\n\n\n48.28 - \u221e - Weathered Basement, harden with depth \n\n\n\nVES 4 Section \n\n\n\nDepth Thickness Description of lithology Infered Lithostrata \n\n\n\n0 \u2013 18.85 18.85 Weathered overburden \n\n\n\n18.85 \u2013 59.60 40.75 Weathered basement \n\n\n\n59.60 - \u221e - Weathered/fractured Basement \n\n\n\nKey/Legend \n\n\n\nTop Soil \n\n\n\nWeathered overburden \n\n\n\nWeathered Basement \n\n\n\nWeathered/Fractured Basement \n\n\n\n7. DISCUSSION\n\n\n\nVES 1: Three geoelectric layers of AK curve type are delineated at this \n\n\n\nlocation. Inferred lithologies are characterized by an 11.56m thick \n\n\n\nweathered overburden with resistivity between 15.0 - 56.0 \u2126m to a depth \n\n\n\nof 11.56m. Below this formation is a 42.08m thick weathered basement \n\n\n\nhardened to a depth of 5.64m. This has a resistivity value between 131-\n\n\n\n738\u2126m. Following the hardened weathered basement is the \n\n\n\nweathered/fractured basement with resistivity value of 513\u2126m with \n\n\n\nundetermined thickness and depth since it makes up the last layer. These \n\n\n\nlayers are probable water-bearing geological structures. \n\n\n\nVES 2: Three geoelectric layers of AK curve type are delineated at this \n\n\n\nlocation. The 5.6m thick top layer to a depth of 5.6m is characterized by \n\n\n\nweathered overburden materials with resistivity between 48 \u2013 164\u2126m. \n\n\n\nUnderlying this layer is a 48.28m thick weathered/fractured basement to \n\n\n\na depth of 53.64m with resistivity between 188 \u2013 502\u2126m. Below this zone \n\n\n\nis also a layer of weathered/fractured basement with resistivity of 287\u2126m \n\n\n\nand undetermined thickness and depth. Layer I is probable water-bearing \n\n\n\nwhile Layers II and III are probable aquiferous geological structures. \n\n\n\nVES 3: Three geoelectric layers of AK curve type are delineated at this \n\n\n\nlocation. Soil layers here are characterized by a porous and permeable \n\n\n\n2.24m thick topsoil with resistivity between 5.0 \u2013 10.0 \u2126m and depth of \n\n\n\n2.24m. Below this layer is a 46.04m thick weathered basement to a depth \n\n\n\nof 48.28m. This is zone has resistivity value between 17.0 \u2013 266.0\u2126m. \n\n\n\nBelow this zone is a layer of weathered basement hardened with depth \n\n\n\nwith resistivity of 800.0\u2126m and undetermined thickness and depth. Layer \n\n\n\nI is dry top soil, Layer II is probably aquiferous while Layer III is probably \n\n\n\nwater-bearing geological structures. \n\n\n\nVES 4: Three geoelectric layers of AK curve type are delineated at this \n\n\n\nlocation. Lithologies here are characterized by an 18.85m thick weathered \n\n\n\noverburden with resistivity between 22.0 \u2013 421.0\u2126m to a depth of 18.85m. \n\n\n\nUnderlying this layer is a 40.75m thick weathered basement to a depth of \n\n\n\n59.60m. This has resistivity value between 135.0 \u2013 222.0\u2126m. Beneath this \n\n\n\nzone is a layer of weathered/fractured basement with resistivity of 213\u2126m \n\n\n\nand undetermined thickness and depth. Layer I is water-bearing, Layer II \n\n\n\nis probably water-bearing while Layer III is probably aquiferos. \n\n\n\n8. CONCLUSION \n\n\n\nThe hydrogeophysical investigations conducted in Salbi farm in \n\n\n\nMasagamu, Magama area, North-Central Nigeria delineated the presence \n\n\n\nof three subsurface geoelectric layers which are the top soil, weathered \n\n\n\noverburden and weathered/fractured basement. These layers correspond \n\n\n\nto the AK-type curve which is a characteristic of the basement complex \n\n\n\nenvironment. This research also revealed the presence of possible aquifers \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 65-69 \n\n\n\nCite the Article: Chaanda M.S.  Alaminiokuma G.I. (2020). Hydrogeophysical Investigation For Groundwater Resource Potential In Masagamu, Magama Area, Fractured \nBasement Complex, North-Central Nigeria. Malaysian Journal of Geosciences, 4(2): 65-69. \n\n\n\nfor groundwater storage in the area.  The study area is predicted to have \n\n\n\nmoderate to good groundwater potential, and this is supported by the \n\n\n\noccurrences and concentration of fractures which can constitute \n\n\n\nweathered/fractured aquifers around these regions.  \n\n\n\n9. RECOMMENDATION \n\n\n\nGroundwater exploitation in the study area should be conducted at VES \n\n\n\nstations I, II and IV as they have higher concentration of fractures, and \n\n\n\nhence possibility of weathered/fractured basement aquifer. The wells to \n\n\n\ndevelop this resource should be drilled to an effective depth of 40 to 60 m \n\n\n\nfor optimum yields. It is also recommended that pumping test be carried \n\n\n\nout on the drilled wells in order to further determine the aquifer efficiency \n\n\n\nand productivity in the area. \n\n\n\nREFERENCES \n\n\n\nAjibade, A.C., Anyanwu, N.P.C., Okoro, A.U., and Nwajide, C.S., 2008. The \n\n\n\nGeology of Minna Area: Explanation of 1:250,000 Sheet 42 (Minna), \n\n\n\nNigeria Geological Survey Agency, Kaduna, Bulletin, No. 43, 112.    \n\n\n\nAlaminiokuma, G.I. and Chaanda, M.S., 2020. Groundwater Potential of \n\n\n\nMando, Kaduna, Crystalline Basement Complex, Nigeria, Journal of Earth \n\n\n\nSciences and Geotechnical Engineering, 10(2). 15-26. \n\n\n\nBawallah, M. A., Aina, A.O., Ozegin, K.O., Akeredolu, B.E., Bamigboye, O.S., \n\n\n\nOlasunkanmi, N.K., Oyedele, A.A., 2019. Integrated Geophysical \n\n\n\nInvestigation of Aquifer and Its Groundwater Potential in Camic Garden \n\n\n\nEstate, Ilorin Metropolis North-Central Basement Complex of Nigeria. \n\n\n\nJAGG, 7(2), 01-08. \n\n\n\nBulus, J.A., Goyit, M.P., Aluwong, K.C., Bala, D.A., and Nimchak, R.N., 2017. \n\n\n\nDetermination of Porous Zones Using Vertical Electrical Sounding Data \n\n\n\nfrom Basement Rocks of Kwal, Kanke, North-Central Nigeria. American \n\n\n\nJournal of Engineering Research, 6(5), 31-34.  \n\n\n\nFerre, E., Caby, R., Peucatt, J.J., Capdevila, R., and Monie, P., 1998. Pan-\n\n\n\nAfrican Postcollisional Ferro-Potassic Granite and Quartz-Monzonite \n\n\n\nPlutons of Eastern Nigeria. Lithos, 45, 255-278.  \n\n\n\nMarkus, U.I., Udensi, E.E., Mufutau, O.J., and Mannir, M., 2018. Geoelectric \n\n\n\nInvestigation of Groundwater Potential of Part of Rafin-Yashi, Minna, \n\n\n\nNorth Central, Nigeria. Am. J. Innov. Res. Appl. Sci. 6(1): 58-66. \n\n\n\nIloeje, N.P., 198). A New Geography of Nigeria. Longman, London, 221p. \n\n\n\nNIMET, 2017. First Quarter Weather Bulletin for 2017. Nigerian \n\n\n\nMeteorological Agency.  \n\n\n\nObaje, N.G., 2009. Geology and Mineral Resources of Nigeria. Springer-\n\n\n\nVerlag, Berlin, 218p. \n\n\n\nObaje, N.G., Lar, U.A., Nzegbuna, A.I., Moumouni, A., Chaanda, M.S., and \n\n\n\nGoki, N.G., 2006. Geology and Mineral Resources of Nasarawa State: an \n\n\n\nInvestors\u2019 Guide. Nasara Scientifique, 2, 1-34. \n\n\n\nRahaman, M.A., 1988. Recent advances in the Study of the Basement \n\n\n\nComplex of Nigeria. In: P.O. Oluyide, W.C. Mbonu, A.E. Ogezi, I.G. \nEgbuniwe, A.C. Ajibade and A.C. Umeji (eds.). Precambrian Geology of \nNigeria, Geological Survey of Nigeria, Kaduna, 315-319. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 32-37 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2020.32.37 \n\n\n\nCite the Article: Akpan Emmanuel F., Akpan Veronica M., Inyang Udeme U. (2020). Geoelectrical Investigation Of Groundwater Quality Through Estimates Of Total \nDissolved Solids And Electrical Conductivity In Parts Of Akwa Ibom State, Southern Nigeria . Malaysian Journal of Geosciences, 4(1): 32-37. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2020.32.37 \n\n\n\nGEOELECTRICAL INVESTIGATION OF GROUNDWATER QUALITY THROUGH \n\n\n\nESTIMATES OF TOTAL DISSOLVED SOLIDS AND ELECTRICAL CONDUCTIVITY IN \n\n\n\nPARTS OF AKWA IBOM STATE, SOUTHERN NIGERIA \n\n\n\nAkpan Emmanuel F*., Akpan Veronica M., Inyang Udeme U. \n\n\n\nDepartment of science Technology, Akwa Ibom State Polytechnic, Ikot Osurua, Ikot Ekpene Local Government Area, Nigeria. \n*Corresponding author\u2019s email: nyaknojimmyg@gmail.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 15 May 2020 \nAccepted 19 June 2020 \nAvailable online 13 July 2020\n\n\n\nThis paper presents the results of a geophysical investigation of groundwater quality in parts of Akwa Ibom \nState, Southern Nigeria. A total of 11 vertical electrical soundings (VES) was carried out in the study area \nusing the Schlumberger electrode configuration. The results of the interpretation show that the area \ncomprises 4 geoelectric layers. The third layer constitutes the major economic hydrogeological unit in the \narea and has resistivity of between 50.3 \u2126m and 2088.9 \u2126m and thickness of between 36.8 m and 149.0m \nrespectively. The groundwater quality was assessed through estimates of the electrical conductivity and \ntotal dissolved solids in water. The conductivity ranges from 74.5 to 604.4\u00b5S/cm with an average value of \n244.0\u00b5S/cm while the TDS values range from 47.7 to 386.8 ppm with an average value of 156.1 ppm. Based \non these values, which are within the permissible limits, the water is considered to be fresh and suitable for \ndrinking and other domestic/agricultural usages. The results show excellent correlation between the \nestimated TDS and the Dar-zarrouk parameters (longitudinal conductance and transverse resistance) on \none hand and the aquifer bulk resistivity on the hand which demonstrate the ease of deriving TDS from \nsurface resistivity data.  \n\n\n\nKEYWORDS \n\n\n\nAquifers, resistivity, water quality, VES, contaminations.\n\n\n\n1. INTRODUCTION \n\n\n\nWater is a good solvent and picks up impurities easily.  Pure water is \ntasteless, colourless, and odourless and is often regarded as a universal \nsolvent. Dissolved solids refer to any minerals, salts, metals, cations or \nanions dissolved in water. Total dissolved solids (TDS) comprise inorganic \nsalts (principally calcium, magnesium, potassium, sodium, bicarbonates, \nchlorides, and sulphates) and some small amounts of organic matter that \nare dissolved in water. TDS in drinking water originates from natural \nsources, sewage, urban run-off, industrial wastewater, and chemicals used \nin the water treatment process, and the nature of the piping or plumbing. \nThe resultant effects of these factors are unwanted taste, odour and \ncolouration of water (Freeze and Cherry, 1979). Thus, the total dissolved \nsolids test is used as an indicator test to determine the general quality of \nthe water.  \n\n\n\nWater is very fundamental to life. Groundwater, a major source of potable \nwater used today for domestic, industrial and agricultural purposes is \noften contaminated by many soluble chemicals (Oladunjoye, et al., 2011). \nVariations in the concentrations of these chemical constituents in \ngroundwater, which may be natural or due to human activities, determine \nthe geochemical character of water and thus, may affect its quality (Gowd, \n2005; Odeyemi et al., 2011). One of the major challenges of the 21st century \nis that of ensuring potable water in adequate amounts to meet the needs \nof the growing human population. Obtaining useful information on \n\n\n\ngroundwater quality of an area is necessary for managing and sustaining \nthe geo-resource to meet man\u2019s increasing water needs.  \n\n\n\nThe resistivity and hence, conductivity of water is directly related to the \namount of total dissolved solids (TDS) in the water. TDS in general, could \nbe determined from electrical conductivity EC (Salufu and Akhirevbulu, \n2015). Electrical conductivity and TDS are two physical parameters \ncommonly used to assess the quality of water (Aweto, 2013; Rusydi, \n2018). EC is very easy to measure by the use of a portable conductivity \nmeter. However, measurements of TDS are expensive and more difficult \nbecause of their requirement of specialized equipment and time \nconsuming (Rice et al., 2017). Electrical resistivity methods have been \nused for many years for groundwater studies (Bello and Makinde 2007, \nYilmaz, and Koc, 2014; George et al., 2015; George et al., 2016). The \nVertical electrical sounding technique (VES) for instance is sensitive to \nvariations in electrical resistivity with depth and could be used to \ndetermine the aquifer bulk resistivity. Relationships exist for estimation of \nwater resistivity from the aquifer bulk resistivity, from where the \nelectrical conductivity could be estimated (Ibanga and George 2016; \nEkanem, et al., 2019). Thus, the surface resistivity method can be used for \nthe assessment of groundwater quality. \n\n\n\nPure water has an electrical conductivity of nearly zero. Allowable limits \nof TDS and EC, according to the world health organization are 500ppm and \n400\u00b5S/cm respectively (USEPA., 2002; WHO, 2004). TDS levels greater \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 32-37 \n\n\n\nCite the Article: Akpan Emmanuel F., Akpan Veronica M., Inyang Udeme U. (2020). Geoelectrical Investigation Of Groundwater Quality Through Estimates Of Total \nDissolved Solids And Electrical Conductivity In Parts Of Akwa Ibom State, Southern Nigeria . Malaysian Journal of Geosciences, 4(1): 32-37. \n\n\n\nthan the above limits can have an effect on water taste and usually are \nindicative of high hardness or alkalinity (Wetzel, 2001). High levels of TDS \nin water alone are not indicative of health hazard. Nonetheless, high levels \nof certain ions which are included in the TDS, for example copper, nitrates, \naluminium, lead or arsenic might be life threatening (Kurl et al., 2019). The \naquifers in the study area are usually exploited without proper assessment \nof the groundwater quality as most of the boreholes in the area were \ncompleted without any geochemical analyses to ascertain the water \nquality. Consequently, this study was aimed at using the surface resistivity \nmethod to determine the electrical conductivity and hence, the total \ndissolved solids (TDS) in groundwater in parts of Akwa Ibom State. These \nparameters are useful in assessing the groundwater quality of the aquifers \nin the area. \n\n\n\n2. LOCATION OF STUDY AREA AND ITS GEOLOGY \n\n\n\nThe study area comprises three Local Government areas in the Southern \npart of Akwa Ibom State. These local government areas are Onna, Ikot \nAbasi and Eastern Obolo (Figure 1). The area lies between latitudes 4\u00b032\u2032N \nand 5\u00b000\u2032N and longitudes 7\u00b025\u2032E and 8\u00b025\u2032E in the Niger Delta region of \nSouthern Nigeria. The area has two main seasons, which are the rainy and \nthe dry seasons respectively. The former usually starts from around March \nand ends around October while the later usually spans from around \nNovember to February (George et al., 2014). However, slight changes \nsometimes occur in the upper and lower limits of these seasons as a result \nof global climatic changes (George et al., 2014).  \n\n\n\nGeologically, the study area is located in the Tertiary to Quaternary Coastal \nPlain Sands (CPS) (also known as the Benin Formation) and alluvial \nenvironments of the Niger Delta region of Southern Nigeria. The Benin \nFormation overlays the paralic Agbada Formation and covers up to 80% \nof the study area (George et al., 2014). The sediments of the Benin \nFormation comprise inter fringing units of lacustrine and fluvial loose \nsands, pebbles, clays, and lignite streaks of diverse thicknesses, while the \nalluvial units comprise tidal and lagoonal sedimentary sands, beach sands \nand soils which are mostly found in the Southern parts and along the river \nbanks. The CPS constitutes the main aquifer units in the study area and \ncomprises poorly sorted continental sands (fine, medium, and coarse) and \ngravels that alternate with lignite streaks, thin clay horizons, and lenses at \nseveral locations. The thin clay/shale horizons truncate the vertical and \nlateral extents of the sandy aquifers and in this way, build up multi-aquifer \nsystems in the area (George et al., 2011, George, 2020).  \n\n\n\nFigure 1: Location map of the study area showing its Geology and VES \n\n\n\npoints \n\n\n\n3. BACKGROUND OF STUDY \n\n\n\n3.1 Total dissolved solids (TDS) and electrical conductivity (EC)  \n\n\n\nThe resistivity and hence, conductivity of water is directly related to the \namount of total dissolved solids (TDS) in the water. TDS is the total amount \nof dissolved material present in water.  It typically includes predominantly \ndissolved mineral ions such as sodium, chloride, potassium, calcium and \nmagnesium, chloride, sulphate, nitrate and bicarbonate. TDS equally \ninclude other inorganic ions, dissolved organic material, and non-ionic \nmatter such as dissolved silica. Although a relatively small amount of the \nTDS includes non-ionic matter, which carry no electrical charge, water \nwith high values of TDS usually have lower values of resistivity or higher \nvalues of conductivity. For instance, water with a high concentration of \ndissolved salts will have a low resistivity or high conductivity.  \n\n\n\nThe major application of TDS is in the area of assessment of groundwater \nquality (Salufu and Akhirevbulu, 2015). TDS in water and electrical \nconductivity (EC) are physical parameters commonly used to assess the \nwater quality (Rusydi, 2018). EC is easy to measure by the use of a portable \n\n\n\nconductivity meter. However, measurements of TDS are expensive and \nmore difficult since they require more equipment and take more time \n(Rice et al., 2017). Surface resistivity methods provide an easy and less \nexpensive means of estimating TDS from resistivity data. Mathematically, \nTDS can be calculated in parts per million (ppm) from EC or water \nresistivity \u03c1w by using equation 1 (Aweto, 2011; Aweto et al., 2018) (1): \n\n\n\n6400\n0.64\n\n\n\nw\n\n\n\nTDS EC\n\uf072\n\n\n\n= \uf0b4 =\n   (1) \n\n\n\nwhere 1\n\n\n\nw\n\n\n\nEC\n\uf072\n\n\n\n=\n. The water resistivity can be obtained from the formation \n\n\n\nfactor F, which according to Archie\u2019s equation is given in equation 2 \n(Archie, 1942). \n\n\n\nmb\n\n\n\nw\n\n\n\nF a\n\uf072\n\n\n\n\uf066\n\uf072\n\n\n\n\u2212= =\n          (2) \n\n\n\nwhere \u03c1b is the aquifer bulk resistivity, a is the pore geometry factor, m \n\n\n\nrepresents the cementation exponent and \uf066  is porosity. \n\n\n\n3.2 Dar-zarrouk parameters  \n\n\n\nA given geoelectric layer is characterized by two primary parameters. \nThese parameters are the layer resistivity (\u03c1) and thickness (h). Two other \nsecondary parameters can be obtained from these two primary \nparameters. These secondary parameters are the longitudinal \nconductance (S) expressed in Siemens (S) and transverse resistance (T) \nexpressed in ohmmeters squared (\u2126m2). These other parameters are \njointly known as the Dar-zarrouk parameters (Maillet, 1947; Henriet, \n1976).  \n\n\n\nFor a given layer, the longitudinal conductance S is given mathematically \n\n\n\nas: \n\n\n\nh\nS\n\n\n\n\uf072\n=\n\n\n\n  (3) \n\n\n\nThe transverse resistance of a given layer, on the other hand is defined as:  \n\n\n\n.T h\uf072=           (4) \n\n\n\nFor n layers, these two secondary parameters are respectively given by \nequations (5) and (6) respectively.  \n\n\n\nn\ni\n\n\n\ni i\n\n\n\nh\nS\n\n\n\n\uf072\n=\uf0e5   (5) \n\n\n\n.\nn\n\n\n\ni i\n\n\n\ni\n\n\n\nT h\uf072=\uf0e5   (6) \n\n\n\nwhere i is the number of layers (i = 1, 2, 3..., n). The Dar-Zarrouk \nparameters particularly the longitudinal conductance, may be useful in the \nassessment of aquifer properties and protection studies (Henriet, 1976). \n\n\n\n4. MATERIAL AND METHOD\n\n\n\nThe VES technique utilizing the Schlumberger electrode configuration \nwas adopted in this study. A total of 11 soundings were made at select \nlocations in the study area using a SAS 4000 model of ABEM Terrameter \nand its accessories. A maximum current electrode separation (AB) of \n1400 m was used while maximum potential electrode spacing (MN) was \n50 m. The apparent resistivity \u03c1a were computed from the measured \napparent resistance values Ra by the use of equation (7).  \n\n\n\n2 2\n\n\n\n2 2\n* *a\n\n\n\nAB MN\n\n\n\nR\nMN\n\n\n\n\uf072 \uf070\n\n\n\n\uf0ec \uf0fc\uf0e6 \uf0f6 \uf0e6 \uf0f6\n\u2212\uf0ef \uf0ef\uf0e7 \uf0f7 \uf0e7 \uf0f7\n\n\n\n\uf0ef \uf0ef\uf0e8 \uf0f8 \uf0e8 \uf0f8= \uf0ed \uf0fd\n\uf0ef \uf0ef\n\uf0ef \uf0ef\uf0ee \uf0fe\n\n\n\n  (7) \n\n\n\nThe computed apparent resistivity values were manually plotted against \nhalf of the current electrode separation (\ud835\udc34\ud835\udc35/2) on log-log graph. Manual \nsmoothening of each plotted curve was done where necessary to get rid of \nthe effects of lateral heterogeneities and other forms of noisy signatures \n(Chakravarthi et al., 2007; George et al., 2015).  The smoothening was \nachieved by either taking the mean of the two readings at the crossover \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 32-37 \n\n\n\nCite the Article: Akpan Emmanuel F., Akpan Veronica M., Inyang Udeme U. (2020). Geoelectrical Investigation Of Groundwater Quality Through Estimates Of Total \nDissolved Solids And Electrical Conductivity In Parts Of Akwa Ibom State, Southern Nigeria . Malaysian Journal of Geosciences, 4(1): 32-37. \n\n\n\npoints or discarding any outlier at the crossover points, which was not in \nconformity with the prevailing trend of the curve. Data that stood out as \noutliers in the dominant curve trend for few VES points were also \ndiscarded as they could lead to high root mean square error (RMSE) values \nduring the computer modelling stage. Discarding these outliers in any case \ndid not change the dominant trend of the sounding curves especially as ten \ndata were measured per decade.  \n\n\n\nThus, the trends observed in the smoothened curves were considered to \nbe due to the variation of the electrical resistivity with depth of \ninvestigation. The preliminary interpreted results were improved by the \n\n\n\nuse of computer based VES modelling software called WINRESIST. This \nsoftware performs automated approximation of the initial resistivity \nmodel from the observed data to improve upon the results using the \niterative least square inversion method. The depths and layer thicknesses \nwere constrained by the use of the borehole data at the vicinity of the VES \nstations while the layer resistivities were allowed to vary. The modelling \nsoftware uses the initial layer parameters to do a number of calculations \nand at the end produces theoretical curves in the process shown in figure \n2. These curves allow the layer parameters (resistivity, thickness and \ndepth) to be determined within the maximum depth of current \npenetration. \n\n\n\n5. RESULTS, ANALYSIS, AND DISCUSSION \n\n\n\nTable 1: Summary of VES data interpretation \n\n\n\nVES \nNO \n\n\n\nName of Location \nLongitude \n(Degrees) \n\n\n\nLatitude \n(Degrees) \n\n\n\n  Bulk resistivity \u03c1 (\u2126m) Thickness h (m) Depth d (m) \n\n\n\n\u03c11 \u03c12 \u03c13 \u03c14 h1 h2 h3 d1 d2 d3 \n\n\n\n1 Eastern Obolo 7.6611 4.5136 1423.0 210.0 860.5 8367.5 19.4 43.1 49.9 19.4 62.5 112.4 \n\n\n\n2 Eastern Obolo 7.5970 4.4970 210.7 397.9 1721.4 228.2 0.7 12.8 55.2 0.7 13.5 68.7 \n\n\n\n3 Eastern Obolo 7.7720 4.5600 1006.6 158.4 50.3 5566.4 18.3 44.1 53.2 18.3 62.4 115.6 \n\n\n\n4 Onna 7.8570 4.6222 1241.0 118.1 611.2 4893.0 21.6 56.7 36.8 21.6 78.3 115.1 \n\n\n\n5 Onna 7.8817 4.8218 1360.0 112.4 783.1 551.0 18.7 61.1 37.3 18.7 79.8 117.1 \n\n\n\n6 Onna 7.8931 4.7210 1063.3 1380.7 2088.9 372.5 7.0 54.0 149.0 7.0 61.0 210.0 \n\n\n\n7 Onna 7.8390 4.8300 1146.1 1400.1 1881.1 201.0 5.9 43.4 81.7 5.9 49.3 131.0 \n\n\n\n8 Ikot Abasi  7.5606 4.6500 120.0 223.3 1945.6 62.3 4.2 2.0 68.1 4.2 6.2 74.3 \n\n\n\n9 Ikot Abasi 7.6810 4.6370 999.8 4938.0 103.4 1789.0 10.4 32.2 57.7 10.4 42.6 100.3 \n\n\n\n10 Ikot Abasi  7.5666 4.5657 660.4 1488.6 549.2 1248.7 1.3 3.6 47.8 1.3 4.9 52.7 \n\n\n\n11 Ikot Abasi  7.5632 4.7100 115.8 577.7 115.9 46.1 4.3 6.0 105.3 4.3 10.3 115.6 \n\n\n\nThe results of the interpretation of the VES data are summarized in table \n\n\n\n1. The results show that the area under investigation is made of 4 \n\n\n\ngeoelectric layers within the limit allowed by the current electrode \n\n\n\nseparation employed. The first layer has resistivity ranging from 115.8 \u2126m \n\n\n\nto 1423.0 \u2126m with an average value of 849.7 \u2126m. this layer, interpreted as \n\n\n\nthe Motley topsoil has thickness ranging from 0.7 m to 21.6m and runs \n\n\n\nacross the study area. For the second layer, the resistivity ranges from \n\n\n\n112.4 \u2126m to 4938.0 \u2126m while the thickness ranges from 2.0 m to 61.1 m. \n\n\n\nThis layer was interpreted as gravelly sand and also cuts across the entire \n\n\n\nstudy area. The third layer, interpreted as fine sand has resistivity ranging \n\n\n\nfrom 50.3 \u2126m to 2088.9 \u2126m with an average value of 973.7 \u2126m and \n\n\n\nthickness of 36.8 m to 149.0 m. This third layer constitutes the economic \n\n\n\naquifer from where the dwellers in the area tap their groundwater. The \n\n\n\nfourth layer has resistivity which ranges from 46.1 \u2126m to 8367.5 \u2126m and \n\n\n\nwas interpreted as coarse sand. The thickness of this layer could not be \n\n\n\nascertained as a result of the permissible depth allowed by the maximum \n\n\n\ncurrent electrode separation used. Samples interpreted VES curves are \n\n\n\nshown in figure 2.  \n\n\n\nFigure 2: Sample interpreted VES curves (a) VES 4 at Eastern Obolo (b) \n\n\n\nVES 6 at Onna (c) VES 11 at Ikot Abasi \n\n\n\nFigure 3 shows the variation of the aquifer bulk resistivity. Higher bulk \nresistivity values are observed in the North eastern and south western \nparts of the study area. This trend suggests that the aquifers in these parts \nwith relatively higher resistivity values may have better water quality than \nthose in the remaining parts with relatively lower values. The distribution \nof the water resistivity is shown in the contour map of figure 4. Again, the \nwater resistivity follows the same trend as the aquifer bulk resistivity, \nwith relatively higher values in the North eastern and south western parts \nof the study area. \n\n\n\nFigure 3: Contour map showing the distribution of the aquifer bulk \n\n\n\nresistivity \n\n\n\nFigure 4: Contour map showing the distribution of the water resistivity \n\n\n\n(a) (b) \n\n\n\n(c) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 32-37 \n\n\n\nCite the Article: Akpan Emmanuel F., Akpan Veronica M., Inyang Udeme U. (2020). Geoelectrical Investigation Of Groundwater Quality Through Estimates Of Total \nDissolved Solids And Electrical Conductivity In Parts Of Akwa Ibom State, Southern Nigeria . Malaysian Journal of Geosciences, 4(1): 32-37. \n\n\n\nTo determine the TDS from the interpreted VES data, first the water \n\n\n\nresistivity needs to be determined. This was done by using the expression \n\n\n\nderived in parts of the study area (Ibanga and George, 2016). This \n\n\n\nexpression is given in equation 8.  \n\n\n\n0.0577 13.643w b\uf072 \uf072= +        (8) \n\n\n\nFinally, the TDS was estimated from equation 1. The Dar-zarrouk \nparameters for the aquifer unit were respectively calculated from \nequations 3 and 4. Table 2 shows a summary of these computed results. \nThe distribution of the TDS in the study area is shown in figure 5. The TDS \nvalues vary from 47.7 to 386.8 ppm with an average value of 156.1 ppm. A \ncomparison of Figures 4 and 5 indicates that areas of low water resistivity \ncorrespond to areas of high TDS as predicted by equation 1. \n\n\n\nTable 2: Summary of aquifer/water properties \n\n\n\nVES \nNO \n\n\n\nName of \nLocation \n\n\n\nLatitude \n(Degrees) \n\n\n\nLongitude \n(Degrees) \n\n\n\nBulk \nResistivity  \n\u03c1b (\u2126m) \n\n\n\nThickness \n(m) \n\n\n\nWater \nresistivity  \n\u03c1w (\u2126m) \n\n\n\nWater \nElectrical \nconductivity \nEC (\u00b5S/cm) \n\n\n\nTDS \n(ppm) \n\n\n\nLongitudinal \nconductance \nS (\u2126-1) \n\n\n\nTransverse \nresistance \nT  (\u2126m2) \n\n\n\n1 Eastern Obolo 7.6611 4.5136 860.5 49.9 63.29 158.0 101.1 0.058 42938.95 \n\n\n\n2 Eastern Obolo 7.5970 4.4970 1721.4 55.2 112.97 88.5 56.7 0.032 95021.28 \n\n\n\n3 Eastern Obolo 7.7720 4.5600 50.3 53.2 16.55 604.4 386.8 1.058 2675.96 \n\n\n\n4 Onna 7.8570 4.6222 611.2 36.8 48.91 204.5 130.9 0.060 22492.16 \n\n\n\n5 Onna 7.8817 4.8218 783.1 37.3 58.83 170.0 108.8 0.048 29209.63 \n\n\n\n6 Onna 7.8931 4.7210 2088.9 149.0 134.17 74.5 47.7 0.071 311246.1 \n\n\n\n7 Onna 7.8390 4.8300 1881.1 81.7 122.18 81.8 52.4 0.043 153685.87 \n\n\n\n8 Ikot Abasi  7.5606 4.6500 1945.6 68.1 125.90 79.4 50.8 0.035 132495.36 \n\n\n\n9 Ikot Abasi 7.6810 4.6370 103.4 57.7 19.61 510.0 326.4 0.558 5966.18 \n\n\n\n10 Ikot Abasi  7.5666 4.5657 549.2 47.8 45.33 220.6 141.2 0.087 26251.76 \n\n\n\n11 Ikot Abasi  7.5632 4.7100 115.9 105.3 20.33 491.9 314.8 0.909 12204.27 \n\n\n\nFigure 5: Contour map showing the distribution of the TDS in the study \n\n\n\narea \n\n\n\nThe distribution of the water electrical conductivity is shown in the \ncontour map of figure 6. The EC values ranges from 74.5 to 604.4\u00b5S/cm \nwith an average value of 244.0\u00b5S/cm.  Lower values of the conductivity \nare observed in the central part and the far north eastern part of the study \narea. Figures 7 and 8 respectively show the distributions of the \nlongitudinal conductance and transverse resistance in the study area. \nHigher values of longitudinal conductance are observed in the central part \nof the part of the study area while the reverse is the case with the \ntransverse resistance. On the whole, the high values of the transverse \nresistance imply that the area has good groundwater potential. \n\n\n\nFigure 6: Contour map showing the distribution of water conductivity in \n\n\n\nthe study area \n\n\n\nFigure 7: Contour map showing the distribution of the aquifer \n\n\n\nlongitudinal conductance in the study area \n\n\n\nFigure 8: Contour map showing the distribution of the aquifer transverse \n\n\n\nresistance in the study area \n\n\n\nFigure 9 shows a correlation of the estimated TDS with the aquifer bulk \nresistivity. The high coefficient of determination (0.97) implies that TDS \ncan be estimated with high accuracy from bulk resistivity according to \nequation 9: \n\n\n\n0.5884852.7 bTDS \uf072 \u2212= (9) \n\n\n\nThe relation between TDS and the Dar-zarrouk parameters are shown in \nthe regression plots of figures 10 and 11 respectively. Both plots show \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 32-37 \n\n\n\nCite the Article: Akpan Emmanuel F., Akpan Veronica M., Inyang Udeme U. (2020). Geoelectrical Investigation Of Groundwater Quality Through Estimates Of Total \nDissolved Solids And Electrical Conductivity In Parts Of Akwa Ibom State, Southern Nigeria . Malaysian Journal of Geosciences, 4(1): 32-37. \n\n\n\npower relations between TDS and longitudinal conductance and \ntransverse resistance respectively. The power inverse relation of TDS and \nlongitudinal conductance is given by equation 10 with a coefficient of \ndetermination of 0.82 while equation 11 given the relation between TDS \nand transverse resistance with a coefficient of determination of o.94 \nrespectively. \n\n\n\nFigure 9: regression plot of TDS against aquifer bulk resistivity \n\n\n\nFigure 10: Regression plot of TDS against aquifer longitudinal \n\n\n\nconductance \n\n\n\nFigure 11: Regression plot of TDS against aquifer transverse resistance \n\n\n\n6. CONCLUSION \n\n\n\nThe surface resistivity method was adopted in this study to estimate water \nconductivity and total dissolved solids with the sole aim of using these \nparameters to assess the water quality in parts of Akwa Ibom State, \nSouthern Nigeria. The study area is shown to comprise of 4 geoelectric \nlayers with the third layer as the major aquifer from where the inhabitants \nobtain their water for drinking and other purposes. The estimated water \nconductivity ranges from 74.5 to 604.4\u00b5S/cm with an average value of \n244.0\u00b5S/cm while the TDS values range from 47.7 to 386.8 ppm with an \naverage value of 156.1 ppm. These values, which are well within the \npermissible limits imply that the water in the aquifers could be considered \nto be fresh water and thus, is suitable for drinking and other domestic or \nagricultural purposes.  The regression plots demonstrate that TDS could \nbe predicted with high degree of confidence from the Dar-zarrouk \nparameters which are readily derivable from VES data. Although results \nshow that the aquifers sampled have freshwater based on EC and TDS \nvalues, follow up research involving hydrogeochemical and \nmicrobiological   analyses should be carried out in order to firm up the \nresults obtained in this present study. \n\n\n\nREFERENCES  \n\n\n\nAweto, K.E., 2013. Resistivity methods in hydrogeophysical investigation \n\n\n\nfor groundwater in Aghalokpe, Western Niger Delta. Global Journal of \n\n\n\nGeological Science, 11, Pp. 47 \u2013 55. \n\n\n\nAweto, K.E., Chiyem, F.I., Asuma, O.O., 2018. An approach to estimate total \n\n\n\ndissolved solids in groundwater using electrical resistivity sounding \n\n\n\nand geochemical methods. Global Journal of Geological Sciences, 6, Pp. \n\n\n\n37 \u2013 43.  \n\n\n\nBello, A.A., Makinde, V., 2007. Delineation of the aquifer in the south \u2013\n\n\n\nwestern part of the Nupe Basin, Kwara state Nigeria. J. Am.  Sci., 2, Pp. \n\n\n\n36 \u2013 44. \n\n\n\nChakravarthi, V., Shankar, G.B.K., Muralidharan, D., Harinarayana, T., \n\n\n\nSundararajan, N., 2007. An integrated geophysical approach for imaging \n\n\n\nsub-basalt sedimentary basins: Case study of Jam River basin, India. \n\n\n\nGeophysics, 72 (6), Pp. B141\u2013B147. \n\n\n\nEkanem, A.M., George, N.J., Thomas, J.E., Nathaniel, E.U., 2019. Empirical \n\n\n\nRelations between Aquifer Geohydraulic\u2013Geoelectric Properties \n\n\n\nDerived from Surficial Resistivity Measurements in Parts of Akwa Ibom \n\n\n\nState, Southern Nigeria. Natural Resources Research, \n\n\n\nhttps://doi.org/10.1007/s11053-019-09606-1. \n\n\n\nFreeze, R.A., Cherry, J.A., 1979. Groundwater. 2nd Edn., Prentice Hall Inc., \n\n\n\nEnglewood, NJ., USA., ISBN-13: 9780133653120, Pp. 604. \n\n\n\nGeorge, N.J., 2020. Appraisal of hydraulic flow units and factors of the \n\n\n\ndynamics and contamination of hydrogeological units in the littoral \n\n\n\nzones: A case study of Akwa Ibom State University and its Environs, \n\n\n\nMkpat Enin L.G.A, Nigeria, Natural Resources Research \n\n\n\nhttps://doi.org/10.1007/s11053-020-09673-9. \n\n\n\nGeorge, N.J., Akpan, A.E., Ekanem, A.M., 2016. Assessment of Textural \n\n\n\nVariational Pattern and Electrical Conduction of Economic and \n\n\n\nAccessible Quaternary Hydrolithofacies via Geoelectric and Laboratory \n\n\n\nMethods in SE Nigeria. A Case Study of Select Locations in Akwa Ibom \n\n\n\nState. J Geologic Soc India, 88 (4), Pp. 517\u2013528. \n\n\n\nGeorge, N.J., Ibuot, J.C., Obiora, D.N., 2015. Geo-electrohydraulic \n\n\n\nparameters of shallow sandy aquifer in Itu, Akwa Ibom State (Nigeria \n\n\n\nusing geoelectric and hydrogeological measurements. J. Afr. Earth Sci., \n\n\n\n110, Pp. 52 \u2013 63. \n\n\n\nGeorge, N.J., Nathaniel, E.U., Etuk, S.E., 2014. Assessment of economically \n\n\n\naccessible groundwater reserve and its protective capacity in Eastern \n\n\n\nObolo Local Government Area of Akwa Ibom State, Nigeria, using \n\n\n\nelectrical resistivity method. International Journal of Geophysics, Pp. l \u2013 \n\n\n\n10. \n\n\n\nGeorge, N.J., Obianwu, V.I., Obot, I.B., 2011. Estimation of Groundwater \n\n\n\nReserve in Unconfined Frequently Exploited Depth of Aquifer Using a \n\n\n\nCombined Surficial Geo-Physicaland Laboratory Techniques in The \n\n\n\nNiger Delta, South \u2013 South, Nigeria. Advances in Applied Science \n\n\n\nResearch vol. 2. Pelagia Research Library (USA) AASRFC, Pp. 163\u2013177. \n\n\n\nGowd, S.S., 2005. Assessment of groundwater quality for drinking and \n\n\n\nirrigation purposes: A case study of Peddavanka watershed, Anantapur \n\n\n\nDistrict, Andhra Pradesh, India; Environmental Geology, 48, Pp. 702 \u2013 \n\n\n\n712. \n\n\n\nHenriet, J.P., 1976. Direct application of Dar-Zarrouk parameters in \n\n\n\ngroundwater survey. Geophysical Prospecting, 24, Pp. 344 - 353. \n\n\n\nIbanga, J.I., George, N.J., 2016. Estimating geohydraulic parameters, \n\n\n\nprotective strength, and corrosivity of hydrogeological units: a case \n\n\n\nstudy of ALSCON, Ikot Abasi, southern Nigeria. Arabian Journal of \n\n\n\nGeoscience, 9, Pp. 363. https://doi.org/10.1007/s12517-016-2390-1. \n\n\n\nKur1, A., Alaanyi, A.T., Awuhe, S.T., 2019. Determination of quality of water \n\n\n\nused by students of College of Education, Katsina-Ala through physical \n\n\n\nand electro-chemical parameters. Science World Journal, 14 (1), Pp. 78 \n\n\n\n\u2013 83. \n\n\n\nMaillet, R., 1947. The fundamental equations of electrical prospecting. \n\n\n\nGeophysics, 12, Pp. 529 \u2013 556. \n\n\n\n\nhttps://doi.org/10.1007/s11053-019-09606-1\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 32-37 \n\n\n\nCite the Article: Akpan Emmanuel F., Akpan Veronica M., Inyang Udeme U. (2020). Geoelectrical Investigation Of Groundwater Quality Through Estimates Of Total \nDissolved Solids And Electrical Conductivity In Parts Of Akwa Ibom State, Southern Nigeria . Malaysian Journal of Geosciences, 4(1): 32-37. \n\n\n\nOdeyemi, A.T., Akinjogunla, O.J., Ojo, M.A., 2011. Bacteriological, \n\n\n\nphysiochemical and mineral studies of water samples from artesian \n\n\n\nborehole, spring and hand dug well located at Oke-Osun, Ikere-Ekiti, \n\n\n\nNigeria. Archives of Applied Science Research, 3 (3), Pp. 94-108. \n\n\n\nOladunjoye, M.A., Olayinka, A.I., Amidu, S.A., 2011. Geoelectrical Imaging at \n\n\n\nan Abandoned Waste Dump Site in Ibadan, Southwestern Nigeria. \n\n\n\nJournal of Applied Sciences, 11, Pp. 3755 - 3764. \n\n\n\nRice, A., Baird, E.W., Eaton, R.B., 2017. APHA 2017 Standard Methods for \n\n\n\nExamination of water and wastewater. Washington: American Public \n\n\n\nHealth Association, American Water Works Association, Water \n\n\n\nEnvironment Federation.  \n\n\n\nRusydi, A.F., 2018. Correlation between conductivity and total dissolved \n\n\n\nsolid in various type of water: A review. IOP Conference Series: Earth \n\n\n\nand Environmental Science, 118, 01201. \n\n\n\nSalufu, S.O., Akhirevbulu, O.E., 2015. Correlation for Predicting TDS and \n\n\n\nElectrical Conductance of Groundwater: Using Electrical Resistivity \n\n\n\nMethod.  Journal of Multidisciplinary Engineering Science and \n\n\n\nTechnology (JMEST), 2 (11), Pp. 2972 \u2013 2975. \n\n\n\nUSEPA. 2002. National standard for drinking water. United States \n\n\n\nEnvironment Protection Agency, 816-F-02-013. \n\n\n\nWetzel, R.G., 2001. Limnology: Lake and River ecosystem (3rd ed.). San \n\n\n\nDiego, CA: Academic Press. \n\n\n\nWorld Health Organization. 2004. Guidelines for drinking-water quality. \n\n\n\nWorld Health Organization, Geneva. \n\n\n\nYilmaz, E., Koc, C., 2014. Physically and chemically evaluation for the water \n\n\n\nquality criteria in a farm on Akcay. J. Water Resour. Prot., 6, Pp. 63-67.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 56-63 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2021.56.63 \n\n\n\nCite the Article: Rakesh Sunari Magar, Pradeep Kumar Shrestha, Prabin Kayastha (2021). Manifestation of Optimal Route Alignment Selection of Rural Road Using GIS \nand Least Cost Path (LCP) Model with Engineering and Environmental Suitability Perspective: A Case Study in Nepal. Malaysian Journal of Geosciences, 5(2): 56-63. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2021.56.63 \n\n\n\nMANIFESTATION OF OPTIMAL ROUTE ALIGNMENT SELECTION OF RURAL ROAD \nUSING GIS AND LEAST COST PATH (LCP) MODEL WITH ENGINEERING AND \nENVIRONMENTAL SUITABILITY PERSPECTIVE: A CASE STUDY IN NEPAL \n\n\n\nRakesh Sunari Magara, Pradeep Kumar Shresthaa*, Prabin Kayasthab  \n\n\n\naPulchowk Campus, Institute of Engineering, Tribhuvan University, Lalitpur, Bagmati, Nepal  \nbShellharbour City Council, NSW, Australia  \n*Corresponding Author E-mail: pradeep.shrestha@pcampus.edu.np \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 23 July 2021 \nAccepted 28 August 2021 \nAvailable online 15 September 2021\n\n\n\nFor the economic growth and sustainable development of any country, the road networks play a pivotal role. \nHence, the selection of best route alignment for the road networks becomes even more significant. The \nGeographical Information System (GIS) integration with the Least Cost Path (LCP) model is used to determine \nthe optimum route to address sustainable road development.  In this study, Dupcheswor Rural Municipality, \nNuwakot, Nepal and part of Langtang National Park was taken as a study area; and engineering and \nenvironmental parameters were selected to create a cost layer. Using the Least Cost Path (LCP) model, fifteen \nroutes were generated in the GIS. All the generated fifteen routes were compared based on cost, and the \noptimum route was selected based on the least cost. The optimum route in this study was derived from the \nhybrid theme of engineering and environmental perspectives. This study suggests further research can be \ndone to improve preliminary to detailed road alignment planning and design coordination by considering \nother factors.  \n\n\n\nKEYWORDS \n\n\n\nGIS, Least Cost Path (LCP), Optimum route alignment, Nepal.\n\n\n\n1. INTRODUCTION \n\n\n\nRoute alignment is one of the important tasks in any transport \n\n\n\ninfrastructure development.  In any transportation infrastructure \n\n\n\ndevelopment, today\u2019s proper decision of appropriate linear positioning of \n\n\n\nthe road alignment determines or plays a great role on the future \n\n\n\nperformance/service of land utilization/exploitation scenario for cost \n\n\n\neffective, efficient and sustainable accessibility and mobility to carry out \n\n\n\nspatial human activities such as business, recreation, education etc. \n\n\n\n(Mahini and Abedian, 2014). New routes that are proposed should be \n\n\n\nmade such that they (i) consider all the dominating and sensitive costs, (ii) \n\n\n\narticulate all constraints, (iii) produce realistic alignment; (iv) satisfy the \n\n\n\npossibility of the construction, (v) satisfy the current and future demands \n\n\n\n(Singh and Singh, 2017; Acharya et al., 2017). \n\n\n\nTo find the optimum balance between transport infrastructural \n\n\n\ndevelopment and engineering and environmental concerns is challenging \n\n\n\nand high demanding in these days. Traditional methods of optimal routing \n\n\n\nare expensive, tedious, protracted and time consuming methods \n\n\n\n(Ebrahimpoor et al., 2009; Mahini and Abedian, 2014; Wahdana et al., \n\n\n\n2019). In the traditional method, route alignment begins with the source \n\n\n\nto the destination plan. This method includes acquiring information from \n\n\n\npublished maps, field surveys, aerial photos, and satellite imagery. If the \n\n\n\nproposed route encounters undefeatable constraints such as engineering, \n\n\n\ngeological, environmental, socio-economic, political etc., a new route must \n\n\n\nbe searched, and the data collection process should be instigated again \n\n\n\n(Humber, 2004). The Geographical Information System (GIS), remote \n\n\n\nsensing and network analysis methods can be used as efficient tools by the \n\n\n\nexperts in route alignment exploring the route selection, route planning \n\n\n\nand finding the optimal route. Among different network analysis methods, \n\n\n\nthe Least Cost Path (LCP) model is widely used as this method allows the \n\n\n\nusers to identify the most economical way to link two locations within a \n\n\n\ncost surface, which can be calculated by combining multiple criteria, and \n\n\n\ntherefore by accounting for different concerns such as engineering, \n\n\n\ngeological, environmental, socio-economic, political etc. (Effat and Hassan, \n\n\n\n2013; Sunusi et al., 2015). \n\n\n\nDifferent researchers in the last three decades used the LCP model in \n\n\n\ntransportation infrastructure planning such as Collischonn and Pillar \n\n\n\n(2000) used the LCP model to identify the best path based on the \n\n\n\ntopography, the initial and end-points of the linear feature (canal or road) \n\n\n\nand a function relating slope, distance and cost; Yu et al. (2003) tested  the \n\n\n\nLCP model on two small mountainous regions in Venango County of \n\n\n\nPennsylvania, USA  by considering spatial distances, anisotropic costs and \n\n\n\nthe presence of bridges and tunnels in the routes; Atkinson et al. (2005) \n\n\n\nused the LCP model for the route of an all-weather road in Nunavur, \n\n\n\nCanada;  Mahini and Abedian (2014) employed the LCP model in Golestan \n\n\n\nProvince, Iran by considering the important parameters such as slope, \n\n\n\ngeology, landslide, etc.; Loganathan and Elangovan (2017) produced the \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 56-63 \n\n\n\n\n\n\n\n \nCite the Article: Rakesh Sunari Magar, Pradeep Kumar Shrestha, Prabin Kayastha (2021). Manifestation of Optimal Route Alignment Selection of Rural Road Using GIS \n\n\n\nand Least Cost Path (LCP) Model with Engineering and Environmental Suitability Perspective: A Case Study in Nepal. Malaysian Journal of Geosciences, 5(2): 56-63. \n \n\n\n\n\n\n\n\nbest alignment  in the corridors  from Perundurai to Palani, Tamil Nadu, \n\n\n\nIndia by using LCP; Singh and Singh (2017) used the LCP model by \n\n\n\nconsidering the environmental, technical, social, and economic criteria in \n\n\n\nthe outer area of Allahabad City, India; \u015eari and \u015een (2017) used the LCP \n\n\n\nmodel in Konya city with economic, environmentally and hybrid \n\n\n\napproaches; Mahavar et al. (2019) used the LCP model in the Dahod \n\n\n\ndistrict of Gujarat, India by considering the topography and land use \n\n\n\npattern; Rao et al. (2019) used the LCP model to design optimum route \n\n\n\nalignment between two locations in the Himalayan region of India; Sekulic \n\n\n\net al. (2020) applied spatial multi-criteria evaluation and the LCP analysis \n\n\n\nto find the optimal by-pass road alignment in the Tlokweng Planning Area \n\n\n\nin Botswana. \n\n\n\nIn the developing country like Nepal, it is found that road alignment design \n\n\n\nhas been carried out manually with decisive actions which takes a lot of \n\n\n\neffort and time which might not be efficient or at optimum level. Several \n\n\n\nfactors which are crucial in the selection of route alignment such as socio-\n\n\n\neconomic conditions, land features, topography, and environmental \n\n\n\nconditions are not holistically considered. This study documents to \n\n\n\ndetermine the possible optimal alignment theoretically and check field \n\n\n\napplicable appropriateness by selecting the factors affecting the road \n\n\n\nroute alignment. Among several possible generated route alignments of \n\n\n\nthe proposed road, this study selects the best alternative. Hence, this study \n\n\n\naims to design optimum route alignment for developing a rural road in the \n\n\n\nHimalayan region of Nepal by considering engineering and environmental \n\n\n\nfactors and selected from the several possible route alignments. The \n\n\n\nspecific objectives to address the main objective of this study are to \n\n\n\ndetermine the factors or variables affecting road route alignment \n\n\n\nselection, to know the accumulated cost required at each grid cell level of \n\n\n\nroad corridor for surpassing them which represents the suitability surface \n\n\n\nmaps for road alignment and to generate the least cost path against several \n\n\n\nsuitability surface maps. \n\n\n\n2. MATERIALS AND METHODOLOGY \n\n\n\n2.1 Study area \n\n\n\nThe study area comprises Dupcheswor Rural Municipality and part of \n\n\n\nLangtang National Park. Dupcheswor Rural Municipality is located in the \n\n\n\neastern part of the Nuwakot district of Nepal's Bagmati Province as shown \n\n\n\nin Figs. 1(a) and (b). This study area lies between latitudes 27\u00b0 52\u2032 0\u2033 to \n\n\n\n28\u00b0 04\u2032 30\u2033 N and longitudes 85\u00b0 22\u2032 00\u2033 to 85\u00b0 30\u2032 00\u2033 E as shown in Fig. \n\n\n\n2.  The study area covers an area of about 220 sq km. The Dupcheswor \n\n\n\nRural Municipality is bounded by the Langtang National Park  on the north, \n\n\n\nthe Helambu Rural Municipality on the east, the Tadi Rural Municipality, \n\n\n\nthe Panchakanya Rural Municipality and the Rasuwa District on the west, \n\n\n\nand the Shivapuri Rural Municipality on the south (DRM, 2020). The \n\n\n\nterrain mostly consists of hills and mountains. The terrain is highly rugged \n\n\n\nwith elevations ranging from 838 m to 5051 m above mean sea level (msl) \n\n\n\nas shown in Fig. 2. This rural municipality belongs to a sub-tropical climate \n\n\n\nwith few upper tropical climate in the southern part and sub-alpine and \n\n\n\nalpine climate in the northern part. The annual precipitation ranges from \n\n\n\n935 mm to 2280 mm throughout the study area. Similarly, the annual \n\n\n\nmean temperature of this study area varies from 8oC at the northern part \n\n\n\nto 22oC at the southern part. This study area also constitutes very rich in \n\n\n\nbiodiversity with vegetation and cultivated area occupying most of its land \n\n\n\ncover area including some portion area of the Langtang National Park. The \n\n\n\nhilly steep areas of the rural municipality are vulnerable to landslides and \n\n\n\nsufficient all-weathered roads are unavailable. \n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Map of study area showing (a) Nepal with seven provinces  and (b) Nuwakot district \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 56-63 \n\n\n\n\n\n\n\n \nCite the Article: Rakesh Sunari Magar, Pradeep Kumar Shrestha, Prabin Kayastha (2021). Manifestation of Optimal Route Alignment Selection of Rural Road Using GIS \n\n\n\nand Least Cost Path (LCP) Model with Engineering and Environmental Suitability Perspective: A Case Study in Nepal. Malaysian Journal of Geosciences, 5(2): 56-63. \n \n\n\n\n\n\n\n\nFigure 2: Digital Elevation Model (DEM) of study area \n\n\n\n2.2 Model description \n\n\n\nThe least-cost path (LCP) tool determines least-cost path from a \n\n\n\ndestination point to a source. This path is one cell wide, travels from the \n\n\n\ndestination to the source, and is assured to be the cheapest route relative \n\n\n\nto the cost units defined by the original cost raster that was input into the \n\n\n\nweighted-distance tool.  In GIS software, a process of determination of the \n\n\n\noptimum path on a surface analysis is carried out in following three steps. \n\n\n\nStep 1: Generation of the cost distance \u2013 In this step, one has to calculate \n\n\n\nthe cost in relation to the start point. For instance, for routing the highway, \n\n\n\nthe cost matrix would be the slope. It is obvious that the higher the slope, \n\n\n\nthe more costly it would be. \n\n\n\nStep 2: Cost backlink \u2013 This analysis provides direction to cost path \n\n\n\nmodel. In this case, it would be the direction in which the path will follow \n\n\n\nwith all the eight possible cardinal directions from one cell to the next cell. \n\n\n\nStep 3: Cost path \u2013 The least cost path can be calculated by using the cost \n\n\n\ndistance, cost backlink and destination source.  It should be noted that \u201cno \n\n\n\ndata\u201d cells are excluded from the possibility of travel. \n\n\n\n2.3 Methodology \n\n\n\nThe methodology consists of the procedure of collecting the spatial data \n\n\n\nfrom several available maps, digitizing and making processable then \n\n\n\nclassifying each criterion into its distinguishable/scalable categories and \n\n\n\nthen employing their attribute data, analyzing (cartographic processes, \n\n\n\nstandardize/reclassify, weighted overlays, LCP (Least Cost Path) etc.) and \n\n\n\nfinally observing results, following the framework determined as shown \n\n\n\nin Fig. 3.  The first stage includes the identification and determination of \n\n\n\nclasses of different criteria considered in this study. The second stage \n\n\n\nconsists of selecting the region for sample data study, which includes the \n\n\n\ncollection of available spatial data of the subject study area. In the third \n\n\n\nstage, the collected data of several scales is brought down to the same \n\n\n\nscale, which is known as reclassifying or standardizing. The fourth stage \n\n\n\nincludes preparation of friction surface map as per predetermined \n\n\n\nscenarios. Next, in the final stage, LCP function is used to generate several \n\n\n\nLCPs as per the corresponding scenarios, which are further compared to \n\n\n\nselect the optimal one. \n\n\n\n\n\n\n\nFigure 3: Framework of methodology \n\n\n\n2.3.1 Stage 1:  Identification and Classification of Themes, Factors and \n\n\n\nCriteria \n\n\n\nSeveral criteria and factors could be identified and selected for the road \n\n\n\nalignment. However, the most relevant factors are slope, stream order, \n\n\n\nvulnerable area, i.e. landslide area, land cover, slope aspect, etc., as shown \n\n\n\nin Table 1.  All the selected factors are obligatory in terms of route \n\n\n\nalignment. However, the priority level of these factors may differ.  The \n\n\n\ndifferences in priority level bring out ideas of use of multi-criteria \n\n\n\ndecisions in route alignment selection activities. \n\n\n\nTable 1: Factors and decision rules \n\n\n\nThematic Map \nFactors \n/Criteria \n\n\n\nDecision rules \n\n\n\n \n \nEngineering \n\n\n\n1. Slope Maximizing route length in flat and mild \nslopes to reduce cut and fill costs \n\n\n\n2. Stream \norder \n\n\n\nMinimizing  route  length  intersecting  \nstream  networks with high order to  \navoid the costly constructions of \ncauseways, bridges etc. \n\n\n\n3. Landslide  Maximizing distance from landslide areas \nto avoid the risk problems lessen the \nexpense of mitigation measures of costly \nstructures \n\n\n\n \n \nEnvironmental \n\n\n\n1. Land use Minimizing route length in agriculture, \nurban areas, archaeological sites to avoid \nor lessen land acquisition \n\n\n\n2. Slope \naspect \n\n\n\nMaximizing the south face of land mass \nfor regular sun light/ray to avoid \nmoistness, dampness \n\n\n\n3. Protected \nArea \n\n\n\nMinimizing occupy of land of protected \nareas \n\n\n\n2.3.2 Stage 2:  Selection of Study Area and Data Collection \n\n\n\nIn this study, Dupcheswor Rural Municipality of the Nuwakot district of \n\n\n\nthe Bagmati Province of Nepal was selected.  Several thematic data on \n\n\n\nfactors which have been identified in Stage 1 were collected from the \n\n\n\ndifferent organizations such as remotely sensed data from USGS, \n\n\n\ntopographical data, drainage maps, land cover maps, aerial photos from \n\n\n\nthe Department of Survey, Government of Nepal, and satellite imageries \n\n\n\nfor the different period from Google Earth. \n\n\n\n2.3.3 Stage 3:  Reclassification \n\n\n\nScores from the different map attributes can only be compared if the \n\n\n\nmeasurements units are the same. As all data have a different \n\n\n\nmeasurement scale, the measurement units are to be made uniform \n\n\n\nthrough the standardization procedure, i.e. reclassification. In this study, \n\n\n\nthe linear transformation was used to convert the criteria attributes into a \n\n\n\ncost scale that ranges from 1 to 9, where the value 1 is the least cost, and 9 \n\n\n\nis the highest cost. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 56-63 \n\n\n\nCite the Article: Rakesh Sunari Magar, Pradeep Kumar Shrestha, Prabin Kayastha (2021). Manifestation of Optimal Route Alignment Selection of Rural Road Using GIS \nand Least Cost Path (LCP) Model with Engineering and Environmental Suitability Perspective: A Case Study in Nepal. Malaysian Journal of Geosciences, 5(2): 56-63. \n\n\n\n2.3.4 Stage 4:  Preparation of Cost Friction Surface Maps as per Themes \n\n\n\nOnce the criteria maps (factors and constraints) have been developed and \n\n\n\nassociated weights have been assigned to each input layer, the cost friction \n\n\n\nmaps for each scenario are generated using the Weighted Overlay function \n\n\n\nas shown in Fig. 4. \n\n\n\nFigure 4: Schematic diagram for generating the cost maps as per various \n\n\n\nscenarios \n\n\n\n2.3.5 Stage 5:  Least Cost Path (LCP) Function and Alternative Route \n\n\n\nAlignments as per the Scenarios \n\n\n\nLeast Cost Path (LCP) functions with the source and destination points on \n\n\n\none hand and resistive friction surface cost map, on the other hand, as \n\n\n\nshown in Fig. 5. Cost distance function uses two tools, i.e. cost distance and \n\n\n\ncost back link as per the source point position and resistive friction surface \n\n\n\ncost map. It is to be noted that the cost backlink is used to retrace the least \n\n\n\ncostly route from the destination to the source over the cost distance \n\n\n\nsurface. The next step is the cost path function, which gives the shortest or \n\n\n\nleast cost path according to the destination/ terminating point against the \n\n\n\ncost distance generated as output from the cost distance function. \n\n\n\nFigure 5: Least Cost Path (LCP) function \n\n\n\n3. RESULTS AND DISCUSSIONS \n\n\n\n3.1 Creation of each cost-criteria map and calculation of the relative \n\n\n\nweights for the cost factors \n\n\n\nSix cost criteria maps based on engineering and environmental themes \n\n\n\nwere generated (Figs. 6a- 6f) using ESRI ArcGIS 10. Descriptions of each \n\n\n\ncriterion maps are as follows: \n\n\n\na) Slope: The slope map (Fig. 6a) is of the study area was prepared \n\n\n\nfrom the Digital Elevation Model (DEM) retrieved from the USGS. \n\n\n\nThe sub-classes of slope along with coverage area and rating values \n\n\n\nare given in Table 2. \n\n\n\nb) Stream order: The stream order (Fig. 6b) in the study area was \n\n\n\nprepared from the drainage map provided by the Department of \n\n\n\nSurvey, Government of Nepal. The stream order was prepared on \n\n\n\nthe basis of the Strahler method (Strahler, 1957). The sub-classes of \n\n\n\nstream order along with coverage area and rating values are given \n\n\n\nin Table 2. \n\n\n\nc) Landslide: The landslide (Fig. 6c) in the study area was prepared \n\n\n\nfrom the aerial photos, land cover maps from the Department of \n\n\n\nSurvey, Government of Nepal and Google earth images taken on \n\n\n\ndifferent periods.  The sub-classes of landslides, along with coverage \n\n\n\narea and rating values are given in Table 2. \n\n\n\nd) Land use: The land use (Fig. 6d) in the study area was produced \n\n\n\nfrom the land cover/ land use map provided by the Department of \n\n\n\nSurvey, Government of Nepal which was updated based on the \n\n\n\nGoogle earth images. The sub-classes of land use, along with \n\n\n\ncoverage area and rating values, are given in Table 2. \n\n\n\ne) Slope aspect: The slope aspect (Fig. 6e) is of the study area was \n\n\n\nprepared from the Digital Elevation Model (DEM) retrieved from the \n\n\n\nUSGS. The sub-classes of slope aspect, along with coverage area and \n\n\n\nrating values are given in Table 2. \n\n\n\nf) Protected area: The protected area (Fig. 6f) in the study area was \n\n\n\nproduced from the land cover map provided by the Department of \n\n\n\nSurvey, Government of Nepal. The sub-classes of the protected area \n\n\n\nalong with coverage area and rating values are given in Table 2. \n\n\n\nFigure 6: (a) Slope map of study area \n\n\n\nFigure 6: (b) Stream order map of study area \n\n\n\nScenario Cost Map i \ni = 1, 2 \u2026.. n \n\n\n\nFactor 1 Map \n\n\n\nSum (Weight x Attribute) \n\n\n\nFactor 2 Map Factor \u2026. Map Factor n Map \n\n\n\nCost Path Function \n\n\n\nCost Distance Function \n\n\n\nInitiating and Terminating \nPoints \n\n\n\nResistance Surface Map \ni = 1, 2, 3 \n\n\n\nCost Distance Cost Back Link \n\n\n\nRaster to Polyline \n\n\n\nLeast Cost Path \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 56-63 \n\n\n\nCite the Article: Rakesh Sunari Magar, Pradeep Kumar Shrestha, Prabin Kayastha (2021). Manifestation of Optimal Route Alignment Selection of Rural Road Using GIS \nand Least Cost Path (LCP) Model with Engineering and Environmental Suitability Perspective: A Case Study in Nepal. Malaysian Journal of Geosciences, 5(2): 56-63. \n\n\n\nFigure 6: (c) Landslide occurrence map of study area \n\n\n\nFigure 6: (d) Land use map of study area \n\n\n\nFigure 6: (e) Slope aspect map of study area \n\n\n\nFigure 6: (f) Protected area map of study area \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 56-63 \n\n\n\nCite the Article: Rakesh Sunari Magar, Pradeep Kumar Shrestha, Prabin Kayastha (2021). Manifestation of Optimal Route Alignment Selection of Rural Road Using GIS \nand Least Cost Path (LCP) Model with Engineering and Environmental Suitability Perspective: A Case Study in Nepal. Malaysian Journal of Geosciences, 5(2): 56-63. \n\n\n\nTable 2: Rating values of different sub-classes of cost factor maps \n\n\n\nFactor/ Sub-classes Description \nCoverage \n%  \n\n\n\nCost \nRating \n\n\n\nEngineering Theme \n(A) Slope \n1. 00-110 Very Low costly 1.83% 1 \n\n\n\n2. 110-170 \nLow to very low \ncostly \n\n\n\n4.92% 2 \n\n\n\n3. 170-220 Low costly 8.36% 3 \n\n\n\n4. 220-270 \nLow to moderate \ncostly \n\n\n\n12.84% 4 \n\n\n\n5. 270-310 Moderate costly 16.91% 5 \n\n\n\n6. 310-350 \nModerate to high \ncostly \n\n\n\n19.83% 6 \n\n\n\n7. 350-390 High costly 18.13% 7 \n\n\n\n8. 390-450 \nHigh to very high \ncostly \n\n\n\n11.94% 8 \n\n\n\n9. >450 Very high costly 5.23% 9 \n(B) Stream Order \n1. No Drainage Very low costly 96.28% 1 \n2. 1st order Low costly 0.90% 3 \n\n\n\n3. 2nd order \nLow to moderate \ncostly \n\n\n\n0.95% 4 \n\n\n\n4. 3rd order \nModerate to high \ncostly \n\n\n\n1.05% 6 \n\n\n\n5. 4th order \nHigh to very high \ncostly \n\n\n\n0.76 % 8 \n\n\n\n6. 5th order Very high costly 0.05% 9 \n(C) Landslide area \n\n\n\n1. No Landslide \nVery low risk or \ncostly \n\n\n\n99.00% 1 \n\n\n\n2. Very small \n(<3,588 m2) \n\n\n\nLow risk or costly 0.02% 3 \n\n\n\n3. Small (3,588m2 to\n15,944m2) \n\n\n\nLow to moderate \nrisk or costly \n\n\n\n0.38% 4 \n\n\n\n4. Medium \n(15,944m2 to\n38,037 m2) \n\n\n\nModerate to high \nrisk or costly \n\n\n\n0.10% 6 \n\n\n\n5. Large (38,037 m2 \nto 85,459m2) \n\n\n\nHigh to very high \nrisk or costly \n\n\n\n0.07% 8 \n\n\n\n6. Extreme\n(>85,459 m2) \n\n\n\nVery high risk or \ncostly \n\n\n\n0.30% 9 \n\n\n\nEnvironmental Theme \n(D) Land use \n1. Barren Land Very Low costly 15.50% 1 \n\n\n\n2. Bush Land \nLow to very low \ncostly \n\n\n\n3.85% 2 \n\n\n\n3. Grass Land \nLow to very low \ncostly \n\n\n\n8.26% 2 \n\n\n\n4. Vegetation Forest Low costly 46.03% 3 \n\n\n\n5. Cultivation Land\nLow to moderate \ncostly \n\n\n\n24.81% 4 \n\n\n\n6. Sand High costly 1.15% 7 \n7. River and stream High costly 0.18% 7 \n\n\n\n8. Pond and lake \nHigh to very high \ncostly \n\n\n\n0.03% 8 \n\n\n\n9. Embankment Very high costly 0.18% 9 \n(E) Slope aspect \n1. South Favourable 15.02% 1 \n\n\n\n2. South East \nModerately \nfavourable \n\n\n\n14.59% 3 \n\n\n\n3. South West \nModerately \nfavourable \n\n\n\n16.25% 3 \n\n\n\n4. East Less favourable 12.36% 5 \n5. West Less favourable 13.40% 5 \n6. North East Unfavourable 8.74% 7 \n7. North West Unfavourable 11.02% 7 \n\n\n\n8. North \nVery \nunfavourable \n\n\n\n8.61% 9 \n\n\n\n(F) Protected area \n1. No Protected Area No restriction 38.86% 1 \n\n\n\n2. Buffer Zone \nModerate \nrestriction \n\n\n\n30.06% 5 \n\n\n\n3. National Park High Restriction 31.08% 9 \n\n\n\n3.2 Creation of the cost factor maps \n\n\n\nIn this study, fifteen cost factor maps were created based on fifteen \n\n\n\nscenarios, as shown in Eqs. 1 to 15. \n\n\n\n1. Engineering Thematic Cost Value - Slope only scenario = \n100%*Slope                                                                                                 (1)\n\n\n\n2. Engineering Thematic Cost Value - Stream order only scenario = \n100%*Stream order                                                                                 (2)\n\n\n\n3. Engineering Thematic Cost Value - Landslides only scenario = \n100% Landslides                                                                                       (3) \n\n\n\n4. Engineering Thematic Cost Value - Combination of Slope and \nStream Order scenario = 50% *Slope + 50% * Stream Order      (4) \n\n\n\n5. Engineering Thematic Cost Value - Combination of Stream Order \nand Landslides scenario = 50% * Stream Order + 50% *Landslides \n\n\n\n (5) \n6. Engineering Thematic Cost Value - Combination of  Landslides \n\n\n\nand Slope scenario =  50% *Landslides + 50% *Slope               (6) \n7. Engineering Thematic Cost Value - Combination of Slope, Stream \n\n\n\nOrder and Landslides scenario = 33.33% *Slope + 33.33% * \nStream Order + 33.33%* Landslide                                                                      (7) \n\n\n\n8. Environmental Thematic Cost Value \u2013 Land use only scenario = \n100%* Land use                                     (8) \n\n\n\n9. Environmental Thematic Cost Value \u2013 Slope aspect only scenario \n= 100%* Slope aspect                                                                                                        (9)\n\n\n\n10. Environmental Thematic Cost Value - Protected area only \nscenario = 100%* Protected area                                                     (10) \n\n\n\n11. Environmental Thematic Cost Value - Combination of Land use \nand Slope aspect scenario = 50% * Land use + 50% * Slope aspect\n(11) \n\n\n\n12. Environmental Thematic Cost Value - Combination of Slope aspect\nand Protected Area scenario = 50% * Slope aspect + 50% * \nProtected area                                                      (12) \n\n\n\n13. Environmental Thematic Cost Value - Combination of  Protected \narea and Land use scenario =  50% * Protected Area + 50% * Land \nuse                                                                                                               (13) \n\n\n\n14. Environmental Thematic Cost Value - Combination of Land use, \nSlope aspect and Protected area scenario = 33.33% * Land use + \n33.33% * Slope aspect + 33.33% * Protected Area             (14) \n\n\n\n15. Hybrid Thematic Cost Value - Combination of Slope, Stream Order, \nLandslides, Land use, Slope aspect and Protected area scenario = \n50% * Combination of Slope, Stream Order and Landslides \nscenario + 50% * Combination of Land use, Slope aspect and \nProtected area scenario                                                                         (15)\n\n\n\n3.3 Designing the fifteen routes using the Least Cost Path (LCP) \n\n\n\nThe cost path algorithm of ESRI ArcGIS determines the Least Cost Path \n\n\n\n(LCP) model. This model was used for the aforementioned fifteen \n\n\n\nscenarios. The fifteen least cost routes (Fig. 7) were generated from the \n\n\n\nsource to destination calculated by the function as per the input cost \n\n\n\ndistance, backlink raster dataset and destination position. The LCP model \n\n\n\nuses the cost-weighted distance and the direction surfaces for an area to \n\n\n\ndetermine a cost-effective route between source and destination (Singh \n\n\n\nand Singh, 2017). In the LCP model, the eight neighbors of a cell are \n\n\n\nevaluated, and the path moves to the cell with the smallest accumulated \n\n\n\nvalue. The process would repeat itself until the source and destination are \n\n\n\nconnected (Xu and Lathrop, 1994). \n\n\n\nFigure 7: 15 scenarios of least cost paths with the existing scenario \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 56-63 \n\n\n\n\n\n\n\n \nCite the Article: Rakesh Sunari Magar, Pradeep Kumar Shrestha, Prabin Kayastha (2021). Manifestation of Optimal Route Alignment Selection of Rural Road Using GIS \n\n\n\nand Least Cost Path (LCP) Model with Engineering and Environmental Suitability Perspective: A Case Study in Nepal. Malaysian Journal of Geosciences, 5(2): 56-63. \n \n\n\n\n\n\n\n\n3.4 Comparison of fifteen routes to select the best alignment \n\n\n\nThe fifteen route alignments were converted to linear features, and a 50 m \n\n\n\nbuffer was created on each side of the centre-line of the route alignment. \n\n\n\nThe area covered by buffered polygons of each alternative route \n\n\n\nconcerning each sub-classes of criteria is derived and analyzed in MS \n\n\n\nExcel. The rating cost values of sub-classes of criteria were multiplied with \n\n\n\nthe number of count of cells to derive the cost. The relative costs for fifteen \n\n\n\nscenarios with the route lengths are summarized in Table 3, and calculated \n\n\n\nrelative costs are compared. Except for the Scenario 2 i.e. based on \n\n\n\nenvironmental theme - stream order only, all other scenarios are \n\n\n\ncomparable in their distance and relative cost. In this study, Scenario 15 \n\n\n\n(hybrid theme) shows the least cost and Scenario 2 (environmental theme \n\n\n\n- stream order only) shows the highest cost. These results depict that the \n\n\n\nbest route alignment in the study area is the one which considers the \n\n\n\nfactors such as slope, stream order, landslide, land use, slope aspect and \n\n\n\nprotected area. Hence, the route alignment derived from Scenario 15 \n\n\n\nbased on the hybrid theme is the optimal path for this study. \n\n\n\nTable 3: Summary of 15 least cost path scenarios \nS. No. Scenario Cost  Rank Length \n\n\n\n0 Existing Road 16,974.00 \n10th \n \n\n\n\n10.96 \n\n\n\n1 Slope only \n18,018.00 \n \n\n\n\n13th \n \n\n\n\n10.03 \n\n\n\n2 Stream order only \n99,552.00 \n \n\n\n\n16th \n \n\n\n\n62.92 \n\n\n\n3 Landslides only \n16,724.00 \n \n\n\n\n8th \n \n\n\n\n8.87 \n\n\n\n4 \nSlope and stream order - \nEqual combo \n\n\n\n17,630.00 \n \n\n\n\n12th \n \n\n\n\n10.30 \n\n\n\n5 \n Stream order and \nlandslides - Equal combo \n\n\n\n15,195.00 \n \n\n\n\n2nd \n \n\n\n\n8.93 \n\n\n\n6 \nLandslides and slope -  \nEqual combo \n\n\n\n18,741.00 \n \n\n\n\n15th \n \n\n\n\n10.13 \n\n\n\n7 \nSlope, stream order and \nlandslides - Equal combo \n\n\n\n17,223.00 \n \n\n\n\n11th \n \n\n\n\n10.24 \n\n\n\n8 Land use only \n18,469.00 \n \n\n\n\n14th \n \n\n\n\n10.65 \n\n\n\n9 Slope aspect only \n15,682.00 \n \n\n\n\n6th \n \n\n\n\n9.58 \n\n\n\n10 Protected area only \n16,793.00 \n \n\n\n\n9th \n \n\n\n\n8.87 \n\n\n\n11 \nLand use and slope aspect - \nEqual combo \n\n\n\n15,418.00 \n \n\n\n\n3rd \n \n\n\n\n9.67 \n\n\n\n12 \nSlope aspect and protected \narea - Equal combo \n\n\n\n15,499.00 \n \n\n\n\n4th \n \n\n\n\n9.51 \n\n\n\n13 \nProtected area and land use \n- Equal combo \n\n\n\n15,862.00 \n \n\n\n\n7th \n \n\n\n\n9.05 \n\n\n\n14 \nLand use, slope aspect and \nprotected area - Equal \nCombo \n\n\n\n15,591.00 \n \n\n\n\n5th \n \n\n\n\n9.53 \n\n\n\n15 All six factors - Equal combo \n15,101.00 \n \n\n\n\n1st \n \n\n\n\n9.18 \n\n\n\n4. CONCLUSIONS \n\n\n\nIdentifying the best route alignment in transport infrastructure \n\n\n\nmanagement is one of the cumbersome tasks as it requires detail analysis \n\n\n\nof the enormously large quantity of data and different criteria, parameters \n\n\n\nand factors depending upon the size of the project. \n\n\n\nThis study presents the combination of Geographic Information System \n\n\n\n(GIS) and Least Cost Path (LCP) model for different scenarios in identifying \n\n\n\nthe various route alignment alternatives. Hence, by avoiding steep to the \n\n\n\nvery steep slope, moderate to the high order of stream, instabilities such \n\n\n\nas landslides, land use such as sand, water bodies, cliff, slope aspect \n\n\n\norientating in the north and protected area such as National Park, the \n\n\n\nplanned route should be technically feasible and environmentally sound. \n\n\n\nIn this study, fifteen alternative routes are generated using different \n\n\n\nscenarios. Among fifteen routes, fourteen routes are comparable in their \n\n\n\ndistance and relative cost. Only one route which is based on environmental \n\n\n\ntheme i.e. stream order only shows the highest cost due to the length of \n\n\n\nroad. In this study area, the existing road is about 11 km long, while the \n\n\n\nLCP model's best route is about 9.2 km long. Hence, the LCP model applied \n\n\n\nin these routes were quite successful in addressing the issues as \n\n\n\nmentioned above. As this model is simple and flexible, similar approaches \n\n\n\ncan be used in different parts of rural areas of Nepal for the selection of the \n\n\n\nbest route alignment to link different rural areas via road. \n\n\n\nThe study results can be used to select the best route alignment in the \n\n\n\nstudy area by the concerned authorities, planners and engineers. This \n\n\n\nstudy provides valuable information so that attention can be paid to the \n\n\n\nsteep to the very steep slope, high order stream, the occurrence of \n\n\n\nlandslides, different land use types, slope aspect and protected areas for \n\n\n\nany kind of road development works. In this study, the LCP model has been \n\n\n\napplied by considering the engineering and environmental aspects of the \n\n\n\nroad. The results would have been different if the social, economic and \n\n\n\npolitical aspects were considered. This study recommends further studies \n\n\n\non the determination of impacts of socio-economic and political \n\n\n\nparameters depending upon data and information availability in the study \n\n\n\narea. Similarly, the other techniques such as unequal overlay weightage, \n\n\n\nAnalytic Hierarchy Process (AHP), fuzzy logic, genetic algorithm \n\n\n\ntechniques etc. can also be applied for this study's improvisation. \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe authors would like to thank the Department of Survey, Government of \n\n\n\nNepal for providing the topographic and digital data. The authors are also \n\n\n\ngrateful to the members of Dupcheswor Rural Municipality, Nepal, for \n\n\n\ntheir support in this research. The authors would like to acknowledge the \n\n\n\nDepartment of Civil Engineering, Pulchowk Campus, Institute of \n\n\n\nEngineering (IOE), Tribhuvan University for laboratory facilities. The \n\n\n\nauthors would also like to thank anonymous reviewer and Editor-in-Chief \n\n\n\nDr. Rodeano Roslee for their constructive comments. \n\n\n\nREFERENCES \n\n\n\nAcharya, T., Yang, I.T., 2017. GIS-based Preliminary Feasibility Study for \n\n\n\nthe Optimal Route Selection for China-India Railway through Nepal. \n\n\n\nJournal of the Korean Society of Surveying, Geodesy, Photogrammetry \n\n\n\nand Cartography 35(4): 281-290. doi: 10.7848/ksgpc.2017.35.4.281 \n\n\n\nAtkinson, M., Deadman, P., Dudycha, D., Traynor, S., 2005. Multi-criteria \n\n\n\nevaluation and least cost path analysis for an arctic all-weather road. \n\n\n\nApplied Geography 25(4): 287-307. \n\n\n\nhttps://doi.org/10.1016/j.apgeog.2005.08.001 \n\n\n\nColloschonn, W., Pliar, J.V., 2000. A direction dependent least-cost-path \n\n\n\nalgorithm for roads and canals. International Journal of Geographical \n\n\n\nInformation Science 14(4): 397-406. \n\n\n\nhttps://doi.org/10.1080/13658810050024304 \n\n\n\nDupcheswor Rural Municipality (DRM), 2020. Village Profile. Office of \n\n\n\nDupcheswor Rural Municipality, Samundratar, Nuwakot, Nepal. 58 pp.  \n\n\n\n Ebrahimipoor, A. R.,  Alimohamadi,  A. , Alesheikh, A.A. ,  Aghighi, H., 2009.  \n\n\n\nRouting of Water Pipeline Using GIS and Genetic Algorithm. Journal of \n\n\n\nApplied Sciences, 9: 4137-4145. doi: 10.3923/jas.2009.4137.4145 \n\n\n\nEffat, H.A., Hassan, O.A., 2013. Designing and evaluation of three \n\n\n\nalternatives highway routes using the Analytical Hierarchy Process and \n\n\n\nthe least-cost path analysis, application in Sinai Peninsula, Egypt. The \n\n\n\nEgyptian Journal of Remote Sensing and Space Science 16(2): 141-151. \n\n\n\nhttps://doi.org/10.1016/j.ejrs.2013.08.001 \n\n\n\nHumber, J. , 2004. Enhancing the pipeline route selection process. In: GITA \n\n\n\nOil and Gas Conference Proceedings, Texas. pp. 1-5 \n\n\n\nLoganathan, S. and Elangovan, K., 2017. Route alignment between the \n\n\n\ncorridors through the applications of RS and GIS \u2014 Perundurai to Palani, \n\n\n\nTamil Nadu, India. In: 2017 International Conference of Electronics, \n\n\n\nCommunication and Aerospace Technology (ICECA), Coimbatore, India. \n\n\n\nMahavar, V., Prakash, I., Pham, B.T., 2019. Development and Application of \n\n\n\nGIS Model in the Selection of Cost Effective Road Alignment. \n\n\n\nInternational Journal of Applied Engineering Research 14(15): 3413-\n\n\n\n3428 \n\n\n\nMahini, A., Abedian, S., 2014. Routing of Road Network through Least Cost \n\n\n\nPathway Algorithm to Minimize Impacts on Environment. Journal of the \n\n\n\nIndian Society of Remote Sensing 42: 517-527. \n\n\n\nhttps://doi.org/10.1007/s12524-013-0351-7 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 56-63 \n\n\n\n\n\n\n\n \nCite the Article: Rakesh Sunari Magar, Pradeep Kumar Shrestha, Prabin Kayastha (2021). Manifestation of Optimal Route Alignment Selection of Rural Road Using GIS \n\n\n\nand Least Cost Path (LCP) Model with Engineering and Environmental Suitability Perspective: A Case Study in Nepal. Malaysian Journal of Geosciences, 5(2): 56-63. \n \n\n\n\n\n\n\n\nRao, K. R., Sreekeshava, K.S., Dharek, M.S.,  Sunagar, P., 2019.  Raster Least \n\n\n\nCost Approach for Automated Corridor Alignment in Undulated Terrain. \n\n\n\nInternational Journal of Recent Technology and Engineering (IJRTE) 8-\n\n\n\n1 (C):1-4 \n\n\n\n\u015eari, F., \u015een, M., 2016. Least Cost Path Algorithm Design for Highway Route \n\n\n\nSelection. In: International Scientific Conference on Applied Sciences, \n\n\n\nAntalya, Turkey, 2016. pp. 69-76. \n\n\n\nSekulic, M., Siljeg, A.,  Mansourian,  A., Watson, L. ,  Cavric, B.I. , 2020. Multi-\n\n\n\ncriteria spatial-based modelling for optimal alignment of roadway by-\n\n\n\npasses in the Tlokweng planning area, Botswana, Journal of Spatial \n\n\n\nScience, 1-19. : https://doi.org/10.1080/14498596.2020.1755731 \n\n\n\nSingh, M.P., Singh, P., 2017. Multi-criteria GIS modeling for optimum route \n\n\n\nalignment planning in outer region of Allahabad City, India. Arabian \n\n\n\nJournal of Geosciences, 10: 294. https://doi.org/10.1007/s12517-017-\n\n\n\n3076-z \n\n\n\nStrahler, A.N., 1957. Quantitative analysis of watershed geomorphology. \n\n\n\nEos, Transactions American Geophysical Union 38 (6): 913-\n\n\n\n920.https://doi.org/10.1029/TR038i006p00913 \n\n\n\nSunusi, A., Agrawal, V.C., Lal, D., Suleiman, S., 2015. Selection of road \n\n\n\nalignment location in Kano-Nigeria using GIS and Analytical Hierarchy \n\n\n\nProcess (AHP) Model. International Journal of Scientific Engineering and \n\n\n\nTechnology Research 4(43):9449-9455. \n\n\n\nWahdan, A., Effat, H., Abdallah, N., Elwan, K., 2019. Design an Optimum \n\n\n\nHighway Route using Remote Sensing Data and GIS-Based Least Cost \n\n\n\nPath Model, Case of Minya-Ras Ghareb and Minya-Wahat-Bawiti \n\n\n\nHighway Routes, Egypt. American Scientific Research Journal for \n\n\n\nEngineering, Technology, and Sciences 56 (1): 157-181  \n\n\n\nXu, J., Lathrop Jr., R.G., 1994. Improving cost-path tracing in a raster data \n\n\n\nformat. Computers & Geosciences 20(10):1455-1465. \n\n\n\nhttps://doi.org/10.1016/0098-3004(94)90105-8 \n\n\n\nYu, C., Lee, J., Munro-Stasiuk, M.J., 2010. Extensions to least-cost path \n\n\n\nalgorithms for roadway planning. International Journal of Geographical \n\n\n\nInformation Science 17(4): 361-376. \n\n\n\nhttps://doi.org/10.1080/1365881031000072645. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 21-31 \n\n\n\nCite The Article: Pipas Kumar, Varun Joshi (2019). A Geospatial- Statistical Approach To Alienate Priority Area Of Upper Watershed Of River Subarnarekha Using \nMorphometric Assessment Framework. Malaysian Journal Of Geosciences, 3(1) : 21-31. \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\nArticle History: \n\n\n\nReceived 19 November 2018  \nAccepted 21 December 2018 \nAvailable online 4 January 2019\n\n\n\nABSTRACT\n\n\n\nMicro-watershed planning and management play an important role in addressing the issues and challenges of water \nscarcity of a region. The assessment of hydrological framework using morphometric calculation and GIS technology \nis a recognized tool for carrying out planning activities for the watershed. The uncertainties associated with the earlier \nwork of watershed prioritization can be easily addressed with new emerging geospatial-statistical correlation \ntechniques. A sum weightages methodology is applied on the upper watershed of river Subarnarekha in the state of \nJharkhand, India, for identification of critical subwatershed, a priority level ranking and zonation mapping of each \nindividual subwatershed is developed using the morphometric calculation coupled with GIS tools. This approach \nhelps in identification and bifurcation of subwatershed with three level of priority, i.e, Low, Medium, High. The result \nreveals that 5.8 % of subwatershed lies in high priority zone that signifies the area as critical and susceptible zones of \nsoil erosion, which requires immediate attention from planners and policymakers. The high-level priority region \nshould be considered for soil, water, and land conservation work. This approach of prioritization techniques can \nfurther be extended to another critical watershed of India for better decision support system and planning  \n\n\n\n KEYWORDS \n\n\n\nSubarnarekha, Watershed, Jharkhand, Morphometric, Ranchi.\n\n\n\n1. INTRODUCTION \n\n\n\nWatershed rejuvenation and development is an integrated approach to \nmaintain ecological balance. It helps in the recharge of the water table, \nenhancing agricultural productivity thus reducing hunger and poverty. \nThe Indian peninsula of southern Asia is endowed with rich footprints of \nmighty Himalaya, which is the origin of three rivers, namely the Ganges, \nYamuna, and the Brahmaputra, can be called as \u201cEcological Fabric of \nHindustan\u201d. Based on a study, the last decade has witnessed climatic \nchange scenarios that may affect the sustainability of an ecosystem [1]. \nAccording to a 2006 World Bank study, water stress and crisis situations \nexist in river basins of India that has affected the health of the aquifer. With \nthe current rate of utilization, in 2050 water demands would exceed all \navailable sources of supply. Solutions are needed that could address the \non-going crisis and constraints on both ground and surface water. \nImplementing good watershed management practices and approaches can \nbe potentially useful. \n\n\n\nWatersheds are self-sustained, composite hydrological entity \nincorporating many components and mechanisms, which play a significant \nrole in sustained growth and development. The implementation of \nwatershed management programs at sub-watershed and the micro-\nwatershed level is a challenging task. The other limiting factor that \ninterferes the sustainability practices related to watershed management \nis the missing data or data gaps. The management of water resource can \nbe done using watershed prioritization techniques, an approach that is \nhelpful in soil conservation, barren land rejuvenation, and rural \nemployment generation. A scientific approach to watershed planning and \nmanagement involves capturing different forms of precipitation for \n\n\n\nstorage (e.g., ponds, tanks) in the wet season, thus increasing soil moisture \ncontent and water availability for future use. The deviation from \nwatershed management practices may result in deterioration of rivers and \nlandform around many catchments, especially, in those regions where \nprecipitation is limited for specific months. Post monsoon water \nconservation practices are utmost necessary for maintaining the balance \nbetween water availability and demand for agricultural, drinking and \nindustrial use. \n\n\n\nIn India, extensive research regarding the morphometric analysis of river \nbasin has been carried at various levels for resource planning and \nwatershed management. Earlier attempt has been made to characterize \nthe hydrogeological behavior of the upper watershed of river \nSubarnarekha through morphometric analysis [2]. According to a \nresearch, the identification of groundwater recharge potential and \nsurface-water augmentation is done at Kurzadi watershed in West-Central \nIndia [3]. Similarly, the Geo-Hydrological study is conducted on Vishav \ndrainage basin to identify low, moderate and high groundwater potential \nzones [4]. A statistical technique, like, the correlation between \nmorphometric variables is also used to understand the nature, erosional \nstatus and hydrologic response of Pinjaur in the West and Sub-Himalayan \nMountains [5]. The morphometric calculation is also exercised in \nestablishing the level of erosion susceptibility in Rembiara watershed [6]. \nSeveral other techniques like geomorphologic instantaneous unit \nhydrograph (GIUH) is used for the derivation of hydrographs, which helps \nin integrated watershed [7]. Similarly, Mapping of the vulnerability of \nflooded area in the arid region is done in Gharda\u00efa-Algeria [8]. According \nto a research, Semi-quantitative method of the sediment yield index (SYI) \nmodel for watershed prioritization is also a good technique for watershed \nprioritization [9]. Morphometric parameters have been used to \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : http://doi.org/10.26480/mjg.01.2019.21.31 \n\n\n\nREVIEW ARTICLE \n\n\n\nA GEOSPATIAL- STATISTICAL APPROACH TO ALIENATE PRIORITY AREA OF UPPER \nWATERSHED OF RIVER SUBARNAREKHA USING MORPHOMETRIC ASSESSMENT \nFRAMEWORK \n\n\n\nPipas Kumar1, Varun Joshi2 \n\n\n\n1Research Scholar, University School of Environment Management, Guru Gobind Singh Indraprastha University, New Delhi, India \n2Professor, University School of Environment Management, Guru Gobind Singh Indraprastha University, New Delhi, India \n*Corresponding Author E-mail: pipasx2@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\n\nmailto:pipasx2@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 21-31 \n\n\n\nCite The Article: Pipas Kumar, Varun Joshi (2019). A Geospatial- Statistical Approach To Alienate Priority Area Of Upper Watershed Of River Subarnarekha Using \nMorphometric Assessment Framework. Malaysian Journal Of Geosciences, 3(1) : 21-31. \n\n\n\nobtain the terrain of Limbang river basin Borneo [10]. Classification \ntechniques like K-means Cluster Analysis (KCA), Fuzzy Cluster Analysis \n\n\n\n(FCA), and Kohonen Neural Networks (KNN) are used to prioritize 25 \nmicro-watersheds of Kherthal watershed of Rajasthan [11]. Watershed  \n\n\n\nprioritization using the weighted sum model and Snyder\u2019s synthetic unit \nhydrograph can be used for obtaining flash flood risk [12]. Groundwater \nrecharge potential zones mapping in upper Manimuktha Sub-basin, Vellar \nriver Tamil Nadu India is obtained using GIS and remote sensing \ntechniques [13]. A micro level basin hydromorphological relationships \nwith morphometric parameters have been presented by [14]. A scholar \ndemonstrated the Application of weights-of-evidence (WoE) and \nevidential belief function (EBF) models for the delineation of soil erosion \nvulnerable zones on Pathro river basin, Jharkhand, India [15]. \n\n\n\nThe compound parameter calculation and ranking method is a common \nmethod used in watershed prioritization. In this method, the entire \nmorphometric variables are assigned equal weightages. It is evident that \nall the associated variables in the watershed have a different level of \nimpact on the overall geohydrological characteristics of the watershed. \nTherefore, assigning equal weightages may create a significant level of \nvariation on the results. To overcome this level of variation, a statistical \napproach, WSA (Weighted Sum Analysis) [16] a correlation technique is \nused for ranking and prioritization of each subwatershed. The impact of \nmorphometric input variables is considered individually to determine \nwhich parameter should be considered in the final combination for rank \nallocation. \n\n\n\n2. STUDY AREA \n\n\n\nFigure 1:  Location of Subarnarekha River basin in state Jharkhand, India \n\n\n\nThe selected area for the present study is the upper watershed of river \nSubarnarekha, covering an area of 12831 Km2, in the state of Jharkhand, \nIndia (figure 1). The origin of river Subarnarekha is a small rivulet in the \nvillage name Piskanagri, almost 15 km from the Ranchi city. The place of \norigin is surrounded by lush green small hillocks and agricultural land. The \narea surrounding the rivulet is also a holy place for the existing ethnic \ngroup of people (tribals), which they worship annually in the month of \nJanuary every year. The origin point of the river is called as rayen jhuma in \nthe nagpuri language of this region. This river flows through three districts \nof Jharkhand, Ranchi, East Singhbhum, and Saraikela Kharsawa, one \ndistrict of West Bengal, i.e, West Medniapore and finally it joins the Bay of \nBengal near Talsari, Odisha. Its main tributaries are Kanchi, Karkari, \nKharkai, Raru, Garru, and Dulang. This river passes through one of the \nrichest mineral belts of world chiefly consisting of ores of iron, copper, \nmica. This region also endowed with the thick cover of the mixed \ndeciduous forest, primarily consisting of Sal (Shorea robusta). The \nlatitudinal and longitudinal extension of the study area is 23\u00b0 18' N and \n85\u00b0 11' E respectively. On the scale of 1: 2,50,000, this area is represented \non Survey of India topographical map no. 73 E. Geomorphologically, this \narea is characterized by high spurs and water divide, gorges, deep valleys \nand waterfalls with deposits of red and laterite soil. The river course \nconsists of rocks of granite, pegmatite [17]. The removal of the \nsuperincumbent load of overlying rocks from the earth\u2019s surface can be \nseen throughout the river channel due to continued erosion [18]. The \nclimate of this region is characterized by hot summer from March to May \nand cold winter during the month of November to February. The mean \nmonthly temperature varies from 40.5\u00b0 C in the month of May to 9.00 \u00b0 C \nin December whereas annual average maximum and minimum \ntemperatures vary from 32.4\u00b0 C to 18.0\u00b0C respectively. This basin receives \nits rainfall from South-West monsoon, which starts from June and ends in \nOctober. The average annual rainfall for the basin is around 1800 mm. The \ncultivation is practiced for two seasons. In Kharif season, (June-October) \nrice is sown and in Rabi (November\u2013March) wheat is the principal crop. \n\n\n\n3. METHODOLOGY \n\n\n\n3.1 Morphometric analysis and watershed management \n\n\n\nThe management of watershed from a micro-catchment level to macro \ninvolves a complex integration of various physical processes and \ngeomorphological units. The complexity of behavior of any watershed \ntowards future scenario becomes a challenging task because it involves a \nbalance between lithospheres and hydrosphere across spatial and \ntemporal scales. The hydrogeological component like surface morphology, \nsurface runoff, elevation, soil texture, landform, land use, etc., play \nsignificant role in devising a plan for integrated watershed management \nwith advent of technology and application of analytical tools, these \ncomponents can be studied with the help of a hydrological assessment \nframework called as morphometric analysis. Mathematical modeling has \nalso emerged as a superb tool to address the challenges in watershed \nmanagement. Extensive representation of watershed through the \nintegration of its physical process and representing it in a digital format \nhas given an edge for decision-makers and planners. Computer modeling \nof a watershed coupled with statistics, remote sensing and Geographic \nInformation Systems (GIS) proved to be effective and efficient tools to \naddress the issue of demand and supply. This integration of scientific and \ntechnological expertise has enhanced the efficiency and accuracy in the \noutput of morphometric analysis. The Morphometric calculation process \nanalyzes the extensive stream network to describe the geomorphological \nfeature of the area. The output can further be extended to study and \ncompare basins of different sizes, prioritization of subwatershed, and \nevaluation of groundwater recharge potential, suitable sites for rainwater \nharvesting. \n\n\n\n3.2 Preparation of geospatial inputs \n\n\n\nIn the present study, to achieve the goals of morphometric analysis, GIS \ntechnique is used for the extraction of the drainage pattern of the river \nbasin. The remotely sensed satellite data dated 20th April 2015 (path-140, \nrow-44, and path-139, row-44) which represents the study area is \ndownloaded from the freely available site, viz, https://www.landsat.org. \nThese data are then processed in Erdas Imagine 9.0 of Leica Geosystems \nand Arc Gis 10.1 of Esri. The techniques and tools, like, image \nenhancement, radiometric correction, transformation, classification, and \nspatial analysis are used to derive various geo hydrological information of \nthe study area. The elevation plays an important role in providing \ninformation about the river drainage pattern. The elevation data at a \nresolution of 90 m acquired through the shuttle radar topography mission \n(SRTM) available for the globe is downloaded from the \nhttp://srtm.usgs.gov/data/obtaining.html (dated 20th April 2015) and \nprocessed in Arc Gis 10.1, using hydrology tool [19]. Based on the data, the \nslope, aspects and other required information are extracted to prepare \nbase maps. According to the digital elevation model (DEM), the study area \nshows the lowest elevation of 48 m and the highest elevation of 1043 m \n(figure 2a). The SRTM cover the entire globe with a 3 arc-second digital \nelevation model. The recent availability of data with 30-meter resolution \ngives finer details of topography. In this study, a comparison of data \nquality, comparing in detail of 30-meter resolution with 90-meter \nresolution does not give a significant difference.  \n\n\n\nFigure 2(a): Digital Elevation \nModel (DEM) of upper watershed \nof River Subarnarekha \n\n\n\nFigure 2(b): Trunk stream \nnetwork of upper watershed of \nRiver Subarnarekha \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 21-31 \n\n\n\nCite The Article: Pipas Kumar, Varun Joshi (2019). A Geospatial- Statistical Approach To Alienate Priority Area Of Upper Watershed Of River Subarnarekha Using \nMorphometric Assessment Framework. Malaysian Journal Of Geosciences, 3(1) : 21-31. \n\n\n\nThe obtained topographic details of the DEM with 90-meter resolutions \nare sufficient to accomplish the present objective of this study. The trunk \nstream network (figure 2b) is extracted with the help of hydrology tools of \nArc Gis 10.1. Apart from this, the comprehensive ground survey is also \n\n\n\nconducted to incorporate ground truth inputs in preparation of base and \ndrainage maps. The present study area is delineated into 10 \nsubwatershed, i.e, SWD 1 to SWD 10. \n\n\n\n4. RESULTS \n\n\n\n4.1 Morphometric Characterization \n\n\n\nThe computation of morphometric parameters is done by calculating linear aspects, areal aspects and relief aspects. The calculated values are based on the \nmathematical formulae (Table 1-3). \n\n\n\nTable 1: Method of calculating linear aspects of the drainage basin \n\n\n\nS.no Parameters Symbol Formula Reference \n\n\n\n1 Stream length  Lu Length of the stream [20] \n\n\n\n2 Stream order  Nu Hierarchical Rank [21] \n\n\n\n3 Bifurcation ratio Rb \nRb = Nu / Nu +1 \nNu = No. of stream segments of a given order \nNu+1= No. of stream segments of next higher order. \n\n\n\n[22] \n\n\n\n4 Stream length ratio  RL \nRL = Lu / Lu -1 \nLu = total stream length of order \u2018u\u2019,  \nLu -1= the total stream length of its next lower order \n\n\n\n[20] \n\n\n\n5 Length of overland flow  Lg \nLg = 1/D* 2 where Lg = length of overland flow, D = drainage \ndensity \n\n\n\n[20] \n\n\n\n6 Length of the main channel  Lm \nLength along longest water course from the outflow point of to the \nupper limit of \ncatchment boundary \n\n\n\n[20] \n\n\n\n7 Mean stream length  Lsm Lsm = Lu / Nu [21] \n\n\n\n8 Basin length  Lb Distance between outlet and farthest point on the basin boundary [21] \n\n\n\n9 Basin perimeter P Length of the watershed divide which surrounds the basin [21] \n\n\n\nTable 2: Method of calculating relief aspects of drainage basin \n\n\n\nS.no Parameters  Symbol Formula Reference \n\n\n\n1 Basin Relief  R \nH = Z \u2013 z \nWhere, Z = Maximum elevation of the basin (m)  \nz = Minimum elevation of the basin (m) \n\n\n\n[21] \n\n\n\n2 Basin Relief Ratio  Rh \nRh = H / Lbmax \nWhere, H = Maximum basin relief (m)  \nLbmax = Maximum basin length (m) \n\n\n\n[21] \n\n\n\n3 Ruggedness number  Rn Rn= Maximum basin relief (H) *drainage density (D) [23] \n\n\n\n4 Dissection index DI \nDI = H /Ra \nWhere, H = basin relief (m) \nRa = Absolute relief (m) \n\n\n\n[24] \n\n\n\nTable 3: Method of calculating aerial aspects of drainage basin \n\n\n\nS.no Parameters  Symbol Formula Reference \n\n\n\n1 Basin area  (A) Area enclosed within the boundary of the watershed divide - \n\n\n\n2 Stream Density \nDd \n(Km/Km2 ) \n\n\n\nDd = L\u03bc/A \nWhere, Dd = Stream density  \nL\u03bc = Total stream length of all orders and \nA = Area of the basin (Km2). \n\n\n\n[20] \n\n\n\n3 Stream Frequency Fs \n\n\n\nFs = N\u03bc/A \nWhere, Fs = Stream frequency. \nN\u03bc = Total no. of streams of all orders and A = Area of the \nbasin (Km2). \n\n\n\n[20] \n\n\n\n4 Texture Ratio Fs \nFs = N\u03bc /P \nWhere, N\u03bc = No. of streams in a given order and P = Perimeter \n(Kms) \n\n\n\n[20]\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 21-31 \n\n\n\nCite The Article: Pipas Kumar, Varun Joshi (2019). A Geospatial- Statistical Approach To Alienate Priority Area Of Upper Watershed Of River Subarnarekha Using \nMorphometric Assessment Framework. Malaysian Journal Of Geosciences, 3(1) : 21-31. \n\n\n\n5 Form Factor  Rf \nRf = A/Lb2 \nWhere, A = Area of the basin and \nLb = (Maximum) basin length \n\n\n\n[20] \n\n\n\n6 \nConstant Channel \nmaintenance  \n\n\n\n(C) \nC = 1/D \nD= Stream density \n\n\n\n[21] \n\n\n\n7 Elongation ratio  (Re) Re= \u221a(4*A/p)/Lb [21] \n\n\n\n8 Circulatory ratio  (Rc) Rc = 4 * Pi * A/P2 [25] \n\n\n\n9 Compactness constant  (Cc) Cc= 0.2821 P/A 0.5 [20] \n\n\n\n4.2 Linear aspects \n\n\n\nTable 4: Linear aspect \n\n\n\nSub watershed identity codes Area (Km2) Perimeter (Km) Basin length (Km) \n\n\n\nSWD-1 3347.18 325.91 89 \n\n\n\nSWD-2 1992.14 433 85 \n\n\n\nSWD-3 360.42 100.84 21 \n\n\n\nSWD-4 1144.64 167.11 39 \n\n\n\nSWD-5 560.36 128.8 37 \n\n\n\nSWD-6 904.37 181.31 44 \n\n\n\nSWD-7 747.47 256.42 34 \n\n\n\nSWD-8 1005.56 278.9 40 \n\n\n\nSWD-9 1376.06 353.28 48 \n\n\n\nSWD-10 1392.92 326.53 55 \n\n\n\nA linear aspect of a basin resembles the channel patterns of the drainage network. It includes stream order (U), stream length (L\u03bc), mean stream length \n(Lsm), bifurcation ratio (Rb), stream length ratio (RL), length of overland flow (Lg) (Table 1). \n\n\n\n4.3 Stream Order (U) \n\n\n\nTable 5: Order wise total stream length (Km) \n\n\n\nSub watershed \nidentity codes \n\n\n\n1 2 3 4 5 6 7 8 Total stream (Km) \n\n\n\nSWD-1 1370.7 623.17 376.22 89.14 171.64 1.23 0 0 2632.1 \n\n\n\nSWD-2 828.18 407.49 145.34 113.15 69.75 89.12 31.65 0 1684.68 \n\n\n\nSWD-3 296.12 181.07 77.9 21.83 26.52 5.7 12.06 0 621.2 \n\n\n\nSWD-4 480.25 227.85 112.05 52.43 69.43 67.96 0 0 1009.97 \n\n\n\nSWD-5 396.54 221.4 124.42 20.34 10.98 34.37 14.08 0 822.13 \n\n\n\nSWD-6 344.51 172.44 105.14 48.08 32.7 0 0 0 702.87 \n\n\n\nSWD-7 512.65 296.76 166.66 55.78 17.4 11.2 49.35 0 1109.8 \n\n\n\nSWD-8 409.9 200.5 91.91 63.68 26.69 0 0 0 792.68 \n\n\n\nSWD-9 555.56 256.03 124.15 40.27 35.63 1.63 0 40.31 1053.58 \n\n\n\nSWD-10 1032.2 575.77 262.17 147.41 30.88 0 0 58.68 2107.11 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 21-31 \n\n\n\nCite The Article: Pipas Kumar, Varun Joshi (2019).A Geospatial- Statistical Approach To Alienate Priority Area Of Upper Watershed Of River Subarnarekha Using \nMorphometric Assessment Framework. Malaysian Journal Of Geosciences, 3(1) : 21-31. \n\n\n\nFigure 3: river network and stream order of upper watershed of River \nSubarnarekha \n\n\n\nThe Strahler\u2019s system (1952) of classification is a slight modification of \nHorton\u2019s system (1945) of classification. In this system of classification, \nthe smallest, un-branched fingertip streams are designated as 1st order, \n\n\n\nthe confluence of two 1st order streams give stream of 2nd order; two 2nd \norder streams join to form a stream of 3rd order and so on. This way all \nsuccessive streams join and forms stream of next order. The trunk stream \nis the stream segment of the highest order. Stream of order 1 to 3 are \ntermed as headwater streams and constitute waterways in the upper \nreaches of the catchment. Nearly 80 % of world\u2019s waterways are of order \n1 to 3. The largest stream order known is 12, for example, river Amazon \nhas stream order of 12 [26]. Similarly, streams 4 to 6 are medium-size \nstream. As per the Strahler\u2019s (1964) ordering scheme, the upper \nwatershed of river in Jharkhand is eighth-order stream (figure 3). The \nprevious morphometric study conducted on river Subarnarekha also \nreveals that this basin is of eighth order [27]. The numerous Ist order \nstream are said to be formed by the continuous erosion of the river banks \n[28]. The main river stream joined by the major tributaries from its both \nbanks resulting in increase of stream order. The increase in stream order \ndirectly affects the size of the river basin. The selected study area \ncatchment has a size of 12831.12 Km2. The area of the catchment \nrepresents the area of closed curve forming the horizontal projection of \nthe catchment boundary. In this study, all sub-watersheds are \nsubsequently subjected to hydrology tool of ARC GIS for extraction of \ndetail stream network and other attributes (Table 5). The SWD-1 and \nSWD-4 is 6th order basins covering an area of 3347.18 Km2, 1114.64 Km2 \nrespectively. Similarly, SWD-3, SWD-2, SWD-5, SWD-7 are 7th order basin \nhaving area 360.42 Km2, 1992.14 Km2, 560.36 Km2, 747.47 Km2 \nrespectively. The 8th order basin is present in two subwatershed, i.e, SWD-\n9 ad SWD-10 which covers an area of 1376.06 Km2 and 1392.92 Km2 \nrespectively. \n\n\n\n4.4 Stream Length (Lu) \n\n\n\nTable 6: Order wise total stream length (Km) \n\n\n\nSub watershed \nidentity codes \n\n\n\n1 2 3 4 5 6 7 8 Total stream (Km) \n\n\n\nSWD-1 1370.7 623.17 376.22 89.14 171.64 1.23 0 0 2632.1 \n\n\n\nSWD-2 828.18 407.49 145.34 113.15 69.75 89.12 31.65 0 1684.68 \n\n\n\nSWD-3 296.12 181.07 77.9 21.83 26.52 5.7 12.06 0 621.2 \n\n\n\nSWD-4 480.25 227.85 112.05 52.43 69.43 67.96 0 0 1009.97 \n\n\n\nSWD-5 396.54 221.4 124.42 20.34 10.98 34.37 14.08 0 822.13 \n\n\n\nSWD-6 344.51 172.44 105.14 48.08 32.7 0 0 0 702.87 \n\n\n\nSWD-7 512.65 296.76 166.66 55.78 17.4 11.2 49.35 0 1109.8 \n\n\n\nSWD-8 409.9 200.5 91.91 63.68 26.69 0 0 0 792.68 \n\n\n\nSWD-9 555.56 256.03 124.15 40.27 35.63 1.63 0 40.31 1053.58 \n\n\n\nSWD-10 1032.2 575.77 262.17 147.41 30.88 0 0 58.68 2107.11 \n\n\n\nThe attribute table of the vector layer of the study area as obtained is used \nto compute and calculate the stream length. Horton\u2019s second law suggests \nthat the total length of stream segments is maximum in first-order streams \nand decreases with the increase in stream order. The stream of relatively \nsmaller length is characteristics of areas with larger slopes and finer \ntexture, whereas the streams which are relatively longer, indicate a flatter \n\n\n\ngradient. There is an inverse relationship between the number of streams \nwith stream order. As the stream order increases, the SWD-1, SWD- 2, \nSWD- 4, SWD- 7 show the slight variation in stream order length (table 6). \nThis variation is caused due to the different elevation pattern, undulating \nlandform, and flatter gradient.\n\n\n\n4.5 Stream Number (N\u03bc) \n\n\n\nTable 7: Stream number in different order of upper watershed of river Subarnarekha \n\n\n\nSub watershed \nidentity codes \n\n\n\n1 2 3 4 5 6 7 8 Total \nstream \n\n\n\nSWD-1 1012 464 192 83 19 1 0 0 1771 \n\n\n\nSWD-2 967 425 201 55 15 11 1 0 1675 \n\n\n\nSWD-3 206 86 49 11 1 1 2 0 356 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 21-31 \n\n\n\nCite The Article: Pipas Kumar, Varun Joshi (2019). A Geospatial- Statistical Approach To Alienate Priority Area Of Upper Watershed Of River Subarnarekha Using \nMorphometric Assessment Framework. Malaysian Journal Of Geosciences, 3(1) : 21-31. \n\n\n\nSWD-4 478 206 81 32 5 2 0 0 804 \n\n\n\nSWD-5 396 167 51 14 2 1 1 0 632 \n\n\n\nSWD-6 461 216 93 31 10 0 0 0 811 \n\n\n\nSWD-7 141 49 12 2 1 2 2 0 209 \n\n\n\nSWD-8 512 275 105 26 7 0 0 0 925 \n\n\n\nSWD-9 712 375 121 37 0 1 0 1 1247 \n\n\n\nSWD-10 701 364 111 39 10 0 0 1 1226 \n\n\n\nThe total number of stream segments in a particular order is known as \nstream number (Horton 1945) Stream number is directly proportional to \nthe size of the total drainage basin area. The total count of the stream \nsegment (Table 7) is found to decrease as the stream order increase in the \n\n\n\nbasin. A higher stream number indicates a high rate of infiltration and less \npermeability to the soil. The 8th order stream is present in SWD-9 and \nSWD-10 only. \n\n\n\n4.6 Stream length ratio (RL) \n\n\n\nTable 8: Stream length ratio of upper watershed of river Subarnarekha \n\n\n\nSub watershed identity \ncodes \n\n\n\n2/1 3/2 4/3 5/4 6/5 7/6 8/7 \n\n\n\nSWD-1 0.45 0.60 0.24 1.93 0.01 0.00 0.00 \n\n\n\nSWD-2 0.49 0.36 0.78 0.62 1.28 0.36 0.00 \n\n\n\nSWD-3 0.61 0.43 0.28 1.21 0.21 2.12 0.00 \n\n\n\nSWD-4 0.47 0.49 0.47 1.32 0.98 0.00 0.00 \n\n\n\nSWD-5 0.56 0.56 0.16 0.54 3.13 0.41 0.00 \n\n\n\nSWD-6 0.50 0.61 0.46 0.68 0.00 0.00 0.00 \n\n\n\nSWD-7 0.58 0.56 0.33 0.31 0.64 4.41 0.00 \n\n\n\nSWD-8 0.49 0.46 0.69 0.42 0.00 0.00 0.00 \n\n\n\nSWD-9 0.46 0.48 0.32 0.88 0.05 0.00 0.00 \n\n\n\nSWD-10 0.56 0.46 0.56 0.21 0.00 0.00 0.00 \n\n\n\nStream length ratio is calculated as the ratio of mean stream length of any \ngiven order (u) to the predecessor order of mean stream length (u-1). \nStream length ratio signifies the relationship between the surface flow \ndischarge and erosion stage of the basin. The erosion pattern over a long \nperiod of time also indicates the geomorphic development stages of the \nstream. The value obtained for different watershed shows a slight increase \nin stream length ratio from lower to higher stream order (Table 8). There \n\n\n\nis a slight variation for stream length ratio for SWD-5 and SWD-7. The \nvariation of stream length ratio between successive stream orders is due \nto the differences in slope and topographic conditions. The overall values \nof stream length of subwatershed suggest the developed geomorphic stage \nof a stream. \n\n\n\n4.7 Mean Bifurcation Ratio (Rb) \n\n\n\nTable 9: Bifurcation ratio of upper watershed of river Subarnarekha \n\n\n\nSub watershed \nidentity codes \n\n\n\n1/2 2/3 3/4 4/5 5/6 6/7 7/8 total Mean \n\n\n\nSWD-1 2.18 2.42 2.31 4.37 19.00 0.00 0.00 30.28 4.33 \n\n\n\nSWD-2 2.28 2.11 3.65 3.67 1.36 11.00 0.00 24.07 3.44 \n\n\n\nSWD-3 2.40 1.76 4.45 11.00 1.00 0.50 0.00 21.10 3.01 \n\n\n\nSWD-4 2.32 2.54 2.53 6.40 2.50 0.00 0.00 16.29 2.33 \n\n\n\nSWD-5 2.37 3.27 3.64 7.00 2.00 1.00 0.00 19.29 2.76 \n\n\n\nSWD-6 2.13 2.32 3.00 3.10 0.00 0.00 0.00 10.56 1.51 \n\n\n\nSWD-7 2.88 4.08 6.00 2.00 0.50 1.00 0.00 16.46 2.35 \n\n\n\nSWD-8 1.86 2.62 4.04 3.71 0.00 0.00 0.00 12.23 1.75 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 21-31 \n\n\n\nCite The Article: Pipas Kumar, Varun Joshi (2019). A Geospatial- Statistical Approach To Alienate Priority Area Of Upper Watershed Of River Subarnarekha Using \nMorphometric Assessment Framework. Malaysian Journal Of Geosciences, 3(1) : 21-31. \n\n\n\nSWD-9 1.90 3.10 3.27 0.00 0.00 0.00 0.00 8.27 1.18 \n\n\n\nSWD-10 1.93 3.28 2.85 3.90 0.00 0.00 0.00 11.95 1.71 \n\n\n\nThe bifurcation ratio is calculated by dividing the number of streams in the \nlower order by the number of streams in the higher of the two orders. In \nthis study, the mean bifurcation ratio lies in between 1.18-4.33 (Table 9). \nThe SWD-9 has lowest bifurcation ratio where as SWD-1 has the highest \nbifurcation ratio of 4.33. The higher the bifurcation ratio is an indicator of \nstructural disorientation. In general, the Rb values lies between 3 to 5 for \nwhich geological structures of the watersheds do not disturb the drainage \n\n\n\npattern. So, as per the obtained result for the SWD-1, the value is within \nthe range, which suggests there is no possible structural disorientation. \nThe bifurcation ratio, having a value about 2 to 3 is of the flat region. Thus, \nconsidering the obtained value, SWD-4, SWD-5, SWD-7 have flatter \ngradient. \n\n\n\n4.8 Relief aspects \n\n\n\nTable 10: Relief aspect of upper watershed of river Subarnarekha \n\n\n\nSub watershed \nidentity codes \n\n\n\nMinimum relief (M) Maximum relief (M) Total Relief \n(M) \n\n\n\nRuggedness number Relative relief Dissection Index \n\n\n\nSWD-1 209 1043 834 1.82 0.08 0.80 \n\n\n\nSWD-2 171 906 735 1.67 0.08 0.81 \n\n\n\nSWD-3 169 655 486 1.16 0.24 0.74 \n\n\n\nSWD-4 150 913 763 1.77 0.16 0.84 \n\n\n\nSWD-5 132 463 331 0.78 0.08 0.71 \n\n\n\nSWD-6 207 880 673 1.44 0.15 0.76 \n\n\n\nSWD-7 115 885 770 2.22 0.55 0.87 \n\n\n\nSWD-8 179 643 464 0.86 0.09 0.72 \n\n\n\nSWD-9 86 919 833 1.58 0.12 0.91 \n\n\n\nSWD-10 48 738 690 1.33 0.10 0.93 \n\n\n\nThe relief aspects include total relief (H), relief ratio (Rh), relative relief \nand ruggedness number (Rn) (Table 10). Based on geophysical and \ntopographic conditions of the terrain, relief aspects are used to evaluate \nthe direction of stream flow and represent the denudation progression \noccurring within the catchment. \n\n\n\n4.9 Basin relief (H) \n\n\n\nBasin relief determines the geomorphic processes and landform \ncharacteristics. It is the elevation difference between the lowest and the \nhighest point on the watershed. The lowest basin relief of 48 m is observed \nin the plains and highest of 1,043 m in the plateau region dominated by \nmountainous structures. The maximum basin relief is associated with \nSWD-1, whereas the lowest basin relief of 48 m is found in SWD-10. The \nmaximum relief is a possible indication of available potential energy in the \nform of water channel moving down the slope, which can be harassed \njudicially for energy production. \n\n\n\n4.10 Basin relief ratio (Rh) \n\n\n\nRelief ratio (Rh) measures the overall steepness of a drainage basin and is \nan indicator of the intensity of the erosional process operating on the slope \nof the basin. There is a direct relationship between the relief and the \ngradient of the channel. High relief ratio of the basin is an indicator of the \n\n\n\nhilly region. The Rh depicts an inverse relationship with shape parameters \nand increases with decreasing drainage area and size of sub-watersheds \nof a given drainage basin. The overall steepness of the drainage basin can \nbe ascertained using this parameter, which is useful as an indicator of the \nintensity of the erosion process in the watershed. A high value of relief \nratio is the characteristics of the hilly region. The value of relief ratio of all \nsubwatershed is between 8.65 to 23.14. The maximum value of relief ratio \nis obtained for the SWD-3 and minimum of for SWD-2. Since the obtained \nvalue is high, so, it can be inferred that the drainage basin has a steep slope. \nThis may be the fact that region is predominantly dominated by a plateau \nwith undulating landforms which make the surface steep. \n\n\n\n4.11 Ruggedness number (Rn) \n\n\n\nIt is the product of maximum basin relief (H) and drainage density (D), \nwhere both parameters are in the same unit. Extreme values of ruggedness \nnumber occur when both variables are large, when a slope is not only \nsteep but long, as well (Strahler, 1958). In the present study, the value of \nruggedness number is 0.78 indicate a steep slope. The watershed areas \nhaving low relief, but high drainage density is rugged in comparison to the \nareas of higher relief having less dissection. In this study, the value of \nruggedness number is found to be in the range of 0.78 to 2.22. (Table 10). \nThe highest value of Rn is observed in SWD-7, where both total relief and \ndrainage density values are high. \n\n\n\n4.12 Areal aspect \n\n\n\nTable 11: Areal aspects of upper watershed of river Subarnarekha \n\n\n\nSub \nwatershed \nidentity codes \n\n\n\nStream \nfrequency \n\n\n\nDrainage \ndensity \n\n\n\nDrainage \ntexture \n\n\n\nForm \nfactor \n\n\n\nElongation \nratio \n\n\n\nCirculatory \nratio \n\n\n\nCompactness \nconstant \n\n\n\nConstant of \nchannel \nmaintenance \n\n\n\nSWD-1 0.53 0.79 5.43 0.42 0.73 0.40 5.00 1.27 \n\n\n\nSWD-2 0.84 0.85 3.87 0.28 0.59 0.13 2.68 1.18 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 21-31 \n\n\n\nCite The Article: Pipas Kumar, Varun Joshi (2019). A Geospatial- Statistical Approach To Alienate Priority Area Of Upper Watershed Of River Subarnarekha Using \nMorphometric Assessment Framework. Malaysian Journal Of Geosciences, 3(1) : 21-31. \n\n\n\nSWD-3 0.99 1.72 3.53 0.82 1.01 0.45 1.50 0.58 \n\n\n\nSWD-4 0.70 0.88 4.81 0.75 0.97 0.51 1.39 1.13 \n\n\n\nSWD-5 1.13 1.47 4.91 0.41 0.72 0.42 1.53 0.68 \n\n\n\nSWD-6 0.90 0.78 4.47 0.47 0.77 0.35 1.70 1.29 \n\n\n\nSWD-7 0.28 1.48 0.82 0.65 0.9 0.14 2.65 0.67 \n\n\n\nSWD-8 0.92 0.79 3.32 0.63 0.89 0.16 2.48 1.27 \n\n\n\nSWD-9 0.91 0.77 3.53 0.60 0.87 0.14 2.69 1.31 \n\n\n\nSWD-10 0.88 1.51 3.75 0.46 0.76 0.16 2.47 0.66 \n\n\n\nThe areal aspects (Table 11) determine various relationships between \nstream area, its length, basin shape, etc. It includes drainage density (Dd), \ndrainage frequency (Fs), texture ratio (Rt), form factor (Rf), constant \nchannel maintenance (Cm), circulatory ratio (Rc), compactness constant \n(Cc), Infiltration number (Ig), elongation ratio (Re). \n\n\n\n4.13 Stream frequency \n\n\n\nThe number of stream segments per unit area is termed stream frequency \n(Fs). There is a direct relationship between surface runoff with stream \nfrequency and drainage density. The stream frequency of all sub-\nwatersheds is given in table 10. The value ranges from 0.28 to 1.13. It \ndepends on the lithological characteristics of the basin and reflects the \ntexture of the drainage network. The phenomenon that affects the \nvariation of stream frequency is duration and type of precipitation. The \nprecipitation in the form of thundershowers will immediately result in a \nlarge volume of surface runoff. This creates more surface drainages lines. \nThe other parameters, which affect the stream frequency are vegetation \ncover, basin relief, subsurface material permeability. The stream \nfrequency of all SWD shows that the basin has moderate vegetation, high \ninfiltration capacity, forest cover, and barren land and later peak \ndischarges owing to low surface runoff rate. \n\n\n\n4.14 Stream density \n\n\n\nThe stream density determines the time travel by water. The \nmeasurement of Dd is a useful numerical measure of landscape dissection \nand runoff potential15. It reflects the land use and affects infiltration and \nthe basin response time between precipitation and discharge. Drainage \nbasin with high Dd indicates that a large proportion of the run-off activity \ndue to precipitation. On the other hand, a low drainage density indicates \nthe most rainfall infiltrates the soil surface and few streams are required \nto carry the run-off. Dd is the result of interacting factors controlling the \nsurface runoff and in turn influences the output of water and sediment \nfrom the drainage basin. The Dd of the SWD-3, SWD-5, SWD-7, SWD-10 is \nhigh indicating low permeability of the subsurface material. \n\n\n\n4.15 Stream texture \n\n\n\nHorton (1945) defined the drainage texture as the total number of stream \nsegments of all order in a basin per perimeter of the basin. Smith (1950) \nhas classified drainage texture into five different textures, i.e., very coarse \n(<2), coarse (2 to 4), moderate (4 to 6), fine (6 to 8) and very fine (>8). \nSome natural factor like climate, type of vegetation cover and its density, \nrock surface, soil type, infiltration capacity, relief and stage of \ndevelopment influences drainage texture coarse drainage texture is a \nresult of low drainage density while fine drainage texture gives high \ndrainage density. More value of texture ratio more will be a dissection, \ncontributing more soil erosion. According to Smith classification, SWD-7 is \nof very coarse stream texture, while SWD-2, SWD-3, SWD-8, SWD-9, SWD-\n10 are of coarse stream texture and SWD-4, SWD-5, SWD-6 are of \nmoderate stream texture. \n\n\n\n4.16 Form factor \n\n\n\nThe form factor is the numerical index commonly used to identify different \nbasin shapes. It is the ratio of basin area (A) to the square of basin length \n(Lb). Smaller the value of form factor, more elongated will be the basin \n\n\n\nwhile the larger value is the representative of the circular basin. The form \nfactor value of all sub watershed lies between 0.28 to 0.82, which is quite \nlow, indicating an elongated shape basin. \n\n\n\n4.17 Length of overland flow \n\n\n\nIt represents the total length of flow of water over the ground surface \nbefore it becomes concentrated in specific stream channels. The surface \nwater moves over the land and leads to the stream tracing a particular \nstream channel whose characteristics depend on the steepness of the \nslope and land cover conditions. A scholar defined the length of overland \nflow as the length, projected to the horizontal, of non-channel flow from a \npoint on the drainage divide to a point on the stream channel. The geo-\nhydrological development of the drainage basin is greatly affected by the \nlength of overland flow. The length of overland flow value of sub-\nwatershed ranges from 0.29 (SWD-3) to 0.64 (SWD-1, SWD-6). Shorter the \nlength of overland flows quicker the surface run-off. The obtained value \nlength of overland flow suggests that the SWD-3, SWD-5, SWD-7 have high \nsurface runoff. \n\n\n\n4.18 Constant channel maintenance \n\n\n\nA scholar used the inverse of drainage density as a property termed as \n\u201cconstant of channel maintenance\u201d. Constant channel maintenance \ndepends on the basin relative relief, lithology, climatic condition, etc. \nHigher values suggest more area is required to produce surface flow which \nimplies that part of water may get lost by evaporation, percolation etc. \nlower value indicates fewer chances of percolation/infiltration and hence \nmore surface runoff. The value of constant of channel maintenance for all \nthe sub watershed of the study area lies within 0.58 to 1.27. The constant \nchannel maintenance value of SWD-1, SWD-2, SWD-4, SWD-6, SWD-8, \nSWD-9 is high indicating that higher basin area is required for the \nmaintenance of stream length of 1 km as compared to others. \n\n\n\n4.19 Circulatory ratio \n\n\n\nIt is estimated as the ratio of the area of the basin (A) to the circular area \n(Ac) having a circumference equal to the perimeter of the river basin. \nWhen the value of circulatory ratio approaches unity, the basin shape \ntends to be circular. The value of the circulatory ratio of the study area lies \nwithin 0.13 to 0.51, which is less than unity. It signifies that the basin is \nelongated in shape. The various factors that predominantly affect the basin \nshape are relief and stream pattern that arises due to a continuous \nerosional activity of the land surface. \n\n\n\n4.20 Compactness constant \n\n\n\nIt is the ratio between basin perimeters to the perimeter of a circle to the \nsame area of the watershed. It derives the relationship between actual \nhydrologic basins to the exact circular basin having the same area as that \nof a hydrologic basin. The value of compactness constant is an indicator of \nerosion risk factors. Lower values signify less vulnerability while higher \nvalues indicate great vulnerability for erosion. It is one of the major \naspects considered for evaluation and conservative measures to be \nimplemented in a watershed for management and planning. The value of \nSWD-1 has the value of 5 indicating high erosion risk assessment factor. It \nreflects that this sub-watershed needs conservation measures as it is more \nvulnerable to erosion.\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 21-31 \n\n\n\nCite The Article: Pipas Kumar, Varun Joshi (2019). A Geospatial- Statistical Approach To Alienate Priority Area Of Upper Watershed Of River Subarnarekha Using \nMorphometric Assessment Framework. Malaysian Journal Of Geosciences, 3(1) : 21-31. \n\n\n\n5. PRIORITIZATION OF SUB-WATERSHED\n\n\n\nTable 12: Rank allocation to areal aspect of sub watershed of upper watershed of river Subarnarekha \n\n\n\nSub \nwatershed \nidentity codes \n\n\n\nStream \nfrequency \n\n\n\nBifurcation \nratio \n\n\n\nDrainage \ndensity \n\n\n\nDrainage \ntexture \n\n\n\nForm factor Elongation \nratio \n\n\n\nCirculatory ratio Compactness \nconstant \n\n\n\nSWD-1 9 1 2 2 2 3 8 9 \n\n\n\nSWD-2 7 2 5 5 7 10 6 10 \n\n\n\nSWD-3 2 3 1 1 9 7 1 2 \n\n\n\nSWD-4 8 5 10 10 8 5 5 5 \n\n\n\nSWD-5 1 4 6 6 10 4 4 4 \n\n\n\nSWD-6 5 9 9 9 6 2 9 7 \n\n\n\nSWD-7 10 6 8 8 1 8 3 8 \n\n\n\nSWD-8 3 7 7 7 5 1 7 1 \n\n\n\nSWD-9 4 10 4 4 3 6 10 6 \n\n\n\nSWD-10 6 8 3 3 4 9 2 3 \n\n\n\nThe calculation of morphometric parameters helps in knowing various \naspects of the geohydrological behavior of the upper watershed of river \nSubarnarekha. In the present study, the prioritization of watershed is done \nby considering the areal and linear aspects, whereas the role of relief \naspects is not undertaken. There exists a correlation among various \nmorphometric parameters, which is used for prioritization of \nsubwatershed for management and planning. The earlier approaches of \nprioritization through the concept of only compound parameter ranking \nare not considered because this method gives equal importance to all \nmorphometric parameters. In the case of risk evaluation and conservation \npractices, no input constraints can be treated equally because it gives rise \nto biases. Moreover, every sub-watershed has its own unique geo \nhydrological characters, which cannot be neglected in priority \nidentification. Linear and areal parameters that are considered as erosion \nrisk estimation factor are the length of overland flow, bifurcation ratio, \ndrainage density, circularity ratio, compactness coefficient, drainage \ntexture, stream frequency, form factor, and elongation ratio. The erosion \nfactors are directly proportional to the linear aspect parameters. Thus, \nconsidering the relationship and to avoid the possibility of equal \nweightages assigning techniques, the methodology of WSA (Weightages \nSum Analysis) is adopted. This method considers the statistical correlation \ntechniques to decide which parameter should be considered for final \nanalysis and subsequently for ranking purposes. The earlier studies \nrelated to the watershed prioritization reveals that the shape parameters \nshow a negative correlation with surface runoff as well as soil erosion, \nwhereas all the other linear parameters show a positive correlation with \nsoil erosion17. So, considering, there is a direct relationship of land and \nwater degradation factors with parameters, like, stream frequency, \ndrainage density, drainage texture ratio and bifurcation ratio, the ranking \nis assigned to each of the values obtained from the morphometric \ncalculation (Table 12). The assigning of highest priority, i.e., 1 for the SWD \nhaving the maximum calculated value of the parameter, and least priority \nranking are given to the SWD having a minimum value. Remaining \nparameters (circulatory ratio, form factor, elongation ratio, compactness \nconstant and basin shape) has an inverse relationship with the land and \nwater degradation factors, so, rating is done by assigning highest priority, \ni.e., 1 for the SWD having minimum value of the parameter, and a similar\nprocedure is followed until the last priority number is assigned. The ranks \nwere assigned to all the parameters in consideration and a correlation \nmatrix (Table 13) is developed. The analysis of the correlation matrix \nreveals that circulatory ratio shows a negative correlation with most of the \nmorphometric parameters except stream frequency and compactness \nconstant. Similarly, drainage density shows a positive correlation with all \nmorphometric parameters except circulatory ratio. Consequently, \nweighted sum methodology is applied to calculate the sum of all \ncorrelation. The grand total obtained from the sum of all correlation \nvariables is 11.030. Then, individual weightages are calculated for each \nparameter by dividing the sum of the correlation coefficient by grand total. \nBased on the individual parameter, a prioritization model with the \nfollowing equation is formulated for actual and final ranking. \n\n\n\n5.1 Prioritization model equation  \n\n\n\n= (0.151 * stream frequency) \u2013 (-0.045 * form factor) + (0.242 * drainage \ndensity) \u2013 (0.125 * circulatory ratio) + (0.242 * drainage texture) + (0.169 \n* compactness constant) \u2013 (0.018 * elongation ratio) + (0.099 * mean \nbifurcation ratio) \n\n\n\nFigure 5: Subwatershed priority rank of upper watershed of River \nSubarnarekha \n\n\n\nThis prioritization model equation is applied to subwatershed and the \nvalue obtained is called as a compound parameter value, which is used for \npriority assessment (Table 14). Subwatershed having the lowest value is \nassigned the highest priority level. Similarly, the lowest priority is given to \nthe subwatershed whose compound parameter value is highest. A Sub \nwatershed priority rank of all individual sub-watersheds is represented in \nfigure 4. In this study, the SWD-7 (1.275) receives the highest priority and \nis allotted rank 1 followed by SWD-8 (1.718). The least priority is given to \nSWD-1 having a compound parameter value of 2.815 and this \nsubwatershed is allotted rank 10. Based on compound parameter value of \nall 10 subwatershed, three class of priority level (Table 15) is constructed \ni.e, low, medium and high. The priority level \u201chigh\u201d is assigned to the values \nfrom 1.275-1.718. The subwatershed falling under this range is SWD-7, \ncovering 5.8% only. Consequently, the subwatershed SWD-1, SWD-2, and \nSWD-5 are given lowest priority that covers 46 % of the total area. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 21-31 \n\n\n\nCite The Article: Pipas Kumar, Varun Joshi (2019). A Geospatial- Statistical Approach To Alienate Priority Area Of Upper Watershed Of River Subarnarekha Using \nMorphometric Assessment Framework. Malaysian Journal Of Geosciences, 3(1) : 21-31. \n\n\n\nBased on the defined level of priority of all 10 subwatershed, prioritization \nzonation map is prepared. (figure 5). The high value of priority signifies \nthe extent of vulnerability of soil and water loss from the surface of a \nwatershed. Based on the map, it can be ascertained that 5.8 % of the upper \n\n\n\nwatershed of river Subarnarekha are in susceptible zone of risk. This area \nneeds immediate attention from planners and policymakers for watershed \nplanning and conservation. \n\n\n\nTable 13: Correlation matrixes of morphometric parameters of upper watershed of river Subarnarekha \n\n\n\nStream \nfrequency \n\n\n\nBifurcation \nratio \n\n\n\nDrainage \ndensity \n\n\n\nDrainage \ntexture \n\n\n\nForm \nfactor \n\n\n\nElongation \nratio \n\n\n\nCirculatory \nratio \n\n\n\nCompactness constant \n\n\n\nStream frequency 1 -0.188 0.224 0.224 0.067 -0.636 0.285 0.685 \n\n\n\nBifurcation ratio -0.188 1.000 0.345 0.345 0.309 -0.273 -0.152 -0.297 \n\n\n\nDrainage density 0.224 0.345 1.000 1.000 0.224 0.079 -0.309 0.103 \n\n\n\nDrainage texture 0.224 0.345 1.000 1.000 0.224 0.079 -0.309 0.103 \n\n\n\nForm factor 0.067 0.309 0.224 0.224 1.000 -0.309 -0.527 0.394 \n\n\n\nElongation ratio -0.636 -0.273 0.079 0.079 -0.309 1.000 -0.055 -0.382 \n\n\n\nCirculatory ratio 0.285 -0.152 -0.309 -0.309 -0.527 -0.055 1.000 0.261 \n\n\n\nCompactness \nconstant 0.685 -0.297 0.103 0.103 0.394 -0.382 0.261 1.000 \n\n\n\nSum of correlation 1.661 1.091 2.667 2.667 1.382 -0.497 0.194 1.867 \n\n\n\nGrand total 11.030 11.030 11.030 11.030 11.030 11.030 11.030 11.030 \n\n\n\nFinal weightages 0.151 0.099 0.242 0.242 0.125 -0.045 0.018 0.169 \n\n\n\nTable 14: Compound parameter constant and priority ranking of sub- watershed of upper watershed of river Subarnarekha \n\n\n\nSub watershed identity codes Compound parameter constant Priority ranking \n\n\n\nSWD-1 2.815 tenth \n\n\n\nSWD-2 2.048 eight \n\n\n\nSWD-3 1.934 sixth  \n\n\n\nSWD-4 1.901 fifth \n\n\n\nSWD-5 2.199 ninth \n\n\n\nSWD-6 1.807 fourth  \n\n\n\nSWD-7 1.275 first \n\n\n\nSWD-8 1.718 second \n\n\n\nSWD-9 1.743 third \n\n\n\nSWD-10 1.980 seventh \n\n\n\nFigure 5: Zonation map of upper watershed of River Subarnarekha \n\n\n\nTable 15: Priority level and type allocation of sub- watershed of upper \nwatershed of river Subarnarekha \n\n\n\nPriority type Priority level Sub watershed \nidentity codes \n\n\n\nPercentage of \narea (%) \n\n\n\nHigh 1.718-1.275 7 5.8 \n\n\n\nMedium 1.980-1.718 3,4,6,9,8,10 48.2 \n\n\n\nLow 2.815-1.980 1,2,5 46 \n\n\n\n6. CONCLUSION \n\n\n\nThe advancement of GIS and remote sensing techniques has resulted in an \nefficient and effective study of geo-morphometric aspects of the drainage \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(1) (2019) 21-31 \n\n\n\nCite The Article: Pipas Kumar, Varun Joshi (2019). A Geospatial- Statistical Approach To Alienate Priority Area Of Upper Watershed Of River Subarnarekha Using \nMorphometric Assessment Framework. Malaysian Journal Of Geosciences, 3(1) : 21-31. \n\n\n\nbasins. GIS-based tools facilitate the analysis of various geomorphological\n parameters of the drainage basin like the lithology, surface run off \npotential, infiltration capacity, etc. The morphometric parameters like \ndrainage density, frequency is very significant classification of the \ndrainage basin that controls and determines the pattern of various \ngeomorphological parameters like runoff pattern, sediment yield, etc. The \nDd of the basin reveals that the nature of subsurface strata is more or less \npermeable mainly due to the rocky structure presents on the river bed and \nadjoining river banks. The basin as a whole has low texture ratio, which \nreveals the high infiltration capacity and low runoff rate. Various \nmorphometric parameters reveal the elongated shape of the basin and a \ndelay in surface runoff. Based on final weightages analysis of the \nSubarnarekha, it is found that SWD-9 covering an area of 747.47 km2 has \nreceived the highest priority. Accordingly, the priority classification map \nidentifies the potential areas that should be considered for soil, water, and \nland conservation work. The division of priority into three different class \nhelp in preparation of susceptible zoning aspects. This zoning map is \nuseful in classifying the area, which is under threat of high soil and water \nloss due to various hydro-geomorphologic conditions. The classification \nmap reveals that the SWD-3, SWD-4, SWD-6 SWD-8, SWD-9, SWD-10 falls \nunder the medium priority. These areas are quite satiable for \nimplementation of conservation practices at this stage to avoid any \nanticipated land degradation phenomena. Thus, quantification and \ncorrelation among various individual morphometric parameters give a \nmore logical approach to watershed prioritization. \n\n\n\nACKNOWLEDGMENTS \n\n\n\nThe authors wish to express their sincere gratitude to the Dean, University \nSchool of Environment Management, and all organizations mentioned \nabove who provided data for the present research work. The author also \nwishes thanks to Guru Gobind Singh Indraprastha University, New Delhi \nfor providing research fellowship. \n\n\n\nREFERENCES \n\n\n\n[1] IPCC. 2014. Climate Change 2014: Synthesis Report. Contribution of \nWorking Groups I, II and III to the Fifth Assessment Report of the \nIntergovernmental Panel on Climate Change, Core Writing Team, R.K. \nPachauri and L.A. Meyer (eds.). IPCC, Geneva, Switzerland, 151. \n\n\n\n[2] Kumar, P., Joshi, V. 2015. Characterization of hydro geological behavior \nof the upper watershed of river - Subarnarekha through morphometric\nanalysis using remote sensing and GIS approach. International Journal of \nEnvironmental Sciences, 6(2), 429- 447 \n\n\n\n[3] Pakhmode, V., Himanshu, K., Deolankar, S.B. 2003. Hydrological-\ndrainage analysis in watershed-programme planning: A case from the \nDeccan basalt, India. Hydrogeology Journa1 595\u2013604. \n\n\n\n[4] Hajam, R.A., Hamid, A., Bhat, S. 2013. Application of Morphometric \nanalysis for Geo-hydrological studies using Geo-Spatial technology \u2013A case \nstudy of Vishav drainage basin. Hydrology Current Research 4, 157 \n\n\n\n[5] Pratik, D., Aggarwal, S.P., Verma, N. 2013. Correlation based \nmorphometric analysis to understand drainage basin evolution: a case \nstudy of sirsa river basin, western Himalaya India. Scientific annals of \nalexandru ioan cuza University of Ia\u015fi. \n\n\n\n[6] Altaf, S., Meraj, G., Romshoo, S.A. 2014. Morphometry and land cover \nbased multi-criteria analysis for assessing the soil erosion susceptibility of \nthe western Himalayan watershed. Environment Monitoring Assessment \n186, 8391\u20138412 \n\n\n\n[7] Jain, M.K., Kothyari, U.C. 2000. Estimation of soil erosion and sediment\nyield using GIS. Hydrological Science Journal, 45(5), 771\u2013786 \n\n\n\n[8] Yamani, K., Hazzab, A., Sekkoum, M. 2016. Modeling Earth Systems and\nEnvironment, (2), 147. \n\n\n\n[9] Jang, T., Vellidis, G., Hyman, J.B., Brooks, E., Kurkalova, L.A., Boll, J., Cho, \nJ. 2013. Model for Prioritizing best management practice implementation:\nsediment load reduction. Environment Management, 51, 209\u2013224 \n\n\n\n[10] Krishnan, M.V.N., Prasanna, M.V., Vijith, H. 2017. Modeling Earth \nSystems and Environment, (3), 1477. \n\n\n\n[11] Srinivasa Raju, K., Nagesh Kumar, D. 2011. Classification of \nmicrowatersheds based on morphological characteristics, Journal of \nHydro-environment Research, 101-109 \n\n\n\n[12] Kadam, A.K., Jaweed, T.H., Umrikar, B.N. 2017. Modeling Earth \nSystems and Environment, (3), 1663. \n\n\n\n[13] Deepa, S., Venkateswaran, S., Ayyandurai, R. 2016. Modeling Earth \nSystems and Environment, (2), 137. \n\n\n\n[14] Prasad, R.N., Pani, P. 2017. Modeling Earth Systems and Environment,\n(3), 1491. \n\n\n\n[15] Gayen, A., Saha, S. 2017. Modeling Earth Systems and Environment,\n(3), 1123. \n\n\n\n[16] Aher, P.D., Adinarayana, J., Gorantiwar, S.D. 2014. Quantification of \nmorphometric characterization and prioritization for management \nplanning in semi-arid tropics of India: A remote sensing and GIS approach. \nJournal of Hydrology, 511, 850\u2013860 \n\n\n\n[17] Gupta, D.B., Mitra, S. 2013. Sustaining Subernarekha river basin. \nInternational Journal of Water Resources Development 20, 431-444 \n\n\n\n[18] Rasool, Q., Singh, V.K., Singh, U.C. 2011. The evaluation of \nMorphometric characteristics of Upper Subarnarekha Watershed \ndrainage basin using Geoinformatics as a tool Ranchi Jharkhand. \nInternational Journal of Environmental Sciences, 1(7) \n\n\n\n[19] Tetford, P.E., Desloges, J.R., Nakassis, D. 2017. Modeling Earth Systems \nand Environment, (3), 1229. \n\n\n\n[20] Rabus, B., Eineder, M., Achim, R., Bamler, R. 2013. The shuttle radar \ntopography mission\u2014a new class of digital elevation models acquired by \nspace borne radar. ISPRS Journal of Photogrammetry & Remote Sensing, \n57, 241\u2013262 \n\n\n\n[21] Horton, R.E. 1945. Erosional development of streams and their \ndrainage basins; hydrological approach to quantitative morphology. \nGeological Society of America Bulletin, 56, 275\u2013370 \n\n\n\n[22] Strahler, A.N. 1957. Quantitative analysis of watershed \ngeomorphology Trans Am Geophys Union 38, 913\u2013920 \n\n\n\n[23] Schumm, S.A. 1956. Evolution of drainage systems and slopes in \nBadlands at Perth Amboy New Jersey. Geological Society of America \nBulletin, 67, 597\u201364 \n\n\n\n[24] Melton, M.A. 1958. Correlations structure of morphometric \nproperties of drainage systems and their controlling agents. Journal of \nGeology, 66, 442\u2013460 \n\n\n\n[25] Magesh, N.S., Chandrasekar, N., Soundranayagam, J.P. 2012. \nDelineation of groundwater potential zones in Theni district Tamil Nadu \nusing remote sensing GIS and MIF techniques. Geoscience Frontiers, (3), \n189-196 \n\n\n\n[26] Subramanyam, K. 2013. Engineering hydrology. Mc Graw Hill \nEducation 4th Edition, 170 \n\n\n\n[27] Satpathi, D.D.P. 1981. An outline of Indian Geomorphology a study in \nregional Geomorphology of Singhbhum, Classical publishing company, 1st \nEdition, 126 \n\n\n\n[28] Chorley, R.J., Donald, E.G., Pogorzelski, H.A. 1957. New Standard for \nEstimating Drainage Basin Shape. American Journal of Science, 255, 138-\n141\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosc i ences 2(1) (2018) 34-37 \n\n\n\nCite the Article: Nicole Lee Siew Len, Nurmin Bolong, Rodeano Roslee, Felix Tongkul, Abdul Karim Mirasa, Janice Lynn Ayog (20 18).  \nFlood Vulnerability of Critical Infrastructures - Review. Malaysian Journal of Geosciences, 2(1) : 34-37. \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nFlood event is one of the natural disasters that increasingly threaten the safety of the people in an area. Critical \ninfrastructure albeit important, has been shown to be vulnerable to flooding and damages to critical infrastructure \nelement may affect large areas over a longer time period. Critical infrastructures play an important role in \nfunctioning of industries and communities and also responding against flooding to reduce their impacts. Critical \ninfrastructures such as hospital, school, road networks and other infrastructures are important during flood event \nto serve as emergency services. It was found that there is difference in understanding the concept of vulnerability \nwith varying assessments and different view. This paper briefly reviews the concept of vulnerability and discusses \non the approach used for flood vulnerability of critical infrastructure by past researchers to identify and fortify the \nvulnerable critical infrastructure ahead of time reducing the potential damage due to flood. This paper focuses the \nvulnerability of critical infrastructure during flood event and also describes several approaches with a discussion \non the application of the approaches used and the relevance results. \n\n\n\nKEYWORDS \n\n\n\nCritical Infrastructure, Flood, Vulnerability, Vulnerability of Critical Infrastructure.\n\n\n\n1. INTRODUCTION \n\n\n\nFlood is a common natural event in places where rain falls in excess. It \ncould be extremely dangerous due to its potential to wipe away the \nentirety of the area, thus causing extensive damage to life and property. \nFlood can occur gradually by the minutes or hours; sometimes even in a \nsudden, without warning due to causes such as cracking of the \nembankment, spilling or even heavy rain. The catastrophic flood can cause \nhuge damage, destroying villages, towns, farmland and critical \ninfrastructures [1]. Generally, flooding caused great damage to property \nand physical infrastructure of many affected communities [2]. Depending \non the strength and extent, flood has undoubted potential to destroy \nbridges, damage traffic infrastructure, disrupting communications \nsystems, power supply and many more. Therefore, it is also possible for \nflood to destroy critical infrastructures that are needed as shelter and \nemergency relief for flood victims. This makes it difficult to predict the \nimpact and consequences of the flood, particularly to critical \ninfrastructures [1]. \n\n\n\nDespite the technological development of mankind, societies are \nincreasingly threatened by the natural disaster [1]. Natural disasters such \nas flood and their impact on people and critical infrastructure cannot be \nprevented, but prediction and early warning of disasters can be improved \nfor faster and more efficient revitalization of endangered values and goods \ncan be increased. Therefore, it is important to identify and fortify the \nvulnerable critical infrastructure ahead of time to significantly reduce the \npotential damage due to flooding.  In this regard, this paper gives a brief \nreview of the concept of vulnerability, discussion on the approach used, \nand the resulting awareness into the vulnerability of critical infrastructure \nfor flooding by past researchers. \n\n\n\n2. VULNERABILITY TERMINOLOGY\n\n\n\nIn assessing the losses caused by natural disaster such as flooding, it is \nessential to understand the how vulnerable are those affected by the \n\n\n\ndisaster. This means the vulnerability of the people, the land as well as the \nproperties such as infrastructures in the affected areas. The concept of \nvulnerability can be extracted from the concept of risk caused by disaster \nthat can be interpreted and understood in different ways by different \npeople [3]. Regardless, by understanding the concept of risk, it helps those \ninvolved with disaster management to measure and also manage the \npotential risk of the disaster event.  \n\n\n\nRisk is the probability of a loss and this depends on three elements, which \nare hazard, vulnerability and exposure. Risk can be expressed in a \nmathematical form as: \n\n\n\nRisk = Hazard \u00d7 Exposure \u00d7 Vulnerability   (1) \n\n\n\nHazard refers to natural disasters that may occur on a random basis. \nExposure means the population, infrastructure and environment. It also \nmeans the populations that are present during a flood occur.  \n\n\n\nVulnerability is an important concept in human-environment research \nwhere past researchers have provided admirable reviews of main \nliterature review on the development of the concept of vulnerability [4-6]. \nIn general, the root meaning of vulnerable is \u201cto wound\u201d, therefore, \nvulnerability can be described as \u201cthe capacity to be wounded\u201d [7]. \nVulnerability refers to the core characteristics of the hazard\u2019s receptors \n(which can be people, infrastructure, economic activities or others), and \ndefines the extent to which these receptors are susceptible to harm from, \nor unable to cope with hazards [8]. Vulnerability is the potential for loss, \nbut the definition differs in other assessments with different view for \ngeography, sociology or political science [5]. The framework of UN\u2019s \nInternational Decade of Natural Disaster Reduction (IDNDR), vulnerability \nassessments are used to determine the potential damage and loss of life \nfrom extreme natural events.  \n\n\n\nIn flood risk management, vulnerability is the main construct [9]. \nTherefore, flood vulnerability is one of the significant components in flood \nrisk management and damage assessment [10]. A group researcher \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : https://doi.org/10.26480/mjg.01.2018.34.37\n\n\n\nFLOOD VULNERABILITY OF CRITICAL INFRASTRUCTURES - REVIEW \n\n\n\nNicole Lee Siew Len1*, Nurmin Bolong1, Rodeano Roslee2,3, Felix Tongkul2,3, Abdul Karim Mirasa1, Janice Lynn Ayog1 \n1 Faculty of Engineering, University Malaysia Sabah (UMS), Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia \n2 Faculty of Science and Natural Resources, University Malaysia Sabah (UMS), Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia \n3 Natural Disaster Research Centre (NDRC), University Malaysia Sabah (UMS), Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia \n*Corresponding Author Email: ninicole92@hotmail.com\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \n\n\n\nCODEN : MJGAAN \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 34-37\n\n\n\nCite the Article: Nicole Lee Siew Len, Nurmin Bolong, Rodeano Roslee, Felix Tongkul, Abdul Karim Mirasa, Janice Lynn Ayog (20 18).  \nFlood Vulnerability of Critical Infrastructures - Review. Malaysian Journal of Geosciences, 2(1) : 34-37. \n\n\n\ndefined flood vulnerability as the possibility of negative effects such as \nharm, damage or causalities caused directly or indirectly by floods [11]. \nOther researcher defined that flood vulnerability as the degree of \nsusceptibility to damage from the flood [12]. When flood physically attacks \non people and infrastructure, then the vulnerability of people and \ninfrastructure is based on the degree of harm and damage [13]. Since \nvulnerability is found to be the main reason of disasters, it seems \nnecessary to develop the perception of the vulnerability [14]. Since \nvulnerability cannot directly measure; several methods have been \nproposed to estimate it.  \n\n\n\n3. CRITICAL INFRASTRUCTURE \n\n\n\nInfrastructures is described as the basic facilities, services, and \ninstallations needed for the functioning of a community or society such as \ntransportation and communications systems, water and power lines, and \npublic institutions including schools, post offices and prisons. Therefore, \ninfrastructure plays an important role in the daily living life. However, \nmost infrastructures are usually designed using codes and standards \nbased on the historic climate data which is no longer adequate for climate \nloads experienced by the infrastructure today. \n\n\n\nMany definitions of critical infrastructures were found by past researchers \n[11,15]. The concept of critical infrastructure came into public view \naround the middle of the last decade of the 20th century, when the US \nstarted to acknowledge that is had identified that there were a set of \nfacilities and services that came together to provide the elements that \nwere \u2018critical\u2019 to the running of a country and the well-being of its citizens \n[16,17]. Critical infrastructure plays a very important role not only for \nsustaining industries and communities but also in properly responding \nagainst natural disaster such as flood to reduce their impacts [18]. \nAccording to, critical infrastructure stands for the infrastructure which is \nessential for the functioning of society, whose failure would seriously \naffect many people [11].  \n\n\n\nIn addition to the definition, the clear scope of critical infrastructure is also \nessential [11]. The compounds as critical infrastructure that identified by \nmost researchers are water supply and drainage networks, \ncommunication related infrastructures, schools, hospitals, sanitation, \ntransportation, telecommunication, administrative and emergency \nservice buildings and road networks. Therefore, there are group \nresearcher also defined that critical infrastructure includes all networks \nand buildings that are essential for the functioning of society during the \nflood event and for the recovery from the flood event [11]. It is considered \nas critical because an outage of the infrastructure has a serious effect on \nmany people over a long period. In other studies, stated that critical \ninfrastructure includes physical resources, services, information \ntechnology facilities, networks and infrastructure assets which, if \ndestroyed by the flood, would have a serious impact on the health, safety \nor economic of the victims [19]. \n\n\n\nUntil now, the term critical infrastructures has become central in the \nemergency preparedness work of many nations, but there is yet no \nentirely accepted definition of the term [20]. According to the documents \nof the United Nations, critical infrastructure represents the infrastructure \nthat consists of physical and information technology facilities, networks, \nservices and property, which if collapsed or destroyed can have a serious \nimpact on health, safety and economic well-being and effective functioning \nof government [21]. Therefore, critical infrastructure is very important to \nbe identified early so that it can reduce the impact of the flood. \n\n\n\n4. VULNERABILITY ASSESSMENT OF CRITICAL INFRASTRUCTURE\n\n\n\nNatural disaster such as flood has increasingly threatened people and their \nproperty, critical infrastructure day by day [1]. The impacts of flooding on \ncritical infrastructure can be seen through the understanding of the \nphysical characteristics of the flood, that is, destructiveness that is \ndetermined by destructive force and possibility of propagation in the \nterritory. Taking the definition of vulnerability from a study, the \nvulnerability of critical infrastructure is in strong correlation with the \nintensity of flooding which affect community in the flooding area [8]. \n\n\n\nAnother group researchers developed a decision support system for \nidentifying critical infrastructure as well as its vulnerability to flood events \nby applying critically and vulnerability assessments [18]. The decision \nsupport system was developed to support emergency agencies and \nindustries to prepare mitigation strategies and plans for preparedness, \nresponse and recovery using the critically and vulnerability analyses. The \ndata in forms of questionnaires, interviews and site investigation in terms \nof technical, social and economic were collected from the affected \ncommunities. Critically analysis was based on the dependency of a \ncommunity or an industry on critical infrastructure in terms of their daily \n\n\n\nroutine activities and it is important to measure the level of inter-\nrelationship between critical infrastructure, industries and communities. \n\n\n\nVulnerability analysis was based on the vulnerability level of each \ninfrastructure based on the varied timeline (before, during and after) of \nthe flood.  After the developed decision support system, Bayesian Network \nTheory was used for calculating probabilities of failure of each component \nwhile System Dynamics Simulation method is to simulate the vulnerability \nassessment of critical infrastructure allied industries and communities. \nThe result can be used for immediate actions to save life, property and \nenvironment. Data for disaster impact analysis in term of social, economic \nand technical aspect can be collected for assistance and improving \npreparedness against the impact of natural disaster. Additionally, the \nassessment can help to examine the conditions of critical infrastructures \nwhich significant to protect industries and communities. \n\n\n\nA study on finding a pattern for the differences of the vulnerability in the \nurban areas has been conducted by a group researcher [22]. The variables \nin vulnerability assessment have been used, which is the physical \ninfrastructures including element at risk and intensifying elements. The \nelements at risk are the old structures are occupied by many people and \ncan be easily covered by flooding. Physical elements consisted of variables \nof old texture blocks, population concentration, land use and distance to \nbridges, while intensifying elements are drainage network, water courses \nand slope that are effective in flood assessment. Analytical Hierarchy \nProcess (AHP) has been used in this study to convert relative variables into \nratio scales and getting weights of relative preferences. The relative \nweights were used as the magnitude of influence of each variable in \nvulnerability. The equation below is defined as a function of the \nvulnerability in front of the flood. \n\n\n\nV = (0.3825P) + (0.2504T) + (0.1596T) + (0.1006B) + (0.641S) + (0.0428D)          (2) \n\n\n\nWhere V is vulnerability, P is population concentration, T is old texture \nblocks, B is distance to bridges, S is the slope and D is drainage network \nconcentration. The result shown in a vulnerability map will indicate which \narea as very high and low vulnerable towards flood.  \n\n\n\nOther researchers reported two approaches of flood vulnerability \nassessment in their study [23]. First was the economic damage that is \nfundamentally a quantification of the expected or actual damages to a \nstructure expressed in economic terms or through an evaluation of the \npercentage of the expected loss. While the other approach deals with the \nphysical vulnerability of individual structures and on the estimation of the \nlikelihood of occurrence of physical damages or collapse of a single \nelement such as, building. Empirical method was used based on the \nanalysis of observed consequences which by collection of actual flood \ndamage information from the respondents after the event through \ninterviews, questionnaires and field mapping was used for this study. The \nmain advantage of these methods is the use of real data. However, the \nresults were very much dependent on the respondents\u2019 risk perception for \nthe first two approaches of data availability and the methodology for \ncollection method. \n\n\n\nOn the following year, the vulnerability of critical infrastructure to flood \nby analysing the vulnerability and coping capacity of infrastructures [24]. \nThe approach is used by using questionnaires survey, interview with 150 \nselected respondents from each zone and infrastructure analysis. The \nsurvey covered in the zone of areas that had experienced heavy damage, \nmedium and light damage. The questionnaires discovered the local \ncommunity\u2019s characteristic and interview in face to face interaction before \nand after the flood disaster, different coping mechanisms and analysis of \ninfrastructure such as school, roads, hospitals and markets. Risk index is \nto determine the strength of resilience of the zone. It was calculated with \nthe calculated coping strength of the zone and hazard occurrences using \nthe UNDP (1992) formula: \n\n\n\nVulnerability = Hazard / Coping Strategies    (3) \n\n\n\nThe results show variations between zones in coping strength and \nvulnerability which indicates varying local coping capacities. The \nmeasurement of vulnerability to flood has helped to identify the capacities \nof local communities to manage and to overcome emergencies and \ndisaster situations. And also determine the critical infrastructures to flood \nand thus support their preparedness for disaster situation. \n\n\n\nA research of reducing future vulnerability of critical infrastructure to \nflooding, which was also to build a more resilient society [19]. In order to \nreduce the flood vulnerability and increase the resilience of the critical \ninfrastructure networks in future, detailed evidence-based analysis and \nvulnerability assessment is needed. Therefore, methods used were the \n\n\n\n35\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 34-37\n\n\n\nCite the Article: Nicole Lee Siew Len, Nurmin Bolong, Rodeano Roslee, Felix Tongkul, Abdul Karim Mirasa, Janice Lynn Ayog (20 18).  \nFlood Vulnerability of Critical Infrastructures - Review. Malaysian Journal of Geosciences, 2(1) : 34-37. \n\n\n\nstoryline approach and CIrcle tool. The storyline method is to analyse the \nsequence of flood events including the responses of the most relevant \nactors while CIrcle tool obtains information from the relevant actors about \nthe vulnerability of critical infrastructure and to study cascade effects. \nNote that these methods needed the responses of relevant actors such as \nstate agencies, local authorities, metrological service, emergency \nresponders and general public before, during and after the flood event. \nThe result from this research help the actors and others to arrange flood \ndefence and mitigation measures, including emergency management plan. \nIt also shows a valuable outline of the locations of critical infrastructures \nin the flood-prone area in and around the study area and in knowledge on \nthe effects and the impacts on society. \n\n\n\nMeanwhile, an assessment in identifying elements at risk, vulnerabilities \nof the people, determining triggers for vulnerability to flood disaster and \nto suggest remedial measures for vulnerability reduction in the study area \nhas been done by a group researcher [25]. The data was obtained through \nquestionnaire and interview schedule with total of 60 respondents. Then, \nliterature review was conducted by gathering information from \ngovernment and non-government departments, agencies, research \nreports, journal articles, newspapers and electronic databases. The data \nwas analysed with the software of Statistical Product and Service Solution \n(SPSS) and Microsoft Excel to calculate the frequency and percentage of \neach variable. Through this study, it can help to identify which area is \nhighly exposed to floods due to its geographical location and physical \ninfrastructures that are weak and cannot use as evacuation centre. it was \nfound that physical infrastructure in the study area is weak due to \ninadequate construction material and lack of mitigation measures. \n\n\n\nIn summary, it can be seen that data analysis based on questionnaires, \ninterviews and site investigation has been practised in flood vulnerability \nassessment for development of their decision support system [18]. This \napproach was also adopted later by a group of researchers in their flood \nvulnerability assessment [23-25]. However, the focus of the \nquestionnaires, interviews and site investigation were differing in each \nstudy conducted. Some of researcher focused more on economic and \nphysical damages while the others also take into account of dependency of \nthe community or industry on critical infrastructures in preparing for \nflood events [23,25]. A researcher takes the coping capacity of \ninfrastructures for the study while in other study, gathered data together \nwith literature reviews and analysed by using software to identify physical \ninfrastructures that cannot sustain flood [24,26]. On the other hand, some \nof researcher also used analysis based on taking responses from the \nrespondent before, during and after flood event [19]. Nevertheless, it \nshows that post-event study is essential in identifying critical \ninfrastructures in flood prone areas as well as estimating the flood \nvulnerability on the life and property in that areas. \n\n\n\n5. CONCLUSION \n\n\n\nThere are many methods have been done by past researchers on \ndeveloping a vulnerability assessment on critical infrastructure during \nflood event. Each assessment has the similarities such as by interviewing \nthe citizens or giving out questionnaires about the floods. It shows how \nimportant is gathering information from affected population. By gathering \nthe information, this can help to identify the infrastructure that is critical \nduring flooding. This is because flood vulnerability assessments \nconducted has proven that it involves not only the people living or affected \nby the flood in certain areas, but also the condition of infrastructure in the \narea especially the critical infrastructure. Thus, with such assessment, \nflood mitigation plans can include proper or emergent protection methods \nfor safeguarding those important infrastructures. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe authors would like to acknowledge the financial support from \nFundamental Research Grant Scheme (FRGS) of Ministry of Higher \nEducation (MOHE) Grant No. FRG0421-TK-1/2015. \n\n\n\nREFERENCES \n\n\n\n[1] Cvetkovi, V.M., Mijalkovi\u0107, S. 2015. Vulnerability of Critical \nInfrastructure by Natural Disasters. Conference Paper, (January 2013). \n\n\n\n[2] Ecology, P., Frontiers, R. 2015. Physical, Economical, Infrastructural \nand Social Flood Risk - Vulnerability Analyses in GIS. \n\n\n\n[3] Kron, W. 2002. Keynote lecture: Flood risk = hazard \u00d7 exposure \u00d7 \nvulnerability. Proceedings of the Flood Defence, 82\u201397. \n\n\n\n[4] Wu, S.Y., Yarnal, B., Fisher, A. 2002. Vulnerability of coastal \ncommunities to sea-level rise: a case study of Cape May county, New \nJersey, USA. Climate Research, 22 (3), 255\u2013270. \n\n\n\n[5] Cutter, S.L. 1996. Vulnerability to Environmental Hazards. Progress in \nHuman Geography. \n\n\n\n[6] Change, G. 2013. Vulnerability: A Short Review, (February), 1\u20137. \n\n\n\n[7] Kates, R.W. 1985. Foreword, Part I Overview, Part II Biophysical \nImpacts, Part III Social and Economic Impacts and Adjustments, Part IV \nIntegrated Assessment, in Robert W. Kates, Jesse H. Ausubel and Mimi \nBerberian (eds). Climate Impact Assessment: Studies of the Interaction of \nClimate and Society, John Wiley, 1\u20132, 103\u2013104, 247\u2013249, 465\u2013467. \n\n\n\n[8] Cavan, G. 2011. Landscape and Urban Planning Surface water flooding \nrisk to urban communities: Analysis of vulnerability, hazard and exposure, \n103, 185\u2013197.  \n\n\n\n[9] Nasiri, H., Mohd Yusof, M.J., Mohammad Ali, T.A. 2016. An overview to \nflood vulnerability assessment methods. Sustainable Water Resources \nManagement, 2 (3), 331\u2013336.  \n\n\n\n[10] Nasiri, H., Shahmohammadi-kalalagh, S. 2013. Flood Vulnerability \nIndex as a Knowledge Base for Flood Risk Assessment in Urban Area. \nJournal of Novel Applied Sciences, 2 (8), 269\u2013272. \n\n\n\n[11] Heilemann, K., Balmand, E., Lhomme, S., de Bruijn, K.M., Nie, L., Serre, \nD. 2013. Identification and analysis of most vulnerable infrastructure in \nrespect to floods, 56. \n\n\n\n[12] Gallop\u00edn, G.C. 2006. Linkages between vulnerability, resilience, and \nadaptive capacity. Global Environmental Change, 16 (3), 293\u2013303.  \n\n\n\n[13] Tingsanchali, T. 2012. Urban Flood Disaster Management. Procedia \nEngineering, 32, 25\u201337.  \n\n\n\n[14] Hinkel, J., Klein, R.J.T. 2007. Integrating Knowledge for Assessing \nCoastal Vulnerability to Climate Change. Managing Coastal Vulnerability: \nAn Integrated Approach, 1\u201320. \n\n\n\n[15] Fekete, A. 2009. Validation of a social vulnerability index in context to \nriver-floods in Germany. Natural Hazards and Earth System Science, 9 (2), \n393\u2013403.  \n\n\n\n[16] Bach, C., Gupta, A.K., Sreeja, S., Nair, J.B. 2013. Critical Infrastructures \nand Disaster Risk Reduction. \n\n\n\n[17] Abouzakhar, N. 2015. Critical Infrastructure Cybersecurity: A Review \nof Recent Threats and Violations, 1, 11. \n\n\n\n[18] Oh, E.H., Deshmukh, A., Hastak, M. 2010. Vulnerability Assessment of \nCritical Infrastructure, Associated Industries, and Communities during \nExtreme Events Ph. D. Candidate, Construction Engineering & \nManagement, School of Civil Engineering, Purdue University, 550 Stadium \nMall Dr., West. Construction Research Congress 2010: Innovation for \nReshaping Construction Practice, 449\u2013458. \n\n\n\n[19] de Bruijn, K.M., Cumiskey, L., N\u00ed Dhubhda, R., Hounjet, M., Hynes, W. \n2016. Flood vulnerability of critical infrastructure in Cork, Ireland. E3S \nWeb of Conferences, 7, 7005.  \n\n\n\n[20] Utne, I.B., Hokstad, P., Kj\u00f8lle, G., Vatn, J., T\u00f8ndel, I. A., Bertelsen, D., \nR\u00f8stum, J. 2008. Risk and Vulnerability Analysis of Critical Infrastructures \n- The DECRIS Approach. Proceedings of SMARISK Conference, 1\u201310.  \n\n\n\n[21] Perl, R.F. 2008. Protecting Critical Energy Infrastructures Against \nTerrorist Attacks: Threats, Challenges and Opportunities for International \nCo-operation, (September), 1\u20136. \n\n\n\n[22] Tali, M.G., Sarvati, M.R., Sarrafi, M., Pourmousavi, M., Derafshi, K. 2012. \nFlood vulnerability assessment in Tehran city. Journal of Rescue and \nRelief, 4 (3), 79\u201392.  \n\n\n\n[23] Ciurean, R.L., Schroter, D., Glade, T. 2013. Conceptual Frameworks of \nVulnerability Assessments for Natural Disasters Reduction. Approaches to \nDisaster Management - Examining the Implications of Hazards, \nEmergencies and Disasters, 3\u201332. \n\n\n\n[24] Nabegu, A.B. 2014. Analysis of Vulnerability to Flood Disaster in Kano \nState, Nigeria. Greener Journal of Physical Sciences, 4 (2), 22\u201329. \n\n\n\n36\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 34-37\n\n\n\nCite the Article: Nicole Lee Siew Len, Nurmin Bolong, Rodeano Roslee, Felix Tongkul, Abdul Karim Mirasa, Janice Lynn Ayog (20 18).  \nFlood Vulnerability of Critical Infrastructures - Review. Malaysian Journal of Geosciences, 2(1) : 34-37. \n\n\n\n[25] Khan, A.N., Khan, S.N., Ullah, S., Qasim, S. 2016. Flood Vulnerability \nAssessment in Union Council Jahangira, District Nowshera, Pakistan. J. Sc. \n& Tech. Univ. Peshawar, 40 (2), 23\u201332. \n\n\n\n[26]UN-ISDR United Nations. 2004. International Strategy for Disaster \nRisk Reduction. \n\n\n\n[27] Abouzakhar, N. (2015). Critical Infrastructure Cybersecurity: A \nReview of Recent Threats and Violations, 1, 11. \n\n\n\n[28] Cavan, G. (2011). Landscape and Urban Planning Surface water \nflooding risk to urban communities\u202f: Analysis of vulnerability , hazard and \nexposure, 103, 185\u2013197.  \n\n\n\n[29] Change, G. (2013). Vulnerability\u202f: A Short Review, (February), 1\u20137. \n\n\n\n[30] Ciurean, R. L., Schroter, D., & Glade, T. (2013). Conceptual \nFrameworks of Vulnerability Assessments for Natural Disasters \nReduction. Approaches to Disaster Management - Examining the \nImplications of Hazards, Emergencies and Disasters, 3\u201332.  \n\n\n\n[31] Claudia Bach, Anil K. Gupta, Sreeja S. Nair, J. B. (2013). Critical \nInfrastructures and Disaster Risk Reduction. \n\n\n\n[32] Cutter, S. L. (1996). Vulnerability to Environmental Hazards. Progress \nin Human Geography. \n\n\n\n[33] Cvetkovi, V. M., & Mijalkovi\u0107, S. (2015). VULNERABILITY OF CRITICAL \nINFRASTRUCTURE BY NATURAL DISASTERS. Conference Paper, (January \n2013). \n\n\n\n[34] de Bruijn, K. M., Cumiskey, L., N\u00ed Dhubhda, R., Hounjet, M., & Hynes, \nW. (2016). Flood vulnerability of critical infrastructure in Cork, Ireland. \nE3S Web of Conferences, 7, 7005.  \n\n\n\n[35] Ecology, P., & Frontiers, R. (2015). Physical, Economical, \nInfrastructural and Social Flood Risk - Vulnerability Analyses in GIS. \n\n\n\n[36] Fekete,  a. (2009). Validation of a social vulnerability index in context \nto river-floods in Germany. Natural Hazards and Earth System Science, \n9(2), 393\u2013403.  \n\n\n\n[37] Gallop\u00edn, G. C. (2006). Linkages between vulnerability, resilience, and \nadaptive capacity. Global Environmental Change, 16(3), 293\u2013303.  \n\n\n\n[38] Ghahroudi Tali, M., Sarvati, M. R., Sarrafi, M., Pourmousavi, M., & \nDerafshi, K. (2012). Flood vulnerability assessment in Tehran city. Journal \nof Rescue & Relief, 4(3), 79\u201392.  \n\n\n\n[39] Heilemann, K., Balmand, E., Lhomme, S., de Bruijn, K. M., Nie, L., & \nSerre, D. (2013). Identification and analysis of most vulnerable \ninfrastructure in respect to floods, 56. \n\n\n\n[40] Hinkel, J., & Klein, R. J. T. (2007). Integrating Knowledge for Assessing \nCoastal Vulnerability to Climate Change. Managing Coastal Vulnerability: \nAn Integrated Approach, 1\u201320. \n\n\n\n[41] Kates, R. W. (1985). Foreword, Part I Overview, Part II Biosphysical \nImpacts, Part III Social and Economic Impacts and Adjustments, Part IV \nIntegrated Assessment, in Robert W. Kates, Jesse H. Ausubel and Mimi \nBerberian (eds). Climate Impact Assessment: Studies of the Interaction of \nClimate and Society, John Wiley, 1\u20132, 103\u2013104, 247\u2013249, 465\u2013467. \n\n\n\n[42] Khan, A. N., Khan, S. N., Ullah, S., & Qasim, S. (2016). Flood \nVulnerability Assessment in Union Council Jahangira, District Nowshera, \nPakistan. J. Sc. & Tech. Univ. Peshawar, 40(2), 23\u201332. \n\n\n\n[43] Kron, W. (2002). Keynote lecture: Flood risk= hazard\u00d7 exposure\u00d7 \nvulnerability. Proceedings of the Flood Defence, 82\u201397.  \n\n\n\n[44] Nabegu, A. B. (2014). Analysis of Vulnerability to Flood Disaster in \nKano State, Nigeria. Greener Journal of Physical Sciences, 4(2), 22\u201329. \n\n\n\n[45] Nasiri, H., Mohd Yusof, M. J., & Mohammad Ali, T. A. (2016). An \noverview to flood vulnerability assessment methods. Sustainable Water \nResources Management, 2(3), 331\u2013336.  \n\n\n\n[46] Nasiri, H., & Shahmohammadi-kalalagh, S. (2013). Flood Vulnerability \nIndex as a Knowledge Base for Flood Risk Assessment in Urban Area. \nJournal of Novel Applied Sciences, 2(8), 269\u2013272. \n\n\n\n[47] Oh, E. H., Deshmukh, A., & Hastak, M. (2010). Vulnerability \nAssessment of Critical Infrastructure , Associated Industries , and \nCommunities during Extreme Events Ph . D . Candidate , Construction \nEngineering & Management , School of Civil Engineering , Purdue \nUniversity , 550 Stadium Mall Dr ., West. Construction Research Congress \n2010: Innovation for Reshaping Construction Practice, 449\u2013458. \n\n\n\n[48] Perl, R. F. (2008). Protecting Critical Energy Infrastructures Against \nTerrorist Attacks\u202f: Threats , Challenges and Opportunities for \nInternational Co-operation, (September), 1\u20136. \n\n\n\n[49] Tingsanchali, T. (2012). Urban Flood Disaster Management. Procedia \nEngineering, 32, 25\u201337.  \n\n\n\n[50] Utne, I. B., Hokstad, P., Kj\u00f8lle, G., Vatn, J., T\u00f8ndel, I. A., Bertelsen, D., \u2026 \nR\u00f8stum, J. (2008). Risk and Vulnerability Analysis of Critical \nInfrastructures - The DECRIS Approach. Proceedings of SMARISK \nConference, 1\u201310.  \n\n\n\n[51] UN-ISDR United Nations (2004). International Strategy for Disaster \nRisk Reduction. \n\n\n\n[52] Wu, S.-Y., Yarnal, B., & Fisher, A. (2002). Vulnerability of coastal \ncommunities to sealevel rise: a case study of Cape May county, New Jersey, \nUSA. Climate Research, 22(3), 255\u2013270. \n\n\n\n37\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 86-89 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2020.86.89 \n\n\n\nCite the Article: J. A. Yakubu, J. C. Agbedo, N.M. Ossai (2020). Geophysical Investigation To Estimate The Curie Point Depth, Heat Flow And Geothermal Gradient In \nSoko And Ankpa, Benue Trough Nigeria. Malaysian Journal of Geosciences, 4(2): 86-89.\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2020.86.89\n\n\n\nGEOPHYSICAL INVESTIGATION TO ESTIMATE THE CURIE POINT DEPTH, HEAT \nFLOW AND GEOTHERMAL GRADIENT IN SOKO AND ANKPA, BENUE TROUGH \nNIGERIA \n\n\n\nJ. A. Yakubu*, J. C. Agbedo, N.M. Ossai \n\n\n\nDepartment of Physics and Astronomy, University of Nigeria, Nsukka, Enugu State, Nigeria  \n\n\n\n*Corresponding Author Email: john.yakubu@unn.edu.ng\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 04 April 2020 \nAccepted 06 May 2020 \nAvailable online 21 May 2020\n\n\n\nThis work presents the interpretation of the aeromagnetic data over Soko and Ankpa area using spectral \nanalysis method. The study area was divided into eight (8) equal spectral blocks in order to estimate the depth \nto the top boundary, centroid, Curie point depth, heat flow and geothermal gradient of the study area. The \nresult of the analysis shows the range of the depths to the top boundary and centroid varies between 1.085 to \n1.984 km and 6.151 to 8.730 km respectively. The Curie temperature isotherm ranges between 11.112 km \nand 15.476 km and the geothermal gradients associated with it ranges from 39.967 and 52.196 0 \ud835\udc36 \ud835\udc58\ud835\udc5a\u2044 . The \ncorresponding values of heat flow ranges from 93.697 \ud835\udc5a\ud835\udc4a\ud835\udc5a2 and 130. 490 \ud835\udc5a\ud835\udc4a\ud835\udc5a2. From this analysis, it was \nobserved that areas with high geothermal gradient correspond to high heat flow and an inverse relationship \nexists between the heat flow and the Curie point depth. With the high geothermal gradient especially at the \nsoutheastern part of the study area, there is a possibility of enough geothermal energy for exploration in order \nto boost and generate clean energy for electricity.  \n\n\n\nKEYWORDS \n\n\n\nAnkpa, Soko, depth to the centroid, depth to the top and geothermal energy.\n\n\n\n1. INTRODUCTION \n\n\n\nDeveloped and developing countries have successfully used geothermal \nenergy in generation of electricity to meet the rapid growth in demand for \nelectricity to power industries and households across the world. In power \ngeneration, geothermal energy is consider to be one of the most clean \nenergy source and has appealed the growing attention worldwide (Okorie \net al., 2019). It has several advantages over fossil fuels of having clean, \nlarge reserves and environmentally friendly (Croteau and Gosselin, 2015). \nThere will be a reduction in the emission of harmful gases and particulate \nmatter resulting from the combustion of fossil fuels if there is an increase \nin global utilization of geothermal resources. \n\n\n\nAccording to power base line in 2015, Nigeria has one of the lowest rate of \nconsumption of electricity per capita in Africa. Nigeria generated about \n4,000 MW of electricity for her population of over 170 million in which \n80% of the total electricity produced is consumed on demand by \nresidential and commercial sectors in Nigeria (Abraham and Nkitnam, \n2017). As a result of these challenges, there is need to emphasize the \nexploration of various energy resources available particularly the \ngeothermal energy to aid the new energy map for the country. In long term \nvision for providing reliable and renewable environmental-friendly \nsources of energy for Nigeria, the importance of geothermal energy can \nnever be over emphasize.  \n\n\n\nIn Nigeria, geothermal energy has been given little or no attention in the \npast. Several researchers have carried out work within the Benue trough \nusing aeromagnetic data for the purpose of determining the presence of \n\n\n\nmineral and geothermal investigation (Obiora et al., 2016; Ekwueme et al., \n2018; Yakubu et al., 2020). This paper focuses on Ankpa and Soko area for \nthe purpose of investigating the geothermal potential of the study area \n\n\n\n2. LOCATION AND GEOLOGY OF THE STUDY AREA \n\n\n\nThe area of study is located within the lower Benue Trough, Nigeria and is \ncomprised of Soko and Dekina areas. The Benue Trough of Nigeria is one \nof the most prominent geologic features in West Africa. It extends over a \ndistance of 800km trending in the NNE-SSW direction starting from the \nNiger-Delta to the South-West of the lake Chad and it ranges from about \n130 to 250 km in width (Obaje et al., 2006). Because of the large regional \narea, the studies in the Benue Trough are usually separated geographically \ninto the lower, middle and upper Benue Trough. The depocenters of the \nlower Benue Trough is made up of the area around Anambra basin and \nAbakaliki Anticlinorium (Uplift) whereas Middle Benue Trough comprise \nthe area around Makurdi through Yandev, Lafia, Awe, Akiri, Keana, Wukari \nto Jangwa. In the Upper Benue Trough, the depocenter comprise Gombe, \nNafada (Gongola arm) and Bambam, Jessus, Lakun and Numan (Yola arm) \n(Fatoye et al., 2014). \n\n\n\n3. DATA SOURCE AND METHODOLOGY \n\n\n\nThe data of the study area (Soko and Ankpa) was obtained from the \n\n\n\nNigeria geological survey agency (NGSA) Abuja. The airborne geophysical \n\n\n\nwork was carried out by Fugro Airborne survey, in which an aircraft was \n\n\n\nflown at 80 m terrain clearance and 500 m line spacing. The digitized data \n\n\n\nby FURGRO airborne surveys was obtained from the Nigeria geological \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 86-89 \n\n\n\nCite the Article: J. A. Yakubu, J. C. Agbedo, N.M. Ossai (2020). Geophysical Investigation To Estimate The Curie Point Depth, Heat Flow And Geothermal Gradient In \nSoko And Ankpa, Benue Trough Nigeria. Malaysian Journal of Geosciences, 4(2): 86-89.\n\n\n\nsurvey agency in XYZ format. X is the latitude, Y is the longitude and the Z \n\n\n\nis the magnetic field intensity. \n\n\n\nThe data was gridded to form the total magnetic intensity (TMI) map. The \n\n\n\nTMI of the study area was subdivided into eight (8) equal blocks each in \n\n\n\norder to accommodate longer wavelength. The obtained magnetic data \n\n\n\nwas transformed into radial energy spectrum for each block using \n\n\n\nmicrosoft excel and Fourpot software. The graphs of log of energy against \n\n\n\nfrequency was plotted.  Two linear segments were identified, which \n\n\n\nindicates the presence of two magnetic source layers in the study area. The \n\n\n\nslope of the deep and shallow line segments were calculated for each \n\n\n\nblock, then depth to centroid Zo and depth to top boundary, Zt were also \n\n\n\nevaluated using equations 2 and 3 respectively. \n\n\n\nIn performing the spectral analysis, the first  stage is to estimate the depth \n\n\n\nto the centroid (Zo) and depth to the top boundary (Zt) of the magnetic \n\n\n\nsource from the gradient of the graph of logarithm of energy against \n\n\n\nfrequency that is given by equation 1; \n\n\n\n\ud835\udc59\ud835\udc5c\ud835\udc54\ud835\udc38\n\n\n\n\ud835\udc39\ud835\udc5f\ud835\udc52\ud835\udc5e\ud835\udc62\ud835\udc52\ud835\udc5b\ud835\udc50\ud835\udc66 \n  (1) \n\n\n\nWhere frequency is measured in cycle/metre\nFrom the graph, linear segments were drawn from the high frequency \nportion of the spectral, from which the slopes were estimated for the \ncalculation of the depth to the centroid (Zo) using the equation (2):  \n\n\n\n\ud835\udc4d\ud835\udc5c = \u2212 \n\ud835\udc5a1\n\n\n\n4\u03c0\n (2) \n\n\n\nAlso, the depth to the top boundary (Zt) was estimated from the low \nfrequency portion from the gradient of the line drawn from the low \nfrequency part of the spectral (Okubo et al., 1985).  \n\n\n\n\ud835\udc4d\ud835\udc61 = \u2212 \n\ud835\udc5a2\n\n\n\n4\u03c0\n (3) \n\n\n\nwhere, \ud835\udc5a1and \ud835\udc5a2slopes of the high and low segments of the plots while \ud835\udc4d\ud835\udc5c \nand \ud835\udc4d\ud835\udc61 are depths to centroid Zo and depth to top boundary, Zt respectively.  \nThe Curie point dephth (basal depth of the magnetic source) was \nevaluated using equation (4) as (Tanaka et al., 1999; Okubo et al., 1985);   \n\n\n\n\ud835\udc4d\ud835\udc4f= 2\ud835\udc4d\ud835\udc5c -\ud835\udc4d\ud835\udc61   (4) \n\n\n\nThe geothermal gradient (change in temperature per unit length) and the \n\n\n\nCurie point depth are related by equation (5) and the Curie temperature \n\n\n\nwith value 580\ud835\udc5cC was used standard for magnetite (Maden, 2010; \n\n\n\nStampolidis et al., 2005; Tanaka et al., 1999; Frost and Shive, 1986).   \n\n\n\n\ud835\udc51\ud835\udc47\n\n\n\n\ud835\udc51\ud835\udc4d\n= \n\n\n\n580\ud835\udc5cC\n\n\n\n\ud835\udc4d\ud835\udc4f\n  (5) \n\n\n\nFurthermore, the Curie point depth and the geothermal gradient are \nrelated to the heat flow (q) as shown in equation 6 (Tanaka et al., 1999; \nTurcotte and Schubert, 1982): \n\n\n\nq = \u03bb\n580\u030a \u030aC\u030a\n\n\n\n\ud835\udc4d\ud835\udc4f\n= \u03bb\n\n\n\n\ud835\udc51\ud835\udc47\n\n\n\n\ud835\udc51\ud835\udc4d\n  (6) \n\n\n\nThe thermal conductivity (lambda, \u03bb) of 2.5 Wm-1C-1 was used as a \nstandard in the study area (Nwankwo et al., 2011). These calculated values \nof the geothermal gradient, Curie point depth and heat flow were inputted \nseparately into Suffer software to construct the 2D contour map of \ngeothermal gradient, Curie point depth and heat flow of the study area. \n\n\n\n4. PRESENTATION OF RESULTS AND DISCUSSION\n\n\n\nThe total magnetic intensity of Soko and Ankpa area, after gridding \nshowed that the magnetic intensity varies between -124.6 and 112.2nT as \nshown in Figure 1. These variations in the magnetic signatures may be due \nto either difference in lithology, faults, depth to the magnetic source, \nsusceptibility contrast or degree of geologic strike in the study area.  \n\n\n\nThe areas of low magnetic anomaly likely indicate lower concentration of \nmagnetically susceptible minerals, while areas with high positive \nmagnetic anomaly will likely indicate higher concentration of magnetically \nsusceptible minerals. \n\n\n\nFigure 1: Division of TMI grid into 8 spectral blocks for geothermal \n\n\n\nanalysis \n\n\n\nIn computing the Curie point depth, the TMI map of the study area was \ndivided into eight (8) equal spectral blocks as shown in Figure 1.  \nFOURPOT and Excel computer software was used in carrying out the \nanalysis. The graphs of logarithms of energy versus frequency were \nplotted for each of the spectral blocks shown in Figure 2. The results of the \ndepth to the centroid, top, geothermal gradient, Curie point depth and heat \nflow are shown in Table 1. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 86-89 \n\n\n\nCite the Article: J. A. Yakubu, J. C. Agbedo, N.M. Ossai (2020). Geophysical Investigation To Estimate The Curie Point Depth, Heat Flow And Geothermal Gradient In \nSoko And Ankpa, Benue Trough Nigeria. Malaysian Journal of Geosciences, 4(2): 86-89.\n\n\n\nFigure 2: The graphs of logarithms of energy versus frequency \n\n\n\nTable 1: The calculated Curie point depth, Geothermal gradient and \nHeat flow \n\n\n\nBlocks Depth to \ncentroid \n(\ud835\udc81\ud835\udfce) in \nkm \n\n\n\nDepth to \ntop \nboundary \n(\ud835\udc81\ud835\udc95) \nin km \n\n\n\nCurie \npoint \ndepth \n(\ud835\udc81\ud835\udc83) \nin km \n\n\n\nGeothermal \n\n\n\ngradient (\n\ud835\udc1d\ud835\udc13\n\n\n\n\ud835\udc1d\ud835\udc19\n) \n\n\n\nin \u00b0C/km \n\n\n\nHeat \nflow (q) \nin \n\ud835\udc8e\ud835\udc7e\ud835\udc8e\u2212\ud835\udfd0 \n\n\n\n1 8.730 1.984 15.476 37.477 93.693 \n\n\n\n2 8.163 1.814 14.512 39.967 99.9173 \n\n\n\n3 6.151 1.190 11.112 52.196 130.490 \n\n\n\n4 6.548 1.705 11.391 50.917 127.293 \n\n\n\n5 7.540 1.058 14.022 41.364 103.409 \n\n\n\n6 7.407 1.178 13.636 42.534 106.336 \n\n\n\n7 6.548 1.389 11.707 49.543 123.858 \n\n\n\n8 7.341 1.752 12.930 44.857 112.142 \n\n\n\nFrom table 1, the contour map of geothermal gradient, Curie point depth \nand heat flow was constructed with the aid of Surfer 13 software. The \nCurie point depth varies between 11.112km and 15.47km. The south \nwestern portion of the study area is having relatively high value of \n15.47km Curie point depth. The shallowest region is at the south east and \nsouth west central portion of the study area which ranges from 11km-\n11.4km as shown in Figure 3. In figure 4, the map defines a region of high \ngeothermal gradient of 51.10 \ud835\udc36 \ud835\udc58\ud835\udc5a\u2044  which is in the southwestern and \naround the southwestern parts of the study area and a low value of \ngeothermal gradient of 36.50 \ud835\udc36 \ud835\udc58\ud835\udc5a\u2044  occurred at the southern (Ankpa) part \nof thestudy area. it was observed that area of high heat flow corresponds \nwith the area of high geothermal gradient and vice versa. The heat flow \nvaries between 93.697 and 130.490mW\ud835\udc5a\u22122.  The highest heat flow value \nis 130mW\ud835\udc5a\u22122 and it is observed in the southwestern and southeastern \npart of the area while the lowest value of heat flow of about 92mw\ud835\udc5a\u22122 \noccurred at the southern part of the study area as shown in Figure 5. \n\n\n\nFigure 3: The Curie point depth map of the study area. \n\n\n\nFigure 4: The geothermal gradient map of the study area. \n\n\n\nFigure 5: The heat flow contour map of the study area (Contour interval \n\n\n\nof 1 mWm-2). \n\n\n\nMeasurements have shown that, a typical high value of heat flow and \ngeothermal gradient are attributed to the region with significant \ngeothermal energy potential. In view of this, the area of study with high \nvalue of geothermal gradient has a good potential for the generation of \ngeothermal energy (Barbier et al., 2011). Usually, shallow Curie point \ndepth is linked to area of active geothermal gradient and heat flow. This \nassertion is clearly revealed by the contoured map of geothermal gradient \n(Figure 4) and that of heat flow (Figure 5). The result of Curie point depth \ndeduced from the analysis of aeromagnetic data using spectral method in \ncombination with the calculated values of heat flow values showed an \ninverse correlation between Curie point depth and heat flow as shown in \nFigure 6. \n\n\n\nFigure 6: The graph of heat flow against Curie point depth. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 86-89 \n\n\n\nCite the Article: J. A. Yakubu, J. C. Agbedo, N.M. Ossai (2020). Geophysical Investigation To Estimate The Curie Point Depth, Heat Flow And Geothermal Gradient In \nSoko And Ankpa, Benue Trough Nigeria. Malaysian Journal of Geosciences, 4(2): 86-89.\n\n\n\n5. CONCLUSION \n\n\n\nThe Curie point depth, heat flow and geothermal gradient of the area of \nstudy has been determined by employing spectral analysis. The results \nhave thrown more light on the understanding of contributions of Curie \npoint depth, heat flow and geothermal gradient, particularly to \ngeophysicists and other geoscientists. Furthermore, with the high \ngeothermal gradient especially at the southeastern part of the study area, \nthere is a possibility of enough geothermal energy potential geothermal \nfor exploration. \n\n\n\nREFERENCES \n\n\n\nAbraham, E.M., Nkitnam, E.E., 2017. Review of Geothermal Energy \n\n\n\nResearch in Nigeria: The Geoscience Front. International Journal of \n\n\n\nEarth Science and Geophysics, 3, Pp. 015. \n\n\n\nBarbier,  E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, A.C., Silliman, B.R.,  \n\n\n\n2011. The value of estuarine and coastal ecosystem services. Ecological \n\n\n\nSociety of America, 81(2), Pp. 169\u2013193. \n\n\n\nCroteau, R., Gosselin, L., 2015. Correlations for cost of ground\u2010source heat \n\n\n\npumps and for the effect of temperature on their performance. \n\n\n\nInternational journal of energy research, 39(3), Pp. 433-438. \n\n\n\nEkwueme, O.U., Obiora, D.N., Igwe, E.A., Abangwu, J.U., 2017. Study of \n\n\n\naeromagnetic anomalies of Idah and Angba areas, north central Nigeria, \n\n\n\nusing high resolution aeromagnetic data. Modeling Earth Systems and \n\n\n\nEnvironment,  \n\n\n\nFatoye, F.B., Ibitomi, M.A., Omada, J.I., 2014. Lead-Zinc-Barytes \n\n\n\nmineralization in the Benue Trough, Nigeria: Their geology, occurrences \n\n\n\nand economic prospective. Advances in Applied Science Research, 5(2), \n\n\n\nPp. 86-92. \n\n\n\nFrost, B.R., Shive, P.N., 1986. Magnetic mineralogy of the lower continental \n\n\n\ncrust. Journal of Geophysical Research, 91, Pp. 6513\u20136521. \n\n\n\nMaden, N., 2010. Curie-point depth from spectral analysis of magnetic \n\n\n\ndata in Erciyes stratovolcano (Central Turkey). Pure Applied \n\n\n\nGeophysics, 167, Pp. 349\u2013358. \n\n\n\nNwankwo, L.I., Olasehinde, P.I., Akoshile, C.O., 2011. Heat flow anomalies \n\n\n\nfrom the spectral   analysis of Airborne Magnetic data of Nupe Basin, \n\n\n\nNigeria. Asian Journal of Earth Sciences, 1, Pp. 1\u20136. \n\n\n\nObaje, N.G., Attah, D.O., Opeloye, S.A., Moumouni, A., 2006. Geochemical \n\n\n\nevaluation of the hydrocarbon prospects of sedimentary basins in \n\n\n\nNorthern Nigeria. Geochemical Journal, 40, Pp. 227 \u2013 243. \n\n\n\nObiora, D.N., Yakubu, J.A., Okeke, F.N., Chukudebelu, J.U., Oha, A.I., 2016. \n\n\n\nInterpretation of aeromagnetic data of Idah area in north central Nigeria \n\n\n\nusing combined methods\", Journal of the Geological Society of India, 88, \n\n\n\nPp. 98-106. \n\n\n\nOkorie, A.C., Obiora, D.N., Igwe, E., 2019. Geophysical study of Ubiaja and \n\n\n\nIllushi area in northern Anambra basin, Nigeria, using combined \n\n\n\ninterpretation methods of aeromagnetic data\", Modeling Earth Systems \n\n\n\nand Environment,  \n\n\n\nOkubo, Y.J., Graf, R., Hansen, R.O., Ogawa, K., Tsu, H., 1985. Curie point \n\n\n\ndepth of the Island of Kyushu and surrounding areas, Japan. Geophysic, \n\n\n\n53, Pp. 481\u2013491. \n\n\n\nStampolidis, A., Kane, I., Tsokas, G.N., Tsourlos, P., 2005. Curie point depths \n\n\n\nof Albania inferred from ground total field magnetic data. Surveys in \n\n\n\nGeophysics, 26, Pp. 461\u2013480. \n\n\n\nTanaka, A., Okubo, Y., Matsubayashi, O., 1999. Curie point depth based on \n\n\n\nspectrum analysis of the magnetic anomaly data in East and Southeast \n\n\n\nAsia. Tectonophysics, 306, Pp. 461\u2013470.  \n\n\n\nTurcotte, D.L., Schubert, G., 1982. Geodynamics. Cambridge University \n\n\n\nPress, New York.  \n\n\n\nYakubu, J.A., Okeke, F.N., Obiora, D.N., 2020. Estimation of Curie point \n\n\n\ndepth, geothermal gradient and heat flow within the lower Benue \n\n\n\ntrough, Nigeria using high resolution aeromagnetic data. Modeling Earth \n\n\n\nSystems and Environment. \n\n\n\n\nhttps://esajournals.onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Barbier%2C+Edward+B\n\n\nhttps://esajournals.onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Kennedy%2C+Chris\n\n\nhttps://esajournals.onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Silliman%2C+Brian+R\n\n\nhttps://esajournals.onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Silliman%2C+Brian+R\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 12-18 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2022.12.18 \n\n\n\n \nCite The Article: Otto A. Ihunda, Ifiok M. Ibanga, Ndubuisi Ukpabi (2022). Biozonation and Age Reconstruction of 4000ft To 4540ft Section of Well-X, Niger Delta, \n\n\n\nNigeria. Malaysian Journal of Geosciences, 6(1): 12-18. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nREVIEW ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2022.12.18 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBIOZONATION AND AGE RECONSTRUCTION OF 4000FT TO 4540FT SECTION OF \nWELL-X, NIGER DELTA, NIGERIA \n \nOtto A. Ihunda, Ifiok M. Ibanga, Ndubuisi Ukpabi \n \naDepartment of Geology, Rivers State University of Science and Technology, River\u2019s state \u2013Nigeria. \nbDepartment of Geology, Akwa Ibom State University, Mkpat Enin \u2013 Nigeria. \ncDepartment of Geology, University of Port Harcourt, Rivers State \u2013 Nigeria. \n* Corresponding Author Email: tinandaotto01@yahoo.com \n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 07 December 2021 \nAccepted 09 January 2022 \nAvailable online 11 January 2022 \n\n\n\n\n\n\n\nBiozonation and Age reconstruction of an offshore Well-X in the Niger Delta was carried out \nusing10composite ditch cutting samples, Palynological studies was carried out on the samples to determine \nthe age of the formation penetrated in the well, generate a range chart for the palynomorphs and generate \nbiozones. The samples were described and prepared using the standard non mineral acid method of \npreparing Palynological samples. The interval studied was between 1219m (4000ft) -1384m (4540ft) \nthickness, belonging to the Agbada Formation which is of late Miocene. Lithologically the section penetrated \nby the well varies from sandstone bed to shaly bed. One biozone of the SPDC scheme P850 has been erected. \nThe P850 zone palynomorphs observed within this interval which coincides with the P860 subzone   include   \nabundance   of   Zonocostitesramonae, rich occurrence of Stereisporitessp, Retibrevitricolporites obodoensis, \nand Psilatricolporites crassus, scanty Nympheapollislotus, and Multiareolites formosus as well as the presence \nof Peregrinipollis nigericus. The age of the sediment is of the late Miocene. \n\n\n\nKEYWORDS \n\n\n\nPalynology, Biozonation, Range chart, Palynomorphs, late Miocene \n\n\n\n1. INTRODUCTION \n\n\n\nBiostratigraphy is the branch of stratigraphy which focuses on correlating \n\n\n\nand assigning relative ages of rocks trata by using the fossil assemblages \n\n\n\ncontained within them. Biozones are intervals of geological strata \n\n\n\ndemarcated based on their typical fossil taxa found in them. These may be \n\n\n\na single taxon or combination of taxa. The process of designating zones in \n\n\n\nstratigraphic units is called biozonation. The boundary of two different \n\n\n\nbiostratigraphic units is called a biohorizon. The main data of \n\n\n\nbiostratigraphic analysis are: the occurrence or nonexistence of a fossil \n\n\n\ntaxon in a geologic horizon; the First Downhole Occurrence (FDO)/Last \n\n\n\nAppearance Datum (LAD); the first Appearance Datum(FAD)/Last Down \n\n\n\nhole Occurrence (either local or global).Rock unit categorized by one or \n\n\n\nmore taxa can be differentiated from next rock units with one or more \n\n\n\nother taxa into biozone or zone. One way of reconstructing ancient \n\n\n\nenvironment, decipher the conditions and timing under which sediments \n\n\n\nwere deposited is by the application of palynological studies.  \n\n\n\nThis involves the study of pollen and spores, particulate organic matters \n\n\n\n(POM) kerogen in sedimentary rocks and certain minute Planktonic \n\n\n\norganisms, in fossils and living form. (Brooks and summons 2003; \n\n\n\nOmorogieva, 2008). Palynology is the study of microscopic bodies \n\n\n\ngenerally known as palynomorphs; pollen and spores and certain other \n\n\n\nmicroscopic sized structures, either of plant or of uncertain origin. These \n\n\n\nother structures include acritarchs, dinoflagellates and their cysts \n\n\n\n(dinocysts), algal spores and fungal spores. The diameters of \n\n\n\npalynomorphs fall within the range of 5\u00b5m-500\u00b5m. Thus, \u201cPalynology\u201d can \n\n\n\nalso be defined as the study of organic-walled microfossils \n\n\n\n(Erdtman,1969). \n\n\n\nThe term \u201cPalynology\u201d was coined and since then, it has become a new \n\n\n\nsub-division of Botanical science with different applications (Hyde and \n\n\n\nWilliam, 1944). At the beginning, Palynology was confined to the study of \n\n\n\nthe morphology of pollen and spores alone. Palynological Methods have \n\n\n\nbeen successfully applied in collaboration with plant and geologic science \n\n\n\nin reconstructing ancient environments for oil and gas exploration, crime \n\n\n\ninvestigation, Climatic conditions, correlation and in the determination of \n\n\n\nthe age of sediment through time by the analysis of pollen and spores as \n\n\n\nwell as microorganisms like Dinoflagellate cyst, Acritarchs, Chitinozoans \n\n\n\nand conodonts found in sediments (Behling, 2005; Dawson and Mayes, \n\n\n\n2015). According to Pollen grains transported by wind, insects and other \n\n\n\nanimals may find their way into deposits of lakes, oceans, swamps \n\n\n\nmangrove and peatbogs (Behling, 2005). Pollen is usually very small \n\n\n\ngrains between 20 and 40\u00b5m and can be observed under a light \n\n\n\nmicroscope. It is also possible to illustrate past plant diversity, stability \n\n\n\nand dynamic of ecosystem through the study of palynology (Ajipe and \n\n\n\nAdebayo, 2018). \n\n\n\n2.  LOCATION OF THE STUDY AREA \n\n\n\n\nmailto:tinandaotto01@yahoo.com\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 12-18 \n\n\n\n\n\n\n\n \nCite The Article: Otto A. Ihunda, Ifiok M. Ibanga, Ndubuisi Ukpabi (2022). Biozonation and Age Reconstruction of 4000ft To 4540ft Section of Well-X, Niger Delta, \n\n\n\nNigeria. Malaysian Journal of Geosciences, 6(1): 12-18. \n\n\n\n\n\n\n\nWell X is an offshore well located in the western part of the Niger Delta. \nThe well is located between two latitude and two longitudes. Latitude \n5\u00b040\u2019 and 5\u00b050\u2019N and Longitude5\u00b000'and5\u00b010\u2019E. (Figure 1). \n\n\n\n\n\n\n\nFigure 1: Location map showing the study area in the western Niger \nDelta and simplified bathymetry (after Bakare etal., 2009) \n\n\n\n2.1    Regional Setting of the Niger delta \n\n\n\nThe Niger Delta clastic wedge formed along a failed arm of a triple junction \nsystem (aulacogen)that originally developed during breakup of the South \nAmerican and African plates in the late Jurassic (Burke etal.,1972; \nWhiteman, 1982). The two arms that followed the southwestern and \nsoutheastern coast of Nigeria and Cameroon developed into the passive \ncontinental margin of West Africa, whereas the third failed arm formed the \nBenue Trough. Other depo centers along the African Atlantic coastal so \ncontributed to deltaic build-ups, Syn rift sediments accumulated during \nthe Cretaceous to Tertiary, with the oldest dated sediments of Albian age. \nA delta is a large accumulation of sediments deposited at the mouth of a \nriver where it is discharged into the sea with more than one channel called \ndistributaries. It results from a stream reaching a body of water such as \nthe sea and building a deposit of sediments because of the reduction of its \nvelocity offlow.  \n\n\n\nStratigraphy of Niger Delta In an advancing delta such as that of the \nTertiary Niger delta, sediments are stratigraphically superimposed. The \nsubmarine delta fringe will encroach on sediments and will in turn, be \ncovered by a younger lower deltaic plain. In the Niger delta, this sequence \nis modified by the numerous transgressions which have occurred from \ntime to time, breaking the continuity of the main overall regression, and \nbecoming stratigraphically superimposed (ShortandStauble,1967). The \nthick wedge of the Niger delta is considered to consist of three units Benin, \nAgbada and Akata formations (Figure 2). These formations are strongly \ndiachronous and cut across the time stratigraphic units which are \ncharacteristically S-shaped in cross section. The typical sections of these \nformations are described and summarized in a variety of papers (Short \nand Stauble, 1967; Avbovbo, 1978; Doust and Omatsola, 1990; Kulke, \n1995). \n\n\n\n3. MATERIALS AND METHOD \n\n\n\n3.1 Materials \n\n\n\nThe Materials used for sample description are: \n\n\n\n\uf0d8 Dilute Hcl \n\n\n\n\uf0d8 Ditch cutting samples ranging between 4000-4540ft depth \n\n\n\n\uf0d8 Weighing balance \n\n\n\n\uf0d8 Pipette. \n\n\n\nMaterials used for sample preparation: \n\n\n\n\uf0d8 Sodium hexametaphosphate \n\n\n\n\uf0d8 Running distilled tap water \n\n\n\n\uf0d8 Palynological slides \n\n\n\n\uf0d8 Coverslips \n\n\n\n\uf0d8 Ten(10)microns nylon mesh sieve \n\n\n\n\uf0d8 Hotplate \n\n\n\n\uf0d8 Norland adhesive glue \n\n\n\n\uf0d8 Phial \n \nMaterials used for sample analysis are: \n\n\n\n\uf0d8 Transmitting light microscope \n\n\n\n3.2 Methods of Study \n\n\n\nA total often (10) ditch cuttings were palynologically analyzed. The \nsamples were composited at18m (60ft) intervals and ranged between \n1219m (4000ft) to 1384m (4540ft). The non-mineral standard method of \npreparing palynological samples were followed, this involves the use of \nsodium hexametaphosphate. \n\n\n\n3.2.1 Lithological Sample Description \n\n\n\nLithostratigraphic analysis was carried out on the samples by visual \ninspection. Physical characteristics such as colour, texture, fissility, \nfriability, consolidated and or indurated physical state of the rock type \nwere noted. The uses of chemical test to determine the presence of \ncalcareous materials was also carried out using10% dilute HCl. \nPalynological sample preparation on the other hand adopted the sodium \nhexametaphosphate procedure for thorough disintegration of samples as \noutlined (Riding and Hughes, 2004). The detailed sample preparation \nprocedures are outlined below. \n\n\n\n3.2.2 Palynological Sample preparation \n\n\n\nSoaking: Bowls were labeled to indicate sample depths contained and \n\n\n\nsoaked with sodium hexametaphosphate and distilled water for 30min to \n\n\n\ndissolve the samples before washing. \n\n\n\nWashing: the soak sample were washed thoroughly under running \n\n\n\ndistilled water with10\u03bcm mesh nylon sieve, the samples were washed \n\n\n\nuntil the water coming out under the sieve becomes clean. The purpose of \n\n\n\nwashing is to remove clay content from the samples. Precaution measure \n\n\n\nwhile washing: Don't apply pressure to avoid deforming the \n\n\n\nmorphological features of the palynomorphs (structure and sculptures). \n\n\n\nConcentration: After the samples were washed and the clay content \n\n\n\nremoved the next step was to concentrate the samples by separating all \n\n\n\nother grains including the mineral walls from the palynomorphs (the \n\n\n\norganic walls). This was done by swirling a process of gravity separation. \n\n\n\nThe faunas were at the bottom while the palynomorphs were at the top. \n\n\n\nThe swirling glass was shaken a little and all the grains allowed to settle at \n\n\n\nthe base, and the palynomorphs to float, repeat the process until the \n\n\n\nsample is free from grains leaving only palynomorphs, label the sieve out \n\n\n\nsample in the Phial. \n\n\n\nMounting: coverslip and slides were used to mount, place the coverslip on \n\n\n\nthe Hotplate and use a pipette to drop the sample on the coverslip. The Hot \n\n\n\nplate was switched on and the samples on the coverslip were allowed to \n\n\n\ndry. Afterward apply Norland adhesive glue to the coverslip and glue it \n\n\n\ntogether with the slide, apply gentle pressure while gluing the cover slip \n\n\n\nwith the slide to avoid air traps. \n\n\n\nThe microscopy: The prepared slides were viewed under Transmitting \n\n\n\nlight microscope to identify the specific palynomorphs and its features, a \n\n\n\nchart was created after identifying the palynomorphs and all data \n\n\n\ngenerated are presented as a chart using the biostratigraphic software \n\n\n\ncalled stratabugs. \n\n\n\n3.2.3  Sample Analysis (Microscopy) \n\n\n\nDuring sample analysis (microscopy), the prepared slides were scanned \n\n\n\nand the frequency of occurrence of the recovered fossils noted and \n\n\n\nrecorded. Each of the fossils recovered were carefully studied under the \n\n\n\nMicroscope with emphasis on systematics which involved systemic \n\n\n\nidentification of subtle morphological features used for taxonomic \n\n\n\nclassification of the fossils. \n\n\n\n4. RESULTS AND DISCUSSION \n\n\n\n4.1 Results Lithological Description \n\n\n\nA total of ten (10) ditch cuttings were palynologically analyzed. The \n\n\n\nsamples were composited at 18m (60ft) intervals and ranged \n\n\n\nbetween1219m (4000ft) to 1384m (4540ft). The lithology comprises of \n\n\n\nalternation of sandstone and shale beds characteristic of the Agbada \n\n\n\nFormation. The sandstone beds showed considerable thickness compared \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 12-18 \n\n\n\n\n\n\n\n \nCite The Article: Otto A. Ihunda, Ifiok M. Ibanga, Ndubuisi Ukpabi (2022). Biozonation and Age Reconstruction of 4000ft To 4540ft Section of Well-X, Niger Delta, \n\n\n\nNigeria. Malaysian Journal of Geosciences, 6(1): 12-18. \n\n\n\n\n\n\n\nwith the shale beds indicative of the upper Agbada Formation. The \n\n\n\nsandstone is sub angular to sub rounded in shape and range from silt size \n\n\n\nto very coarse grained. The shale is fissile and moderately calcareous. \n\n\n\n \nFigure 2: Litholog of wellX \n\n\n\n4.1.1 Palynology Chart \n\n\n\n \nFigure 3: Distribution Range chart and biozonation of well X showing the \n\n\n\nforms recovered. \n\n\n\nThe depositional environment of the well was evaluated following detailed \nanalysis and characterization of the biogenic and physical features of the \nsedimentary lithofacies coupled with the palynological characteristics. \nThe major groups utilized in the study are pollen/spores and \ndinoflagellates; other associated element includes foraminiferal test \nlinings. From the charts it can be seen that terrestrial derived \npalynomorphs dominated the assemblage. \n\n\n\n4.2   Discussion \n\n\n\nThe lithology comprises of alternation of sandstone and shale beds \ncharacteristic of the Agbara Formation. The sandstone beds showed \nconsiderable thickness compared with the shale beds indicative of the \nupper Agbada formation. The sandstone is subangular to subrounded in \nshape and range from siltsize to very coarse grained. The shale is fissile \nand moderately calcareous. Palynomorph recovery within the studied \ninterval was fairly rich. The result of the analysis reveals that the \nsediments were deposited during the Late Miocene times (Messinian). One \nbiozone of the SPDC schemes; P850 were identified in the studied interval \n(Evamy et al., 1978).  \n\n\n\nDetails of the interpretation and analysis are given below: \n\n\n\nP-Zone: P850 \n\n\n\nInterval: 1219m(4000ft)-1384m(4540ft).  \n\n\n\nAge:  \n\n\n\nLate Miocene: \n\n\n\nThe top of P850 zone coincides with the base of the overlying P860 \nsubzone and corresponded to Quantitative base of \nNympheapollislotus.The top of P850 zone was not met in the studied \nsection. \n\n\n\nThe base however was not penetrated in this study as Peregrinipollis \nnigericus did not show a Quantitative top.Other characteristic P850 zone \npalynomorphs observed within this interval included abundance of \nZonocostites ramonae, rich occurrence of Stereisporitessp, \nRetibrevitricolporites obodoensis, and Psilatricolporites crassus, scanty \nNympheapollislotus, and Multiareolites formosus as well as the presence \nof Peregrini pollisnigericus. \n\n\n\nTable 1: Quantitative count of the palynomorphs \n\n\n\n\n\n\n\nTable 2: Analysis result. 4000ft \n\n\n\nS/No TaxonName Count \n\n\n\n1 Zonocostites ramonae 140 \n\n\n\n2 Psilastephano colporites laevigatus 5 \n\n\n\n3 Psilatricolporitessp 2 \n\n\n\n4 Echiperiporitesestelae 3 \n\n\n\n5 Psilatricolporites crassus 7 \n\n\n\n6 Verrucatosporite salienus 34 \n\n\n\n7 Laevigatosporites discordatus 41 \n\n\n\n8 Pachydermitesdiederixi 17 \n\n\n\n9 Acrostichumaureum 7 \n\n\n\n10 Nympheapollislotus 2 \n\n\n\n11 Retibrevitricolporites obodoensis 2 \n\n\n\n12 Crassoretitriletes vanraadshooveni 2 \n\n\n\n13 Fungalspore 2 \n\n\n\n14 Brevicolporitesguinetii 1 \n\n\n\n15 Stereisporitessp 4 \n\n\n\n16 Verrucatosporite stenellis 1 \n\n\n\n17 Lycopodiumsporites sp 3 \n\n\n\n18 Selaginellamyosurus 1 \n\n\n\n19 Monoporites annulatus 2 \n\n\n\n20 Aletesporites sp 1 \n\n\n\n21 Magnastriatites howardi 1 \n\n\n\n22 Pterospermellasp 1 \n\n\n\n23 Botryococcusbraunii 1 \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 12-18 \n\n\n\n\n\n\n\n \nCite The Article: Otto A. Ihunda, Ifiok M. Ibanga, Ndubuisi Ukpabi (2022). Biozonation and Age Reconstruction of 4000ft To 4540ft Section of Well-X, Niger Delta, \n\n\n\nNigeria. Malaysian Journal of Geosciences, 6(1): 12-18. \n\n\n\n\n\n\n\nTable 3: 4060ft \n\n\n\nS/No TaxonName Count \n\n\n\n1 Zonocostites ramonae 280 \n\n\n\n2 Pachydermitesdiederixi 42 \n\n\n\n3 Psilatricolporites crassus 10 \n\n\n\n4 Verrucatosporites alienus 30 \n\n\n\n5 Brevicolporites guinetii 2 \n\n\n\n6 Psilastephanocolporites laevigatus 1 \n\n\n\n7 Verrucatosporitestenellis 1 \n\n\n\n8 Fungalspore 2 \n\n\n\n9 Retitricolporites irregularis 2 \n\n\n\n10 Laevigatosporites discordatus 21 \n\n\n\n11 Psilatricolporitessp 2 \n\n\n\n12 Magnastriatites howardi 2 \n\n\n\n13 Polypodiaceio sporites retirugatus 1 \n\n\n\n14 Polypodiaceiosporitessp 1 \n\n\n\n15 Stereisporitessp 1 \n\n\n\n16 Acrostichumaureum 2 \n\n\n\n17 Klukisporitespseudoreticulatus 1 \n\n\n\n\n\n\n\nTable 4: 4120ft \n\n\n\nS/No TaxonName Count \n\n\n\n1 Acrostichumaureum 9 \n\n\n\n2 Verrucatosporites alienus 32 \n\n\n\n3 Zonocostitesramonae 280 \n\n\n\n4 Pachydermitesdiederixi 144 \n\n\n\n5 Crassoretitriletesvanraads hooveni 5 \n\n\n\n6 Klukisporitespseudo reticulatus 2 \n\n\n\n7 Monoporites annulatus 1 \n\n\n\n8 Brevicolporites guinetii 1 \n\n\n\n9 Polypodiaceiosporites sp 3 \n\n\n\n10 Laevigatosporites discordatus 3 \n\n\n\n11 Lycopodium neogenicus 1 \n\n\n\n12 Dinocystin determinate 1 \n\n\n\n13 Multiareolites formosus 1 \n\n\n\n14 Retitricolporites irregularis 3 \n\n\n\n15 Psilatricolporites crassus 7 \n\n\n\n16 Stereisporitessp 1 \n\n\n\n17 Retibrevitricolporites obodoensis 2 \n\n\n\n18 Psilatricolporitessp 1 \n\n\n\n19 Ctenolophoniditescostatus 2 \n\n\n\n20 Verrucatosporites tenellis 1 \n\n\n\n21 Psilastephanocolporites laevigatus 3 \n\n\n\n22 Psilatricolporites digitatus 1 \n\n\n\n23 Magnastriatites howardi 1 \n\n\n\n24 Lycopodiumsporites sp 2 \n\n\n\n25 Fungal spore 3 \n\n\n\n26 Polypodiaceio sporites retirugatus 1 \n\n\n\n \nTable 6: 4280ft \n\n\n\nS/No TaxonName Count \n1 Psilatricolporites crassus 3 \n2 Verrucatosporites tenellis 1 \n\n\n\n3 Zonocostites ramonae 205 \n4 Pachydermitesdiederixi 92 \n\n\n\n5 Fungal spore 1 \n6 Selaginellamyosurus 1 \n\n\n\n7 Verrucatosporites alienus 11 \n\n\n\n8 Acrostichumaureum 11 \n\n\n\n9 Botryococcusbraunii 1 \n\n\n\n10 Magnastriatites howardi 2 \n\n\n\n11 Crassoretitriletesvanraadshooveni 4 \n\n\n\n12 Aletesporitessp 1 \n\n\n\n13 Psilatricolporitessp 1 \n\n\n\n14 Nympheapollislotus 1 \n\n\n\n15 Matonisporissp 1 \n\n\n\n16 Ctenolophoniditescostatus 2 \n\n\n\n17 Retibrevitricolporites obodoensis 1 \n\n\n\n18 Monoporites annulatus 1 \n\n\n\n19 Dinocystindeterminate 2 \n\n\n\n20 Stereisporitessp 1 \n\n\n\n21 Lycopodiumsporitessp 1 \n\n\n\n22 Echiperiporites estelae 1 \n\n\n\n\n\n\n\nTable 7: 4360ft \n\n\n\nS/No TaxonName Count \n\n\n\n1 Psilatricolporites crassus 7 \n\n\n\n2 Zonocostites ramonae 400 \n\n\n\n3 Pachydermitesdiederixi 40 \n\n\n\n4 Verrucatosporites alienus 11 \n\n\n\n5 Laevigatosporites discordatus 2 \n\n\n\n6 Acrostichumaureum 5 \n\n\n\n7 Psilastephanocolporites laevigatus 2 \n\n\n\n8 Echiperiporitesestelae 5 \n\n\n\n9 Stereisporitessp 1 \n\n\n\n10 Magnastriatites howardi 2 \n\n\n\n11 Verrucatosporites tenellis 1 \n\n\n\n12 Brevicolporitesguinetii 2 \n\n\n\n13 Klukisporitespseudo reticulatus 1 \n\n\n\n14 Botryococcusbraunii 1 \n\n\n\n15 Fungalspore 1 \n\n\n\n16 Psilastephanocolporitesminor 1 \n\n\n\n17 Echiperiporitessp 1 \n\n\n\n18 Psilaperiporites minimus 1 \n\n\n\n19 Ctenolophonidites costatus 2 \n\n\n\n20 Retitricolporites amazoensis 1 \n\n\n\n21 Dinocystindeterminate 1 \n\n\n\nTable 5: 4180ft \n\n\n\nS/No TaxonName Count \n1 Zonocostitesramonae 140 \n2 Psilastephanocolporites laevigatus 2 \n3 Polypodiaceiosporitessp 1 \n4 Psilatricolporitescrassus 5 \n5 Laevigatosporitesdiscordatus 3 \n6 Verrucatosporitesalienus 8 \n7 Nympheapollislotus 1 \n8 Magnastriatites howardi 1 \n9 Psilatricolporitessp 1 \n\n\n\n10 Ctenolophoniditescostatus 1 \n11 Crassoretitriletesvanraadshooveni 1 \n12 Alchorneacordifolia 1 \n13 Psilatricolporites annuliporis 1 \n14 Pachydermitesdiederixi 19 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 12-18 \n\n\n\n\n\n\n\n \nCite The Article: Otto A. Ihunda, Ifiok M. Ibanga, Ndubuisi Ukpabi (2022). Biozonation and Age Reconstruction of 4000ft To 4540ft Section of Well-X, Niger Delta, \n\n\n\nNigeria. Malaysian Journal of Geosciences, 6(1): 12-18. \n\n\n\n\n\n\n\nTable 8: 4420ft \n\n\n\nS/No TaxonName Count \n1 Acrostichumaureum 34 \n\n\n\n2 Zonocostites ramonae 320 \n\n\n\n3 Pachydermitesdiederixi 135 \n\n\n\n4 Psilatricolporites crassus 32 \n\n\n\n5 Crassoretitriletesvanraadshooveni 5 \n\n\n\n6 Retitricolporiteirregularis 3 \n\n\n\n7 Echiperiporitesestelae 3 \n\n\n\n8 Verrucatosporitesalienus 57 \n\n\n\n9 Brevicolporitesguinetii 1 \n\n\n\n10 Retibrevitricolporites obodoensis 4 \n\n\n\n11 Psilastephanocolporites laevigatus 7 \n\n\n\n12 Selaginellamyosurus 2 \n\n\n\n13 Pterospermellasp 1 \n\n\n\n14 Magnastriatiteshowardi 2 \n\n\n\n15 Psilatricolporitessp 2 \n\n\n\n16 Verrucatosporitestenellis 2 \n\n\n\n17 Laevigatosporites discordatus 28 \n\n\n\n18 Gemmamonoporitessp 1 \n\n\n\n19 Echistephanoporites echinatus 2 \n\n\n\n20 Stereisporitessp 1 \n\n\n\n21 Lycopodiumsporitessp 2 \n\n\n\n22 Nympheapollislotus 1 \n\n\n\n23 Fungalspore 1 \n\n\n\n24 Dinocystindeterminate 1 \n\n\n\n25 Psilatriporitessp 1 \n\n\n\n26 Psilastephanocolporitesminor 1 \n\n\n\n27 Peregrinipollis nigericus 1 \n\n\n\n28 Multiareolitesformosus 2 \n\n\n\n29 Retitricolporites amazoensis 1 \n\n\n\n30 Monoporites annulatus 2 \n\n\n\n\n\n\n\nTable 9: 4480ft \n\n\n\nS/No TaxonName Count \n\n\n\n1 Zonocostitesramonae 319 \n\n\n\n2 Verrucatosporitesalienus 14 \n\n\n\n3 Pachydermitesdiederixi 96 \n\n\n\n4 Psilasyncolporitessp 1 \n\n\n\n5 Laevigatosporites discordatus 43 \n\n\n\n6 Psilatricolporitessp 2 \n\n\n\n7 Psilastephanocolporitessp 1 \n\n\n\n8 Retibrevitricolporites obodoensis 3 \n\n\n\n9 Psilastephanocolporites laevigatus 8 \n\n\n\n10 Acrostichumaureum 14 \n\n\n\n11 Striatricolpites catatumbus 2 \n\n\n\n12 Brevicolporitesguinetii 1 \n\n\n\n13 Retitricolporites irregularis 5 \n\n\n\n14 Stereisporitessp 1 \n\n\n\n15 Ctenolophonidites costatus 6 \n\n\n\n16 Crassoretitriletesvanraadshooveni 2 \n\n\n\n17 Polypodiaceiosporites retirugatus 2 \n\n\n\n18 Echiperiporitesestelae 3 \n\n\n\n19 Echitriletes sp 1 \n\n\n\n20 Foraminiferal lining 1 \n\n\n\n21 Fungalspore 1 \n\n\n\n22 Psilatricolporites crassus 6 \n\n\n\n5. CONCLUSION \n\n\n\nThis study reports on palynomorphs assemblage recovered from \nsediment penetrated by WellX in Western Niger Delta Basin. One biozone \nhave been erected based on the abundance of the palynomorphs and used \nto characterize the age of the sediment, as late Miocene. Zone boundaries \nwere placed where significant changes occurred in the abundance of the \nspecies. The Palynological P850zone identified which coincides with the \nbase of the overlying P860 subzone include abundance of Zonocostites \nramonae, rich occurrence of Stereisporitessp, Retibrevitricolporites \nobodoensis,and Psilatricolporitescrassus,scanty Nympheapollislotus, and \nMultiareolites formosus as well as the presence of Peregrinipollisnigericus. \n\n\n\nREFERENCES \n\n\n\nAvbovbo, A.A., 1978. Tertiary lithostratigraphy of Niger Delta: American \nAssociation of Petroleum Geologists Bulletin, 62, Pp. 295-300. \n\n\n\nDoust, B., and Omatsola, E., 1990. Niger Delta, in, Edwards, J.D., and \nSantogrossi, P.A., eds., Divergent/passive Margin Basins., AAPG Memoir \n48: Tulsa, American Association of Petroleum Geologists, Pp. 239- 248. \n\n\n\nEvamy, B.D., Haremboure, J., Kamerling, P., Knaap, W.A., Molloy, F.A., and \nRowlands, P.H., 1978. Hydrocarbon habitat of Tertiary Niger Delta: \nAmerican Association of Petroleum Geologists Bulletin, 62, Pp. 277-298. \n\n\n\nFawcett, P., Green, D., Holleyhead, R., Shaw, G., 1970. Application of \nradiochemical techniques to the determination of the hydroxyl content \nof some sporopollenins. Grana,10, Pp. 246-247. \n\n\n\nFlenley, J.R., 1971. Measurements of the specific gravity of the pollenexine. \nPollenet Spores, 13, Pp. 179-186.  \n\n\n\nJuvign\u00e9, E. 1973a. Unem\u00e9thode des\u00e9parationde spollens applicable \nauxs\u00e9diments min\u00e9raux. Annalesdela Soci\u00e9t\u00e9 G\u00e9ologiquede Belgique, \n96, Pp. 253-262. \n\n\n\nJuvign\u00e9, E., 1973b. Densit\u00e9 desexines dequelque sesp\u00e8ces depollens et \nspores fossiles. Annalesdela Soci\u00e9t\u00e9 G\u00e9ologiquede Belgique, 96, Pp. 363-\n374. \n\n\n\nLitwin, R.J., Traverse, A., 1989. Basic guidelines for palynomorph \nextraction and preparation from sedimentary rocks. In: R.M. Feldmann; \n\n\n\nTable 10: 4540ft \n\n\n\nS/No TaxonName Count \n\n\n\n1 Pachydermites diederixi 93 \n\n\n\n2 Acrostichum aureum 19 \n\n\n\n3 Zonocostites ramonae 234 \n\n\n\n4 Verrucatosporites alienus 28 \n\n\n\n5 Laevigatosporites discordatus 32 \n\n\n\n6 Botryococcus braunii 4 \n\n\n\n7 Psilatricolporites crassus 11 \n\n\n\n8 Echiperiporites estelae 13 \n\n\n\n9 Retitricolporites irregularis 2 \n\n\n\n10 Polypodiaceiosporites retirugatus 4 \n\n\n\n11 Crassoretitriletesvanraadshooveni 2 \n\n\n\n12 Psilatricolporites sp 1 \n\n\n\n13 Echiperiporites sp 1 \n\n\n\n14 Psilastephanocolporites laevigatus 9 \n\n\n\n15 Ctenolophonidites costatus 6 \n\n\n\n16 Retibrevitricolporites obodoensis 7 \n\n\n\n17 Psilastephanocolporites sp 1 \n\n\n\n18 Monoporites annulatus 1 \n\n\n\n19 Echitricolporites spinosus 1 \n\n\n\n20 Psilatricolporites digitatus 1 \n\n\n\n21 Echistephanoporites echinatus 2 \n\n\n\n22 Stereisporites sp 1 \n\n\n\n23 Magnastriatites howardi 1 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 12-18 \n\n\n\n\n\n\n\n \nCite The Article: Otto A. Ihunda, Ifiok M. Ibanga, Ndubuisi Ukpabi (2022). Biozonation and Age Reconstruction of 4000ft To 4540ft Section of Well-X, Niger Delta, \n\n\n\nNigeria. Malaysian Journal of Geosciences, 6(1): 12-18. \n\n\n\n\n\n\n\nR.E. Chapman & J.T. Hannibal (eds.) Paleotechniques, The \nPaleontological Society Special Publication, 4, Pp. 87-98. \n\n\n\nNichols, G., Obaje., M.K., Musa., A.N., Odomaand, H., Hamza, 2011. Journal \nof Petroleum and Gas Exploration Research, 1 (1), Pp. 001-013. \n\n\n\nOloto, I.N., 2014. Biostratigraphy of the Upper Tertiary Western offshore \nNiger Delta, Nigeria. International Journal of Scientific &Technology \nResearch, 3 (2). \n\n\n\nPeter, S.O., Adewale, K.B., 2015. Foraminiferal Assemblage & \nPalaeoenvironment: A Case Study of Meren 31SideTract-2Well, Offshore \nNiger Delta. Journal of Environment and EarthScience, 4 (22), Pp. 9-21. \n\n\n\nReijers, T.J.A., 2011. Stratigraphy and Sedimentogy of the Niger Delta. \nGeologos, The Netherlands, 17 (3), Pp. 133-162. \n\n\n\nRowley, J.R., 1976. Dynamic changes in pollen wall morphology. In: I.K. \nFerguson & J. Muller (eds.). The evolutionary significance of the exine. \nLinnean Society Symposium Series,1, Pp. 39-66. \n\n\n\nRowley, J.R., 1990. The fundamental structure of the pollen exine. Plant \nSystematic Evolution, supplement, 5, Pp. 13-29. \n\n\n\nShaw, G., 1971. The chemistry of sporopollenin. In: J. Brooks, P.R. Grant, M. \nMuir, P. VanGijzel and G. Shaw(eds.) Sporopollenin. Proceedings of a \nSymposium held at the Geology Department, Imperial College, London, \n23rd-25thSeptember, 1970. AcademicPress, Pp. 305-350. \n\n\n\nShaw, G., Yeadon, A., 1964. Chemical studies on the constitution of some \npollen and spore membranes. Grana Palynologica, 5, Pp. 245-252. \n\n\n\nShort, K.C., and Stauble, A.J., 1967. Outline Geology of Niger Delta: AAPG \nBulletin, 51, Pp. 761-779.  \n\n\n\nStacher, P., 1995. Present understanding of the Niger Delta hydrocarbon \nhabitat, in M.N. Oti, & G. Postma, (eds). Geology of Deltas: Rotterdam, A.A. \nBalkema, Pp. 257-267. \n\n\n\nWadeetal, 2011. Biozonation and biochronology of Miocene through \nPleistocene calcareous nanno fossils from low and middle latitudes, Pp. \n25-70. \n\n\n\nAPPENDIX \n\n\n\n\n\n\n\nPalynomorphs micrograms \n\n\n\n(a)Nympheae pollisclarus; (b)Pachydermitesdiederixi; (c) Peregrinipollis \nnigericus; (d) Proteacidites cooksonni; (e) Proxapertites cursus; (f) \nPsilatricolporites  crassus; (g) Psilamonocolpites sp.; (h) \nPsilastephanocolpites sp.; (i) Psilatricolpites operculatus; (j) \nRetitricolporites irregularis; (k) Retimonocolpites sp.; (l) \nRetibrevitricolporites obodoensis. \n\n\n\n\n\n\n\n(a) Retistephanocolpites gracilis; (b) Striatricolpites catatumbus; \n(c) Zonocostites duquei; (d) Zonocostites ramonae; (e) \nBotryococcusbraunii; (f) Concentricytes circulus; (g) Pediastrumsp.; (h) \nDiatomfrustule1; (i) Diatomfrustule2; (j) Scolecodontsp.; (k) \nDistephanus boliviensis(Silicoflagellatesp.1);(l)Naviculopsis \nrobusta(Silicoflagellatesp.2). \n\n\n\n\n\n\n\n    (a) Acrostichumaureum; (b) Crassoretitriletes vanraadshooveni; (c) \nLaevigatosporitessp.; (d) Polypodiaceoisporitessp.; (e) \nStereisporitessp.; (f) Verrucatospo rites sp.; (g) Arecipitesexilimuratus; \n(h) Chenopodipollis sp.; (i) Crototricolpites densus; (j) Cyperaceaepollis \nsp.; (k) Gemmamonoporites sp.; (l) Monoporites annulatus. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 12-18 \n\n\n\n\n\n\n\n \nCite The Article: Otto A. Ihunda, Ifiok M. Ibanga, Ndubuisi Ukpabi (2022). Biozonation and Age Reconstruction of 4000ft To 4540ft Section of Well-X, Niger Delta, \n\n\n\nNigeria. Malaysian Journal of Geosciences, 6(1): 12-18. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1. Echiperiporites estalae; 2. Retitricolporites irregulari; 3. \nSpirosyncolpites bruni; 4. Magnastriatites howardi; 5. Operculodinium \ncentrocarpum; 6. Sumatradiniumsp.; 7. Peregrinipollisnigericus; 8. \nRacemonocolpiteshians\u2019 9. Selenopemphixnephroides; 10.? \nRetibrevitricolporitesobodoensis/protrudens; 11. \nCrassoretitriletesvanraadshooveni; 12. Leiosphaeridiasp.\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 01-06 \n\n\n\nCite The Article: Rodeano Roslee (2019). Engineering Geological Investigation On Karambunai -Lok Bunuq Landslides, Kota Kinabalu, Sabah . \nMalaysian Journal of Geosciences, 3(2): 01-06. \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 04 January 2019 \nAccepted 19 February 2019 \nAvailable Online 22 February 2019\n\n\n\nABSTRACT\n\n\n\nThis paper describes landslide occurrences in debris materials, together with its engineering geological and \ngeotechnical setting. The predictions from conventional geotechnical slope stability analyses, taking into account \ntopography, hydrological, geotechnical and engineering geological effects, are compared with the observed pattern \nof instability. Physical and mechanical properties of eight (8) soil samples indicated that the failure materials mainly \nconsist of poorly graded materials of sandy clay soils and characterized by low to intermediate plasticity, containing \nof normal clay (0.42 to 0.95), very high degree of swelling (5.63 to 10.35), variable low to high water content (11.95 \n% to 19.92 %), specific gravity ranges from 2.60 to 2.68, low permeability (6.68 X 10-4 to 1.52 X 10-4 cm/s), friction \nangle (\uf066) ranges from 18.50\u02da to 34.20\u02da and cohesion (C) ranges from 3.36 kN/m2 to 19.50 kN/m2 with very soft to \nsoft of undrained shear strength (9.47 kN/m2 to 32.30 kN/m2). Geotechnical limit equilibrium stability analyses of \nentire slopes are rarely able to predict the smaller-scale initiation events leading to landslide occurrences, because \nthese are controlled by local topography, water runoff and groundwater conditions, weathered materials and \nengineering geological setting. Slope stability analysis shows that the factor of safety value is ranges from 0.805 to \n0.817 (unstable). It is concluded that the failures was debris flow and resulted from a combination of factors. \nEngineering geological evaluation should be prioritized and take into consideration in the initial step in all \ninfrastructure program. Development planning has to consider the geohazard and geoenvironmental management \nprogram. This engineering geological study may play a vital role in slope stability assessment to ensure public safety. \n\n\n\nKEYWORDS \n\n\n\ntopography, hydrological, geotechnical and engineering geological, Geotechnical limit equilibrium. \n\n\n\n1. INTRODUCTION \n\n\n\nThe study area approximately located about twenty (20) km far from Kota \nKinabalu City, Sabah (Figure 1). In 30h June 2006, a large landslide \noccurred suddenly on embankment of road side at the Karambunai area, \nKota Kinabalu. This landslide destroyed 3 residents and caused one \nfatality. After a few months from that incident, on 10th October 2006 a \nsame large landslide occurred nearby (approximately one (1) kilometer \nfrom the Karambunai area) at hilly side of the Lok Bunuq area, Kota \nKinabalu. This second landslide has been recorded occurred on previous \nyear 2001, which destroyed 15 residents and caused 6 fatalities. And now \ntoday it\u2019s to be occurred again destroyed 7 residents and fortunately no \nrecorded for any fatality. These two (2) landslide incidents received wide \nmedia coverage and raised concern from the authorities and local \npopulation over the stability of the surrounding slopes. \n\n\n\nFigure 1: Location of the study area \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : http://doi.org/10.26480/mjg.02.2019.01.06 \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN : MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nENGINEERING GEOLOGICAL INVESTIGATION ON KARAMBUNAI-LOK BUNUQ LANDSLIDES, KOTA \nKINABALU, SABAH \n\n\n\nRodeano Roslee1,2 \n\n\n\n1Natural Disaster Research Centre (NDRC), Universiti Malaysia Sabah \n2Faculty of Science and Natural Resources, Universiti Malaysia Sabah \n*Corresponding Author Email: rodeano@ums.edu.my \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 01-06 \n\n\n\nCite The Article: Rodeano Roslee (2019). Engineering Geological Investigation On Karambunai -Lok Bunuq Landslides, Kota Kinabalu, Sabah . \nMalaysian Journal of Geosciences, 3(2): 01-06. \n\n\n\nThe aims of this study are to analyses the physical and engineering \nproperties of the local material, to evaluate the main factors contributing \nto landslide and to compute the factor of safety (FOS) of the slopes. \nAlthough little damage of any real consequence was caused, the slip is of \ngeotechnical interest because of its geomorphological and geological \nsetting, the speed with which the slip debris finally moved down slope, and \nthe large number of potentially contributing factors in triggering the \nfailure which were heavy rainfall event. The slope failures estimated to \nhave involved approximately about 15 000 cubic meters of each slopes. \n\n\n\n2. METHODOLOGY\n\n\n\nThe laboratory works such as classification tests (grain size, atterberg \nlimit, shrinkage limit, specific gravity and water content), permeability \ntest and consolidated isotropically undrained (CIU) test were carried out \nin compliance and accordance to British Standard Code of Practice BS \n5930-1981 (Site Investigation) and British Standard Code of Practice BS \n\n\n\n1377-1990 (Method of Test for Soils for Civil Engineering Purposes).  \n\n\n\nFor the slopes stability analysis, using the \u201cSLOPE/W\u201d software through \nthe Morgenstern and Price method was done successfully to determine \nsusceptibility of the slopes to shallow non-circular slides based on the \ndetermination of factor of safety values, which are common in the study \narea [1]. The advantage of these methods is that in its limit equilibrium \ncalculations, forces and moments on each slice is considered. \n\n\n\n2.1 Climatic Setting \n\n\n\nEvaluation of rainfall records in study area and it\u2019s surrounding for the \nyear 2006 indicated that the average monthly rainfall is ranging 10 mm to \n640 mm (Figure 2). The mean annual temperature for the same period is \nrecorded ranges from 30o.9 C to 32.8o C, and the lowest from 23.2o C to \n24.1o C. \n\n\n\nFigure 2: Total annual rainfall for 2006 (Source from Climatological Services Department of Sabah) \n\n\n\n2.2 Topography and Drainage Systems \n\n\n\nThe study area lies between the South China Sea and the Crocker Range. \nThe area consists of swamps, coastal plains, valleys, small isolated foothills \nand a linear belt of hills parallel to the Crocker Range towards the east \n(Figure 3). The coastal plains and valleys vary from 2 to 5 km in width \nwhile the linear belt of hills is about a kilometre wide. The height of the \nhills range from 6 to 45 m; rising to over 60 m towards the east at the foot \nof the Crocker Range, which rises about 180 m. The complexity of the \noverall geomorphology of the study area is a combination of erosion, \nweathering process, faulting, folding and mass movement.  \n\n\n\nThe watershed lies in the Crocker Formation and river flows westward \ninto the South China Sea. Most of the rivers flow through mangrove swamp \nbefore discharging towards the South China Sea (Figure 3). Structurally, a \nnumber of linear river segments that different watershed systems indicate \nthe existence of major fractures. This structural control of many of the \ntributary streams is evident in the areas of sedimentary rocks; faults and \nless competent shale beds are preferentially eroded. The sedimentary \nrocks are intensely dissected and form a trellis and parallel drainage \npatterns. \n\n\n\nFigure 3: Topography and drainage map with their sampling locations\n\n\n\n0\n\n\n\n100\n\n\n\n200\n\n\n\n300\n\n\n\n400\n\n\n\n500\n\n\n\n600\n\n\n\n700\n\n\n\nM\nil\n\n\n\nim\ne\n\n\n\nte\nr\n\n\n\nMonths\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 01-06 \n\n\n\nCite The Article: Rodeano Roslee (2019). Engineering Geological Investigation On Karambunai -Lok Bunuq Landslides, Kota Kinabalu, Sabah . \nMalaysian Journal of Geosciences, 3(2): 01-06. \n\n\n\n2.3 Geology and Tectonic Setting  \n\n\n\nBorneo forms an extension of Sundaland, a cratonic core built of accreted \ncontinental fragments, which stabilized towards the end of Mesozoic. \nThroughout the Late Mesozoic and Tertiary additional terrains where \nadded to this core, by subduction of oceanic sea floor. This subduction is \nbelieved to be the result of the expansion of this region, which was related \nto the collision of India with the southern margin of the Asian continent \nduring Early Tertiary and to spreading in the Indian and Pacific Oceans [2].  \n\n\n\nThe geology of the study area is made up of two sedimentary rock \nformations: the Crocker Formation (Miocene to Late Eocene age) and \nQuaternary Alluvium (Recent age) (Figure 4). The effect of faulting and \nfolding activities can be observed on the lithologies in the study area \n(Figure 5). This was confirmed by the existence of transformed faulted \nmaterial consisting of angular to sub angular sandstone fragments, with \nfine recrystallined quartz along the joint planes, poorly sorted sheared \nmaterials and marked by the occurrence of fault gouge with fragments of \nslickensided surfaces. Breaks and fractures were developed by shearing \nstresses that caused the rapid disintegration and weathering of the rocks \ninto relatively thick soil deposit. As a corollary to this, in rock bodies, the \nsurface roughness of joint are generally smooth to rough planar. A \nrelatively smooth surface decreases the frictional resistance to expose the \nfractures, therefore effected the possibility of landslide occurrences in \n\n\n\nstudy area. \n\n\n\nFigure 4: Geological map of the study area \n\n\n\nFigure 5: The presence of faulting and folding activities on the lithologies (a: Telipok & b: Kastam quarters) \n\n\n\n2.4 Groundwater Conditions \n\n\n\nThe study area consists mainly of beach deposits along the beach area, \nalluvial deposit mostly in the valley and swamp area and sedimentary \nrocks of the Crocker Formation. Based on the field observation, the study \narea is located within the area of sedimentary rocks which has a locally \nsignificant occurrence of groundwater, while some area are of \nunconsolidated sediment (beach deposit and alluvial deposit) and areas \nwith no significant occurrence of groundwater. These sedimentary rocks \n\n\n\nare made up principally of interbedded sandstone-shale sequences, \noccasional breccias units and alluvial deposit that have limited primary \nporosity and moderately well-developed secondary porosity. The areas \nwhere there are no significant occurrences of groundwater are made up \nprincipally of shale unit sequences. On the others view, it is indicated that \nthe spring flowing follow the topography from highland toward the road \nand the valley sides (Figure 6). The weathered materials are weak and \ncaused landslide due to high fractured porosity and high pore pressure \nsubjected by both shallow and deep groundwater. \n\n\n\nFigure 6: The surface water shows that it\u2019s flowing strongly into the road drain and local villages (a: Karambunai landslide & b: Lok Bunuq landslide) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 01-06 \n\n\n\nCite The Article: Rodeano Roslee (2019). Engineering Geological Investigation On Karambunai -Lok Bunuq Landslides, Kota Kinabalu, Sabah . \nMalaysian Journal of Geosciences, 3(2): 01-06. \n\n\n\n2.5 Geotechnical Properties and Stability Analysis \n\n\n\nTable 1: Laboratories analysis results of soil materials \n\n\n\nType of failure Rotational Slide \nLocation Karambunai Lok Bunuq \nGeological Formation Crocker Formation Crocker Formation \nLithology Sedimentary rock Sedimentary rock \nWeathering grade IV to VI IV to VI \n\n\n\nVolume (1) Large Large \n\n\n\nBoreholes AH 1 AH 2 AH 3 AH4 AH 5 AH 6 AH 7 AH 8 \nSand (%) 34.75 50.27 34.98 45.57 35.56 49.41 51.79 46.26 \nSilt (%) 32.50 19.11 41.91 33.06 20.36 27.82 27.25 30.49 \n\n\n\nClay (%) 32.75 30.62 23.11 21.37 41.90 22.76 20.96 23.25 \n\n\n\nLiquid limit (%) 40 36 30 40 30 28 34 33 \nPlastic limit (%) 22 22 19 26 19 20 20 19 \nPlasticity index (%) 18 14 11 14 11 8 14 14 \nLiquidity index (%) - 0.14 - 0.13 - 0.72 - 0.52 - 0.27 - 0.66 - 0.25 - 0.62 \nClay activity 0.95 0.89 0.56 0.65 0.60 0.42 0.66 0.68 \nShrinkage limit (%) 10.35 7.14 8.09 7.86 8.57 8.70 9.16 5.63 \nMoisture content (%) 19.60 16.48 17.53 18.98 16.05 11.95 19.92 18.87 \nSpecific gravity 2.61 2.65 2.63 2.62 2.68 2.60 2.64 2.62 \nPermeability (cm/s) 1.52 X 10-4 4.83 X 10-4 1.40 X 10-4 1.57 X 10-4 4.08 X 10-4 6.68 X 10-4 6.59 X 10-4  4.34 X 10-4 \nUnit Weight (kN/m3) 17.36 22.83 17.85 18.05 17.46 17.95 21.58 18.25 \nCohesion, C (kN/m2) (Ave.) 7.20 3.40 6.78 6.27 7.31 19.50 8.29 3.36 \nFriction angle (o) (Ave.) 26.30 34.20 28.90 32.90 29.20 35.50 20.76 18.50 \n\n\n\nUndrained shear strength (\uf074) \n(kN/m2) \n\n\n\n15.78 18.91 16.63 17.95 17.07 32.30 16.47 9.47 \n\n\n\nFactor of safety 0.817 0.805 \nMain factors causing failures (2) SA, W, V, GWL, M, C, G and AC \n\n\n\nNote: (1) Volume: small (10 \u2013 50 m3), Medium (50 \u2013 500 m3) and Large (> \n500 m3) and (2) Slope angle (SA), Weathering (W), Vegetation (V), \nGroundwater level (GWL), Material characteristics (M), Climatological \nsetting (C), Geological characteristics (G), Over burden or vibration (OBV), \nDrainage system (DS), Embankment construction (EC) and Artificial \nchanging (AC) \n\n\n\nResults of laboratory analyses for the soil materials are presented in Table \n1. The failure volume scale involved generally large in size for each slopes \n(15 000 cubic meters) endangering road users and villagers. In term of \nweathering grades, the materials that underwent failure were in the \nranges from grade IV to VI (Figures 7 to 9). Climatic setting is the main \nfactors causing failure with the depth of intensive weathering influencing \nthe volume of material that fails. It appears that grade IV to grade V \nmaterials actually failed with the overlying grade VI material sliding or \nflowing down together with this debris materials during failure. Physical \n\n\n\nand engineering properties of eight (8) soil samples indicated that the \nfailure materials mainly consist of poorly graded materials of sandy clay \nsoils, which characterized by low to intermediate plasticity, containing of \nnormal clay (0.42 to 0.95), very high degree of swelling (5.63 to 10.35), \nvariable low to high water content (11.95 % to 19.92 %), specific gravity \nranges from 2.60 to 2.68, low permeability (6.68 X 10-4 to 1.52 X 10-4 \ncm/s), friction angle (\uf066) ranges from 18.50\u02da to 34.20\u02da and cohesion (C) \nranges from 3.36 kN/m2 to 19.50 kN/m2 with very soft to soft of undrained \nshear strength (9.47 kN/m2 to 32.30 kN/m2). Slope stability analysis \nshows that the factor of safety value is ranges from 0.805 to 0.817 \n(unstable) (Figures 10 & 11). The presence of ground water, slope angle, \nremoval of vegetation cover, lack of proper drainage system, artificial \nchanging, intensive weathering process and geological characteristics are \nadditional factors contributing to the failures. \n\n\n\nFigure 7: Landslides generally consisting of fine texture, cohesive materials, completely weathered to residual soil materials (a: Karambunai landslide & b: \nLok Bunuq landslide) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 01-06 \n\n\n\nCite The Article: Rodeano Roslee (2019). Engineering Geological Investigation On Karambunai -Lok Bunuq Landslides, Kota Kinabalu, Sabah . \nMalaysian Journal of Geosciences, 3(2): 01-06. \n\n\n\nFigure 8: Rotational Slide showing the failure movement are starting from the hill side to the road and/or village sides through the development of water \nrunoff (a: Karambunai landslide & b: Lok Bunuq landslide) \n\n\n\nFigure 9: Failure materials which containing of clayey soil materials (a: Karambunai landslide & b: Lok Bunuq landslide) \n\n\n\nNo. \nDescription Minimum Factor of Safety \nAnalysis method Moment Force \n\n\n\n1 Ordinary 0.749 - \n2 Bishop 0.826 - \n3 Janbu - 0.741 \n4 Morgenstern \u2013 Price 0.819 0.817 \nSlip surface # = 17 576 of 17 576 \n\n\n\nFigure 10: The results of slope stability analysis (Location: Karambunai \nlandslide)  \n\n\n\nNo. \nDescription Minimum Factor of Safety \nAnalysis method Moment Force \n\n\n\n1 Ordinary 0.785 - \n2 Bishop 0.819 - \n3 Janbu - 0.781 \n4 Morgenstern \u2013 Price 0.812 0.805 \nSlip surface # = 9261 of 9261 \n\n\n\nFigure 11: The results of slope stability analysis (Location: Lok Bunuq \nlandslide) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 01-06 \n\n\n\nCite The Article: Rodeano Roslee (2019). Engineering Geological Investigation On Karambunai -Lok Bunuq Landslides, Kota Kinabalu, Sabah . \nMalaysian Journal of Geosciences, 3(2): 01-06. \n\n\n\n3. DISCUSSIONS \n\n\n\nIt is clear from records existing before the landslide occurred that the \nstudy area was one of the pre-existing instability. The slope stability \nanalysis shows the area of the eventual slip as being underlain by \nbrecciated rock with the sandy clay soil (debris materials) of the Crocker \nFormation.  It seems likely that fracturing associated with faulting is a \ncontrolling mechanism in the production of the debris materials that is \nfound intermittently on these hillsides [3]. The aerial photograph clearly \nshows the location of a number of areas of previous instability, one which \ncoincides with and is slightly larger than the eventual area that slipped.  \n\n\n\nSlope stability analysis reveals that most of it occurring in sandy clay soil. \nLow to intermediate plasticity, high elasticity, friability and shrink \u2013 swell \nnature of the soil are just some properties, which characterized the sandy \nclay soils. The relatively high liquid limit and water content lead to a \ndecrease in strength during rainy period when the limiting condition is \nreached, the material tends to behave like viscous liquid, which then \neasily slides. The sandy clay soils with a low permeability of inert \nparticles in the study area have a high porosity and more porous. Porous \nsoils allow for rapid infiltration of surface water to the lower layers. \nAdditionally, the surface water was found flowing strongly into the \nroad drain and local villages [4]. This limits the destabilizing effect of \naccumulated water on the upper soil layer. The draw back here is that \nclayey soils tend to be very weak, friable and easily transported by \nflowing surface water and this would seriously weaken the shear \nstrength of the soils slopes, causing high chance for slope failure to \noccur. Slope stability analysis shows that the factor of safety value for \nthe soil slope failures ranges from 0.805 to 0.817 (Unstable).\n\n\n\naccurately, it is essential to recognize features such as a sequence of weak \nbeds, thin marker beds, old surfaces, fault or shear zones and hydro \ngeological effects. \n\n\n\n4. CONCLUSIONS \n\n\n\nA rotational slide has been described involving the flow of a large mass of \nfailure materials in the study area. The Karambunai-Lok Bunuq landslides \noccurred in an area of previous instability, on a steep slope, and involved \na large mass of debris material flowing rapidly down slope. Climatic \nsetting is the main factor causing soil slope failures. Physical and \nengineering properties of eight (8) soil samples indicated that the failure \nmaterials mainly consist of poorly graded materials of clayey soils, which \ncharacterized by low to intermediate plasticity, containing of normal clay \n(0.42 to 0.95), very high degree of swelling (5.63 to 10.35), variable low to \nhigh water content (11.95 % to 19.92 %), specific gravity ranges from 2.60 \nto 2.68, low permeability (6.68 X 10-4 to 1.52 X 10-4 cm/s), friction angle \n(\uf066) ranges from 18.50\u02da to 34.20\u02da and cohesion (C) ranges from 3.36 kN/m2 \nto 19.50 kN/m2 with very soft to soft of undrained shear strength (9.47 \nkN/m2 to 32.30 kN/m2). Slope stability analysis shows that the factor of \nsafety value is ranges from 0.805 to 0.817 (unstable). \n\n\n\n5. RECOMMENDATION\n\n\n\nTo correct or prevent the mass movement in the study area, the following \nrecommendations are proposed: \n\n\n\n1. Installation of piezometric and clinometers to monitor seasonal build \n\n\n\n-ups of pore water pressure and creep movement respectively. \n\n\n\n2. Surface drainage, which include:The compositions of the slip mass, the speed of slope movement, the \nmanner in which it moved, and the lobate form of its toe, classify the \nmajor movement as a debris flow. Rotational slide occur when poorly \nsorted brecciated and clayey materials, saturated with water, surge down \nslopes under gravitational action and characterized by the sudden \ncollapse and extensive, very to extremely rapid run-out of a mass of \ndebris material following some disturbance [5]. An essential feature is \nthat the material involved has unstable, loose or high porosity structure. \nAs a result of disturbance this collapses, transferring the overburden load \nwholly or partly onto the pore fluid, in which excess pore pressures are \ngenerated. The consequent sudden loss of strength gives the failing The \nconsequent sudden loss of strength gives the failing material, briefly a \nsemi-fluid character and allows a debris flow to develop. \n\n\n\nIn understanding the geology of the region is of paramount importance in \ntackling problems associated with slopes and slope development. Local \ngeological details such geometry of the sub-surface; soil properties and \ngroundwater have a considerable influence on the performance of \nindividual slopes. The slope stability evolution is an interdisciplinary \nendeavour requiring concepts and knowledge from engineering geology \n[6]. Any slope stability method of analysis must give due consideration to \nsignificant geological features. Awareness of geology is necessary for \nappropriate idealization of ground conditions and the subsequent \ndevelopment of realistic geotechnical model. In order to understand the \nrelationship between slope failure and geology, it is prerequisite to have \nknowledge on the types, characteristics and features of geological \nmaterials (soil and rock). Besides that, geological structures of the slope \nforming materials are a dominant feature in slope behaviour. The \nsuccession, thickness and attitude of beds are direct relevance to \nconsideration of potential instability especially in sedimentary rock of the \nCrocker Formation. These geological structures play an important role in \nunderstanding slope development processes, formation of valleys, ridges \nand the development of residual soil. In order to predict slope stability \n\n\n\nREFERENCES \n\n\n\n[1] Morgenstern, N., Price, V.E. 1965. The analysis of the stability of the \nstability of generalized slip surfaces. \n\n\n\n[2] Hamilton, W. 1979. Tectonics of the Indonesian region. U.S. Geol. \nSurvey Prof. Paper 1078. \n\n\n\n[3] Roslee, R., Tahir, S., Omang, S.A.K.S. 2006. Engineering Geology of the \nKota Kinabalu Area, Sabah, Malaysia. Bull. of Geol. Soc. of Malaysia, 52, 17-\n25. \n\n\n\n[4] Nelson, P.H. 1997. Monte Carlo Simulation (on-line)\nhttp://www.circle4.com/pww/mc.index.html \n\n\n\n[5] British Standard BS 5930. 1981. Site Investigation. London: British\nStandard Institution. \n\n\n\n[6] British Standard BS 1377. 1990. Methods of Test for Soils for Civil \nEngineering Purposes. London: British Standard Institution. \n\n\n\na) Sealing off of the cracks;\nb) Shot Crete or other means of reducing erosive action of \n\n\n\nrainwater runoff.\nc) Retaining wall with bore piles.\nd) Subsurface drainage, i.e. horizontal drainage method. \n\n\n\n\nhttp://www.circle4.com/pww/mc.index.html\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 12-22 \n\n\n\nCite The Article: Jong E Cheng (2019). Implication Of Reservoir Characteristics Based On Overview Of Structure And Sedimentology Of Ou tcrops Along Bintulu-Niah-Miri Areas. \nMalaysian Journal of Geosciences, 3(2): 12-22. \n\n\n\n\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN : MJGAAN \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 04 January 2019 \nAccepted 07 February 2019 \nAvailable Online 4 March 2019\n\n\n\nABSTRACT\n\n\n\nSix-day fieldwork was conducted in the north-west coast of Sarawak to examine the outcrops along Bintulu- Niah- \n\n\n\nMiri areas which cover southern part of Balingian Province and Baram Delta Province. The aim of this fieldwork is \n\n\n\nto synthesize the observations of structure and sedimentology of outcrops along Bintulu-Niah-Miri areas and \n\n\n\ndiscuss the implication of reservoir characteristics based on observation. The study was conducted by sketching \n\n\n\nthe main structural elements of outcrops followed by detailed sedimentological analysis which include observation \n\n\n\nand facies description were conducted on different outcrops along Bintulu-Niah- Miri areas using sedimentary logs. \n\n\n\nThe findings show that Bintulu- Niah- Miri areas outcrops consists of mixed-environment deposited succession with \n\n\n\ntidal and wave characteristic. This resulted in reservoir architect will be different and result in different in reservoir \n\n\n\nproperties included horizontal and vertical permeability of fluids. Niah Cave is a good place to study the distribution \n\n\n\nof the types of breccia due to collapsed paleokarst at reservoir scale and also good analog for Central Luconia \n\n\n\nPlatform where large resources of hydrocarbon have been discovered due to the its environment setting or forming \n\n\n\nprocess is same as Central Luconia Platform. In addition, Miri Airport Outcrop succession consist of Type 4- Fracture \n\n\n\nCreate Flow Barriers which could lead to potential production problems. \n\n\n\nKEYWORDS \n\n\n\nReservoir characteristics, sedimentology, depositional environment. \n\n\n\n1. INTRODUCTION \n\n\n\nSix-day fieldwork was conducted in the north-west coast of Sarawak to \nexamine the outcrops along Bintulu- Niah- Miri areas from 15th to 20th \nFebruary 2016 guided by Prof. Madya Ng Tham Fatt, Dr. Meor Hakif bin \nAmir Hassan and Dr. Ralph L. Kugler. This fieldwork will cover southern \npart of Balingian Province and Baram Delta Province. The objective of this \nfieldwork is to discuss the implication of reservoir characteristics based \non synthesize the observations of structure and sedimentology of outcrops \nalong Bintulu-Niah-Miri areas. The importance of this fieldwork is the \nsurface outcrops in studied area can be used as analogue for subsurface \nreservoir studies of offshore hydrocarbon field especially in Balingian \nProvince and Baram Delta Province. An understanding of the \nsedimentological characteristics and facies architecture of outcrops may \nallow us to understand production behavior of hydrocarbon and sweep \nefficiency in waterfloods to encounter the problems related to \ncompartmentalization of reservoirs and other issues in field development \nplanning. \n\n\n\n2. REGIONAL BACKGROUND \n\n\n\nThis fieldwork will cover the southern part of Balingian Province and \nBaram Delta Province (Figure 1). The Balingian Province is part of \nperipheral foreland basin fill of Sarawak Basin which formed due to the \nclosure of the Rajang Sea and the Sarawak Orogeny during the Late Eocene \n(Figure 2) [1]. The Nyalau Formation dominates the onshore geology of \nthe southern part of the Balingian Province [1,2]. Some researchers \nidentified common structural features in deforming foreland basins which \nindicate penecotemporaneous deformation in Nyalau Formation [3].  \n\n\n\nFigure 1: Geological Province map of Sarawak showing the study area \nwithin the southern part of Balingian Province and Baram Delta Province \n[4].. \n\n\n\nFigure 2: Illustration depicting the formation of Sarawak Basin by the \nsubduction of Rajang Sea beneath Borneo [4]. \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : http://doi.org/10.26480/mjg.02.2019.12.22 \n\n\n\n  REVIEW ARTICLE \n\n\n\nIMPLICATION OF RESERVOIR CHARACTERISTICS BASED ON OVERVIEW OF STRUCTURE AND \nSEDIMENTOLOGY OF OUTCROPS ALONG BINTULU-NIAH-MIRI AREAS \n\n\n\nJong E Cheng \n\n\n\nCoal Resources Department, Sarawak Energy Berhad, 93050 Kuching, Sarawak. \n*Corresponding Author E-mail: echengjong@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\nmailto:echengjong@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 12-22 \n\n\n\nCite The Article: Jong E Cheng (2019). Implication Of Reservoir Characteristics Based On Overview Of Structure And Sedimentol ogy Of Outcrops Along Bintulu-Niah-Miri Areas. \nMalaysian Journal of Geosciences, 3(2): 12-22. \n\n\n\nThe Balingian Province is a Tertiary basin that developed in Late Eocene \ntimes after orogenic uplift and folding of Cretaceous to Eocene sediment \n[1]. The Balingian Province consists of Tertiary succession of age ranging \nfrom Oligocene to Late Miocene (Figure 3). The Tertiary succession \nincluded the Tatau Formation, Buan Formation, Nyalau Formation, Setap \nShale Formation and Liang Formation. The Nyalau Formation conformably \noverlies Oligocene Buan Formation Oligocene and Eocene Tatau \nFormation (Figure 3). However, the Nyalau Formation is unconformably \noverlain by Pleistocene\u2013Holocene coastal deposits and alluvium in most of \nthe area around Bintulu [5]. \n\n\n\nFigure 3: Stratigraphic framework for the onshore northwest coast of \nSarawak represent the cycle and age of Nyalau Formation [5]. \n\n\n\nNyalau Formation is characterized by alternating heterolithic beds, cross-\nbedded sandstones and coal-bearing mudstones [6]. Based on \nforaminifera, Nyalau Formation gives age from Early Oligocene (Rupelian) \nto the Early Miocene (Burdigalian) [6]. Detailed sedimentological study of \nupper Nyalau Formation aged Early Miocene indicates Nyalau Formation \nconsists wave-, tide- and fluvial-influenced coastal deposits [5]. Nyalau \nFormation contains effective source rock whereby shales from Nyalau \nFormation was interpreted as gas-prone while the coal and carbargilite \nsediment from Nyalau Formation have the potential to generate oil [7]. \n\n\n\nThe Baram Delta Province is a Tertiary basin that developed in Late \nEocene times after orogenic uplift and folding of Cretaceous to Eocene \nsediment [1]. The Baram Delta Province consists of Tertiary succession of \nage range from Oligocene to Late Miocene (Figure 4). The Tertiary \nsuccession included the Setap shale, Lambir Formation, Miri Formation, \nTukau Formation, Seria Formation and Liang Formation. The Miri \nFormation is interfingering with the Lambir Formation and Tukau \nFormation [1]. \n\n\n\nFigure 4: Stratigraphic framework for the onshore northwest Sarawak \nrepresents the units of Miri Formation [8]. \n\n\n\nThe sedimentary rocks of the Miri Formation belong to the age range in \nbetween Middle Miocene and Late Miocene (13-9 Ma) [9]. Miri Formation \nwas divided into Lower Unit and Upper Unit based on lithology and \nbenthonic foraminifera assemblage [6,10]. Lower Miri Formation unit \nconsists of interbedded shale and sandstone overlying the Setap Shale \nFormation while Upper Miri Formation which is characterised by irregular \nsandstone-shale alternations, and more arenaceous laterally [6]. The \ndeposition environment of Miri Formation was interpreted as in a littoral \nto inner neritic shallow marine environment [6]. \n\n\n\nThe age of Lambir Formation ranges from the late Middle to Late Miocene \n(14-10 Ma) [9]. The Lambir Formation is characterized by alternating \nsandstone and shale with some limestone and marl in certain location [6]. \n\n\n\n3. METHODOLOGY \n\n\n\nThe main structural elements of outcrops are sketched followed by \ndetailed sedimentological analysis which include observation and facies \ndescription were conducted on different outcrops along Bintulu-Niah- \nMiri areas using sedimentary logs. Based on lithology, textures, \nsedimentary structures, geometry, bioturbation and trace fossil content, \nthe formations are divided into different types of facies followed by facies \nassociation before depositional environment was deduced.  Lastly, the \nimplication of reservoir characteristics will be discussed based on \noverview of structure and sedimentology of outcrops along Bintulu-Niah-\nMiri areas. \n\n\n\n4. BINTULU (LIANG FORMATION & NYALAU FORMATION)\n\n\n\nGenerally, the succession of Airport Road Stop (1) Outcrops (Figure 5) \ncontains high net-to-gross or sand rich. The succession is fining-upward in \ngrain size, from very fine to medium-grained accompanied by decreasing \nin the size of the preserved bedding. Strike of bedding is ranging from 100-\n210 and dip reading 100 -150 toward South-East. The facies can be \nobserved from the outcrop below unconformity included Facies NF2: Low \nangle cross-stratified sandstone; Facies NF3: Lenticular bedded \nheterolithic sandstone; Facies NF8: Wavy and flaser stratified heterolithic \nsandstone and Facies NF5: Trough cross stratified sandstone with \nchannel-like geometry. The top section is characterized by dark grey, \nstructureless mudstone facies and a coal layer on top. \n\n\n\nFigure 5: View of Airport Road Stop (1) Outcrops. \n\n\n\nGenerally, the succession of Rangsi Hill Outcrops (Figure 6) consists of \nthick sandstone with poorly sorted mixture of sand, mud and rock \nfragments or conglomerates inclusion range from different size (cm - dm) \n(alluvial deposit) overlie on tilted thinly interbedded (4 cm-11 cm) \nsandstone and shale succession which separated by an angular \nunconformity. The angular unconformity could be due to Sarawak \nOrogeny which related to the closure of the Rajang Sea during the Late \nEocene [2,6].  Strike of bedding below the angular unconformity is ranging \nfrom 1200-1700 and dip reading 600 -790 toward South - West. The \nunderlain tilted interbedded sandstone and shale succession could have \nformed due to tectonic event (Sarawak Orogeny) followed by erosion and \ndeposition of thick sandstone with poorly sorted conglomerate inclusion. \nThe underlain sandstone bed consists of fine grained sandstone with sand \ncontent of 60%. The younging direction is toward North West based on \nevidence of flute clast below the sandstone beds. The facies can be \nexamined from the lower section of outcrop included fine-grained \nboudinaged sandstone facies and structureless mudstone facies. However, \nthe sedimentary structure of the thin sandstone bed could not be \ndetermined due to high degree of weathering. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 12-22 \n\n\n\nCite The Article: Jong E Cheng (2019). Implication Of Reservoir Characteristics Based On Overview Of Structure And Sedimentol ogy Of Outcrops Along Bintulu-Niah-Miri Areas. \nMalaysian Journal of Geosciences, 3(2): 12-22. \n\n\n\nFigure 6: View of Rangsi Hill. \n\n\n\nStrike of beddings at Sungai Mas Camp Outcrop (Figure 7) are ranging \nfrom 2950-3270 and dip reading 240 -320 toward North-East. The facies can \nbe observed from the outcrop included Facies NF1: Hummocky cross-\nstratified sandstone; Facies NF2: Low angle cross-stratified sandstone; \nFacies NF3: Lenticular bedded heterolithic sandstone; Facies NF4: Coal \nbed; Facies NF5: Structureless mudstone; Facies NF6: Trough cross-\nbedded sandstone showing bundling; Facies NF7: Herringbone cross \nstratification sandstone; Facies NF8: Wavy and flaser stratified hetrolithic \nsandstone; Facies NF9: Trough cross stratified sandstone; Facies NF10: \nSigmoidal cross stratification sandstone and Facies NF11: Bioturbated \nmudstone. (*Detail Facies and Facies Association of Sungai Mas Camp \nOutcrop will be discussed in section 5.0.) \n\n\n\nFigure 7: View of Sungai Mas Camp Outcorp. \n\n\n\nStrike of beddings at Meteorology Department Outcrop (Figure 8) are \nranging from 2700- 2780 and dip reading 200 -280 toward North. The facies \ncan be observed from the outcrop included Facies NF1: Hummocky cross-\nstratified sandstone; Facies NF2: Low angle cross-stratified sandstone; \nFacies NF3: Lenticular bedded heterolithic sandstone; Facies NF4: Coal \n\n\n\nbed; Facies NF5: Structureless mudstone; Facies NF8: Wavy and flaser \nstratified hetrolithic sandstone and Facies NF9: Trough cross stratified \nsandstone. (*Detail Facies and Facies Association of Meteorology Outcrop \nwill be discussed in section 5.0.) \n\n\n\nFigure 8: View of Meteorology Department Outcrop. \n\n\n\nStrike of beddings at Similanjau Outcrop (Figure 9) are ranging from 700- \n730 and dip reading 80 -90 toward South-East. According to Dr Meor, the \ntop of succession of this outcrop is Pleistocene\u2013Holocene (20, 000 year \nold) deposits followed by Liang Formation (about 1 million year old) and \nNyalau Formation (20 million year old). Pleistocene\u2013Holocene deposit is \ncharacterized by white colour, fined-grained, flaser bedded sandstone \nwith rootlets facies and medium-grained, bioturbated sandstone with \nherringbone facies and medium-grained sandstone with trough cross \nbedding facies. Liang Formation is characterized by dark grey, \nstructureless mudstone facies and a coal layer on top. The facies of Nyalau \nFormation can be observed from the outcrop included Facies NF2: Low \nangle cross-stratified sandstone and Facies NF9: Trough cross stratified \nsandstone. This succession can be correlate with succession of Airport \nRoad Stop (1) Outcrops. \n\n\n\nFigure 9: View of Similanjau Outcrop. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 12-22 \n\n\n\nCite The Article: Jong E Cheng (2019). Implication Of Reservoir Characteristics Based On Overview Of Structure And Sedimentol ogy Of Outcrops Along Bintulu-Niah-Miri Areas. \nMalaysian Journal of Geosciences, 3(2): 12-22. \n\n\n\n4.1 Niah Cave (Subis Limestone) \n\n\n\nBased on the geometry and exposed inclined bedding layer at left side of \nthe Niah Mountain due to cementation at an early stage of marine burial \ndiagenesis (Figure 10), Niah Mountain could have made up of limestone as \nan isolated carbonate platform which known as the Subis Limestone. The \nSubis Limestone carbonate platform is built up from antecedent surface \n(seabed), as corals and other framework-building organisms were \ngrowing upward, keeping up with rising sea level from transgressive tract \nuntil highstand system tract. The current Niah Mountain (Subis \nLimestone) is subaerial exposed and in lowstand systems tract (Figure 11) \nand resulted in formation of karst feature by weathering processes. \n\n\n\nFigure 10: View of isolated carbonate platform which known as the Subis \nLimestone. Niah Cave is part of Subis Limestone \n\n\n\nFigure 11: Model for an Isolated Carbonate \n\n\n\nIn addition, Subis Limestone could have undergone terrestrial processes \nincluding erosion and fluvial deposition. The evidence for fluvial processes \nincluded transported breccia and sediment due to paleoriver as well as \neroded feature can be seen in Niah Cave (Figure 13) supported by flowing \nriver across limestone can be seen along the way toward Niah Cave (Figure \n14). \n\n\n\nNiah Cave is part of Subis Limestone with geometry of laterally extensive \n(Figure 10) but vertically restricted. The cave systems are separated by \nunaltered thick host limestones. The laterally extensive nature of the \npaleo-caves is characteristic of phreatic environments [11]. Based on \nCaribbean Karst Model, Niah Cave could be Phreatic Caves formed at \nancient water table or in between vadose and phreatic which is right above \nof ancient old meteoric lens (Figure 12) and now in Vadose Zone. This idea \nis supported by presence of chaotic breakdown breccia on the floor of cave \n(Figure 13); ceiling crackle breccia on roof resulted in breakout dome; \npresence of stalactite feature which formed due to precipitation of \ndissolute calcium carbonate via the fracture (proven by water dropping \nfrom top of cave). \n\n\n\nFigure 12: Caribbean Karst Model [11]. \n\n\n\nFigure 13: Niah Cave presence of chaotic breakdown breccia on the floor \nof cave; ceiling crackle breccia on roof resulted in breakout dome (Top \nphoto) and presence of stalactite feature which formed due to \nprecipitation of dissolute calcium carbonate via the fracture (down photo). \n\n\n\nFigure 14: Flowing river across limestone \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 12-22 \n\n\n\nCite The Article: Jong E Cheng (2019). Implication Of Reservoir Characteristics Based On Overview Of Structure And Sedimentol ogy Of Outcrops Along Bintulu-Niah-Miri Areas. \nMalaysian Journal of Geosciences, 3(2): 12-22. \n\n\n\n4.2 Miri (Lambir Formation & Miri Formation) \n\n\n\nGenerally, the succession of Bukit Song Outcrops contains high net-to-\ngross or sand rich. The succession consists of mainly thin bedded \nsandstone with thickening-upward in the size of the preserved bedding. \nStrike of bedding is ranging from 750-800 and dip reading 150 -200 toward \nSouth-East. \n\n\n\nFive lithofacies have been determined from the Bukit Song Outcrop based \non lithology, sedimentary structures, bioturbation, fossil traces content \nand bed geometry. These five major lithofacies included: Facies LF1: \nHetrolithic sandstone; Facies LF2: Low angle cross-stratified sandstone; \nFacies LF3: Trough cross-bedded sandstone; Facies LF4: Structureless \nsandstone with coal clast shell fragments and Facies LF5: Bioturbated \nmudstone.  \n\n\n\n4.2.1 Facies LF1: Hetrolithic sandstone \n\n\n\nFacies LF1 is characterized by yellowish to light grey, moderately sorted, \nvery fine grained with heterolithic sandstones. Wavy and flaser \nstratification can be observed in this facies. Soft sediment deformation can \nbe observed in heterolithic sandstone. The sandstone is sparsely \nbioturbated. The thickness of sandstone bed range from 3 cm to 9 cm. \n\n\n\n4.2.2 Facies LF2: Low angle cross-stratified sandstone \n\n\n\nFacies LF2 is characterized by light yellow, well-moderate sorted, very fine \nto fine grained with low angle cross-stratified sandstones. Parallel \ncarbonaceous lamination can be observed in this facies. The sandstone is \nsparsely bioturbated. The trace fossil (Ophiomorpha) can be observed \nthroughout the facies. The thickness of sandstone bed range from 0.10 m \nto 0.70 m. \n\n\n\n4.2.3 Facies LF3: Trough cross-bedded sandstone \n\n\n\nFacies LF3 is characterized by light yellow, moderate sorted, fine grained \n\n\n\nwith trough cross-bedded sandstones. Mud drape can be observed in this \nfacies. The sandstone is sparsely bioturbated. The trace fossil \n(Ophiomorpha) can be observed throughout the facies. The thickness of \nsandstone bed range from 0.10 m to 0.30 m with channel-like geometry. \n\n\n\n4.2.4 Facies LF4: Structureless sandstone with coal clast shell \nfragments \n\n\n\nFacies LF4 is characterized by light grey, well to moderate sorted, medium \ngrained sandstones with coal clast and shell fragments. The size of coal \nclasts range from 0.2cm to 6.0 cm while the white colour shell fragments \nis about 1mm. The sandstone is absent to sparsely bioturbated. The \nthickness of sandstone bed is about 0.3 m. \n\n\n\n4.2.5 Facies LF5: Bioturbated mudstone \n\n\n\nFacies LF5 is characterized by dark grey bioturbated mudstone. The \nmudstone is moderate to commonly bioturbated. The trace fossil can be \nobserved throughout the facies. The thickness of mudstone bed range \nfrom 0.10 m to 0.70 m. \n\n\n\nGenerally, Miri Formation (Figure 15) has very high net to gross. Five \nlithofacies have been determined from the outcrops of Miri Formation \nbased on lithology, sedimentary structures, bioturbation, fossil traces \ncontent and bed geometry. These five major lithofacies included: Facies \nMFA: Herringbone cross stratification sandstone; Facies MFB: \nHummocky-cross stratification sandstone; Facies MFC: Laminated \nsandstone; Facies MFD: Heterolithic sandstone and Facies MFE: Low angle \ncross-stratified sandstone. However, it can be noted that generally the \nlower part of Miri Airport Outcrop is characterised by sand body with tidal \nsignal included Facies MFA: Herringbone cross stratification sandstone; \nFacies MFC: Laminated sandstone and Facies MFD: Heterolithic sandstone \nfollowed by upper part of Miri Airport Outcrop is characterized by sand \nbody with wave signal included Facies MFB: Hummocky-cross \nstratification sandstone and Facies MFE: Low angle cross-stratified \nsandstone. \n\n\n\nFigure 15: Overview of Miri Airport Road outcrop. \n\n\n\n4.2.6 Facies MFA: Herringbone cross stratification sandstone \n\n\n\nFacies MFA is characterized by light yellow to light grey, well sorted, very \nfine to fine grained with herringbone cross-stratified/ bipolar cross-\nstratified sandstones. The sandstone is sparsely bioturbated. The trace \nfossil (Ophiomorpha) can be observed throughout the facies. The \nthickness of sandstone bed range from 0.3m to 1.8m. \n\n\n\n4.2.7 Facies MFB: Hummocky-cross stratification sandstone \n\n\n\nFacies MFB is characterized by light grey, well sorted, very fine to fine \ngrained with hummocky cross-stratified sandstones. The sandstone is \nsparsely bioturbated. The trace fossil (Ophiomorpha) can be observed \nthroughout the facies. The thickness of sandstone bed range from 0.1m to \n1.1m. \n\n\n\n4.2.8 Facies MFC: Laminated sandstone \nFacies MFC is characterized by yellowish to light grey, well sorted, very \nfine to fine grained with laminated sandstones.  Parallel carbonaceous \nlamination can be observed in this facies. The sandstone is sparsely \nbioturbated. The thickness of sandstone bed range from 0.4m to 0.8m. \n\n\n\n4.2.9 Facies MFD: Heterolithic sandstone \n\n\n\nFacies MFD is characterized by yellowish to light grey, moderately sorted, \nvery fine grained with heterolithic sandstones. Wavy stratification can be \nobserved in this facies. The sandstone is sparsely bioturbated. The \nthickness of sandstone bed range from 4 cm to 8 cm. \n\n\n\n4.2.10 Facies MFE: Low angle cross-stratified sandstone \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 12-22 \n\n\n\nCite The Article: Jong E Cheng (2019). Implication Of Reservoir Characteristics Based On Overview Of Structure And Sedimentol ogy Of Outcrops Along Bintulu-Niah-Miri Areas. \nMalaysian Journal of Geosciences, 3(2): 12-22. \n\n\n\nFacies MFA is characterized by light yellow, well sorted, very fine to fine \ngrained with low angle cross-stratified sandstones. The sandstone is \nsparsely bioturbated. The trace fossil (Ophiomorpha) can be observed \nthroughout the facies. The thickness of sandstone bed range from 0.3 m to \n1.2 m. \n\n\n\nNumerous normal faults can be observed at Airport Road Anticline \noutcrop. The density of fault more concentrated at the crest of the anticline \nstructure. Listric normal fault F1 with strike of 2250 and dip of 560 towards \nNorth-West with fault throw of 0.55 m. Normal fault F2 with strike of 460 \nand dip of 610 towards South-East with fault throw of 0.31 m. Normal fault \nF3 with strike of 480 and dip of 660 towards South-East with fault throw of \n0.35 m. Normal fault F4 with strike of 2280 and dip of 700 towards North-\nWest with fault throw of 0.33 m. The normal fault zone is characterized by \na few structural features included drag feature; fault-bounded lens shaped \nstructure; telescoping on parallel strands; deformation band and fault \ngouge. In addition, juxtaposition associated with the normal fault can be \nobserved in this outcrop. \n\n\n\nFault- bounded lens shaped structure can be observed at the outcrop, the \nsize of lens can range from meter to centimeter. There is an increase in dip \nof lamination inside lens compared to dip of lamination outside the lens. \nHowever, the dip of lamination at the lens tip is significantly higher than \ndip of lamination at the center of lens. The deformation band density \ninside the lens is higher compare to outside. \n\n\n\nFurthermore, there is an increase in number of deformation bands \ntowards the fault plane. The spacing of deformation bands is narrow (mm) \nas getting near the fault plane while spacing of deformation bands is wider \nas away from fault plane (cm). Deformation band in fault zone could \nreduce the permeability of sandstone [9]. \n\n\n\nTelescoping along parallel strands were common at the normal fault zone \nin Airport Road Anticline outcrop. Telescoping along parallel strands were \ncontribute to formation of thick clay between sand beds and not as thick \nclay gouge. \n\n\n\nSystematic joint perpendicular to sandstone bedding can be observed at \nMiri Airport Road outcrop. The length of joint is around 0.5m with spacing \nranging from 3cm to 25cm. Strike is ranging from 2480-2510 and dipping \ntoward North-West. \n\n\n\n5. SEDIMENTOLOGY AND IMPLICATION FOR RESERVOIR \nCHARACTERISTIC \n\n\n\n5.1 Sedimentary facies characteristics of Bintulu Outcrops (Nyalau \nFormation) \n\n\n\n11 lithofacies have been determined from the Bintulu Outcrops (Nyalau \nFormation) based on lithology, sedimentary structures, bioturbation, \nfossil traces content and bed geometry. These 11 major lithofacies \nincluded: Facies NF1: Hummocky cross-stratified sandstone; Facies NF2: \nLow angle cross-stratified sandstone; Facies NF3: Lenticular bedded \nheterolithic sandstone; Facies NF4: Coal bed; Facies NF5: Structureless \nmudstone; Facies NF6: Trough cross-bedded sandstone showing \nbundling; Facies NF7: Herringbone cross stratification sandstone; Facies \nNF8: Wavy and flaser stratified hetrolithic sandstone; Facies NF9: Trough \ncross stratified sandstone; Facies NF10: Sigmoidal cross stratification \nsandstone and Facies NF11: Bioturbated mudstone. \n\n\n\n5.1.1 Facies NF1: Hummocky cross-stratified sandstone \n\n\n\nFacies NF1 is characterized by light yellow, well - sorted, very fine to fine \ngrained with hummocky cross stratified sandstones. Parallel \ncarbonaceous lamination and mud clast can be observed in this facies. The \nsandstone is sparsely bioturbated. The trace fossil (Ophiomorpha, \nSkolithos) can be observed throughout the facies. The thickness of \nsandstone bed range from 0.20 m to 0.70 m. \n\n\n\n5.1.2 Facies NF2: Low angle cross-stratified sandstone \n\n\n\nFacies NF2 is characterized by light yellow, well sorted, very fine to fine \ngrained with low angle cross-stratified sandstones. Parallel lamination can \nbe observed in this facies. The sandstone is sparsely bioturbated. The trace \nfossil (Ophiomorpha) can be observed throughout the facies. The \nthickness of sandstone bed range from 0.20 m to 2.70 m. \n\n\n\n5.1.3 Facies NF3: Lenticular bedded heterolithic sandstone  \n\n\n\nFacies NF3 is characterized by yellowish, moderately sorted, very fine \ngrained with heterolithic sandstones. The sand content of this facies is \nabout 20% - 30%. Lenticular bedding and rhythmic lamination can be \nobserved in this facies. The sandstone is sparsely bioturbated. The trace \nfossil (Ophiomorpha, Skolithos) can be observed throughout the facies. \nThe thickness of sandstone bed range from 3 cm to 9 cm. \n\n\n\n5.1.4 Facies NF4: Coal bed \n\n\n\nFacies NF4 is characterized by black coal bed. The coal bed is non \nbioturbated. The thickness of coal bed is about 25cm. \n\n\n\n5.1.5 Facies NF5: Structureless mudstone \n\n\n\nFacies NF5 is characterized by dark grey structureless mudstone. The \nmudstone is sparsely bioturbated. Occasionally, rootlets and organic \ndebris laminae can be observed throughout the facies. The thickness of \nmudstone bed range from 0.17 m to 2.1 m. \n\n\n\n5.1.6 Facies NF6: Trough cross-bedded sandstone showing bundling \n\n\n\nFacies NF6 is characterized by light yellow, well-moderate sorted, fine \ngrained with trough cross-bedded sandstones showing bundling. Mud \ndrape and tidal bundles can be observed in this facies. Alternating \npackages of thick bundles (Spring) and thin bundles (Neap) can be \nobserved in this facies. The sandstone is sparsely bioturbated. The trace \nfossil (Ophiomorpha) can be observed throughout the facies. The \nthickness of sandstone bed range from 0.10 m to 0.70 m. \n\n\n\n5.1.7 Facies NF7: Herringbone cross stratification sandstone \n\n\n\nFacies NF7 is characterized by light yellow to light grey, well sorted, fine \ngrained with herringbone cross-stratified/ bipolar cross-stratified \nsandstones. The sandstone is sparsely bioturbated. The trace fossil \n(Ophiomorpha) can be observed throughout the facies. The thickness of \nsandstone bed range from 0.20cm - 0.80 cm. \n\n\n\n5.1.8 Facies NF8: Wavy and flaser stratified hetrolithic sandstone \n\n\n\nFacies NF8 is characterized by yellowish to light grey, moderately sorted, \nvery fine grained with heterolithic sandstones. Wavy and flaser \nstratification can be observed in this facies. The sandstone is sparsely \nbioturbated. The thickness of sandstone bed range from 0.3 m to 1.4 m. \n\n\n\n5.1.9 Facies NF9: Trough cross stratified sandstone \n\n\n\nFacies NF9 is characterized by light yellow, well to moderate sorted, fine \ngrained with trough cross stratified sandstones. The sand and mud \ncontent of this facies is about 90% and 10% respectively. Mud drape can \nbe observed in this facies. The sandstone is sparsely bioturbated. The trace \nfossil (Ophiomorpha) can be observed throughout the facies. The \nthickness of sandstone bed range from 0.10 m to 4.0 m with channel-like \ngeometry. \n\n\n\n5.1.10 Facies NF10: Sigmoidal cross stratification sandstone \n\n\n\nFacies NF10 is characterized by light yellow to light grey, well sorted, fine \ngrained with sigmoidal cross-stratified sandstones. Mud drape and tidal \nbundles can be observed in this facies The sandstone is sparsely to \nmoderately bioturbated. The trace fossil (Ophiomorpha) can be observed \nthroughout the facies. The thickness of sandstone bed range from 0.20cm \n- 0.60 cm \n\n\n\n5.1.11 Facies NF11: Bioturbated mudstone \n\n\n\nFacies NF11 is characterized by grey structureless mudstone. The \nmudstone is moderate to commonly bioturbated. Trace fossil can be \nobserved throughout the facies. The thickness of mudstone bed range \nfrom 0.20 m to 2.1 m. \n\n\n\n5.2 Facies Association of Bintulu Outcrops (Nyalau Formation) \n\n\n\nSix facies association have been determined from the Sungai Mas Camp \noutcrop. These six facies association included Facies Association NF1: \nShoreface; Facies Association NF2: Muddy Tidal Flats; Facies Association; \nNF5: Offshore; Facies Association NF3: Tidal Channel Facies Association \nNF6: Point Bar and Facies Association NF4: Tidally Reworked Bars and \nDunes (Figure 16). The succession is interpreted as shoreface and offshore \nsuccession overlain by tidal bar and tidal channel successions (Figure 16).  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 12-22 \n\n\n\nCite The Article: Jong E Cheng (2019). Implication Of Reservoir Characteristics Based On Overview Of Structure And Sedimentol ogy Of Outcrops Along Bintulu-Niah-Miri Areas. \nMalaysian Journal of Geosciences, 3(2): 12-22. \n\n\n\nFigure 16: Sedimentary log showing facies and facies association of \nSungai Mas Outcrop \n\n\n\nThree facies association have been determined from the Meteorology \nDepartment outcrop. These three facies association included Facies \nAssociation NF1: Shoreface; Facies Association NF2: Muddy Tidal Flats \nand Facies Association NF3: Tidal Channel (Figure 17). The succession is \ninterpreted as shoreface succession overlain by muddy tidal flat and tidal \nchannel successions (Figure 17).  \n\n\n\nFigure 17: Sedimentary log showing facies and facies association of \nMeteorology Department Outcrop. \n\n\n\n5.2.1 Facies Association NF1: Shoreface \n\n\n\nFacies Association NF1 is characterized by very fine to fine-grained, well-\nsorted sandstones with low angle cross-stratification (Facies NF2) and \nhummocky cross-stratification (Facies NF1) which can be isolated or \namalgamated. The sandstones are sharp-based with coarsening-upward \npackages. The sandstone is sparsely bioturbated. The trace fossil \n(Ophiomorpha, Skolithos) can be observed throughout the facies.  \nInterpretation: \n\n\n\nA researcher suggested that hummocky cross-stratification is commonly \nconfined to shallow marine sedimentary rock formed by relatively large \nstorm waves in the ocean [12]. Other researcher also stated hummocky \ncross-stratification and low angle cross-stratification are recognized as \ninfluence of waves [13]. Hence, Facies Association NF1 has been \ninterpreted as wave-dominated shoreface deposits based on its \nstratigraphic position and relation with other facies associations.  \n\n\n\n5.2.2 Facies Association NF2: Muddy Tidal Flats \n\n\n\nFacies Association NF2 is characterized by coal bed (Facies NF4), \nstructureless mudstone (Facies NF5) with rootlets and plant fragments, \nsmall-scale channelled unit of trough cross stratified sandstone (Facies \nNF9) and lenticular bedded heterolithic sandstone (Facies NF3). \n Interpretation: \n\n\n\nMudstone with plant fragments and roots in place which reworked by \npedogenic processes are interpreted as the uppermost of tidal mud flats \n[14]. Lenticular bedding (Facies NF3) with marine trace fossils typically \nindicates deposition from reversing tidal current [15]. The small-scale \nchannelled sandstone trough cross stratified sandstone (Facies NF9) \nindicate depositional in tidal gullies that crossed the tidal flats [16]. Hence, \nFacies Association NF2 has been interpreted as muddy tidal flats deposits \nbased on its stratigraphic position and relation with other facies \nassociations. \n\n\n\n5.2.3 Facies Association NF3: Tidal Channel \n\n\n\nFacies Association NF3 is characterized by wavy and flaser stratified \nhetrolithic sandstone (Facies NF8), trough cross-bedded sandstone \nshowing bundling (Facies NF6), herringbone cross-stratified/ bipolar \ncross-stratified sandstones (Facies NF7), lenticular bedded heterolithic \nsandstone (Facies NF3) with channel-like bed geometry and trough cross \nstratified sandstone (Facies NF9) which are amalgamated with channel-\nlike bed geometry. The fining-upward packages association has basal \nerosion and mud drapes are ubiquitous through the Facies Association \nNF3. \n\n\n\nInterpretation: \n\n\n\nThe channel-shape, ubiquitous mud drapes and marine/ brackish trace \nfossils suggest deposition in tidal channel [14]. Mud / organic debris \ndrapes and herringbone cross-stratified/ bipolar cross-stratified \nsandstones indicate strong tidal influence. Hence, Facies Association NF2 \nhas been interpreted as tidal channel deposits based on its stratigraphic \nposition and relation with other facies associations. \n\n\n\n5.2.4 Facies Association NF4: Tidally Reworked Bars and Dunes \n\n\n\nFacies Association NF4 is characterized by wavy and flaser stratified \nhetrolithic (Facies NF8); sigmoidal cross stratification sandstone (Facies \nNF10) and Trough cross stratified sandstone (Facies NF9). They are well \nto moderate sorted. The sandstone is sparsely to moderate bioturbated. \nThe trace fossil (Ophiomorpha) can be observed throughout the facies. \nThe succession is coarsening-upward in grain saiz from fine to medium-\ngrained accompanied by an increase in the size of the preserved bedding. \nThe lower part of sand body consists of heterolithic wavy laminae while \nthe upper part of sand body consists of heterolithic flaser laminae followed \nby trough cross stratified sandstone (two-dimensional dune). The sand \nbody are characterized by thick inclined heterolithic bed (~3m) which can \nbe traced up to 60m. \nInterpretation: \n\n\n\nThe facies association is interpreted as tidal reworking of sand bars. \nLarger dunes climbing over smaller dunes produced thickening and \nupward coarsening patterns during the reworking process. Features \nobserved in sand bodies indicate tidal influence included sigmoidal cross-\nstratification and mud drapes and tidal bundles [14]. Moderate to well \nsorted, with sub-angular grains, sigmoidal cross-stratified, coarsening \nupwards sand with degree of bioturbation of moderate and dominated by \nDactyloidites ottoi, although Ophiomorpha, Planolites are also present are \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 12-22 \n\n\n\nCite The Article: Jong E Cheng (2019). Implication Of Reservoir Characteristics Based On Overview Of Structure And Sedimentol ogy Of Outcrops Along Bintulu-Niah-Miri Areas. \nMalaysian Journal of Geosciences, 3(2): 12-22. \n\n\n\ninterpreted as tidally reworked bars and dunes [13]. Although a group \nresearchers suggested upward-coarsening facies model for features in the \nAger Basin (Eocene), northern Spain which interpreted to be a \u201ctidal bar\u201d, \nothers explained this feature should be caused by deposit of a compound \ndune [17,18]. Hence, based on its stratigraphic position and relation with \nother facies associations, Facies Association NF4 has been interpreted as \ntidally reworked bars and dunes. \n\n\n\n5.2.5 Facies Association NF5: Offshore \n\n\n\nFacies Association NF5 is characterized by bioturbated muddy deposits \n(Facies NF11), very fine to fine-grained, well-sorted sandstones with low \nangle cross-stratification (Facies NF2) and lenticular bedded heterolithic \nsandstone (Facies NF3) The degree of bioturbation is moderate to \ncommon. Facies Association NF5 gradually coarsening upwards as the \nmud content decrease and transition into Facies Association NF1 \n(shoreface) deposits. \n\n\n\nInterpretation: \n\n\n\nRossi & Steel stated offshore or offshore transition is characterised by \nabundance of trace fossil, abundance of fine-grained, heterolithic deposits \nand marine mudstone [13]. Hence, Facies Association NF5 has been \ninterpreted as offshore deposits based on its stratigraphic position and \nrelation with other facies associations. \n\n\n\n5.2.6 Facies Association NF6: Point Bar \n\n\n\nFacies Association NF6 is characterized by concave upward channel \nprofiles with lenticular bedded heterolithic sandstone (Facies NF3) \n\n\n\ncommonly forming inclined stratification (IHS) and coal bed (Facies NF4). \nInterpretation: \n\n\n\nThe inclined heterolithic strata are interpreted as lateral accretion \ndeposits on tidal point bar surface and in high-sinuosity tidal channel [14]. \nCoal layer marks abandonment of channels [14]. \n\n\n\n6. IMPLICATION FOR RESERVOIR CHARACTERISTIC \n\n\n\n6.1 Deposition Environment \n\n\n\nCorrect interpretation of depositional environment plays an important \nrole in estimating the geometry of sand body as different sedimentological \nprocesses could have different impact on the reservoir architecture and \naffect the heterogeneity on fluid flow within the hydrocarbon reservoir. \nRecognition of the mixed-energy character of depositional system is \ncrucial for reservoir characterization which would have impact on \nreservoir modelling and characterization [13]. \n\n\n\nIn general, Bintulu areas outcrops consists of mixed-environment \ndeposited succession with tidal and wave characteristic. Tide-influenced \nand wave-influenced deposits are better sorted than fluvial-dominated \ndeposits [13]. Hence, this could indicate that the successions have good \nreservoir properties in general. \n\n\n\n6.1.1 Case 1: Meteorology Department Outcrop \n\n\n\nThe Meteorology Department outcrop succession is interpreted as \nshoreface (FA: NF1) succession overlain by muddy tidal flat (FA: NF2) and \ntidal channel (FA: NF3) (Figure 18). \n\n\n\nFigure 18: The Meteorology Department outcrop succession is interpreted as shoreface (FA: NF1) succession overlain by muddy tidal flat (FA: NF2) and \ntidal channel (FA: NF3) \n\n\n\nFrom bottom, shoreface (FA: NF1) sand body could be a good reservoir \nwhich characterized by thick (>18m) and laterally extensive sheets. This \nreservoir is expected to have good horizontal flow properties and vertical \nflow between beds in this case. However, the Shoreface (FA: NF1) sand \nbody is overlain or separated by mudstone horizon of muddy tidal flat \ndeposit (FA: NF2) which is poor in both permeability and porosity. This \ncharacteristic of muddy tidal flat deposit (FA: NF2) mudstone horizon \nallows the shoreface (FA: NF1) sand body (which could be reservoir) be \nsealed to prevent migration of hydrocarbon. \n\n\n\nWithin the muddy tidal flat deposit (FA: NF2) mudstone horizon, there is \nan isolated channel-like sand body enclosed by mud and a coal layer bed, \nfollowed by depositions of tidal channel deposits (FA: NF3) in between \nmuddy tidal flat deposit (FA: NF2) mudstone horizon. Coal bed could be \nimportant for seismic data as a key horizon marker for stratigraphic \ncorrelation.  The isolated sand body and channel-like tidal channel \ndeposits (FA: NF3) sand body enclosed within muddy tidal flat (NF2) \n\n\n\ndeposited mud horizon could lead to overpressure which may resulted in \ndrilling problem such as blowouts. However, tidal channel deposits (FA: \nNF3) channel-like sand body is still could be good reservoir with geometry \nof sand body perpendicular to shoreline and laterally thinning away from \nchannel axis. This reservoir is expected to have good horizontal flow \nproperties but limited vertical flow between beds due to individual \nchannel was separated by muddy tidal flat deposit (FA: NF2) mudstone \nhorizon. In addition, wavy-bedded mudstone layer and mud-draped \nsurfaces within tidal channel deposits (FA: NF3) channel-like sand body \ncould be barriers baffles or barriers to fluid flow within the reservoir. \n\n\n\n6.1.2 Case 2: Sungai Mas Camp Outcrop \n\n\n\nThe Sungai Mas Camp outcrop succession is interpreted as shoreface (FA: \nNF1) and offshore (FA: NF5) succession overlain by muddy tidal flat (FA: \nNF2), tidally reworked bars and dunes (NF4) and tidal channel (FA: NF3) \n(Figure 19,20). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 12-22 \n\n\n\nCite The Article: Jong E Cheng (2019). Implication Of Reservoir Characteristics Based On Overview Of Structure And Sedimentol ogy Of Outcrops Along Bintulu-Niah-Miri Areas. \nMalaysian Journal of Geosciences, 3(2): 12-22. \n\n\n\nFigure 19: The Sungai Mas Camp outcrop succession is interpreted as \nshoreface (FA: NF1) and offshore (FA: NF5) succession overlain by muddy \ntidal flat (FA: NF2), tidally reworked bars and dunes (NF4) and tidal \nchannel (FA: NF3). \n\n\n\nFigure 20: The Sungai Mas Camp outcrop succession is interpreted as \nshoreface (FA: NF1) and offshore (FA: NF5) succession overlain by muddy \ntidal flat (FA: NF2), tidallyreworked bars and dunes (NF4) and tidal \nchannel (FA: NF3). \n\n\n\nFrom bottom, shoreface (FA: NF1) sand body could be a good reservoir \nwhich characterized by and laterally extensive sheets. This reservoir is \nexpected to have good horizontal flow properties and vertical flow within \nshoreface (FA: NF1) sand body in this case. However, the shoreface (FA: \nNF1) sand body is overlain or separated by mudstone horizon of offshore \n(FA: NF5) deposited mudstone horizon which is poor in both permeability \nand porosity. This characteristic of offshore (FA: NF5) deposited \nmudstone horizon allows the shoreface (FA: NF1) sand body (which could \nbe reservoir) be sealed to prevent migration of hydrocarbon. \n\n\n\nAt the depth of 20m of the logged outcrop, which is right above the \noffshore (FA: NF5) deposited mudstone horizon, shoreface (FA: NF1) sand \n\n\n\nbody is overlain by tidal channel (FA: NF3) deposited mud horizon. As \nmentioned above shoreface (FA: NF1) could be a good reservoir for \nhydrocarbon and sealed by mud horizon and coal within tidal channel (FA: \nNF3). Coal bed could be important for seismic data as a key horizon \nmarker for stratigraphic correlation. In addition, it is noted that tidal \nchannel (FA: NF3) in this outcrop section contain thin-bedded sandstone \nwhich could be the reservoir which always been overlook by geologist as \nthey are below logs and seismic resolution. \n\n\n\nAt the depth of 36 m of the logged outcrop, tidally reworked bars and \ndunes (NF4) sand body is underlain by shoreface (FA: NF1) sand body \nfollowed by muddy tidal flat deposit (FA: NF2) mudstone horizon. Tidally \nreworked bars and dunes (NF4) sand body and shoreface (FA: NF1) sand \nbody both could be a good reservoir. Tidally reworked bars and dunes \n(NF4) sand body are thin laterally away from channel axis and also thins \naway seaward. Numerous tidally reworked bars and dunes (NF4) sand \nbody are coalesce to form broad parallel sand sheet in this outcrop. This \nreservoir is expected to have good horizontal flow properties and vertical \nflow within tidally reworked bars and dunes (NF4) sand body and also \nbetween tidally reworked bars and dunes (NF4) sand body and shoreface \n(FA: NF1) sand body. Muddy tidal flat deposit (FA: NF2) in this section \ncould be seal of the below reservoir to prevent migration of hydrocarbon. \nHowever, the isolated sand body enclosed by muddy tidal flat (FA: NF2) \ndeposited mud horizon could lead to overpressure which could cause \ndrilling problem such as blowouts. \n\n\n\nAt the top section of outcrop is characterized by tidal channel deposits (FA: \nNF3) channel-like sand body which could be good reservoir with geometry \nof sand body perpendicular to shoreline and laterally thinning away from \nchannel axis. This reservoir is expected to have good horizontal flow \nproperties but limited vertical flow between beds as the individual \nchannel could be separated by muddy tidal flat deposit (FA: NF2) \nmudstone horizon. In addition, mud-draped surfaces within tidal channel \ndeposits (FA: NF3) channel-like sand body could be barriers baffles or \nbarriers to fluid flow within the reservoir. \n\n\n\n6.2 Karst Reservoirs \n\n\n\nLoucks have described the how carbonate paleo-cave could become \nreservoir started from near-surface process (formation of phreatic cave; \nbrecciation; localized fracturing; chemical precipitation; collapse of cave \nwalls and ceiling) until it being buried into subsurface where breccia clasts \nare rebrecciated (Figure 21) [19]. Hence, Subis Limestone which is \nproduct of near-surface karst processes could be good carbonate reservoir \neven after buried at the subsurface of offshore.  \n\n\n\nCarbonate reservoir are known to be challenging due to their complex \npore system make then difficult to characterize and develop efficiently. \nHowever, by understanding the forming process of the limestone unit \ncould help in roughly predict the geometry of the pore system. Niah Cave \nprovide the opportunity for us to observe the distribution of the types of \nbreccia due to collapsed paleokarst at reservoir scale. \n\n\n\nFigure 21:  Diagram depicting the collapse of a cave during the passage \nfrom the phreatic zone to the vadose zone and further brecciation in the \ndeep subsurface [19]. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 12-22 \n\n\n\nCite The Article: Jong E Cheng (2019). Implication Of Reservoir Characteristics Based On Overview Of Structure And Sedimentol ogy Of Outcrops Along Bintulu-Niah-Miri Areas. \nMalaysian Journal of Geosciences, 3(2): 12-22. \n\n\n\nFurthermore, due to the its environment setting or forming process is \nsame as Central Luconia Platform, Niah Cave can be good analog for \nCentral Luconia Platform where large resources of hydrocarbon have been \ndiscovered. Central Luconia is a gas-producing carbonate province \noffshore Sarawak formed by carbonate build-ups and influenced by \nterrestrial processes including erosion and fluvial deposition at exposed \nshelf during lowstands [20]. Terrestrial processes could lead to deposition \nof siliciclastic deposits which may act as thief bed migrating hydrocarbons \naway from carbonate reservoir [21]. \n\n\n\n6.3 Anticline structure, Faults, Joints and Beddings \n\n\n\nAnticline structure could be good structural trap for hydrocarbon. \nHowever, it can be observed that the crest of Miri Airport Outcrop anticline \nstructure consists of numerous of fold-related faults related strain which \ncould lead to leakage (tertiary migration) of hydrocarbon or \ncompartmentalization due to fault juxtapositions. Characteristics of fault \nplay an important role in hydrocarbon basin analysis and design of field-\nscale development reservoirs as fault can be barrier and conduit for fluid \nflow. Deformation band in fault zone could reduce the permeability of \nsandstone [9]. \n\n\n\nSeveral flow paths of fluid can be expected at fault zone: \n\n\n\na) Fluid flow across fault \n\n\n\nCross-fault leakage connectivity allows fluid flow across fault and resulted \nin inter-reservoir fluid communication. However, juxtaposition fault may \nincrease or decrease cross fault leakage connectivity [22]. Hence, \njuxtaposition fault could lead to reservoir compartmentalization [23]. \n\n\n\nb) Fluid flow along fault \n\n\n\nFault could act as a conduit which induces fluid flow or migration of \nhydrocarbon.  \n\n\n\nc) Leakage over spill point (Juxtaposition spill point/ gouge failure spill \npoint/ filled to seal capacity) \n\n\n\nAt faulted trap, fluid might spill at spill point under several conditions as \nintroduced` included juxtaposition spill point, gouge failure spill point or \nfilled to seal capacity and lead to leakage or tertiary migration [24]. These \nspill points are known as \u201cCryptic\u201d spill points which cannot be seen on \nseismic or reconstructed from the structural/stratigraphic framework of \nthe trap. \n\n\n\nIn addition, beddings and joints within bedding across the sandstone bed \nis also important parameter for reservoir characterization or modelling. \nThin beds (Bukit Song Outcrop) and joints (Miri Outcrops) could be \noverlook in seismic section due to resolution. High net-to-gross (Bukit \nSong Outcrop) thin-bedded succession could be good reservoir which \nalways over look from logs and seismic. In addition, joints can act as \nconduit which enhance the hydrocarbon or fluid flow from reservoir. \nFurthermore, joints patterns and orientation measured from the outcrop \ncould be crucial information in design of well direction. The reservoir \nshould be drilled perpendicular to the joint to get the maximum \nhydrocarbon from the reservoir bed (sandstone). It is a good idea to plan \na borehole trajectory with bedding orientation in mind, because, even in \ncomplex structures, fractures tend to be perpendicular to bedding [25]. \n\n\n\nGenerally, Miri Airport Outcrop succession consist of Type 4 Fractured \nReservoir Type - Fracture Create Flow Barriers. Type 4 Fractured \nReservoir Type could lead to potential production problems included \nreservoir commonly highly compartmentalized; wells underperform \ncompared to matrix capabilities; recovery factor highly variable across \nfield and permeability anisotropy opposite to other adjacent fractured \nreservoirs of other fracture types [26]. \n\n\n\n7. CONCLUSIONS \n\n\n\nBintulu- Niah- Miri areas outcrops consists of mixed-environment \ndeposited succession with tidal and wave characteristic. This resulted in \nreservoir architect will be different and result in different in reservoir \nproperties included horizontal and vertical permeability of fluids. Niah \nCave is a good place to study the distribution of the types of breccia due to \ncollapsed paleokarst at reservoir scale and also good analog for Central \nLuconia Platform where large resources of hydrocarbon have been \ndiscovered due to the its environment setting or forming process is same \nas Central Luconia Platform. Miri Airport Outcrop succession consist of \nType 4- Fracture Create Flow Barriers which could lead to potential \nproduction problems included reservoir commonly highly \n\n\n\ncompartmentalized; wells underperform compared to matrix capabilities; \nrecovery factor highly variable across field and permeability anisotropy \nopposite to other adjacent fractured reservoirs of other fracture types. \n\n\n\nACKNOWLEDGEMENT  \n\n\n\nI would like to thank Prof. Madya Ng Tham Fatt, Dr. Meor Hakif bin Amir \nHassan and Dr. Ralph L. Kugler. for their guidance during the fieldtrip.  \n\n\n\nREFERENCES \n\n\n\n[1] Hutchison, C.S. 2005. Geology of North-West Borneo: Sarawak, Brunei \nand Sabah. Elsevier, Amsterdam. \n\n\n\n[2] Mazlan, M. 1999. Geological setting of Sarawak. In: The Petroleum \nGeology and Resources of Malaysia. PETRONAS, Kuala Lumpur, pp. 275\u2013\n290. \n\n\n\n[3] Mazlan, M., Abdul H.A.R. 2007. Penecontemporaneous deformation in \nthe Nyalau Formation (Oligo-Miocene), Central Sarawak. Geological \nSociety of Malaysia, Bulletin 53, 67- 73. \n\n\n\n[4] Mazlan, M., Abolins, P. 1999. Balingian Province. In: The Petroleum \nGeology and Resources of Malaysia. PETRONAS, Kuala Lumpur, pp. 343\u2013\n368. \n\n\n\n[5] Meor, H.A.H., Howard, D.J., Peter, A.A., Wan, H.A. 2013. Sedimentology \nand stratigraphic development of the upper Nyalau Formation (Early \nMiocene), Sarawak, Malaysia: A mixed wave- and tide-influenced coastal \nsystem. Journal of Asian Earth Sciences, 76, 301\u2013311. \n\n\n\n[6] Liechti, P., Roe, F.W., Haile, N.S. 1960. The geology of Sarawak, Brunei \nand the western part of North Borneo. Geological Survey Department, \nBritish Territories in Borneo Bulletin 3. \n\n\n\n[7] Wan, H.A. 1999. Oil-generating potential of Tertiary coals and other \norganic-rich sediments of the Nyalau Formation, onshore Sarawak. \nJournal Asian Earth Science, 17, 255-267. \n\n\n\n[8] Numair, A.S., Abdul, H.A.R., Chow, W.S., Manoj, J.M., David, M. 2014. \nFacies Characteristics and Static Reservoir Connectivity of Some \nSiliciclastic Tertiary Outcrop Successions in Bintulu and Miri, Sarawak, \nEast Malaysia, presented at AAPG International Conference & Exhibition, \nIstanbul, Turkey: Search and Discovery Article #90194. \n\n\n\n[9] Wannier, M., Lesslar, P.H., Lee, C.H., Raven, H., Sorkhabi, R., Ibrahim, A. \n2011. Geological Excursions Around Miri, Sarawak. EcoMedia, Miri, \nMalaysia, pp. 39. \n\n\n\n[10] Wilford, G.E. 1961. The geology and Mineral Resource of Brunei and \nAdjacent Parts of Sarawak with descriptions of Seria and Miri oilfield. \nBorneo Geological Survey Mem., 10, pp.319. \n\n\n\n[11] Paul, W.V., Baceta, Ignacio, J., Lapointe., Philippe, A. 2014. Paleokarstic \nmacroporosity development at platform margins: Lessons from the \nPaleocene of north Spain Interpretation, 2 (3) p. SF1\u2013SF16. \nhttp://dx.doi.org/10.1190/INT-2013-0175.1. \n\n\n\n[12] Boggs, Sam. 2006. Principle of Sedimentology and Stratigraphy (4th \ned.). Oerson Prentice Hall, New Jersey, pp 93. \n\n\n\n[13] Rossi, V.M., Steel, R.J. 2016. The role of tidal, wave and river currents \nin the evolution of mixed-energy deltas: Example from the Lajas \nFormation (Argentina). Sedimentology. doi: 10.1111/sed.12240. \n\n\n\n[14] Plink-Bjorklund, P. 2016. Stacked fluvial and tide-dominated \nestuarine deposits in high-frequency (fourth-order) sequences of the \nEocene Central Basin, Spitsbergen. Sedimentology.doi:10.1111/j.1365-\n3091.2005.00703. x. \n\n\n\n[15] Reineck, H.E., Singh, I.B. 1980. Depositional Sedimentary \nEnvironments, 2nd edn. Springer-Verlag, New York, 549 pp. \n\n\n\n[16] Dalrymple, R.W., Yasuhiko, M., Zaitlin, B.A. 1991. Temporal and \nspatial patterns of rythmite deposition on mud flats in the macrotidal \nCobequid Bay-Salmon River estuary, Bay of Fundy, Canada. In: Clastic \nTidal Sedimentology (Eds D.G. Smith, G.E. Reinson, B.A. Zaitlin and R.A. \nRahmani). Canadian Society of Petroleum Geologists, 16, 3\u201328. \n\n\n\n[17] Mutti, E., Rosell, J., Allen, G.P., Fonnesu, F., Sgavetti, M. 1985. The \nEocene Baronia tide dominated delta-shelf system in the Ager Basin. In: \n\n\n\n\nhttp://dx.doi.org/10.1190/INT-2013-0175.1\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 12-22 \n\n\n\nCite The Article: Jong E Cheng (2019). Implication Of Reservoir Characteristics Based On Overview Of Structure And Sedimentol ogy Of Outcrops Along Bintulu-Niah-Miri Areas. \nMalaysian Journal of Geosciences, 3(2): 12-22. \n\n\n\nMila, M.D., Rosell, J. (Eds.), Excursion Guidebook, 6th European Regional \nMeeting. International Association of Sedimentologists, Lleida, Spain, pp. \n579\u2013600. \n\n\n\n[18] Dalrymple, R.W., Choi, K. 2007. Morphologic and facies trends \nthrough the fluvial\u2013marine transition in tide-dominated depositional \nsystems: A schematic framework for environmental and sequence-\nstratigraphic interpretation. Earth-Science Reviews 81, pp 135\u2013174 \n\n\n\n[19] Loucks, R.G. 1999. Paleocave Carbonate Reservoirs: Origins, Burial -\nDepth Modifications, Spatial Complexity, and Reservoir Implications. \nAAPG Bulletin, 83, (11), pp. 1795 -1834. \n\n\n\n[20] Ko\u0161a, E. 2013. The Rivers of Luconia: The Effects of Sea-Level \nLowstands on the Stratigraphy  of a Mixed Carbonate/Clastic Province; \nMiocene-Present, Offshore Sarawak, NW Borneo.In: Petroleum Geoscience \nConference and Exhibition (PGCE), Kuala Lumpur, Malaysia, 18-19 March \n2013. \n\n\n\n[21] Ko\u0161a, E., Warrlich, G.M.D., Loftus, G. 2015. Wings, mushrooms, and \nChristmas trees: The carbonate seismic geomorphology of Central \nLuconia, Miocene\u2013 present, offshore Sarawak, northwest Borneo. AAPG \nBulletin, 99 (11), pp. 2043\u20132075 \n\n\n\n[22] Van der Zee, W., Urai, J.L. 2005. Processes of normal fault evolution in \na siliciclastic sequence: a case study from Miri, Sarawak, Malaysia. Journal \nof Structural Geology, 27, 2281\u20132300. \n\n\n\n[23] Morris, A.P., Smart, K., Ferril, D.A., Reish, N., Cowel, P.F. 2012. Fault \nCompartmentalization in a Mature Clastic Reservoir: An Example from Elk \nHills Field, California, Presented at AAPG Annual Convention and \nExhibition, Long Beach, California: Search and Discovery Article #40907 \nCarter, D.C., 2003. 3-D seismic geomorphology: Insights into fluvial \nreservoir deposition and performance, Widuri field, Java Sea. American \nAssociation of Petroleum Geologists Bulletin, 87, 909-934. \n\n\n\n[24] Sales, J.K. 1997. Seal strength vs. trap closure\u2014a fundamental control \non the distribution of oil and gas, in R.C. Surdam, ed., Seals, traps, and the \npetroleum system. AAPG Memoir, 67, 57\u201383.  \n\n\n\n[25] Berg, C.R., Newson, A. 2010. The Importance of Bedding Orientation \nWhen Looking for Fractures, presented at AAPG International Convention \nand Exhibition, Calgary, Alberta, Canada: Search and Discovery Article \n#90108. \n\n\n\n[26] Pranter, M.J., Ellison, A.I., Cole, R.D., Patterson, P.E. 2007. Modelling \nand analysis of intermediate-scale reservoir heterogeneity based on a \nfluvial point-bar outcrop analog, Williams Fork Formation, Piceance Basin, \nColorado, USA. American Association of Petroleum Geologists Bulletin, 91, \n1025-1051. \n\n\n\n[27] Olayinka, S.T., Wan, H.A., Mohammed, H.H., Pedro, J.B. 2015. Organic \ngeochemical and petrographic characteristics of Neogene organic-rich \nsediments from the onshore West Baram Delta Province, Sarawak Basin: \nImplications for source rocks and hydrocarbon generation potential. \nMarine and Petroleum Geology, 63, 115-126. \n\n\n\n\n\n"
"\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 24-28 \n\n\n\n\n\n\n\nCite The Article:Mohamed A. Abdel-Maksou, Kholoud M. Abdel-Maksoud (2017). Appraisement Of The Geologic Features As A Geo-Heritage In Abu-Roash Area, Cairo- Egypt. \nMalaysian Journal Geosciences, 1(2) : 24-28\n\n\n\n  ARTICLE DETAILS \n\n\n\nARTICLE HISTORY: \n\n\n\nReceived 12 May2017  \nAccepted 12 July 2017  \nAvailable online 10 September 2017 \n\n\n\nKEYWORDS: \n\n\n\nGeo-heritage, Abu-Roash, Geo-\ndiversity \n\n\n\nABSTRACT\n\n\n\nEgypt contains Geologic Heritage that create much opportunity to develop educational and recreational programs as \nwell as tourism projects. Enhancement of Geologic Heritage and awareness of the importance of Geologic Heritage is \na great challenge. This paper focuses on a neglected area inside Cairo that is facing a great destruction from the people \nliving there.  The Abu-Roash archaeological site is located at 31\u1d52 02\u2032 42\u2033 E longitude and 30\u1d52 02\u2032 42\u2033 N latitude. It is \none of the most important areas for education and scientific study inside Cairo. Although the area is not suited as a \ngeo-heritage or even a protected area, it contains Cretaceous to upper Eocene sedimentary beds and fossils, and a \ngreat variety of structural features. Not only an important geologic aspect found in the area but also an archeological \nsite is present which provide the area of a great scientific, cultural/historical, aesthetic and/or social/economic value. \nThese different criteria   qualifies the study are to have a regional/provincial rank for its Geo-heritage. Abu Roash area \nare possess good geo-diversity, geo abundance and geo richness which lead us to start point for establishing potential \ngeo-heritage that should be conserved the area also need to be recognized as a geological conservation sites, the area \nshould be Stated as a protected area of a heritage legislation to protect geo-heritage. \n\n\n\n1. INTRODUCTION \n\n\n\nIn the most recent few decades, there emerged another pattern for \nprotection and management of geologic offers through worldwide \nassociation. In 1972, the all gathering for UNESCO received the convention \nconcerning those security of the universe social and common heritage\u201d [1]. \nThis gathering gives those definition about two sorts from claiming \nheritage, the place \u201c\u2026natural legacy will be characterized similarly as those \ncomplex about bio-ecological and geomorphological components of nature \ndeserving about protection. This twofold point of view may be additionally \nrecognized at authoritative level, notably Toward those first parts of the EU \ndirective 92/43, and during experimental level Toward the endeavors will \njoin geomorphology Furthermore nature [2,3]. Geo- heritage evaluation \narised in the recent years with growing importance, leading to a place for \ngeodiversity concepts alongside biodiversity [4-11]. \n\n\n\nThe assessment study on the topic geoheritage are recent studies but this \ntype of studies is fast growing and depend on quantitative methods [4,6]. \nGeological features are presenting different contents which displaying \nvariable heritage values, depending on the meaning that we attribute to \nthem. As pointed by a scientist, the diversity of contents and the different \nprotection criteria leads to the existence of a great variety of legal \nregulations [12-14]. As a result, the geological heritage of the planet is \nirregularly protected all over the world, and objects with different contents \nmay be or not at risk, depending on a wide range of factors, most of them \nnot related with its contents. \n\n\n\nThe term geoheritage is not applied widely in Egypt, although there are \nmany valuable geologic areas. The area of Abu-Roash represents a unique \nand easily accessible geologic feature.  Unfortunately, the area is not \ncurrently monitored by a geologic organization as a geoheritage place or \neven not recorded as a protected area. \n\n\n\n2. GEOLOGIC SETTING FOR ABU-ROASH AREA \n\n\n\nAbu Roash constitutes a complex Cretaceous sedimentary succession with  \n\n\n\noutstanding tectonic features. The area lies on the edge of the western \ndesert, west of Cairo, Egypt (figure 1), at distance of 9 km north of the great \npyramid of Giza. Its name is derived from the neighboring village of Abu-\nRoash. \n\n\n\nThe Abu-Roash area is within the western end of the Syrian-arc folds of \nwhich extends from northern Egypt to Syria [13]. The upper Cretaceous \nrocks in the northwestern desert of Egypt underwent many different \ntectonic regimes since Paleozoic time. These regimes caused the formation \nof many sub-basins, ridges, trenches and platforms. The exposed \nlithostratigraphic sequence of the area includes Cretaceous, Middle and \nUpper Eocene, Oligocene, and Quaternary rock units. The units in the \nfollowing ascending order; Sandstone series, Rudista series, Limestone \nseries,Acteonella series, Flint series, Pilcatula series, Chalk- Maddi \nFormation, Sands, and Basalt and Gravel terraces and alluvial deposits The \nAbu Roash Massif is also characterized by heterogeneous fold styles with \ndifferent directions [14-17]. The folds are plunging anticlines and synclines \noriented In a NE-SW direction. The northeast trending folds of the area \nresulted from the combination of compressional stresses initiated from \nwrenching in addition to arching of the basement. These folds are believed \nto have developed during the Late Cretaceous - Early Eocene time. The \nCretaceous tectonics were severe to the degree that in many parts of Egypt \nthey formulate the present day structurally related land forms [18]. Among \nthe latter, some domal structures were selected by the petroleum industry \nto test by drilling like what found in Abu Roash area [19].   \n\n\n\nThe major structural elements in Abu Roash area are folding and faulting. \nThese structural elements reflect the structural pattern of the north-\nWestern Desert that are hidden below the younger sediments. These \nstructures were developed during the late Cretaceous and characterized by \ncompression tectonic regime. Besides the folds, faults are extensively \ndeveloped in specific directions: The E- W, the ENE and WNW trending \nfaults are the masters with almost a dextral-sense of movement, while those \nof NW trend are normals. N-S, NNE and NNW sinistral-slip faul ts and NE \nthrusts are subordinately developed [20-23]. The en echelon arrangement \nof both folds and faults in addition to the restriction of deformation in \n\n\n\nContents List available at RAZI Publishing \n\n\n\nMalaysian Journal of Geosciences \nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/ \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (online)\n\n\n\nAPPRAISEMENT OF THE GEOLOGIC FEATURES AS A GEO-HERITAGE IN \nABU-ROASH AREA, CAIRO- EGYPT \nMohamed A. Abdel-Maksou1, Kholoud M. Abdel-Maksoud2* \n1Faculty of Science- Cairo University \n2Institute of African Research and Studies- Cairo University \n*Corresponding author e-mail: Kholoud.mohamedali@gmail.com ,  kholoud.mali@staff.cu.edu.eg\n\n\n\nhttps://doi.org/10.26480/mjg.02.2017.24.28\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\nmailto:Kholoud.mohamedali@gmail.com\n\n\nmailto:kholoud.mali@staff.cu.edu.eg\n\n\nhttps://doi.org/10.26480/mjg.02.2017.24.28\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 24-28 \n\n\n\nCite The Article:Mohamed A. Abdel-Maksou, Kholoud M. Abdel-Maksoud (2017). Appraisement Of The Geologic Features As A Geo-Heritage In Abu-Roash Area, Cairo- Egypt. \nMalaysian Journal Geosciences, 1(2) : 24-28\n\n\n\n25 \n\n\n\ncertain narrow belts; weak development of the conjugate sinistral-slip \nfaults and conspicuous rotation of the structural elements indicate a dextral \nshear-couple. Such a regime principally prevailed with little convergence \nalong the ENE master faults, and divergence along the EW wrenches. \nFolds in Abu-Roash are the most important structural elements that played \na major role in the deformational history of the area [24]. A series of \nanticlines and synclines are recognized obviously in the area, folds range \nfrom 100 m to 0.5 km in width and from 300 m to 2.5 km in length. They \nare disturbed by longitudinal and reverse faults [25]. Some folds are open \nand form symmetrical structures, whereas others are rather complicated, \nasymmetric, plunging. Besides the individual folds, there are domal \nstructure (El-Hassana dome and El Ghigiga dome) \n\n\n\n3. GEO-SITES IN ABU-ROASH \n\n\n\nAbu Roash area is one of the most interesting sites inside Cairo, with \nimportant geologic features that could be investigated easily [26,27]. This \narea is used as the main field trip locale for students in University (geology, \ngeography and Archaeology) since the 1980s, which indicates how valuable \nthe area is and that the area is used for; scientific, educational and \narchaeological purposes. \n\n\n\nThe area contains the following types of geo-sites; \na- Stratigraphic type: the area consists of different stratigraphic \n\n\n\nsequences that include Cretaceous and upper to middle Eocene \nstrata (figure 2) \n\n\n\nb- Structure type: the area consists of folds, faults, unconformities, \nand domal structures. (figure 3-8) \n\n\n\nc- Economical type: the area is considered one of the important \nplaces for chalk quarries in Cairo. There are 4 quarries inside of \nthe Abu Roash area (Figures 9a & b) \n\n\n\nd- Paleontological type: there are different types of fossils are well \npreserved within the sedimentary succession at Abu Roash \n(figures 10a-d). Ph. Colelntrata (Anthoozoa), Ph. Porifera, Ph \nMollusca ( Gastropoda and Bivalivia) , Ph. Echinodermata, These \nfossil collection include rudists and echinoidea that reflect open \nmarine conditions. Rudist fragments in some strata were \nreworked from the rudist biostrome and re-deposited in quiet, \ndeep subtidal conditions. \n\n\n\ne- Sedimentary type: The snow-white chalky limestone. This Chalk \nwas deposited under open marine, outer shelf environmental \nconditions as mentioned from Issawi et al., (2009), are of great \ninterest because such rocks are rare in the geological record. \nAnd rare to be found inside Cairo it was recorded in Bahariya \noasis 500 Km from Cairo (Figures 11& 12). \n\n\n\nf- Igneous type: Tertiary age basalt is found in the Abu Roash area.\ng- Archaeological site: Old pyramid, sculpted in chalky limestone, \n\n\n\nknown of the 4th lost pyramid in Egypt. (Figures 13 & 14). \n\n\n\n4. DISCUSSION\n\n\n\nThe rank of geologic heritage in the Abu Roash area according to the \nclassification of Ruban and Kuo. The typology of the area is; Stratigraphical, \npaleonotical, sedimentary, Igneous, economical, Structural, \nPaleogeographical, geomorphological, geohistrocal [28]. Which indicate a \ndiversity in the geosites in this area, makes the area ranking from low to \nmoderate in its geologic heritage. And according to the typology of the area \ncontain different facies according to the geologic age recorded in the area \nfrom the upper createous which represented in the Chalk facie \u2013Eocene \nrepresented in Shallow marine (bivalaves,, nummulites) [29-31]. \n\n\n\nThis is one of the unique cases that the archaeological sites are linked with \nthe geological sites represented in the area in an old pyramid for the \nancient Egyptian, the area needs good understanding to support a correct \nassessments of geological heritage value, geo-conservation and geo-\ntourism planning.  Although the great importance of the area it is treated \nwith caution.  \n\n\n\nAfter calculating the geodiversity index, for Abu-Roash area, the linear \nscale is 0.55 which indicate that the area ranked as Regional/provincial in \nits geosite importance [32]. \n \u201cstated two types of geomophosite; \n(\u2170) a geomorphosite is a landform to which a value can be attributed; \n(\u2171) a geomorphological resource is a geomorphosite that can be used by \nsociety. \nThe attributes that may confer value to a geomorphosite are: scenic; socio-\neconomic; cultural; scientific. The scenic (aesthetic) criterion is to a great \nextent, of an intuitive nature. In this case, the approach to Nature depends \nupon the individual contemplating it and his/her state of mind at the time. \nIt is derived from feelings which, being personal perceptions, are highly \nsubjective, it is therefore difficult to value and compare with the feelings \n\n\n\nand perceptions of others\u201d. \nIn Abo-Roash area can be classified as a type (i) geomorphosite and a \ngeomorphological resource (ii) which can be used by society, where the \narea contains both unique landforms and used for scio-ecnomic, culture, \nand scientific study. \n\n\n\n5. CONCLUSION\n\n\n\nThe geologic heritage of Abu Roash is of regional/provincial rank where \nthe area represents geodiversity, geoabundance and georichness in its geo-\nheritage. The area is not currently recorded as a geoheritage site or \nprotected area. The only place recorded as a protected area is the Domal \nStructure (El Hassan dome), while the aforementioned folds, fossils, and \nfacies are not being monitored by the country, thus there is a high \nprobability of losing Abu Roash as a geoheritage site.  Thus, it would be \ndesirable to put this area under control from a specialized organization to \nsave the geologic heritage. \nAlso, the area should be: \n\n\n\n\u2022 Stated as a protected area of geo- heritage legislation that \ndirectly protects its geo and archeological heritage. \n\n\n\n\u2022 Vulnerability assessment should also identify tenure status.\n\n\n\n\u2022 An expert working groups should achieve enhanced and \npractical protection approaches for the geosites. \n\n\n\nREFERENCES  \n[1] UNESCO. 1972. Convention concerning the protection of world cultural \nand natural heritage. UNESCO, Paris. \n\n\n\n[2] Urban, M.A., Daniels, M. 2006. Exploring the links between \ngeomorphology and ecology. Geomorphology, 77(3\u20134), 203. \n\n\n\n[3] Rovere, A., Bellati, S., Parravicini, V., Firpo, M., Morri, C., Bianchi, C.N. \n2009. Abiotic and biotic links work two ways: effects on the deposit at the \ncliff foot induced by mechanical action of datemussel harvesting \n(Lithophaga lithophaga). Estuaries Coasts 32, 333\u2013339. \n\n\n\n[4] Brilha, J. 2002. Geoconservation and protected areas. Environmental \nConservation, 29 (3), 273\u2013276. \n\n\n\n[5] Zouros, N. 2004. The European Geoparks Network. Geological heritage \nprotection and local development. Episodes, 27 (3), 165\u2013171. \n\n\n\n[6] Zouros, N. 2005. Assessment, protection and promotion of \ngeomorphological and geological sites in the Aegean area, Greece. \nG\u00e9omorphol relief processus environnement, 3, 227\u2013234. \n\n\n\n[7] Staces, H., Larwood, J.G. 2006. Natural foundations: geodiversity for \npeople, places and nature. English Nature, Peterborough. \n\n\n\n[8] Carcavilla, L., L\u00f3pez-Mart\u00ednez, J., Dur\u00e1n, J. 2007. Patrimonio geol\u00f3gicoy \ngeodiversidad: investigaci\u00f3n, conservaci\u00f3n, gesti\u00f3n y relaci\u00f3ncon lo \nespacios naturals protegidos. Istituto Geol\u00f3gico y Minero de Espa\u00f1a, \nMadrid. \n\n\n\n[9] Panizza, M. 2009. The Geomorphodiversity of the Dolomites (Italy): a \nkey of geoheritage assessment. Geoheritage, 1 (1), 33\u201342. \n\n\n\n[10] Zouros, N. 2007. Geomorphosite assessment and management in \nprotected areas of Greece. The case of the Lesvos island coastal \ngeomorphosites. Geographica Helvetica, 62 (3), 169\u2013180. \n\n\n\n[11] Zouros, N., Ramsey, T., McKeever, P., Patzak, M. (eds) 2009. European \nGeoparks. EGN, Sigri Lesvos. \n\n\n\n[12] Zouros, N., McKeever, P.J. 2009. European Geoparks Network and \ngeotourism. In Neto de Carvalho C, Rodrigues J (eds) New challenges with \ngeotourism. Proceedings of the VIII European Geoparks Conference, \nIdanha-a-Nova 14\u201316 September (2009), Portugal: 19\u201323. \n\n\n\n[13] Abdel Khalek, M.L., El Sharkawi, M.A., Darwish, M., Hagras, M., Sehim, \nA. 1989. Structural history of Abu Roash district, Western Desert, Egypt. \nJournal of African Earth Sciences 9 (3-4), 435\u2013443. \n\n\n\n[14] Abdel-Gawad, G.I., Saber, S.G., El Shazly, S.H., and Salama, Y.F. 2011. \nTuronian Rudist Facies from Abu Roash Area, North Western Desert, Egypt. \nJournal of African Earth Sciences, 59 (2011) 359\u2013372. \n\n\n\n[15] Abu Khadrah, A.M., Helba, A.A., Abdel-Gawad, G.I., Badawy, H.S.M. \n2005. Progradational peritidal depositional pattern of the Turonian clastics \n(Sandstone series) of the Abu Roash Formation, Abu Roash area, Giza, \n\n\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 24-28 \n\n\n\nCite The Article:Mohamed A. Abdel-Maksou, Kholoud M. Abdel-Maksoud (2017). Appraisement Of The Geologic Features As A Geo-Heritage In Abu-Roash Area, Cairo- Egypt. \nMalaysian Journal Geosciences, 1(2) : 24-28\n\n\n\n26 \n\n\n\nEgypt. In: Fourth International Conference on the Geology of Africa, Asuit \nUniversity, 2, 639\u2013654. \n\n\n\n[16] Barettino, D., Wimbledon, W.A.P., Gallego, E. (eds). 2000. Patrimonio \nGeol\u00f3gico: conservaci\u00f3ny gesti\u00f3n. Istituto Geol\u00f3gico y Minero de Espa\u00f1a, \nMadrid. \n\n\n\n[17] Bruno, D.E., Crowley, B.E., Gutak, J. M., Moroni, A., Nazarenko, O.V., \nOheim, K.B., Ruban, D.A., Tiess, G., Zorina, S.O. 2014. Paleogeography as \ngeological heritage: Developing geosite classification. Earth-Science \nReviews, 138, 300-312. \n\n\n\n[18] Carreras, J., Druguet, E. 1998. The geological heritage of the Cap de \nCreus Peninsula (NE Spain): some keys for its conservation. Geologica \nBalcanica, 28 (3\u20134), 43\u201347. \n\n\n\n[19] Dauvin, J.C., Lozachmeur, O., Capet, Y., Dubrulle, J.B., Ghezali, M., \nMesnard, A.H. 2004. Legal tools for preserving France\u2019s natural heritage \nthrough integrated coastal zone management. Ocean Coast Manage, 47,\n463\u2013477. \n\n\n\n[20] Firpo, M., Guglielmin, M., Queirolo, C. 2006. Relict block fields in the \nLigurian Alps (Mount Beigua, Italy). Permafrost Periglac Process, 17 \n(1),71\u201378. \n\n\n\n[21] Issawi, B., Francis, M., Youssef, A., Osman, R., (2009). The \nPhanerozoic of Egypt: A 474 geodynamic approach. Geological Survey of \nEgypt, Cairo, 589 pp. Geologists' Association 121, 326-333. \n\n\n\n[22] Jux, U. 1954. Zur geologic des kridegebites von Abu Roash bei Kairo- \nNeues Jahrb-Geol. U. Palaonto, 100 (2), 159-207. \n\n\n\n[23] Krenkel, E. 1924. Der Syrische Bogen. Cetrable. Min. 9, 274-281; 10, \n301-313. \n\n\n\n[24] Panizza, M. 2001. Geomorphosites: concepts, methods and example \nof geomorphological survey. Chinese Science Bulletin, 46 (1), 4\u20136. \n\n\n\n[25] Panizza, M., Piacente, S. 1993. Geomorphological asset evaluation. Z \nGeomorph NF 87:13\u201318. \n\n\n\n[26] Panizza, M. 1996. Environmental Geomorphology, Amsterdam: \nElsevier, 268. \n\n\n\n[27] Plyusnina, E.E., Sallam, E.S., Ruban, D.A. 2016. Geological heritage of \nthe Bahariya and Farafra oases, the central Western Desert of Egypt. \nJournal of African Earth Sciences, doi: 10.1016/j.jafrearsci.2016.01.002.\n\n\n\n[28] Ruban, D.A., Kuo, I. 2010. Essentials of geological heritage site \n(geosite) management: a conceptual assessment of interests and conflicts. \nNatura Nascosta, 41, 16-31. \n\n\n\n[29] Said, R. 1962. The Geology of Egypt, Amsterdam. Science, 140 (3562), \n41. \n\n\n\n[30] Sehim, A. 1993. Cretacues tectonics in Egypt. Journal of Geology, 37 \n(1), 335-372. \n\n\n\n[31] Wimbledon, W.A.P. 1996. GEOSITES, a new IUGS initiative to compile \na global comparative site inventory, an aid to international and national \nconservation activity. Episodes, 19, 87\u201388. \n\n\n\n[32] Wimbledon, W.A.P., Benton, M.J., Bevins, R.E., Black, G.P., Bridgland, \nD.R., Cleal, C.J., Cooper, R.G., May, V.J. 1995. The development of a \nmethodology for the selection of British sites for conservation. Part 1. \nModern Geology, 20, 159\u2013202. \n\n\n\nFigures Captions: \n\n\n\nFigure 1a: Geologic map of Abu-Roash area (after Abu Khadra et al, \n2005). \n\n\n\nFigure 1b: Location map for Au-Roash area. \n\n\n\nFigure 2: Accessible road in the entrance to the of Abu-Roash area, open \nfold with different facies appear also recognize. \n\n\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 24-28 \n\n\n\nCite The Article:Mohamed A. Abdel-Maksou, Kholoud M. Abdel-Maksoud (2017). Appraisement Of The Geologic Features As A Geo-Heritage In Abu-Roash Area, Cairo- Egypt. \nMalaysian Journal Geosciences, 1(2) : 24-28\n\n\n\n27 \n\n\n\nFigure 3: Plunged fold consisting of different beds from ferruginous \nsandstone and chalky limestone. \n\n\n\nFigure 4a and b: Over view for the area, showings the anticline and \nsyncline folds with alternative beds of sandstone and limestone. S, \n\n\n\nsinkholes also appear in the folded strata. \n\n\n\nFigure 5: Symmetrical and unsymmetrical folds Varieties of folds are \nobvious (symmetrical and unsymmetrical folds) appear in this area. \n\n\n\nFigure 6: Tilted chalk limestone beds (Eocene) as they occur in Abu \nRoash., The human impact is clear in this photo with garbage dumped \n\n\n\nwithin the Abu Roash area. Where they used this area to throw their \ngarbage. \n\n\n\nFigure 7: An angular unconformity is clearly shown clear unconformity \nbetween tilted and horizontal beds and horizontal one. \n\n\n\nFigure 8a: El- Hassana Dome, (domal structure) near the end of the Abu \nRoash Aburoash area and the end of the deformation, this dome is \n\n\n\ncurrently within recorded as a protected area. \n\n\n\nFigure 8b: The core of El Hassana Dome. \n\n\n\nFigure 9a and b: Limestone quarries in the area. \n\n\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 24-28 \n\n\n\nCite The Article:Mohamed A. Abdel-Maksou, Kholoud M. Abdel-Maksoud (2017). Appraisement Of The Geologic Features As A Geo-Heritage In Abu-Roash Area, Cairo- Egypt. \nMalaysian Journal Geosciences, 1(2) : 24-28\n\n\n\n28 \n\n\n\nFigure 10: Different fossils record different facies and geologic ages in the \nAbu Roash area: \n\n\n\na- nummilites gizahensis (, Eocene), b & c rudist bivalves, d- \nRudist rudist bearing limestone (after Abdel-Gawad et al, \n2011). A,b, and c are samples of fossils were collected during a\n\n\n\nfield trip with students. \n\n\n\nFigure 11 & 12: The snow-white chalky limestones in Abou-Roash area. \n\n\n\nFigure 13: The lost pyramid, the Djedefre pyramid. The remaining blocks \nof the Pyramid. \n\n\n\nFigure 14: Inside the pyramid. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 94-100 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2023.94.100 \n\n\n\n \nCite The Article: Matthew Coffie Wilson, Dinah Wankyimah Quaye, Kweku Ofori Agyemang, Daniel Apau  (2023). Using Alteration Patterns to Characterize Gold \n\n\n\nMineralization at Magnetic Hinge in Chirano Gold Mines Limited: Application of Analytical Spectral Device. Malaysian Journal of Geosciences, 7(2): 94-100. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2023.94.100 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUSING ALTERATION PATTERNS TO CHARACTERIZE GOLD MINERALIZATION AT \nMAGNETIC HINGE IN CHIRANO GOLD MINES LIMITED: APPLICATION OF \nANALYTICAL SPECTRAL DEVICE \n\n\n\nMatthew Coffie Wilsona,*, Dinah Wankyimah Quayea, Kweku Ofori Agyemangb, Daniel Apauc \n\n\n\na Kwame Nkrumah University of Science and Technology (KNUST), Department of Geological Engineering, Kumasi-Ghana \nb AngloGold Ashanti, Obuasi-Ghana \nc Chirano Gold Mines Limited, Chirano-Ghana \n*Corresponding Author: Email: regimatt2003@yahoo.co.uk; mcwilson.coe@knust.edu.gh \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 25 June 2023 \nRevised 15 July 2023 \nAccepted 22 August 2023 \nAvailable online 25 August 2023 \n\n\n\n\n\n\n\nThe Magnetic Hinge deposits from Chirano Gold Mine lie within the Paleoproterozoic Birimian rocks of \nsouthwestern Ghana on the West Africa Craton. The Analytical Spectral Device (ASD) Terraspec Spectrometer \nis used to measure the composition and abundance of a variety of alteration minerals. The results obtained \nthrough the ASD on rock cores are plotted as strip logs plots, box plots and the creation of geological models \nusing Leap Frog Geo. The different alterations identified using the ASD were sericite, chlorite, carbonate, \namphibole, chlorite-amphibole, kaolinite and epidote. Cutoff grades lesser than 0.2 ppm were considered \ninsignificant mineralization and hence were of no interest. The Magnetic Hinge boasts of carbonate, sericite, \nand sericite-chlorite alterations hosting good gold grades in rocks and this was observed from the results \nobtained. Moreover, Magnetic Hinge is a new project and reverse circulation was done to obtain information \non the subsurface alterations, open pit mining may be done and as progress is made, diamond drilling can be \ndone to obtain subsurface information for much deeper depth. \n\n\n\nKEYWORDS \n\n\n\nAnalytical Spectral Device, mineral alteration, core orientation, structural control, cutoff grade. \n\n\n\n\n\n\n\n1.   INTRODUCTION \n\n\n\nWhen a rock is altered, the original minerals are modified or replaced by a \nnew suite of minerals with a different chemical composition. The new \ngroup of minerals called a mineral assemblage, reflects the rock \ncomposition and properties or amount of the fluid that altered the rock \n(Kylie, 2021). Because the variations in alteration mineralogy are \ngenerally commensurate with mass transfer, litho-geochemical data can \nbe used to assess the magnitude and spatial distribution of fluid-rock \ninteraction (Eilu and Mikucki, 1998). Litho-geochemical data is the data \nacquired in observing the distribution pattern of elements related to \nmineralization. Therefore, litho-geochemical data helps to understand the \nchemical compositional changes in rock and how this change in \ncomposition relates to mineralization. \n\n\n\nIn this study, the litho-geochemical signature of hydrothermal alteration \nat the Chirano\u2013Magnetic Hinge gold deposit which is the location under \nstudy for this article is to be characterized. The distribution of alteration \npatterns using Analytical Spectral Device (ASD) datasets would be \nmapped. The Analytical Spectral Device is an instrument that accurately \nmeasures reflectance, transmittance, radiance, or irradiance in the full \nspectrum range of 350-2500 nanometres. The main aim is to study the \ndifferent alterations in rocks and how these alterations relate to gold \nmineralization. The specific objectives are: to identify the different \nalterations in the rocks and the depths at which they occur; to establish a \nrelationship with the different alteration types and gold mineralization; \nand, to identify how the alteration relationships with gold relate to the \nmain Chirano structures. Therefore, this paper seeks to throw more light \non the usage of ASD as another analytical method. \n\n\n\n1.1 Problem Statement \n\n\n\nGold is one of the most valuable mineral resources with increasing \ndemand, however, for gold to be discovered, the geological assurance must \nbe high. In relation to the points stated in the background above, there is a \nlink between hydrothermal alteration and gold mineralization. Using the \nAnalytical Spectral Device (ASD), the obtained litho-geochemical data \nshows the alteration patterns in rocks due to an interaction between \nhydrothermal fluids and the surrounding rocks. The ASD helps us to \ndelineate alterations related to gold mineralization. To elaborate, current \nexploration activities are done to discover new deposits in Chirano Gold \nMines and the new area under study is the Chirano Magnetic Hinge, which \nis also our area of interest for this project. The problem we aim to solve is \nthe identification of gold deposits within the Chirano Magnetic Hinge with \nthe intention of increasing the life of the mine and increasing gold \nproduction as a whole. Also, there are setbacks in the usage of \nconventional methods such as visual core logging. Results from ASD can \nbe compared with results from other analytical methods such as the X-ray \nFluorescence device to increase geological assurance. \n\n\n\n2.   GEOLOGICAL SETTING \n\n\n\nGhana is located along the Gulf of Guinea and the Atlantic Ocean, in the \nsub-region of West Africa. The geology of Ghana is primarily very ancient \ncrystalline basement rock, volcanic belts and sedimentary basins, affected \nby periods of igneous activity and two major orogeny mountain-building \nevents. Ghana is lithologically divided into; (a) The western units found at \nthe eastern margin of the West African Craton; (b) The Precambrian \nmobile belt units located in the south-eastern parts of Ghana; (c) The \n\n\n\n\nmailto:regimatt2003@yahoo.co.uk\n\n\nmailto:mcwilson.coe@knust.edu.gh\n\n\nhttps://en.wikipedia.org/wiki/Orogeny\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 94-100 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Dinah Wankyimah Quaye, Kweku Ofori Agyemang, Daniel Apau  (2023). Using Alteration Patterns to Characterize Gold \n\n\n\nMineralization at Magnetic Hinge in Chirano Gold Mines Limited: Application of Analytical Spectral Device. Malaysian Journal of Geosciences, 7(2): 94-100. \n \n\n\n\nVoltaian found in the central parts of Ghana; (d) The coastal sedimentary \nbasins; and (e) The Tertiary to Recent deposits (Gyamera, 2018).   \n\n\n\nThe Chirano gold deposits (Figure 1) are hosted near the tonalite-intruded \nfaulted boundary of Birimian mafic igneous rocks with Tarkwaian \nsedimentary rocks. Tonalite hosted in hydrothermally altered mafic \nigneous rocks has been overestimated volumetrically in recent exposures \n\n\n\nin open pits. Intrusions of the Sehwi-Bibiani volcanic belt dominate the \nwestern domain, whilst the Central domain is composed of the Tarkwaian \nGroup rocks and the Kumasi basin comprises the eastern domain (Allibone \net al., 2004). Between the western and central domains is the Chirano \nShear Zone and the Bibiani Shear Zone which lies north of the Chirano gold \ndistrict.   \n\n\n\n\n\n\n\nFigure 1: Geological Map of Ghana showing the study area (Baratoux et al., 2011)) \n\n\n\n3.   METHODOLOGY \n\n\n\nThe drilling of rock cores plays a vital role in obtaining litho-geochemical \ndatasets for the various alteration patterns. The Analytical Spectral Device \n(ASD) Terraspec Spectrometer can be used to measure the composition \nand abundance of a variety of alteration minerals. By matching data from \nsamples in the Magnetic Hinge against a known spectral library which is \ncalled \u201cThe Spectral Geologist,\u201d alteration minerals which may be difficult \nto detect with the eye can easily be identified. Moreover, in order to map \nout the spatial distribution, the drilled rock cores must be properly \norientated. \n\n\n\n3.1   Core Orientation \n\n\n\nOrienting the core shows the insert direction of the DD core and the depth \nat which the geological structures can be measured. The core orientation \nbasically is for measuring structures and mapping out the spatial \nalteration of minerals. \n\n\n\n3.2   The Analytical Spectral Device \n\n\n\nA variety of alteration minerals can have their composition and abundance \nmeasured using the Terraspec (Figure 2A). Between 350 and 2500 \nnanometers, it functions in the short-wavelength infrared portion of the \nspectrum. In this range, a number of chemical bonds in the minerals \nabsorb energy matching to certain light wavelengths, resulting in \nreflectance profiles with pronounced dips at those specific wavelengths. \nThe absorption characteristics in the short-wave infrared range are \ncaused by water, hydroxyl bonds, carbonates, and sulfates (Spectral \nInternational Inc., 2005). \n\n\n\nThe field of remote sensing made the discovery of reflectance \nspectroscopy. Reflectance spectroscopy is the term for a method of \nmineral analysis that makes use of electromagnetic energy in the visible \n\n\n\n(0.4-0.7 m), near-infrared (0.7-1.3 m), and short wave infrared (1.3-2.5 m) \nwavelength areas. Minerals' spectral reflectance features are a result of \ntheir many physical and chemical attributes. Absorption features at \nparticular wavelengths are a manifestation of changes in composition and \nenergy levels (Figure 2B). In the Visible to Short Wave Infrared spectrum, \na number of different electronic processes are in action (Spectral \nInternational Inc., 2005). \n\n\n\n3.2.1  Procedure for using the Analytical Spectral Device in Data \nCollection \n\n\n\n\u2022 Ensure the device is well connected to a power source and turn on \nthe device. \n\n\n\n\u2022 Clean the optic fibre probe and perform optimization and white \nreferencing.  \n\n\n\n\u2022 Create a folder on the control computer of the ASD where spectrum \ndata would be saved. Name the folder at your discretion and select \nthe interval for the data collection. \n\n\n\n\u2022 Place the fibre optic probe (Figure 2C) on the core and take the data. \nThe probe should not be placed on veins or any form of ink on the \ncore. The probe should also be placed so that the emitted light \ndirectly covers the core. \n\n\n\n\u2022 Wait for the absorption and reflectance readings to be shown on the \ncomputer as seen in Figure 2E \n\n\n\n\u2022 Double-click on the enter key to save the data. \n\n\n\n\u2022 After every 45 to 60 minutes of using the ASD, the device must be \noptimized and white referenced to ensure high data quality is \nobtained. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 94-100 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Dinah Wankyimah Quaye, Kweku Ofori Agyemang, Daniel Apau  (2023). Using Alteration Patterns to Characterize Gold \n\n\n\nMineralization at Magnetic Hinge in Chirano Gold Mines Limited: Application of Analytical Spectral Device. Malaysian Journal of Geosciences, 7(2): 94-100. \n \n\n\n\n(A) \n\n\n\n\n\n\n\n(B) \n\n\n\n\n\n\n\n(C)  \n\n\n\n(D) \n\n\n\n\n\n\n\n(E)\n\n\n\n\n\n\n\n(F) \n\n\n\n\n\n\n\n \nFigure 2: Some of the useful equipment and materials: (A) The Analytical Spectral Device Terraspec Spectrometer and Computer; (B) Reflectance and \nAbsorption Profiles of Compounds (Halley, 2011); (C) Optic Fibre Probe on rock cores; (D) Dark cap placed on Optic fibre probe; (E) Absorption and \n\n\n\nreflectance profile indicating alteration minerals; (F) Optic fibre probe on rock core \n\n\n\n4.   RESULTS AND DISCUSSION \n\n\n\nThis chapter deals with the results obtained by plotting data as strip logs \nplots, box plots and creating geological models using Leap Frog Geo. \n\n\n\n4.1   Overview of the Lithologies at the Chirano Magnetic Hinge  \n\n\n\nThe Chirano Magnetic Hinge consists of dolerite, diorite, tonalite, felsic \ndyke, quartz vein and others, obtained from surface mapping and through \nremote sensing.  \n\n\n\n4.2   General Alterations at Magnetic Hinge \n\n\n\nThe different alterations identified using the analytical spectral device \nwere sericite, chlorite, carbonate, amphibole, chlorite amphibole, kaolinite \nand epidote. The Spectral Geologist is able to group the alterations and \nalso specify a particular mineral dominant or responsible for the \nalteration. Figure 3 represents a section of Mag\u2013Hinge (Section 28125mN) \nwhich shows the various alterations recorded with the respective gold \ngrades. The cutoff grade used for analysis is 0.2 ppm. Cutoff grade lesser \nthan 0.2 ppm is considered an insignificant mineralization and hence it is \nof no interest. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 94-100 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Dinah Wankyimah Quaye, Kweku Ofori Agyemang, Daniel Apau  (2023). Using Alteration Patterns to Characterize Gold \n\n\n\nMineralization at Magnetic Hinge in Chirano Gold Mines Limited: Application of Analytical Spectral Device. Malaysian Journal of Geosciences, 7(2): 94-100. \n \n\n\n\n\n\n\n\nFigure 3: Alterations in the various rock types in Magnetic Hinge against their gold grades and modelled 3D domains (Exploration, Chirano gold mines, \n2022) \n\n\n\n4.3   Alterations Related to Mineralization  \n\n\n\nOut of all the alterations present in Figure 4 the major alteration \nresponsible for the gold mineralization is the sericite alteration. The \n\n\n\nsericite has wide zones correlated with gold grades above the cutoff grade. \nThe cutoff grade used is 0.2 ppm. The main minerals related to the sericite \nare muscovite and phengite. \n\n\n\n\n\n\n\nFigure 4: Section indicating alterations related to gold mineralization in Mag-Hinge \n\n\n\n\n\n\n\nAlteration legend \n\n\n\nMag Hinge \u2013  X Section 28125mN \n\n\n\n\n\n\n\nMain lode \n\n\n\nmineralized domain. \n\n\n\n\n\n\n\n\n\n\n\nW E \n\n\n\nWestern splay \n\n\n\nmineralized domain \n\n\n\n\n\n\n\nSericite Alteration \n\n\n\nMain lode \n\n\n\nmineralized domain. \n\n\n\n\n\n\n\n \nWestern splay \n\n\n\nmineralized domain \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 94-100 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Dinah Wankyimah Quaye, Kweku Ofori Agyemang, Daniel Apau  (2023). Using Alteration Patterns to Characterize Gold \n\n\n\nMineralization at Magnetic Hinge in Chirano Gold Mines Limited: Application of Analytical Spectral Device. Malaysian Journal of Geosciences, 7(2): 94-100. \n \n\n\n\nFrom Figure 4, it is seen that there are two main zones characterizing gold \nmineralization. The main lode mineralized domain is the main zone and \nthe western splay mineralized domain is the secondary zone. The main \nzone shows the original pathway  of the  hydrothermal  fluids  resulting in \n\n\n\n the concentration of gold in these areas while the secondary zone results \nfrom other fractures in the west causing a diversion from the normal flow \npath. \n\n\n\n4.4   Statistical Analysis \n\n\n\n\n\n\n\nFigure 5: Strip Log showing various rocks and its relative alterations at distinct depths \n\n\n\nIn analyzing the alterations and gold concentration in these rocks, \nstatistical tools such as the box whisky plot and strip log were employed. \nThe strip log plot shows the various depths of the rocks below the earth's \nsurface and their various alterations. Moreover, the strip log is a drilling \ncorrelation  as it  correlates  lithology to  alteration  to assay. Furthermore, \n\n\n\n we are looking at the correlation downhole. Alongside these, there is a box \nplot showing the gold concentrations at various depths. A peak correlate \nwith gold values above the cutoff grade required by Chirano Gold Mines \nLimited which is 0.2 ppm. Straight lines indicate a gold grade of 0.001 ppm.  \n\n\n\n4.5   Geological Modelling  \n\n\n\n\n\n\n\nFigure 6: Alteration Mineralogy section indicating a 2.0g/t grade shell in red (Section looking North) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 94-100 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Dinah Wankyimah Quaye, Kweku Ofori Agyemang, Daniel Apau  (2023). Using Alteration Patterns to Characterize Gold \n\n\n\nMineralization at Magnetic Hinge in Chirano Gold Mines Limited: Application of Analytical Spectral Device. Malaysian Journal of Geosciences, 7(2): 94-100. \n \n\n\n\nAfter analyzing every drill hole in our area of interest and capturing data \nfor all recovered rock cores and chips, major alteration in close relation \nwith gold mineralization has been identified at various depths through the \naid of statistical analysis. A zone of gold mineralization has been modelled \nusing the Leap Frog Geo.  \n\n\n\nThe Magnetic Hinge boasts of carbonate, sericite, and sericite-chlorite \nalterations hosting good gold grades in rocks. There were several drill \nholes but for this paper/article we focused on two drill holes namely \n\u2018CHRC3059\u2019 and \u2018CHRC3060.\u2019 With reference to the strip log plot seen in \nFigure 5, the subsurface lithologies are shown together with the gold \ngrades (Au ppm) and their respective alterations. Codes have been used to \nreference these lithologies. Below are the codes and their respective \nlithological names; \n\n\n\nnsr \u2013 No samples recorded  \n\n\n\nosp \u2013 Saprolite  \n\n\n\ngto -  Tonalite \n\n\n\ngdl - Dolerite \n\n\n\ngdr \u2013 Diorite  \n\n\n\ngqdl \u2013 Quartz Dolerite \n\n\n\nrqz \u2013 Quartz Replaced \n\n\n\nrvq \u2013 Quartz Vein \n\n\n\nAlso, the Au grade ranges from 0.01-19.5ppm and is indicated with a red \n\n\n\nline showing peaks and valleys. These peaks and valleys show the gold \n\n\n\ngrades for the respective alterations at depths. The alterations that can be \n\n\n\nseen on the strip log plot include epidote, chlorite, sericite, etc. It is \n\n\n\nobserved that for some of the lithologies such as gdl, the Au line is straight \n\n\n\nshowing low i.e. 0.001ppm gold concentration.  \n\n\n\nLooking at the strip log plot of drill hole CHRC3060 in Figure 5, the first \n\n\n\ngto (tonalite closer to the surface) shows 0.001ppm gold grade with \n\n\n\nsericitic-chloritic alteration. But in the same hole, at the bottom, we see \n\n\n\nthat the tonalite present has similar alteration to the tonalite near the \n\n\n\nsurface yet there is a peak in the gold grade. This brings to our attention \n\n\n\nanother contributing factor to good gold grades in Chirano Gold Mines \n\n\n\nwhich is disseminated pyrite. Therefore, there may be all factors for gold \n\n\n\noccurrence but without disseminated pyrite, a good gold grade wouldn\u2019t \n\n\n\nbe obtained. Again, the tonalite near the surface of the drill hole possesses \n\n\n\npyrite which is cubic in shape indicating a fresh form of pyrite. This \n\n\n\nobservation shows that in areas where pyrite was seen to be disseminated, \n\n\n\na deformational event occurred and in Chirano, the gold deposits are \n\n\n\nstructurally controlled. There must be a lot of structures and \n\n\n\ninterconnected fractures to allow for the movement and entrapment of \n\n\n\nhydrothermal fluids under favorable conditions i.e. the required \n\n\n\ntemperature and pressure for formation of deposits.  \n\n\n\nThis is proven by looking at the peaks of gold grades (Figure 5) in areas of \n\n\n\nthe drill log where quartz dolerite and quartz veins occur. These intrusions \n\n\n\ncause the fracturing of existing rocks present, to allow for hydrothermal \n\n\n\nfluid entrapment and the respective alteration of minerals to give \n\n\n\ncarbonate, sericite and sericite-chlorite alteration. For Chirano Magnetic \n\n\n\nHinge to be specific, the type of lithology that gives gold grade is the quartz \n\n\n\ndolerite which is more dominant and tonalite following next in line. \n\n\n\nMoreover, in the gold zone, the main alteration is the carbonate, sericite \n\n\n\nand sericite-chlorite alteration. This conclusion was drawn with results \n\n\n\nfrom the Analytical Spectral Device (ASD) and not from the intuition of the \n\n\n\ngeologist. However, there are limitations with the usage of the strip log \n\n\n\nplot as it does not give any clue of the concentration and the variation or \n\n\n\nspread of alteration minerals. \n\n\n\nThe subsequent statistical tool employed is the box and whisker plot \n\n\n\nwhich defeats the limitations of the strip log plot and also throws more \n\n\n\nlight on the interpretation of the data obtained from the ASD. The gold \n\n\n\ngrades and their respective alterations are used in plotting the box and \n\n\n\nwhisker plot. In Figure 5C, the red dots represent the gold grades when \n\n\n\ntraced to the Au ppm Axis. The box and whisker plot indicates the \n\n\n\nconcentration and variability or spread of altered minerals. The box and \n\n\n\nwhisker plot is divided into quartiles (25%) with 4 quartiles equaling \n\n\n\n100%. \n\n\n\nIn the case where the length of a whisker and box is short, it indicates that \n\n\n\ndata is concentrated in an area and has less variability. Therefore, for a \n\n\n\nrock core length of 100m, ammonium-illite which ends in the first quartile \n\n\n\nas seen in Figure 5 above would be concentrated in only 25% of the rock \n\n\n\ncore. Also, in the case where a box and whisker are long, indicates that the \n\n\n\ndata is less concentrated in an area but has good variability. This can be \n\n\n\nseen in carbonate and sericite alterations where they cover more than two \n\n\n\nquartiles (>50%). \n\n\n\nObserving the pattern of the box of sericite and carbonate, sericite has a \n\n\n\nrelatively short box indicating that sericite has a relatively good \n\n\n\nconcentration compared to carbonate which has a long box indicating a \n\n\n\nrelatively lower concentration. Moreover, carbonate has a high spread but \n\n\n\nless concentration whereas sericite has more concentration but less \n\n\n\nspread. Furthermore, it is observed that the altered minerals with high \n\n\n\nvariability are associated with high gold grades. Therefore, in the Magnetic \n\n\n\nHinge, with respect to the results from the strip log plots and box and \n\n\n\nwhisker plots, any rock core that has carbonate and sericite with high \n\n\n\nvariability has good gold grades.  \n\n\n\nWith the statistical methods used above, zones of gold mineralization have \n\n\n\nbeen delineated using a geological model known as the Leap Frog Geo. This \n\n\n\ncan be seen in Figure 6. The gold mineralization hovers around areas on \n\n\n\nthe rock cores where carbonate and sericite alterations occurred. This is \n\n\n\nbecause, as discussed above, these alterations host good gold grades. \n\n\n\nMoreover, carbonate alterations are relatively more dominant than \n\n\n\nsericite alterations.  \n\n\n\n5.  CONCLUSION \n\n\n\nThe gold mineralization in Chirano gold deposits is largely located in \n\n\n\naltered mafic rocks such as the quartz dolerite and porphyry intrusive \n\n\n\nrocks. This study sought to create a link between alterations and gold \n\n\n\nmineralization at the Chirano Magnetic \u2013 Hinge. There are about 11 \n\n\n\n(eleven) alterations affecting the rocks. From the results obtained, the \n\n\n\nfollowing conclusions were made; \n\n\n\n\u2022 The various alteration types identified at the site were sericite, \n\n\n\nchlorite, carbonate, amphibole, chlorite amphibole, kaolinite and \n\n\n\nepidote. However, not all were related to gold mineralization. The \n\n\n\nalteration depths range from 20m to about 150m. \n\n\n\n\u2022 The sericite minerals are related to high gold mineralization while the \n\n\n\nother identified alterations are not. \n\n\n\n\u2022 The mineralization can be found in the main Chirano shear zone and \n\n\n\nthe western splay zone. \n\n\n\nThe Analytical Spectral Device which was used in this work is relatively a \n\n\n\nvery fast method and accurate. Alteration patterns at various depths of the \n\n\n\ncore can be identified simply with this device without any visual core \n\n\n\nlogging. Results obtained with the aid of The Spectral Geologist is able to \n\n\n\ndecode and name the alterations found. In general, rock cores play a vital \n\n\n\nrole in determining the alteration and hence it is necessary the orientation \n\n\n\nof the rock cores are done properly to depict the exact depth in the ground. \n\n\n\nSince there has been some level of deformation, geological structures play \n\n\n\ninfluences alteration and the degree of wall rock interaction. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe Authors acknowledge with much appreciation, the crucial role of \n\n\n\nsome of the staff of Chirano Gold Mines Limited for helping us finalize this \n\n\n\nproject within the limited time frame. \n\n\n\nREFERENCES \n\n\n\nAllibone, A., Hayden, P., Cameron, G., and Duku, F., 2004. Paleoproterozoic \nGold Deposits Hosted by Albite- and Carbonate-Altered Tonalite in \nthe Chirano District, Ghana, West Africa. Economic Geology, 99, Pp. \n479-497. \n\n\n\nBaratoux, L., Metelka, V., Naba, S., Jessell, M.W., Gr\u00e9goire, M., Ganne, J., \n2011. Juvenile Paleoproterozoic crust evolution during the \nEburnean orogeny (\u223c2.2\u20132.0Ga), Western Burkina Faso. \nPrecambrian Research, 191, Pp. 18\u201345. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 94-100 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Dinah Wankyimah Quaye, Kweku Ofori Agyemang, Daniel Apau  (2023). Using Alteration Patterns to Characterize Gold \n\n\n\nMineralization at Magnetic Hinge in Chirano Gold Mines Limited: Application of Analytical Spectral Device. Malaysian Journal of Geosciences, 7(2): 94-100. \n \n\n\n\nEilu, P., and Mikucki, E.J., 1998. Alteration and primary geochemical \ndispersion associated with the Bulletin lode-gold deposit, Wiluna, \nWestern Australia. J. Geochem. Explor., 63, Pp. 73\u2013103 \n\n\n\nExploration Department, 2022. Chirano Gold Mines Limited. \n\n\n\nGyamera, E., 2018. Geophysical investigation report on Komenda sugar \nfactory. Global Journal of Environmental Science and Technology: \nISSN-2360-7955, 5 (6), Pp. 460-477. \n\n\n\nHalley, S., 2011. Geochemistry of the Chirano Gold System: Unpublished \n\n\n\nreport prepared for Chirano Gold Mine Ltd. by Mineral Mapping \n\n\n\nconsulting. \n\n\n\nKylie Williams, 2021.  \u201cWhat the -ic? An introduction to Alteration\u201d       \n\n\n\nhttps://www.geologyforinvestors.com/ic-introduction-alteration/ \n\n\n\nAccessed on 2nd April 2022 \n\n\n\nSpectral International Inc. 2005. Applied Reflectance Spectroscopy: \n\n\n\nManual provided as an introduction to the process of collecting and \n\n\n\ninterpreting data with ASD, version 4.1 \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geoscien  ces 2(1) (2018) 42-44 \n\n\n\nCite the Article: Nicklos Jefrin, Nurmin Bolong, Justin Sentian, Ismail Abustan, Thamer Ahmad Mohammad, Janice Lynn Ayog (201 8). Comparison of GEV and Gumble's \nDistribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah . Malaysian Journal of Geosciences, 2(1) : 42-44. \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nRainfall data has a significant role in hydrological design which is, it\u2019s produce the intensity duration frequency \ncurve. IDF curve gives critical information that needed in the design of water management infrastructure, it gives \ninformation by showing the mathematical relation of rainfall intensity, recurrence interval of the storm and \nduration of storm. This paper aims to compares and develop IDF curve using two frequency distribution which is \ngeneralized extreme value distribution (GEV) and Gumbel distribution (EV1). Then, the best fit distribution for \nflood-prone area in Sabah will be choose and determined from the two-mentioned distribution. The goodness of fit \ntest that used to determine the best distribution is chi-square test, it works by determining the differences between \nobserve data value from Weibull formula and the estimated values from GEV and Gumbel\u2019s distribution method. \nAfter that the chi-square value for GEV and Gumbel is compared to the critical value from chi-square table at \nsignificant level of 5%. From the Chi-square test, it is concluded that Gumbel\u2019s (chi square value Tandek:0.47952, \npatiu:1.0531, babagon: 1.026931, Ulu Moyog:0.382415) shows a better fit distribution compared to GEV \ndistribution (chi square value Tandek:59.7598, patiu:16.5746, babagon: 3.3555347, Ulu Moyog:22.1315) \n\n\n\nKEYWORDS \n\n\n\nGumbel\u2019s distribution, GEV, flood, rainfall intensity, return period, Sabah.\n\n\n\n1. INTRODUCTION \n\n\n\nRainfall data played an important role in hydrological designed because it \nwas used to develop IDF curve and designed water management \ninfrastructures, bridge, spillways, flood protection structures, and many \nother civil engineering structures involving hydrologic flows [1]. Extreme \nrainfall was a complicated phenomenon and its marginal distributions \nwere not necessarily similar or distributed as normal [2]. According to, \nhistorical rainfall data obtained from various rainfall stations can be used \nto develop Intensity-Duration- Frequency (IDF) curve and it was the most \nfrequently used tool to estimate rainfall [3]. Extreme rainfall was a \ncomplicated phenomenon and its marginal distributions were not \nnecessarily similar or distributed as normal [2]. In order to get accurate \nhydrologic analyses, reliable rainfall intensity estimations were necessary. \nThe IDF relationship included the estimations of rainfall intensities of \ndifferent durations and returned periods [4]. After the IDF curve was \nderived, the most suitable probability distribution to analyse the data \nmust be determined. There are frequently used theoretical distribution \nfunctions that were applied in different regions all over the world for \nexample Generalized Extreme Value Distribution (GEV), Gumbel, Log \nnormal, Log Pearson Type III (LP3) distribution [5, 6]. According to, GEV \nused 3 parameters distributions whereas Gumbel used 2 parameters [7]. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nSecondary rainfall data for selected rainfall station is supplied from \ndepartment of Irrigation and Drainage Sabah (DID). All rainfall station \nselected is located around Penampang district and Kota Marudu district \nwhich is a flood prone area that hit by flood every year. Kota Marudu has \nsuffered frequent occurrences of flooding which occurred at least a year \nwith the latest case is in 18th January 2017 that effecting 2874 peoples. For \npenampang district ulu moyog and babagon station is selected, and for \nKota marudu district tandek PH and Patiu station is selected. Gumble\u2019s \ndistribution and GEV distribution is used to analyse the rainfall data and \n\n\n\nconstruction of the IDF curve for the selected station. \n\n\n\nExtreme value distribution and is commonly known as Gumbel\u2019s \ndistribution. The following equation used to provide the inflow for every \nperiod of return. \n\n\n\nWhere, Q is value of variate with a return period, T,  is Mean of the \n\n\n\nvariate, S is standard deviation of the sample, K is frequency factor \n\n\n\nChi-square test decides the best fit distribution. The equation for chi-\nsquare test is:  \n\n\n\nWhere, Oi is the observed rainfall and Ei is the expected rainfall and will \nhave chi-square distribution with (N-k-1) degree of freedom, By \ncomparing both distribution Chi-square value, the smallest Chi-square \nvalue will be chosen as the best probability distribution, at the 5% \nsignificance level as it was used by researches [4].  \n\n\n\nAs mentioned before, GEV distribution used three parameters which were \nlocation parameter (\ud835\udf09), scale parameter (\ud835\udefc) and the shape parameter (\ud835\udc58) \nfor the estimation of extreme rainfalls. In order to find the three \nparameters of GEV, Probability Weighted Moments (4 PWM\u2019s (M100, \nM110, M120, M130).) are needed for the calculation of L-Moments. Firstly, \nthe data obtained must be arranged in ascending order and then be \napplied. After that, 4 L-Moments (\ud835\udf061, \ud835\udf062, \ud835\udf063, 4) are determined by using \nthe PWMs. Finally, using the desired return period, all parameters were \napplied to the return period to estimated return value Qt. \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : https://doi.org/10.26480/mjg.01.2018.42.44\n\n\n\nCOMPARISON OF GEV AND GUMBLE\u2019S DISTRIBUTION FOR DEVELOPMENT OF \nINTENSITY DURATION FREQUENCY CURVE FOR FLOOD PRONE AREA IN SABAH \n\n\n\nNicklos Jefrin1*, Nurmin Bolong1, Justin Sentian2, Ismail Abustan3, Thamer Ahmad Mohammad4, Janice Lynn Ayog1 \n1Civil Engineering Program, Engineering Faculty, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia \n2Faculty of Science and Natural Resources, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia \n3School of engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, P.Pinang, Malaysia \n4Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor Darul Ehsan, Malaysia \n* Corresponding author email: nicklosjefrin@gmail.com \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \n\n\n\nCODEN : MJGAAN \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 42-44\n\n\n\nCite the Article: Nicklos Jefrin, Nurmin Bolong, Justin Sentian, Ismail Abustan, Thamer Ahmad Mohammad, Janice Lynn Ayog (201 8). Comparison of GEV and Gumble's \nDistribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah . Malaysian Journal of Geosciences, 2(1) : 42-44.\n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nFigure 1: Tandek rainfall station \n\n\n\nFigure 1 shows the comparison of Gumbel and GEV distribution for 2- \nyears and 100-years return period at Tandek station. Based on the result \nobtained, for the return period of 2-year, GEV distribution show slightly \nhigher rainfall intensity estimation. On the other hand, for the return \nperiod of 100-year, GEV distribution show higher rainfall estimation \ncompared to 100 years Gumbel.  \n\n\n\nFigure 2: Patiu Rainfall station \n\n\n\nFigure 2 represents the comparison of Gumbel and GEV at Patiu Station. \nBased on Figure 2, for the return period of 2-years, it was shown that GEV \ndistribution gave a higher estimation of rainfall intensity compare to the \nGumbel distributions. For the return period of 100-years, Gumbel \ndistribution yielded slightly higher intensity expectation with a smooth \ncurve, compared to GEV distribution that gives a fluctuated curve.  \n\n\n\nFigure 3: Babagon rainfall station \n\n\n\nFigure 3 shows the comparison of Gumbel and GEV distribution for 2- \nyears and 100-years return period at Babagon Station. In accordance to \nFigure3, for the return period of 2-years, it was obviously shown that GEV \ndistribution gave the same estimation of rainfall with Gumbel . However, \nOn the other hand, for the return period of 100-years, Gumbel distribution \nhave slightly higher and smooth curve compared to GEV same as patiu \nRainfall station  \n\n\n\nFigure 4: Ulu Moyog rainfall station \n\n\n\nFigure 4 shows the comparison of Gumbel and GEV distribution for 2-\nyears and 100-years return period at ulu Moyog Station.  Rainfall intensity \nof 2-years and 100-years return period was chosen to be compare because \nthe minimum and maximum of the intensity. In accordance to Figure 4, for \nthe return period of 2-years, it was obviously shown that GEV distribution \nyielded higher estimation of rainfall compared to the Gumbel distribution. \nOn the other hand, for the return period of 100-years, Gumbel gave slightly \nhigher estimation of rainfall compared to GEV distributions. \n\n\n\nTable 1: Comparison of Chi square value for both frequency distributions \n\n\n\nRainfall station Gumbel\u2019s distribution GEV \nTandek 0.469 16.573 \nPatiu 2.46 12.736 \nBabagon 1.865 18.537 \nUlu Moyog 2.847 24.856 \n\n\n\nTable 1 shows the chi-square value for 4 stations by Gumbel\u2019s distribution \nand GEV distribution. Significant level of 5% is used for this research. \nThere are 2 parameters used to compare therefore, the degree of freedom \nis 1 and critical value used from chi-square table is 3.84. Following the \nwork[7], any value greater than 3.84 will be rejected and value lower that \n3.84 will be accepted as the best fit distribution [7]. The distribution that \nhas a lower Chi-square value compare to other distribution will be chosen \nas the best fit distribution [4]. From the comparison of Chi-square value of \n3.84 with the chi-square from both distribution, its clearly show that \nGumbel\u2019s distribution has a lower Chi-square value  and accepted as the \nbest distribution. Overall all 4 Station favor GumbeL distribution for \nanalysis of rainfall intensity and development of IDF curve in Kota Marudu \nand Penampang area.  \n\n\n\n4. CONCLUSION \n\n\n\nFrom the IDF curve, it shows that GEV method may overestimate the \nrainfall intensity compare to Gumbel. From the goodness of fit test, \nGumbel\u2019s distribution shows better fit than GEV, therefore Gumbel\u2019s \ndistribution is more suitable distribution for flood frequency analysis and \ndevelopment of IDF curve in Kota Marudu and Penampang \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThis research has been financially supported by Malaysia ministry of \neducation (MOE), research acculturation collaborative (RACE) grant \nscheme: RACE0020-TK-2015 \n\n\n\n1\n\n\n\n10\n\n\n\n100\n\n\n\n1000\n\n\n\n10000\n\n\n\n1 10 100 1000 10000\n\n\n\nR\na\n\n\n\nin\nfa\n\n\n\nll\n I\n\n\n\nn\nte\n\n\n\nn\nsi\n\n\n\nty\n (\n\n\n\nm\nm\n\n\n\n/h\nr)\n\n\n\nDuration (min)\n\n\n\nComparison of GEV &  Gumbel\n\n\n\n2\nyears--\nGumble\n2\nyears--\nGEV\n100\nyears--\nGumbel\n100\nyears--\nGEV\n\n\n\n1\n\n\n\n10\n\n\n\n100\n\n\n\n1000\n\n\n\n1 10 100 1000 10000\n\n\n\nR\nai\n\n\n\nn\nfa\n\n\n\nll\n I\n\n\n\nn\nte\n\n\n\nn\nsi\n\n\n\nty\n (\n\n\n\nm\nm\n\n\n\n/h\nr)\n\n\n\nDuration (min)\n\n\n\ncomparison of GEV and Gumbel\n\n\n\n2 years-\n-gumbel\n\n\n\n2 years-\n-GEV\n\n\n\n100\nyears--\nGumbel\n100\nyears--\nGEV\n\n\n\n1\n\n\n\n10\n\n\n\n100\n\n\n\n1000\n\n\n\n1 10 100 1000 10000\n\n\n\nR\na\n\n\n\nin\nfa\n\n\n\nll\n I\n\n\n\nn\nte\n\n\n\nn\nsi\n\n\n\nty\n (\n\n\n\nm\nm\n\n\n\n/h\nr)\n\n\n\nDuration (min)\n\n\n\nComparison of GEV & Gumbel\n\n\n\n2 years\nGumbel\n\n\n\n2 years GEV\n\n\n\n100 years\nGumbel\n\n\n\n100 years\nGEV\n\n\n\n1\n\n\n\n10\n\n\n\n100\n\n\n\n1000\n\n\n\n1 10 100 1000 10000\n\n\n\nra\nin\n\n\n\nfa\nll\n\n\n\n I\nn\n\n\n\nte\nn\n\n\n\nsi\nty\n\n\n\n (\nm\n\n\n\nm\n/h\n\n\n\nr)\n\n\n\nDuration (min)\n\n\n\nComparison of GEV & Gumbel\n\n\n\n2 years\nGumbel\n\n\n\n2 years GEV\n\n\n\n100 years\nGumbel\n\n\n\n100 years\nGEV\n\n\n\n43\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 42-44\n\n\n\nCite the Article: Nicklos Jefrin, Nurmin Bolong, Justin Sentian, Ismail Abustan, Thamer Ahmad Mohammad, Janice Lynn Ayog (201 8). Comparison of GEV and Gumble's \nDistribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah . Malaysian Journal of Geosciences, 2(1) : 42-44. \n\n\n\n[1] Hailegeorgis, T.T., Thorolfsson, S.T., Alfredsen, K. 2013. Regional \nfrequency analysis of extreme precipitation with consideration of \nuncertainties to update IDF curves for the city of Trondheim. Journal of   \nHydrology, 498, (8), 305\u2013318. \n\n\n\n[2] Ariff, N.M. et al., 2012. IDF relationships using bivariate copula for \nstorm events in Peninsular Malaysia. Journal of Hydrology, 470\u2013471, 158\u2013\n171.  \n\n\n\n[3] Mirhosseini, G., Srivastava, P., Stefanova, L. 2013. The impact of climate \nchange on rainfall Intensity-Duration-Frequency (IDF) curves in Alabama. \nRegional Environmental Change, 13 (1), 25\u201333.  \n\n\n\n[4] Elsebaie, I.H. 2012. Developing Rainfall Intensity-Duration-Frequency \nRelationship for Two Regions in Saudi Arabia. Journal of King Saud \nUniversity-Engineering Sciences, 24, 131-140 \n\n\n\n[5] AlHassoun, S.A. 2011. Developing an empirical formulae to estimate \nrainfall intensity in Riyadh region. Journal of King Saud University - \nEngineering Sciences, 23(2), 81\u201388. \n\n\n\n[6] Al-anazi, K.K., El-Sebaie, I.H. 2013. Development of Intensity-Duration-\nFrequency Relationships for Abha City in Saudi Arabia. International \nJournal of Computational Engineering Research, 3, (10), 58-65. \n\n\n\n[7] Millington, N., Das, S., Simonovic, S.P. 2011. The Comparison of GEV, \nLog-Pearson Type 3 and Gumbel Distributions in the Upper Thames River \nWatershed under Global Climate Models. Water Resources Research \nReport \n\n\n\n44\n\n\n\nREFERENCES \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 26-31 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2020.26.31 \n\n\n\nCite the Article: Rodeano Roslee (2020). Geological Assisted On Water Resources Planning In Mountainous Catchments In Kundasang, Sabah, Malaysia . \n Malaysian Journal of Geosciences, 4(1): 26-31. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2020.26.31 \n\n\n\nGEOLOGICAL ASSISTED ON WATER RESOURCES PLANNING IN MOUNTAINOUS \n\n\n\nCATCHMENTS IN KUNDASANG, SABAH, MALAYSIA \n\n\n\nRodeano Roslee a,b* \n\n\n\na Universiti Malaysia Sabah, Natural Disaster Research Center (NDRC), Jalan UMS 88400 Kota Kinabalu, Sabah. \nb Universiti Malaysia Sabah, Faculty of Science & Natural Resources, Jalan UMS 88400 Kota Kinabalu, Sabah. \n\n\n\n*Corresponding Author Email: rodeano@ums.edu.my\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 05 January 2020 \nAccepted 10 February 2020 \nAvailable online 28 February 2020\n\n\n\nBased on geological mapping and geohydrologic data, water resources planning in mountainous catchment \nareas in Kundasang are outlined. The area is underlain by thick Paleogene clastic sediment and old \nQuaternary gravels. These rock units are carved by numerous lineaments with complex structural styles \ndeveloped during series of regional Tertiary tectonic activities. The tectonic complexities reduced the \nphysical and mechanical properties of the rock units and produced intensive displacements and \ndiscontinuities among the strata, resulting in high degree of weathering process and instability. The \nweathered materials are unstable and may cause subsidence and sliding induced by high pore pressure \nsubjected by both shallow and deep hydrodynamic processes. Evaluation of 60 boreholes data in the study \narea reveals that the depth of the groundwater table ranges from 1.90 m (6 feet) to 11.20 m (35 feet) deep. \nThe groundwater level in the study area fluctuates even within a short period of any instability of climatic \nchange. The Quaternary sand and gravel layers with variable thickness defined the major shallow aquifers \nwithin the underlying weathered materials while the highly fractured sedimentary rocks defined the major \ndeep aquifers. Most of the aquifers within the top unconsolidated weathered clastic material are under \nunconfined condition. The sedimentary formations are coarse-grained clastic materials generally contain \nfractured porosity and exhibit higher permeability. However, below subsurface, much of the groundwater is \npartially confined. Movements of groundwater are sufficiently restricted area to cause slightly different in \nhead depth zones during periods of heavy pumping. During periods of less draught, the various groundwater \nlevels will be recovered to their respective original level. This condition resulted from discontinuous nature \nof sediments where zones of permeable sand and gravel are layered between less permeable beds of silt and \nclay. Aquifer characterization and geological data are given to assist the local agencies on the water resources \nplanning of the study area.  \n\n\n\nKEYWORDS \n\n\n\nGeology, Water Resources, Mountainous Catchments, Hydrology, Sabah, Malaysia.\n\n\n\n1. INTRODUCTION \n\n\n\nThe population growth in Malaysia has resulted in an increase in water \n\n\n\ndemand, greater shortage of water supply and pollution of water \n\n\n\nresources. Decisions affecting the utilization of groundwater resources \n\n\n\nmust be based on knowledge of the geologic and geohydrologic aspects of \n\n\n\nthe study area and its surrounding areas. The geology as it pertains to \n\n\n\ngroundwater can be perceived by examining the physiographic setting, \n\n\n\ngeologic history, and nature and water-bearing characteristics of the \n\n\n\nunderlying rocks. The tropical climate in Sabah provides abundant rain \n\n\n\n(>2000 mm annually) but the present water is hardly sufficient to cover \n\n\n\nthe need of the population because most of the rain consumed by \n\n\n\nevapotranspiration and generally there is poor management of water \n\n\n\nresources discharge in most of the area. The water shortage problem in \n\n\n\nSabah demands that all possible sources should be studied, analyzed and \n\n\n\ndeveloped for optimum utilization.  \nFigure 1. Location of study area \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 26-31 \n\n\n\nCite the Article: Rodeano Roslee (2020). Geological Assisted On Water Resources Planning In Mountainous Catchments In Kundasang, Sabah, Malaysia . \n Malaysian Journal of Geosciences, 4(1): 26-31. \n\n\n\nGroundwater has been identified, as an alternative source of water supply, \n\n\n\ndue to its economic importance. Managing of groundwater resources \n\n\n\nincluding monitoring its quality has become important as preparatory \n\n\n\nsteps in facing water supply problem as experience during the drought in \n\n\n\n1998. The study area covers an area of about 100 km2. It is bounded by \n\n\n\nlongitude E 116o 30.946\u2019 to E 116o 39.268\u2019 and latitude N 06o 00.598\u2019 to N \n\n\n\n05o 56.635\u2019 (Figure 1). The study area is part of the largest catchment \n\n\n\nareas in Sabah and can contribute a large amount of water to the state of \n\n\n\nSabah. \n\n\n\n2. METHODOLOGY \n\n\n\nSoil and rock samples from the study area were collected during field \n\n\n\nmapping for detailed analysis. The laboratory works such as classification \n\n\n\ntests (grain size, atterberg limit, specific gravity and moisture content) and \n\n\n\npermeability test were carried out in compliance and accordance to British \n\n\n\nStandard Code of Practice BS 5930-1981 (Site Investigation) and British \n\n\n\nStandard Code of Practice BS 1377-1990 (Method of Test for Soils for Civil \n\n\n\nEngineering Purposes). Sixty boreholes log data were obtained from the \n\n\n\nJ.W. Geotechnical Consultant Sdn Bhd, which were reinterpreted and \n\n\n\ncorrelated in order to have clearer idea of the subsurface soil profile and \n\n\n\nlithological units. \n\n\n\n3. PHYSIOGRAPHY AND LANDUSE \n\n\n\nThe major geomorphologic regions distinguished are the summit dome of \n\n\n\nMount Kinabalu above 9,000 feet; the steepness slopes and cliffs of the \n\n\n\nmountain from 2,500 to above 9,000 feet; the areas of partly dissected \n\n\n\nsolifluction material; the foothills of dissected ultrabasic rocks; and the \n\n\n\nfoothills of dissected sedimentary rocks, the accordant summits of which \n\n\n\nprobably formed part of the early Pleistocene erosion surfaces.  \n\n\n\nThe land use area in the study area consists of 3 categories: state land and \n\n\n\nagricultural area (50.0 %), protected and commercial forest reserve (36.0 \n\n\n\n%) and parks (14.0 %). Most of the villagers are farmers. Other activities \n\n\n\ninclude handicraft, selling agricultural products, animal husbandry, \n\n\n\nforestry, administrative services and tourism. The business centers are \n\n\n\nKundasang and Ranau towns, while the tourism is active along the Sabah \n\n\n\nParks at Mt. Kinabalu to Ranau area. \n\n\n\n4. CLIMATOLOGY \n\n\n\nSabah is located within the equatorial belt and enjoys a warm and wet \n\n\n\ntropical climate. Generally, the seasons are divided into the northeast \n\n\n\nmonsoon (November to March) and the southwest monsoon (May to \n\n\n\nAugust). Daily temperature of the lowland area ranges from 25\uf0b0C to 30\uf0b0C \n\n\n\nwith significantly cooler at higher altitude areas. There is no record of \n\n\n\ntyphoons passing through Sabah, but unexpected tropical storms are \n\n\n\nunavoidable. There are five climatic regions in Sabah based on dry and wet \n\n\n\nseasons, which in turn are induced by minimum and maximum rain \n\n\n\nperiods (Figure 2). Regionally, the study area belongs to the third type \n\n\n\n(100\u201d \u2013 120\u201d). The driest seasons occurs during the northern monsoon and \n\n\n\nthe wettest seasons during the southwest monsoon. The maximum \n\n\n\ntemperature is 30\uf0b0C and the minimum is 15\uf0b0C, although the usual range is \n\n\n\nfrom 23\uf0b0C to 32\uf0b0C at the lowland areas.  \n\n\n\nInland, the climate is somewhat colder due to altitude changes. In order to \n\n\n\nunderstand the climatologic setting of the study area, some statistical \n\n\n\nanalysis was applied for this study (Figure 3). Rainfall provides the major \n\n\n\nsource of inflow to the Kinabalu basin while surface runoff, \n\n\n\nevapotranspiration, groundwater extraction, spring discharge and other \n\n\n\nconstitute the out-flow component (Faisal et al., 1997). The average \n\n\n\nannual rainfall in the study area (1971 \u2013 2003) is ranging from 1220.0 mm \n\n\n\nto 3358.8 mm with total wet days ranging from 49 mm to 233 mm (half of \n\n\n\nthe days), most of which falls in second half of the year. Rainfall is heavy \n\n\n\nduring the inter monsoon season, drenching the slopes with more than \n\n\n\n1400 mm of rains in a single month. Apart from the monsoon rains, due to \n\n\n\nits altitude also receives regular mountainous rain, especially in the \n\n\n\nafternoon. Deep weathering profiles seen in this study area is attributed \n\n\n\nto the heavy rainfall. Analysis of the rainfall data (Figure 3) shows that \n\n\n\nthere is a noticeable decrease rainfall in the early 90\u2019s compared to the \n\n\n\nhigh rainfall of the late 80\u2019s. \n\n\n\nFigure 2. Distribution of Sabah means annual rainfall data Climatologic \n\n\n\nServices (After Faisal, et al. 1997) \n\n\n\nFigure 3: Total annual (Source after Sabah Department) \n\n\n\n5. HYDROLOGIC AND GEOHYDROLOGIC SETTING \n\n\n\nThe study area and its surrounding areas are controlled by heavy drainage \n\n\n\nsystem of different patterns (Trellis, Annular and Parallel) (Figure 1). The \n\n\n\nregion has a high drainage density, being the cradle and origin of major \n\n\n\nrivers in the study area. The watershed of the Mount Kinabalu region feeds \n\n\n\nrivers like the Kinasaraban, Liwagu, Mesilau, Kuaman and countless other \n\n\n\ndrainage basins surrounding the mountain, those flow either to the South \n\n\n\nChina Sea or to the Sulu Sea. Structurally, a number of linear river \n\n\n\nsegments belong to different watershed systems indicate the existence of \n\n\n\nmajor tectonic fractures. The structural control of the river tributaries of \n\n\n\nthe area is evidenced by the physical characteristics of sedimentary rocks; \n\n\n\nhighly fractured areas and less competent shale beds. The sedimentary \n\n\n\nrocks are more intensely dissected by fault zones than the ultrabasic rocks \n\n\n\nand formed trellis drainage pattern. Groundwater occurs and moves \n\n\n\nthrough interstices or secondary pore openings in the rock formations. \n\n\n\nSuch openings can be the pore spaces between individual sedimentary \n\n\n\ngrains, open joints and fractures in hard rocks or solution and cavernous \n\n\n\nopening in brecciated layers and cataclasites. The amount of water that \n\n\n\ncan be stored by a rock is measured by its porosity and permeability. \n\n\n\nHowever, the amount of water that can be withdrawn from the rock \n\n\n\ndepends largely on its permeability. Therefore the more permeable a rock \n\n\n\nis, the better aquifer it is. The factors that control the above coefficients \n\n\n\nare the size or degree of openings, interconnection of the openings and \n\n\n\nmolecular attraction between the grains and the water. The direction of \n\n\n\nsubsurface water movement is generally under the influence of gravity. \n\n\n\nWithin the zone of aeration, movement is generally vertical until the water \n\n\n\nreaches the water table, which defines the upper surface of the zone of \n\n\n\nsaturation. Within the zone of saturation, the subsurface water is now \n\n\n\nreferred to as groundwater. Here, the direction of groundwater movement \n\n\n\nis generally toward the point of lines of discharge, also influenced by \n\n\n\ngravity but at a greater horizontal component. In artesian aquifers, where \n\n\n\nthe groundwater is confined between two impermeable layers, movement \n\n\n\ncan go against the pull of gravity depending on the shape of the confined \n\n\n\naquifer. Since the water is under hydrostatic pressure, the elevation to \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 26-31 \n\n\n\nCite the Article: Rodeano Roslee (2020). Geological Assisted On Water Resources Planning In Mountainous Catchments In Kundasang, Sabah, Malaysia . \n Malaysian Journal of Geosciences, 4(1): 26-31. \n\n\n\nwhich the water level rises is usually above the top of the aquifer and in \n\n\n\nsome cases above the ground surface. \n\n\n\n6. GEOLOGY\n\n\n\nThe geology of the study area is made up of three sedimentary rock \n\n\n\nformations: the Trusmadi Formation (Palaeocene to Eocene age), the \n\n\n\nCrocker Formation (Late Eocene age) and the Pinousuk Gravel (Late \n\n\n\nPleistocene to Holocene age) (Figure 4). The Trusmadi Formation consists \n\n\n\nof interbedded dark shale and sandstone. The Trusmadi Formation is \n\n\n\nexposed at the foot of the Mount Kinabalu and Ranau area. Low-grade \n\n\n\nmetamorphism has occurred in some of the rocks of the Trusmadi \n\n\n\nFormation. The rock associations are highly sheared and brecciated with \n\n\n\nsome cataclasites. The dark argillaceous rocks are thickly bedded or \n\n\n\ninterbedded with sandstone and siltstone beds. The thickness of the \n\n\n\nargillaceous beds is about 100 feet, whereas the sandstone beds are about \n\n\n\n120 feet at the foot of Mount Kinabalu area. Some volcanic rocks mainly \n\n\n\nspilite, extruded through the Trusmadi Formation. Quartz veins are quite \n\n\n\ncommon in this Formation. The Trusmadi Formation can be divided into 4 \n\n\n\nmain lithological units: shale, interbedded sandstone and shale \n\n\n\n(turbidities), cataclasites and thick sandstones. The Crocker Formation \n\n\n\nforms the main exposure in the area where outcrops can be found along \n\n\n\nroad-cuts, paths and excavations. Major exposures are moderately to \n\n\n\nhighly weathered materials. The sedimentary rocks of the area are partly \n\n\n\nmade up of the Crocker Formation to the southwest of Mount Kinabalu and \n\n\n\npartly consist of the Trusmadi formation, forming part of the Crocker \n\n\n\nRange and Trusmadi Range respectively, were mapped as the Crocker \n\n\n\nFormation on the basis of their lithological features (Collenette, 1958). The \n\n\n\nCrocker Formation is characterized by monotonous rock facies, repetition \n\n\n\nof interbedded sandstone and shale strata, by isoclinal foldings and faults. \n\n\n\nThe rough structure has been determined from statistical analysis of \n\n\n\nvarious structural elements and by the analysis of aerial photograph \n\n\n\n(Kasama et al., 1970). The Crocker Formation can be divided into four \n\n\n\nmain lithological units; namely thick bedded sandstone, thinly bedded \n\n\n\nsandstone and siltstone, red and dark shale and slumped deposits. The \n\n\n\nPinousuk Gravels are preserved south and west of Mount Kinabalu in three \n\n\n\nmain areas: the Pinousuk Plateau, the Tohubang Valley, and near \n\n\n\nTenompok. It is proposed to regard as the type section exposures those \n\n\n\nalong Mantaki River, Mesilau River, Tawaras River and Bambangan River, \n\n\n\nflow through the Pinousuk Plateau at east - southern. The solifluction \n\n\n\nmaterial is continuously distributed within the alluvium in which terraces \n\n\n\nhave been developed along the Liwagu Valley and in the Tohubang Valley \n\n\n\nnear Ranau. The angular to rounded clasts found in the Pinousuk Gravels \n\n\n\nare mainly granites and sedimentary rocks, embedded in a light brown to \n\n\n\nred brown matrix of sandy, silty and clayey materials. Rare wood \n\n\n\nfragments are also found in the Pinousok Gravels. The Pinousok Gravels \n\n\n\nunconformable overlies the above discussed rock units and reach a \n\n\n\nmaximum thickness of about 450 feet. The beds consist of poorly \n\n\n\ncemented gravel of various compositions and are considered as tilloid \n\n\n\ndeposits. Division is possible into a lower unit consisting of silty to sandy \n\n\n\ngravel of angular to subangular clasts of either sedimentary or ultra-basic \n\n\n\nrocks, and an upper unit that is composed of clayey to sandy gravel of \n\n\n\nangular to rounded clasts of varied composition. The lower and upper \n\n\n\nunits have been interpreted as pre-glacial solifluction deposits and as \n\n\n\nprobable mudflow sediment containing reworked till, respectively (Tjia, \n\n\n\n1974). Laboratory analysis for petrography was conducted on selected 17 \n\n\n\nsedimentary rock samples in order to study the mineralogy and porosity \n\n\n\nof the rock samples by using polarized microscope (Table 1). From this \n\n\n\nlaboratory analysis, a thin \u2013 section was used as a delineation of the \n\n\n\nmineral constituents to show the mineral characteristics. The \n\n\n\npetrographic study of rock samples in the study area revealed only slight \n\n\n\ndifferences between the sedimentary rocks of Trusmadi Formation and \n\n\n\nCrocker Formation.  \n\n\n\nTable 1: Modal analysis for sedimentary rocks from Trusmadi Formation and Crocker Formation \n\n\n\nROCK \n\n\n\nUNIT \n\n\n\nROCK \n\n\n\nTYPE \n\n\n\nLOCA-\n\n\n\nTION \n\n\n\nSAM-\n\n\n\nPLE NO. \n\n\n\nDETRITAL GRAINS (%) \nMATRIX & \n\n\n\nCEMENT \n\n\n\n(%) \n\n\n\nPORO-\n\n\n\nSITY \n\n\n\n(%) \nQUARTZ \n\n\n\nPLAGIO-\n\n\n\nCLASE \n\n\n\nK - \n\n\n\nFELD-\n\n\n\nSPAR \n\n\n\nMICA \n\n\n\nROCK \n\n\n\nFRAG -\n\n\n\nMENT \n\n\n\nCHERT \n\n\n\nTRUS-\n\n\n\nMADI \n\n\n\nFORM-\n\n\n\nATION \n\n\n\nQuartz \n\n\n\nWacke \n\n\n\n11 RSL 11 58.00 1.80 1.80 0.45 0.70 0.90 24.35 12.00 \n\n\n\n12 RSL 15 62.00 1.50 2.00 0.30 0.75 1.05 21.40 11.00 \n\n\n\n13 RSL 13 48.50 2.00 1.60 0.50 0.90 1.20 32.80 12.50 \n\n\n\n15 RSL 15 48.00 2.50 1.75 0.65 0.85 1.05 32.70 12.50 \n\n\n\n18 RSL 18 53.50 2.85 1.85 0.55 0.80 1.35 27.60 11.50 \n\n\n\n19 RSL 19 52.00 2.65 1.95 0.60 0.80 1.20 27.80 13.00 \n\n\n\n23 RSL 23 58.50 2.55 1.85 0.55 0.90 1.15 22.00 12.50 \n\n\n\nAVERAGE 54.36 2.26 1.83 0.51 0.81 1.13 26.95 12.15 \n\n\n\nCROC-\n\n\n\nKER \n\n\n\nFORM-\n\n\n\nATION \n\n\n\nQuartz \n\n\n\nWacke \n\n\n\n1 RSL 1 42.00 4.50 0.80 0.45 3.50 3.50 32.75 12.50 \n\n\n\n2 RSL 2 48.50 3.00 0.95 0.55 2.50 4.00 27.50 13.00 \n\n\n\n3 RSL 3 45.00 5.50 1.00 0.50 3.00 4.00 27.00 14.00 \n\n\n\n4 RSL 4 48.00 3.50 0.95 0.45 2.00 4.50 28.10 12.50 \n\n\n\n7 RSL 7 47.50 5.00 0.75 0.40 1.50 3.00 28.35 13.50 \n\n\n\n8 RSL 8 46.50 6.00 0.80 0.60 2.50 2.50 26.60 14.50 \n\n\n\n9 RSL 9 49.50 4.00 0.90 0.45 1.50 4.50 25.15 14.00 \n\n\n\n10 RSL 10 51.00 4.00 0.95 0.50 2.00 2.50 22.50 16.50 \n\n\n\n26 RSL 26 55.00 5.00 0.90 0.45 1.85 3.50 17.80 15.50 \n\n\n\n27 RSL 27 50.00 5.50 0.85 0.55 2.00 3.00 22.60 15.50 \n\n\n\nAVERAGE 48.30 4.60 0.89 0.49 2.24 3.50 25.84 14.14 \n\n\n\n7. GEOLOGICAL CHARACTERISTICS AND WATER RESOURCES \n\n\n\nTable 2 indicates the aquifer types in the study area with their water \n\n\n\nbearing properties, engineering properties and physical characteristics. \n\n\n\nTwo aquifer types are present in the study area; the unconsolidated \n\n\n\naquifers and consolidated aquifers (Figure 5). The unconsolidated \n\n\n\naquifers consist of unconsolidated sediments, chiefly sand and gravel. \n\n\n\nThese deposits are usually found along watercourses, plains or valley, \n\n\n\nburied or abandoned river channels and sand lenses. They are commonly \n\n\n\ncovered by interlayered with clay and silt (Table 2). Being unconsolidated, \n\n\n\nthe permeability of sand and/or gravel is generally higher than other \n\n\n\nnatural material. In unconsolidated aquifers, groundwater occurs in the \n\n\n\npore openings between the granular material and within individual \n\n\n\nporous grain. In the study area, large portions of the developed aquifers \n\n\n\nare found in the unconsolidated aquifers. The consolidated aquifer can be \n\n\n\ndivided into four types based on host rocks (Figure 5): aquifer within \n\n\n\nkarstic rocks, aquifer within sandstones rocks, aquifer within sandstone \n\n\n\nand associated volcanic rocks and aquifers within igneous and \n\n\n\nmetamorphic rocks.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 26-31 \n\n\n\nCite the Article: Rodeano Roslee (2020). Geological Assisted On Water Resources Planning In Mountainous Catchments In Kundasang, Sabah, Malaysia . \n Malaysian Journal of Geosciences, 4(1): 26-31. \n\n\n\nThe karstic rock covers only very limited areas in the study area. It has \n\n\n\nlittle importance to groundwater resources. The sandstone aquifers cover \n\n\n\nlarge areas in study area. Their potential to groundwater depends on it \n\n\n\nporosity, specific yield and permeability (Table 2), cementing materials  \n\n\n\nand presence or absence of geologic discontinuities. Partly cemented \n\n\n\nfractured sandstone can be a good aquifer, especially if the deposits are \n\n\n\nsufficiently thick and widely distributed. Individual beds are commonly \n\n\n\nthin, (between a few centimeters to a few feet) but in some instances, they \n\n\n\nTable 2: Types of Aquifer with their Water Bearing Properties, Engineering Properties and Physical Characteristics \n\n\n\nRock \n\n\n\nFormation \nRock Unit General Character \n\n\n\nWater-\n\n\n\nBearing \n\n\n\nProperties \n\n\n\nEngineering \n\n\n\nProperties \n\n\n\nPhysical Characteristics \n\n\n\nSoil \n\n\n\nTypes \n\n\n\nPorosity, \n\n\n\nnp (%) \n\n\n\nSpecific \n\n\n\nYield \n\n\n\n(%) \n\n\n\nPermeability, K  \n\n\n\n(m/day) \n\n\n\nAlluvium  \n\n\n\n(Quaternary) \n\n\n\n- \n\n\n\nUnconsolidated gravel, \n\n\n\nsand and silt with \n\n\n\nminor amounts of clay \n\n\n\ndeposited along the \n\n\n\nrivers or streams and \n\n\n\ntheir tributaries. \n\n\n\nIncludes natural levee \n\n\n\nand flood plain deposit. \n\n\n\nGravelly and \n\n\n\nsandy, \n\n\n\nportions are \n\n\n\nhighly \n\n\n\npermeable and \n\n\n\nyield large \n\n\n\nquantities of \n\n\n\nwater. \n\n\n\nImportant to \n\n\n\ngroundwater \n\n\n\ndevelopment. \n\n\n\nGenerally poorly \n\n\n\nconsolidated. \n\n\n\nHence not \n\n\n\nsuitable for heavy \n\n\n\nstructures and \n\n\n\nsubsidence under \n\n\n\nheavy load. \n\n\n\nGravelly \n\n\n\nSAND \n15 to 25 16 to 18 52 to 8.20 X 10 -2 \n\n\n\nPinousuk \n\n\n\n(Gravel Upper \n\n\n\nPleistocene to \n\n\n\nHolocene) \n\n\n\n- \n\n\n\nPoorly consolidated \n\n\n\ntilloid deposits. \n\n\n\nUnconformable overlie \n\n\n\nultrabasic granitic and \n\n\n\nTertiary sedimentary \n\n\n\nrocks. \n\n\n\nGood aquifer \n\n\n\npresent in \n\n\n\npoorly \n\n\n\nfractures \n\n\n\nconsolidated \n\n\n\ndeposit. \n\n\n\nPoorly \n\n\n\nconsolidated. Not \n\n\n\nsuitable for heavy \n\n\n\nstructure. Born to \n\n\n\nbe heavy sliding. \n\n\n\nFine \n\n\n\nSAND \n25 to 32 22 to 25 16 to 1.92 X 10 -2 \n\n\n\nCrocker \n\n\n\nFormation  \n\n\n\n(Late Eocene \n\n\n\nto Early \n\n\n\nMiocene) \n\n\n\nShale \n\n\n\nThis unit composed \n\n\n\ntwo types of shale red \n\n\n\nand grey. It is a \n\n\n\nsequence of alteration \n\n\n\nof shale with siltstone \n\n\n\nof very fine. \n\n\n\nIt has no \n\n\n\nsignificant to \n\n\n\ngroundwater \n\n\n\ndevelopment \n\n\n\ndue to its \n\n\n\nimpermeable \n\n\n\ncharacteristic. \n\n\n\nVery dangerous \n\n\n\nsite for heavy \n\n\n\nstructures and \n\n\n\nthe main causes \n\n\n\nof mass \n\n\n\nmovement. \n\n\n\nSilty \n\n\n\nCLAY  \n40 to 45 3 to 5 1.82 to 9.25 X 10 -5 \n\n\n\nShale-\n\n\n\nSandstone \n\n\n\nInter \n\n\n\nbedded \n\n\n\nIt is a sequence of \n\n\n\ninterlayering of \n\n\n\npermeable sediment \n\n\n\nsandstone with \n\n\n\nimpermeable sediment \n\n\n\nof shale. The \n\n\n\npermeability of this \n\n\n\nunit is quite variable. \n\n\n\nGroundwater in this \n\n\n\nunit tends to be under \n\n\n\nsemi-confine to confine \n\n\n\nsystem. \n\n\n\nLittle \n\n\n\nimportance to \n\n\n\ngroundwater \n\n\n\nprovides some \n\n\n\nwater but not \n\n\n\nenough for \n\n\n\ngroundwater \n\n\n\ndevelopment. \n\n\n\nDangerous site \n\n\n\nfor heavy \n\n\n\nstructures and \n\n\n\nhigh potential for \n\n\n\nmass movement. \n\n\n\nSandy \n\n\n\nCLAY  \n30 to 35 2 to 8 \n\n\n\n5.68 X 10 \u20134 to \n\n\n\n7.95 X 10 -7 \n\n\n\nSandstone \n\n\n\nLight grey to cream \n\n\n\ncolour, medium to \n\n\n\ncoarse -grained and \n\n\n\nsome time pebbly. It is \n\n\n\nhighly folded, faulted, \n\n\n\njointed, fractured \n\n\n\noccasionally \n\n\n\ncavernous, surfically \n\n\n\noxidized and exhibit \n\n\n\nspheriodal weathering. \n\n\n\nImportance to \n\n\n\ngroundwater. \n\n\n\nGood site for \n\n\n\nheavy structures \n\n\n\nwith careful \n\n\n\ninvestigation. \n\n\n\nStable from mass \n\n\n\nmovement and \n\n\n\nprovide some \n\n\n\nmodification like \n\n\n\nclosing of \n\n\n\ncontinuous \n\n\n\nstructure. \n\n\n\nGravelly \n\n\n\nSAND  \n12 to 20 8 to 10 482 to 6.32 X 10 -2 \n\n\n\nTrusmadi \n\n\n\nFormation \n\n\n\n(Paleocene to \n\n\n\nEocene) \n\n\n\nTrusmadi \n\n\n\nSlate and \n\n\n\nTrusmadi \n\n\n\nPhylites \n\n\n\nComprise of dark \n\n\n\ncolored argillaceous \n\n\n\nrock either in thick \n\n\n\nbedded or interbedded \n\n\n\nwith thin sandstone \n\n\n\nbeds reported along \n\n\n\nwith isolated \n\n\n\nexposures of volcanic \n\n\n\nrock is a common \n\n\n\nfeature of this \n\n\n\nformation. \n\n\n\nFracture bed \n\n\n\nof sandstone \n\n\n\nhas significant \n\n\n\nto \n\n\n\ngroundwater. \n\n\n\nDangerous site \n\n\n\nfor heavy \n\n\n\nstructure. \n\n\n\nImprovement \n\n\n\nshould be \n\n\n\nconducted before \n\n\n\nany project. \n\n\n\nSandy \n\n\n\nGRAVEL \n18 to 25 15 to 20 565 to 1.35 X 10 -2 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 26-31 \n\n\n\nCite the Article: Rodeano Roslee (2020). Geological Assisted On Water Resources Planning In Mountainous Catchments In Kundasang, Sabah, Malaysia . \n Malaysian Journal of Geosciences, 4(1): 26-31. \n\n\n\n116 E\n0\n\n\n\n117 E\n0\n\n\n\n118 E\n0\n\n\n\n119 E\n0\n\n\n\n116 E\n0\n\n\n\n117 E\n0\n\n\n\n118 E\n0\n\n\n\n119 E\n0\n\n\n\n07\n0\n\n\n\nN\n07\n\n\n\n0\n\n\n\nN\n\n\n\n06\n0\n\n\n\nN\n06\n\n\n\n0\n\n\n\nN\n\n\n\n05\n0\n\n\n\nN\n05\n\n\n\n0\n\n\n\nN\n\n\n\n600 120 KM\n\n\n\nU\n\n\n\nIgneous / Metamorphic Rocks\n\n\n\nSandstone and Associated Volcanics\n\n\n\nSandstone\n\n\n\nKarstic Rocks\n\n\n\nTerrace Sand, Gravel and Corals; \nCoastal / Alluvial Sand\n\n\n\nLocation of study area\n\n\n\nCONSOLIDATED AQUIFERS\n\n\n\nUNCONSOLIDATED AQUIFERS\n\n\n\nLegend :\n\n\n\nSULU\nSEA\n\n\n\nSULAWESI\n  SEA\n\n\n\nSOUTH \nCHINA\n  SEA\n\n\n\nKudat\n\n\n\nKota Kinabalu\n\n\n\nLahat Datu\n\n\n\nTawau\n\n\n\nBeaufort\n\n\n\nRanau\nSandakan\n\n\n\nTelupid\n\n\n\nLamag\n\n\n\nBeluran\n\n\n\nTuaran\n\n\n\nKota Belud\n\n\n\nPapar\n\n\n\nSipitang\nTenom\n\n\n\nKeningau\n\n\n\nTambunan\n\n\n\nKota Marudu\n\n\n\nTaritipan\n\n\n\nPitas\n\n\n\nLangkon\n\n\n\nPensiangan\n\n\n\nSemporna\n\n\n\nSg. Tingkayu\n\n\n\nS\ng\n\n\n\n. \nP\n\n\n\na\nd\na\ns\n\n\n\nS\ng\n. P\n\n\n\ne\ng\na\nla\n\n\n\nn\n\n\n\nS\ng. K\n\n\n\nua\nm\n\n\n\nut\n\n\n\nSg. Kinabatangan\n\n\n\nSg. Labuk\n\n\n\nSg. Sugut\n\n\n\nS\ng.\n\n\n\n S\neg\n\n\n\nam\na\n\n\n\nP. Gaya\n\n\n\nP. Jambongan\n\n\n\nP. Malawali\n\n\n\nP. Banggi\n\n\n\nP. Balambangan\n\n\n\nW.P. Labuan\n\n\n\nLayer 5\n\n\n\nWeak to Medium Strong, Dark Grey, Highly \n\n\n\nWeathered, Highly Fractured, Interbedded \n\n\n\nSANDSTONE and SHALE.\n\n\n\nLayer 3\n\n\n\nVery Stiff  to Hard,Brown and Light to Dark Grey, \n\n\n\nClayey Sandy SILT.\n\n\n\nLayer 2\n\n\n\nMedium dense to Very dense, Yellowish Orange to\n\n\n\nbrown, Gravelly Silty SAND with some CLAY\n\n\n\nLayer 1\n\n\n\nYellowish Brown to Light Grey, Sandy SILT with   \n\n\n\n some CLAY, BOULDERS and GRAVEL.\n\n\n\nLegend\n\n\n\nLayer 6\n\n\n\nHard, Dark to Light Bluish Grey to Soft Grey and \n\n\n\nYellowish Orange to Reddish Brown, Slightly \n\n\n\nWeathered, Slightly Fractured, Siliceous SILSTONE\n\n\n\nwith quarzite interbedded, Highly Decomposed,\n\n\n\nHighly Fragmented SHALE and Matrix.\n\n\n\nX\n\n\n\nX__\n\n\n\nX X X\n\n\n\nX X_XO.X\n_X XX_ X\n\n\n\nXO _XXO_X\n\n\n\nXX\n\n\n\nX\nX\n\n\n\nXOX\n\n\n\nO\nO\n\n\n\nO\n\n\n\n_X\nXX\n\n\n\nX\n\n\n\n- 10\n\n\n\n- 20\n\n\n\n- 40\n\n\n\n- 50\n\n\n\n0\n\n\n\n- 70\n\n\n\n- 60\n\n\n\nHorizontal  (m)\n\n\n\nElevation (m)\n\n\n\nX\n\n\n\nX\n\n\n\nX\n\n\n\nX\n\n\n\nX\n\n\n\nX\n\n\n\nX X\n\n\n\nX_X\nX\n\n\n\n.X\n\n\n\nX\n\n\n\nX\n\n\n\nX\n\n\n\nX\nX\n\n\n\nX\n\n\n\n_X\n\n\n\nX\n\n\n\n_X\n\n\n\n_X\nX\n\n\n\n_X\n\n\n\nX\n\n\n\nX\n\n\n\nX\n\n\n\nX\n\n\n\n_X\n\n\n\n_X\n\n\n\nX\n\n\n\nX\n\n\n\n_X\nX\n\n\n\nX\n\n\n\n_X\n\n\n\nX\n\n\n\nX\n\n\n\nOXX_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\nX_X_O_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n_ _\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\nX_\n\n\n\nX_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\nO_\n\n\n\nO_\n\n\n\nX_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\nX_\n\n\n\n_\n\n\n\nO_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\nO_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n__\n\n\n\nX_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\nOO_\n\n\n\nO_\n_O_\n\n\n\nO_\n\n\n\n_\n\n\n\n_\n\n\n\nO_\n\n\n\nO_\n\n\n\nO_\n\n\n\nO_\n\n\n\nO__\n\n\n\n_\n\n\n\nX_\n\n\n\nX_\n\n\n\nX_\n\n\n\n_\n\n\n\nXX_\n\n\n\n_X_\n\n\n\n_\n\n\n\n_\n\n\n\nO_\n.\n. .\n\n\n\n.\n\n\n\nX.\nX.\n\n\n\n.\n\n\n\n...\n\n\n\n...\n\n\n\n....\n..\n. .\n\n\n\n..\n.\n\n\n\n. .\n....\n\n\n\n.X.\n\n\n\n.\n\n\n\n.\n.\n\n\n\nX \n\n\n\n.\nX...\n..\n.\n\n\n\n......\n\n\n\n.\n\n\n\n.\n\n\n\n.\n..\n.\n\n\n\nX \n\n\n\n.\n\n\n\n......\n\n\n\nX...\n\n\n\n..\n\n\n\n.\n......\n\n\n\n. . .. .\n\n\n\n..\n\n\n\n_.\nO.O.\n. O\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\n.XO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nX.O\n\n\n\nO\n\n\n\nXO\nO\n\n\n\nO\n\n\n\n_OO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nXO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\n_O\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\nO\n\n\n\n........\n\n\n\n... ..\n\n\n\nX.O..........O..\n...\n\n\n\n....\n\n\n\n...\n\n\n\n.\n\n\n\n...\n\n\n\n...\n\n\n\n.\n\n\n\n... .\n\n\n\n...\n\n\n\n.\n...\n\n\n\n.\n\n\n\n...\n\n\n\n.\n\n\n\n..\n....\n\n\n\n...\n\n\n\n..\n\n\n\n.\n\n\n\n....\n\n\n\n..\n\n\n\n.\n\n\n\n...\n... ..\n\n\n\n.....\n\n\n\n.....\n\n\n\n.\n\n\n\n...\n\n\n\n.......\n\n\n\n.\nX_\n\n\n\n*\n\n\n\n*\n\n\n\n**\n\n\n\n*\n*\n\n\n\n*\n**\n\n\n\n**\n\n\n\n**\n\n\n\n**\n\n\n\n**\n\n\n\n*\n*\n\n\n\n**\n\n\n\n**\n\n\n\n*\n**\n\n\n\n*\n**\n\n\n\n**\n\n\n\n*\n\n\n\nLayer 1\n\n\n\nLayer 1\nLayer 1\n\n\n\nLayer 1\n\n\n\nLayer 1\n\n\n\nLayer 3\n\n\n\nLayer 3\n\n\n\nLayer 2\n\n\n\nLayer 2\n\n\n\nLayer 2\n\n\n\nLayer 2\n\n\n\nLayer 3\nLayer 3\n\n\n\nLayer 3\n\n\n\nLayer 3\n\n\n\nLayer 3\n\n\n\nLayer 2\n\n\n\nLayer 2\n\n\n\nLayer 2\n\n\n\nLayer 2Layer 2\n\n\n\nLayer 4\n\n\n\nLayer 4\nLayer 4\n\n\n\nLayer 4\n\n\n\nLayer 4\nLayer 4\n\n\n\nLayer 4Layer 4\nDatum = 5500La fty\n\n\n\ner 4\n\n\n\nLayer 5\n\n\n\nLayer 5\n\n\n\nLayer 5\n\n\n\nLayer 5\n\n\n\nLayer 5\n\n\n\nLayer 6\nLayer 6\n\n\n\nLayer 6\n\n\n\nLayer 6\nLayer 6\n\n\n\nLayer 6\nLayer 6\n\n\n\nLayer 1\n\n\n\nLayer 1\nX\n\n\n\nX\n\n\n\nOX\n\n\n\nX\nX\n\n\n\nX\n\n\n\nLayer 1\n\n\n\nLayer 1\n\n\n\n..X...\n\n\n\n......\n\n\n\n......\n......\n\n\n\n......\n.....\n.........\n\n\n\n.......\n......\n\n\n\n..\n\n\n\n....\n\n\n\n....\n\n\n\n.....\n\n\n\n.... .....\nO_\n\n\n\n_\n\n\n\nX_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n_\n\n\n\n._\n\n\n\nEnd of BH = 13.60 m\nG.W.L. = 7.50 m\n\n\n\nEnd of BH = 24.30 m\n\n\n\nGD.atWu.Lm . ==  1510.0200  ftm\n\n\n\nEnd of BH = 21.00 m\nG.W.L. = 8.10 m\n\n\n\nEnd of BH = 12.10 m\n\n\n\nG.W.L. = 2.50 m\n\n\n\nEnd of BH = 22.61 m\nG.W.L. = 2.40 m\n\n\n\nEnd of BH = 23.10 m\n\n\n\nG.W.L. = Full\n\n\n\nDatum = 5000 ftE\nnd of BH = 11.50 m\n\n\n\nG.W.L. = 7.50 m\nEnd of BH = 18.10 m\n\n\n\nG.W.L. = 1.90 m\n\n\n\nEnd of BH = 24.3 m\n\n\n\nG.W.L. = Full\n\n\n\nEnd of BH = 15.50 m\n\n\n\nG.W.L. = 9.30 m\n\n\n\nLayer 4\n\n\n\nMedium Dense to Very Dense, Dark Brown to Grey, \n\n\n\nSandy Gravelly SAND to Silty Clayey SAND.\nX X X.X\n\n\n\nX\n_X\n\n\n\nXXX\n\n\n\nX_\n._.X.\n.\n\n\n\nX_\n\n\n\nCorrelation Line\n\n\n\nGroundwater Level Line\n\n\n\nA B\n\n\n\nDatum = 4800 ft\n\n\n\n- 30\n\n\n\n- 80\n\n\n\nDatum = 5500 ft\n\n\n\nDatum = 5500 ft\n\n\n\nDatum = 5300 ft\nDatum = 4700 ft\n\n\n\nDatum = 5500 ft\n\n\n\nDatum = 5200 ft\n\n\n\nKEDAMAIAN\n    FAULT\n\n\n\nRANAU FAULT\n\n\n\nMENSABAN\n\n\n\n    FAULT\n\n\n\nRANAU\n\n\n\nKUNDASANGK\nBUNDU TUHAN\n\n\n\nTENOMPOK\n\n\n\nRANDAGONG\n\n\n\nSg. Melaut\nSg. Melaut\n\n\n\nSg. Kenipir\n\n\n\nSg. Liwagu\n\n\n\nSg. Liodan\n\n\n\nS\ng\n\n\n\n. \nR\n\n\n\ned\nap\n\n\n\nTrusmadi\n\n\n\nTrusmadi\n\n\n\nTrusmadi\n\n\n\nCrocker\n\n\n\nTrusmadi\n\n\n\nTrusmadi\n\n\n\nTrusmadi\n\n\n\nT\nru\n\n\n\nsm\na\n\n\n\nd\ni\n\n\n\nT\nru\n\n\n\nsm\na\nd\n\n\n\niC\nro\n\n\n\nck\ner\n\n\n\nCrocker\n\n\n\nC\nro\n\n\n\nck\ner\n\n\n\nC\nro\n\n\n\nck\ner\n\n\n\nC\nro\n\n\n\nck\ner\n\n\n\nT\nru\n\n\n\nsm\na\nd\n\n\n\ni\n\n\n\nTrusmadi\n\n\n\nCrocker\n\n\n\nCrocker\n\n\n\n0 1 2 3 4 5 6 Kilometer\n\n\n\nLEGEND\n\n\n\n3 (A) TRACE OF FAULT ZONE.\n\n\n\n2 (A) LIKE 1 (A) BUT INDICATING\nSENSE OF LOW ANGLE REVERSE\nFAULTING OR OVER THRUSTING. \n\n\n\n1 (B) DITTO, BUT DETERMINED \nFROM STEREO PLOT OF \nSTRUCTURAL ELEMENTS ALONG A \nPARTICULAR\nSECTION.\n\n\n\n1 (A) DIRECTION OF HORIZONTAL \nCOMPRESSION DETERMINED BY \nVISUAL INSPECTION OF S - PLANES \n(BEDDING AND FOLIATION) FOR \nPARTICULAR SECTIONS ALONG THE \nTRAVERSE.\n\n\n\nRIVER\n\n\n\nROAD\n\n\n\n2 (B) LIKE 1 (B) BUT INDICATING\nSENSE OF LOW ANGLE REVERSE\nFAULTING OR OVER THRUSTING. \n\n\n\n3 (B) DIRECTION OF FAULT \nMOVEMENT.\n\n\n\n3 (A) TRACE OF FAULT ZONE.\n\n\n\n4 (B) ULTRABASIC ROCK.\n\n\n\n4 (A) ADAMELITE.\n\n\n\n4 (C) TERTIARY SEDIMENTARY \nROCKS.\n\n\n\n4 (D) CRYSTALLINE BASEMENT.\n\n\n\nLOHAN\n\n\n\nCrocker\n\n\n\nCrocker\n\n\n\nScale : 1 : 25, 000\n\n\n\n116 40\u2019E116 35\u2019E 116 45\u2019E\n\n\n\n06 00\u2019N\n\n\n\n116 30\u2019E\n\n\n\n05 50\u2019N\n\n\n\n05 55\u2019N\n\n\n\ncould be sufficiently thick (more than 65 feet). The sandstone and \n\n\n\nassociated volcanic aquifer types vary widely in porosity and permeability \n\n\n\ndepending upon the degree of consolidation, degree of association and/or \n\n\n\nthe development of permeable zones after deposition. The drilling of \n\n\n\nsuccessful production wells in this type of aquifer depends very much on \n\n\n\nthe borehole penetrating fracture zones. Igneous and metamorphic rocks \n\n\n\nare relatively impermeable and are generally poor aquifers. Appreciable \n\n\n\nporosities and permeability however can be developed through fracturing \n\n\n\nand weathering of both types of the rocks. Limited yields for low \n\n\n\nproduction, domestic purposes may also be obtained from leached or \n\n\n\nhighly fractured zones of both rock types. The porosity and permeability \n\n\n\nof igneous and metamorphic rocks, generally, decreases rapidly with \n\n\n\ndepth of emplacement and the crystallization period.  \n\n\n\nThe rock formations exhibit a high degree of weathering and covered by \n\n\n\nthick residual soil, that extends to more than 65 feet in thickness. \n\n\n\nEvaluation of more than 60 boreholes drilled (Figure 1) and the cross-\n\n\n\nsection constructed (Figure 6) from those boreholes in the study area \n\n\n\nindicated that the groundwater table in the study area is shallow and \n\n\n\nranges from 6 feet to about 35 feet. It is also indicated that the water table \n\n\n\nfollowing the topography from highland toward the road and the valley \n\n\n\nside. The weathered materials are weak and may cause sinking, \n\n\n\nsubsidence and sliding due to high pore pressure subjected by both \n\n\n\nshallow and deep groundwater. Faults and joints (fractures) will tend to \n\n\n\nclose at depth due to compaction of overburden. The occurrence of \n\n\n\ngroundwater in the study area is greatly influenced by these geologic \n\n\n\ndiscontinuities due to faulting or fracturing control and enhancing the \n\n\n\nsecondary porosity and permeability.  \n\n\n\nThe effect of faulting activity can be observed on the lithologies of the \n\n\n\nstudy area. This was confirmed by the existence of transformed faulted \n\n\n\nmaterial consisting of angular to sub angular sandstone fragments, with \n\n\n\nfine recrystallized quartz along the joint planes, poorly sorted sheared \n\n\n\nmaterials and marked by the occurrence of fault gouge with fragments of \n\n\n\nsubphyllite and slickened sided surfaces. The geometry of these fault lines \n\n\n\nis not well known but is expected to be complex due to the fact that there \n\n\n\nare intersection zone of different type (Figure 7). Highly fractured and \n\n\n\nsheared sandstones indicate the result from long history of tectonic \n\n\n\nactivities; most of faulting shears exist within the interbedded sandstone-\n\n\n\nshale. Breaks and fractures were developed by shearing stresses that \n\n\n\ncaused the rapid disintegration and weathering of the rocks into relatively \n\n\n\nthick soil deposit. As a corollary to this, in rock bodies, the surface \n\n\n\nroughness of joint are generally smooth to rough planar. A relatively \n\n\n\nsmooth surface decreases the frictional resistance to expose the fractures, \n\n\n\ntherefore effected the possibility of ground water movement in study area. \n\n\n\nFigure 4: Regional geologic map of the area (Modified after Jacobson, \n\n\n\n1970) \n\n\n\nFigure 5: Types of aquifers in Sabah (After Faisal et al., 2001) \n\n\n\n\n\n\n\n. \n\n\n\nFigure 6: Cross \u2013 sectional of groundwater table (From Figure 1) \n\n\n\nFigure 7: Structural geology map (Modified after Tjia, 1974) \n\n\n\n8. CONCLUSIONS \n\n\n\nBased on evaluation of the studies, the followings were concluded: \n\n\n\n1. The geologic and topographic setting controlled the groundwater \n\n\n\noccurrences and movements in Kundasang Mountainous area. Various \n\n\n\ncombinations of geologic discontinuities and stratigraphy resulted in \n\n\n\ndifferent groundwater systems. The general movement of \n\n\n\ngroundwater flow in the study area is from highland to lowland. \n\n\n\nGroundwater level in the study area is ranging between 6 feet to 35 \n\n\n\nfeet depth. \n\n\n\n2. The geology of the study area indicates that the sandy layers of \n\n\n\nsedimentary rocks and the Quaternary sediments can be considered \n\n\n\nas important groundwater reservoirs. Sand and gravel of varying \n\n\n\nthickness define the major aquifers within the unconsolidated \n\n\n\nsediments. The older rocks are highly compacted layers at which \n\n\n\ngroundwater development is not economically feasible. Shallow clay \n\n\n\nbeds occasionally act as aquicludes resulting in semi-confined \n\n\n\nconditions in some unconsolidated sediments. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 26-31 \n\n\n\nCite the Article: Rodeano Roslee (2020). Geological Assisted On Water Resources Planning In Mountainous Catchments In Kundasang, Sabah, Malaysia . \n Malaysian Journal of Geosciences, 4(1): 26-31. \n\n\n\n3. The other groundwater reservoir in the study area is the confined \n\n\n\npermeable bed that occurs as irregular masses of sandstones \n\n\n\nintercalated with impervious beds of shale, clay and silt. Shale beds or \n\n\n\nlenses locally interbedded with sandstone might be extensive enough \n\n\n\nto separate water-bearing layers into general aquifers. \n\n\n\nRECOMMENDATIONS \n\n\n\nThis study recommends some suggested measures as stated below to \n\n\n\nfacilitate the understanding of groundwater system and provide water \n\n\n\nresources planning for the future in Kundasang Mountainous area: \n\n\n\n1. Continuous monitoring of groundwater level to determine actual \n\n\n\nresponse of water level to climatic change. \n\n\n\n2. Monitoring of groundwater system to avoid the adverse impact.\n\n\n\n3. Detailed geophysical survey to delineate the geological structures \n\n\n\nthose are suspected to act as groundwater conduit from the watershed \n\n\n\ntoward the valley. \n\n\n\n4. The identification of individual layers within the sedimentary \n\n\n\nsuccession gave overall planning and management of groundwater \n\n\n\nresources in the study area. \n\n\n\nREFERENCES \n\n\n\nBedient, P.B., Rifai, H.S., Newel, C.J., 1994. Groundwater contamination - \n\n\n\ntransport and remediation. Englewood Cliffs, New Jersey: Prentice \n\n\n\nHall. \n\n\n\nFaisal, M.M., 1990. Effects of Marikina faults on Groundwater. Ph.D. Thesis, \n\n\n\nCollege of Science, University of the Philippines, Diliman, Quezon City \n\n\n\n(Unpublished). \n\n\n\nFaisal, M.M., Siong, L.P., Anton, A., 2001. Application of Geology to \n\n\n\nGroundwater Exploration in Sabah. Proceedings of the International \n\n\n\nWater Association Conference on Water and Waste Water \n\n\n\nManagement for Developing Countries. Kuala Lumpur, Malaysia. \n\n\n\nJacobson, G., 1970. Gunong Kinabalu area, Sabah, Malaysia. Geological \n\n\n\nSurvey Malaysia. Report 8. \n\n\n\nMartin, W., Robet, K., Ron, E., 1997. Hydrology: Water Quantity and Quality \n\n\n\nControl. 2nd Edition. New York: John Wiley & Sons Inc.  \n\n\n\nRobie, R.B., 1981. Evaluation of groundwater resources South San \n\n\n\nFrancisco Bay. Department of Water Resources, Sate of California, 4. \n\n\n\nRoslee, R., Faisal, M.M., Tahir, S., Omang, S.A.K., 2002. Effect of geology on \n\n\n\nmass movement in Bundu Tuhan area, Sabah, Malaysia.\u201d Southeast \n\n\n\nAsian Natural Resources and Environmental Management Conference \n\n\n\n(SANREM 2002). Kota Kinabalu, Sabah, Malaysia. \n\n\n\nRoslee, R., Faisal, M.M., Tahir, S., Omang, S.A.K., 2003a. Assessment of \n\n\n\nGroundwater Potential in Kundasang Mountainous Area, Sabah, \n\n\n\nMalaysia. International Conference on Hydrology and Water \n\n\n\nResources in Asia Pacific Region (APHW  2003). Kyoto, Japan. \n\n\n\nTjia, H.D., 1974. Sense of tectonic transport in intensely deformed \n\n\n\nTrusmadi and Crocker sediments, Ranau - Tenompok area, Sabah. \n\n\n\nSains Malaysiana, 3 (2), 129 \u2013 166. \n\n\n\nWalton, W.C., 1970. Ground water resources evaluation. McGraw-Hill, Inc., \n\n\n\nNew York, U.S.A. \n\n\n\nWanielista, M., Kersten, R., Eaglin, R., 1997. Hydrogy, Water Quantity and \nQuality Control. 2nd edition. New York: John Wiley and Sons. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 51-55 \n\n\n\nCite the Article: Rizki Satria Rachman, Winantris (2021). Large Foraminifera from Limestone in The Rajamandala Formation, Sukabumi, West Java. \nMalaysian Journal of Geosciences, 5(2): 51-55. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\n\n\n\n\nREVIEW ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2021.51.55 \n\n\n\n\n\n\n\n\n\n\n\nLARGE FORAMINIFERA FROM LIMESTONE IN THE RAJAMANDALA FORMATION, \nSUKABUMI, WEST JAVA \n\n\n\nRizki Satria Rachman*, Winantris \n \n\n\n\nDepartment of Geological Engineering, Faculty of Geological Engineering, Padjadjaran University, Jl. Raya Bandung \u2013 Sumedang Km.21, \nSumedang Regency, West Java, 45363, Indonesia. \n*Corresponding Author Email: rizkisatriarachman@gmail.com \n\n\n\n\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS \n \n\n\n\nABSTRACT \n \n\n\n\nArticle History: \n \n\n\n\nReceived 20 July 2021 \nAccepted 24 August 2021 \nAvailable online 14 September 2021 \n\n\n\n\n\n\n\nRajamandala Formation is a limestone reef formation that is exposed in the Rajamandala - Sukabumi area, \nWest Java. Different from those in the Rajamandala area, this formation which is located in the Sukabumi area \nis still rarely analyzed, especially from its large foraminifera. This study aims to assess the content, age, and \ndepositional environment of reef limestone from the Rajamandala Formation based on large foraminifera. \nResearch method was carried out in several stages. Sampling was done by spot sampling as many as 6 \nsamples. Then, thin section observations was carried out using Olympus CX-22 binocular microscope. Finally, \nage and environmental analysis were carried out using basic biostratigraphic methods and cluster analysis \nbased on the fossil content of large foraminifera. Result, the limestone reefs of Rajamandala Formation in \nSukabumi area have varied material content. These rock ages were found to be in the Late Oligocene range \n(Chattian). This rock depositional environment is in shallow marine environment which is divided into 3 \nmain clusters. The first cluster is dominated by Austrotrilina and Borelis which shows the Backreef shelf \nenvironment. The second cluster is dominated by Heterostegina and Cycloclypeus which shows the Forereef \nshelf environment. Finally, the third cluster is dominated by all large foraminifera representing the Reef \nenvironment. When compared with previous studies, the limestone of Rajamandala Formation in Sukabumi \nand Rajamandala areas has the same age and depositional environment. \n\n\n\nKEYWORDS \n\n\n\nContent, Rajamandala Formation, Large Foraminifera, Reef, Sukabumi. \n\n\n\n\n\n\n\n1. INTRODUCTION \n\n\n\nIndonesia is low latitude archipelagic country located in Southeast Asia \nwith tropical climate (Schneider, 1998; Julismin, 2013; Hall, 2017). This \ncondition makes Indonesia have complex atmosphere and sea circulation \nwhich is very important for the surrounding area (Song et al., 2006; \nSprintall et al., 2014). From this, Indonesia is a country that has very high \nmarine diversity, one of which is shown by the richness of coral reefs \n(Renema, 2007). Limestone is sedimentary rock resulting from organic, \nchemical, or mechanical processes associated with the development of \ncoral reefs in an area (Natasia and Alfadli, 2018). The organic process \noccurs by the development of the carbonate skeleton and shell by various \nbiota (CaCO3). These biota include protists, algae, and other animals that \nmake up biogenic sediments (Hallock, 1997). Biota in limestone in the \nform of foraminifera can be used as indicator of how the condition of \nlimestone grows and develops in an area. (Langer and Hottinger, 2000). \n\n\n\n \nForaminifera are marine organisms belonging to unicellular protists with \nshells mostly composed of CaCO3 (Hottinger, 1982; Terakado et al., 2010; \nSaraswati and Srinivasan, 2015). These creatures live in the intertidal \nregion to the bottom of photic zone because the foraminifera are in \nsymbiosis with the presence of algae to form additional energy. \n(Hohenegger et al., 1999). Large foraminifera is one of the divisions of \nforaminifera based on their size characteristics (3-300 \u00b5). These large \n\n\n\nforaminifera can live in water environments with warm, shallow, and \ntropical characteristics in oligotrophic conditions (Saraswati and \nSrinivasan, 2015). \n\n\n\n \nRajamandala Formation is one of formations in the Bogor Basin which has \nan old limestone lithology. The overall thickness of this formation reaches \n60 \u2013 100 m (Hutabarat, 1971; Sudrajat, 1973). This formation is generally \ndivided into 2 members, the Limestone Member at the bottom and the \nMudstone Member at the top (younger) (Figure 1c). Rajamandala \nFormation extends in southwest-northeast direction from the Sukabumi \narea to the Rajamandala Padalarang area. Rajamandala Formation in the \nPadalarang area has been widely studied before (Siregar, 2005; Siregar \nand Mulyadi, 2007). Meanwhile, Rajamandala Formation in the Sukabumi \narea has not been studied much, especially from large foraminifera aspect. \nBased on previous studies that were analyzed using small foraminifera \nand nanofossils, Rajamandala Formation has Late Oligocene \u2013 Early \nMiocene age range. Furthermore, this formation is interpreted to have \nShallow Sea depositional environment (Effendi et al., 1998; Martodjojo, \n2003; Siregar, 2005; Wibowo and Kapid, 2014). \n\n\n\n \nQuick Response Code Access this article online \n\n\n\n\n\n\n\n\n\n\n\n \nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\n \nDOI: \n\n\n\n10.26480/mjg.02.2021.51.55 \n\n\n\n\nhttp://doi.org/10.26480/mjg.02.2021.51.55\n\n\nmailto:rizkisatriarachman@gmail.com\n\n\nhttp://www.myjgeosc.com/\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 51-55 \n\n\n\nCite the Article: Rizki Satria Rachman, Winantris (2021). Large Foraminifera from Limestone in The Rajamandala Formation, Sukabumi, West Java. \nMalaysian Journal of Geosciences, 5(2): 51-55. \n\n\n\n\n\n\n\n\n\n\n\nTable 1: Large Foraminifera Rajamandala Formation \n\n\n\nLarge Foraminifera S.1 S.2 S.3 S.4 S.5 S.6 \n\n\n\nAustrotrillina asmariensis   1 4 1  \n\n\n\nHeterostegina borneensis 11 4    2 \n\n\n\nLepidocyclina sumatrensis 5 3    11 \n\n\n\nCycloclypeus sp. 18 3    3 \n\n\n\nAmphistegina sp. 1 3    1 \n\n\n\nBorelis sp.  2 5 6   \n\n\n\nOperculina sp.  4     \n\n\n\nRed Algae 1 3 2    \n\n\n\nTotal 36 22 8 10 1 17 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 1: Location and Stratigraphy of Research Area; a. Research \n\n\n\nlocation in relation to Java; b. Research location in relation to Base Map; \n\n\n\nc. Results of Stratigraphy and Lithology \n \n\n\n\nResearch location is on the Cibatu River, Sukadamai Village, Cicantayan \nDistrict, Sukabumi Regency, West Java with coordinates 06o 56\u2019 36,57\u201d \u2013 \n06o 56\u2019 56,13\u201d S to 106o 50\u2019 30,85\u201d \u2013 106o 50\u2019 32,10\u201d E (Figure 1a \u2013 1b). \nFrom this description, it can be seen that it is interesting to analyze the \nlimestone content of Rajamandala Formation in Sukabumi area. In \naddition, further analysis of large foraminifera was also carried out to \ndetermine the condition of age and depositional environment from this \nRajamandala Formation limestone. \n\n\n\n2. MATERIAL AND METHODS \n\n\n\nThis research was conducted in several stages. First, the rocks were taken \nby spot sampling on several limestone outcrops of Rajamandala \nFormation on the Cibatu River, Sukabumi Regency, West Java (Liu et al., \n2014). As a result, 6 rock samples were obtained at several points from the \nRajamandala Formation. Rock samples were described in the field \nmegascopically with the help of rock parameters. After that, the sample is \nprepared by making a thin section. Then the samples were described, \nidentified, and determined using an Olympus CX-22 binocular microscope \nto find out the content of each research sample. Next, analysis is carried \nout on limestone content in the research sample which is compared with \nthe previous reference (Adams, 1984; BouDagher-Fadel, 2018). The \nresults of the limestone content are then analyzed by grouping and \ndrawing ranges based on biostratigraphic principles, both age and \ndepositional environment of each biota, especially from large \nforaminifera. \n\n\n\n \nIn addition, cluster analysis was also carried out to confirm the results of \nanalysis. Cluster analysis is a multivariate technique that aims to classify \nobjects into different groups. It is used to identify the similarities in the \ncharacteristics of objects that indicate certain conditions of influence \n(Romesburg, 2004). All of these steps are carried out to be able to interpret \nthe conditions of Rajamandala Formation when these limestones grow \nand develop (Jambak et al., 2014; Patriani et al., 2016). After all \ninterpretation and analysis is done. This research was compared with \nthose in the Rajamandala Formation in the Padalarang area from previous \nstudies. The results of the comparison are then concluded to determine \nthe similarities and differences that occur from the Rajamandala \nFormation of these two regions. \n\n\n\n3. RESULTS \n\n\n\n3.1 Limestone Content \n \n\n\n\nReef limestone outcrops were found to be located on the Cibatu River. This \nlimestone has light white to yellowish color characteristic with a lot of \ncoral and foraminifera content. Moreover, this limestone looks massive \nwith 7.2 meters thickness. From these outcrops, 6 samples were taken \nsequentially (S.1 \u2013 S.6) which represented each of the rock outcrops in \nstudy area (Figure 1c). \n\n\n\n\n\n\n\nIn general, Rajamandala Formation has quite diverse limestone content, \nsome samples are dominated by large amounts of calcite (S.1 and S.2), \nother sample are dominated by large foraminifera (S.6), and the other \nsamples have a wide variety of limestone content. When viewed from the \nlarge foraminifera content, at least 95 individual foraminifera which are \ndivided into 7 species were identified. The most dominant foraminifera \nwere obtained from the Cycloclypeus sp as many as 24 individuals. While \nthe least identified foraminifera came from the Operculina sp as many as \n4 individuals. When viewed from the sample, sample S.1 has the most \nidentified foraminifera as many as 36 individuals and sample S.5 has the \nleast identified foraminifera as many as 1 individual (Table 1 - 2). \n\n\n\n\n\n\n\n3.2 Age Analysis \n \n\n\n\nFigure 2: Age Range of Rajamandala Formation from Large Foraminifera \nAnalysis (Adams, 1976; BouDagher-Fadel, 2018) \n\n\n\n\n\n\n\nAge analysis was carried out by drawing the biostratigraphic zone from all \nresearch samples. As a result, Rajamandala Formation was deposited at \nLate Oligocene (Chattian) age which belongs to the large foraminiferal \nzone Te1-4. This age was drawn based on interval zone between the early \nappearance of Lepidocyclina sumatrensis and the late appearance of \nAustrotrillina asmariensis (Figure 2). Basically, Lepidocyclina sumatrensis \nwas only present and developed in Southeast Asia starting in Late \nOligocene as a result from evolution of Lepidocyclina isolepidinoides \nspecies. This L. sumatrensis then evolved into L. verbeeki with changes \ninfluenced by the nepionic chamber conditions of each of these large \nforaminifera (Adams, 1976; BouDagher-Fadel, 2018). While Austrotrillina \nasmariensis is species that developed until the Late Te which then evolved \ninto Austrotrillina howchini in Tf1 and became extinct throughout the \nAustrotrillina genus in Indonesian territory at Early Miocene age (Tf1). In \naddition, the development of large foraminifera from Heterostegina \nborneensis and Cycloclypeus sp. that were present in the study sample \nshowed that the study sample has age not far from Oligocene. (Adams, \n1976; BouDagher-Fadel, 2018). \n\n\n\nTable 2: Material Content Rajamandala Formation \n\n\n\nContent (%) S.1 S.2 S.3 S.4 S.5 S.6 \n\n\n\nCalcite 50 20 10 15 60 25 \n\n\n\nCoral 0 5 15 0 5 5 \n\n\n\nLarge Foraminifera 30 20 20 30 5 45 \n\n\n\nSmall Foraminifera 5 15 20 15 10 5 \n\n\n\nAlgae 5 25 10 0 5 10 \n\n\n\nOther Fragments 5 10 15 30 10 5 \n\n\n\nMatrix 5 5 10 10 5 5 \n\n\n\nTotal 100 100 100 100 100 100 \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 51-55 \n\n\n\nCite the Article: Rizki Satria Rachman, Winantris (2021). Large Foraminifera from Limestone in The Rajamandala Formation, Sukabumi, West Java. \nMalaysian Journal of Geosciences, 5(2): 51-55. \n\n\n\n\n\n\n\n\n\n\n\n3.3 Depositional Environmental Analysis \n \n\n\n\n \nFigure 3: Depositional Environments Range of Rajamandala Formation \n\n\n\nfrom Large Foraminifera Analysis (BouDagher-Fadel, 2018) \n \n\n\n\nThe analysis of depositional environment was interpreted based on \nforaminifera content in research sample. Result, Rajamandala Formation \nin this study can be divided into 3 depositional environments. First, \nsamples S.3, S.4, and S.5 showed that these three samples were formed in \nBackreef shelf environment. This can be seen by the large number of large \nforaminifera from Austrotrilina and Borelis species (Alveolinids). These \ntwo large foraminifera basically developed well in Backreef shelf \nenvironment compared to other foraminifera. In addition, the abundance \nof calcite, small foraminifera, large foraminifera, and coral growth in \nsample S.3 indicates that this sample has very close formation site to the \nmain reef. Meanwhile, in samples S.4 and S.5, large amounts of calcite, \nalgae, and other fragments indicate that these samples have site of \nformation that is further away from the presence to the main reef. \nTherefore, it can be interpreted that these three samples were formed in \nBackreef shelf depositional environment with tendency for S.3 samples to \nbe closer to the main reef development. \n\n\n\n \nSecond, samples S.1 and S.6 showed that they were formed in Forereef \nshelf environment. This can be seen by the presence of large foraminifera \nfrom Heterostegina and Cycloclypeus types. Basically, these two types of \nlarge foraminifera can develop well in Forereef shelf environment. In \naddition, the large number of Lepidocyclina that can live in wider \nenvironmental range also shows that samples S.1 and S.6 have \ndepositional environment in the Forereef shelf region. When viewed from \nthe composition, large amount of foraminifera in this sample indicates the \nformation area is very close to the main reef. Therefore, these two samples \nare interpreted to have formed in Forereef shelf depositional environment \nclose to the main reef growth. \n\n\n\n \nLast, sample S.2 shows that it was formed in Reef environment. This can \nbe seen with the same number of large foraminifera originating from the \nForereef and Backreef environment in this sample. Furthermore, when \nviewed from the sample content, the large number of calcite, small \nforaminifera, large foraminifera, and the growth of coral in this sample \nalso indicate the conditions for main reef. Therefore, this sample is \ninterpreted to have formed in main Reef depositional environment. \n\n\n\n\n\n\n\n4. DISCUSSION \n\n\n\nThe analysis carried out in previous studies was mostly carried out on the \nRajamandala Formation in Rajamandala area. Age of this formation is \nfound to be in Late Oligocene \u2013 Early Miocene range (Effendi et al., 1998; \nMartodjojo, 2003). When compared with this study, rock from \nRajamandala Formation in Sukabumi area were formed at the same time \nas those in the Rajamandala area. This can be seen from large foraminifera \nanalysis which shows that these rocks were formed at Late Oligocene age \n(Chattian). \n\n\n\n\n\n\n\n \nFigure 4: Cluster Analysis Results of Research Samples \n\n\n\nResults from cluster analysis show that the research sample can be divided \ninto three main clusters. The first cluster is cluster that shows the Backreef \nshelf environment. This cluster consists of 3 samples (S.3, S.4, and S.5). In \nthis first cluster, foraminifera from Austrotrilina and Borelis types \n(Alveolinids) show that this limestone formed in shallow marine \nenvironment with very low current conditions. These current conditions \ncan occur in Backreef shelf environment because the currents that occur \nin this environment are blocked by the presence of main Reef. These two \ntypes of foraminifera are associated with each other at the rear of main \nReef. When viewed based on its classification, this environmental area is \nin Euphotic lighting zone with a tropical index that is classified as \nOligotrophic \u2013 Mesotrophic. (BouDagher-Fadel, 2018). Therefore, this first \ncluster is interpreted to have environment on Backreef shelf when viewed \nfrom the association of large foraminifera. \n\n\n\n \nThe second cluster is cluster that shows the Forereef depositional \nenvironment. This second cluster consists of 2 main samples (S.1 and S.6). \nIn this second cluster, the large number of foraminifera from \nHeterostegina and Cycloclypeus types indicate that the limestones in this \ncluster were formed in very shallow environment (< 30 m) with soft \nsediments or hard sediments substrate conditions. This is interpreted \nbecause genus Cycloclypeus has low tolerance for lack of light and changes \nin temperature in shallow sea areas. In addition, the presence of \nHeterostegina and Lepidocyclina indicates that limestone was formed in \nhigh energy environment. When viewed based on its classification, this \nenvironmental area is in Oligophotic lighting zone with tropical index that \nis classified as Oligotrophic \u2013 Mesotrophic. (BouDagher-Fadel, 2018). \nTherefore, this second cluster is interpreted to have an environment \nformed on Forereef shelf with conditions close to the main Reef body \nwhich has shallow depth. \n\n\n\n \nLast, the third cluster is cluster formed in the Reef environment which \nconsists of only one sample (S.2). In this last cluster, all foraminifera \ndominated the research sample, both those in the Forereef shelf and \nBackreef shelf environments. When viewed based on its classification, this \nenvironmental area is in Mesophotic lighting zone with tropical index that \nis classified as Oligotrophic \u2013 Mesotrophic (BouDagher-Fadel, 2018). Due \nto the abundance of foraminifera with various types, it is interpreted that \nthis cluster formed on the main Reef of Rajamandala Formation. \n\n\n\n \nFrom all these explanations, it can be seen that the results of cluster \nanalysis confirm the results of analysis of depositional environment based \non large foraminifera. The research sample can be divided into 3 main \nenvironments, namely Forereef shelf, Backreef shelf, and Reef. The results \nof this study are in line with previous studies which stated that the \nRajamandala Formation was formed in shallow marine environment \nwhich was divided into several facies, namely Reef facies, Forereef shelf, \nand also Backreef shelf (Siregar, 2005; Wibowo & Kapid, 2014). However, \nit is true that the Rajamandala Formation located in Sukabumi region \ncontains foraminifera and coral that are not as abundant as those found in \nRajamandala area. \n\n\n\n \nFrom this description, it can be interpreted that the reef limestones in \nSukabumi and Rajamandala areas are continuous reef limestones and are \nformed at the same age and environment. This reef limestone was formed \nat Late Oligocene age with Shallow Sea depositional environment on the \nReef, Forereef shelf, and Backreef shelf. These limestones were formed \nwhen the Bogor Basin began to develop and became more visible due to \nthe changing of northern and southern parts of Java into higher elevations. \nThis elevation is caused by development of volcanoes associated with the \nJampang Formation (Martodjojo, 2003). \n\n\n\n5. CONCLUSIONS \n\n\n\nFrom the results of this study, several conclusions can be drawn. Reef \nlimestones of Rajamandala Formation in Sukabumi region contain both \ncalcite, coral, large foraminifera, small foraminifera, algae, and other \nfragments. From the age of the formation, these rocks of Rajamandala \nFormation has Late Oligocene (Chattian) age. This age was drawn based \non interval zone between the early appearance of Lepidocyclina \nsumatrensis and the late appearance of Austrotrillina asmariensis. \nMeanwhile, from the depositional environment, this formation has \nshallow marine depositional environment which is divided into 3 clusters. \nThe first cluster is dominated by foraminifera from Austrotrilina and \nBorelis types which show Backreef shelf environment. The second cluster \nis dominated by Heterostegina and Cycloclypeus which shows Forereef \nshelf environment. Finally, the third cluster is dominated by all large \nforaminifera that represent Reef environment. When compared with \nprevious studies, the limestones of Rajamandala Formation in Sukabumi \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 51-55 \n\n\n\nCite the Article: Rizki Satria Rachman, Winantris (2021). Large Foraminifera from Limestone in The Rajamandala Formation, Sukabumi, West Java. \nMalaysian Journal of Geosciences, 5(2): 51-55. \n\n\n\n\n\n\n\n\n\n\n\nand Rajamandala regions have the same age and depositional \nenvironment. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThanks to the Chancellor of Padjadjaran University for funding research \nthrough the HIU-RKDU program, the paleontology laboratory team, \nFaculty of Geological Engineering, Padjadjaran University and all parties \nwho have helped and provided encouragement in this research. \n\n\n\n\n\n\n\nREFERENCES \n\n\n\nAdams, C.G., 1968. A Revision of the Foraminiferal Genus Austrotrillina \n\n\n\nParr, In: Bulletin of The British Museum (Natural History). The British \n\n\n\nMuseum (Natural History), London, Pp. 71-97. \n\n\n\nAdams, C.G., 1976. Larger foraminifera and the late cenozoic history of the \nmiditerranean region. Palaeogeography, Palaeoclimatology, \nPalaeoecology, 20 (1-2), Pp. 47-66. https://doi.org/10.1016/0031- \n\n\n\n0182(76)90025-0. \n \n\n\n\nBoudaugher-Fadel, M.K., 2018. Evolution and Geological Significance of \nLarger Benthic Foraminifera, Second Edition. UCL Press, London. \n\n\n\n\n\n\n\nEffendi, Kusnama, Hermantos, 1998. Geological Map Sheet Bogor, West \n\n\n\nJava, Scale 1: 100,000. Geological Research and Development Center, \n\n\n\nBandung. \n\n\n\nHall, R., 2017. Southeast Asia: New Views of the Geology of the Malay \nArchipelago. Annual Review of Earth and Planetary Sciences, 45, Pp. \n331-358. https://doi.org/10.1146/annurev-earth-063016-020633. \n\n\n\n\n\n\n\nHallock, P., 1997. Reefs and Reef Limestones in Earth History, In: Life and \n\n\n\nDeath Of Coral Reefs. Chapman & Hall, New York, Pp. 13-42. \n\n\n\nHohenegger, J., Yordanova, E., Nakano, Y., Tatzreiter, F., 1999. Habitats of \n\n\n\nlarger foraminifera on the upper reef slope of Sesoko Island, Okinawa, \n\n\n\nJapan. Marine Micropaleontology, 36 (2-3), Pp. 109-168. \n\n\n\nhttps://doi.org/10.1016/S0377-8398(98)00030-9. \n\n\n\nHottinger, L., 1982. Larger foraminifera, giant cells with a historical \n\n\n\nbackground. Naturwissenschaften, 69 (8), Pp. 361-371. \n\n\n\nhttps://doi.org/10.1007/BF00396687. \n\n\n\nHutabarat, M., 1971. Stratigrafi Batuan Karbonat daerah Tagogapu, \n\n\n\nBandung. Thesis ITB (not published), Bandung. \n\n\n\nJambak, M.A., Ovinda, Nababan, U.P., 2014. Asosiasi Fosil dan Paleoekologi \n\n\n\nBatuan Karbonat Formasi Rajamandala, Padalarang, Jawa Barat. \n\n\n\nMindagi, 8 (2), Pp. 1-12. \n\n\n\nJulismin, 2013. Dampak dan Perubahan Iklim di Indonesia. Jurnal Geografi, \n\n\n\n5 (1), Pp. 39-46. https://doi.org/10.24114/jg.v5i1.8083. \n \n\n\n\nLanger, M.R., Hottinger, L., 2000. Biogeography of selected\" larger\" \nforaminifera. Micropaleontology, 46, Pp. 105-126. \n\n\n\n\n\n\n\nLiu, D., Li, Z., Lian, Z., 2014. Compaction quality assessment of earth-rock \n\n\n\ndam materials using roller-integrated compaction monitoring \n\n\n\ntechnology. Automation in Construction, 44, Pp. 234-246. \n\n\n\nhttps://doi.org/10.1016/j.autcon.2014.04.016. \n\n\n\nMartodjojo, S., 2003. Evolusi Cekungan Bogor, Jawa Barat. Institut \n\n\n\nTeknologi Bandung, Bandung. \n\n\n\nNatasia, N., Alfadli, M.K., 2018. Pengaruh Parameter Sementasi Pada \n\n\n\nPerhitungan Saturasi Air Pada Reservoir Batugamping. Bulletin of \n\n\n\nScientific Contribution: Geology, 16 (1), Pp. 57-64. \n \n\n\n\nPatriani, E.Y., Rijani, S., Sundari, D., 2017. Perubahan Biofasies \n\n\n\nForaminifera pada Batugamping di Pantai Baron dan Serpeng, Provinsi \n\n\n\nDI Yogyakarta. Jurnal Geologi dan Sumberdaya Mineral, 17 (2), Pp. 61- \n\n\n\n71. \n\n\n\nRenema, W., 2007. Fauna Development of Larger Benthic Foraminifera in \n\n\n\nthe Cenezoic of Southeast Asia, In: Biogeography, Time, and Place: \n\n\n\nDistributions, Barriers, and Islands. Springer, Leiden, Pp. 179-215. \n\n\n\nRomesburg, C., 2004. Cluster Analysis for Researchers. Lulu Press, North \n\n\n\nCarolina. \n\n\n\nSaraswati, P.K., Srinivasan, M.S., 2015. Micropaleontology Principles and \nApplications. Springer, London. \n\n\n\n\n\n\n\nSchneider, N., 1998. The Indonesian Throughflow and the global climate \n\n\n\nsystem. Journal of Climate, 11 (4), Pp. 676-689. \n \n\n\n\nSiregar, M.S., 2005. Sedimentasi dan Model Terumbu Formasi \n\n\n\nRajamandala di Daerah Padalarang, Jawa Barat. RISET \u2013 Geologi dan \n\n\n\nPertambangan, 15 (1), Pp. 61-81. \n\n\n\nhttps://doi.org/10.14203/risetgeotam2005.v15.189 \n \n\n\n\nSiregar, M.S., Mulyadi, D., 2007. Fasies dan Diagenesa Formasi \nRajamandala di Daerah Padalarang, Jawa Barat. LIPI Geotechnology \nResearch Center, Bandung, Pp. 19-23. \n\n\n\n\n\n\n\nSong, Q., Vecchi, G.A., Rosati, A.J., 2007. The Role of the Indonesian \n\n\n\nThroughflow in the Indo\u2013Pacific Climate Variability in the GFDL Coupled \n\n\n\nClimate Model. Journal of Climate, 20 (11), Pp. 2434-2451. \n\n\n\nhttps://doi.org/10.1175/JCLI4133.1. \n\n\n\nSprintall, J., Gordon, A.L., Koch-Larrouy, A., Lee, T., Potemra, J.T., Pujiana, \n\n\n\nK., Wijffels, S.E., 2014. The Indonesian seas and their role in the coupled \n\n\n\nocean\u2013climate system. Nature Geoscience, 7 (7), Pp. 487-492. \n\n\n\nhttps://doi.org/10.1038/ngeo2188. \n\n\n\nSudrajat, D., 1973. Stratigrafi batuan karbonat daerah Gunung Manik, \n\n\n\nFormasi Rajamandala, Jawa Barat, Thesis UNPAD (not published), \n\n\n\nBandung. \n\n\n\nTerakado, Y., Ofuka, Y., Tada, N., 2010. Rare Earth Elements, Sr, Ba, Fe, and \n\n\n\nMajor Cation Concentrations in Some Living Foraminiferal Tests \n\n\n\nCollected from Iriomote Island, Japan: An Exploration for Trace Element \n\n\n\nBehavior During Biogenic Calcium Carbonate Formation. Geochemical \n\n\n\nJournal, 44, Pp. 315-322. https://doi.org/10.2343/geochemj.1.0068. \n\n\n\nWibowo, U.P., Kapid, R., 2014. Biostratigrafi Nannoplankton Daerah \n\n\n\nRajamandala. Jurnal Geologi Sumber Daya Mineral, 15 (4), Pp. 185-194. \n\n\n\n \nAPPENDICES \n\n\n\n \nPlate 1 \n\uf0b7 Sample S.1 \n\n\n\na. Amphistegina sp.; Vertical Incision \nb \u2013 d, f. Cycloclypeus sp.; Vertical Incision \ne, h \u2013 j. Heterostegina borneensis; Vertical Incision \ng. Red algae \nk. Lepidocyclina sumatrensis; Horizontal incision \n\n\n\nPlate 2 \n\uf0b7 Sample S.4 \n\n\n\na. Austrotrillina asmariensis; Vertical Incision \nb \u2013 c. Borelis sp.; Vertical \u2013 Horizontal incision \n\uf0b7 Sample S.3 \n\n\n\nd \u2013 h. Borelis sp.; Vertical \u2013 Horizontal incision \ni. Red algae \n\uf0b7 Sample S.2 \n\n\n\nj. Amphistegina sp.; Vertical Incision \nk. Borelis sp.; Horizontal incision \nl. Operculina sp.; Horizontal incision \nm. Lepidocyclina sumatrensis; Vertical Incision \nn. Cycloclypeus sp.; Vertical Incision \no. Red algae \np. Heterostegina borneensis; Vertical Incision \n\n\n\nPlate 3 \n\uf0b7 Sample S.5 \n\n\n\na. Austrotrillina asmariensis; Vertical Incision \n\uf0b7 Sample S.6 \n\n\n\nb. Amphistegina sp.; Vertical Incision \nc \u2013 e. Cycloclypeus sp.; Vertical Incision \nf. Heterostegina borneensis; Vertical Incision \ng \u2013 m. Lepidocyclina sumatrensis; Vertical \u2013 Oblique Incision \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 51-55 \n\n\n\nCite the Article: Rizki Satria Rachman, Winantris (2021). Large Foraminifera from Limestone in The Rajamandala Formation, Sukabumi, West Java. \nMalaysian Journal of Geosciences, 5(2): 51-55. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPlate 1 Plate 2 \n \n\n\n\n \nPlate 3 \n\n\n\n \n\n\n\n\n\n"
"\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 20-23 \n\n\n\nCite the article:Jun Wang, Hongtai xu (2017). The Crust And Uppermost Mantle S-Wave Velocity Structure Beneath Japan Islands Revealed By Joint Analysis Of P - And S-\nWave Receiver Functions. Malaysian Journal Geosciences, 1(2) : 20-23\n\n\n\n  ARTICLE DETAILS \n\n\n\nARTICLE HISTORY:\n\n\n\nReceived 12 May2017  \nAccepted 12 July 2017  \nAvailable online 10 September 2017 \n\n\n\nKEYWORDS:  \n\n\n\nReceiver functions, velocity \nstructure, joint Inversion, Japan \nIslands, S wave \n\n\n\nABSTRACT\n\n\n\nWe have studied the crust and uppermost mantle S-wave velocity structure beneath Japan Islands by using the \nteleseismic waveform data above Mw 6.0 recorded from January 2006 to February 2017 by 15 Hi-Net stations and \nthe joint inversion technique of P- and S-wave receiver functions based on the Bayes theory. The results show that, \nbeneath Japan Islands, the crust appear the characteristic of thinner in the south and east, and thicker in the north \nand west. The thinnest and thickest crust in the study region locate at station JSD (26km) and JGF(44km), \nrespectively. The horizontal distribution of S-wave velocity in the study region are relatively complicated within \nupper and middle crust depth, while from the lower crust to the uppermost mantle depth, the velocity distribution \nis relatively uniform. The large earthquakes (above Mw 6.0) mainly took place at the edge of the high and low \nvelocity zones.\n\n\n\n1. INTRODUCTION \n\n\n\nThe Japan Islands locate among four active lithospheric plates (shown in \nFigure1). The subduction and collision of these plates has caused many \ndestructive earthquakes in the islands or the surrounding region [1,2]. Many \nseismic researches have been conducted in order to detect the crust and \nupper mantle structure beneath Japan Islands. Using seismic body wave \ntravel time tomography method and the seismic data recorded by more and \nmore densely distribution of seismic stations in Japan, some scientists have \nobtained detailed P wave and S wave velocity perturbation images within \nupper mantle depth which has greatly improved the understanding about the \ndeep structure and the tectonics beneath Japan and the relationship between \nlarge earthquakes and the deep velocity structure [3-8]. To the purpose of \nexploring the deep tectonic causes of 2011 Tohoku earthquake, some \nscientists studied the seismic attenuation of Northeast of Japan arc [9]. By the \ncombination of seismic waveform and strong motion data and GPS \nobservations, scholars also studied the rupture process of 2011 Sariku-Oki \nearthquake [7]. Some of researchers analyzed the generating mechanism of \n2011 Tohoku earthquake and the induced tsunami by high resolution seismic \nimages of the northeast Japan forearc [10]. \n\n\n\nComparing with seismic travel time tomography technique, teleseismic \nreceiver function method takes advantage of velocity discontinuities \nidentification and obtaining absolute velocity information within crust and \nupper mantle depth (about 100km depth). The 410km and 660km \ndiscontinuities depth beneath Japan subduction zone have been studied by \nusing P wave receiver function migration and summation technique [11,12]. \nThey proposed a new receiver function migration method based on wave-\nequation post stack, and applied it to study the subsurface structures of the \nJapan subduction zone, and obtained the slab image and the dipping angle of \nPacific plate subduction down to Japan [13]. However, by using the receiver \nfunction migration method, only the interfaces lateral variation within crust \nand upper mantle depth could be obtained.  \n\n\n\nP- and S- wave receiver function joint inversion method has only been \nproposed and developed in recent years, by which the layered P-wave \nand S- wave velocity structure models within crust and uppermost \nmantle depth (about 300km) beneath some study region could be \nobtained [14-16]. In this paper, we would like to use P- and S- wave \nreceiver function joint inversion technique based on Bayes theory to \nanalyze the crust and uppermost mantle S-wave velocity structure, \nwhich is much different in many aspects comparing with global \ninversion method [14-17]. \n\n\n\nFurthermore, only S-wave velocity structure within 100km depth is \ndetermined by the observed P-wave and S-wave receiver functions, \nwhich implies fewer target variables, less inversion uncertainties. \n\n\n\n2. DATA AND METHOD \n\n\n\nThe seismic waveforms used in this study are recorded by 15 stations \nof Hi-net (Figure 1), which are operated by the Japanese national \nuniversities, Japan Meteorological Agency (JMA) and National Research \nInstitute for Earth Science and Disaster Prevention (NIED) for the High \n-sensitivity seismic network. Totally 292 records of teleseismic events \nwith Mw\u22656.5 from January 2006 to February 2017 with epicentral \ndistances in the range of 30-90 degrees for P-wave receiver functions \n(PRF) and 60-80 degrees for S-wave receiver functions (SRF), are \ncollected. We selected records with high signal-to-noise ratio and clear \nonset of P-waves and S-waves. The waveforms were rotated from the \nnorth-east-vertical (N-E-Z) to the radial-transverse-vertical (R-T-Z) \ncoordinate using the back-azimuth. For the PRFs, the three- component \nrecords were then cut in the time window of 5s prior to and 20s after \nthe P-arrival. We then constructed the receiver functions by \ndeconvolving the vertical component from radial component using an \niterative approach [18]. For the SRFs, the time window is selected from \n-25s to 25s and also constructed by the iterative approach. A low-pass \nGaussian filter was applied to smooth the PRFs to 1.5Hz and the SRFs \n\n\n\nContents List available at RAZI Publishing \n\n\n\nMalaysian Journal of Geosciences \nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/ \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (online)\n\n\n\nTHE CRUST AND UPPERMOST MANTLE S-WAVE VELOCITY \nSTRUCTURE BENEATH JAPAN ISLANDS REVEALED BY JOINT ANALYSIS \nOF P- AND S-WAVE RECEIVER FUNCTIONS \nJun Wang*, Hongtai xu \n**Institute of Earthquake Forecasting, China Earthquake Administration, Beijing, China. \nShandong Earthquake Agency, Jinan, China. \nCorresponding author e-mail: wjun_923@163.com\n\n\n\nhttps://doi.org/10.26480/mjg.02.2017.20.23\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\nmailto:wjun_923@163.com\n\n\nmailto:wjun_923@163.com\n\n\nmailto:wjun_923@163.com\n\n\nmailto:wjun_923@163.com\n\n\nhttps://doi.org/10.26480/mjg.02.2017.20.23\n\n\n\n\n\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 20-23 \n\n\n\nCite the article:Jun Wang, Hongtai xu (2017). The Crust And Uppermost Mantle S-Wave Velocity Structure Beneath Japan Islands Revealed By Joint Analysis Of P - And S-Wave \nReceiver Functions. Malaysian Journal Geosciences, 1(2) : 20-23\n\n\n\n21\n\n\n\nto 0.5Hz. We then did the summation of PRFs and SRFs from different \nazimuth for each station, which could suppress the laterally \nheterogeneity of the media beneath each station and could also enhance \nthe amplitudes of conversion waves. The PRF and SRF waveform data \nafter summation of each station then were transformed into spectral \ndomain by FFT algorithm. So far, the data for joint inversion were ready as \nour inversion would be processed in spectral domain, which would speed \nup the calculation and reduce the computation \n\n\n\nFigure 1: Distribution of seismic stations used in this study.  \n\n\n\nThe upper characters of the triangles denote the station codes. The \npurple and yellow circles denote the piercing points at 100km depth of \nPRFs and SRFs used in this study, respectively. The red star shows the \nepicenter of the 2011 Tohoku-Oki earthquake (Mw 9.0). The white \ncurves denote the plate boundaries. The red lines show the three \nprofiles in Figure 3\u20125. \n\n\n\nIt is necessary to point out that we only invert the S-wave velocity \ninstead of both P and S, which means the more reliable results could be \nexpected for the numbers of target variables were reduced. The \ndetailed processes of joint inversion would not be introduced here (For \nthe details, the reader is referred to the other paper by Wang and Liu \n[17]) \n\n\n\nSome examples for the joint inversion in Japan Islands have been given \n(Figure 2). Three different and possible initial models (PREM, IASP91, \nand CRUST 1.0) (Left panel of each subgraph in Figure 2) were \nattempted for the inversion of each station to test the inversion \nreliability and stability. From the final models (Middle panels), much \nsimilar results could be observed from different initial models (Figure 2 \n(a) and (b)), but the final model\u2019s consistency for the third station JSD is \nnot so good as the upper two stations (ASAJ and JMM). We infer to two \nreasons, the first of which is that the station JSD located on the sea \n(Figure 1), the crust structure of which is much different with the \nstations located on the continent. The second reason might be the \nforward calculation in the inversion process was based on the global \ncontinental model. \n\n\n\nFigure 2: Examples of the P- and S- wave receiver functions joint \ninversion results in Japan. \n\n\n\n(a) Station ASAJ; (b) Station JMM; (c) Station JSD (Stations location \nshown in Figure 1). In each subgraph, the left panel represents Initial \n\n\n\nmodels (PREM (red), IASP91 (black) and CRUST 1.0 (green)) for the \ninversion. The middle panel shows final models after inversion. The line \ncolor is the same as shown in the left panel. Moho depth is denoted by \nblack arrow. The waveform fitting of P- and S-wave receiver functions \nof each station for the joint inversion are shown in the right panel. Black \nlines represent Observed receiver functions while color lines represent \npredicted theoretical receiver functions after joint inversion from \ndifferent initial models. P-to-s and S-to-p conversion waves from Moho \nare marked out by black arrows corresponding to the Moho depth in \nthe middle panel. \n\n\n\n3. RESULTS \n\n\n\nBy using the PRF and SRF joint inversion method mentioned above, \nwe obtained 15 stations results, including PRFs and SRFs profile, and \nS-wave velocity model of each station. Three profiles (Figure 1) were \ndesigned in order to analyze the horizontal variation characteristic of \nS-wave velocity beneath Japan Islands. Obvious lateral variations \nof Moho depth could be observed from profile A-A\u2019 (Figure 3), which \nis along near south-to-north direction from Philippine Sea plate \npass through east of Japan Island to the northeast of Japan (Figure 1). \nThe Moho depth increase from South to North along profile A-A\u2019, \nwhich could be supported by both PRF profile (Figure 3 (a)) and \nS-wave velocity profile (Figure 3(c)). The shallow Moho depth in this \nprofile is about 30km, located at Philippine Sea plate, while the \nstation JEW located at northeast Japan has the deepest Moho, which \nis about 40km. We couldn\u2019t catch much deep structure information \nfrom SRF profile (Figure 3 (b)), but it would provide effective \nconstrains for the joint inversion. The sedimentary layers beneath \ntwo stations (JHJ2 and JEW) could be observed in the PRF profile \n(special waveforms nearby 0 sec) and S-wave velocity model \n\n\n\nFigure 3: The P receiver functions (a) and S receiver functions (b) \nalong profile A-A\u2019 (location shown in Figure 1).  \n\n\n\nThe upper characters on each subgraph represent the station codes. \nThe black dashed line in (a) denotes the P-to-s conversion wave from \nMoho. The S wave velocity structures within 100km depth (c) from \njoint analysis of P- and S receiver functions. The black arrows in each \nsubgraph represent the Moho depth of each station. The red arrows on \nthe top of (a) and (c) has marked out the stations with sediments.  \n\n\n\nObvious low velocity values at 0km depth could be observed in Figure \n3 (a) and (c), which are marked out by red arrows. \n\n\n\nAnother profile B-B\u2019 along near south-to- north direction passes \nthrough the west part of Japan Islands. The remarkable lateral \nfluctuation of Moho along this profile could be easily observed from \nboth PRFs (Figure 4 (a)) and S velocity models (Figure 4 (c)). Taking \nthe station JSD as the center, to the south, The Moho is about 42-44km, \nand 36-42km to the north. The Moho depth is only 26km beneath the \nstation JSD located in the sea. \n\n\n\n\n\n\n\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 20-23 \n\n\n\nCite the article:Jun Wang, Hongtai xu (2017). The Crust And Uppermost Mantle S-Wave Velocity Structure Beneath Japan Islands Revealed By Joint Analysis Of P - And S-\nWave Receiver Functions. Malaysian Journal Geosciences, 1(2) : 20-23\n\n\n\n22\n\n\n\nFigure 4: The same as Figure 3, but for profile B-B\u2019 (location shown in \nFigure 1). \n\n\n\nThe profile C-C\u2019 (Figure 5) passes through the center of Japan Islands \nalong southwest-to-northeast direction (Figure 1). The Moho \nappears much deeper and with good con- \n\n\n\nFigure 5: The same as Figure 3, but for profile C-C\u2019 (location shown in \nFigure 1). sistence (42-44km) to the west of station MAJ, and shallower \n\n\n\n(30-34km) to the east of MAJ. An obvious positive phase (red color) at \nabout 3-4 sec could be identified between station JWT to station MAJ \nfrom the PRF profile (Figure 5 (a)), which might be the conversion \nwave from Conrad interface. This phase could also be observed in west \nof Japan Islands (Figure 4 (a)), but couldn\u2019t be found in the east of the \nIslands. (Figure 3 (a)). \n\n\n\nTo further analyze the results, the S-wave velocity images at different \ndepths from 0km to 100km with 5km or 10km interval were drawn by \nthe two-dimensional in  \n\n\n\nFigure 6: The obtained S-wave velocity distribution at different depth \n(0 to 25km with 5km interval).  \n\n\n\nRed and blue colors represent low and high velocities, respectively. The \nabsolute velocity value is shown at right. The purple triangles denote \nstations. The yellow circles, blue circles and the red star represent \nepicenters of earthquakes above Mw6.0, Mw6.5 and Mw7.0, \nrespectively. \n\n\n\ntrepidation technique (Figure 6-8). The S-wave velocity changes \ngradually with depth increasing, which could be observed from 0-25km \nimages (Figure 6). The relatively uniform image at 0km (Figure 6 (a)) \nbecomes much more complicated to the depth of 5-25km (Figure 6 (b-\nf)). We could also find that the epicenter of large earthquakes (up to \nMw 6.0) mainly locate at the edge of high S-wave velocity bodies and \nthe low ones. From the image of 25km (Figure 6 (f)), at which depth the \nTohoku Mw 9.0 earth \n\n\n\nFigure 7: The same as Figure 6, but for the depth of 30 to 55km with \n5km interval. \n\n\n\nFigure 8: The same as Figure 6, but for the depth of 60 to 100km with \n10km interval. \n\n\n\nquake, 2011 took place, we couldn\u2019t get the exact velocity structure \nbeneath the epicenter, but we could observe that to the southwest \n\n\n\nof the epicenter the S-wave velocity shows special characteristic of low-\nhigh-low. \n\n\n\nThis feature extends to 35km depth (Figure 7 (a), (b)). From 40km to \n100km, the other simple characteristic could be concluded that the high \nvelocity shown in the large part of the northeast of Japan Islands, and \nthe low velocity appear in a relatively small region of the south- \nwest of the Islands. The exceptions appear in the images of 50km \n(Figure 7(e)) and 70km (Figure 8 (b)), which show more complicated \nlateral changes. The upper boundary of the subducting Pacific slab \nappear no close relationship with the velocity distribution (Figure 8 (a), \n(c), (e)). \n\n\n\n\n\n\n\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 20-23 \n\n\n\nCite the article:Jun Wang, Hongtai xu (2017). The Crust And Uppermost Mantle S-Wave Velocity Structure Beneath Japan Islands Revealed By Joint Analysis Of P - And S-Wave \nReceiver Functions. Malaysian Journal Geosciences, 1(2) : 20-23\n\n\n\n23\n\n\n\n4. DISCUSSION \n\n\n\nIn this paper, we studied the crust and uppermost mantle S-wave \nvelocity structure beneath Japan Islands by using the teleseismic \nwaveform data above Mw 6.0 recorded from January 2006 to February \n2017 by 15 Hi-Net stations and the joint inversion technique of P- and S-\nwave receiver functions based on the Bayes theory. The results show \nthat, beneath Japan Islands, the crust depth appear the characteristic of \nthinner in the south and east, and thicker in the north and west. The \nthinnest and thickest crust in this region locate at station JSD (26km) \nand JGF(44km), respectively. The horizontal distribution of S-wave \nvelocity in the study region are relatively complicated within upper and \nmiddle crust depth, while from the lower crust to the uppermost \nmantle depth, the velocity distribution is relatively uniform. The large \nearthquakes (above Mw 6.0) mainly took place at the edge of the high \nand low velocity zones. \n\n\n\nThis paper aims at testing the new joint inversion technique in Japan \nIslands, so only the data recorded by 15 stations of Hi-Net have been \nused. More detailed crust and upper mantle structure information \ncould be expected, if more data recorded by dense seismic array were \nprocessed by using this method. For the complication of inversion \nproblems, all the inversion techniques must face the problem of results \nnon-uniqueness. The joint inversion technique used in this paper could \nbe developed by adding surface wave or ambient noise dada as the \ninversion constrains. \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe authors would like to thank IRIS DMC for providing the waveform \ndata used in this paper. \n\n\n\nREFERENCES \n\n\n\n[1] Ishida, M. 1992. Geometry and relative motion of the \nPhilippine Sea plate and Pacific plate beneath the Kanto-Tokai district, \nJapan. Journal of Geophysical Research, 97 (B1), 489-531. \n\n\n\n[2] Zhao, D. P., Om, P. M., Ryohei, S., Kazushige, O., Norihito, U., \nand Akira, H. 2002. Seismological evidence for the influence of fluids \nand magma on earthquake. Bulletin of the Earthquake Research \nInstitute, 76, 271-289. \n\n\n\n[3] Zhao, D. P., Huang, Z. C., Norihito, U., Akira, H., and Hiroo, K. \n2011. Structural heterogeneity in the megathrust zone and mechanism of \nthe 2011 Tohoku-oki earthquake (Mw 9.0). Geophysical Research \nLetters, 38 (17), L17308. \n\n\n\n[4] Zhao, D. P., Hiroki, K., and Genti, T. 2015a. A water wall in the \nTohoku forearc causing large crustal earthquakes. Geophysical Journal \nInternational, 200 (1), 149-172. \n\n\n\n[5] Zhao, D. P. 2015b. The 2011 Tohoku earthquake (Mw 9.0) \nsequence and subduction dynamics in Western Pacific and East Asia.\nJournal of Asian Earth Sciences, 98, 26-49. \n\n\n\n[6] Sun, A. H., Zhao, D. P., Michiharu, I., Chen, Y., and Chen, Q. F. \n2008. Seismic imaging of southwest Japan using P and PmP data: \nImplications for arc magmatism and seism tectonics. Gondwana \nResearch, 14 (3),\n535-542. \n\n\n\n[7] Shao, G. F., Chen, J., and Zhao, D. P. 2011. Rupture process of \nthe 9 March 2011 Mw 7.4 Sanriku-Oki, Japan earthquake constrained \nby jointly inverting teleseismic waveform, strong motion data and GPS \nobservations. Geophysical Research Letters, 38 (7), L00G20. \n\n\n\n[8] Liu, X., Zhao, D. P., and Li, S. Z. 2011. Seismic attenuation \ntomography of the Northeast Japan arc: Insight into the 2011 Tohoku \nearthquake (Mw 9.0) and subduction dynamics. Journal of Geophysical \nResearch: Solid Earth, 119, 1094-1118. \n\n\n\n[9] Tong, P., Zhao, D. P., and Yang, D. 2012. Tomography of the \n2011 Iwaki earthquake (M 7.0) and Fukushima nuclear power plant \narea. Solid Earth, 3, 43-51. \n\n\n\n[10] Huang, Z. C., and Zhao, D. P. 2013. Mechanism of the 2011 \nTohoku-Oki earthquake (Mw 9.0) and tsunami: Insight from seismic \ntomography, Journal of Asian Earth Sciences, 70 (71), 160-168. \n\n\n\n[11] Li, X. Q., Sobolev, S. V., Kind, R., Yuan, X. H., and Estabrook, C. \n2000. A detailed receiver function image of the upper mantle \ndiscontinuities in the Japan subduction zone, Earth and Planetary \nScience Letters, 183 (3-4), 527-541. \n\n\n\n[12] Yuan, X. H., Ni, J., Kind, R., Mechie, J., and Sandvol, E. 1997. \nLithospheric and upper mantle structure of southern Tibet from a \nseismological passive source experiment. Journal of Geophysical \nResearch: Solid Earth, 102 (B12), 27491-27500. \n\n\n\n[13] Chen, L., Wen, L. X., and Zheng, T. Y. 2005. A wave equation \nmigration method for receiver function imaging: 2. Application to the \nJapan subduction zone. Journal of Geophysical Research: Solid Earth, \n110 (B11), B11310. \n\n\n\n[14] Vinnik, L. P., Reigber, C., Aleshin, I. M., Kosarev, G. L., Kaban, \nM. K., Oreshin, S. I., and Roecker, S. W. 2004. Receiver function \ntomography of the central Tien Shan. Earth and Planetary Science \nLetters, vol. 225 (1-5),131-146. \n\n\n\n[15] Vinnik, L. P., Singh, A., Kiselev, S.,and Kumar, M. R. 2007. \nUpper mantle beneath foothills of the western Himalaya: subducted \nlithospheric slab or a keel of the Indian shield. Geophysical Journal \nInternational, 171 (3), 1162-1171. \n\n\n\n[16] Vinnik, L. P., Erduran, M., Oreshin, S. I., Kosarev, G. L., Kutlu, \nYu, A., Cakir, O., and Kiselev, S. G. 2014. Joint inversion of P- and S-\nreceiver functions and dispersion curves of Rayleigh waves: The results \nfor the Central Anatolian Plateau. Physics of the Solid Earth, 50 (5), 622-\n631. \n\n\n\n[17] Wang, J., and Liu, Q. Y. 2013. Joint P- and S-receiver function \ninversion based on the Bayesian theory. Chinese Journal of Geophysics, \n56 (1), 69-78. \n\n\n\n[18] Ligorria, J. P., and Ammon, C. J. 1999. Iterative deconvolution \nand receiver-function estimation. Bulletin of the Seismological Society \nof America, 89 (5), 1395-1400. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal Geosciences (MJG) 1(1) (2017) 27-31\n\n\n\nAssessing Water Quality Index in River Basin : Fuzzy Inference System Approach\n1 Herman Umbau Lindang, 2 Zamali Hj Tarmudi, 3 Ajimi Jawan \n1School of Biological Sciences, Faculty of Applied Sciences, Universiti Teknologi MARA Shah Alam, 40450 Selangor. 2 Department of Mathematics, Faculty \nof Computer and Mathematical Sciences, Universiti Teknologi MARA Sabah, Locked Bag 71, 88997 Kota Kinabalu, Sabah. 3 Department of Biological \nSciences, Faculty of Applied Sciences, Universiti Teknologi MARA Sabah, Locked Bag 71, 88997 Kota Kinabalu, Sabah 1 Corresponding Author : email: \nhermanumbau@gmail.com Tel.: 60013-8362140, Fax: 6088-325164\n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle history:\n\n\n\nReceived 22 January 2017  \nAccepted 03 February 2017  \nAvailable online 05 February 2017\n\n\n\nKeywords:\n\n\n\n Fuzzy Inference System (FIS), Water \nQuality Assessment, River Basin\n\n\n\nWater Quality Index is an important water assessment that sustain and conserve the aquatic ecosystem. In Malaysia, \nthe current classi ication practice on Department of Environmental Water Quality Index (DOE WQI) shows rigid \nvalue in term of assessing the input of parameters that close to a class boundary. Hence, this study proposed a \ntechnique to assess the parameters in a holistic manner by using the Fuzzy Inference System (FIS). The approach \nas an assessment tool represents the classes of various ranges and aggregating the parameters using membership \nfunction and Centroid Function respectively. A numerical example based on actual data from one of the sampling \nstation from Inanam Likas River Basin was adapted in this study. It was adapted to demonstrate the proposed \napproach. Findings shown using the proposed methods indicate that the river has Poor water status. Overall, FIS \nis able to assess the parameters and execute into a single index that represent the condition from poor to excellent \nscales of the water quality\n\n\n\n1.0 INTRODUCTION\n\n\n\nland and this include the river basin (Liu & Zou, 2012). With the external factors \nthat act upon the river, it is important for every river to be monitor and assessed \nin respect to the physicochemical and biological factor. \nDepartment of Environment (DoE) in Malaysia uses DoE Water Quality Index \n(DoE WQI) and National Water Quality Standards (NWQS) to assess the quality \nstatus of the river. WQI have 5 classes in the form of range and the level of water \n\n\n\nClass 4 and Class 5 (DoE, 2014). WQI is an assessment of water that involves \nlocal necessity pollution status on the river basin. Table 1 represents selected \n\n\n\nselected parameters according to DoE WQI and NWQS for Malaysia.\n\n\n\nwith a comprehensive assessment and reliable computational framework of data \n\n\n\nin the managing the environment as shown by Chen, Rui, Li, & Zhang (2014), \nFranz et al. (2013) ,Ocampo-Duque et al. (2013) and Gharibi et al. (2012). \nComplimentary to fact, assessing the surface water quality involve with human \n\n\n\ninformation for the assessment of surface water tends to be complex when it \n\n\n\ncomplexity of assessing the water can be managed well by using fuzzy sets theory \napproach. One of the Fuzzy\u2019s application approaches is Fuzzy Inference System \n\n\n\nand complex nature of assessing and managing the river basin. Study done by \nCarbajal-Hern\u00e1ndez et al. (2012) and Mahapatra et al. (2011) has shown that \n\n\n\nnumber of water variables.\n\n\n\nproblems that are being highlighted in this study; Section 3 and 4 both discuss \nthe background theory and implementation of proposed methods for illustration \n\n\n\nthe paper.\n\n\n\nCite this article as: Assessing Water Quality Index in River Basin : Fuzzy Inference System Approach.  Herman Umbau Lindang,  Zamali Hj Tarmudi,  Ajimi \nJawan / Mal. J. Geo 1(1) (2017) 27-31\n\n\n\nContents List available at RAZI Publishing\n\n\n\nMalaysian Journal of Geosciences\nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg\n\n\n\nISSN: 2521-0920  (Print) \nISSN: 2521-0602 (Online)\n\n\n\nhttps://doi.org/10.26480/mjg.01.2017.27.31\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\nhttp://doi.org/10.26480/mjg.01.2017.27.31\n\n\nhttps://doi.org/10.26480/mjg.01.2017.27.31\n\n\n\n\n\n\nHerman Umbau Lindang,  Zamali Hj Tarmudi,  Ajimi Jawan / Malaysian Journal Geosciences (MJG) 1(1) (2017) 27-31 28\n\n\n\nCite this article as: Assessing Water Quality Index in River Basin : Fuzzy Inference System Approach.  Herman Umbau Lindang,  Zamali Hj Tarmudi,  Ajimi \nJawan / Mal. J. Geo 1(1) (2017) 27-31\n\n\n\n2.0 PROBLEM IDENTIFICATION\nNowadays, different water quality assessment proposed by international \nresearchers shows that each assessment have their own emphasize based \non the water bodies main function. The current range implemented \nby Department of Environment through DOE-WQI for classi\ufffdication of \nparameters indexes showed less \ufffdlexibility in judging the concentration of \na parameter that is close to the boundary. Inputs of hydrological data into \ninterval sets of data had shown the uncertainties of inputs in evaluating the \ndata. Uncertainties of inputs can be evaluate effectively by using fuzzy logic \nin water assessment. Therefore, this paper proposes to assess the water \nparameter by applying into FIS to execute the water assessment\n3.0 THE BASIC CONCEPT AND PROPOSED METHOD\n3.1 Fuzzy Inference Systems\nHydrology data exist in a crisp data and being classi\ufffdied into certain range \nrespective to its parameter. The existence of interval data in classifying \nthe water quality shows the existence of subjectivity in the classi\ufffdication \nprocess. Similarly Fuzzy Inference System (FIS) has been an effective and \nholistic tool to evaluate and execute any forms of subjective data into a \nsingle output.\nIn this paper, the current water quality classi\ufffdication will be adapted into \nFIS for the execution of water status. The constructed outcome from the \n\n\n\n  .rooP dna rooP yletaredoM ,dooG ,dooG yreV ,tnellecxE saw sisylana esoporp\nCounter back strategy implemented in this paper to solve the evaluation of \n\n\n\ndata on the boundary of a range was resolve as well by using FIS. That is to \nsay, FIS is a process of formulating a mapping from a given multiple input \nto a single output using fuzzy logic. The process of fuzzy inference involves \nthree important concepts: membership function, logical operations and If \n\u2013 Then rules (Carbajal-Hern\u00e1ndez, S\u00e1nchez-Fern\u00e1ndez, Carrasco-Ochoa, & \nMart\u00ednez-Trinidad, 2012; Ocampo-Duque, Osorio, Piamba, Schuhmacher, & \nDomingo, 2013)\n3.2 Proposed Methods of FIS into Water Quality Assessment\nStep 1: Fuzzy Inputs\n This paper proposes the uses the inputs of hydrology data into the \nmembership functions as the \ufffdirst steps. It was implemented in the inputs \nof the FIS as membership functions. Every classi\ufffdication of the parameters \nwith regards to its possible class were represented using membership \nfunctions. A membership functions (\u03bc) transforms the real value obtained \ninto a [0,1] value. Trapezoidal membership functions (TrapMFs) de\ufffdine the \ninput transformation of the FIS and were represented as in expression (1).\n\n\n\n\u03bc(x,a,b,c,d)=min\u2061{(x-a)/(b-a),1,(d-x)/(d-c)}    \n   (1)\n\n\n\nwhere x is a water quality variable; a, b, c and d are membership parameters. \n\n\n\nTable 3 shows the value of each membership parameters to be adapted into \nexpression (1).\n\n\n\n\n\n\n\n\nHerman Umbau Lindang,  Zamali Hj Tarmudi,  Ajimi Jawan / Malaysian Journal of Geosciences 1(1)(2017) 27-31 29\n\n\n\nCite this article as: Assessing Water Quality Index in River Basin : Fuzzy Inference System Approach.  Herman Umbau Lindang,  Zamali Hj Tarmudi,  Ajimi \nJawan /  Mal. J. Geo 1(1) (2017) 27-31\n\n\n\n\n\n\n\n\nHerman Umbau Lindang,  Zamali Hj Tarmudi,  Ajimi Jawan / Geological Behavior 1(2) (2017) 27-31 30\n\n\n\nCite this article as: Assessing Water Quality Index in River Basin : Fuzzy Inference System Approach.  Herman Umbau Lindang,  Zamali Hj Tarmudi,  Ajimi \nJawan / Geo. Behav.. 1(2) (2017) 27-31\n\n\n\nFigure 2: Membership functions for Water Quality Index \nFigure 1 shows the representation of classi\ufffdication of dissolved oxygen (DO), \nammoniacal nitrogen (NH3N), pH and turbidity in TrapMFs. Figure 2 shows \nthe representation of Water Quality Index in TrapMFs. The constructed \nTrapMFs was adapted from the DoE WQI that was still used in Malaysia. \n\n\n\nStep 2: Fuzzy Operators\n The membership degree of each part of rule antecedent is \ncomputed after the inputs are fuzzi\ufffdied. Three fuzzy operators as had shown \nin expression (2), (3) and (4) were used. The operators are union (OR), \nintersection (AND) and negation (NOT).\n\n\n\nUnion (OR)    \u03bc_(A\u222aB) (x)=max\u2061{\u03bc_A (x),\u03bc_B \n(x)}    (2)\nIntersection (AND)   \u03bc_(A\u2229B) (x)=min\u2061{\u03bc_A (x),\u03bc_B (x)}   \n  (3)\nNegation (NOT)  )x( A_\u3017 \u03bc\u3016 -1=)x(  \u0305 A_\u3017 \u03bc\u3016    \n  (4)\n\n\n\nStep 3: Inference Rules (Reasoning Process)\nSubjectivity may refer to the speci\ufffdic interpretations of any aspect of \nexperiences. Likewise in this paper, it refers to the possibilities of the crisp \ndata input in the classi\ufffdication of data in the forms of interval set used in \nevaluating the water. As reported in the annual report of Department of \nEnvironment (2014), water experts\u2019 uses linguistic expression such as Class \n1, Class 2, Class 3, Class 4 and Class 5 to represent the status of the water. \nThe sets of classi\ufffdication constructed used in this paper were described.\nDissolved Oxygen = DO = {Class 1, Class2, \nClass3, Class 4, Class 5} = {C1, C2, C3, C4, C5}\nAmmoniacal Nitrogen = NH3N = {Class 1, \nClass2, Class3, Class 4, Class 5} = {C1, C2, C3, C4, C5}\npH = pH = {Class 1, Class2, Class3, Class 4, \nClass 5} = {C1, C2, C3, C4, C5}\nTurbidity = Turb = {Class 1, Class2} = \n{C1, C2}\n\n\n\nThe terms representing each set have the following meaning: C1 as Class 1, \nC2 as Class 2, C3 as Class 3, C4 as Class 4 and C5 as Class 5. \nAs an illustration of application on River A, if the dissolved oxygen (DO) in \nthe water is Class 1, the ammoniacal nitrogen (NH3N) level is Class 1, the \npH is Class 1 and the level of turbidity is Class 1, then the expected water \nquality is excellent. These linguistic forms of information can be interpreted \ninto fuzzy language. The robustness of the systems also depends on the \nnumber and quality of the rules constructed for the evaluation using FIS. As \ndemonstrated in this paper, there were 250 rule  constructed and it re\ufffdlects \nthe possible inputs of the total parameter involve in the assessment. To \nillustrate some of the sets constructed to represent the parameters used in \nthis paper, the \ufffdirst 6th rules and the 250th rules were described as follows.\n\n\n\nRules 1:  \nIf DO is C1 AND  NH3N is C1 AND pH is C1 and Turb is C1 then WQI is \nExcellent.\nRules 2:\nIfDO is C1 AND  NH3N is C1 AND pH is C1 and Turb is C2 then WQI is \nExcellent.\nRules 3:\nIf DO is C1 AND NH3N is C2 AND pH is C1 and Turb is C1 then WQI is \nExcellent.\nRules 4:If DO is C1 AND  NH3N is C2 AND pH  is C1 and Turb is C2 then \nWQI is Very Good.\nRules 5:\nIf DO is C1 AND  NH3N is C3 AND pH is C1 and Turb is C1 then WQI  is \nExcellent.\nRules 6:\nIf DO is C1 AND  NH3N is C3 AND pH  is C1 and Turb is C2 then WQI is Very \nGood.\nRules 250:\nIf DO is C5 AND  NH3N is C5 AND pH is C5 and Turb is C5 then WQI is Poor.\nThe output fuzzy rule then computed using the fuzzy operator AND,\n\n\n\nRm = min\nDO\n\n\n\nim ,\nNH3N\n\n\n\njm ,\nP H\n\n\n\nkm ,\nTurb\n\n\n\nlm{ }\n       \nwhere i, j, k and l are the different levels of concentration (Class 1, Class 2, \nClass 3, Class 4, Class 5 respectively) depends on each parameters. \nStep 4: Aggregation\n The membership function will be aggregated and produce a \nsingle output after the being used different set of rules and being matched \nwith fuzzy outputs (\u03bc_R). The combination of the rules is called aggregation. \nThe aggregation used  to fuzzy union all output in the FIS is the maximum \nmethods (Carbajal-Hern\u00e1ndez et al., 2012).\nStep 5: Defuzi\ufffdication\nNext, the different water quality condition obtained in a graph will have be \nobtained. Centroid function (CF) returns the center of area under the curved \nformed by the output fuzzy function according to expression 6:\n\n\n\nCF =\nxmout (x)dx\u00f2\nmout (x)dx\u00f2\n\n\n\nThe output of the center of area by centroid function determines the input \nvalue to be classi\ufffdied into the classi\ufffdication of water status from Poor to \nExcellent accordingly. The different water quality status from poor to \nexcellent can be within this range and normalization of results was done \nusing expression 7. The output value of the \ufffdinal evaluation was in the range \nof [0,1].               \n\n\n\n\n\n\n\n\nHerman Umbau Lindang,  Zamali Hj Tarmudi,  Ajimi Jawan / Malaysian Journal of Geosciences 1(1) (2017) 27-31 31\n\n\n\nCite this article as: Assessing Water Quality Index in River Basin : Fuzzy Inference System Approach.  Herman Umbau Lindang,  Zamali Hj Tarmudi,  Ajimi \nJawan / Mal. J. Geo 1(1) (2017) 27-31\n\n\n\nWQI = CF - min(CF)\nmax(CF) - min(CF)\n\n\n\n4.0 IMPLEMENTATION AND DISCUSION\nTo demonstrate our proposed method applied, we adapted one of \nour water quality data taken from Inanam River, Sabah. The data was \ntabulated in Table 4\n\n\n\n* Average range of each parameters \nGiven a situation that obeys the rule constructed in Rule 196, 197, 198 and \nRule 199, having their parameters DO, NH3N, pH and Turbidty and their \nvalues of 1.347 mgl-1 ,0.673 mgl-1,8.84 mgl-1 and 643.667 NTU respectively. \n\n\n\nUsing the propose methods stated in expression (1) untill (7), the water \nquality index can be evaluate using the FIS. The execution of assessment \nwas computed using Matlab 2015b as had shown in Figure 3\n\n\n\nFigure 3: Fuzzy inference diagram for the water quality problem with four \nparameters and 4 rules. Rules 196, 197, 198 and 199 were used to exemplify \nthe defuzzi\ufffdication process.\n\n\n\nBased on the calculation computed using the data taken, the river in Inanam \nLikas River at the speci\ufffdied location was 0.155 and classi\ufffdied as Poor. It \nindicates the river is at it worst water quality. As re\ufffdlected in Table 2, Class \n5 of water status was not suitable as habitat for the aquatic ecosystem \nand utilize by humans. Poor condition of the river shows that the river \nis unhealthy.It affects the food web and the natural function of the river \n(Aweng, Imis, & Maketab, 2011).\n Even though the numerical example only implies only on four-\nselected parameters, the expected results can be derived using other \nparameters as long as the representing the range of parameters involves is \nadapted into the TrapMFs. \n5.0 CONCLUSION\nIn this paper, we have applied the Fuzzy Inference System (FIS) to evaluate \nthe water assessment by using our own hydrological data obtained from the \nKlombong Industrial area. It is clearly seen that the proposed method are \ncapable to evaluate the status of the water and the process are less complex \nand straightforward. Furthermore, this reduces the time required to analyze \nthe hydrological data to determine the status of the water. In short, FIS have \nshown to be one of the effective and less complex tools to assess the quality \nof water in a river basin. In the future, the proposed method can be validated \nusing Sensitivity Analysis\nAcknowledgements\nThe author would like to acknowledge Ministry of Higher Education for \nproviding the fund through 600-RMI/RACE 16/6/2 (17/2013). The author \nis also very grateful to his supervisor and co-supervisor for their fruitful \nreview and constructive comments to improve the content of this paper.\n\n\n\nReferences\nCarbajal-Hern\u00e1ndez, J. J., S\u00e1nchez-Fern\u00e1ndez, L. P., Carrasco-Ochoa, J. a., \n& Mart\u00ednez-Trinidad, J. F. (2012). Immediate water quality assessment \nin shrimp culture using fuzzy inference systems. Expert Systems with \nApplications, 39(12), 10571\u201310582. \n\n\n\nChen, Q., Rui, H., Li, W., & Zhang, Y. (2014). Analysis of algal bloom risk \nwith uncertainties in lakes by integrating self-organizing map and fuzzy \ninformation theory. The Science of the Total Environment, 482-483, 318\u201324.\nFranz, C., Makeschin, F., Wei\u00df, H., & Lorz, C. (2013). Geochemical signature \nand properties of sediment sources and alluvial sediments within the Lago \nParano\u00e1 catchment, Brasilia DF: a study on anthropogenic introduced \nchemical elements in an urban river basin. The Science of the Total \nEnvironment, 452, 411\u201320.\nGharibi, H., Mahvi, A. H., Nabizadeh, R., Arabalibeik, H., Yunesian, M., & \nSowlat, M. H. (2012). A novel approach in water quality assessment based \non fuzzy logic. Journal of Environmental Management, 112, 87\u201395.\nMahapatra, S. S., Nanda, S. K., & Panigrahy, B. K. (2011). A Cascaded Fuzzy \nInference System for Indian river water quality prediction. Advances in \nEngineering Software, 42(10), 787\u2013796. \nOcampo-Duque, W., Osorio, C., Piamba, C., Schuhmacher, M., & Domingo, J. \nL. (2013). Water quality analysis in rivers with non-parametric probability \ndistributions and fuzzy inference systems: application to the Cauca River, \nColombia. Environment International, 52, 17\u201328. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 61-68 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2022.61.68 \n\n\n\nCite The Article: Oluyemi. E. Faseki (2022). Geotechnical and Geophysical Evaluati on of Subsoils in Ikate Area, Southwestern \nNigeria: Implications on Foundation Integrity and Corrosivity. Malaysian Journal of Geosciences, 6(2): 61-68. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2022.61.68\n\n\n\nGEOTECHNICAL AND GEOPHYSICAL EVALUATION OF SUBSOILS IN IKATE AREA, \nSOUTHWESTERN NIGERIA: IMPLICATIONS ON FOUNDATION INTEGRITY AND \nCORROSIVITY \n\n\n\nOluyemi. E. Faseki* \n\n\n\nDepartment of Earth Sciences, Adekunle Ajasin University, Akungba Akoko, Ondo State, Nigeria \n*Corresponding Author Email: oluyemi.faseki@aaua.edu.ng; oluyemi.faseki@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 15 March 2022 \nAccepted 20 July 2022 \nAvailable online 27 July 2022\n\n\n\nGeophysical and geotechnical methods remain one of the most effective approaches for the evaluation of \nsubsoil properties prior to engineering construction. This is important in the delineation of soil sequence and \nestimation of soil parameters necessary for ensuring sufficient supports for structures. This study therefore \npresents the results of subsoil integrity and corrosivity probability evaluation using Vertical Electrical \nSounding (VES) and Standard Penetration (SPT) in Ikate Area, Lagos, Nigeria. Data were acquired in eight (8) \nVES positions using Pasi terameter deploying the Schlumberger array system along four traverses while SPT-\nN values were obtained at every 1.5m within six (6) boreholes drilled to 30.0m depth in the study area. The \ncombined results of VES and SPT delineate four to five geologic units corresponding to topsoil, different \nshades of clays, peat, sandy/clayey sand and sand. The low resistivity values of the clayey, peaty, and sandy \nlayers especially along traverse four was used to rate them as highly or extremely susceptible to corrosion. \nThe presence of highly compressible organic clay and peat in the second layer precludes the adoption of \nshallow foundation in the medium dense sandy layer that made up the topsoil (0.0 \u2013 2.50m). Foundation \nanalysis carried out with N-values shows that pile installed within the clayed sand/sand clay and sand layers \nencountered between 12.0 \u2013 27.m with diameter range of 300 \u2013 600mm could mobilize ultimate and \nallowable loads ranges of [702.9 \u2013 5012.4KN] and [234.3 \u2013 1670.8KN]. The correlations of resistivity with N-\nvalues returned high to weak positive linear relationships suggesting that resistivity values may not be \neffective in estimation of the strength and stiffness of subsoil. Conclusively, the study demonstrated the \ncomplimentary role of both VES and SPT as an effective geoengineering characterization tool. \n\n\n\nKEYWORDS \n\n\n\nSoil Integrity, SPT, Ikate Area, Pile Bearing Capacity, VES, Corrosivity \n\n\n\n1. INTRODUCTION \n\n\n\nThe success of every major engineering construction project is dependent \n\n\n\non several factors which includes proper detailing, use of competent \n\n\n\nprofessionals, quality of construction materials, foundation integrity and \n\n\n\nsubsoil competency. Unlike the engineering materials whose properties \n\n\n\nand behaviours are quite predicTable, the subsoil is dynamic due to its \n\n\n\nheterogeneous nature and unpredicTable behaviour. This make adequate \n\n\n\nsite investigation a sine qua non to every large engineering construction \n\n\n\nprojects especially in urban centres like Lagos metropolis where many \n\n\n\nwetland areas hitherto considered unsuiTable for civil engineering \n\n\n\napplications are now being hydraulically sand filled and used for \n\n\n\nconstruction. Hence, for a safety compliance foundation construction, it is \n\n\n\nimportant that subsoil conditions at any proposed engineering site be \n\n\n\nproperly assessed prior to commencement of the final design or \n\n\n\nconstruction activities. Such Pre-foundation evaluation helps policy \n\n\n\nmakers, developers and engineers in prediction and amelioration of risks \n\n\n\nassociated with infrastructural development. Detailed understanding of \n\n\n\nthe nature of underlying formations will aid in the construction of proper \n\n\n\nfoundation which integrity determine the overall safety, service life and \n\n\n\nstability of the structure. It is therefore necessary to carry out site \n\n\n\ninvestigation for sustainable building constructions that will stand the test \n\n\n\nof time (Akpabot, et al., 2019; Ede, et. al., 2017, Faseki et al., 2016). Among \n\n\n\nthe techniques that have been used for critical assessment of soil integrity \n\n\n\nand potential for corrosivity are conventional geophysical and \n\n\n\ngeotechnical methods. Standard Penetration Test (SPT) is one of the oldest \n\n\n\nand most used geotechnical methods for deriving foundation parameters \n\n\n\nfor design. In-situ testing like SPT provides direct information concerning \n\n\n\nthe subsurface conditions, geo-stratigraphy and engineering properties \n\n\n\nprior to design, bids and construction on the ground (Faseki, et al., 2018). \n\n\n\nIt provides information about the resistance of soils to penetration which \n\n\n\nhave useful correlations with various soil parameters. The inside nature of \n\n\n\nthe test means that site specific information about the behaviour of soil \n\n\n\nunder field condition can be obtained. Geophysical survey on its part \n\n\n\ncomprises the measurement of physical properties, usually at the ground \n\n\n\nsurface level without disturbing the soil structure, followed by \n\n\n\ncomprehensive processing and interpretation of the recorded data to \n\n\n\ngather information on the soil type and structure (Marius, et al., 2020). Its \n\n\n\nnon-invasive nature, large area coverage, low costs and quick results, as \n\n\n\nwell as an ability to create a spatial subsoil model without the use of heavy \n\n\n\n\nmailto:oluyemi.faseki@aaua.edu.ng\n\n\nmailto:oluyemi.faseki@gmail.com\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 61-68 \n\n\n\nCite The Article: Oluyemi. E. Faseki (2022). Geotechnical and Geophysical Evaluati on of Subsoils in Ikate Area, Southwestern \nNigeria: Implications on Foundation Integrity and Corrosivity. Malaysian Journal of Geosciences, 6(2): 61-68. \n\n\n\nequipment makes it an accepTable method for pre-foundation evaluation.  \n\n\n\nAmong the various site investigation tools, the Vertical Electrical Sounding \n\n\n\n(VES) and Standard Penetration Test (SPT) is considered to play \n\n\n\ncomplimentary role. This combination has become very reliable and \n\n\n\npopular to investigate the subsurface underground features for \n\n\n\napplications ranging from environmental, geohydrology as well as \n\n\n\nengineering site investigation. The VES and SPT is often desirable among \n\n\n\npractitioners and researchers since they both mirror the vertical sequence \n\n\n\nof soils, their resistivities and stiffness/strength. The VES method is based \n\n\n\non the assumption that soil and rocks, as well as other materials, conduct \n\n\n\nelectricity. It is affected by the soil moisture content and water saturation, \n\n\n\nits porosity and contamination level of water in the soil pores and change \n\n\n\nof stress conditions (Stopinski, 2003; McCarter, 2006). Employed \n\n\n\ngeoelectrical resistivity and seismic refraction methods to characterize the \n\n\n\nnear surface of a proposed conference center with a view to understand \n\n\n\nthe weathered profile at the site (Oladunjoye, et al., 2017). A study \n\n\n\nsuccessfully used the integration of geotechnical and geophysical methods \n\n\n\nto characterize the near surface for possible cracks and ground distress in \n\n\n\nSaudi Arabia (Al-fouzan and Dafalla, 2013). This paper aimed to evaluate \n\n\n\nthe corrosion rating and competency of the shallow formations within the \n\n\n\nstudy area. \n\n\n\n2. STUDY LOCATION AND GEOLOGIC SETTING\n\n\n\nThe study area is in Ikate Ancient Community along Lekki-Epe expressway \n\n\n\nin Eti-Osa local government area of Lagos State. It in the southeastern part \n\n\n\nof the state (Figure 1) and lies between latitudes 60 30I 37II and 60 30I 18II \n\n\n\nN and longitude 30 36I 3II and 3O 35I 34II E in southwestern Nigeria. It is in \n\n\n\nthe zone of coastal creeks and lagoons developed by barrier beaches \n\n\n\nassociated with sand deposition. It is situated in the Nigeria sector of the \n\n\n\nDahomey Basin and near the eastern margin of the basin (Figure 2). The \n\n\n\nBasin is one of the sedimentary basins on the continental margin of the \n\n\n\nGulf of Guinea extending from southeastern Ghana to the Western flank of \n\n\n\nthe Niger Delta (Adeniran, 2015). The eastern half of the basin occurs \n\n\n\nwithin the Nigeria territory and it is a marginal pull-apart basin that \n\n\n\ndeveloped in the Mesozoic as the African and the American lithospheric \n\n\n\nplates separated (Adeniran, 2015). The Basin is divided into the Northern \n\n\n\nand Southern zones (Okosun, 1990). The stratigraphy sequence spans \n\n\n\nfrom Cretaceous to Recent and the succession started with the Abeokuta \n\n\n\ngroup, comprising of the Ise formation overlying the basement complex, \n\n\n\nfollowed by the Araromi formation and terminating the Cretaceous with \n\n\n\nthe Afowo formation, a coarse to medium-grained sandstone, as the \n\n\n\nyoungest sediment (Minapuye, et al., 2018). The Cretaceous Abeokuta \n\n\n\ngroup comprises Ise, Afowo, and the Araromi formation, the Paleocene \n\n\n\nEwekoro formation; the late Paleocene to early Eocene Akinbo formation; \n\n\n\nthe Eocene Oshosun and Ilaro formations and the Pleistocene to recent \n\n\n\nBenin formation (Omatsola and Adegoke, 1981). The geological formation \n\n\n\nof the study area is composed of sediments that are typical of the marine \n\n\n\nenvironments, which is the intercalation of sand and clay. These sediments \n\n\n\nalso grade into one another and vary widely in both lateral extent and \n\n\n\nthickness. The Quaternary sediments within the area serve as foundation \n\n\n\nof engineering structures. \n\n\n\nFigure 1: Location and Base Map of the Study Area\n\n\n\nFigure 2: Geological map of the Eastern Dahomey Basin (Adapted from Gebhardt, et al., 2010) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 61-68 \n\n\n\nCite The Article: Oluyemi. E. Faseki (2022). Geotechnical and Geophysical Evaluati on of Subsoils in Ikate Area, Southwestern \nNigeria: Implications on Foundation Integrity and Corrosivity. Malaysian Journal of Geosciences, 6(2): 61-68. \n\n\n\n3. MATERIALS AND METHODS \n\n\n\n3.1   Electrical Resistivity Survey \n\n\n\nElectrical resistivity survey deploying the Vertical Electrical Soundings \n\n\n\n(VES) technique was carried in the study area. The electrode spacing AB \n\n\n\nvaries between 2 and 120m. The study area was geo- referenced by using \n\n\n\nGarmin Etrex model Global Positioning System (GPS). A total of eight \n\n\n\npositions (VES1, VES2, VES3, VES4, VES5, VES6, VES7 AND VES8) were \n\n\n\nsounded along four traverses (Figure 1). The PASI Terrameter was used \n\n\n\nfor data acquisition using Schlumberger array system as enumerated \n\n\n\n(Milton, 2007).  \n\n\n\n3.2   Standard Penetration Test \n\n\n\nSix boreholes were drilled using cable percussion rig to a maximum depth \n\n\n\nof 30.0m below existing ground level. Standard Penetration Test (SPT) \n\n\n\nwere conducted on all the six drilled holes (BH1, BH2, BH3, BH4, BH5 and \n\n\n\nBH6) at 1.50m intervals and at depths where sudden change in lithology \n\n\n\nwere observed. The horizontal distance between one borehole and the \n\n\n\nadjacent one is approximately 100.0m.  The SPT were conducted as per \n\n\n\nmethod suggested by ASTM D1586 (ASTM 2002). Water samples were \n\n\n\ntaken from the borehole and tested for pH, sulphate, resistivity, \n\n\n\nconductivity as well as salinity contents in the laboratory. \n\n\n\n3.3   Data Processing \n\n\n\nThe acquired VES data were processed to suppress noise from \n\n\n\ninstrumentation error and environmental factors. The manual curve \n\n\n\nmatching was done using both master and auxiliary curves as outlined in \n\n\n\nCampagnie, 1963. The true resistivities of the successive strata \n\n\n\nunderneath each VES point was obtained using one-dimensional \n\n\n\nresistivity inversion carried out using Winresist software program. The \n\n\n\nprocedure follows an iterative way and stopped when the root mean \n\n\n\nsquare (RMS) value is less than an accepTable value or convergence after \n\n\n\ncertain iterations. The model parameters obtained (layer resistivity and \n\n\n\nthickness) were used to produce four geo-electric sections using surfer \n\n\n\nsoftware. The interpretation and classification of the subsurface strata on \n\n\n\nthe basis of resistivity viz-a-vis corrosiveness was done using the \n\n\n\ncombined works of (Edeye and Eteh, 2021; Oki, et al., 2016; Bhattarai, \n\n\n\n2013; Gopal, 2010; Escalante, 1995 and Robinson, 1993).  \n\n\n\nThe SPT processing, analysis and interpretation was carried out as per \n\n\n\nmethod proposed by Peck et al., 1953 while the computation of the \n\n\n\nultimate pile capacity (Qu) and allowable pile capacity (Qa) using N-Values \n\n\n\nwas done as per the method of Meyerhof, 1976. The water analysis results \n\n\n\nwere interpreted by comparing with NSDWQ, (2007). \n\n\n\n4. RESULTS AND DISCUSSION \n\n\n\nThe samples of resistivity curves obtained from iteration is as shown in \n\n\n\nFigures 3 \u2013 4. The qualitative interpretation revealed six distinct curve \n\n\n\ntypes which are HKH, KHK, HA, QHK, QHA and QH. Also, a summary of the \n\n\n\ninterpreted VES data and corrosion status is presented in Table 1. The \n\n\n\ngenerated geo-electric sections beneath the VES points are presented as \n\n\n\nshown in Figures 5 \u2013 8. The result of boring and N-value of each \n\n\n\nstratigraphic unit is as shown in the borehole log in Figure 9 and Table 2 \n\n\n\nwhile the water test results is as summarized in Table 3. \n\n\n\n4.1   Vertical Electrical Sounding \n\n\n\nTraverse One: VES (1) and VES (2) probed five and four distinct geoelectric \n\n\n\nlayers respectively (Figure 5 and Table 1). These layers consist of topsoil, \n\n\n\nsand, clay and another layer of sand. The first layer is interpreted to be \n\n\n\ntopsoil (medium dense sand) with layer resistivity range of 102 to 76.2 \n\n\n\nohm-m under VES (1) and VES (2) respectively. The layer thickness is \n\n\n\napproximately 0.5m and 0.8m under VES (1) and VES (2). It is deemed \n\n\n\nfairly competent to sustain superstructure loads while it is considered \n\n\n\nmildly corrosive. The second layer under VES (1) is thin column of clay \n\n\n\nthat pinch out before entering VES (2) and have a thickness of 0.70m. The \n\n\n\nthird layer (sand) under VES (1) correspond to the second layer under VES \n\n\n\n2. It has layer resistivity range of 210.6 \u2013 79.8 ohm-m. It occurs at depth \n\n\n\nrange of 1.2 \u2013 3.2m under VES (1) and 0.8 \u2013 4.6m below VES (2). The \n\n\n\ncorrosion potential within the layer is rated negligible (Gopal, 2010). The \n\n\n\nfourth layer (clay) under VES 1 correspond to the third layer (clay) under \n\n\n\nVES 2 and it has layer resistivity range of 29.3 \u2013 33.4 ohm-m with depth \n\n\n\nrange of 3.2 -16.0m under VES (1) and 4.6 \u2013 17.0m under VES (2). It poses \n\n\n\nhigh corrosive potential to concrete foundation and order metallic \n\n\n\ninfrastructures installed within the layer, hence the need for adequate \n\n\n\ncathodic protection. The fifth layer (sand) under VES (1) correlate with the \n\n\n\nfourth layer (sand) under VES (2) having resistivity range of 116.9 \u2013 212.3 \n\n\n\nohm-m. The terminating depth of the layer could not be ascertained since \n\n\n\ncurrent terminated within the zone. It has low corrosive potential due to \n\n\n\nthe relative high resistivity. \n\n\n\nTraverse Two: the geoelectric section shows that VES (3) and VES (4) \n\n\n\nmirrored four and five distinct layers respectively (Figure 7 and Table 1). \n\n\n\nThe first layer under VES (4) is diagnostic of medium dense sand with no \n\n\n\ncorresponding layer in VES (3). It has a thickness approximately 0.4m with \n\n\n\nlayer resistivity of 104.0 ohm-m indicating a relatively good engineering \n\n\n\nmaterial with low corrosivity rating. The second layer under VES (4) \n\n\n\ncorrespond to the first layer under VES (3) and is symptomatic of clayey \n\n\n\nlayer. It has a layer resistivity range of 46.1\u2013 23.8 ohm-m with depth \n\n\n\nranges of 0.0 \u2013 0.8m and 0.4 \u2013 1.5m under VES (3) and VES (4) respectively. \n\n\n\nThe low resistivity value is suggestive of poor engineering material with \n\n\n\nvery high potential for corrosivity (Escalante, 1995).  The second layer \n\n\n\nunder VES (3) correlate with the third layer under VES (4) having layer \n\n\n\nresistivity range of 12.7 \u2013 14.6 0hm-m. It is also symptomatic of clayey \n\n\n\nlayer but with very poor engineering property and high corrosivity rating \n\n\n\ncompared to the upper layers. The third layer under VES (3) is indicative \n\n\n\nof sandy clay to clayey sand with layer resistivity of 66.5 ohm-m. It pinched \n\n\n\nout before getting to VES (4), it has a strata depth of 8.1 \u2013 23.5m with \n\n\n\nmoderate geotechnical properties and high corrosivity rating. \n\n\n\nThe fourth layer under VES (3) and VES (4) is suggestive a sand with layer \n\n\n\nresistivity of 258.1 \u2013 256.7 ohm-m. It has strata depth of 23.5 \u2013 30.0m \n\n\n\nunder VES 3 and 10.1 \u2013 51.9m under VES (4). Such layer generally \n\n\n\npossesses good geotechnical properties and good cathodic protection \n\n\n\nrating. The last layer under this traverse was encountered under VES (4). \n\n\n\nIt is a peaty layer with resistivity of 7.2 ohm-m and extending at depth \n\n\n\nbeyond 51.9m, the current electrode terminated within the layer. It has \n\n\n\nextremely poor engineering properties and extremely high corrosion \n\n\n\nrating. \n\n\n\nTraverse Three: it is made up of four to five geoelectric layers (Figure 6) \n\n\n\nwith VES (5) and VES (6) constituting this profile. These layers are \n\n\n\nrepresentative of top soil, clay, sand and peat. The first geo-electric layer \n\n\n\nsignifies topsoil characterized by resistivity values ranging from 66.1 to \n\n\n\n81.2 ohm-m with layer thickness between 0.30 and 0.70m. These \n\n\n\nresistivity values are indicative of materials with low to moderate \n\n\n\nengineering properties and high corrosion rating. The second geoelectric \n\n\n\nlayer denotes clay with resistivity values between 21.0 and 15.0 ohm-m \n\n\n\nwith layer thickness range of 2.20 to 1.30m. The low resistivity values \n\n\n\npinpoint a geomaterial with poor geotechnical properties and very high \n\n\n\ncorrosion potential status.  The third layer was only observed below VES \n\n\n\n(5) and is diagnostic of peat, the signature resembles a wedge. It has \n\n\n\nresistivity value of 5.6 and layer thickness of 3.6m. the very low resistivity \n\n\n\nvalue is a pointer to a very poor foundation material with extremely high \n\n\n\ncorrosion potential rating. The next geoelectric layer is suggestive of clay, \n\n\n\nsandy clay or clayey sand, it has resistivity values that ranges between 58.0 \n\n\n\nto 58.4 ohm-m and thickness between 11.2 \u2013 58.2m. It has high potential \n\n\n\ncorrosion. \n\n\n\nThe last geoelectric layer denotes sand with resistivity value of 102.1 ohm-\n\n\n\nm, the layer thickness could not be defined because current terminated on \n\n\n\nit. The resistivity value infers low corrosion rating. \n\n\n\nTraverse Four: the traverse comprises VES (7) and VES (8) delineating \n\n\n\nfour geoelectric layers. These layers correspond to topsoil, clay, sandy \n\n\n\nclay/clayed sand. The first layer of this section, the topsoil, is characterized \n\n\n\nwith resistivity values in the range of 50.4 and 52.4 ohm-m with layer \n\n\n\nthickness 1.4m providing high corrosion vulnerability. The second \n\n\n\ngeoelectric layer is representative of sandy clay to clayey sand with \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 61-68 \n\n\n\nCite The Article: Oluyemi. E. Faseki (2022). Geotechnical and Geophysical Evaluati on of Subsoils in Ikate Area, Southwestern \nNigeria: Implications on Foundation Integrity and Corrosivity. Malaysian Journal of Geosciences, 6(2): 61-68. \n\n\n\nthickness of 2.6m and resistivity value of 16.7 \u2013 17.7 ohm-m. This zone \n\n\n\nreflects very high corrosive rating. The third horizon signifies another \n\n\n\nlayer of clay with resistivity values between 11.0 12.0 ohm-m with \n\n\n\nthickness of 5.3 \u2013 5.5. The zone also denotes very high corrosive \n\n\n\nprobability. The fourth geoelectric layer delineates sandy clay to clayey \n\n\n\nsand with layer resistivity of 70.7 \u2013 71.1 ohm-m. The horizon points to high \n\n\n\ncorrosive probability. These shows that traverse four layers generally \n\n\n\nposes high probability for corrosion. \n\n\n\nFigure 3:  Resistivity curvse for VES 1 and 3 \n\n\n\nFigure 4:  Resistivity curvse for VES 5 and 7\n\n\n\nFigure 5: Geoelectric section for transverse 1 \n\n\n\nFigure 6: Geoelectric section for transverse 3 \n\n\n\nFigure 7: Geoelectric section for transverse 2 \n\n\n\nFigure 8: Geoelectric section for transverse 4 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 61-68 \n\n\n\nCite The Article: Oluyemi. E. Faseki (2022). Geotechnical and Geophysical Evaluati on of Subsoils in Ikate Area, Southwestern \nNigeria: Implications on Foundation Integrity and Corrosivity. Malaysian Journal of Geosciences, 6(2): 61-68. \n\n\n\nTable 1: Summary of Vertical Electricity Sounding Model and Corrosion Status. \n\n\n\nVES NO LAYERS RESISTIVITY (\u2126m) THICKNESS (m) DEPTH (m) CURVE TYPE LITHOLOGY CORROSIVITY RATING \n\n\n\n1 \n\n\n\n1 102.0 0.5 0.5 \n\n\n\nHKH \n\n\n\n\u03c11 > \u03c12 < \u03c13>\u03c14<\u03c15 \n\n\n\nTopsoil Mildly Corrosive \n\n\n\n2 39.1 0.7 1.2 Clay High \n\n\n\n3 210.6 2.0 3.2 Sand Negligible \n\n\n\n4 29.3 12.8 16.0 Clay High \n\n\n\n5 116.9 ------ ----- Sand Low \n\n\n\n2 \n\n\n\n1 76.2 0.8 0.8 \n\n\n\nKHK \n\n\n\n\u03c11< \u03c12 > \u03c13<\u03c14 \n\n\n\nTopsoil High \n\n\n\n2 79.8 3.8 4.6 Clayey sand High \n\n\n\n3 33.4 12.4 17.0 Clay Very High \n\n\n\n4 212.3 ----- ----- Sand Negligible \n\n\n\n3 \n\n\n\n1 46.1 0.8 0.8 \n\n\n\nHA \n\n\n\n\u03c11 > \u03c12 < \u03c13< \u03c14 \n\n\n\nTopsoil Very High \n\n\n\n2 12.7 7.3 8.1 Clay Very High \n\n\n\n3 68.5 15.5 23.6 \n\n\n\nSandy \n\n\n\nclay/clayey \n\n\n\nsand \n\n\n\nHigh \n\n\n\n4 258.1 ----- ----- Sand Negligible \n\n\n\n4 \n\n\n\n1 104.6 0.4 0.4 \n\n\n\nQHK \n\n\n\n\u03c11> \u03c12 > \u03c13 < \u03c14> \n\n\n\n\u03c15 \n\n\n\nTopsoil Low \n\n\n\n2 23.8 1.1 1.5 Clay Very High \n\n\n\n3 14.6 8.6 10.1 Clay Very High \n\n\n\n4 253.7 41.8 51.9 Sand Negligible \n\n\n\n5 7.2 ----- ----- Peat Extremely High \n\n\n\n5 \n\n\n\n1 66.1 0.7 0.7 \n\n\n\nQHA \n\n\n\n\u03c11 > \u03c12 > \u03c13 <\u03c14< \n\n\n\n\u03c15 \n\n\n\nTopsoil High \n\n\n\n2 21.0 2.2 2.9 Clay Very High \n\n\n\n3 5.8 3.6 6.5 Peat Extremely High \n\n\n\n4 58.4 50.2 56.7 \n\n\n\nSandy \n\n\n\nclay/clayey \n\n\n\nsand \n\n\n\nHigh \n\n\n\n5 102.1 ----- ----- Sand Low \n\n\n\n6 \n\n\n\n1 81.2 0.3 0.3 \n\n\n\nQH \n\n\n\n\u03c11 > \u03c12 >\u03c13< \u03c14 \n\n\n\nTopsoil High \n\n\n\n2 37.2 1.3 1.6 Clay Very High \n\n\n\n3 15.0 11.2 12.9 Clay Very High \n\n\n\n4 58.0 ----- ----- \n\n\n\nSandy \n\n\n\nclay/clayey \n\n\n\nsand \n\n\n\nHigh \n\n\n\n7 \n\n\n\n1 50.4 1.4 1.4 \n\n\n\nQH \n\n\n\n\u03c11 >\u03c12 >\u03c13 < \u03c14 \n\n\n\nTopsoil High \n\n\n\n2 17.7 2.6 4.0 Clay Very High \n\n\n\n3 12.0 5.3 9.3 Clay Very High \n\n\n\n4 71.1 ----- ----- \n\n\n\nSandy \n\n\n\nclay/clayey \n\n\n\nsand \n\n\n\nHigh \n\n\n\n8 \n\n\n\n1 52.5 1.4 1.4 \n\n\n\nQH \n\n\n\n\u03c11 >\u03c12 >\u03c13 < \u03c14 \n\n\n\nTopsoil High \n\n\n\n2 16.7 2.6 3.9 Clay Very High \n\n\n\n3 11.0 5.5 9.4 Clay Very High \n\n\n\n4 70.7 ----- ----- Sandy clay High \n\n\n\n4.2   Geotechnical Investigation Analysis \n\n\n\n4.2.1   Water Analysis \n\n\n\nThe result shows that the ground water pH is acidic while the sulphate \ncontent is high (Table 2). The other parameters such as conductivity, \n\n\n\nresistivity, total dissolved solid and salinity of the tested water samples \nfrom the drilled borehole are within the standard specification (Table 2). \nThe high sulphate content and acidity is implicated to be responsible for \nthe relatively low resistivity values of the soil sequence encountered \nwithin the area.  \n\n\n\nTable 2: Summary of Water Samples Analysis \n\n\n\nBH Water pH@ 250C Hardness ppm Sulphate Ppm Conductivity (us/cm) Resistivity Total Dissolved Solid(mg/l) Salinity \n\n\n\nBH Sample 4.97 100.0 350 137.7 - 1.0 82.2 500 \n\n\n\nStandard 6.5 \u2013 8.5 < 150 100 <1000 <18.2 <500 <700 \n\n\n\nRemark Fail Pass Fail Pass Pass Pass Pass \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 61-68 \n\n\n\nCite The Article: Oluyemi. E. Faseki (2022). Geotechnical and Geophysical Evaluati on of Subsoils in Ikate Area, Southwestern \nNigeria: Implications on Foundation Integrity and Corrosivity. Malaysian Journal of Geosciences, 6(2): 61-68. \n\n\n\n4.2.2   Foundation Assessment \n\n\n\nThe borehole log shows that the shallow subsurface comprises \nheterogeneous materials of varying engineering properties (Figure 9 and \nTable 3). The variation of N-values with depth is presented in Figure 10. It \nclearly shows that the N-value is sensitive to the strength/stiffness of each \ngeologic units. The geologic layers delineated includes loose to medium \ndense sand (0.0 \u2013 2.50m), soft silty sandy clay and peat (2.50 \u2013 6.0m), loose \nsand (6.0 \u2013 9.0m), loose to medium dense sand (9.0 \u2013 16.50m) and medium \ndense coarse sand (16.0 \u2013 30.0m). The presence of soft organic clay and \npeat immediately beneath the relatively competent topsoil preclude the \nadoption of shallow foundation in the area especially for high rise \nstructures. The results of pile foundation analysis using the generally \naccepted N-value approach is as presented in Table 4. Assuming pile \ndiameter range between 300 \u2013 600mm, the pile ultimate (Qu) and \nallowable (Qa) capacity is predicted to range from [702.9 \u2013 2311.0KN (Qu) \nand 234.3 \u2013 770.3KN (Qa)], [802.2 \u2013 2601.3KN (Qu) and 267.4 \u2013 867.1 \n(Qa)], [968.4 \u2013 3069.1KN (Qu) and 322.8 \u2013 1023.0KN (Qa)], [1131.0 \u2013 \n3641.7KN (Qu) and 377.1 \u2013 1213.9KN], [1403.7 \u2013 3765.8KN (Qu) and \n467.6 \u2013 1255.2KN] and [1625.4 \u2013 5012.4KN (Qu) and 541.8 \u2013 1670.8KN \n(Qa)] for 12.0m, 15.0m, 18.0m, 21.0m, 24.0m and 27.0m respectively.   The \nvariation of Qu and Qa with depth is as shown in Figures 11 \u2013 14. It clearly \nindicates an increase in bearing capacity with depth.  \n\n\n\n4.3   Correlations of SPT-N with Electrical Resistivity  \n\n\n\nThe relationship of electrical resistivity of soil and N-values from BH1 \u2013 \nBH6 are as shown in Figures 15 - 17. The obtained linear relationship \nbetween electrical resistivity and N-value reveal that; BH1 (R2 =0.93), BH2 \n(R2 = 0.56), BH3 (R2 = 0.74), BH4 (R2 = 0.26), BH5 (R2 = 0.91) and BH6 (R2 \n\n\n\n= 0.83). BH1, BH5 and BH6 returned strong positive linear correlations, \nBH2 and BH3 shows average linear correlations while BH2 pinpoint very \nweak positive linear correlation. The high variability in the correlations is \na little bit different from the strong positive linear relationship reported \nby Fahad, 2012. The inference to be drawn from this is that electrical \nresistivity is not a particularly preferable method for estimating the \nstrength and stiffness of soil like the N-value. \n\n\n\nFigure 9: Borehole Log Showing the Litho-Stratigraphy \n\n\n\nFigure 10: Variation of N-Values with Depth \n\n\n\nTable 3: Summary of Stratigraphic Profile \n\n\n\nStratum \n\n\n\nRange (m) \n\n\n\nThickness of \n\n\n\nStratum (m) \nDescriptions of Stratum SPT-N \n\n\n\nGround level \n\n\n\n0.00 \u2013 2.5 \n2.50 \n\n\n\nLoose grading to medium dense \n\n\n\nwhitish fine to medium grained \n\n\n\nsand. \n\n\n\n13 \u2013 15 \n\n\n\n2.5 \u2013 6.0 3.50 \n\n\n\nSoft dark grey silty sandy clay \n\n\n\nwith traces of organic clay and \n\n\n\npeat in parts \n\n\n\n1 \u2013 3 \n\n\n\n6.0 \u2013 9.0 3.0 \n\n\n\nGrey very loose grading to loose \n\n\n\nfine to medium grained silty \n\n\n\nsand. \n\n\n\n4 \u2013 9 \n\n\n\n9.0 \u2013 16.50 7.50 \n\n\n\nGreyish loose grading to \n\n\n\nmedium dense fine grained silty \n\n\n\nmedium with sea shell. \n\n\n\n8 \u2013 21 \n\n\n\n16.50 \u2013 30.0 13.50 \n\n\n\nGrey medium dense fine to \n\n\n\ncoarse grained sand with traces \n\n\n\nof clay in parts \n\n\n\n19 \u2013 34 \n\n\n\nTable 4: The values of safe working Loads at Various depth \n\n\n\nFigure 11: Variation of pile capacity with Depth @300mm Diameter \n\n\n\nFigure 12: Variation of pile capacity with Depth @400mm Diameter \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 61-68 \n\n\n\nCite The Article: Oluyemi. E. Faseki (2022). Geotechnical and Geophysical Evaluati on of Subsoils in Ikate Area, Southwestern \nNigeria: Implications on Foundation Integrity and Corrosivity. Malaysian Journal of Geosciences, 6(2): 61-68. \n\n\n\nFigure 13: Variation of pile capacity with Depth @500mm Diameter \n\n\n\nFigure 14: Variation of pile capacity with Depth @500mm Diameter \n\n\n\nFigure 15: Correlation of Resistivity (VES 1, 3) with N-number for BH1 \nand BH2 \n\n\n\nFigure 16: Correlation of Resistivity (VES 5, 6) with N-number for BH3 \nand BH4 \n\n\n\nFigure 17: Correlation of Resistivity (VES 7, 8) with N-number for BH7 \nand BH8 \n\n\n\n5. CONCLUSION \n\n\n\n\u2022 The results of Standard Penetration Test and Vertical Electrical \nSounding analyses showed that the shallow subsurface within the \nstudy area is underlain by heterogeneous layers which isncludes \nsands with varying densities, different shades of clays, peat, sandy \nclay/clay sand. \n\n\n\n\u2022 The VES and SPT results indicate that corrosive vulnerability and \ncompetency of the lithologic units are functions of the resistivity \nand N-value respectively. \n\n\n\n\u2022 Also, the acidity and high sulphate content of water is believed to be \nresponsible for the relatively low resistivity of the competent sand \nformation in the area. \n\n\n\n\u2022 The adoption of shallow foundation in the uppermost medium \ndense sandy layer especially for higher loading structures is \nconstrained by the presence of highly compressible soft silty clay \n(2.50 \u2013 6.0m) immediately beneath it. Therefore, pile installed \nwithin the clayed sand/sand clay and sand layers encountered \nbetween 12.0 \u2013 27.m with diameter range of 300 \u2013 600mm are \npredicted to mobilize ultimate and allowable loads ranges of [702.9 \n\u2013 5012.4KN] and [234.3 \u2013 1670.8] respectively. \n\n\n\n\u2022 The correlations of resistivity with N-values returned high to weak \npositive linear relationships suggesting that resistivity values may \nnot be effective in estimation of the strength and stiffness of subsoil. \n\n\n\n\u2022 This study has shown that both VES and SPT are complimentary \ntool in subsurface evaluation and it recommends that these \ntechniques be deployed prior to the execution of major engineering \nprojects. \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe authors wish to thank Mr. Jumbo Femi for his assistance during the \nfieldworks. \n\n\n\nREFERENCES \n\n\n\nAdeniran, O. A., 2015. Upper Cretaceous to Paleogene Palynosequence \nStratigraphy of H-1 well Offshore Eastern Dahomey Basin, South \nWestern Nigeria. International Journal of Research and Innovations in \nEarth Science, Volume 2, JSSN (online). \n\n\n\nAkpabot, A. I., Ede, A. N., Olofinnade, O. M., and Bamigboye, G. O., 2019. \nRisks of seismic activities on built environment in Nigeria. \nInternational Journal of Environment and Sustainable Development, \n18(3), pp. 259\u2013269. doi:10.1504/IJESD.2019.10022557 \n\n\n\nAl Fouzan, F., and Dafalla, M. A., 2013. Study of cracks and fissures \nphenomenon in Central Saudi Arabia by applying geotechnical and \ngeophysical techniques. Arabian Journal of Geosciences, 7(3), pp. \n1157\u20131164. doi:10.1007/s12517-013-0884-7 \n\n\n\nASTM., 2002. Standard test method for penetration test and split-barrel \nsampling of soils (D 1586-1999). Annual Book of ASTM Standards, pp \n146-150.  \n\n\n\nBhattarai, J., 2013. Study on the corrosive nature of soil towards the \nburied-structures. Scientific World, 11, pp. 43-47. \n\n\n\nCampagnie Generale de Geophysique., 1963. Master curve for electrical \nresistivity sounding. European Association of Exploration \nGeophysicists, 2nd Edition. \n\n\n\nEde, A. N., Olofinnade, O. M., Bamigboye, G. O., Shittu, K. K., and Ugwu, E. I., \n2017. Prediction of fresh and hardened properties of normal concrete \nvia choice of aggregate sizes, concrete mix-ratios and cement. \nInternational Journal of Civil Engineering and Technology, 8(10), pp. \n288\u2013301. \n\n\n\nEdeye, E., and Eteh, D.E., 2021.: Geoelectric Investigation of Corrosivity \nand Competence Soils Using Geospatial Technology: A Case Study in \nPart of Yenagoa, Bayelsa State, Nigeria. International Journal of \nEnvironmental & Earth Sciences, 1(1), pp. 1 \u2013 7. \n\n\n\nEscalante, E., 1995. Soils, Corrosion Test and Standards. ASTM, pp. 5. \n\n\n\nFahad I. (2012). Use of Vertical Electrical Sounding (VES) method as an \nAlternative to Standard Penetration Test (SPT). Proceedings of the \nTwenty-second (2012) International Offshore and Polar Engineering \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(2) (2022) 61-68 \n\n\n\nCite The Article: Oluyemi. E. Faseki (2022). Geotechnical and Geophysical Evaluati on of Subsoils in Ikate Area, Southwestern \nNigeria: Implications on Foundation Integrity and Corrosivity. Malaysian Journal of Geosciences, 6(2): 61-68. \n\n\n\nConference Rhodes, Greece, June 17\u201322. Pp. 871 \u2013 875. \n\n\n\nFaseki, O. E., Olatinpo, O. A., Akinsile, A.R., 2016. Assessment of Sub-Soil \nGeotechnical Properties for Foundation Design in Part of Reclaimed \nLekki Pennisula, Lagos, Nigeria. International Journal of Advanced \nStructures and Geotechnical Engineering, 5(4), pp. 120 \u2013 128. \n\n\n\nFaseki, O. E., Olatinpo, O. A., Akinsile, A.R., 2018. Geotechnical Properties \nof Soil Sequence for Foundation Design at Addo, Lagos Nigeria. Mayfeb \nJournal of Material Science, 1, pp. 12 \u2013 25. \n\n\n\nGebhardt, H., Adekeye, O. A., Akande, S. O., 2010. Late Paleocene to initial \nEocene thermal maximum foraminifera biostratigraphy and \npaleoecology of the Dahomey Basin, southwestern Nigeria. Gjahrbuch \nDer Geologischem Bundesantalt, 150, pp. 407\u2013 419 \n\n\n\nGopal, M., 2010. Corrosion potential assessment: The Geology of Part of \nSouth-Western Nigeria. Geological Survey of Nigeria, pp. 31-87.  \n\n\n\nMariusz, L., Zdzis\u0142aw, S., Marek, B., and Katarzyna, M. L., 2020. Applications \nof Electrical Resistivity Surveys in Solving Selected Geotechnical and \nEnvironmental Problems. Appl. Sci, 10, 2263; \ndoi:10.3390/app10072263 \n\n\n\nMcCarter, W. J., Blewett, J., Chrisp, T. M., Starss, G., 2006. Electrical property \nmeasurements using a modified hydraulic oedometer. Can. Geotech. J, \n42, pp. 655\u2013662. \n\n\n\nMeyerhof, G. G., 1976. Bearing capacity and settlement of pile foundation. \nJ.of the Geotechnical Engineering division, ASCE, 102, pp. 196 \u2013 228. \n\n\n\nMilton, J., 2007. Field Geophysics. John Wiley and Sons Limited, Chichester, \nEngland, pp. 93 \u2013 103. \n\n\n\nMinapuye, I. O., Soronnadi-Ononiwu, G. C., Princes, M., and Oduah, A. A., \n\n\n\n2018. Tectonostratigraphy of Benin Basin, West Africa American. \nScientific Research Journal for Engineering, Technology and Sciences, \npp. 1-16. \n\n\n\nNSDWQ., 2007. Nigeria Standard for Drinking Water Quality. Nigeria \nIndustrial Standard NIS, Standard Organization of Nigeria, pp. 30.  \n\n\n\nOki, O. A., Egai, A. O., Akana, T. S., 2016. Soil corrosivity assessment in the \npre-design of SUB-Surface water pipe distributary network in \nYenagoa, South-South Nigeria using electrical resistivity. Geosciences, \n6, pp. 13-20. \n\n\n\nOkosun, E. A.., 1990. A Review of the Cretaceous Stratigraphy of the \nDahomey Embayment, West Africa. Cretaceous Research, 11, pp. 17-\n27.  \n\n\n\nOladunjoye, M. A., Salami, A. J., Aizebeokhai, A. P., Sanuade, O. A., and Kaka, \nS. I., 2017. Preliminary geotechnical characterization of a site in \nSouthwest Nigeria using integrated electrical and seismic methods. \nJournal of the Geological Society of India, 89(2), pp. 209\u2013215. \ndoi:10.1007/s12594-017-0585-z \n\n\n\nOmatsola, M.E., and Adegoke, O.S., 1981. Tectonic Evolution and \nCretaceous Stratigraphy of the Dahomey Basin. Journal of Mining and \nGeology, 18, pp. 130-137. \n\n\n\nPeck, R. B., Hanson, W. E., and Thornburn, T.  H., 1953. Foundation \nEngineering. New York, John Willey and Sons.  \n\n\n\nRobinson, W., 1993. Materials Performance, 32, pp. 56-58. \n\n\n\nStopi\u0144ski, W., 2003. Bedrock monitoring by means of the electric \nresistivity method during the construction and operation of the \nCzorsztyn-Niedzica dam. Acta Geophys, 51, pp. 215\u2013256. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 96-102 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2020.96.102 \n\n\n\nCite the Article: Rodeano Roslee,  Jeffery Anak Pirah,  Mohd Fauzi Zikiri, Ahmad Nazrul Madri (2020). Applicability Of The Rock Mass Rating (RMR) System For The \nTrusmadi Formation At Sabah, Malaysia. Malaysian Journal of Geosciences, 4(2): 96-102. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2020.96.102 \n\n\n\nAPPLICABILITY OF THE ROCK MASS RATING (RMR) SYSTEM FOR THE TRUSMADI \n\n\n\nFORMATION AT SABAH, MALAYSIA \n\n\n\nRodeano Rosleea,b*,  Jeffery Anak Piraha,c,  Mohd Fauzi Zikiria,d, Ahmad Nazrul Madria,d \n\n\n\na Universiti Malaysia Sabah, Faculty of Science and Natural Resources, UMS Road, 88400 Kota Kinabalu, Sabah, Malaysia. \nb Universiti Malaysia Sabah, Natural Disaster Research Centre (NDRC), UMS Road, 88400 Kota Kinabalu, Sabah, Malaysia. \nc Alamega Konsult, 2nd Floor, Block B, Lot 12-2, Plaza Utama, Jalan Penampang By Pass, 88300 Kota Kinabalu, Sabah, Malaysia. \nd Department of Public of Work (Sabah State), Slope Branch, Sembulan Road, 88538 Kota Kinabalu, Sabah, Malaysia. \n\n\n\n*Correspondence Author Email: rodeano@ums.edu.my \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 02 October 2020 \nAccepted 05 November 2020 \nAvailable Online 18 November 2020\n\n\n\nRock Mass Classification Systems (RMCS) can be of considerable use in the initial stage of a project when little \n\n\n\nor no detailed information is available. There is a large number of RMCS developed for general purposes but \n\n\n\nalso for specific applications such as Rock Quality Designation (RQD), Rock Mass Rating (RMR), Rock Structure \n\n\n\nRating (RSR), Geological Strength Index (GSI), Slope Mass Rating (SMR), etc. In this paper, we present the \n\n\n\nresults of the applicability of the Rock Mass Rating (RMR) System for the Trusmadi Formation in Sabah, \n\n\n\nMalaysia. The RMR system is a RMCS incorporated with five (5) parameters:  Strength of intact rock material,  \n\n\n\nRock Quality Designation (RQD), Spacing of joints, Condition of joints, and Groundwater conditions. A total of \n\n\n\nten (10) locations were selected on the basis of exposures of the lithology and slope condition of the Trusmadi \n\n\n\nFormation. Trusmadi Formation is Paleocene to Eocene in aged. The Trusmadi Formation generally shows \n\n\n\ntwo major structural orientations NW-SE and NE-SW. Trusmadi Formation is characterized by the present of \n\n\n\ndark colour argillaceous rocks, siltstone and thin-bedded turbidite in well-stratified sequence. Some of the \n\n\n\nTrusmadi Formation rocks have been metamorphosed to low grade of the greenish-schist facies; the sediment \n\n\n\nhas become slate, phyllite and metarenite. Cataclastic rocks are widespread and occur as black phyllonite \n\n\n\nenclosing arenitic and lutitic boudins with diameter up to a meter or demarcating thin to thicker fault zones \n\n\n\nor as flaser zones with hardly any finer grain matrix or as zones of closely spaced fractures. Quartz and calcite \n\n\n\nveins are quite widespread within the crack deformed on sandstone beds. The shale is dark grey when fresh \n\n\n\nbut changes light grey to brownish when weathered. The RMR system for 10 outcrops ranges from 33.0 to \n\n\n\n50.0 and its classified as \u201cFair\u201d (Class III) to \u201cPoor\u201d (Class IV) rocks. The Fair Rock (Class III) recommended \n\n\n\nthat the excavation should be top heading and bench 1.5 m \u2013 3 m advance in the top heading. Support should \n\n\n\nbe commencing after each blast and complete support 10 m from face. Rock bolts should be systematic with 4 \n\n\n\nm long spaced 1.5 m - 2 m in crown and walls with wire mesh in crown. Shotcrete should be 50 mm \u2013 100 mm \n\n\n\nin crown and 30 mm in sides. While for the Poor Rock (Class IV), the excavation should be top heading and \n\n\n\nbench 1.0 m \u2013 1.5 m advance in top heading. Support should be installed concurrently with excavation, 10 m \n\n\n\nfrom face. Rock bolt should be systematic with 4 m \u2013 5 m long, spaced 1.5 m \u2013 1.5 m in crown and walls with \n\n\n\nwire mesh. Shotcrete of 100 m \u2013 150 mm in crown and 100 mm in sides. The steel sets should be light to \n\n\n\nmedium ribs spaced 1.5 m only when required.  \n\n\n\nKEYWORDS \n\n\n\nRock Mass Rating (RMR) System, Rock Mass Classification Scheme (RMCS) &  Trusmadi Formation. \n\n\n\n1. INTRODUCTION \n\n\n\nRock Mass Classification Systems (RMCS) can be of considerable use in the \n\n\n\ninitial stage of a project when little or no detailed information is available. \n\n\n\nThere is a large number of RMCS developed for general purposes but also \n\n\n\nfor specific applications. Most of the multi-parameter were developed from \n\n\n\ncivil engineering case histories in which all of the components of the \n\n\n\nengineering geological characteristics of the rock mass were included in \n\n\n\nRMCS (Wickham et al ., 1972; Bieniawski, 1973; 1989; and Barton et al., \n\n\n\n1974). The RMCS take into consideration several factors, which are \n\n\n\nbelieved to affect the stability. The parameters are therefore often related \n\n\n\nto the discontinuities such as the number of joint sets, joint distance, \n\n\n\nroughness, alteration and filling of joints, groundwater conditions, and \n\n\n\nsometimes also the strength of the intact rock and the stress magnitude.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 96-102 \n\n\n\n\n\n\n\n \nCite the Article: Rodeano Roslee,  Jeffery Anak Pirah,  Mohd Fauzi Zikiri, Ahmad Nazrul Madri (2020). Applicability Of The Rock Mass Rating (RMR) System For The \n\n\n\nTrusmadi Formation At Sabah, Malaysia. Malaysian Journal of Geosciences, 4(2): 96-102. \n \n\n\n\n\n\n\n\nRMCS is an indirect approach and does not measure the mechanical \n\n\n\nproperties as well. The result is an estimate of the stability quantified in \n\n\n\nsubjective terms such as e.g. bad, acceptable, good or very good. The value \n\n\n\nobtained by some of the RMCS is used to estimate or calculate the rock mass \n\n\n\nstrength using a failure criterion. It can also be used to estimate necessary \n\n\n\nrock support. Therefore, it is important to understand that the use of a \n\n\n\nRMCS cannot replace some of the more elaborate design procedures. \n\n\n\nHowever, the use of these design procedures requires access to relatively \n\n\n\ndetailed information on in situ stresses, rock mass properties and planned \n\n\n\nexcavation sequence, none of which may be available at an early stage in \n\n\n\nthe project. As this information becomes available, the use of the RMCS \n\n\n\nshould be updated and used in conjunction with site specific analyses.  \n\n\n\n\n\n\n\nBieniawski published the details of Rock Mass Rating (RMR) system \n\n\n\n(Bieniawski, 1976). Over the years, this system has been successively \n\n\n\nrefined as more case records have been examined and the reader should \n\n\n\nbe aware that Bieniawski has made significant changes in the ratings \n\n\n\nassigned to different parameters. In applying this RMR system, the rock \n\n\n\nmass is divided into a number of structural regions and each region is \n\n\n\nclassified separately. The boundaries of the structural regions usually \n\n\n\ncoincide with a major structural feature such as a fault or with a change in \n\n\n\nrock type. In some cases, significant changes in discontinuity spacing or \n\n\n\ncharacteristics, within the same rock type, may necessitate the division of \n\n\n\nthe rock mass into a number of small structural regions. \n\n\n\n2. BACKGROUND OF STUDY AREA \n\n\n\nStudy area is located about 110km from Kota Kinabalu city center. It is \n\n\n\nbounded between longitude line E 116o 30\u2019 to E 116o 40\u2019 and latitude line \n\n\n\nN 06o 09\u2019 to N 06o15\u2019 (Figure 1). Due to this study only concentrated on the \n\n\n\nTrusmadi Formation, all activities such mapping, sampling, observation \n\n\n\nand monitoring is more focused on the slopes under this formation. The \n\n\n\nTrusmadi Formation consists of dark argillaceous rocks, siltstone, and \n\n\n\nsandstone with rare volcanic (Jacobson, 1970; Rodeano et al., 2010; \n\n\n\nNorbert et al., 2016; Rodeano et al., 2018). The age of the Trusmadi \n\n\n\nFormation ranges from late Paleocene to early Eocene (Table 1) (Jacobson, \n\n\n\n1970). Low-grade metamorphism has occurred in some of the rocks of the \n\n\n\nTrusmadi Formation. The rocks are sheared and brecciate and cataclasites \n\n\n\nare common. The dark argillaceous rocks are thickly bedded or \n\n\n\ninterbedded with sandstone and siltstone beds. The thickness of the \n\n\n\nargillaceous beds is about 30 m, whereas the sandstone beds are about 37 \n\n\n\nm in the Gunung Kinabalu area (Jacobson, 1970). Rare volcanic rocks, \n\n\n\nmainly spilite also occur in the Trusmadi Formation. Quartz veining is \n\n\n\nquite common in this Formation.  \n\n\n\n3. DETERMINATION OF ROCK MASS RATING (RMR) SYSTEM \n\n\n\nField studies have been carried out to study the lithological and structural \n\n\n\nvariations in rock slopes. A total of ten (10) locations were selected on the \n\n\n\nbasis of exposures of the lithology and slope condition of the Trusmadi \n\n\n\nFormation (Figure 2). Slopes at these locations were studied and classified \n\n\n\nfor their Rock Mass Rating (RMR) System were calculated by using below \n\n\n\nequation: \n\n\n\nRMR = Parameter A + Parameter B + Parameter C + Parameter D + \n\n\n\nParameter E  (1) (Bieniawski, 1989) \n\n\n\nWhere,  \n\n\n\nParameter A= Strength of intact rock material. Uniaxial compressive \n\n\n\nstrength is preferred. For rock of moderate to high strength, point load \n\n\n\nindex is acceptable. \n\n\n\nParameter B= Rock quality designation (RQD) which, as an attempt to \n\n\n\nquantify rock mass quality. RQD only represents the degree of fracturing \n\n\n\nof the rock mass. It does not account for the strength of the rock or \n\n\n\nmechanical and other geometrical properties of the joints.  \n\n\n\nParameter C= Spacing of joints. Average spacing of all rock discontinuities \n\n\n\nis used. \n\n\n\nParameter D= Condition of joints. Condition includes joint aperture, \n\n\n\npersistence, roughness, joint surface weathering and alteration, and \n\n\n\npresence of infilling. \n\n\n\nParameter E= Groundwater conditions. It is to account for groundwater \n\n\n\ninflow in excavation stability. \n\n\n\n\n\n\n\nFigure 1: Location of study area \n\n\n\nTable 1: Local Stratigraphic Column and their Water Bearing and \nEngineering Remarks for the Trusmadi Formation \n\n\n\nAge Unit General \nCharacter \n\n\n\nWater-\nBearing \nProperties \n\n\n\nEngineering \nRemarks \n\n\n\nPaleocene \nto Eocene \n\n\n\n\n\n\n\nTrusmadi \nSlate and \nTrusmadi \nPhyllite \n\n\n\nComprise of \ndark colour \nargillaceous \nrock either \nin thick \nbedded or \ninterbedded \nwith thin \nsandstone \nbeds and \nsiltstone.  \n\n\n\nFractured \nsandstone has \nsignificant to \ngroundwater. \n\n\n\nDangerous \nsite for heavy \nstructure. \nImprovement \nshould be \nconducted \nbefore any \nproject. \n\n\n\n\n\n\n\n\n\n\n\nFigure 2: Selected rock slopes location with their photographs \n\n\n\nTable 2 is the RMR system classification updated in 1989. Part A of the \n\n\n\ntable shows the RMR system classification with the above 5 parameters. \n\n\n\nIndividual rate for each parameter is obtained from the property of each \n\n\n\nparameter. The weight of each parameter has already considered in the \n\n\n\nrating. The overall basic RMR system rate is the sum of individual rates. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 96-102 \n\n\n\n\n\n\n\n \nCite the Article: Rodeano Roslee,  Jeffery Anak Pirah,  Mohd Fauzi Zikiri, Ahmad Nazrul Madri (2020). Applicability Of The Rock Mass Rating (RMR) System For The \n\n\n\nTrusmadi Formation At Sabah, Malaysia. Malaysian Journal of Geosciences, 4(2): 96-102. \n \n\n\n\n\n\n\n\nInfluence of joint orientation on the stability of excavation is considered in \n\n\n\nPart B of the same table. Explanation of the descriptive terms used is given \n\n\n\ntable Part C. With adjustment made to account for joint orientation, a final \n\n\n\nRMR system rating is obtained it can be also expresses in rock mass class, \n\n\n\nas shown in Tables 2 and 3. The tables also gives the meaning of rock mass \n\n\n\nclasses in terms of stand-up time, equivalent rock mass cohesion and \n\n\n\nfriction angle. Part D indicate the meaning of rock classes, Part E described \n\n\n\nthe guidelines for classification of discontinuity conditions and Part F \n\n\n\nexplained the effect of discontinuity strike and dip orientation in \n\n\n\ntunneling. Upon obtaining a RMR System, the value will be matched to a \n\n\n\nfigure of recommended guidelines for support in tunnels and mine (Table \n\n\n\n4). \n\n\n\n\n\n\n\nTable 2: Rock Mass Rating (RMR) System (Bieniawski, 1989) \n\n\n\nA. CLASSIFICATION PARAMETERS AND THEIR RATINGS \n\n\n\nParameter Range of values \n\n\n\n1 \n\n\n\nStrength \n\n\n\nof intact \n\n\n\nrock \n\n\n\nmaterial \n\n\n\nPoint-load strength \n\n\n\nindex \n>10MPa 4 \u2013 10MPa 2 \u2013 4MPa 1 \u2013 2MPa \n\n\n\nFor this low range \u2013 \n\n\n\nuniaxial \n\n\n\ncompressive test is \n\n\n\npreferred \n\n\n\nUniaxial \n\n\n\ncompression \n\n\n\nstrength \n\n\n\n>250MPa 100 \u2013 250MPa 50 \u2013 100MPa 25 \u2013 50MPa \n\n\n\n5-\n\n\n\n25 \n\n\n\nMPa \n\n\n\n1-5 \n\n\n\nMPa \n\n\n\n<1 \n\n\n\nMPa \n\n\n\nRating 15 12 7 4 2 1 0 \n\n\n\n2 \nDrill core quality RQD 90% - 100% 75% - 90% 50% - 75% 25% - 50% < 25% \n\n\n\nRating 20 17 13 8 3 \n\n\n\n3 \nSpacing of discontinuities >2m 0.6 \u2013 2m 200 \u2013 600mm 60 \u2013 200mm <60mm \n\n\n\nRating 20 15 10 8 5 \n\n\n\n4 \n\n\n\n\n\n\n\n\n\n\n\nCondition of discontinuities \n\n\n\n(See E) \n\n\n\nVery rough \n\n\n\nsurfaces \n\n\n\nNot continuous \n\n\n\nNo separation \n\n\n\nUnweathered wall \n\n\n\nrock \n\n\n\nSlightly rough \n\n\n\nsurfaces \n\n\n\nSeparation <1mm \n\n\n\nSlightly weathered \n\n\n\nwalls \n\n\n\nSlightly rough \n\n\n\nsurfaces \n\n\n\nSeparation <1mm \n\n\n\nHighly weathered \n\n\n\nwalls \n\n\n\nSlickensided \n\n\n\nsurfaces or \n\n\n\ngouge <5mm thick \n\n\n\nor \n\n\n\nSeparation 1-5mm \n\n\n\ncontinuous \n\n\n\nSoft gouge >5mm \n\n\n\nthick \n\n\n\nor Separation \n\n\n\n>5mm continuous \n\n\n\nRating 30 25 20 10 0 \n\n\n\n5 \n\n\n\n\n\n\n\nGround \n\n\n\nwater \n\n\n\nInflow per 10m \n\n\n\ntunnel length (l/m) \nNone <10 10 \u2013 25 25 \u2013 125 >125 \n\n\n\n(Joint water press)/ \n\n\n\n(major principal \u03c3) \n0 <0.1 0.1 \u2013 0.2 0.2 \u2013 0.5 >0.5 \n\n\n\nGeneral conditions Completely dry Damp Wet Dripping Flowing \n\n\n\nRating 15 10 7 4 0 \n\n\n\nB. RATING ADJUSTMENT FOR DISCONTINUITY ORIENTATIONS (See F) \n\n\n\nStrike and dip orientations Very Favourable Favourable Fair Unfavourable Very Unfavourable \n\n\n\n\n\n\n\nRatings \n\n\n\nTunnels and mines 0 -2 -5 -10 -12 \n\n\n\nFoundations 0 -2 -7 -15 -25 \n\n\n\nSlopes 0 -5 -25 -50 -60 \n\n\n\nC. ROCK MASS CLASSES DETERMINED FROM TOTAL RATINGS \n\n\n\nRating 100 \uf0df 81 80 \uf0df 61 60 \uf0df 41 40 \uf0df 21 <21 \n\n\n\nClass number I II III IV V \n\n\n\nDescription Very good rock Good rock Fair rock Poor rock Very poor rock \n\n\n\nD. MEANING OF ROCK CLASSES \n\n\n\nClass number I II III IV V \n\n\n\nAverage stand-up time 20 yrs for 15m \n\n\n\nspan \n\n\n\n1 year for 10m \n\n\n\nspan \n\n\n\n1 week for 5m span 10 hrs for 2.5m \n\n\n\nspan \n\n\n\n30 min for 1m span \n\n\n\nCohesion of rock mass (kPa) >400 300 \u2013 400 200 \u2013 300 100 \u2013 200 <100 \n\n\n\nFriction angle of rock mass (deg) >45 35 \u2013 45 25 \u2013 35 15 \u2013 25 <15 \n\n\n\nE. GUIDELINES FOR CLASSIFICATION OF DISCONTINUITY CONDITIONS \n\n\n\nDiscontinuity length (persistence) \n\n\n\nRating \n\n\n\n<1m \n\n\n\n6 \n\n\n\n1.3m \n\n\n\n4 \n\n\n\n3 \u2013 10m \n\n\n\n2 \n\n\n\n10 \u2013 20m \n\n\n\n1 \n\n\n\n>20m \n\n\n\n0 \n\n\n\nSeparation (aperture) \n\n\n\nRating \n\n\n\nNone \n\n\n\n6 \n\n\n\n<0.1mm \n\n\n\n5 \n\n\n\n0.1 \u2013 1.0mm \n\n\n\n4 \n\n\n\n1 \u2013 5mm \n\n\n\n1 \n\n\n\n>5mm \n\n\n\n0 \n\n\n\nRoughness \n\n\n\nRating \n\n\n\nVery rough \n\n\n\n6 \n\n\n\nRough \n\n\n\n5 \n\n\n\nSlightly rough \n\n\n\n3 \n\n\n\nSmooth \n\n\n\n1 \n\n\n\nSlickensided \n\n\n\n0 \n\n\n\nInfilling (gouge) \n\n\n\nRating \n\n\n\nNone \n\n\n\n6 \n\n\n\nHard filling <5mm \n\n\n\n4 \n\n\n\nHard filling >5mm \n\n\n\n2 \n\n\n\nSoft filling <5mm \n\n\n\n2 \n\n\n\nSoft filling >5mm \n\n\n\n0 \n\n\n\nWeathering \n\n\n\nRating \n\n\n\nUnweathered \n\n\n\n6 \n\n\n\nSlightly weathered \n\n\n\n5 \n\n\n\nModerately \n\n\n\nweathered \n\n\n\n3 \n\n\n\nHighly weathered \n\n\n\n1 \n\n\n\nDecomposed \n\n\n\n0 \n\n\n\nF. EFFECT OF DISCONTINUITY STRIKE AND DIP ORIENTATION IN TUNNELLING ** \n\n\n\nStrike perpendicular to tunnel axis Strike parallel to tunnel axis \n\n\n\nDrive with dip \u2013 Dip 45 \u2013 90\u00b0 Drive with dip \u2013 Dip 20 \u2013 45\u00b0 Dip 45 \u2013 90\u00b0 Dip 20 \u2013 45\u00b0 \n\n\n\nVery favourable Favourable Very unfavourable Fair \n\n\n\nDrive against dip \u2013 Dip 45 \u2013 90\u00b0 Drive against dip \u2013 Dip 20 \u2013 45\u00b0 Dip 0 \u2013 20 \u2013 Irrespective of strike\u00b0 \n\n\n\nFair Unfavourable Fair \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 96-102 \n\n\n\n\n\n\n\n \nCite the Article: Rodeano Roslee,  Jeffery Anak Pirah,  Mohd Fauzi Zikiri, Ahmad Nazrul Madri (2020). Applicability Of The Rock Mass Rating (RMR) System For The \n\n\n\nTrusmadi Formation At Sabah, Malaysia. Malaysian Journal of Geosciences, 4(2): 96-102. \n \n\n\n\n\n\n\n\nTable 3: Rock mass classes determined from total ratings and meaning (Bieniawski, 1989) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable 4: Guidelines for excavation and support of 10m span rock tunnels in accordance with the RMR System (Bieniawski, 1989) \n\n\n\nRock Mass Class Excavation \n\n\n\nRock bolts \n\n\n\n(20mm diameter, fully \n\n\n\ngrouted) \n\n\n\nShotcrete Steel sets \n\n\n\nI \u2013 Very good rock \n\n\n\nRMR: 81 \u2013 100 \nFull face, 3m advance Generally no support required except spot bolting \n\n\n\nII \u2013 Good rock RMR: 61 \u2013 \n\n\n\n80 \n\n\n\nFull face, 1 \u2013 1.5m advance. Complete \n\n\n\nsupport 20m from face. \n\n\n\nLocally, bolts in crown 3m \n\n\n\nlong, spaced 2.5m with \n\n\n\noccasional wire mesh \n\n\n\n50mm in crown \n\n\n\nwhere required. \nNone \n\n\n\nIII \u2013 Fair rock RMR: 41 \u2013 60 \n\n\n\nTop heading and bench 1.5 \u2013 3m \n\n\n\nadvance in top heading. Commence \n\n\n\nsupport after each blast. Complete \n\n\n\nsupport 10m from face \n\n\n\nSystematic bolts 4m long, \n\n\n\nspaced 1.5 \u2013 2m in crown and \n\n\n\nwalls with wire mesh in crown \n\n\n\n50 \u2013 100mm in \n\n\n\ncrown and 30mm in \n\n\n\nsides. \n\n\n\nNone \n\n\n\nIV \u2013 Poor rock RMR: 21 \u2013 \n\n\n\n40 \n\n\n\nTop heading and bench 1.0 \u2013 1.5m \n\n\n\nadvance in top heading. Install \n\n\n\nsupport concurrently with \n\n\n\nexcavation, 10m from face \n\n\n\nSystematic bolts 4 \u2013 5m long, \n\n\n\nspaced 1 \u2013 1.5m in crown and \n\n\n\nwalls with wire mesh. \n\n\n\n100 \u2013 150mm in \n\n\n\ncrown and 100mm \n\n\n\nin sides \n\n\n\nLight to medium ribs \n\n\n\nspaced 1.5m where \n\n\n\nrequired \n\n\n\nV \u2013 Very poor rock \n\n\n\nRMR: <20 \n\n\n\nMultiple drifts 0.5 \u2013 1.5m advance in \n\n\n\ntop heading. Install support \n\n\n\nconcurrently with excavation. \n\n\n\nShotcrete as soon as possible after \n\n\n\nblasting \n\n\n\nSystematic bolts 5 \u2013 6m long, \n\n\n\nspaced 1 \u2013 1.5m in crown and \n\n\n\nwalls with wire mesh. Bolt \n\n\n\ninvert. \n\n\n\n150 \u2013 200mm in \n\n\n\ncrown, 150mm in \n\n\n\nsides and 50mm on \n\n\n\nface \n\n\n\nMedium to heavy ribs \n\n\n\nspaced 0.75m with \n\n\n\nsteel lagging and \n\n\n\nforepoling if required. \n\n\n\nClose invert. \n\n\n\n4. ESTIMATION OF ROCK MASS RATING (RMR) SYSTEM \n\n\n\n4.1 Strength of intact rock material \n\n\n\nThe strength of intact rock material of the Trusmadi Formation was \n\n\n\nestablished by testing approximately 10 rock samples. There are 3 tests \n\n\n\nconducted to obtain the strength of intact rock material; Schmidt hammer \n\n\n\nrebound test, Point load test (Is (50)) and Uniaxial compressive strength \n\n\n\n(UCS). However, the Point load test (Is (50)) and UCS are chosen to \n\n\n\ndetermine the RMR System value because it has higher precision and \n\n\n\naccording to the classification scheme introduced (Table 5) (Hoek et al., \n\n\n\n1998). \n\n\n\n\n\n\n\nTable 5: Strength of intact rock material results \n\n\n\nLocation \nN5\u00b055.053\u2019, \n\n\n\nE116\u00b036.859\u2019 \n\n\n\nN5\u00b054.521\u2019, \n\n\n\nE116\u00b035.703\u2019 \n\n\n\nN5\u00b053.901\u2019 , \n\n\n\nE116\u00b035.105\u2019 \n\n\n\nN5\u00b054.683\u2019, \n\n\n\nE116\u00b034.548\u2019 \n\n\n\nN5\u00b053.463\u2019 , \n\n\n\nE116\u00b033.856\u2019 \n\n\n\nDepth (m) 0.30-0.50 0.30-0.50 0.30-0.50 0.30-0.50 0.30-0.50 \n\n\n\nSample No. TR1 TR2 TR3 TR4 TR5 \n\n\n\nRock Strength tests \n\n\n\nWeathering Grade - III \u2013 IV III \u2013 IV III \u2013 IV III \u2013 IV III \u2013 IV \n\n\n\nPoint load test (Is (50)) mPa 0.4727 0.3800 0.4214 0.4694 0.4738 \n\n\n\nUniaxial compressive strength \n\n\n\n(UCS) \nmPa 5.849 5.289 5.645 5.847 5.283 \n\n\n\nDescription Moderately weak Moderately weak Moderately weak Moderately weak Moderately weak \n\n\n\nRating 2.0 2.0 2.0 2.0 2.0 \n\n\n\nLocation \nN5\u00ba53.139\u2019, \n\n\n\nE116\u00ba33.173\u2019 \n\n\n\nN5\u00ba57.472\u2019, \n\n\n\nE116\u00ba30.908\u2019 \n\n\n\nN5\u00ba57.411\u2019, \n\n\n\nE116\u00ba32.893\u2019 \n\n\n\nN5\u00ba58.105\u2019, \n\n\n\nE116\u00ba32.289\u2019 \n\n\n\nN5\u00ba58.648\u2019, \n\n\n\nE116\u00ba31.815\u2019 \n\n\n\nDepth (m) 0.30-0.50 0.30-0.50 0.30-0.50 0.30-0.50 0.30-0.50 \n\n\n\nSample No. TR6 TR7 TR8 TR9 TR10 \n\n\n\nRock Strength tests \n\n\n\nWeathering Grade - III \u2013 IV III \u2013 IV III \u2013 IV III \u2013 IV III \u2013 IV \n\n\n\nPoint load test (Is (50)) mPa 0.3838 0.4849 0.4109 0.3852 0.5237 \n\n\n\nUniaxial compressive strength \n\n\n\n(UCS) \nmPa 5.497 5.225 6.143 5.894 5.362 \n\n\n\nDescription Moderately weak Moderately weak Moderately weak Moderately weak Moderately weak \n\n\n\nRating 2.0 2.0 2.0 2.0 2.0 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 96-102 \n\n\n\n\n\n\n\n \nCite the Article: Rodeano Roslee,  Jeffery Anak Pirah,  Mohd Fauzi Zikiri, Ahmad Nazrul Madri (2020). Applicability Of The Rock Mass Rating (RMR) System For The \n\n\n\nTrusmadi Formation At Sabah, Malaysia. Malaysian Journal of Geosciences, 4(2): 96-102. \n \n\n\n\n\n\n\n\nBased on the results in Table 5, the ranges of point load strength index (Is \n\n\n\n(50)) significantly from 0.3800 MPa to 0.5237 MPa (moderately weak). \n\n\n\nWhile UCS results also indicated the similar category moderately week, \n\n\n\nwhich is varies from the lowest 5.225 MPa to the highest 6.143 MPa. Both \n\n\n\nthe experimental results show that the rocks exhibit very high grade of \n\n\n\nweathering features such as chemical weathering and influences from \n\n\n\nrainfall. Moreover, a new foliated/sheared rock mass category of the \n\n\n\nTrusmadi Formation has thus been considered to better represent thinly \n\n\n\nfoliated and structurally sheared weak rocks. In these rock masses the \n\n\n\nfoliation is the predominant structural feature which prevails over any \n\n\n\nother discontinuity set, resulting in complete lack of blockiness. \n\n\n\n4.2 Rock Quality Designation (RQD)  \n\n\n\nDue to unable to obtain a drill core sample for the selected outcrops, other \n\n\n\nmethod has been used in determining the value of Rock Quality \n\n\n\nDesignation (RQD). By using the method that was introduced by Priest & \n\n\n\nHudson (1976), RQD value has been estimated from the average of joint \n\n\n\nspacing. The value of RQD later was used in classification of GSI (Hoek & \n\n\n\nKarzulovic, 2000). The value of RQD for the 10 selected slopes in the study \n\n\n\narea is shown in Table 6. Based on the RQD results, the rock mass quality \n\n\n\nin the study area can be categorised as fair where the RQD values is \n\n\n\nranging between 50.37 to 65.18 %. This result indicates that the value of \n\n\n\nRQD is directly influenced by the presence of discontinuity characteristics \n\n\n\nin the intact rock. This condition is proved by the presence of the lower \n\n\n\nintensity of joint sets or shale unit, the value of RQD will be higher.  \n\n\n\n\n\n\n\nTable 6: Results for rock quality designation (RQD) \n\n\n\nLocation No. \n\n\n\nRock Quality \n\n\n\nDesignation, 100e-0.1\u03bb \n\n\n\n(0.1 \u03bb+1) \n\n\n\nRock Quality \n\n\n\nDescription \nRating \n\n\n\nTR1 61.51 Fair rock 13.0 \n\n\n\nTR2 54.49 Fair rock 13.0 \n\n\n\nTR3 50.37 Fair rock 13.0 \n\n\n\nTR4 53.90 Fair rock 13.0 \n\n\n\nTR5 61.52 Fair rock 13.0 \n\n\n\nTR6 52.49 Fair rock 13.0 \n\n\n\nTR7 65.18 Fair rock 13.0 \n\n\n\nTR8 60.26 Fair rock 13.0 \n\n\n\nTR9 55.42 Fair rock 13.0 \n\n\n\nTR10 58.25 Fair rock 13.0 \n\n\n\n4.3 Spacing of discontinuities  \n\n\n\nDiscontinuity spacing is a basic measurement of the distance between one \n\n\n\ndiscontinuity and another. Priest stated three forms of discontinuity \n\n\n\nspacing measurements: total spacing, set spacing, and normal set spacing \n\n\n\n(Priest, 1993). Total spacing is the distance between two adjacent \n\n\n\ndiscontinuities, measured along a sampling line but with a specified \n\n\n\nlocation and orientation. Set of spacing is the distance between adjacent \n\n\n\ndiscontinuities from a particular discontinuity set measured along a \n\n\n\nsampling line but with a specified location and orientation. Normal set \n\n\n\nspacing is the set spacing measured along a sampling line that is normal to \n\n\n\nthe mean orientation of a particular set. \n\n\n\n\n\n\n\nDiscontinuity spacing determines the dimensions of the blocks of rocks in \n\n\n\na slope which influences the overall stability of the rock slope. Therefore, \n\n\n\nit is an important parameter in designing appropriate stabilization \n\n\n\nmeasures for rock slopes such as rock bolts and rock fall barriers (Priest \n\n\n\nand Hudson, 1976). Similarly, discontinuity spacing is one of the most \n\n\n\nimportant parameters to describe the quality of a complete rock mass. It \n\n\n\nis widely used in the rock mass classification system such as the rock mass \n\n\n\nrating system (Priest, 1993). A total of 1,258 discontinuity of fractures \n\n\n\n(joints) were measured from the study area. From the data obtained, the \n\n\n\noccurrences of discontinuity spacing were recorded and divided into two \n\n\n\n(2) categories; 60-200 mm (rating = 8) and 200-600 m (rating = 10) (Table \n\n\n\n7). \n\n\n\nTable 7: Results for spacing of discontinuities \n\n\n\nLocation No. Spacing of discontinuities Rating \n\n\n\nTR1 \uf0bb 75-105mm 8.0 \n\n\n\nTR2 \uf0bb 480-560mm 10.0 \n\n\n\nTR3 \uf0bb 65-155mm 8.0 \n\n\n\nTR4 \uf0bb 450-580mm 10.0 \n\n\n\nTR5 \uf0bb 114-135mm 8.0 \n\n\n\nTR6 \uf0bb 240-480mm 10.0 \n\n\n\nTR7 \uf0bb 106-180mm 8.0 \n\n\n\nTR8 \uf0bb 88-176mm 8.0 \n\n\n\nTR9 \uf0bb 450-575m 10.0 \n\n\n\nTR10 \uf0bb 425-550m 10.0 \n\n\n\n4.4 Condition of discontinuities \n\n\n\nA discontinuity of fractures (joints) is an interface face of two contacting \n\n\n\nsurfaces. The surfaces can be smooth or rough; they can be in good contact \n\n\n\nand matched, or they can be poorly contacted and mismatched. The \n\n\n\ncondition of contact also governs the aperture of the interface. The \n\n\n\ninterface can also be filled with intrusive or weathered materials. Joint \n\n\n\nsurface roughness is a measure of the inherent surface unevenness and \n\n\n\nwaviness of the discontinuity relative to its mean plane. The roughness is \n\n\n\ncharacterised by large scale waviness and small scale unevenness of a \n\n\n\ndiscontinuity. It is the principal governing factor the direction of shear \n\n\n\ndisplacement and shear strength, and in turn, the stability of potentially \n\n\n\nsliding blocks. \n\n\n\n\n\n\n\nRoughness can be distinguished between small scale surface irregularity \n\n\n\nor unevenness and large scale undulation or waviness of the discontinuity \n\n\n\nsurface. A classification of discontinuity roughness has been suggested, \n\n\n\nand is reproduced in Table 2 for RMR system. It describes the roughness \n\n\n\nfirst in meter scale (step, undulating, and planar) and then in centimeter \n\n\n\nscale (rough, smooth, and slickensided) (Bieniawski, 1989). The result of \n\n\n\ncondition of discontinuities from the field observation is presented in \n\n\n\nTable 8. Based on the results, the condition of discontinuities of the slopes \n\n\n\nin the study area can be categorized as: \n\n\n\na. Slightly rough surfaces, separation <1mm and highly weathered walls. \n\n\n\nb. Slickensided surfaces with gouge <5mm thick and separation 1-5mm \n\n\n\ncontinuous. \n\n\n\nThe classification is useful to describe the joint surface but does not give \n\n\n\nany quantitative measure. Moreover, filling is material in the rock \n\n\n\ndiscontinuities. The material separating the adjacent rock walls of \n\n\n\ndiscontinuities. The wide range of physical behaviour depends on the \n\n\n\nproperties of the filling material. In general, filling affects the shear \n\n\n\nstrength, deformability and permeability of the discontinuities. \n\n\n\n\n\n\n\nTable 8: Results for condition of discontinuities \n\n\n\nLocation No. Condition of discontinuities Rating \n\n\n\nTR1 \nSlightly rough surfaces, separation <1mm and \n\n\n\nhighly weathered walls \n20.0 \n\n\n\nTR2 \nSlickensided surfaces with gouge <5mm thick \n\n\n\nand separation 1-5mm continuous \n10.0 \n\n\n\nTR3 \nSlightly rough surfaces, separation <1mm and \n\n\n\nhighly weathered walls \n20.0 \n\n\n\nTR4 \nSlickensided surfaces with gouge <5mm thick \n\n\n\nand separation 1-5mm continuous \n10.0 \n\n\n\nTR5 \nSlickensided surfaces with gouge <5mm thick \n\n\n\nand separation 1-5mm continuous \n10.0 \n\n\n\nTR6 \nSlightly rough surfaces, separation <1mm and \n\n\n\nhighly weathered walls \n20.0 \n\n\n\nTR7 \nSlickensided surfaces with gouge <5mm thick \n\n\n\nand separation 1-5mm continuous \n10.0 \n\n\n\nTR8 \nSlightly rough surfaces, separation <1mm and \n\n\n\nhighly weathered walls \n20.0 \n\n\n\nTR9 \nSlickensided surfaces with gouge <5mm thick \n\n\n\nand separation 1-5mm continuous \n10.0 \n\n\n\nTR10 \nSlickensided surfaces with gouge <5mm thick \n\n\n\nand separation 1-5mm continuous \n10.0 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 96-102 \n\n\n\n\n\n\n\n \nCite the Article: Rodeano Roslee,  Jeffery Anak Pirah,  Mohd Fauzi Zikiri, Ahmad Nazrul Madri (2020). Applicability Of The Rock Mass Rating (RMR) System For The \n\n\n\nTrusmadi Formation At Sabah, Malaysia. Malaysian Journal of Geosciences, 4(2): 96-102. \n \n\n\n\n\n\n\n\n4.5 Groundwater conditions \n\n\n\nGroundwater occupying the fractures within a rock mass can significantly \n\n\n\nreduces the stability of a rock slope. Water pressure acting within a \n\n\n\ndiscontinuity reduces the effective normal stress acting on plane, thus \n\n\n\nreducing the shear strength along the plane. Water pressure within \n\n\n\ndiscontinuities that run roughly parallel to a slope face also increase the \n\n\n\ndriving forces acting on the rock mass. In the study area, groundwater \n\n\n\noccurs and moves through interstices or secondary pore openings in the \n\n\n\nrock formations in wet to flowing (Table 9). Such openings can be the pore \n\n\n\nspaces between individual sedimentary and meta-sediment grains, open \n\n\n\njoints and fractures or solution and cavernous opening in brecciated layers \n\n\n\nand cataclasites.  \n\n\n\n\n\n\n\nThe direction of groundwater movement is generally under the influence \n\n\n\nof gravity. The rock formations exhibit a high degree of weathering and \n\n\n\ncovered by thick residual soil that extends to more than 25 meters in \n\n\n\nthickness. Evaluation of more than 60 boreholes in the study area \n\n\n\nindicated that the groundwater table is shallow and ranges from 2 meters \n\n\n\nto about 15 meters (Rodeano, 2020). It\u2019s also seen that the water table \n\n\n\nfollows the topography from highland toward the road and the valley side. \n\n\n\nThe weathered materials are weak due to high fractures porosity and high \n\n\n\npore-water pressures that generated by both shallow and deep \n\n\n\ngroundwater.  \n\n\n\n\n\n\n\n\n\n\n\nTable 9: Results for groundwater conditions \n\n\n\nLocation No. \n\n\n\nGroundwater conditions \n\n\n\nInflow per 10m tunnel \n\n\n\nlength (l/m) \n\n\n\n(Joint water press)/ \n\n\n\n(major principal, \u03c3) \nGeneral conditions Rating \n\n\n\nTR1 10 \u2013 25 0.1 \u2013 0.2 Wet 7.0 \n\n\n\nTR2 > 125 > 0.5 Flowing 0.0 \n\n\n\nTR3 > 125 > 0.5 Flowing 0.0 \n\n\n\nTR4 25 \u2013 125 0.2 \u2013 0.5 Dripping 4.0 \n\n\n\nTR5 10 \u2013 25 0.1 \u2013 0.2 Wet 7.0 \n\n\n\nTR6 25 \u2013 125 0.2 \u2013 0.5 Dripping 4.0 \n\n\n\nTR7 > 125 > 0.5 Flowing 0.0 \n\n\n\nTR8 25 \u2013 125 0.2 \u2013 0.5 Dripping 4.0 \n\n\n\nTR9 10 \u2013 25 0.1 \u2013 0.2 Wet 7.0 \n\n\n\nTR10 25 \u2013 125 0.2 \u2013 0.5 Dripping 4.0 \n\n\n\n5. SUMMARY RESULT OF ROCK MASS RATING (RMR) \n\n\n\nCLASSIFICATION SYSTEM \n\n\n\nThe summary result of Rock Mass Rating (RMR) System are shown in \n\n\n\nTable 10. Based on the Table 10, the strenght of intact rock rating for all \n\n\n\nten rock sample from the outcrop is 2.0. For RQD ratings, it shows that all \n\n\n\nof the samples has 13.0 which indicates that the rock RQD quality ranges \n\n\n\nfrom 50% - 75%.  For TR1, TR3, TR5, TR7 and TR8, the spacing of joint \n\n\n\nrating is 8.0 which indicicates 60 mm \u2013 200 mm spacing of discontinunity, \n\n\n\nwhile TR2, TR4, TR6, TR9 and TR10 spacing of joints is 10.0 indicating a \n\n\n\n200mm \u2013 600mm discontinunity spacing. For the condition of joints \n\n\n\nrating, TR2, TR4, TR5, TR7, TR9 and TR10  rate is 10.0, which means the \n\n\n\ncondition of discontinunity on the outcrop has slickensided surface or \n\n\n\ngouge with less than 5 mm thick and seperation of 1 mm \u2013 5 mm \n\n\n\ncontinuously.  \n\n\n\nMeanwhile TR1, TR3, TR6 and TR8 has condition of joint rating of 20.0 \n\n\n\nindicating that the condition of discontinunities on respective outcrops \n\n\n\nhas slightly rough surfaces with seperation of less than 1mm and slightly \n\n\n\nweathered walls. Groundwater conditions rating for TR2, TR3 and TR7 is \n\n\n\n0.0 indicating that the general condition for the outcrops are completely \n\n\n\ndry, TR4, TR6, TR8 and TR10 has 4.0 rating for groundwater condition \n\n\n\nindicating a dripping water on outcrops while TR1, TR5 and TR9 has 7.0 \n\n\n\ngroundwater conditions rating indicating a flowing water on the outcrops. \n\n\n\nTherefore, the RMR ratings for 10 outcrops ranges from 33.0 to 50.0 and \n\n\n\nclassified as \u201cFair\u201d to \u201cPoor\u201d rocks.  \n\n\n\n\n\n\n\nTable 10: The total summarizes rating for the Rock Mass Rating (RMR) System results. \n\n\n\nStation TR1 TR2 TR3 TR4 TR5 TR6 TR7 TR8 TR9 TR10 \n\n\n\nStrength of intact rock material rating 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 \n\n\n\nRQD rating 13.0 13.0 13.0 13.0 13.0 13.0 13.0 13.0 13.0 13.0 \n\n\n\nSpacing of joints rating 8.0 10.0 8.0 10.0 8.0 10.0 8.0 8.0 10.0 10.0 \n\n\n\nCondition of joints rating 20.0 10.0 20.0 10.0 10.0 20.0 10.0 20.0 10.0 10.0 \n\n\n\nGroundwater conditions rating 7.0 0.0 0.0 4.0 7.0 4.0 0.0 4.0 7.0 4.0 \n\n\n\nTOTAL RMR rating 50.0 35.0 43.0 39.0 40.0 49.0 33.0 47.0 42.0 39.0 \n\n\n\nRMR System Classification \nFair \n\n\n\nrock \n\n\n\nPoor \n\n\n\nrock \n\n\n\nFair \n\n\n\nrock \n\n\n\nFair \n\n\n\nrock \n\n\n\nFair \n\n\n\nrock \n\n\n\nFair \n\n\n\nrock \n\n\n\nPoor \n\n\n\nrock \n\n\n\nFair \n\n\n\nrock \n\n\n\nFair \n\n\n\nrock \n\n\n\nPoor \n\n\n\nrock \n\n\n\n6. APPLICATION OF ROCK MASS RATING (RMR) SYSTEM FOR THE \n\n\n\nTRUSMADI FORMATION \n\n\n\nTable 3 shows the rock mass classes determined from total ratings and its \n\n\n\nmeaning. According to Tables 2 and 3, TR2, TR3, TR4, TR5, TR7, TR9 and \n\n\n\nTR10 falls in rock mass class D. Rock mass class D are generally poor rocks \n\n\n\nwith average stand- up time of 10 hours for 2.5m span with mass cohesion \n\n\n\nranges between 100 kPa \u2013 200 kPa and rock mass friction angle ranges \n\n\n\nfrom 15\u00b0 to 35\u00b0. Meanwhile, TR1, TR6 and TR8 falls on rock mass class C. \n\n\n\nFor rock mass class C, the rock from this class are generally fair rock. A \n\n\n\nclass C rock has average stand-up time of 1 week for 5 m span. The rock \n\n\n\nmass cohesion for this class ranges from 200 kPa \u2013 300 kPa with friction \n\n\n\nangle ranges from 25\u00b0 to 35\u00b0.  \n\n\n\nThe Rock Mass Rating (RMR) System of the Trusmadi Formation is \n\n\n\nclassified as Class III (fair rock) and Class IV (poor rock) (Table 4). The \n\n\n\nguideline for excavation and support of 10m span rock tunnels on Table 4 \n\n\n\nindicate that for fair rock (Class III), the excavation should be top heading \n\n\n\nand bench 1.5 m \u2013 3 m advance in the top heading. Support should be \n\n\n\ncommencing after each blast and complete support 10 m from face. Rock \n\n\n\nbolts should be systematic with 4 m long spaced 1.5 m - 2 m in crown and \n\n\n\nwalls with wire mesh in crown. Shotcrete should be 50 mm \u2013 100 mm in \n\n\n\ncrown and 30 mm in sides.  \n\n\n\nFor poor rock (Class IV), the excavation should be top heading and bench \n\n\n\n1.0 m \u2013 1.5 m advance in top heading. Support should be installed \n\n\n\nconcurrently with excavation, 10 m from face. Rock bolt should be \n\n\n\nsystematic with 4 m \u2013 5 m long, spaced 1.5 m \u2013 1.5 m in crown and walls \n\n\n\nwith wire mesh. Shotcrete of 100 m \u2013 150 mm in crown and 100 mm in \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 96-102 \n\n\n\n\n\n\n\n \nCite the Article: Rodeano Roslee,  Jeffery Anak Pirah,  Mohd Fauzi Zikiri, Ahmad Nazrul Madri (2020). Applicability Of The Rock Mass Rating (RMR) System For The \n\n\n\nTrusmadi Formation At Sabah, Malaysia. Malaysian Journal of Geosciences, 4(2): 96-102. \n \n\n\n\n\n\n\n\nsides. The steel sets should be light to medium ribs spaced 1.5 m only when \n\n\n\nrequired (Table 4).  \n\n\n\n7. CONCLUSION \n\n\n\nIn light of available information, the following conclusions may be drawn \n\n\n\nfrom the present study: \n\n\n\n1. The RMR system for 10 outcrops from the Trusmadi Formation ranges \n\n\n\nfrom 33.0 to 50.0 and its classified as \u201cFair\u201d (Class III) to \u201cPoor\u201d (Class \n\n\n\nIV) rocks.  \n\n\n\n2. The Fair Rock (Class III) recommended that the excavation should be \n\n\n\ntop heading and bench 1.5 m \u2013 3 m advance in the top heading. \n\n\n\nSupport should be commencing after each blast and complete support \n\n\n\n10 m from face. Rock bolts should be systematic with 4 m long spaced \n\n\n\n1.5 m - 2 m in crown and walls with wire mesh in crown. Shotcrete \n\n\n\nshould be 50 mm \u2013 100 mm in crown and 30 mm in sides.  \n\n\n\n3. For the Poor Rock (Class IV), the excavation should be top heading and \n\n\n\nbench 1.0 m \u2013 1.5 m advance in top heading. Support should be \n\n\n\ninstalled concurrently with excavation, 10 m from face. Rock bolt \n\n\n\nshould be systematic with 4 m \u2013 5 m long, spaced 1.5 m \u2013 1.5 m in \n\n\n\ncrown and walls with wire mesh. Shotcrete of 100 m \u2013 150 mm in \n\n\n\ncrown and 100 mm in sides. The steel sets should be light to medium \n\n\n\nribs spaced 1.5 m only when required. \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nDeep gratitude to Universiti Malaysia Sabah (UMS) for providing easy \n\n\n\naccess to laboratories and research equipment. Highest appreciations also \n\n\n\nto Ministry of Higher Education of Malaysia (MOHE) for the research grant \n\n\n\naward (Engineering Properties of The Trusmadi Formation Rocks and Soils \n\n\n\nin Ranau Area, Sabah, Malaysia-FRG0095-ST-1-2006) to finance all the \n\n\n\ncosts of this research. \n\n\n\nREFERENCES \n\n\n\nBarton, N., Lien, R., Lunde, J., 1974. Analysis of rock mass quality and \n\n\n\nsupport practice in tunneling and a guide for estimating support \n\n\n\nrequirements. Internal report No. 106. Norwegian Geotechnical \n\n\n\nInstitute, Oslo. \n\n\n\nBieniawski, Z.T., 1973. Engineering classification of jointed rock masses. \n\n\n\nSouth African Inst. Civil Eng., 15, Pp. 335 \u2013 344. \n\n\n\nBieniawski, Z.T., 1976. The point load test in geotechnical practice. \n\n\n\nEngineering Geology, 9, Pp. 1 \u2013 11. \n\n\n\nBieniawski, Z.T., 1989. Engineering rock mass classification. New York: \n\n\n\nJonh Wiley & Sons.  \n\n\n\nHoek, E., Karzulovic, A., 2000. Rock mass properties for surface mines. In: \n\n\n\nHustralid WA, McCarter MK, van Zyl DJA (eds). Slope stability in surface \n\n\n\nmining. Society for Mining, Metallurgical and Exploration (SME), \n\n\n\nLittleton. \n\n\n\nHoek, E., Marinos, P., Benissi, M., 1998. Applicability of the geological \n\n\n\nstrength index (GSI) classification for weak and sheared rock masses\u2014\n\n\n\nthe case of the Athens schist formation. Bull Eng Geol Env., 57 (2), Pp. \n\n\n\n151\u2013160. \n\n\n\nJacobson, G., 1970. Gunong Kinabalu area, Sabah, Malaysia. Geological \n\n\n\nSurvey Malaysia. Report 8. \n\n\n\nNorbert, S., Rodeano, R., Abdul, G.R., Goh, T.L., Noran, N.N.A., Kamilia, S., \n\n\n\nNightingle, L.M., Azimah, H., Lee, K.E., 2016. Rock Mass Assessment \n\n\n\nusing Geological Strength Index (GSI) along the Ranau-Tambunan Road, \n\n\n\nSabah, Malaysia. Research Journal of Applied Sciences, Engineering and \n\n\n\nTechnology, 12 (1), Pp. 108-115. \n\n\n\nPriest, S.D., 1993. Discontinuity analysis for rock engineering. Chapman & \n\n\n\nHall, London, Pp. 473.  \n\n\n\nPriest, S.D., Hudson, J.A., 1976. Discontinuity spacings in rock. \n\n\n\nInternational Journal of Rock Mechanics and Mining Sciences & \n\n\n\nGeomechanics, 13, Pp. 135\u2212148. \n\n\n\nRodeano, R., 2020. Geological Assisted on Water Resources Planning in \n\n\n\nMountainous Catchments In Kundasang, Sabah, Malaysia. Malaysian \n\n\n\nJournal of Geosciences, 4 (1), Pp. 26-31.  \n\n\n\nRodeano, R., Sanudin, T., Baba, M., Omang, S.A.K.S., 2010. Geological inputs \n\n\n\nfor Landslide Hazard Identification (LHI) in the Trusmadi Formation \n\n\n\nslopes, Sabah, Malaysia. Borneo Science, 26, Pp. 37-51. \n\n\n\nRodeano, R., Felix, T., 2018. Engineering Geological Mapping on Slope \n\n\n\nDesign in the Mountainous Area of Sabah Western, Malaysia.  Pakistan \n\n\n\nJournal of Geology, 2 (2), Pp. 01-10. \n\n\n\nWickham, G.E., Tiedemann, H.R., Skinner, E.H., 1972, Support \n\n\n\ndetermination based on geologic predictions, In: Lane, K.S.a.G., L. A., ed., \n\n\n\nNorth American Rapid Excavation and Tunneling Conference: Chicago, \n\n\n\nNew York: Society of Mining Engineers of the American Institute of \n\n\n\nMining, Metallurgical and Petroleum Engineers. Pp. 43-64. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 76-84 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2021.76.84 \n\n\n\n \nCite The Article: Casmed Charles Amadu, Gordon Foli, Bernard Kissi-Abrokwa, Sylvester Akpah (2021). Geostatistical Approach For The Estimation of Shear-Hosted \n\n\n\nGold Deposit: A Case Study of The Obuasi Gold Deposit, Ghana. Malaysian Journal of Geosciences, 5(2): 76-84. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nREVIEW ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2021.76.84 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGEOSTATISTICAL APPROACH FOR THE ESTIMATION OF SHEAR-HOSTED GOLD \nDEPOSIT: A CASE STUDY OF THE OBUASI GOLD DEPOSIT, GHANA \n \nCasmed Charles Amadua*, Gordon Folib, Bernard Kissi-Abrokwaa, Sylvester Akpahc \n \naDepartment of Earth Science, Faculty of Earth and Environmental Sciences, CK Tedam University of Technology and Applied Sciences, P. O. Box \n24, Navrongo, Ghana \nbDepartment of Geological Engineering, Kwame Nkrumah University of Science and Technology (KNUST), PMB, University Post Office; Kumasi, \nGhana \ncDepartment of Computer Science and Engineering, University of Mines and Technology, P. O. Box 237, Tarkwa, Ghana \n*Corresponding Author E-mail: camadu@uds.edu.gh \n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 10 September 2021 \nAccepted 14 October 2021 \nAvailable online 26 October 2021 \n\n\n\n\n\n\n\nUnderground mining at Obuasi in Ghana has been in operation since 1947. This paper uses geostatistical \nmethods to evaluate gold ore blocks to ensure reliable grades for mining large tonnage and low-grade \nresources. Historically, the principal ores were low tonnage, high grade and relatively homogeneous quartz \nstockwork with simple geometry and average bulk grades in the range of 20-30 g/t that were evaluated using \nconventional polygonal methods and mined by semi-mechanized means. Currently, the ore is a shear-hosted \nmixed quartz vein and disseminated sulphide type deposit of low grade that is mined using highly \nmechanized means. The need therefore arises for a re-assessment of the estimation procedures to ensure \nprolonged and more profitable mining. Both diamond drill (DD) core and stope/cross-cut channel samples \nwere taken from Block 1 at the mine for analyses and re-assessment. A wireframe model was used to \nconstrain the three dimensional (3D) block model of the deposit. Ordinary kriging (OK) and multiple indicator \nkriging (MIK) geostatistical methods were used to estimate gold grades. Grade distribution is positively \nskewed with high spatial variability and extreme values while background values are established as <0.6 g/t. \nThe Spatial variability is characterized by fitting models on experimental variograms. The MIK approach \nmitigates the effects of outliers and establish grades that are consistently lower than the OK and the weighted \naverage method that are widely used at the mine. The MIK method, a non-linear, non-parametric method of \nlocal grade estimation are applicable to the deposit architecture. Profoundly, the MIK method is a more \nreliable approach considering the fact that the MCF based on the estimates at the mine are high despite \noperational deficiencies on the mine. The results from this study demonstrates usefulness of geostatistics to \ndetermine the architecture of Au mineralization at the deposit scale. \n\n\n\nKEYWORDS \n\n\n\nOre reserve estimation, geostatistics, ordinary kriging, multiple indicator kriging. \n\n\n\n1. INTRODUCTION \n\n\n\n1.1 Overview \n\n\n\nMining projects relating to precious metals such as gold (Au) deposits, \nrequire accurate information on tonnage and grade to ensure credible \nresource estimates and good project feasibility (Sinclair and Blackwell, \n2002; Wellmer et al., 2007; Daya, 2012; Gol et al., 2017). This requirement \nhas become very crucial in recent years, due to the negative impacts of \nincreasing depletion of high profit deposits, unstable world market prices, \ncost of production and weak legislation, among others factors (Bloomberg \nCommodity Index, 2015). Notably, many companies failed to live up to \nexpectation due to poor grade and tonnage estimations, as well as \nineffective geological controls (Sinclair and Blackwell, 2002; Wellmer et \nal., 2007; Rossi and Deutsch, 2013: Dominy, 2014). Accurate mineral \nresource and mineral reserve evaluations, therefore, form the basis on \nwhich economic decisions are made on mining project. \n\n\n\nAt the Anglogold Ashanti mine in Ghana, the methods of ore reserve \n\n\n\nestimation have been mainly by polygonal method and a factor applied for \nerror correction (Amadu, 2006). Though historically, these methods might \nhave worked satisfactorily, there are also many drawbacks due to inherent \nassumptions that leads to overestimation (Sinclair and Blackwell, 2002). \nThis is because the Obuasi mine orebodies are characterised by non-\nuniform mineralization distribution and sharp boundaries (Baxter and \nYates, 2001; Perrouty et al., 2012). \n\n\n\nStatistical analysis of Au grades at the Obuasi mine show levels of extreme \nassay values (Amadu, 2006), which have the potential of causing errors in \nestimations of the overall grade and gold content of ore blocks using linear \nestimators (Issaks and Srivsatava, 1989; Al-Hassan and Annels, 1994). \nStatistical and geostatistical analyses of data influence the choice of an \nestimation method; hence, estimation method must be appropriate and \nreasonably applied (Wellmer et al., 2007; Abzalov et al., 2010; Abzalov, \n2016). \n\n\n\n1.2 Research background \n\n\n\n\nmailto:camadu@uds.edu.gh\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 76-84 \n\n\n\n\n\n\n\n \nCite The Article: Casmed Charles Amadu, Gordon Foli, Bernard Kissi-Abrokwa, Sylvester Akpah (2021). Geostatistical Approach For The Estimation of Shear-Hosted \n\n\n\nGold Deposit: A Case Study of The Obuasi Gold Deposit, Ghana. Malaysian Journal of Geosciences, 5(2): 76-84. \n\n\n\n\n\n\n\nThe reconciliation practice being done by AGA involves comparing mined \ngrade to mill head grade, called the Mine Call Factor (MCF). The MCF is \nregarded as the measure of the efficiency of the mining system. It is a ratio \nexpressed as a percentage of the gold \u2018accounted for\u2019 divided by the gold \n\u2018called for\u2019 by the mine's measuring methods (de Jager, 1997): \n\n\n\nMCF = \n100%\n\n\n\nMinegrade\n\n\n\nMillgrade\n\uf0b4\n\n\n\n         Equation 1 \n\n\n\nObservations made from the operations of the mine indicate a consistently \nMCF, which at a time fell as low as -24% (Amadu, 2006). Ideally, MCF \nshould be 100%, however, such low values achieved, despite the severe \ntop cutting of assayed Au values in the data used for resource/reserves \ncould be due to overestimation of grades by the company or some other \nsources of error. Several different techniques exist for the estimation of \nore grades and tonnages (Yasrebi et al., 2009; Daya, 2012; Rossi and \nDeutsch, 2013). Where applicable, geostatistical methods greatly enhance \nthe accuracy of estimation of mineral resources and ore reserves (Daya, \n2012). \n\n\n\nGeostatistics appears to be especially attractive mineral deposits \nevaluation and solving many of the problems of resource/reserve \nestimation relating to sampling distribution and unequal sample support \n(Dowd, 1992; Daya, 2012; Shahbeik et al, 2014). Using conventional \npolygonal method for resource/reserve estimations has various \ndrawbacks, which relate to assumptions that lead to overestimation \n(Sinclair and Blackwell, 2002). It is therefore very necessary to assess the \napplicability of geostatistical methods as alternative approaches to ore \nreserve estimation of the Obuasi deposit and also ascertain whether the \nconsistent overestimation of grade (as evidenced from the MCF) \nencountered by the polygonal method could be addressed. \n\n\n\nIn this research, it has been clearly established from this study that \nmultiple indicator kriging (MIK) geostatistical method is a more suitable \nevaluation method for establishing the ore reserve evaluation parameters \nin large-scale mining of relatively low-grade gold ores being mined at the \nObuasi mine. The outcome will help minimize of challenges with \noverestimation and the subsequent maximization of the efficient \nutilization of the ore resource/reserves of the mine. \n\n\n\n2.  MATERIALS AND METHODS \n\n\n\n2.1 Study aim and objectives \n\n\n\nThe study aims to use geostatistics to evaluate gold ore blocks to ensure \nreliable grades for mining large tonnage and low-grade resources. The \nspecific objective is to: \n\n\n\n\u2022 Apply Ordinary kriging (OK) and multiple indicator kriging (MIK) \ngeostatistical methods to estimate gold grades of the gold ore deposits \nwithin Block 1 at the Obuasi Mine. \n\n\n\nTo preserve confidentiality, Au grades have been multiplied by a factor, \nthat renders meaningless the economic inference or interpretation of the \nresults. \n\n\n\n2.2 Study area \n\n\n\nThe Obuasi mine is located within the Ashanti gold belt of Ghana (Figure \n1) (Oberthur et al., 1997), within the Paleoproterozoic Birimian terrain of \nthe West African craton (WAC), with ages ranging from 2.2 to 2.1 Ga \n(Feybesse et al., 2006; Goldfarb et al., 2017). \n\n\n\n \nFigure 1: Geological map of southern Ghana showing the Obuasi Mine \n\n\n\nThe Birimian is thought to have undergone structural deformation and \ngenerally metamorphosed to greenschist during the Eburnean orogenic \nevent (Perrouty et al., 2012). The major fault zones, thrusts and shears \nresulting from the deformational events acted as channel-ways for \nmineralised hydrothermal fluids (Alliborne et al., 2002; Perrouty et al., \n2012; Fougerouse et al., 2013). Mineralisation is persistent both laterally \nand vertically, and can be traced for over 8 km strike length and over 1700 \nm below surface (Alliborne et al., 2002; Fougerouse et al., 2017).  \n\n\n\nAlmost all gold mineralisation on the mine shows some form of structural \ncontrol at scales from the 8 km strike length of the major reefs to the \nsmaller scale of shear surfaces and folds (Perrouty et al., 2012). \nMineralisation is confined to a series of anastomosing ductile shear zones, \nof widths up to 0.2 to 5 m, and collectively comprise a shear zone that may \nbe up to some 30 m wide (Alliborne et al., 2002). Gold ores occur as two \ndistinct primary ore types; these are, Quartz vein type (QVT) and \nDisseminated sulphide type (DST) (Alliborne et al., 2002; Amposah et al., \n2015). The QVT gold occurs with carbonate vein-filling along fractures in \nthe quartz veins, and previously formed the major orebodies (Alliborne et \nal., 2002). The DST gold occurs in the sheared metasedimentary and \nmetavolcanic rocks. The mineral assemblage comprises of quartz, calcite, \nankerite, iron oxides, chlorite and the diverse kinds of sulphides (Oberthur \net al., 1995; Foli et al., 2015). Typically, high concentration of arsenopyrite, \nespecially if fine grained and disseminated in tuffaceous rocks also \ncorrelates well with high gold values (Amadu, 2006). \n\n\n\n2.3 Sample data and organization \n\n\n\nThe assay data for this study is from an underground mine block with \noutline presented in Figure 2. \n\n\n\nFigure 2: Vertical projection of Block 1 at the Obuasi mine \n\n\n\nThe data consists of analytical results of two types of rock samples, \ndiamond drill (DD) core and stope/cross-cut (X-cut) channel samples \ntaken from irregular locations. Drilling grids were irregular. Holes were \ndrilled from development inclines and working faces at varied inclinations \nand azimuths with LTK46 (with core diameter 35.6 mm) and BQ (core \ndiameter 36.5 mm) coring equipment. All boreholes were surveyed using \nan Eastman single-shot camera with shots taken at an average of 15 m. In \ntotal, 507 borehole cores were sampled at intervals of between 1.0-3.0 m \nto generate a total of 114488 samples and 7667 stope/channel samples \nwere collected and assayed were assayed for Au using the Atomic \nAbsorption Spectrometry (AAS) technique. \n\n\n\nTo carry out geostatistical evaluation of the deposit, a database was \ncreated which involved the production of three main ASCII files, namely \nCOLLAR, ASSAY and SURVEY containing information on borehole collar \nco-ordinates, lithology and downhole orientation survey into a database \nusing the ADHOLE system of the DATAMINE software. The channel sample \ndata set was entered and stored as XCUT file, which contains information \non channel sample assays, reference peg coordinates of the start of the \nchannel sampling line (CSL) and rock type. \n\n\n\nThe development string file and the peg coordinate file of the CSLs were \nused to obtain CSL profiles as pseudo boreholes. ASCII output files from \nthe database data entry system of AGA were imported into the DATAMINE \nsystem. To facilitate the extraction of sections and plans for geological \ninterpretation and digitization of ore outlines, a three-dimensional (3D) \nmodel of boreholes and channel samples were generated within the study \narea. This was done by merging the assay, collar, geology and survey files \ninto one single file referred to as the \u2018desurveyed\u2019 file. \n\n\n\nGeological interpretation based mainly on grade was done on the mine \nlevel plans (MLPs) and vertical section (VS) plots, and envelopes of \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 76-84 \n\n\n\n\n\n\n\n \nCite The Article: Casmed Charles Amadu, Gordon Foli, Bernard Kissi-Abrokwa, Sylvester Akpah (2021). Geostatistical Approach For The Estimation of Shear-Hosted \n\n\n\nGold Deposit: A Case Study of The Obuasi Gold Deposit, Ghana. Malaysian Journal of Geosciences, 5(2): 76-84. \n\n\n\n\n\n\n\neconomic zones digitized using the AutoCAD version 17.1 software and the \nstrings imported into DATAMINE as perimeter string files. The closed \nperimeters formed envelopes of selecting assayed Au values for analyses. \nThe volume enveloped is between Eastings 14650 to 15175; Northings \n10700 to 10950 and elevations between -724.58 to -900.50, where the \nnegative denotes measurements below ground elevation. \n\n\n\n2.4 Statistical analysis on data \n\n\n\nIt is important in resource estimation to work with equal support \n\n\n\n(volume) samples (Isaaks and Srivastava, 1989). To investigate whether \n\n\n\nthe two data sets (DD and X-cut) belong to statistically similar populations, \n\n\n\nthe F- and t- tests were carried out as suggested by Al-Hassan and Annels \n\n\n\n(1994). Having shown that there was no significant difference statistically \n\n\n\nbetween DD core and channel sample types, both data were pooled \n\n\n\ntogether and used for further analysis. Descriptive statistics [mean, \n\n\n\nminimum (min), maximum (max), variance, standard deviation (SD), \n\n\n\ncoefficient of variation (CoV)] for the pooled data were calculated. The \n\n\n\nfrequency distribution analysis was used to determine the choice of cut-\n\n\n\noff grade values chosen for indicator kriging. In addition, histograms and \n\n\n\nprobability plots were used to identify outliers in the data as outlined by \n\n\n\nRezaei et al. (2019). The histogram of the data indicates a strong positive \n\n\n\nskewness (Figure 3), with about 20 % of the data values below 0.1 g/t and \n\n\n\nis not normal. \n\n\n\n \nFigure 3: Histogram of the gold grades of the combined diamond DD \n\n\n\ncore and X-cut samples \n\n\n\nIn figure 3, the skewness of the values indicated a significant departure \n\n\n\nfrom normality. After a lower truncation of the very low-grade values (< \n\n\n\n0.6 g/t), the data was log-transformed to normalize it and then used \n\n\n\nfurther to produce semi-variograms. \n\n\n\n2.5 Variography \n\n\n\n2.5.1 Raw gold values \n\n\n\nThe first step in geostatistical analysis is the calculation and modelling of \n\n\n\nsemi-variograms (Gol et al., 2017). Calculations in several directions, such \n\n\n\nas, along strike, across strike and down dip directions give an insight on \n\n\n\nthe structural and geometric controls on the orebody (Daya, 2012; Rossi \n\n\n\net al., 2014). The variogram represents the variance between sample pairs \n\n\n\nas a function of distance (lag) between the samples in a particular \n\n\n\ndirection (Goovaerts, 1997). An experimental variogram, ( )h\u03b3*   can be \n\n\n\ndefined as (Michel, 1982): \n\n\n\n\uf07b \uf07d\uf0e5\n=\n\n\n\n+\u2212=\nn\n\n\n\n1i\n\n\n\nhiZ()iZ(\n2\n\n\n\n2n\n\n\n\n1\n(h)\u03b3* xx\n\n\n\n             Equation 2; \n\n\n\nwhere, \n=)xiZ(\n\n\n\nthe value of sample grade at point xi ; Z ( =+ h)xi\n\n\n\ngrade of sample at distant h from point \nxi  and, n = the number of \n\n\n\nsample pairs. \n\n\n\nSemi-variograms were constructed in several directions including, the \n\n\n\nstrike-plunge, across-strike and down dip directions for all grade values \n\n\n\ndetermined. After experimenting with several lag distances, it was \n\n\n\nestablished that the most reliable one was 15 m. This appears to \n\n\n\ncorrespond with the crosscut channel sample spacing along strike at the \n\n\n\nmine. Spherical models (Sinclair and Blackwell, 2002) were then fitted to \n\n\n\nthe sample semi-variograms in all cases. \n\n\n\n2.5.2 Indicator variography \n\n\n\nFollowing the convention of Dowd (1992), a series of cut-off grades, being; \n\n\n\n2.0 g/t, 4.6 g/t, 7.2 g/t, 9.8 g/t, 12.4 g/t and 15.0 g/t, were listed and spread \n\n\n\nin ascending order through the data to create seven data groups. \n\n\n\nSrivastava and Parker (1988), and Parker (1991) noted that, the number \n\n\n\nof thresholds, Nk should depend on the number of data points available \n\n\n\nand should be sufficient to ensure proper resolution around and above \n\n\n\nenvisioned cutoffs, that need not rise in equal increments (Royle, 1991). \n\n\n\nThe threshold values for this work were selected based on the cumulative \n\n\n\nfrequency distribution (CFD) curve, which indicates the proportion of the \n\n\n\nsamples with value less than a given upper limit (Davis, 1986). The cut-off \n\n\n\ngrades were listed to correspond to the 45th, 65th, 75th, 80th, 85th and \n\n\n\n95th percentiles and their corresponding indicator semi-variograms \n\n\n\ncomputed to determine the statistics of the samples above the cut-offs as \n\n\n\nshown in Table 1. \n\n\n\nTable 1: Grade statistics \n\n\n\nCut-off grade, g/t % Above cut-off Mean grade g/t \n\n\n\n2.0 55 0.50 \n\n\n\n4.6 35 3.14 \n\n\n\n7.2 25 5.84 \n\n\n\n9.8 20 11.00 \n\n\n\n12.4 15 13.66 \n\n\n\n15 5 19.62 \n\n\n\nAfter the selection of cut-off values, the data were transformed into (0, 1) \n\n\n\nvalues for every cut-off separately. The indicators values were obtained by \n\n\n\napplying the COV (Hohn, 1988) as: \n\n\n\n  1 for Z(Xk) \u2264 Zk \n\n\n\n             i(X: Zk) = \n\n\n\n  0 otherwise   \n                                                                                               Equation 3; \n\n\n\nwhere, Z (Xk ) denotes the grade value of the sample at location X and Zk is \nthe cut-off value. \n\n\n\nDue to the binary nature of the data used in indicator kriging (IK), the \n\n\n\nmethod is resistant to the effects of outliers, which can affect variography \n\n\n\ndata sets (Smith et al., 1993), however problems such as loss of \n\n\n\ninformation during data transformation involving the IK (Goovarts, 1997) \n\n\n\nnecessitated the use of the more robust multiple indicator kriging (MIK) \n\n\n\nto overcome such problems. Different variograms were calculated for each \n\n\n\ncut-off grade Zk, and modelled in the three directions of: across strike, \n\n\n\nalong strike, and down dip. The experimental indicator semi-variograms \n\n\n\nwere fitted with two-structure spherical models. \n\n\n\n2.6 Validating the models \n\n\n\nTo test how adequate the various variogram models were, a process of \n\n\n\ncross validation was carried out by point kriging, as described by Isaaks \n\n\n\nand Srivastava (1989). The procedure involved removing each sample \n\n\n\nvalue in the data set and re-estimating the sample value by point kriging \n\n\n\nfrom the remaining data using the test model. Table 2 is an example of the \n\n\n\nsummary statistics of the cross-validated results for raw grade omni-\n\n\n\ndirectional semi-variogram model. \n\n\n\nFigure 4 is the scatter plot of the estimated values against the actual values \nwithin specified ranges using OK. The omni-directional model was \nconsidered the best fitted model and used for the OK estimation. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 76-84 \n\n\n\n\n\n\n\n \nCite The Article: Casmed Charles Amadu, Gordon Foli, Bernard Kissi-Abrokwa, Sylvester Akpah (2021). Geostatistical Approach For The Estimation of Shear-Hosted \n\n\n\nGold Deposit: A Case Study of The Obuasi Gold Deposit, Ghana. Malaysian Journal of Geosciences, 5(2): 76-84. \n\n\n\n\n\n\n\nTable 2: Summary statistics of the cross-validation for raw grade \nsemi-variogram model data \n\n\n\nParameter Value \n\n\n\nNumber of points kriged 6220 \n\n\n\nNumber of points not kriged 0 \n\n\n\nMean grade Actual 7.1372 \n\n\n\nMean grade Estimated  7.1818 \n\n\n\nVariance, Actual 44.9076 \n\n\n\nVariance, Estimated 25.5895 \n\n\n\n% Error in mean estimation -0.62 \n\n\n\nCorr. coeff of actual & estimate 0.81 \n\n\n\nMean kriging variance 29.5681 \n\n\n\nKriging variance ratio 0.4289 \n\n\n\nMean difference (Actual \u2013 Estimate) -0.0446 \n\n\n\nMean absolute difference 3.2135 \n\n\n\nFigure 4: Cross-validation of semi-variogram parameters for gold data \nset using OK \n\n\n\nAfter validating the models, parameters determined for the non-\n\n\n\ndirectional, across strike, along strike and down dip directions are \n\n\n\npresented in Table 3. \n\n\n\nTable 3: Parameters for Gold Value Semi-variogram models \n\n\n\nDirection C0 \n\n\n\n(g/t)\n2 \n\n\n\nC1(g/t)\n2 \n\n\n\nC2g(/t)\n2 \n\n\n\na1(m\n) \n\n\n\na2(m\n) \n\n\n\nSill \ng(/t)\n2 \n\n\n\nDirectionles\ns \n\n\n\n3.7 20.6 8.4 11.1 48.0 32.7 \n\n\n\nAcross \nStrike \n\n\n\n3.7 19.6 25.2 3.7 7.9 48.5 \n\n\n\nAlong Strike 3.7 16.0 12.8 16.1 49.1 32.5 \n\n\n\nDown Dip 3.7 16.8 11.7 27.5 42.5 32.2 \n\n\n\n2.7 In situ reserve estimation \n\n\n\n2.7.1 Domain envelope definition, wireframing and block modelling \n\n\n\nSectional interpretations of mineralisation boundaries from section 334/5 \n\n\n\nto 354/5 (along strike of orebody) at 25 m intervals, and level plans (level \n\n\n\n32 to 39) were digitized in a clockwise manner, and perimeters linked in \n\n\n\nsection to form a three-3D wireframe solid (Figure 5).  \n\n\n\nA visual check was performed on the wireframe to ensure unique and non-\n\n\n\noverlapping triangular surfaces were created, followed by the generation \n\n\n\nof a 3D block model. Since perimeters were drawn based on the \n\n\n\nmineralized zones without the application of a cut-off grade, the model fits \n\n\n\nthe best approximation to adopt. The model consists of 3D cells or blocks, \n\n\n\neach of which has parameters such as rock type, grade and rock density. \n\n\n\nCell sizes were chosen based on the general conditions of mining in the \n\n\n\narea, which has Block dimensions of 5  2.5  5 were used (Table 4). \n\n\n\nFigure 5: A typical 3-D Wireframe model \n\n\n\nTable 4: Block model framework \n\n\n\nDirection X Y Z \n\n\n\nModel origin (m) 14630.0 10700.0 -985.0 \n\n\n\nModel limits (m) 15200.0 10950.0 -650.0 \n\n\n\nModel dimensions (m) 5.0 2.5 5.0 \n\n\n\nNumber of blocks \n(cell) \n\n\n\n108 96 66 \n\n\n\n2.7.2 Grade interpolation \n\n\n\nTo estimate the gold grades, the OK and MIK (non-parametric) methods \nwere used. The parameters for the isotropic models adopted for the raw \ngold values used for ordinary kriging, and the six indicator parameters \nused for indicator kriging are presented in Tables 5 and 6, respectively. \n\n\n\nTable 5: Parameters for isotropic raw gold value semi-variogram \nmodel adopted \n\n\n\nParameter C0 a1 a2 C1 C2 \n\n\n\nValue, Along strike \ndirection \n\n\n\n3.7 16.1 49.1 16.0 12.8 \n\n\n\nValue, Down dip \ndirection \n\n\n\n3.7 27.5 42.5 16.8 11.7 \n\n\n\nValue, Across strike \ndirection \n\n\n\n3.7 3.7 7.9 15.9 5.6 \n\n\n\n\n\n\n\nTable 6: Parameters for indicator semi-variogram M\\models \n\n\n\nParamet\ner \n\n\n\nC0 a1S\n\n\n\nT \na1D\n\n\n\nIP \na1X\n\n\n\nST \na2S\n\n\n\nT \na2D\n\n\n\nIP \na2X\n\n\n\nST \nC1 C2 \n\n\n\nIndicator \n1 \n\n\n\n0.0\n2 \n\n\n\n16.\n8 \n\n\n\n13.\n5 \n\n\n\n4.4 64.\n0 \n\n\n\n63.\n9 \n\n\n\n14.\n3 \n\n\n\n0.0\n6 \n\n\n\n0.0\n4 \n\n\n\nIndicator \n2 \n\n\n\n0.0\n1 \n\n\n\n16.\n3 \n\n\n\n13.\n7 \n\n\n\n3.7 62.\n0 \n\n\n\n61.\n7 \n\n\n\n13.\n8 \n\n\n\n0.0\n7 \n\n\n\n0.0\n7 \n\n\n\nIndicator \n3 \n\n\n\n0.0\n1 \n\n\n\n16.\n8 \n\n\n\n13.\n2 \n\n\n\n3.4 61.\n7 \n\n\n\n57.\n2 \n\n\n\n13.\n1 \n\n\n\n0.0\n8 \n\n\n\n0.1\n0 \n\n\n\nIndicator \n4 \n\n\n\n0.0\n1 \n\n\n\n16.\n0 \n\n\n\n13.\n0 \n\n\n\n3.3 61.\n9 \n\n\n\n46.\n2 \n\n\n\n12.\n1 \n\n\n\n0.0\n7 \n\n\n\n0.0\n6 \n\n\n\nIndicator \n5 \n\n\n\n0.0\n7 \n\n\n\n16.\n3 \n\n\n\n12.\n3 \n\n\n\n3.0 60.\n0 \n\n\n\n40.\n0 \n\n\n\n12.\n2 \n\n\n\n0.0\n6 \n\n\n\n0.0\n6 \n\n\n\nIndicator \n6 \n\n\n\n0.0\n6 \n\n\n\n16.\n3 \n\n\n\n12.\n3 \n\n\n\n3.0 60.\n3 \n\n\n\n40.\n0 \n\n\n\n11.\n8 \n\n\n\n0.0\n7 \n\n\n\n0.6 \n\n\n\nNote: ST = along strike; DIP= down dip; XST=across strike \n\n\n\nAn ellipsoid search of distances of 15 m, 5 m and 15 m in the strike, across \nstrike, and vertical configurations respectively, were adopted to limit the \nsmearing of grades during the interpolation process. A density of 2.89 \nt/m3, being the average density of the ore material of Block 1 deposit was \nused in tonnage computations. In general, for n classes, the total estimated \nvalue is given as: \n\n\n\ng*\nn\n\n\n\n1n\n\n\n\n1i\n\n\n\np*\ni1g*\n\n\n\ni\n\n\n\n1n\n\n\n\n1i\n\n\n\np*\nig* \uf0b4\n\n\n\n\uf0f7\n\uf0f7\n\n\n\n\uf0f8\n\n\n\n\uf0f6\n\n\n\n\uf0e7\n\uf0e7\n\n\n\n\uf0e8\n\n\n\n\uf0e6\n\uf0e5\n\u2212\n\n\n\n=\n\n\n\n\u2212+\uf0b4\uf0e5\n\u2212\n\n\n\n=\n\n\n\n=\n                        Equation 4;                                     \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 76-84 \n\n\n\n\n\n\n\n \nCite The Article: Casmed Charles Amadu, Gordon Foli, Bernard Kissi-Abrokwa, Sylvester Akpah (2021). Geostatistical Approach For The Estimation of Shear-Hosted \n\n\n\nGold Deposit: A Case Study of The Obuasi Gold Deposit, Ghana. Malaysian Journal of Geosciences, 5(2): 76-84. \n\n\n\n\n\n\n\nwhere, \np*\n\n\n\ni  is the proportion of class i. i = 1, n-1; \ng*\n\n\n\ni  is the estimated value \n\n\n\nof class i, i = 1, n and\ng*\n\n\n\nn  is the estimated value of class n. \n\n\n\nAfter the grade interpolation process, checks were made on interpolation \ntrend and its consistency with mineralization orientation. This was done \nby displaying section slices through the model and ensuring that drill hole \nmineralized assay values coincided with model boundaries (Figure 6).  \n\n\n\n \nFigure 6: Typical N-S section through orebody at 14925 Easting \n\n\n\nIn the light of the above criteria, the resulting grade model was found to \nbe satisfactory. \n\n\n\n3. RESULTS AND DISCUSSIONS \n\n\n\n3.1 Sample data distribution analysis \n\n\n\nFigure 3 shows the histogram as well as statistics for gold grades of the \nstudy area. From the diagram (Figure 3), the frequency distribution model \nshows it is positively skewed, typical of gold grades and other precious \nmetals, where the grade value increases with fewer numbers of samples \n(Krige, 1993). The coefficient of variation (CoV) is 1.63 (Table 7) which is \nhigher than expected for a normal distribution (i.e., must be less than 0.5) \n(Al-Hassan and Annels 1994). \n\n\n\nTable 7: Summary Statistics for combined samples (DD and channel) \n\n\n\nDescripti\non \n\n\n\nNo. of \nSampl\nes \n\n\n\nMin\n. \n(g/t\n) \n\n\n\nMax. \n(g/t) \n\n\n\nMea\nn \n(g/t\n) \n\n\n\nVarian\nce \n\n\n\n(g/t)2 \n\n\n\nCoef. of \n\n\n\nvariatio\nn \n\n\n\nPooled \nsamples \n\n\n\n12215\n5 \n\n\n\n0.01 289.6\n0 \n\n\n\n3.77 37.92 1.63 \n\n\n\nThe variance associated with the distribution is 37.92 (g/t)2 As mentioned \nearlier, the distribution of the raw data is not normal and had to be \ntransformed. Plots of logarithms and cumulative probability graphs of \ngold grades of the pooled data are shown in Figure 7. \n\n\n\n\n\n\n\n \nFigure 7: Statistics for pooled data sets. (A) Log histogram; and (B) \n\n\n\nCumulative probability plot \n\n\n\nIn Figure 7A, the probability plots of both the raw and log-transformed \nvalues show evidence of mixed populations. Inspection of the log-\nhistogram plot shows a polymodal global distribution of the grades. This \nis probably due to the combination of the two main types of \nmineralization, which are the QVT and DST. The cumulative log-\nprobability plot (Figure 7B) of the grades indicates that it is possible to fit \nat least four straight lines to the data points possibly indicating the \npresence of four populations that are distinguished by a threshold value \nbetween 0.01and 0.1 g/t, 0.1 and 10.0 g/t, 10.0 and 40.0 g/t.  \n\n\n\nThe population below about 0.1 to 0.6 g/t represents the \u2018non-mineralised\u2019 \nor background population, while, values beyond about 40.0 g/t can be \nconsidered as extreme values or outliers. Dowd (1997) defined outliers as \n\u2018any value which is significantly higher than or lower than others in the \npopulation. Generally, there is no natural break in the assay value trends \nthat relate to the choice of economic cut-off of 3.43 g/t that is currently \nbeing used at the Obuasi mine for evaluations. \n\n\n\n3.2 Variography \n\n\n\n3.2.1 Raw gold values \n\n\n\nSemi variograms models and cross-semi variograms models and \n\n\n\nparameters are shown in Figure 8 and Table 8.  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 76-84 \n\n\n\n\n\n\n\n \nCite The Article: Casmed Charles Amadu, Gordon Foli, Bernard Kissi-Abrokwa, Sylvester Akpah (2021). Geostatistical Approach For The Estimation of Shear-Hosted \n\n\n\nGold Deposit: A Case Study of The Obuasi Gold Deposit, Ghana. Malaysian Journal of Geosciences, 5(2): 76-84. \n\n\n\n\n\n\n\n\n\n\n\nFigure 8: Non-directional and directional two-structure spherical models fitted to variograms of Au values: (A) Non-directional, (B) Across strike \ndirection, (C) Along strike direction, and (D) Down dip direction\n\n\n\nTable 8: Parameters for Au value semi-variogram models. \n\n\n\nDirection C0(g/t)\n2 \n\n\n\nC1(g/t)\n2 \n\n\n\nC2g(/t)\n2 \n\n\n\na1(m\n) \n\n\n\na2(m\n) \n\n\n\nSill \ng(/t)\n2 \n\n\n\nNon-\ndirectiona\nl \n\n\n\n3.7 20.6 8.4 11.1 48.0 32.7 \n\n\n\nAcross \nstrike \n\n\n\n3.7 19.6 25.2 3.7 7.9 48.5 \n\n\n\nAlong \nstrike \n\n\n\n3.7 16.0 12.8 16.1 49.1 32.5 \n\n\n\nDown dip 3.7 16.8 11.7 27.5 42.5 32.2 \n\n\n\nThe experimental variograms depict two structures, as they fit with two-\nstructure spherical models. Down dip direction was taken as 81\u00ba \ncorresponding to general dip of the Block 1 deposit. Geostatistical analysis \nof the data in the non-directional direction resulted in the model as \nfollows: \n\n\n\n\n\n\n\nThe general equation for this nested model as predicted by Journel and \nHuijbregts (1978) is: \n\n\n\nwhere, C0 = nugget variance, C1 and C2 = spatial variance of first and second \nstructure respectively, a1 and a2 = range of first and second structure, h = \ndistance separating pairs of sample values. \n\n\n\nThe nugget variance C0 is expressed as \u03b3(0) = C0. C0 represents the random \nportion of the variability of the regionalised variable, i.e the variogram \nvalue \u03b3(h) at a distance of zero (i.e., when \u2018h\u2019 equal to zero). It is partly a \nmeasure of the variability between samples at, or very close to zero \ndistance apart and partly the presence of sampling errors. The situation of \nnested structures arises due to the presence of more than one underlying \nstructures in the data set (Sinclair and Blackwell 2002). There is quite a \ndifficulty assigning geological influences for the nested structures for the \nspatial variation exhibited in the variograms, probably due to secondary \nproperties and characteristics of the host rocks, such as intensity of \nmineralization, quartz veining and alteration. \n\n\n\nVariograms for the different directions demonstrate similar spatial \nstructure and variance proportions. The results show varied spatial \ncorrelation between samples, evidenced by different ranges in different \ndirections (Table 9). Experimental variography revealed structured \ndirection with maximum continuity plunging at about 81\u00b0 south-east and \ncorresponds to the geological controls on mineralisation. The variogram \nhaving the longest range is the down dip direction, and the shortest range \nis across strike (Table 9); the range along strike of the deposit is about 11.0 \nm. \n\n\n\n3.2.2 Indicator variography \n\n\n\nAs mentioned earlier, cut-off Au grades at the 2.0 g/t (1st), 4.2 g/t (2nd), \n7.2 g/t (3rd), 9.8 g/t (4th), 13.4 g/t (5th), and 15.0 g/t (6th) were used as \nthreshold values for indicator kriging. Figure 9 present examples of semi-\nvariograms and models fitted for indicators 1 and 2 in the down dip \ndirection \n\n\n\n\n\n\n\n \nFigure 9: Examples of variogram models for indicators at 2.0 and 4.2 g/t \n\n\n\nin the down dip \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 76-84 \n\n\n\n\n\n\n\n \nCite The Article: Casmed Charles Amadu, Gordon Foli, Bernard Kissi-Abrokwa, Sylvester Akpah (2021). Geostatistical Approach For The Estimation of Shear-Hosted \n\n\n\nGold Deposit: A Case Study of The Obuasi Gold Deposit, Ghana. Malaysian Journal of Geosciences, 5(2): 76-84. \n\n\n\n\n\n\n\nVariograms for the different directions demonstrate similar spatial \n\n\n\nstructure and variance proportions. The results also show varied spatial \n\n\n\ncorrelation between samples, evidenced by different ranges in different \n\n\n\ndirections (Table 9). Experimental variography confirm a structured \n\n\n\ndirection with maximum continuity plunging at about 81\u00b0 south-east, \n\n\n\nwhich corresponds to the geological controls on mineralisation. The \n\n\n\nvariogram having the longest range is the down dip direction, and the \n\n\n\n   shortest range is across strike (Table 9); the range along strike of the \n\n\n\ndeposit is about 11.0 m. \n\n\n\nThe parameters derived from indicator semi-variograms, fitted with a \n\n\n\ntwo-structure spherical models for; across strike, along strike and down \n\n\n\ndip directions are presented in Tables 9. \n\n\n\nTable 9: Parameters from semi-variograms, across strike, along strike and down dip directions \n\n\n\nField COG (g/t) C0 C1 C2 a1 a2 \n\n\n\nAcross strike direction \n\n\n\nIndicator 1 2.0 0.02 0.06 0.04 4.4 14.3 \n\n\n\nIndicator 2 4.2 0.01 0.07 0.10 3.7 13.0 \n\n\n\nIndicator 3 7.2 0.01 0.06 0.14 3.3 13.1 \n\n\n\nIndicator 4 9.8 0.01 0.07 0.10 3.1 12.1 \n\n\n\nIndicator 5 13.4 0.06 0.07 0.06 3.0 12.0 \n\n\n\nIndicator 6 15.0 0.07 0.06 0.15 2.8 11.8 \n\n\n\nAlong strike direction \n\n\n\nIndicator 1 2.0 0.02 0.05 0.04 16.8 64.0 \n\n\n\nIndicator 2 4.2 0.01 0.07 0.07 16.3 62.0 \n\n\n\nIndicator 3 7.2 0.01 0.08 0.06 16.8 61.7 \n\n\n\nIndicator 4 9.8 0.01 0.06 0.06 16.0 61.9 \n\n\n\nIndicator 5 13.4 0.06 0.03 0.03 16.3 60.0 \n\n\n\nIndicator 6 15.0 0.07 0.07 0.06 16.3 60.3 \n\n\n\nDown dip direction \n\n\n\nIndicator 1 2.0 0.20 0.11 0.02 13.7 61.7 \n\n\n\nIndicator 2 4.2 0.10 0.17 0.05 13.2 57.2 \n\n\n\nIndicator 3 7.2 0.10 0.06 0.06 13.0 553.7 \n\n\n\nIndicator 4 9.8 0.10 0.01 0.07 13.0 46.2 \n\n\n\nIndicator 5 13.4 0.06 0.03 0.04 12.3 40.0 \n\n\n\nIndicator 6 15.0 0.07 0.02 0.04 12.3 40.0 \n\n\n\nFor deposits with high variability of grades within short scales, MIK method mitigates the effects of outliers and different supports in the data (Fytas et al., \n1990). \n\n\n\n3.3 Reserve estimation \n\n\n\nSummary of results obtained from using, OK and MIK methods to evaluate  \n\n\n\nthe block reserve of the deposit at various cut-offs grades are presented in \nTables 10, while, a cut-off of 3.43 g/t, corresponding to the currently used \neconomic cutoff for the mine was also considered. \n \n\n\n\nTable 10: Summary of reserve estimation using OK and MIK \n\n\n\nCut-off \nGrade (g/t) \n\n\n\nTonnes (t) Grade (g/t) Ounces (oz) \n\n\n\nOK MIK OK MIK OK MIK \n\n\n\n2.0 7802837.5 7802837.5 6.81 6.23 1707956.50 1562418.13 \n\n\n\n3.43 6059638.0 6059638.0 7.94 7.24 1548328.25 1409623.5 \n\n\n\n5.0 4518265.5 4518265.5 9.15 8.27 1328832.50 1201483.38 \n\n\n\n6.0 3677470.0 3677470.0 9.90 8.90 1169713.75 1052944.63 \n\n\n\n9.0 1611197.38 1611197.38 12.19 10.81 631515.13 560229.50 \n\n\n\nEvaluation of mining projects involves taking into accounts various \nparameters including operational parameters and grade tonnage \ndistribution. Estimating ore reserves at various cut-off grades helps in \nmine planning. The choice of an optimal cut-off grade determines the \neconomic viability and return on investment on a mining project (Muriuki \nand Temeng, 2018). \n \n3.4 Comparison of grade estimation \n \nTable 10 presents a comparison of the two reserve estimates for Au using \nOK and MIK methods, which is quite similar, based on the results obtained. \n\n\n\nThe results of the two approaches are quite similar. Also, it can be \nobserved that, MIK estimates are consistently lower than OK estimates. \nThe implication is that by applying MIK method, the observed difference \nbetween estimated mining head grade and mill head grade can be reduced. \nOK provides estimates based on weighted parameters of observed values \nwithin a given area. According to a study, OK do not estimate the \nconditional probabilities; however, due to the coding of grades into \nindicators data, MIK deals with the effects of outliers and is useful for \nskewed data sets (Carras, 1984; Smith et al., 1993). Figure 10 shows the \ngrades estimated by OK and that estimated using MIK method. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 76-84 \n\n\n\n\n\n\n\n \nCite The Article: Casmed Charles Amadu, Gordon Foli, Bernard Kissi-Abrokwa, Sylvester Akpah (2021). Geostatistical Approach For The Estimation of Shear-Hosted \n\n\n\nGold Deposit: A Case Study of The Obuasi Gold Deposit, Ghana. Malaysian Journal of Geosciences, 5(2): 76-84. \n\n\n\n\n\n\n\n \nFigure 10: Comparison of Estimated grades using OK and MIK methods \n\n\n\n4. CONCLUSIONS \n\n\n\nThe study established that, the distribution of the Au grades from the \nObuasi Mine is positively skewed with high variability and extreme values. \nBackground value of mineralization was established as < 0.6 g/t. It has \nbeen demonstrated that geostatistical analysis is a useful way to \ndetermine the architecture of Au mineralisation at the deposit scale. \nSpatial variability is adequately characterized by fitting models on \nexperimental variograms. The study also demonstrated some of the \ndifficulties that are encounted when estimating resources/reserves of \nhighly skewed data if honour have to be made to the statistical and spatial \ncharacteristics of the variable under consideration. Mining is a high risk \nbusiness, as such, choosing the appropriate reserve estimation method \nwith minimum error is important in mining operations. This can improve \non the performance of the mine immensely as it determines what part of \nore deposit can be mined at a profit at any particular time. \n\n\n\nIn this paper, it has been demonstrated that geostatistics is a useful tool in \ndetermining the architecture of Au mineralisation at the deposit scale, and \nalso that, the MIK method, a non-linear, non-parametric method of local \ngrade estimation is applicable to the shear-hosted gold deposit of Obuasi. \nThe approach addresses the challenge of high-grade extreme values. MIK \napproach mitigates the effects of outliers and different supports in the \ndata. The grade estimates obtained using the MIK method are consistently \nlower than the OK and weighted average method of AGA. MIK method \ncould be more reliable approach considering the fact that the MCF based \non the estimates of AGA, are high despite operational deficiencies on the \nmine. \n\n\n\nIn this paper, it has been demonstrated that geostatistics is a useful tool in \ndetermining the architecture of Au mineralisation at the deposit scale, and \nalso that, the MIK method, a non-linear, non-parametric method of local \ngrade estimation is applicable to the shear-hosted gold deposit of Obuasi. \nThe approach addresses the challenge of high-grade extreme values. MIK \napproach mitigates the effects of outliers and different supports in the \ndata. The grade estimates obtained using the MIK method are consistently \nlower than the OK and weighted average method of AGA. MIK method \ncould be more reliable approach considering the fact that the MCF based \non the estimates of AGA, are high despite operational deficiencies on the \nmine. \n\n\n\nREFERENCES \n\n\n\nAbzalov, M. Z. 2016. Applied Mining Geology; Springer: Berlin, Germany. \n\n\n\nAbzalov, M. Z., Menzel, B. B., Wlasenko, M. B., Phillips, J. B. 2010. \nOptimisation of the grade control procedures at the Yandi iron-ore \nmine, Western Australia: geostatistical approach. Trans Inst Min Metall \nSect B. 119(3): 132\u2013142. \n\n\n\nAl- Hassan, S. and Annels, A. E. 1994. Geostatistical Evaluation of \nManganese Oxide Resources at Nsuta Mine, In Whateley, M.K.G and \nHarvey, P.K (Ed) 1994, Case Histories and Methods in Mineral \nResources Evaluation, Geol. Soc. Sec. Pub. 79, 157-169. \n\n\n\nAllibone, A., Hayden, P., Cameron, G., Duku, F. 2004. Paleopaleozoic gold \ndeposits hosted by albite- and carbonate-altered tonalite in the Chirano \ndistrict, Ghana, West Africa. Economic Geology, 99, 479\u2013497. \n\n\n\nAllibone, A., Teasdale, J., Cameron, G., Etheridge, M. A., Uttley, P., Soboh, A., \nAppiah-Kubi, J., Adanu, A., Arthur, R., Mamphey, J., Odoom, B., Zuta, J., \nTsikata, A., Pataye, F., Famiyeh, S., and Lamb, E. 2002. Timing and \n\n\n\nstructural controls on gold mineralization at the Bogoso gold mine, \nGhana, West Africa: Economic Geology, 97, 949\u2013969. \n\n\n\nAmadu, C. C. 2006. Geostatistical mineral resource estimation of part of \nBlock 1, KMS, Obuasi Mine, Ghana: A Case Study, Unpubl. MSc. Thesis, \nKwame Nkrumah University of Science and Technology, Kumasi, Ghana, \n204. \n\n\n\nAmponsah, P. O., Salvi, S., B\u00e9ziat, D., Siebenaller, L., Baratoux, L. and Jessell, \nM. W. 2015. Geology and geochemistry of the shear-hosted Julie gold \ndeposit, NW Ghana. Journal of African Earth Sciences, 112, 505-523. \n\n\n\nBaxter, J. L. and Yates, M. G. 2001. Estimation of Reserves and Resources in \nShear Zone Hosted Gold Deposits, in Mineral Resource and Ore Reserve \nEstimation \u2013 The AusIMM Guide to Good Practice (Ed: A C Edwards), pp. \n125\u2013134 (The Australasian Institute of Mining and Metallurgy: \nMelbourne). \n\n\n\nBloomberg Commodity Index (BCOM), 2015. Tables and Charts (Q1) \n\n\n\nCarras, S. N. 1984. Comparative ore reserve methodologies for gold mine \nevaluation. In Gold mining, metallurgy and geology (Melbourne: \nAustralasian Institute of Mining and Metallurgy) 59-70, Spec. Pub. 4/84. \n\n\n\nCosta, J. F. 2003. Reducing the impact of outliers in ore reserves estimation, \nMathematical Geology, 35(3): 323\u2013345. \n\n\n\nDavis, B. 1984. Indicator as applied to an alluvial gold deposit. \nGeostatistics for Natural Resources Characterisation. Reidel. 1, 337-\n348. \n\n\n\nDavis, J. C. 1986. Statistics and Data Analysis in Geology, John Wiley & Sons, \nNew York. 646. \n\n\n\nDaya, A. A. 2012. Reserve estimation of central part of Choghart north \nanomaly iron ore deposit through ordinary kriging method. \nInternational Journal of Mining Science and Technology, 22, 573\u2013577. \n\n\n\nDe Jager, E. J. 1997. The analysis of the mine call factor in gold mining, with \nspecific reference to Western Holdings Mine. PhD thesis, University of \nthe Witwatersrand, Johannesburg. \n\n\n\nDominy, S. C. 2014. Predicting the unpredictable\u2013evaluating high nugget \neffect gold deposits. In Mineral Resource and Ore Reserve Estimation; \nAustralasian Institute of Mining and Metallurgy: Melbourne, 659\u2013678. \n\n\n\nDominy, S. C., Glass, H. J., Lam, C. K., O\u2019Connor, L., Purevgerel, S., Minnitt, R. \nC. A. 2018. Integrating the Theory of Sampling into underground grade \ncontrol strategies. Minerals, 8, 232. \n\n\n\nDowd, P.A. 1997. Structural controls in the geostatistical simulation of \nmineral deposits. In Geostatistics Wollongong\u201996; Baafi, E. Y., Schofield, \nN. A., Eds.; Kluwer Academic: Dordrecht, The Netherlands, pp. 647\u2013657. \n\n\n\nFeybesse, J.L., Billa, M., Guerrot, C., Duguey, E., Lescuyer, J.L., Milesi, J.-P., \nand Bouchot, V. 2006. The Paleoproterozoic Ghanaian province: \nGeodynamic model and ore controls, including regional stress \nmodeling: Precambrian Research, v. 149, pp. 149\u2013196. \n\n\n\nFoli, G., Gawu, S.K.Y., Nude, P.M., 2015. Arsenic contamination and \nsecondary mineral evaluation in mine drainage using integrated acid\u2013\nbase accounting and toxicity characterisation leaching procedure: the \ncase of Obuasi mine, Ghana. Environ Earth Sci 73:8471\u20138486 \n\n\n\nFougerouse, D., Micklethwaite, S., Ulrich, S., Miller, J., and McCuaig, T.C. \n2013. Multistage mineralization of the Obuasi giant gold deposit, Ghana: \nBiennial SGA Meeting, 12th, 12\u201315 August 2013, Uppsala, Sweden, \nProceedings, 3, 1105\u20131108. \n\n\n\nFougerouse, D., Micklethwaite, S., Ulrich, S., Miller, J., Gode, B., Adams, D. T. \nand McCuaig, C. 2017. Evidence for Two Stages of Mineralization in \nWest Africa\u2019s Largest Gold Deposit: Obuasi, Ghana, Economic Geology, \n112, 3\u201322. \n\n\n\nFytas, C., Chaouai, N.E., Lavigne, M. 1990. Gold Deposit Estimation using \nIndicator Kriging. CIM Bulletin, February, 77-83. \n\n\n\nGol, C., Bulut, S., Bolat, F. 2017. Comparison of different interpolation \nmethods for spatial distribution of soil organic carbon and some soil \nproperties in the Black Sea backward region of Turkey. Journal of \nAfrican Earth Sciences, 134, 85-91. \n\n\n\nGoldfarb, R. J., Andr\u00e9-Mayer, A., Jowitt, S. M. and Mudd, G. M. 2017. West \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 76-84 \n\n\n\n\n\n\n\n \nCite The Article: Casmed Charles Amadu, Gordon Foli, Bernard Kissi-Abrokwa, Sylvester Akpah (2021). Geostatistical Approach For The Estimation of Shear-Hosted \n\n\n\nGold Deposit: A Case Study of The Obuasi Gold Deposit, Ghana. Malaysian Journal of Geosciences, 5(2): 76-84. \n\n\n\n\n\n\n\nAfrica: The World\u2019s Premier Paleoproterozoic Gold Province, Economic \nGeology, 112, 123\u2013143. \n\n\n\nGoovaerts, P. 1997. Geostatistics for Natural Resources Evaluation; Oxford \nUniversity Press: \n\n\n\nHohn, M. E. 1988. Geostatistics and Petroleum Geology, Van Nostrand \nReinhold, New York, 264. \n\n\n\nIsaaks, E. H. and Srivastava, R.M. 1989. Applied Geostatistics. Oxford \nUniversity Press, New York, 561. \n\n\n\nJoint Ore Reserves Committee (JORC) 2012. Australasian Code for \nReporting of Exploration Results, Mineral Resources and Ore \nReserves\u2014The JORC Code; Joint Ore Reserves Committee of the \nAustralasian Institute of Mining and Metallurgy, Australian Institute of \nGeoscientists and Minerals Council of Australia: Melbourne, Australia, \n44. \n\n\n\nJournel, A. G. 1983. Nonparametric estimation of spatial distributions. Jour \nMath Geol., 15(3), 445-468. \n\n\n\nKrige, D. G 1993. Variability in gold grade and spatial structures of \nWitwatersrand reefs. J.S. Afr. Inst. Min. Metall, 93(3), 79-84. \n\n\n\nMichel, D. 1982. Geostatistical ore reserve estimation. New York: Elsevier \nScientific \n\n\n\nMuriuki, J.W. and Temeng, V. A. 2018. \u201cComputer-Aided Cut-off Grade \nOptimisation for Open Pit Mines\u201d, Ghana Mining Journal, Vol. 18, No. 2, \npp. 1 - 8. \n\n\n\nOberthur, T., Hirdes, W., Hohndorf, A., Schmidt Mumm, A., Vetter, U., \nWeiser, T., Davis, D.W., Blenkinsop, T.G., Amanor, J.A., Loh, G., 1995. A \nreview of gold mineralisation in the Ashanti Belt of Ghana and its \nrelation to the crustal evolution of the terrane. Commun Geol Surv \nNamib 10:121\u2013127 \n\n\n\nParker, H. M. 1991. Statistical treatment of outlier data in epithermal gold \ndeposit reserve estimation, Mathematical Geology, 23(2): 175\u2013199. \n\n\n\nPerrouty, S., Aill\u00e8res, L., Jessell, M. W., Baratoux, L., Bourassa, Y. and \nCrawford, B. 2012. Revised Eburnean geodynamic evolution of the gold-\nrich southern Ashanti Belt, Ghana, with new field and geophysical \nevidence of pre-Tarkwaian deformations. Precambrian Research, 204\u2013\n205, 12\u2013 39. \n\n\n\nRezaei, A., Hassani, H., Fard-Mousavi, S. B., and Jabbari, N. 2019. Evaluation \nof heavy metals concentration in Jajarm Bauxite deposit in Northeast of \nIran using environmental pollution indices. Malaysian Journal of \nGeosciences, 3(1), 12\u201320. https://doi.org/10.26480/ \nmjg.01.2019.12.20 \n\n\n\nRossi, M.E. and Deutsch, C. V. 2013. Mineral resource estimation. Springer \nScience & Business Media. \n\n\n\nRoyle, A.G. 1991. Kriging. In: Dowd, P.A.  (Ed.), Leeds University Mining \n\n\n\nShahbeik, S., Afzal, P., Moarefvand, P. and Qumarsy, M. 2014. Comparison \nbetween Ordinary Kriging (OK) and Inverse Distance Weighted (IDW) \nbased on estimation error Case study: in Dardevey iron ore deposit, NE \nIran. Arab J Geosci., 7, 3693\u20133704. \n\n\n\nSinclair, A. J. and Blackwell, G. H. 2002. Applied Mineral Inventory \nEstimation. Cambridge \n\n\n\nSmith, J. L., Halvorsen, J. J., Papendick, R. I. 1993. Using multiple-variable \nindicator kriging for evaluating soil quality. Soil Sci. Soc. Am. J., 57, 743\u2013\n749. \n\n\n\nSrivastava, R. M. and Parker, H. M. 1988. Robust measures of spatial \ncontinuity. In: Amstrong M (ed) Geostatistics. Reidel, Dordrecht, 295\u2013\n308. \n\n\n\nWellmer, F.W., Dalheimer, M. and Wagner, M. 2007. Economic Evaluations \nin Exploration. Springer Science & Business Media. \n\n\n\nYasrebi, J., Saffari, M., Fathi, H., Karimian, N., Moazallahi, M., Gazni, R., 2009. \nEvaluation and comparison of ordinary kriging and inverse distance \nweighting methods for prediction of spatial variability of some soil \nchemical parameters. Res. J. Biol. Sci. 4, 93-102. \n\n\n\n\n\n\n\n \n\n\n\n\nhttps://doi.org/10.26480/\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 43-53 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2020.43.53 \n\n\n\nCite the Article: Md Abdullah Salman, and Faisal Ahmed (2020). Climatology In Barishal, Bangladesh: A Historical Analysis Of Temperature, Rainfall, Wind Speed And \nRelative Humidity Data. Malaysian Journal of Geosciences, 4(1): 43-53. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2020.43.53 \n\n\n\nCLIMATOLOGY IN BARISHAL, BANGLADESH: A HISTORICAL ANALYSIS OF \n\n\n\nTEMPERATURE, RAINFALL, WIND SPEED AND RELATIVE HUMIDITY DATA \n\n\n\nMd Abdullah Salman*, and Faisal Ahmed \n\n\n\nDepartment of Geology and Mining, University of Barishal, Bangladesh \n\n\n\n*Corresponding Author Email: masalman@bu.ac.bd \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 15 July 2020 \nAccepted 17 August 2020 \nAvailable online 3 September 2020\n\n\n\nThe Climatological data (temperature, rainfall, wind speed & relative humidity) recorded at Barishal \ndivisional meteorological station and Bangladesh Meteorological Departments over the period of 1961-2019 \nis used for an assessment of climatological aspects, climate change and the variability of Barishal in \nBangladesh. The trend of variant of yearly average maximum and minimum temperature has been found to \nbe increasing at a rate of 0.0055 \u00baC & 0.0087 \u00baC/year. Analysis of rainfall data observed that for majority of \nstations, the total rainfall showed decreasing trend for pre-monsoon, monsoon and winter seasons, while \nlittle increasing trend was observed for the post-monsoon. Calculated annual total rainfall in Barishal was \nshowed declining at the rate of -0.18488 mm/year and annual average wind speed was increasing by \n0.001783 m/s per year. Likewise, yearly average relative humidity observed to be abrupt rising at a rate of \n0.342975 per year with average of 70.855 at 2 meters.  \n\n\n\nKEYWORDS \n\n\n\nClimate Change, Meteorological Stations, Rainfall variability, Sen\u2019s slope estimator, Trend Analysis.\n\n\n\n1. INTRODUCTION \n\n\n\nClimate change is no longer incredible to take place in future but rather an \n\n\n\nongoing happening. It is now clearly well-known that climate change is \n\n\n\nrealism, and the adversities of climate alterations pose of the greatest \n\n\n\nchallenges facing humanity at the moment (IUCN, 2011). Climate is \n\n\n\naltering at both the regional scales (Gemmer et al. 2004; Kayano & \n\n\n\nSansigolo, 2008) and the global scales (Lambert et al. 2003; Dore, 2005) \n\n\n\ndue to global warming. The InterGovernmental Panel on Climate Change \n\n\n\n(IPCC) states that climate change as \u201ca change in the state of the climate \n\n\n\nthat can be identified by changes in the mean and/or the variability of its \n\n\n\nproperties and that persists for an extended period, typically decades or \n\n\n\nlonger\u201d. From the 1950s, numerous types of research have been conducted \n\n\n\nto recognize climate change, revealing that huge amounts of ice have \n\n\n\nmelted and the sea level has escalated because of the warming of the \n\n\n\natmosphere and ocean (Hartmann and Tank, 2013;IPCC, 2013).The \n\n\n\nintensity of greenhouse gases has augmented, which causes an increase \n\n\n\ninland and sea surface temperature and changes the patterns of rainfall, \n\n\n\nsea level rise and strengthening of El Nino (Basak et al., 2013;Raihan et al., \n\n\n\n2015; Jaiswal et al., 2015;Yu et al., 2016;Islam and Nursey-Bray, 2017).The \n\n\n\nimplications of climate change are principally noteworthy for the regions \n\n\n\nalready under pressure, such as in Bangladesh where hydrological \n\n\n\ndisasters are common phenomena (Shahid & Behrawan, 2008). The \n\n\n\nIntergovernmental Panel on Climate Change (IPCC) has termed \n\n\n\nBangladesh as one of the most vulnerable countries in the world due to \n\n\n\nclimate change (Ali, 1999; IPCC, 2007; Hashizume M. et al., 2007; Shahid, \n\n\n\n2011; Islam and Nursey-Bray, 2017; Vij et al., 2018). IPCC, 2013 has \n\n\n\nstudied that the combine global land and sea surface temperature has \n\n\n\nincreased by 0.89 \u00baC (0.69- 1.08 \u00baC) during 1901 to 2012 and by about 0.72 \n\n\n\n\u00baC (0.49-0.89 \u00baC) during 1951 to 2012, and the atmospheric burden of well \n\n\n\nmixed greenhouse gases has improved from 2005 to 2011 ((IPCC, 2001; \n\n\n\nHartmannand Tank, 2013; Yu et al., 2016). It has been predicted that due \n\n\n\nto climate change, there will be a steady go up of temperature and change \n\n\n\nin rainfall model which might have an amount of implications in \n\n\n\nagriculture (Karmakar S., 2000, 2003 and Khan T.M.A., et al. 2000), water \n\n\n\nresources (Fung et al. 2006) and public health (Shahid, 2008) in \n\n\n\nBangladesh. \n\n\n\nTopographically, Bangladesh is primarily a low-lying plain of about \n\n\n\n147,570 square kilometres (56,980 sq mi), situated on deltas of the largest \n\n\n\nGanges-Brahmaputra-Meghna Rivers flowing from the Himalayas. \n\n\n\nBangladesh is a disaster prone country for its geological condition with the \n\n\n\nHimalayas in the north and Bay of Bengal in the south. The international \n\n\n\ncommunity has recognized that Bangladesh ranks as high in the list of \n\n\n\nmost vulnerable countries on the earth. Bangladesh\u2019s high vulnerability to \n\n\n\nclimate change is due to a number of hydro-geological and socio-economic \n\n\n\ninfluences that include: (a) its geographical location in South Asia; (b) its \n\n\n\nextreme climate variability that is governed by monsoon and which results \n\n\n\nin acute water distribution over space and time; (c) its flat deltaic \n\n\n\ntopography with very low elevation; (d) its majority of population being \n\n\n\ndependent on crop agriculture which is highly influenced by climate \n\n\n\nvariability and change and (e) its high population density and poverty \n\n\n\nincidence. IPCC, 2013 has famed Bangladesh as a risky country due to \n\n\n\nclimate change, where many natural disasters such as increase \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 43-53 \n\n\n\nCite the Article: Md Abdullah Salman, and Faisal Ahmed (2020). Climatology In Barishal, Bangladesh: A Historical Analysis Of Temperature, Rainfall, Wind Speed And \nRelative Humidity Data. Malaysian Journal of Geosciences, 4(1): 43-53. \n\n\n\ntemperature, flood, cyclone, drought, saline water intrusion, sea-level rise, \n\n\n\nand heavy monsoon downpours are very common occurrences (Titumir \n\n\n\nand Basak, 2012;Basak et al., 2013). Due to the ancient climate change, \n\n\n\nBangladesh\u2019s seasonal cycle has altered from six seasons to three, which \n\n\n\ncan be primarily characterized by a hot summer, a shrinking winter, and \n\n\n\nmedium to heavy rains during the monsoon season (Denissen, 2012). \n\n\n\nBangladesh has a sub-tropical humid climate characterized by wide \n\n\n\nseasonal variations in moderately hot temperatures, rainfall and high \n\n\n\nhumidity (Rashid, 1991 and OECD, 2003). The climatic change incidents \n\n\n\nhave become global priorities during the last few decades. It is marked that \n\n\n\nthe global mean surface air temperature has amplified by 0.3 oC to 0.6 \u00baC \n\n\n\ncover last hundred years, with the five global average warmest years being \n\n\n\nin the 1980s-90s (WMO, 1991, World Bank, 2000 & IPCC, 2007). Over the \n\n\n\nsame period global sea level has improved by 10-20 cm (IPCC, 2007). The \n\n\n\neconomic activities of the country, especially the agriculture are \n\n\n\ndependent on the rainfall and temperature. The weather activities of \n\n\n\nBangladesh are dominated by the southwest monsoon. The production of \n\n\n\nagriculture is also relying on temperature variability. In addition, \n\n\n\nBangladesh is considered to become the worst victim of the impacts of \n\n\n\nglobal warming and related climate change. The climate change persuaded \n\n\n\nenhancement of natural disasters will cause its people to suffer \n\n\n\ninnumerable loss to resources and livelihood. Variability of rainfall causes \n\n\n\nflood and droughts. The effect of these can be minimized by proper \n\n\n\nmanagement practices which include preparedness, rescue operation and \n\n\n\nrehabilitation. Again, agriculture plan can be made suitable using the \n\n\n\nknowledge of climatic change. The trends of climatic parameters help the \n\n\n\npolicy maker to develop the country especially in the agriculture sector. \n\n\n\nSince the population of Barishal city is growing, crop yield should be \n\n\n\nacceptable to balance agro-ecosystem. Barishal city is most vulnerable to \n\n\n\nclimate change since the climatic parameters like temperature, rainfall \n\n\n\nvariability, wind velocity and humidity are unpredictable. A strong and \n\n\n\nrobust hydrometeorology monitoring network is therefore fundamental \n\n\n\nto further work on detection and attribution of present-day hydrological \n\n\n\nvariations; in particular, changes in water resources and in the \n\n\n\noccurrences of extreme events like cyclones , floods,  droughts, storms , \n\n\n\nirregular rainfall, cold spells etc. (IUCN Bangladesh, 2011). \n\n\n\nThe dissimilarity in temperature, rainfall, wind speed and relative \n\n\n\nhumidity due to climate change during the past decades has exaggerated \n\n\n\nvarious problems around the world as well as Bangladesh. Presently, \n\n\n\nmany studies have focused on climate change all over the world, but \n\n\n\nBangladesh has not yet advanced in this field. Although a number of \n\n\n\nstudies have been found on temperature variability in Bangladesh (Jones, \n\n\n\n1995; Singh, 2001; Shahid, 2010b, 2011; Shahid et al., 2012; Basak et al., \n\n\n\n2013; Raihan et al., 2015 and Khan et al. 2019). Besides these, most studies \n\n\n\nhave highlighted the daily maximum and minimum temperature variables \n\n\n\nbefore the year 2008, even without considering all the records of the \n\n\n\nmeteorological stations. \n\n\n\nCurrently, Khan et al. (2019) reported that the average monthly maximum \n\n\n\ntemperature (Tmax) and minimum temperature (Tmin) have increased \n\n\n\nsignificantly by 0.35 \u00b0C/decade and 0.16 \u00b0C/decade, respectively. In \n\n\n\ncontrast, the wind speed (WS) has decreased significantly all over the \n\n\n\ncountry and decreased by a higher rate in the north-western (NW) region \n\n\n\n(monsoon, 0.60 and annual, 0.51 kt/decade) than other regions, while the \n\n\n\nmonsoonal and annual precipitation have decreased by 87.35 mm/decade \n\n\n\nand 107 mm/decade, respectively. Similarly, Rahman, M.R., & Latch, H. \n\n\n\n(2015) observed that recent climate change in Bangladesh with a 0.20 \u00b0C \n\n\n\nper decade upward trend of mean temperature over a 40-year period \n\n\n\n(1971 to 2010). They also observed that an upward trend of annual rainfall \n\n\n\n(+7.13 mm per year) and downward pre-monsoon (\u22120.75 mm per year) \n\n\n\nand post-monsoon rainfall (\u22120.55 mm per year) trends during the same \n\n\n\ntime frame. Their evidence\u2019s would lead to approximately 1.0 \u00b0C warmer \n\n\n\ntemperatures in Bangladesh by 2020, compared to that of 1971. \n\n\n\nHowever, numbers of studies have been carried out on trends of change in \n\n\n\nclimate parameters in the context of Bangladesh like Haque et al (1992) \n\n\n\nstated that the average increase in temperature would be 1.3 oC and 2.6 oC \n\n\n\nfor the projected years of 2030 and 2075, respectively. Similar to IPCC \n\n\n\npredictions, the increase in winter temperature in Bangladesh was \n\n\n\npredicted to be higher probably due to momentous rise in monsoon \n\n\n\nprecipitation, which could also cause rigorous flooding in the future. \n\n\n\nChowdhury & Debsarma, (1992) studied that the projected changes will \n\n\n\nbe 1.4 oC in the winter and 0.7 oC in the monsoon months in 2030. For 2075, \n\n\n\nthe variation would be 2.1 oC and 1.7 oC for winter and monsoon \n\n\n\ncorrespondingly. It is also observed that the growing tendency of lowest \n\n\n\nminimum temperature over Bangladesh.   \n\n\n\nAddisu et al. (2015), Warrick et al (1994), Karmakar & Shrestha (2000), \n\n\n\nDebsarma (2003) and Salahuddin A., (2006) studied the variation of \n\n\n\ntemperature and rainfall over Bangladesh. In this study (Warrick et al, \n\n\n\n1994), mean-annual temperatures have been expressed as departures \n\n\n\nfrom the reference period 1951-1980. It is evident that, on this time scale, \n\n\n\nBangladesh region has been getting warmer. Since the later part of the last \n\n\n\ncentury, there has been, on average, an overall increase in temperature by \n\n\n\n0.5 oC which was comparable in magnitude to the observed global \n\n\n\nwarming. Karmaker & Nessa, (1997) and Karmakar (2003) studied on \n\n\n\nclimate change and its impacts on natural disasters and southwest-\n\n\n\nmonsoon in Bangladesh and the Bay of Bengal. They found that the decadal \n\n\n\nmean annual temperature over Bangladesh have shown increasing \n\n\n\ntendency especially after 1961-1970. Chowdhury & Debsarma (1992) and \n\n\n\nMia (2003) reported variations in temperature based on analysis of \n\n\n\nchronological data of some selected weather stations in Bangladesh. \n\n\n\nBasak et al (2013 & 2011), Titumir, R.A.M. and Basak (2012) studied the \n\n\n\ntrend of variation of yearly average maximum temperature has been found \n\n\n\nto be increasing at a rate of 0.0186 oC per year, whereas the rate was \n\n\n\n0.0152 oC per year for yearly average minimum temperature in \n\n\n\nBangladesh for the period of 1976-2008. Shahid, S. (2010) studied an \n\n\n\nincreasing mean, mean maximum and mean minimum temperatures of \n\n\n\nBangladesh at a rate of 0.103\u00b0C, 0.091\u00b0C and 0.097\u00b0C per decade and an \n\n\n\nincreasing of annual and pre monsoon rainfall of Bangladesh are also \n\n\n\nobserved at a rate of 5.53 mm/year and 2.47 mm/year respectively over \n\n\n\nthe time period of 1958-2007. \n\n\n\nTo address climatological challenges, KfW, Swiss Re and Barishal City \n\n\n\nCorporation (2016) teamed up to build up an adaptation strategy for \n\n\n\nBarishal in Bangladesh. Applying the Economics of Climate Adaptation \n\n\n\nmethodology, the team was able to recognize the key risk drivers in \n\n\n\nBarisal: the city\u2019s annual monsoon, cyclones and sea level rise as well as \n\n\n\nurbanization. Frequent flooding, urban sewage problems and damaging \n\n\n\neffects for low-income households are just some of the negative impacts \n\n\n\nthe city has to tackle every year. Communities in Barisal face yearly \n\n\n\ndamages of USD 10 million due to monsoon floods and cyclones. This \n\n\n\nnumber is probable to add to significantly by 2050. The total climate risk \n\n\n\nby 2050 as a result of economic growth and climate change under a \n\n\n\nmoderate state is projected to add up to approximately USD 130 million in \n\n\n\ndamage per year. \n\n\n\nKfw and Barishal City Corporation (2016) observed rapid urban growth \n\n\n\nclaims several challenges for the city of Barishal in Bangladesh. Natural \n\n\n\ndisasters are a key unfavorable factor to sustainable development for the \n\n\n\ncoastal region, including Barisal\u2019s economy. The key reasons are an \n\n\n\narrangement of natural processes and growing anthropogenic activities, \n\n\n\nsuch as a swiftly rising waterfront development. Likewise, land use \n\n\n\nplanning and regulation are fragile and combined with population growth \n\n\n\nand urbanization pressures this has led to an untenable urban growth. \n\n\n\nIn this study, we have tried to build upon a steadily increasing number of \n\n\n\ntheoretical and experimental studies of the climatological factors such as \n\n\n\ntemperature, rainfall, wind speed and relative humidity to assess their \n\n\n\nchange during 1961 to 2019 in Barishal, Bangladesh according to the data. \n\n\n\nThus, wide statistical analysis was performed using meteorological data, \n\n\n\nand an ancient climate change trend was found in the climatic sub-regions \n\n\n\nas well as the entire country. The main objective of this study is to explore \n\n\n\nthe coherent trend of related climate variables in order to explain their \n\n\n\nchanges in the time series and comprehensible correlation during the last \n\n\n\n58 years. The dramatic climate change over the city is also a part of the \n\n\n\nfocus of this study. \n\n\n\nThe present study has provided an assessment of climatological aspects, \n\n\n\nclimate change and variability in Barishal based on analysis of historical \n\n\n\ndata of temperature, rainfall, wind velocity and relative humidity recorded \n\n\n\nat available meteorological stations in Bangladesh. Assessments have been \n\n\n\nmade, in particular, of changes in maximum temperature, changes in \n\n\n\nminimum temperature, changes in rainfall pattern, changes in wind \n\n\n\nvelocity and change in relative humidity. \n\n\n\n2. CLIMATOLOGY OF BANGLADESH \n\n\n\nClimatology, or occasionally known as climate science, is the study of the \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 43-53 \n\n\n\nCite the Article: Md Abdullah Salman, and Faisal Ahmed (2020). Climatology In Barishal, Bangladesh: A Historical Analysis Of Temperature, Rainfall, Wind Speed And \nRelative Humidity Data. Malaysian Journal of Geosciences, 4(1): 43-53. \n\n\n\nEarth's weather models and the systems that cause them. From the ocean \n\n\n\noscillations to trade winds, a pressure that drives temperature, airborne \n\n\n\nparticles that influence local conditions and even the phases of the moon \n\n\n\nand Earth's wobble all affect the climate. Bangladesh has a sub-tropical \n\n\n\nmonsoon and humid climate characterized by wide seasonal variations in \n\n\n\nmoderately warm temperatures, rainfall, wind velocity and high humidity \n\n\n\n(Rashid, 1991). Four dissimilar seasons can be renowned in Bangladesh \n\n\n\nfrom climatological point of view: (i) the dry winter season from \n\n\n\nDecember to February (January is the coldest month, when the average \n\n\n\ntemperature for most of the country is about 10\u00b0C) (BMD, 2019), (ii) the \n\n\n\npre-monsoon hot summer season from March to May (April is the warmest \n\n\n\nmonth in most parts of the country and temperatures range between 30\u00b0C \n\n\n\nand 40\u00b0C) (BMD, 2019), (iii) the rainy monsoon season from June to \n\n\n\nSeptember, and (iv) the post-monsoon autumn season which lasts from \n\n\n\nOctober to November.  \n\n\n\nHeavy rainfall pattern is characteristic of Bangladesh. Rainfall variability \n\n\n\nin space and time is one of the most relevant uniqueness of the climatology \n\n\n\nof Bangladesh. Rainfall in Bangladesh varies from 1400-1600 mm in the \n\n\n\nwest to more than 2400-4400 mm in the east (Hussain AM, & Sultana N., \n\n\n\n1996). Higher rainfall in the northeast is caused by the additional uplifting \n\n\n\neffect of the Meghalaya plateau. About 75-80% of rainfall in Bangladesh \n\n\n\noccurs during the monsoon time, caused by weak tropical depressions that \n\n\n\nare brought from the Bay of Bengal into Bangladesh by the wet monsoon \n\n\n\nwinds. \n\n\n\nAs we know that temperature gradient between places results in \n\n\n\ndifferences in air pressure and ultimately, wind. Wind speeds increase \n\n\n\nwith a greater temperature difference. Bangladesh has high variation of \n\n\n\nwarm temperature and high humidity. A study published in the journal \n\n\n\n\u2018Nature Climate Change\u2019 found that winds across much of North America, \n\n\n\nEurope and Asia have been growing faster since about 2010. In \n\n\n\nBangladesh, annual average wind speed at 30 m height along the coastal \n\n\n\nbelt is above 5 m/s (Khan et al. 2000). Wind speed in northeastern parts \n\n\n\nis above 4.5 m/s while inland wind speed is around 3.5 m/s for most part \n\n\n\nof Bangladesh (Khan et al. 2000). \u2018Nature Climate Change\u2019 also observed \n\n\n\nthat if the speeding-up trend could continue for another decade or longer, \n\n\n\nuntil the next major shift occurs. That could be a boon for the wind power \n\n\n\nindustry in the near future. If the current pattern continues, they suggest \n\n\n\nthat average global power generation could increase by as much as 35-\n\n\n\n37% by 2024. \n\n\n\nHigher Humidity is also characteristic of Bangladesh. As we know that if \n\n\n\nthe water vapor content stays the same and the temperature falls, the \n\n\n\nrelative humidity increases. If the water vapor content stays the same and \n\n\n\nthe temperature rises, the relative humidity decreases. This is because \n\n\n\ncolder air doesn't require as much moisture to become saturated as \n\n\n\nwarmer air. \n\n\n\nThe vapor plays a key role in determining the dynamic properties of the \n\n\n\nclimate system. Humidity is the amount of water vapor in the air, and \n\n\n\nrelative humidity considers the ratio of the actual vapor pressure of the air \n\n\n\nto the saturated vapor pressure which is usually expressed in percentage. \n\n\n\nHumidity affects crops through evaporation, transpiration and \n\n\n\ncondensation (Lenka, 1998). Crop agriculture is highly prejudiced by \n\n\n\nclimatic change and majority of population is relying on agricultural crop \n\n\n\nin Bangladesh. The prediction of atmospheric factors is essential for \n\n\n\nclimate monitoring, harsh weather prediction, drought revealing, \n\n\n\nagriculture and production, development in energy and industry, pollution \n\n\n\ndispersal and communication etc. \n\n\n\n3. METHODOLOGY \n\n\n\n3.1 Study Area \n\n\n\nBarishal is a major city that lies on the bank of Kirtankhola River in south-\n\n\n\ncentral Bangladesh (Figure 1). It is the largest city and the administrative \n\n\n\nheadquarter of both Barishal district and Barishal Division. The area of the \n\n\n\ncity is 58 km2 and located at 22\u00b048\u20320\u2033N 90\u00b030\u20320\u2033E (Figure 1). The climate \n\n\n\nof Barishal city is a tropical wet and dry climate. The Barishal lies on 10m \n\n\n\nabove sea level. In winter, there is much less rainfall in Barishal than in \n\n\n\nsummer. In Barishal, the average annual temperature is 25.9 \u00b0C | 78.7 \u00b0F. \n\n\n\nThe annual rainfall is 2184 mm | 86.0 inch (BMD, 2019). \n\n\n\nFigure 1: Study area map with showing Divisional Weather Station of \n\n\n\nBarishal. \n\n\n\n3.2 Data Collection and Data Range \n\n\n\nDifferent tools were applied to calculate and assess the trends of changes \n\n\n\nof climatic variables in Barishal. The main focus is on the assessment of the \n\n\n\ntrend on changes of temperature. Different instruments, formula, tools \n\n\n\nand techniques were applied to accomplish the study. \n\n\n\nIn this study, data on temperature, rainfall, wind velocity and humidity of \n\n\n\ndivisional weather stations in Barishal were collected from the Bangladesh \n\n\n\nMeteorological Department (BMD). Temperatures data included monthly \n\n\n\naverage and annual mean maximum and minimum temperatures for the \n\n\n\nperiod January 1961 through December 2019, rainfall data, wind speed for \n\n\n\nthe same period and relative humidity data for the period of January 1981 \n\n\n\nthrough December 2019. Fifty Eight years (1961-2019) temperature, \n\n\n\nrainfall, wind velocity and humidity (1981-2019) records of those stations \n\n\n\nare used in the present study to assess the recent change and climatology \n\n\n\nin the Barishal City. It should also be noted that there are some missing \n\n\n\ndata for some months at some stations, which have been included in the \n\n\n\ntrend analysis from the sources of NASA (National Aeronautics and Space \n\n\n\nAdministration) and NOAA (National Oceanic and Atmospheric \n\n\n\nAdministration). \n\n\n\n3.3 Trend Analysis \n\n\n\nMann-Kendall test (Mann 1945; Kendall 1975) for trend and Sen\u2019s slope \n\n\n\n(Sen, 1968) estimates used for detecting and estimates trends in the time \n\n\n\nseries of the annual values of yearly temperature, rainfall and wind \n\n\n\nvelocity. A number of Excel template developed for Mann-Kendall test for \n\n\n\ntrend and Sen\u2019s slope estimation. Of them Mann-Kendall test for trend and \n\n\n\nSen\u2019s slope estimates (MAKESENS) used for detecting and estimating \n\n\n\ntrend (Salmi et al., 2002). There are two phases in trend analysis; first the \n\n\n\npresence of a monotonic increasing or decreasing trend and secondly the \n\n\n\nslope of a linear trend is estimated. Both of the cases nonparametric tests \n\n\n\nwere applied. For monotonic trend analysis the nonparametric Mann-\n\n\n\nKendall test and for slope of linear trend estimation the non-parametric \n\n\n\nSen\u2019s slope estimator used. Correlation coefficient of the meteorological \n\n\n\nvariables and time were also computed to determine the better strength \n\n\n\nand understanding of the linear relationship between variables \n\n\n\n(Olofintoye, 2010). \n\n\n\n\nhttps://www.weatheronline.co.uk/cgi-bin/regframe?3&PRG=cityklima&WMO=41923&INFO=0&PAG=1\n\n\nhttps://www.nature.com/articles/s41558-019-0622-6\n\n\nhttps://en.wikipedia.org/wiki/Kirtankhola\n\n\nhttps://en.wikipedia.org/wiki/Bangladesh\n\n\nhttps://en.wikipedia.org/wiki/Barisal_district\n\n\nhttps://en.wikipedia.org/wiki/Barisal_Division\n\n\nhttps://tools.wmflabs.org/geohack/geohack.php?pagename=Barisal&params=22_48_0_N_90_30_0_E_region:BD_type:city(328278)\n\n\nhttps://en.wikipedia.org/wiki/Tropical_savanna_climate\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 43-53 \n\n\n\nCite the Article: Md Abdullah Salman, and Faisal Ahmed (2020). Climatology In Barishal, Bangladesh: A Historical Analysis Of Temperature, Rainfall, Wind Speed And \nRelative Humidity Data. Malaysian Journal of Geosciences, 4(1): 43-53. \n\n\n\n3.3.1 Mann-Kendall test  \n\n\n\nIn Mann-Kendall trend test (Mann 1945; Kendall 1975) the data are \n\n\n\nestimated as an ordered time series. Each data is compared to all \n\n\n\nconsequent data. The initial value of the Mann-Kendall statistic, S, is \n\n\n\nassumed to be 0 (no trend). If a data from a later time period is greater \n\n\n\nthan a data from an earlier time period, S is incremented by 1. Then again, \n\n\n\nif the data from a later time period is lesser than a data sampled earlier, S \n\n\n\nis decremented by 1. The net result of all such increments and decrements \n\n\n\nproduces the final value of S. If x1, x2, x3 ....... xn represent n data points \n\n\n\nwhere xj represents the data point at time j, then S is given by, \n\n\n\nThe probability associated with S and the sample size, n, are then \n\n\n\ncomputed to statistically quantify the significance of the trend. Normalized \n\n\n\ntest statistic Z is computed as follows: \n\n\n\nAt the 99% significance level, the null hypothesis of no trend is rejected if \n\n\n\n|Z|>2.575; at 95% significance level, the null hypothesis of no trend is \n\n\n\nrejected if |Z|>1.96; and at 90% significance level, the null hypothesis of \n\n\n\nno trend is rejected if|Z|>1.645. More specifics of Mann-Kendall test can \n\n\n\nbe found in Sneyers (1990). \n\n\n\n3.3.2 Sen\u2019s Slope Method  \n\n\n\nSen's Slope method (Sen, 1968) contains computing slopes for all the sets \n\n\n\nof ordinal time points and then using the median of these slopes as an \n\n\n\nevaluation of the total slope. The Sen\u2019s method expected that the trend is \n\n\n\nlinear. This means that the continuous monotonic aggregating or reducing \n\n\n\nfunction of time, f(t), is equal to \n\n\n\nf(t) = Qt + B \n\n\n\nWhere, Q is the slope and B is a constant. To get the slope, Q in equation \n\n\n\nabove first the slopes of all data pairs are calculated, \n\n\n\nWhere, Q/ = slope between data points xt/ and xt ; xt/ = data measurement \n\n\n\nat time t/ \n\n\n\nxt = data measurement at time t \n\n\n\nSen's estimator of slope is simply given by the median slope, \n\n\n\nWhere, N is the number of calculated slopes. Details of Sen\u2019s slope \n\n\n\nestimation can be found in Sen (1968). \n\n\n\n4. RESULT AND DISCUSSION \n\n\n\nThe analysis of temperature, rainfall, wind speed and relative humidity \n\n\n\ntrends reveals abrupt changes found in Bangladesh over the time period \n\n\n\n1961\u20132019. The obtained outcomes are described in the following \n\n\n\nsections. \n\n\n\n4.1 Changes in Monthly Maximum Temperature \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 43-53 \n\n\n\nCite the Article: Md Abdullah Salman, and Faisal Ahmed (2020). Climatology In Barishal, Bangladesh: A Historical Analysis Of Temperature, Rainfall, Wind Speed And \nRelative Humidity Data. Malaysian Journal of Geosciences, 4(1): 43-53. \n\n\n\nFigure 2: Temporal variation of annual average maximum temperature (\u00baC) in Barishal from 1961 to 2019. \n\n\n\n(Jan: January; Feb: February; Mar: March; Apr: April; Jun: June; Jul: July; Aug: August; Sep: September; Oct: October; Nov: November; Dec: December; Dev: \n\n\n\nStandard Deviation) \n\n\n\nThe trend of variation of monthly average maximum temperature was \n\n\n\nanalyzed for all available stations. The monthly maximum temperature \n\n\n\nincreased at most of the months expect January, March and April in \n\n\n\nBarishal during the period of 1961\u20132019 and the rates of changed of \n\n\n\ntemperature were 0.0019 \u00baC, 0.0009 \u00baC, 0.0159 \u00baC, 0.0086 \u00baC, 0.0141 \u00baC, \n\n\n\n0.01 \u00baC, 0.0087 \u00baC, 0.0125 \u00baC and 0.0058 \u00baC per year for February, May, \n\n\n\nJune, July, August, September, October, November and December months \n\n\n\nrespectively (Table 1 & Figure 2). In Barishal City, the increasing trend of \n\n\n\nmaximum temperature has the highest coefficient of determination equal \n\n\n\nto 0.457275 in August month which was significant at 99% level of \n\n\n\nsignificance (Table 1). On an average yearly average maximum \n\n\n\ntemperature of Barishal City has been found to be increasing at a rate of \n\n\n\n0.0055 \u00baC per year with average maximum temperature of 30.38 \u00baC and \n\n\n\naverage coefficient of determination equal to 0.193478 (Table 1). \n\n\n\nSignificant increase of temperature during this 58-years period was \n\n\n\nobserved at June month (0.0159 \u00baC) with correlation of coefficient equal \n\n\n\nto 0.37054 and minimum at January, March and April respectively (- \n\n\n\n0.0089 \u00baC, - 0.002 \u00baC and - 0.0022 \u00baC) (Table 1 & Figure 2). \n\n\n\nTable 1: Total changes, Average changes and Regression equation of trend lines for Average Maximum Temperature from 1961 to 2019. \nMonth Total Changes R\u00b2. Correlation of coefficient r Average Equation \n\n\n\nJanuary - 0.0083 0.0187 0.136748 25.38 y = -0.0083x + 0.2493 \nFebruary 0.0019 0.0006 0.024495 28.39 y = 0.0019x - 0.0556 \n\n\n\nMarch - 0.002 0.0012 0.034641 32.15 y = -0.002x + 0.0603 \nApril - 0.0022 0.0013 0.036056 33.36 y = -0.0022x + 0.0665 \nMay 0.0009 0.0003 0.017321 34.12 y = 0.0009x - 0.0258 \nJune 0.0159 0.1373 0.37054 31.62 y = 0.0159x - 0.4755 \nJuly 0.0086 0.0711 0.266646 30.68 y = 0.0086x - 0.2572 \n\n\n\nAugust 0.0141 0.2091 0.457275 30.81 y = 0.0141x - 0.4217 \nSeptember 0.01 0.1252 0.353836 31.34 y = 0.01x - 0.3005 \n\n\n\nOctober 0.0087 0.0562 0.237065 31.07 y = 0.0087x - 0.2607 \nNovember 0.0125 0.0773 0.278029 29.35 y = 0.0125x - 0.3743 \nDecember 0.0058 0.0119 0.109087 26.28 y = 0.0058x - 0.1746 \n\n\n\nAverage Changes 0.0055 NA 0.193478 30.38 NA \n\n\n\n4.2 Changes in Monthly Minimum Temperature \n\n\n\nTable 2: Total changes, Average and Regression equation of trend lines for Average Minimum Temperature from 1961 to 2019. \n\n\n\nMonth Total Changes R\u00b2 Correlation of coefficient r Average Equation \n\n\n\nJanuary - 0.0021 0.0019 0.04359 12.38 y = -0.0021x + 0.0621 \n\n\n\nFebruary 0.0138 0.0693 0.26324 15.7 y = 0.0138x - 0.4152 \n\n\n\nMarch 0.0094 0.0297 0.17233 20.16 y = 0.0094x - 0.2805 \n\n\n\nApril 0.0085 0.0374 0.19339 23.59 y = 0.0085x - 0.2558 \n\n\n\nMay 0.0116 0.085 0.29154 24.22 y = 0.0116x - 0.3487 \n\n\n\nJune 0.0125 0.183 0.42778 25.62 y = 0.0125x - 0.3756 \n\n\n\nJuly 0.0095 0.1542 0.39268 25.75 y = 0.0095x - 0.2858 \n\n\n\nAugust 0.012 0.2108 0.45913 25.71 y = 0.012x - 0.361 \n\n\n\nSeptember 0.0078 0.0908 0.30133 25.57 y = 0.0078x - 0.2341 \n\n\n\nOctober 0.0082 0.044 0.20976 23.75 y = 0.0082x - 0.2456 \n\n\n\nNovember 0.0051 0.0067 0.08185 19.03 y = 0.0051x - 0.1525 \n\n\n\nDecember 0.0086 0.0279 0.16703 13.94 y = 0.0086x - 0.2576 \n\n\n\nAverage Changes 0.008742 NA 0.250304 21.285 NA \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 43-53 \n\n\n\nCite the Article: Md Abdullah Salman, and Faisal Ahmed (2020). Climatology In Barishal, Bangladesh: A Historical Analysis Of Temperature, Rainfall, Wind Speed And \nRelative Humidity Data. Malaysian Journal of Geosciences, 4(1): 43-53. \n\n\n\nThe monthly minimum temperature showed increasing trend in the \n\n\n\nBarishal City over the study period, 1961 to 2019. On an standard yearly \n\n\n\naverage minimum temperature of Barishal City has been found to be rising \n\n\n\nat a rate of 0.0087 \u00baC per year with average minimum temperature of \n\n\n\n21.29 \u00baC and average coefficient of determination equal to 0.250304 \n\n\n\n(significant at 99% level of significance) (Table 2). An increase trend of \n\n\n\nminimum temperature showed at most of the months expects January (- \n\n\n\n0.0021 \u00baC) in Barishal over the period of 1961-2019 (Table 2 & Figure 3). \n\n\n\nAn increase rates of changed of temperature were 0.0138 \u00baC, 0.0094 \u00baC, \n\n\n\n0.0085 \u00baC, 0.0116 \u00baC, 0.0125 \u00baC, 0.0095 \u00baC, 0.012 \u00baC, 0.0078 \u00baC, 0.0082 \u00baC, \n\n\n\n0.0051 \u00baC and 0.0086 \u00baC per year for February, March, April, May, June, \n\n\n\nJuly, August, September, October, November and December months \n\n\n\nrespectively (Table 2 & Figure 3). In Barishal City, the increasing trend of \n\n\n\nminimum temperature has the highest coefficient of determination equal \n\n\n\nto 0.45913 in August and the lowest equal to 0.04359 in January which \n\n\n\nwas significant at 99% level of significance (Table 2). Momentous increase \n\n\n\nof temperature during this 58-years period was observed at February \n\n\n\nmonth (0.0138 \u00baC) and minimum at January respectively (- 0.0021 \u00baC) \n\n\n\n(Table 2 & Figure 3). \n\n\n\nFigure 3: Temporal variation of annual average minimum temperature (\u00baC) in Barishal from 1961 to 2019. \n\n\n\n(Jan: January; Feb: February; Mar: March; Apr: April; Jun: June; Jul: July; Aug: August; Sep: September; Oct: October; Nov: November; Dec: December; Dev: \n\n\n\nStandard Deviation) \n\n\n\n4.3 Changes in Monthly Average Rainfall \n\n\n\nThe changes in rainfall model are significant climate change phenomena, \n\n\n\nwhich are likely to be observed all over the land. In the study, the annual \n\n\n\nmonthly total rainfall has been showed decreasing trend in Barishal over \n\n\n\nthe study period, 1961 to 2019. Annual monthly total rainfall in Barishal \n\n\n\nhas been decreasing at the rate of -0.18488 mm/year and average rainfall \n\n\n\n185.1252 mm/year with average correlation of coefficient equal to \n\n\n\n0.079871 (significant at 99% level of significance) (Table 3). \n\n\n\nIn this study, changes in rainfall model have been assessed by analyzing \n\n\n\nchanges in total rainfall during four seasons i.e., winter (December-\n\n\n\nFebruary), pre-Monsoon (March-May), Monsoon (June-September) and \n\n\n\npost-Monsoon (October-November) for the period 1961 to 2019; analysis \n\n\n\nwas made separately for available weather stations. In winter, overall \n\n\n\nrainfall was found to be decreasing at an average rate of -0.01293 \n\n\n\nmm/year (Table 3). Similarly, pre-Monsoon and Monsoon periods were \n\n\n\nfound to be decreasing at an average rate of -0.01997 mm/year and -\n\n\n\n0.58508 mm/year (Table 3). However, an increasing trend was observed \n\n\n\nat an average rate of 0.11025 mm/year in the post-Monsoon time (Table \n\n\n\n3). In Barishal City, Momentous decrease of rainfall during this 58-years \n\n\n\nperiod was observed highest at June month (-2.7705 mm/year) and \n\n\n\nminimum at February (-0.0031 mm/year) respectively (Table 3 & Figure \n\n\n\n4). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 43-53 \n\n\n\nCite the Article: Md Abdullah Salman, and Faisal Ahmed (2020). Climatology In Barishal, Bangladesh: A Historical Analysis Of Temperature, Rainfall, Wind Speed And \nRelative Humidity Data. Malaysian Journal of Geosciences, 4(1): 43-53. \n\n\n\nFigure 4: Temporal variation of annual average rainfall (mm) in Barishal from 1961 to 2019. \n\n\n\n(Jan: January; Feb: February; Mar: March; Apr: April; Jun: June; Jul: July; Aug: August; Sep: September; Oct: October; Nov: November; Dec: December; Dev: \n\n\n\nStandard Deviation)\n\n\n\nTable 3: Total changes, Average changes and Regression equation of trend lines for Average Rainfall from 1961 to 2019. \n\n\n\nSeasons Month \nTotal \nChanges \n\n\n\nSeasonal Average \nChanges \n\n\n\nR\u00b2 \nCorrelation of \ncoefficient r \n\n\n\nAverage Equation \n\n\n\nWinter \n\n\n\nDecember -0.0078 \n\n\n\n-0.0129333 \n\n\n\n9.00E-05 0.009487 9.102 y = -0.0078x + 0.2332 \n\n\n\nJanuary -0.0279 0.003 0.054772 6.98 y = -0.0279x + 0.8364 \n\n\n\nFebruary -0.0031 7.00E-06 0.002646 21.08 y = -0.0031x + 0.0929 \n\n\n\npre-Monsoon  \n\n\n\nMarch 0.0384 \n\n\n\n-0.0199667 \n\n\n\n0.0003 0.017321 44.8 y = 0.0384x - 1.152 \n\n\n\nApril 0.689 0.0294 0.171464 120.8 y = 0.689x - 20.67 \n\n\n\nMay -0.7873 0.015 0.122474 219.7 y = -0.7873x + 23.62 \n\n\n\nMonsoon  \n\n\n\nJune -2.7705 \n\n\n\n-0.585075 \n\n\n\n0.0711 0.266646 442.8 y = -2.7705x + 83.115 \n\n\n\nJuly 0.9119 0.0143 0.119583 450.2 y = 0.9119x - 27.356 \n\n\n\nAugust -0.4971 0.0043 0.065574 395.3 y = -0.4971x + 14.914 \n\n\n\nSeptember 0.0154 1.00E-05 0.003162 289.9 y = 0.0154x - 0.4611 \n\n\n\npost-Monsoon  \nOctober -0.1265 \n\n\n\n0.11025 \n0.0005 0.022361 178.4 y = -0.1265x + 3.796 \n\n\n\nNovember 0.347 0.0106 0.102956 42.44 y = 0.347x - 10.411 \n\n\n\nAverage Changes -0.18488 NA NA 0.079871 185.1252 NA \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 43-53 \n\n\n\nCite the Article: Md Abdullah Salman, and Faisal Ahmed (2020). Climatology In Barishal, Bangladesh: A Historical Analysis Of Temperature, Rainfall, Wind Speed And \nRelative Humidity Data. Malaysian Journal of Geosciences, 4(1): 43-53. \n\n\n\n4.4 Changes in Annual Monthly Wind Speed \n\n\n\nTable 4: Total changes, Average changes and Regression equation of trend lines for Average Wind Speed (m/s) from 1961 to 2019. \nMonth Total Changes R\u00b2 Correlation of coefficient r Average (m/s) Equation \n\n\n\nJanuary 0.0122 0.4446 0.666783 1.363 y = 0.0122x - 0.365 \nFebruary - 0.002 0.0203 0.142478 1.595 y = -0.002x + 0.0593 \n\n\n\nMarch - 0.0057 0.0589 0.242693 2.081 y = -0.0057x + 0.1704 \nApril - 0.0134 0.1671 0.408779 3.066 y = -0.0134x + 0.402 \nMay - 0.0022 0.0046 0.067823 2.903 y = -0.0022x + 0.0654 \nJune 0.0076 0.0821 0.286531 2.892 y = 0.0076x - 0.229 \nJuly 0.0073 0.1322 0.363593 3.051 y = 0.0073x - 0.2204 \n\n\n\nAugust 0.0032 0.0262 0.161864 2.754 y = 0.0032x - 0.0963 \nSeptember - 0.0004 0.0005 0.022361 2.137 y = -0.0004x + 0.0133 \n\n\n\nOctober - 0.0005 0.001 0.031623 1.578 y = -0.0005x + 0.0149 \nNovember 0.0064 0.1266 0.355809 1.180 y = 0.0064x - 0.1927 \nDecember 0.0089 0.2202 0.469255 1.188 y = 0.0089x - 0.2676 \n\n\n\nAverage Changes 0.001783 NA 0.268299 2.149 NA \n\n\n\nThe annual monthly average wind speed showed negligible increasing \n\n\n\ntrend in Barishal City over the study period 1961-2019. In Barishal, annual \n\n\n\naverage wind speed was increasing by 0.001783 m/s per year with its \n\n\n\naverage 2.149 m/s (Table 4). In Barishal, the increasing trend of wind \n\n\n\nspeed coefficient of determination equal to r = 0.268299. The highest \n\n\n\nchange has been found in January month at a rate of 0.0122 m/s per year \n\n\n\nand the lowest has been found at a rate of -0.0004 m/s per year in \n\n\n\nSeptember respectively (Table 4 & Figure 5). \n\n\n\nIn Barishal, the increasing rates of changed of wind speed were 0.0122 \n\n\n\nm/s, 0.0076 m/s, 0.0073 m/s, 0.00032 m/s, 0.0064 m/s and 0.0089 m/s \n\n\n\nper year for January, June, July, August, November and December months \n\n\n\nrespectively for the period of 1961\u20132019 (Table 4 & Figure 5). \n\n\n\nConsequently, a decreasing rates of variations of wind speed were \u2013 0.002 \n\n\n\nm/s, -0.0057 m/s, -0.0134 m/s, -0.0022 m/s, -0.0004 m/s and -0.0005 m/s \n\n\n\nper year for February, March, April, May, September and October months \n\n\n\nrespectively (Table 4 & Figure 5). \n\n\n\nFigure 5: Temporal variation of annual average wind speed (m/s) in Barishal from 1961 to 2019. \n\n\n\n(Jan: January; Feb: February; Mar: March; Apr: April; Jun: June; Jul: July; Aug: August; Sep: September; Oct: October; Nov: November; Dec: December; Dev: \n\n\n\nStandard Deviation) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 43-53 \n\n\n\nCite the Article: Md Abdullah Salman, and Faisal Ahmed (2020). Climatology In Barishal, Bangladesh: A Historical Analysis Of Temperature, Rainfall, Wind Speed And \nRelative Humidity Data. Malaysian Journal of Geosciences, 4(1): 43-53. \n\n\n\n4.5 Changes in Annual Monthly Relative Humidity \n\n\n\nTable 5: Total changes, Average changes and Regression equation of trend lines for Average Relative Humidity from 1981 to 2019. \nMonth Total Changes R\u00b2 Correlation of coefficient r Average (%) Equation \nJanuary 0.6335 0.4554 0.674833 55.22 y = 0.6335x - 12.67 \nFebruary 0.4095 4 2 48.55 y = 0.4095x - 8.1905 \nMarch 0.1882 0.1024 0.32 49.86 y = 0.1882x - 3.763 \nApril 0.2156 0.1213 0.348281 60.55 y = 0.2156x - 4.3118 \nMay 0.2771 0.2496 0.4996 71.23 y = 0.2771x - 5.5428 \nJune 0.2735 0.3112 0.557853 83.15 y = 0.2735x - 5.4702 \nJuly 0.1831 0.3772 0.614166 87.84 y = 0.1831x - 3.6625 \nAugust 0.1252 0.3194 0.565155 88.51 y = 0.1252x - 2.5038 \nSeptember 0.1186 0.2364 0.48621 87.68 y = 0.1186x - 2.3723 \nOctober 0.2736 0.353 0.594138 81.09 y = 0.2736x - 5.472 \nNovember 0.5922 0.4811 0.693614 71.73 y = 0.5922x - 11.844 \nDecember 0.8256 0.5023 0.708731 64.85 y = 0.8256x - 16.512 \nAverage Changes 0.342975 NA 0.671882 70.855 NA \n\n\n\nThe annual monthly relative humidity showed severe increasing trend in \n\n\n\nthe Barishal City over the study period, 1961 to 2019. On an standard \n\n\n\nyearly average relative humidity of Barishal City has been found to be \n\n\n\nescalating at a rate of 0.342975 per year with average relative humidity of \n\n\n\n70.855 at 2 meters (%) per year and average correlation coefficient of r \n\n\n\nequal to 0.671882 (significant at 99% level of significance) (Table 5). An \n\n\n\nincrease trend of relative humidity showed at all most of the months in \n\n\n\nBarishal over the period of 1961-2019 (Table 5 & Figure 6). In Barishal \n\n\n\nCity, Momentous increase of relative humidity during this 58-years period \n\n\n\nwas observed highest at December month (0.8256) and minimum at \n\n\n\nSeptember (0.1186) respectively per year (Table 5 & Figure 6). \n\n\n\nFigure 6: Temporal variation of annual average relative humidity at 2 meters (%) in Barishal from 1981 to 2019. \n\n\n\n(Jan: January; Feb: February; Mar: March; Apr: April; Jun: June; Jul: July; Aug: August; Sep: September; Oct: October; Nov: November; Dec: December; Dev: \n\n\n\nStandard Deviation) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 43-53 \n\n\n\n\n\n\n\n \nCite the Article: Md Abdullah Salman, and Faisal Ahmed (2020). Climatology In Barishal, Bangladesh: A Historical Analysis Of Temperature, Rainfall, Wind Speed And \n\n\n\nRelative Humidity Data. Malaysian Journal of Geosciences, 4(1): 43-53. \n \n\n\n\n\n\n\n\n5. CONCLUSIONS AND DISCUSSIONS \n\n\n\nThis study was carried out to assess the climatologically data \n\n\n\n(Temperature, Rainfall and Wind Speed) collected from Bangladesh \n\n\n\nMeteorological Department which was used for the climate change aspects \n\n\n\n(crossed match with National Aeronautics and Space Administration and \n\n\n\nNational Oceanic and Atmospheric Administration data sources) over the \n\n\n\nperiod of 1961-2019.Only Relative Humidity data collected for the study \n\n\n\nover the period of 1981-2019. The regression equation, the coefficient of \n\n\n\ndetermination and standard deviation were calculated for the trend \n\n\n\nanalysis. Mann-Kendall test for trend and Sen\u2019s slope estimator were also \n\n\n\ncalculated for this study. All the temperature extremes in most of the \n\n\n\nregions show a warming trend, while an annual average maximum & \n\n\n\nminimum temperature showed increasing trend about 0.0055 \u00baC & 0.0087 \n\n\n\n\u00baC per year with average coefficient of determination equal to 0.193478 & \n\n\n\n0.250304 (significant at 99% level of significance) in Barishal over the 58 \n\n\n\nyears period from 1961 to 2019. Calculated highest increase rate of \n\n\n\nmaximum temperature was observed at June month (0.0159 \u00baC) and the \n\n\n\nlowest at January, March and April respectively (- 0.0089 \u00baC, - 0.002 \u00baC and \n\n\n\n- 0.0022 \u00baC). Significant increase of minimum temperature during this 58-\n\n\n\nyears period was observed pick point at February month (0.0138 \n\n\n\n\u00baC/year). The magnitude of increase in monthly average maximum and \n\n\n\nminimum temperatures during the 58 years period from 1961 to 2019 is \n\n\n\nunfairly significant.  \n\n\n\nIn the study, an annual recorded total rainfall was observed to be declining \n\n\n\nat the rate of -0.18488 mm/year and average rainfall 185.1252 mm/year \n\n\n\nwith average correlation of coefficient equal to 0.079871 (significant at \n\n\n\n99% level of significance) trend in Barishal over the study period, 1961 to \n\n\n\n2019. In winter, recorded overall rainfall was showed to be declining at an \n\n\n\naverage rate of -0.01293 mm/year. In the same way, pre-Monsoon and \n\n\n\nMonsoon periods were observed to be declining at an average rate of -\n\n\n\n0.01997 mm/year and -0.58508 mm/year. On the other hand, a rising \n\n\n\ntrend was found at an average rate of 0.11025 mm/year in the post-\n\n\n\nMonsoon time. \n\n\n\nLikewise, calculated annually average wind speed was observed minor \n\n\n\nrising by 0.001783 m/s per year with its average speed 2.149 m/s per year \n\n\n\ntrend in Barishal City over the study period 1961-2019. The highest \n\n\n\nchange observed in January month at a rate of 0.0122 m/s per year and \n\n\n\nthe lowest at a rate of -0.0004 m/s per year in September respectively. On \n\n\n\nthe other side, Analysis of annually relative humidity was observed severe \n\n\n\nescalating at a rate of 0.342975 per year with average relative humidity of \n\n\n\n70.855 at 2 meters (%) per year trend in the Barishal City over the study \n\n\n\nperiod. A historic increase of relative humidity during this 58-years period \n\n\n\nwas observed maximum at December month (0.8256) and minimum at \n\n\n\nSeptember (0.1186) respectively per year. \n\n\n\nSignificant negative correlations of rainfall, relative humidity, wind speed \n\n\n\nand temperature variables have been found in Barishal and have reached \n\n\n\nextreme levels in the studied regions. The increase in temperature, \n\n\n\nrelative humidity and decrease in wind circulation speed may cause a \n\n\n\ndecrease in rainfall in these regions. Besides the combination of frequent \n\n\n\nnatural disasters, high population density, rapid urbanization and low \n\n\n\nresilience to economic shocks factors, make Barishal very vulnerable to \n\n\n\nclimatic risks. However, if these changes headway in the future, they will \n\n\n\nlikely be the cause of significant negative impacts on the climate of \n\n\n\nBarishal. Therefore, further advanced studies should be carried out with \n\n\n\nrelated model projections to investigate climate change in Bangladesh and \n\n\n\nits impact on the global climate. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nAuthor is grateful to the Bangladesh Meteorological Department (BMD) \n\n\n\nfor providing the data. \n\n\n\nREFERENCES \n\n\n\nAddisu, S., Selassie, Y.G., Fissha, G. and Gedif, B., 2015. Time series trend \n\n\n\nanalysis of temperature and rainfall in lake Tana Sub-basin, Ethiopia. \n\n\n\nEnvironmental Systems Research, DOI 10.1186/s40068-015-0051-0. \n\n\n\nBasak J.K., Titumir, R.A.M., Dey N.C., 2013. Climate Change in Bangladesh: \n\n\n\nA Historical Analysis of Temperature and Rainfall Data. Journal of \n\n\n\nEnvironment, Vol. 02, Issue 02, pp. 41-46.  \n\n\n\nBasak, J.K., 2011. Implications of Climate Change on Crop Production in \n\n\n\nBangladesh and Possible Adaptation Techniques. In: International \n\n\n\nConference on United Nations Framework Convention on Climate \n\n\n\nChange (UNFCCC), Durban Exebition Centre, Durban, COP 17. Unnayan \n\n\n\nOnneshan - The Innovators, Dhaka-1215, Bangladesh. \n\n\n\nChowdhury, M.H.K., and Debsarma, S.K., 1992. Climate change in \n\n\n\nBangladesh \u2013 A statistical review. Report of IOC-UNEP Workshop on \n\n\n\nImpacts of Sea Level Rise due to Global Warming, NOAMI, November \n\n\n\n16-19, 1992, Dhaka, Bangladesh. Intergovernmental Oceanographic \n\n\n\nCommission. \n\n\n\nDebsarma, S.K., 2003. Intra-annual and inter-annual variation of rainfall \n\n\n\nover different regions of Bangladesh. In: Proceedings of SAARC Seminar \n\n\n\non Climate Variability in the South Asian Region and its Impacts, SMRC, \n\n\n\nDecember 20-24, 2002, Dhaka, Bangladesh. Dhaka, SAARC \n\n\n\nMeteorological Research Centre. \n\n\n\nFung CF, Farquharson F, Chowdhury J., 2006. Exploring the impacts of \n\n\n\nclimate change on water resources - regional impacts at a regional scale: \n\n\n\nBangladesh. In: Demuth, S. et al, (ed.) Climate variability and change: \n\n\n\nhydrological impacts. Wallingford, UK, IAHS Press, Institute of \n\n\n\nHydrology, 389-393. (IAHS Publication, 308) \n\n\n\nHaque, M.Z., Quayyam, H.A., Hossian, M.M., and Islam, M.S., 1992. \n\n\n\nOccurrence of grain sterility in different rice crops. In: Islam, A.K.M.N., \n\n\n\nFattah, Q.A., Muttaqi, I.A., and Aziz, A. eds. Plant Science and Man: \n\n\n\nproblems and Prospects. Proc. Intl. Bot. Conf., January 10-12, 1991, \n\n\n\nBangladesh Bot. Soc., Dhaka, Bangladesh. Dhaka, Bangladesh Bureau of \n\n\n\nStatistics (BBS), pp. 117-124. \n\n\n\nHartmann, D.L., Tank, A.M.G.K., 2013. Observations: Atmosphere and \n\n\n\nSurface.Climate Change 2013: the Physical Science Basis. Contribution \n\n\n\nof Working GroupI to the Fifth. Assessment Report of the \n\n\n\nIntergovernmental Panel on ClimateChange. Cambridge University \n\n\n\nPress Cambridge, United Kingdom, New York,NY, USA. \n\n\n\nHashizume M, Armstrong B, Hajat S, Wagatsuma Y, Faruque A.S.G., Hayashi \n\n\n\nT, Sack D.A., 2007. Association between climate variability and hospital \n\n\n\nvisits for non-cholera diarrhoea in Bangladesh: Effects and vulnerable \n\n\n\ngroups. Internl J of Epidemio l36(5): 1030-1037. \n\n\n\nHussain AM, Sultana N., 1996. Rainfall distribution over Bangladesh \n\n\n\nstations during the monsoon months in the absence of depressions and \n\n\n\ncyclonic storms. Mausam 47: 339\u2013348. \n\n\n\nIslam, M.T., Nursey-Bray, M., 2017. Adaptation to climate change in \n\n\n\nagriculture in Bangladesh: the role of formal institutions. J. Environ. \n\n\n\nManag. 200, 347e358. \n\n\n\nIPCC, 2007. Climate Change 2007: The Physical Science Basis. Summary \n\n\n\nfor Policymakers. Contribution of the Working Group I to the Fourth \n\n\n\nAssessment Report of the Intergovernmental Panel on Climate Change, \n\n\n\nCambridge University Press, Cambridge. \n\n\n\nIPCC, 2013. In: Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., et \n\n\n\nal.(Eds.), Summary for Policymakers, Climate Change 2013: the Physical \n\n\n\nScience Ba-sis, Contribution of Working Group I to the Fifth Assessment \n\n\n\nReport of the Inter-governmental Panel on Climate Change. Cambridge \n\n\n\nUniversity Press, Cambridge,United Kingdom and new York, NY, USA. \n\n\n\nIUCN, Bangladesh, 2011. Protocol for Monitoring of Impacts of Climate \n\n\n\nChange and Climate Variability in Bangladesh. Dhaka, Bangladesh, IUCN \n\n\n\n(International Union for Conservation of Nature), pp. xiv + 182.  \n\n\n\nJaiswal, R.K., Lohani, A.K., Tiwari, H.L., 2015. Statistical analysis for change \n\n\n\ndetection and trend assessment in climatological parameters. Environ. \n\n\n\nProcess. 2(4), 729e749. \n\n\n\nKarmakar, S., 2003. Trends in the annual frequency of cyclonic \n\n\n\ndisturbances and storms in the Bay of Bengal. In: Proceedings of SAARC \n\n\n\nSeminar on Climate Variability in the South Asian Region and its \n\n\n\nImpacts, SMRC, December 20-24, 2002, Dhaka, Bangladesh. Dhaka, \n\n\n\nSAARC Meteorological Research Centre.  \n\n\n\nKarmakar S., and Shrestha M.L., 2000. Recent Climate Change in \n\n\n\nBangladesh. SMRC Series, no. 4. Dhaka, SAARC Meteorological Research \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 43-53 \n\n\n\nCite the Article: Md Abdullah Salman, and Faisal Ahmed (2020). Climatology In Barishal, Bangladesh: A Historical Analysis Of Temperature, Rainfall, Wind Speed And \nRelative Humidity Data. Malaysian Journal of Geosciences, 4(1): 43-53. \n\n\n\nCentre.  \n\n\n\nKendall M.G., 1975. Rank correlation methods. Griffin, London. \n\n\n\nKhan, M.H.R., Rahman, A., Luo, C., Kumar, S., Islam, G.M.A, Hossain, M.A., \n\n\n\n2019. Detection of changes and trends in climatic variables in \n\n\n\nBangladesh during 1988\u20132017. \n\n\n\nhttps://doi.org/10.1016/j.heliyon.2019.e01268 \n\n\n\nKhan T.M.A, Singh O.P, Rahman M.S., 2000. Recent Sea Level and Sea \n\n\n\nSurface Temperature Trends along the Bangladesh Coast in Relation to \n\n\n\nthe Frequency of Intense Cyclones. Marine Geodesy 23(2): 103-116.  \n\n\n\nLambert F, Stott P, Allen M., 2003. Detection and attribution of changes in \n\n\n\nglobal terrestrial precipitation. Geophy Res Abs 5: 06140 \n\n\n\nMia, N. M., 2003. Variations of temperature in Bangladesh. In: Proceedings \n\n\n\nof SAARC Seminar on Climate Variability in the South Asian Region and \n\n\n\nits Impacts, SMRC, December 20-24, 2002, Dhaka, Bangladesh. Dhaka, \n\n\n\nSAARC Meteorological Research Centre. \n\n\n\nOECD, 2003. Development and Climate Change in Bangladesh: Focus on \n\n\n\nCoastal Flooding and the Sundarbans. In: Agrawala S, Ota T, Ahmed AU \n\n\n\n(ed) Organization for Economic Co-operation and Development (OECD) \n\n\n\nReport COM/ENV/EPOC/DCD/DAC(2003)3/FINAL, Paris, France. \n\n\n\nwww.oecd.org/dataoecd/46/55/21055658.pdf Accessed on 25 March \n\n\n\n2009. \n\n\n\nRahman, M.R., Latch, H., 2015. Climate change in Bangladesh: a spatio-\n\n\n\ntemporal analysis and simulation of recent temperature and rainfall \n\n\n\ndata using GIS and time series analysis model. Theor Appl Climatol \n\n\n\n(2017) 128:27\u201341. \n\n\n\nRahman M.R, Salehin M, Matsumoto J., 1997. Trends of monsoon rainfall \n\n\n\npattern in Bangladesh. Bangladesh J of Wat Resour 14\u201318: 121\u2013138.  \n\n\n\nRaihan, F., Li, G., Harrison, S.P., 2015. Detection of recent changes in \n\n\n\nclimate using meteorological data from south-eastern Bangladesh. J. \n\n\n\nClimatol. Weather Fore-cast. 3 (1). \n\n\n\nRashid H.E., 1991. Geography of Bangladesh. University Press Ltd, Dhaka. \n\n\n\nSalahuddin A, Isaac RH, Cutis S, Matsumoto J., 2006. Teleconnections \n\n\n\nbetween the sea surface temperature in the Bay of Bengal and monsoon \n\n\n\nrainfall in Bangladesh. Global and Planetary Change 53(3): 188-197. \n\n\n\nSen P.K., 1968. Estimates of the regression coefficient based on Kendall's \n\n\n\ntau. J of the Am Stat Ass 63: 1379-1389 \n\n\n\nShahid, S., 2010. Recent trends in the climate of Bangladesh, Climate \n\n\n\nResearch, 42(3): 185-193.  \n\n\n\nShahid S., 2008. Spatial and Temporal Characteristics of Droughts in the \n\n\n\nWestern Part of Bangladesh. Hydrol Process 22(13): 2235-2247.  \n\n\n\nShahid S, Behrawan H., 2008. Drought Risk Assessment in the Western \n\n\n\nPart of Bangladesh. Nat Haz 46(3): 391-413.  \n\n\n\nSwiss Re, 2016. Barisal: helping a city prepare for climate change; An \n\n\n\nEconomics of Climate Adaptation study in Barisal, Bangladesh. \n\n\n\nfile:///D:/Climate%20Change%20papers/eca-barisal-bangladesh.pdf.  \n\n\n\nTitumir, R.A.M., and Basak, J.K., 2012. Effects of Climate Change on Crop \n\n\n\nProduction and Climate Adaptive Techniques for Agriculture in \n\n\n\nBangladesh. Social Science Review, the Dhaka University Studies, Part-\n\n\n\nD, 29(1), pp. 215-232.  \n\n\n\nVij, S., Biesbroek, R., Groot, A., Termeer, K., 2018. Changing climate policy \n\n\n\npar-adigms in Bangladesh and Nepal. Environ. Sci. Policy 81, 77e85. \n\n\n\nWarrick, R.A., Bhuiya, A.H., and Mirza, M.Q., 1994. The Greenhouse Effect \n\n\n\nand Climate Change: Briefing Document No.1. Dhaka, Bangladesh \n\n\n\nUnnayan Parishod (BUP), pp. 17-20.  \n\n\n\nWorld Bank, 2000. Bangladesh: Climate Change and Sustainable \n\n\n\nDevelopment. Report No. 21104-BD, the World Bank, Rural \n\n\n\nDevelopment Unit, South Asia Region.  \n\n\n\nYu, W., Liu, H., Wang, J., 2016. Analysis on climate change trend in \nGuangzhouarea in recent 65 years. Int. J. Hosp. Inf. Technol. 9 (12), \n67e76. \n\n\n\n\nhttps://doi.org/10.1016/j.heliyon.2019.e01268\n\n\nfile:///D:/Climate%20Change%20papers/eca-barisal-bangladesh.pdf\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 26-32  \n\n\n\nCite The Article: Rohana Tair, Sheyron Eduin (2018). Heavy Metals In Water And Sediment From Liwagu River And Mansahaban River At Ranau Sabah.  \nMalaysian Journal of Geosciences, 2(2) : 26-32. \n\n\n\n\n\n\n\n    ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 26 June 2018  \nAccepted 2 July 2018  \nAvailable online 1 August  2018 \n\n\n\nABSTRACT\n\n\n\nABSTRACT \n\n\n\nThe Liwagu River is one of the most reliable river systems in Ranau which had experienced a mudflows event due \nto massive landslide of Mount Kinabalu. The aim of this study is to determine the water quality and compare the \nlevel of heavy metals in water and sediment of the Liwagu River and a non-impacted mudflows of Mansahaban River. \nWater and sediment were collected from four sampling stations on each river. Water samples were filtered with \n0.45 \u03bcm membrane filter and acidified to pH<2 and conducted by means of ICP-OES while AAS was used for sediment \nanalysis prior to aqua regia digestion method for the determination of five heavy metals (Cd, Cr, Cu, Pb and Zn). The \nPaired T-test shows that there were significant different between impacted and non-impacted river especially for \nturbidity, conductivity, temperature, Cuw, Crw, Znw, Cds, Crs and Cus, (P<0.05). Liwagu River shows the Cu \nconcentration in sediment and water were exceeded the Guideline for the Protection and Management of Aquatic \nSediment quality in Ontario and Interim National Water Quality Standards (INWQS): >110 mg/kg and >0.02 mg/L, \nrespectively. The correlations coefficient shows that there were significant relationship between heavy metals in \nwater and sediment from Liwagu and Mansahaban River (0.413<r<0.888, p<0.05). For conclusion, the quality of \nwater from both river are fairly the same but not for the sediment. However, further in depth investigation is needed \nto identify a wider perspective towards the behavior of heavy metals prior to mudflows event in Ranau, North \nBorneo of Malaysia.  \n\n\n\n KEYWORDS \n\n\n\nRanau, North Borneo, Mudflow event, Water Quality, Heavy Metal, Sediment.\n\n\n\n1. INTRODUCTION\n\n\n\nThe earthquake triggered in Ranau on 5th June 2015 with 5.9 magnitude \nhas brought massive disturbance not only on the man-made infrastructure \nbut also the natural landscape of Mount Kinabalu itself. A group \nresearchers clarified that the occurrence of an earthquake may create or \ninfluenced other natural phenomena to occur as it may be refer as natural \ndisaster that arise from one factor connected to another [1]. This nature \ncan be seen through the massive landslide which happened as a result \nfrom the earthquake. The removal of part of the land on the foot hill of \nMount Kinabalu at the same time has affected the natural vegetation [2].  \nMoreover, the soils presence in hill side of a mountain is important in \nterms of the security of the land, whereas the erosion due to enormous \namount of rainfall as well as a tremor resulting from earthquake can be \nreduced [3]. In addition, the protection of land on the hilly side is more \nsecure when it is covered by trees, as the root system is highly responsible \nto hold the soils together form sloping down the hill. Primarily, the \nreduction of top soils regarding to the landslide in Mount Kinabalu has \naltered this function which ultimately allows more reduction of soils \ncaused by the tremor resulting from aftershocks. Basically, aftershock has \na small magnitude of earthquake occurs after the main earthquake \ntriggered in certain region [4]. Additionally, the aftershock has lengthened \nthe process of erosion on the foot hill of Mount Kinabalu which eventually \ncaused more soils to rush down the hills. This process has largely \ncontributed to the process of sedimentation resulting from the streams \ncarrying mud [5]. The process of sedimentation can be described when the \nwater associated with mud are flowing through a river channels and \neventually settled down when they encountered obstacle along the way or \nas the water streams will eventually slow down [6]. The settlement of mud \naccumulates in the lower land of Liwagu River can be seen along the river \nwhich overly change the structure of the irrigation.  \n\n\n\nThe intervention of water that mixed with sediment will endangered the \nbiota environment as well as the small vegetation which lies in the river in \nsuch way that these ecosystems will be killed due to the severe condition \n[7]. Fish may die in this situation where they have insufficient amount of \noxygen supply as the water becomes polluted with portion of soils and \nexhibit high amount of metals concentrations caused by the fact that the \nsedimentation exposed heavy metals elements into the river streams \nwhich then diluted along with streams. In fact, the small vegetation which \nlies across the water channel are also reducing either caused by the \nerosion of river bank or the inability of receiving light to carried out \nphotosynthesis due to the presence of turbidity. The reduction of \nvegetation along the Liwagu River has resulted from such event.  \n\n\n\nAs consequence, the removal of large amount of sand and debris which \nallowed the process of accumulation of soil and not only changes the \nstructure of the Liwagu River yet also altering the water supply in Ranau \n[8]. Further emphasize into this case, a researcher added that the strong \nstreams which caused by the discharged of temporary dammed by the \nlandslide sweep tons of vegetation across the river [9]. This situation has \naffected the supply of water in Ranau, where the intake station of water \nplant is forced to be closed caused by the risk of huge streams and the \nquality of the water itself. As the quality of water been a key factor in \ninfluencing the water supply, heavy metals pollution has become one of \nthe concerned pollution in this situation [10].  \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: https://doi.org/10.26480/mjg.02.2018.26.32\n\n\n\nHEAVY METALS IN WATER AND SEDIMENT FROM LIWAGU RIVER AND \nMANSAHABAN RIVER AT RANAU SABAH \n\n\n\nRohana Tair, Sheyron Eduin\n\n\n\nFaculty of Science and Natural Resources, Universiti Malaysia Sabah, UMS Road, 88400 Kota Kinabalu, Sabah, Malaysia. \n*Corresponding Author Email: rohana@ums.edu.my\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\nISSN : 2521-0920 (Print)\nISSN : 2521-0602 (Online) \nCODEN: MJGAAN\n\n\n\n\nhttps://doi.org/10.26480/mjg.02.2018.11.16\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 26-32 \n\n\n\nFigure 1: The sedimentation and the color of water change after \n\n\n\nmudflow event \n\n\n\nWhereby, some researchers claimed that the occurrence of heavy metals \ninto the river system can appear from the mudflows events resulting from \nthe aftershock which lengthen the sedimentation on the hilly side of \nmountain [11]. Primarily, heavy metal is an essential nutrient that \npresence in soils and important for the development of living organism \nhowever hazard at certain point of their concentration and most \nimportantly they are not permanently fixed in soils [12]. In other words, it \nmay be removed or transported by soils and water as a typical removal \nagent in trophic land. Since the Liwagu River triggered by the mud flowing \nstreams situation thus, additional metals concentration may be dispersed \nfrom this event. Furthermore, other researchers has mentioned that river \nare one of the reliable agent of transporting heavy metals contaminants \n[13]. With respect to its mobility, hence this contaminant may be \ndistributed across the irrigation system for agricultural activities.  \n\n\n\nPrevalently, the lower land of Ranau is covered by the ultrabasic soils [14]. \nSince this part of land is uniformly elevated in terms of landscape, thus the \ndistributions of water are accessible through the irrigation which \nchannelled from the Liwagu\u2019s streams. A group researchers added that by \nconsidering the fact that the heavy metals are able to retain their presence \nin sediment and running water, there is a high potential for this \ncontamination to be distributed along the Liwagu River [15]. Additionally, \nthe contamination of heavy metals will endanger the exposed plant as well \nas human through the water supply [16].  \n\n\n\nMoreover, the awareness towards this pollution need to be emphasize. \nThus, this study was carried out to determine the current status of water \nquality and heavy metals (Cd, Cr, Cu, Pb and Zn) in water and sediment \nfrom Liwagu River as impacted river and compared to Mansahaban River \nas non-impacted river after mudflow event. This study focus on the \nanalysis of heavy metals namely, as accordance to the state that claimed \nthat these elements comprised the highest heavy metals concentration \ndetected in fresh water system as well as the surface sediment [13,17]. \nHence, these elements refer as the potential occurring heavy metals in \nLiwagu River. \n\n\n\n2. METODHOLOGY\n\n\n\n2.1 Study sites  \n\n\n\nThe study was conducted in the district of Ranau, Sabah, North Borneo of \nMalaysia. Ranau district experienced an annual rainfall of 2500 mm per \nyear with average temperature at 10\u00b0C on the higher land while increasing \nup to 30\u00b0C on the lower land. Primarily, Ranau is a rural district which \ncomprise of a total population of 94,029 where more than 85% of these \npopulations are Dusun [18]. Geographically, Ranau comprise of a hilly \nelevation of land where the highest land peak presence is the Mount \nKinabalu whereby it is known as the highest mountain in South East Asia \nwith 4096 meter in height. Additionally, the locals in Ranau contributed to \nagriculture, tourism and self-employment and government employees \nwhich composed of 50%, 7% and 13% respectively [19]. To cope with this \nlevel of population, the domestic use of water in Ranau is highly dependent \non the Liwagu River. Prevalently in Ranau, Liwagu River and Mansahaban \nRiver are the main reliable sources of water distribution (Figure 2 & 3). \nThe amount of water is available as a source for agricultural, households \nand commercial activities [9]. \n\n\n\nSampling was conducted in two different geological setting of river \nwhereby the water and sediment samples were taken simultaneously \nfrom four selected location along the Liwagu River and Mansahaban River \nrespectively. The sampling site with coordinates is shown in Table 1. The \nsampling stations was chosen because of the community livelihood that \ndepending on the water supply from the river especially for agricultural \nactivity, paddy plantation, freshwater fish pond and tagal area (Tagal is the \nconservation area for freshwater fish belong to community at an area, \nfishing not allowed for some period of time and punishment will apply if \nunfilled the tagal\u2019s rules) [20]. The Sampling was done few months after \nthe mudflow event; on 9 February 2016 and 9 March 2016. \n\n\n\nTable 1: Sampling stations at Liwagu River, Ranau Sabah \n\n\n\nStation Liwagu River Descriptions Mansahaban River Descriptions \n\n\n\nS1 \n557'4.59''N \n\n\n\n116 38'58.97''E \n\n\n\nWater Catchment area 05 56\u2019 03. 3\u2019\u2019N \n\n\n\n116 41\u2019 09.7\u2019\u2019E \n\n\n\nTagal area \n\n\n\nS2 \n557'27.03''N \n\n\n\n116 39'44.23''E \n\n\n\nAgriculture area 05 56\u2019 44.0\u2019\u2019N \n\n\n\n116 41\u2019 24.1\u2019\u2019E \n\n\n\nFish pond area \n\n\n\nS3 \n557' 25.65''N \n\n\n\n116 40'2.35''E \n\n\n\nPaddy plantation area 05 57\u2019 26.9\u2019\u2019N \n\n\n\n116 41\u2019 28. 7\u2019\u2019E \n\n\n\nTagal area \n\n\n\nS4 \n557'11.13''N \n\n\n\n116 40\u201934.07''E \n\n\n\nPaddy plantation area 05 58\u2019 53. 6\u2019\u2019N \n\n\n\n116 40\u2019 28. 8\u2019\u2019E \n\n\n\nTagal area \n\n\n\n(a) Station 1   (b)   Station 2 \n\n\n\nCite The Article: Rohana Tair, Sheyron Eduin (2018). Heavy Metals In Water And Sediment From Liwagu River And Mansahaban River At Ranau Sabah.  \nMalaysian Journal of Geosciences, 2(2) : 26-32. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG)  2(2) (2018) 26-32 \n\n\n\n(c ) Station 3   (d) Station 4 \n\n\n\nFigure 2: The sampling stations at Liwagu River, Ranau Sabah \n\n\n\n(a) Station 1 (b) Station 2 \n\n\n\n   (c ) Station 3    (d) Station 4 \n\n\n\nFigure 3: The sampling stations at Mansahaban River, Ranau Sabah \n\n\n\n2.2 In-situ parameters and Analysis of water  \n\n\n\nThe in-situ parameter were taken along the Liwagu and Mansahaban River \nwhich were the pH value, turbidity, temperature, Dissolve Oxygen (DO) \nand Electrical conductivity (EC) by using YSI meter. Calibration technique \nwas applied to the portable meter before used to optimize the functions \nfor measurement reading recovery.  \n\n\n\nThe polyethylene bottles were used to collect the water samples from the \n\n\n\nstudy sites. Polyethylene bottles were cleaned by soaked into 5% of HNO3 \n\n\n\nfor all night and rinse with distilled water, additionally this will ensure that \nthe safety of bottles from any undesirable matter. Next, the water sample \n\n\n\nwas taken by submerging the polyethylene bottles into the water surface \n\n\n\nfor a depth of 20 cm to exclude suspended matter and nitric acid was \n\n\n\nadded until pH <2 for preservation before analyzed the samples by using \n\n\n\nPerkin Elmer Optima 5300DV Inductively Coupled Plasma \u2013 Optical \nEmission Spectrometer (ICP-OES) [17].   \n\n\n\n2.3 Sediment Digestion  \n\n\n\nSample sediment were dried thoroughly in an oven for 2 days with \n\n\n\ntemperature of 105 \u00b0C in order to remove all the moisture before being \n\n\n\nsieved through 0.65 \u03bcm stainless steel mesh [21]. In later stage, the \n\n\n\ndigestion method for aqua regia were carried out at a very cautious mode \n\n\n\ndue to the acid presence in this method. As accordance to a study, the total \namount of Cd, Cr, Cu, Pb and Zn in sediments normally applying the aqua \n\n\n\nregia\u2019s method as a procedure due to their simplicity and adaptability [22]. \nThe aqua regia solution made by the mixing of HNO3 and HCL acid and \n\n\n\nadded to 1 g of the sediment (<0.65 \u03bcm). Later, the samples were boiled \n\n\n\nfor a period of 2 hours with maximum temperature of 110\u00b0C. As it settled \n\n\n\ndown, the samples passed through 0.45 \u03bcm membranes filter and added \n\n\n\nthe deionized water until it reach 50 ml before analyzed with FAAS. \n\n\n\n3. RESULTS AND DISCUSSIONS\n\n\n\nTo analyse the ranges of acceptable amount of heavy metals elements in \nthe surface water, Mansahaban River (1km away from Liwagu River) was \nused as a reliable and comparable data as this river does not affected by \nthe mudflows event, in fact Mansahaban River at the same time is a \npreserved river or known as a \u201ctagal\u201d river. Hence it was subjected to \nMansahaban River as a baseline representative river in Ranau to indicate \nthe ranges of heavy metals present in Liwagu River after the mud flows \nevents with a non-impacted river. Thus, the concentration of heavy metals \nin water and sediment from four sampling station along two rivers were \ndetermined respectively in two distinct sampling period. The result shows \nin Table  2. \n\n\n\nCite The Article: Rohana Tair, Sheyron Eduin (2018). Heavy Metals In Water And Sediment From Liwagu River And Mansahaban River At Ranau Sabah.  \nMalaysian Journal of Geosciences, 2(2) : 26-32. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 26-32  \n\n\n\nTable 2: The results (range and mean concentrations) of in-situ parameters and heavy metals in water and sediment from Liwagu and Mansahaban river, \nRanau Sabah. \n\n\n\nMedia Parameters \nLiwagu River Mansahaban River \n\n\n\nSamp 1 Samp 2 Mean SD Samp 1 Samp 2 Mean SD \n\n\n\nIn-situ  \n\n\n\nDissolve oxygen \n(mg/L) \n\n\n\n7.56-9.86 7.50-8.23 8.18 0.73 6.3-7.7 6.1-8.1 7.14 0.67 \n\n\n\npH 7.56-9.00 7.57-8.53 8.16 0.48 7.97-8.24 6.1-8.09 7.58 0.76 \n\n\n\nTurbidity (NTU) 12.22-20.66 \n3.38-\n14.29 \n\n\n\n14.55 5.80 2.31-7.69 2.54-8.71 5.68 2.62 \n\n\n\nConductivity \n(\u00b5s/cm) \n\n\n\n215.30-\n432.56 \n\n\n\n236-380 347.98 78.77 \n275.63-\n340.90 \n\n\n\n224.17-314-\n67 \n\n\n\n281.55 38.59 \n\n\n\nTemperature (oC) 25.53-30.80 \n26.03-\n31.00 \n\n\n\n28.18 2.16 \n25.10-\n29.87 \n\n\n\n25.70-32.47 28.91 2.54 \n\n\n\nWater \n\n\n\nCd (mg/L) \n<0.0001-\n0.0005 \n\n\n\n<0.0001 0.0004 0.0001 \n<0.0001-\n0.0005 \n\n\n\n<0.0001 0.0003 0.0002 \n\n\n\nCr (mg/L) \n0.0072- \n0.0139 \n\n\n\n<0.0001 0.0088 0.0024 \n0.0050-\n0.0138 \n\n\n\n<0.0001-\n0.0042 \n\n\n\n0.0161 0.0339 \n\n\n\nCu (mg/L) \n<0.0001-\n0.0932 \n\n\n\n<0.0001-\n0.0230 \n\n\n\n0.0240 0.0248 \n<0.0001-\n0.0188 \n\n\n\n<0.0001-\n0.0058 \n\n\n\n0.0072 0.0076 \n\n\n\nPb (mg/L) \n0.0026-\n0.0099 \n\n\n\n<0.0001-\n0.0047 \n\n\n\n0.0077 0.0236 \n0.0018-\n0.0054 \n\n\n\n<0.0001 0.0037 0.0010 \n\n\n\nZn (mg/L) \n0.0023-\n0.0424 \n\n\n\n0.0055-\n0.0105 \n\n\n\n0.0161 0.0247 \n0.0012-\n0.0106 \n\n\n\n0.0039-\n0.0110 \n\n\n\n0.0048 0.0039 \n\n\n\nSed \n\n\n\nCd (mg/kg) 0.22 - 2.08 0.60- 4.35 1.81 1.30 0.95-1.10 0.70 -1.20 0.96 0.18 \n\n\n\nCr (mg/kg) \n62.43 - \n174.28 \n\n\n\n60.75 - \n121.45 \n\n\n\n93.61 51.16 \n67.65- \n155.70 \n\n\n\n70.90 - \n162.50 \n\n\n\n106.52 58.14 \n\n\n\nCu (mg/kg) \n114.55- \n390.65 \n\n\n\n10.50 -\n93.50 \n\n\n\n137.16 116.29 \n18.35-\n45.10 \n\n\n\n8.07 - 43.10 29.05 15.08 \n\n\n\nPb (mg/kg) 14.75- 32.55 \n6.55 - \n25.05 \n\n\n\n21.34 7.67 \n4.55 - \n28.60 \n\n\n\n4.45 - 18.15 13.24 11.23 \n\n\n\nZn (mg/kg) 16.02 - 51.35 \n25.7 - \n55.80 \n\n\n\n35.32 22.09 \n29.50 - \n69.30 \n\n\n\n25.70 - \n55.80 \n\n\n\n45.89 24.19 \n\n\n\n(Below detection limit = <0.0001) \n\n\n\n3.1 In-situ parameters \n\n\n\nAccording to a study, the water quality of Liwagu River was considered as \nexcellent before the mud flow event in year 2011, respectively [23]. \nSimilar to this study where the concentrations of the parameters (DO, pH, \nEC and temperature) were found safe for human consumption but \nunfortunately not for drinking water because of the turbidity is exceeded \nthe permissible limit of INQWS and Drinking water standard (Table 6). \nThe result shows that the Liwagu River has seriously impacted by mud \nflow after the earthquake natural disaster. The Paired T-test proved that \nthere were significant different between first sampling and second \nsampling that shows the Liwagu River condition is still unstable and the \nwater quality keep on changing (p<0.05, Table 3). In additions, the high \namount of annual rainfall per year may cause the river is not easy to \nrecover again as before. However, the mud flow event has significantly \nintroduced an interesting impact to the water quality where the \nconcentration of DO, EC and temperature were on considerable range of \nINQWS and its shows that the water received more nutrients and energy \nfrom new sediment that traps in the areas. The correlation coefficient \nshows that there were interaction relationship between in-situ \nparameters in water was occurred actively at Liwagu River compared to \nMansahaban River (0.742<r<0.798, p<0.05, Table 4).  \n\n\n\n3.2 Heavy metals in Water and Sediment \n\n\n\nThe heavy metals concentrations in water at Liwagu River and \nMansahaban River show significant different between two sampling \nperiod (p<0.05). While, in sediment there is only Liwagu River shows the \nsignificant different especially for Cd, Cr and Cu (Table 3). The \nconcentration of heavy metals in water and sediment at Liwagu River were \nstill below the concentration that found from Mamut River [24,25]. Mamut \nRiver has known as acid mine drainage area due to the Mamut Copper \nMining (MCM) activities since 1975 until 1999 [25]. The comparisons of \nthe heavy metals were at Table 6 and 7. In additions, the mean \nconcentrations of heavy metals in water and sediment have found below \nthe Interim National Water Quality Standard for Malaysia (Class II) \n(INWQS) and The Guideline for the Protection and Management of Aquatic \nSediment quality in Ontario. However, some of the heavy metal has more \nthan both guideline levels when maximum concentration counted \nespecially for Cu (Table 2). According to the correlation coefficient \nanalysis, there were significant different between Cu in water and \nsediment also with other elements (-0.431<r<0.608, p<0.05, Table 4 & 5). \nMaybe it was due to the MCM historical activity where Cu was the key of \nconnectors element for both water and sediment at Liwagu and \nMansahaban River [26-28].  \n\n\n\nCite The Article: Rohana Tair, Sheyron Eduin (2018). Heavy Metals In Water And Sediment From Liwagu River And Mansahaban River At Ranau Sabah.  \nMalaysian Journal of Geosciences, 2(2) : 26-32. \n\n\n\nCite The Article: Rohana Tair, Sheyron Eduin (2018). Heavy Metals In Water And Sediment From Liwagu River And Mansahaban River At Ranau Sabah.  \nMalaysian Journal of Geosciences, 2(2) : 26-32. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 26-32  \n\n\n\nTable 3: Paired T-test between sampling at Liwagu and Mansahaban River, Ranau Sabah. \n\n\n\nLocation Heavy Metals Mean Variance Observations df t -Stat t -Critical  P-value Sign \n\n\n\nLiwagu River \n(Impacted) \n\n\n\nIn-situ \n\n\n\nDissolve oxygen \n(mg/L) 8.43 0.9879 12 11 1.298879 0.2848 3.1824 > 0.05 \n\n\n\npH 8.24 0.3477 12 11 0.717561 0.5249 3.1824 > 0.05 \n\n\n\nTurbidity (NTU) 18.2575 16.2804 12 11 11.18392 0.0015 3.1824 < 0.05 \nConductivity \n(\u00b5s/cm) 357.465 9537.1129 12 11 1.119735 0.3444 3.1824 > 0.05 \n\n\n\nTemperature (oC) 28.34 6.1998 12 11 0.572478 0.6071 3.1824 > 0.05 \n\n\n\nWater \n\n\n\nCd (mg/L) 0.0001 3.45E-08 12 11 1.8638 0.0892 2.2010 > 0.05 \nCr (mg/L) 0.0088 5.62E-06 12 11 12.8158 5.90E-08 2.2010 < 0.05 \n\n\n\nCu (mg/L) 0.0235 0.0006 12 11 1.4994 0.1619 2.2010 > 0.05 \n\n\n\nPb (mg/L) 0.0124 0.0005 12 11 1.6799 0.1211 2.2010 > 0.05 \n\n\n\nZn (mg/L) 0.0236 0.0005 12 11 2.3757 0.0368 2.2010 < 0.05 \n\n\n\nSediment \n\n\n\nCd (mg/kg) 1.1058 0.4132 12 11 -3.5210 2.2010 0.0048 < 0.05 \n\n\n\nCr (mg/kg) 106.0333 1571.5733 12 11 3.0111 2.2010 0.0118 < 0.05 \n\n\n\nCu (mg/kg) 220.0667 10818.6574 12 11 4.9129 2.2010 0.0005 < 0.05 \nPb (mg/kg) 22.6750 26.5657 12 11 1.1994 2.2010 0.2556 > 0.05 \n\n\n\nZn (mg/kg) 34.2108 205.1815 12 11 -0.3946 2.2010 0.7007 > 0.05 \n\n\n\nMansahaban \nRiver (Non-\nImpacted) \n\n\n\nIn-situ \n\n\n\nDissolve oxygen \n(mg/L) 7.1750 0.38 12 11 0.440732 0.6892 3.1824 > 0.05 \npH 8.0800 0.01 12 11 2.852232 0.0650 3.1824 > 0.05 \n\n\n\nTurbidity (NTU) 5.1725 8.53 12 11 -0.90294 0.4331 3.1824 > 0.05 \nConductivity \n\n\n\n(\u00b5s/cm) 301.1325 908.53 12 11 7.548012 0.0048 3.1824 < 0.05 \n\n\n\nTemperature (oC) 28.0275 4.48 12 11 -4.37849 0.0221 3.1824 < 0.05 \n\n\n\nWater \n\n\n\nCd (mg/L) 0.0001 2.70E-08 12 11 1.7578 0.1065 2.2010 > 0.05 \n\n\n\nCr (mg/L) 0.0192 0.0014 12 11 1.6547 0.1262 2.2010 > 0.05 \n\n\n\nCu (mg/L) 0.0066 5.74E-05 12 11 2.3877 0.0360 2.2010 < 0.05 \n\n\n\nPb (mg/L) 0.0037 1.08E-06 12 11 12.4514 7.95E-08 2.2010 < 0.05 \n\n\n\nZn (mg/L) 0.0030 7.45E-06 12 11 -3.0701 0.0107 2.2010 < 0.05 \n\n\n\nSediment \n\n\n\nCd (mg/kg) 0.9875 0.0082 12 11 1.1957 2.2010 0.2570 > 0.05 \n\n\n\nCr (mg/kg) 94.5767 1778.7496 12 11 -1.3878 2.2010 0.1927 > 0.05 \nCu (mg/kg) 30.4958 101.5102 12 11 0.9922 2.2010 0.3424 > 0.05 \n\n\n\nPb (mg/kg) 15.2042 80.5852 12 11 1.0884 2.2010 0.2997 > 0.05 \nZn (mg/kg) 51.1875 312.4437 12 11 1.2120 2.2010 0.2509 > 0.05 \n\n\n\nTable 4: The correlations between parameters for Liwagu River, Ranau Sabah. \n\n\n\nDO pH Turbidity EC \nTem\n\n\n\np \nCdw Crw Cuw Pbw Znw Cds Crs Cus Pbs Zns \n\n\n\nDO 1 \n\n\n\npH .569 1 \n\n\n\nTurbidity -.221 -.434 1 \n\n\n\nEC \n-\n\n\n\n.768* \n-\n\n\n\n.742* \n.756* 1 \n\n\n\nTemp -.706 -.402 .484 .798* 1 \n\n\n\nCdw -.177 -.050 -.499 -.178 -.234 1 \n\n\n\nCrw .573 .191 .476 -.141 -.246 .249 1 \n\n\n\nCuw -.429 -.224 -.500 .054 .038 .143 .058 1 \n\n\n\nPbw \n-.007 -.467 -.073 .105 -.232 .535*\n\n\n\n*\n.242 .158 1 \n\n\n\nZnw \n.215 -.097 -.361 -.222 -.379 \n\n\n\n.587*\n*\n\n\n\n.339 .226 \n.888*\n\n\n\n*\n1 \n\n\n\nCds -.111 .495 -.293 -.174 -.007 -.160 -.574** -.015 -.205 -.175 1 \n\n\n\nCrs \n.456 .084 .491 -.054 -.211 .192 .598** \n\n\n\n-\n.487* \n\n\n\n.109 .071 -.493* 1 \n\n\n\nCus -.411 -.124 -.115 .208 .343 -.040 .584** .358 .090 .180 -.533** -.075 1 \n\n\n\nPbs -.029 -.014 .238 .104 .101 -.088 .339 -.107 -.068 .058 .137 .190 .186 1 \n\n\n\nZns \n.217 -.036 -.024 -.168 -.412 .395 .003 -.158 .193 .234 .413* .216 \n\n\n\n-\n.431* \n\n\n\n.27\n0 \n\n\n\n1 \n\n\n\nCite The Article: Rohana Tair, Sheyron Eduin (2018). Heavy Metals In Water And Sediment From Liwagu River And Mansahaban River At Ranau Sabah.  \nMalaysian Journal of Geosciences, 2(2) : 26-32. \n\n\n\n\n\n\n\n\nTable 5: The correlations between parameters for Mansahaban River, Ranau Sabah. \n\n\n\nDO pH \nTurbidit\n\n\n\ny \nEC \n\n\n\nTem\np \n\n\n\nCdw Crw Cuw Pbw Znw Cds Crs Cus Pbs Zns \n\n\n\nDO 1 \npH .358 1 \n\n\n\nTurbidity -.152 -.649 1 \nEC -.612 .444 -.277 1 \n\n\n\nTemp .733* -.163 .254 \n-\n\n\n\n.723* \n1 \n\n\n\nCdw -.619 -.398 .203 .280 -.278 1 \nCrw .295 -.335 .514 -.529 .509 -.031 1 \n\n\n\nCuw \n.029 \n\n\n\n-\n.740* \n\n\n\n.271 -.606 .326 -.002 \n.608*\n\n\n\n*\n1 \n\n\n\nPbw .393 -.008 .212 -.396 .025 .143 .299 .423* 1 \n\n\n\nZnw \n-.074 \n\n\n\n-\n.749* \n\n\n\n.320 -.537 .116 -.195 -.213 -.357 \n-\n\n\n\n.478* \n1 \n\n\n\nCds .036 -.270 .378 -.065 .354 -.022 .027 .030 .165 -.177 1 \nCrs .136 -.008 -.249 .112 .225 -.198 .260 .390 -.369 .128 .048 1 \nCus .408 .501 .177 .100 .046 -.265 .165 .070 .221 -.305 .354 .161 1 \n\n\n\nPbs \n.151 .274 .354 .187 .057 .245 -.075 -.228 .249 -\n\n\n\n.492* \n.387 -.204 .509* 1 \n\n\n\nZns \n-.060 .072 .406 .205 .127 .473* -.171 -.289 .244 -\n\n\n\n.473* \n.291 -.328 .222 .871** 1 \n\n\n\n*. Correlation is significant at the 0.05 level (2-tailed)     **. Correlation is significant at the 0.01 level (2-tailed). \n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 26-32  \n\n\n\nTable 6: The water quality standard and drinking water standard \n\n\n\nParameters \n\n\n\nInterim National Water \n\n\n\nQuality Standard for \n\n\n\nMalaysia (Class I) \n\n\n\n(INWQS) \n\n\n\nMamut River \n\n\n\n(Ali  et al., \n2004) \n\n\n\nKipungit  \n\n\n\nRiver (Ali  et \n\n\n\nal., 2004) \nLiwagu River \n\n\n\n(Fera et al,. 2013) \nThis Study \n\n\n\nDO (mg/L) 7.00 7.7\u00b10.3 7.5\u00b10.2 6.19-7.79 6.1-8.23 \n\n\n\npH 6.5-8.5 6.15\u00b10.43 7.30\u00b10.9 6.34- 8.30 6.1-8.53 \n\n\n\nTurbidity (NTU) 5 - - - 2.31-20.66 \n\n\n\nEC (\u00b5s/cm) 1000 318\u00b16.0 40.2\u00b11.6 0.05- 0.14 215-432 \n\n\n\nTemperature (oC) - - - 19.27-24.87 25-32 \n\n\n\nCd (mg/L) -(Class IIA=0.01) 0.37 0.15 - 0.0004 \n\n\n\nCr (mg/L) -(Class IIA=0.05) 0.71. - - 0.0161 \n\n\n\nCu (mg/L) - (Class IIA=0.02) - - - 0.0240 \n\n\n\nPb (mg/L) - (Class IIA=0.05) 2.08 - - 0.0077 \n\n\n\nZn (mg/L) -(Class IIA=5.00) 2.18 - - 0.0161 \n\n\n\nTable 7: The sediment quality standard \n\n\n\nParameters \n\n\n\nThe Guideline for the Protection and Management of \n\n\n\nAquatic Sediment quality in Ontario \n\n\n\n (Persaud et al,. 1993) \n\n\n\nMamut River \n\n\n\n(Bibi et al,. 2015) \nThis Study \n\n\n\nCd (mg/kg) 0.6-10 - 0.96-1.81 \n\n\n\nCr (mg/kg) 26-110 - 93.61-106.52 \n\n\n\nCu (mg/kg) 16-110 40.8-1347.54 29.05-137.16 \n\n\n\nPb (mg/kg) 31-250 0.49-49.68 13.24-21.34 \n\n\n\nZn (mg/kg) 120-820 43.87-68.41 35.32-45.89 \n\n\n\n4. CONCLUSION\n\n\n\nThe turbidity, Cuwater and Cusediment were the best indicator to determine the \nwater and sediment recovery for the Liwagu River after impacted by mud \nflow event. The quality of water from both river (Liwagu and Mansahaban \nRiver) are fairly the same but not for the sediment. However, further in \ndepth investigation is needed to identify a wider perspective towards the \nbehavior of heavy metals prior to mudflows event in Ranau, North Borneo \nof Malaysia. \n\n\n\nREFERENCES \n\n\n\n[1] Budimir, M.E.A., Atkinson, P.M., Lewis, H.G. 2014. Earthquake-and-\nlandslide events are associated with more fatalities than earthquakes \n\n\n\nalone. Journal of Natural Hazard, 72, 895-914. \n\n\n\n[2] Neg\u2019rel, P., Merly, C., Gourcy, L., Cerdan, O., Giraud, E. P., Kralik, M., \nWirdum, G. V., Vegter, J. 2014. Soil sediment river connections: catchment \nprocesses delivering pressures to river catchments. The Handbook of \nEnvironmental Chemistry, (29). \n\n\n\n[3] Bot, A., Benites, J. 2005. The importance of soil organic matter key to \n\n\n\ndrought resistant soil and sustained food and production. Food and \n\n\n\nAgriculture Organization of the United Nations, Rome. Retrieved at: \nhttp://www.fao.org/3/a-a0100e.pdf Accessed on: 22. 09. 2015. \n\n\n\n[4] Sena, L., Michae, K.W. 2006. Disaster Prevention and Preparedness. \nRetrieved at: \nhttp://www.cartercenter.org/resources/pdfs/health/ephti/library/lect\nure_notes/health_extension_trainees/DisasterPreventionPreparedness.p\ndf Accessed on: 26. 09. 2015. \n\n\n\n[5] Morpi, M. 2015. High Alert over Mud Flood. 2015. The Borneo Post \n\n\n\nOnline, 17 June. Retrieved at: \nmud-http://www.theborneopost.com/2015/06/17/high-alert-over- \n\n\n\nflood/ Accessed on: 05. 09. 201 \n\n\n\n[6] Clapcott, J.E., Young, R.G., Harding, J.S., Matthaei, C.D., Quinn, J.M., Death, \n\n\n\nR.G. 2011. Sediment Assessment Methods: Protocols and guidelines for \n\n\n\nrassessing the effects of deposited fine sediment on in-stream values. \nCawthron Institute, Nelson, New Zealand. \n\n\n\nCite The Article: Rohana Tair, Sheyron Eduin (2018). Heavy Metals In Water And Sediment From Liwagu River And Mansahaban River At Ranau Sabah.  \nMalaysian Journal of Geosciences, 2(2) : 26-32. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 26-32  \n\n\n\n[7] Poff, N.L., Hart, D.D. 2002. How dams vary and why it matters for the \nemerging science of dam removal. American Institute of Biological \nSciences, 52, (8), 659-668.\n\n\n\n[8] Chan, J. 2015. Sabah to study rainfall harvest to address looming water \ncrisis. Malay mail Online. 15 July. 2015 Retrieved at: http://\nwww.themalaymailonline.com/malaysia/article/sabah-to-study-rainfall-\nharvest-to-address-looming-water-crisis Accessed on: 7. 08. 2015\n\n\n\n[9] Joseph, J. 2015. Ranau villagers move as \u201craging\u201d flood expected. FMT \nNews. 19 June. Retrieved at: 80http://www.freemalaysiatoday.com/\ncategory/nation/2015/06/19/ranauvillager s-move-as-raging-flood-\nexpected/ Accessed on: 22. 08. 2015\n\n\n\n[10] Tair, R., Dell, L. 2018. Spring-Water As An Alternative Resource After \nEarthquake For Villagers, Kota Belud Sabah. Geological Behavior, 2 (1), \n05-11.\n\n\n\n[11] Purohit, K.K., Mukherjee, P.K., Khanna, P.P., Saini, N.K., Ra, M.S. 2001. \nHeavy metal distribution and environmental status of Doon Valley soils, \nOuter Himalaya, India. Journal of Environmental Geology, 40, (6), \n716-724.\n\n\n\n[12] Roozbahani, M.M., Sobhanardakani, S., Karimi, H., Sorooshnia, R. \n2015. Natural and anthropogenic source of heavy metals pollution in the \nsoil samples of an industrial complex; a case study. Iranian Journal of \nToxicology, 9, (29), 1336-1341.\n\n\n\n[13] Jepkoech, J.K., Simiyu, G.M., Arusei, M. 2013. Selected heavy metals in \nwater and sediments and their bioconcentrations in plant (polygonum \npulchrum) in Sosiani River, Uasin Gishu County, Kenya. Journal of \nEnvironmental Protection, 4, 796-802.\n\n\n\n[14] Roslaili, A.A., Sahibin, A.R., Ismail, S., Wan, M.R.I. 2015. Speciation and \nAvailability of Heavy Metals On Serpentinized Paddy Soil and Paddy \nTissue. Procedia - Social and Behavioral Sciences, 195, 1658 \u2013 1665.\n\n\n\n[15] Wogu, M.D., Okaka, C.E. 2011. Pollution Studies on Nigerian Rivers: \nHeavy metals in surface water of Warri River, Delta State. Journal of \nBiodiversity and Environmental Sciences (JBES), 3, 7-12.\n\n\n\n[16] Angelovicova, L., Fazekasova, D. 2014. Contamination of the soil and \nwater environment by heavy metals in the former mining area of \nRud\u0148any (Slovakia). Journal of Soils and Water Resources, 9, 18-24.\n\n\n\n[17] Obaidy, A.H.M.J.A., Mashhady, A.A.M.A., Awad, E.S., Kadhem, A.J. 2014. \nHeavy metals pollution in surface water of Mahrut River, Diyala, Iraq. \nInternational Journal of Advanced Research, 2 (10), 1039-1044.\n\n\n\n[18] Tangit, M.T., Hassim, A.K., Adanan, A. 2014. Rural Tourism at its Peak: \nSocio- cultural impacts towards host communities of Kinabalu Park, Sabah \n\n\n\n(Malaysian Borneo). EDP Sciences, 2014. Retrieved at: http://www.shs- \nconferences.org/articles/shsconf/pdf/2014/09/\nshsconf_4ictr2014_01097.pdf Accessed on: 12.09.2015\n\n\n\n[19] Lintangah, W., Mojito, A.R., Kodoh, J., Solimun, M. 2010. An \nAssessment of tree plantation activity among smallholders in the district \nof Ranau, Sabah. Journal of Modern Applied Science, 4, (9), 58-65.\n\n\n\n[20] Jephrin, Z.W., Seiichi, E., Arthur, B.S. 2009. Towards Sustainable \nCommunity-based Fishery Resources Management: The Tagal System of \nSabah, Malaysia. Southeast Asian Fisheries Development Center, 7 (2), \n18-23.\n\n\n\n[21] Udosen, E.D., Benson, N.U., Essien, J.P., Ebong, G.A. 2002. Relation \nbetween aqua-regia extractable heavy metals in soils and manihot \nutilissima within a municipal dumpsite. International Journal of Soil \nSciences, 1 (1), 27-32.22] Zeng, Y.H., Zueng, H.C., Chen, C.T., Chun, C.T., \nShuang, F.C., Chyan, C.L., Haw, T.L. 2002. Digestion method for total heavy \nmetal in sediment and soils. Journal of Air, and Soil Pollution, 141, 189\u2013\n205.\n\n\n\n[23] Fera, N.C., Feona, I., Lee, K.H., Kawi, B. 2013. Water quality status of \nLiwagu River, Tambunan, Sabah, Malaysia. Journal of tropical biology and \nconservation, 10, 67-73.\n\n\n\n[24] Ali, M.F., Heng, L.Y., Ratnam, W., Nais, J., Ripin, R. 2004. Metal \ndistribution and contamination of the mamut river, malaysia, caused by \ncopper mine discharge. Bulletin Environment Contamination Toxicology, \n73, 535\u2013542.\n\n\n\n[25] Bibi, N.M.A., Chin, Y.L., Fera, C., Mohd, H.A., Baba, M. 2015. Assessment \nof heavy metals contamination in Mamut river sediments using sediment \nquality guidelines and geochemical indices. Environmental Monitoring \nAssessment, 187, 4190.\n\n\n\n[26] Ahmad, A.K., Mushrifah, I., Muhammad, S.H. 2009. Water quality and \nheavy metal concentrations in sediment of sungai Kelantan, Kelantan, \nMalaysia: A Baseline Study. Sains Malaysiana, 38 (4), 435\u2013442.\n\n\n\n[27] Adlina, A.H., Jawan, J.A., Ismail, S.R., Othman, N., Masnin, M.H. 2013. \nTraditional knowledge and environmental conservation among \nindigenous people in Ranau, Sabah. Global Journal of Human Social \nScience, 13 (3), 5-11.\n\n\n\n[28] Asael, D., Matthews, A., Matthews, M.B., Halicz, L. 2007. Copper \nisotope fractionation in sedimentary copper mineralization (Timna Valley, \nIsrael). Journal of Chemical Geology, 243, 238\u2013254.\n\n\n\nCite The Article: Rohana Tair, Sheyron Eduin (2018). Heavy Metals In Water And Sediment From Liwagu River And Mansahaban River At Ranau Sabah.  \nMalaysian Journal of Geosciences, 2(2) : 26-32. \n\n\n\n\n\n\n \nBlank Page\n\n\nBlank Page\n\n\nBlank Page\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 12-21 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2021.12.21 \n\n\n\nCite the Article: Ali Mohammad, E.N. Dhanamjayarao (2021). The Impact Of Seasonal Changes On Heavy Minerals Concentration From A Part Of East Coast Of India.  \nMalaysian Journal of Geosciences, 5(1): 12-21. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2021.12.21 \n\n\n\nTHE IMPACT OF SEASONAL CHANGES ON HEAVY MINERALS CONCENTRATION \nFROM A PART OF EAST COAST OF INDIA \n\n\n\nAli Mohammada*, E.N. Dhanamjayaraob  \n\n\n\na Research Scholar, Department of Geology, Andhra University, Visakhapatnam-530003. \nb Professor, Department of Geology, Andhra University, Visakhapatnam-530003, India. \n*Corresponding Author E-mail: ali.mooh89@Gmail.com; ali.mooh89@andhrauniversity.edu.in \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 18 September 2020 \nAccepted 20 October 2020 \nAvailable online 20 November 2020\n\n\n\nPre and post monsoonal changes in the environment have led to a noticeable variation in sediment \n\n\n\ncharacteristics, heavy mineral concentrations and their distribution. The current study aimed to find out the \n\n\n\neffect of seasonal fluctuations on the concentration of heavy minerals along the coast and the variations in \n\n\n\nsediment textures and distribution. The study has revealed the effect of seasons on the sediments supply and \n\n\n\nits distribution along coast in the study area. The total heavy minerals concentrations are more in post \n\n\n\nmonsoon than in pre monsoon and the concentration also increases from south to north in parts of the study \n\n\n\narea because of seasonal circulation of currents from south to north along the shore. The micro textural study \n\n\n\nof the heavy mineral grains from different locations in the study area revealed the mechanical and chemical \n\n\n\nerosions on the grain surfaces.  \n\n\n\nKEYWORDS \n\n\n\nSeasonal Variations, Coastal Sediments, Texture characteristics, Heavy Minerals, Grain-Microtextures, \nVishakhapatnam Coast.\n\n\n\n1. INTRODUCTION \n\n\n\nClimate fluctuations play a major role in the accumulation or removal of \nsediments in a specific area. Typically, the transport processes of big-sized \nand high-density sediments are active during seasons with heavy rains. \nWhereas, these sediments accumulate in the source area during the dry \nseasons. Grain size, density, and shape of the minerals also have an effect \non the accumulation of heavies in different coastal environments (i.e. \nforeshore, backshore, and dunes). In the post-deposition stage, selective \nsorting and lateral transport work to concentrate the heavy minerals as \nplacer deposits on the beach area (Bryan et al., 2007; Garzanti et al., 2009; \nArmstrong-Altrin et al., 2012).  In general, most of the detrital minerals are \nformed in markedly different conditions (pressure and temperature) from \nthe conditions on the Earth\u2019s surface (Nair et al., 2009; And\u00f2 et al., 2012).  \nWeathering of igneous, metamorphic and sedimentary rocks by chemical, \nphysical, and biological processes is the first step in the chain of processes \nthat produce heavy mineral deposits (Boggs, 2006). The erosion of these \nrocks results in an increase in the concentrations of more resistant and \nhigher specific gravity minerals (2.89). Physical agents (mainly rivers) \ntransport the weathering products from their source area towards the \ndepositional basins. Depending on the mode of origin and transportation, \nthe placer deposits can be broadly classified into eluvial, deluvial, \nproluvial, alluvial, lateral (subdivided into lacustrine, beach, marine beach, \nand offshore placers), glacial and aeolian placers (Suresh and Raja, 2014). \nIn general, the coastal placer deposits are the most widespread, due to the \nfact that most of the rivers terminate on the coastal zone, as this region is \nthe last point of the sediments\u2019 journey. \n\n\n\nThe studies of the beach areas extremely vary around the world (Ergin et \nal., 2007; \u00d6rg\u00fcn et al., 2007; Ibrahim et al., 2015; Ergin et al., 2018). The \nspecial characteristics of a particular beach change in time with the change \nof external processes, such as waves and currents. The temporal and \nspatial differences are not only correspondent with depositional \nconditions, but also with the hydrodynamic behaviour in the environment. \nAnnual cycle of wind and seasonal changes in atmospheric circulation, \nwhich are known as Monsoons, are the main determiners of the beach \ncharacteristics along the east coast of India, as this coast is under two \nphases of stormy condition, which are South West and North East \nmonsoons (SW and NE monsoons) (Aagaard et al., 2005; Magesh et al., \n2014). SW monsoon is the major condition which produces most of beach \nprocesses. During this period, erosion becomes very active and the beach \nmorphology changes significantly (Albino and Suguio, 2010; Gervais et al., \n2012; Karunarathna et al., 2014; Jarmalavicius et al., 2016). Moreover, \nbeach processes vary during monsoon periods and the sediment \ncharacteristics respond to these processes (Chauhan, 1995).  \n\n\n\nThus, studying the variable parameters during successive periods can give \nclear perception of the beach processes, which can be used to understand \nthe characteristics of old beaches. The modern sediments along the east \ncoast of India contain considerable amounts of heavy minerals. The \ncommon heavy mineral placers include magnetite, ilmenite, garnet, rutile, \nmonazite, zircon, etc. Several studies along the east coast of India have \ninvestigated the seasonal changes on the textural parameters and the \nconcentration of heavy minerals (Chauhan, 1995; Quartel et al., 2008; \nSrinivasalu et al., 2010; Gandhi et al., 2011; Joevivek and Chandrasekar, \n2014; Chauhan et al., 2014). By comparing these studies, it can be seen that \n\n\n\n\nmailto:ali.mooh89@Gmail.com\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 12-21 \n\n\n\n\n\n\n\n \nCite the Article: Ali Mohammad, E.N. Dhanamjayarao (2021). The Impact Of Seasonal Changes On Heavy Minerals Concentration From A Part Of East Coast Of India.  \n\n\n\nMalaysian Journal of Geosciences, 5(1): 12-21. \n \n\n\n\n\n\n\n\nthe seasonal changes are not uniformed in nature. Besides being related to \nthe monsoon, they are also related to several patterns that characterize \neach region such as wave height, current direction, coastal \ngeomorphology, sediments supply, etc. Therefore, it is necessary to study \neach area separately and try to compare the results with neighbouring \nareas, thus it becomes possible to obtain a comprehensive picture of the \nspatial and temporal changes. \n \nVisakhapatnam coast is located in the central part of the east coast of India. \nThis area receives huge amounts of terrigenous materials transported \nfrom the Eastern Ghat Mobile Belt (EGMB) by major rivers, such as Sarada \nand Gosthani, in addition to several small streams. Moreover, this coast is \nconsidered one of the rich areas of heavy minerals (mainly, ilmenite and \ngarnet) in the Indian peninsula (Cheepurupalli et al., 2012; Karuna, 2019). \nEarlier studies have investigated the concentration placer mineral \ndeposits (i.e. garnet, ilmenite, zircon, monazite, etc.) along the coastal area \nbetween Yarada village and Bhimunipatnam (Mahadevan and Sriramadas, \n1948; Sastry et al., 1987; Jagannadha Rao et al., 2005; Cheepurupalli et al., \n2012; Murali et al., 2016; Rezaye et al., 2018; Karuna, 2019, Mohammad et \nal., 2020). These studies have found promising concentration of placer \nminerals in the modern sediments along Visakhapatnam coast. In spite of \nthe multiplicity of studies in this region, the researchers only focused on \nstudying the concentrations of heavy minerals in one season without \nexamining the seasonal effects on the sediment characteristics and/or \nseasonal concentration of the heavy minerals along Visakhapatnam coast.  \nTherefore, the present study is an attempt to examine the seasonal \ndistribution of heavy mineral suites in sediments sourced from the beach \nsediments deposited in the area between the Sarada River mouth and the \nGosthani River mouth. The study aims to estimate the influence of \nmonsoon processes on the concentration of heavy minerals in the coastal \nsediments. This study is expected to provide economic information on the \nrichest locations in heavy minerals that can be extracted in economic \nquantities. On the other hand, the micro textural study of the grain \nsurfaces has proven its importance in determining the different \nenvironmental conditions to which these grains were subjected, such as \nphysical and geochemical processes in the source and sedimentary \nregions (Madhavaraju et al., 2006; Hossain, et al., 2014; Costa et al., 2012; \nHossain et al., 2020). This work also aims to determine the effect of \nseasonal variations on sediment characteristics, concentration and \ndistribution of heavies in beaches along the study area. \n\n\n\n2. THE STUDY AREA \n\n\n\n\n\n\n\nFigure 1:  Location map of the study area. \n\n\n\nThe present study area is a part of Visakhapatnam coast of the Bay of \nBengal (Figure 1). This coast runs roughly in the NE - SW direction with a \nwidth that varies from few meters to several tens of meters. The coastal \n\n\n\narea is characterized by numerous hills, bad land and sand dunes \n(Jagannadha et al., 2012). The study area is located between 17\u00b0 23\u02c8 & 17\u00b0 \n55\u02c8 N and 82\u00b0 23\u02c8 & 83\u00b0 29\u02c8 E with a beach stretch of approximately 100 \nkm. Visakhapatnam is the headquarter of Visakhapatnam district and it is \nlocated in the central part of the study area, consequently, the present \ninvestigation has been divided into two parts, namely, southern and \nnorthern sectors, which are relative to the city position. \n \nThis area falls under tropical climate, i.e. humid mega thermal with \nseasonal rainfall controlled by the monsoon. The rainfall occurs mainly in \nthe south-west monsoon period (July-September) and in the north-east \nmonsoon (October). The average rainfall in the area varies from 900 to \n1500 mm per year. The wave system along this area has two directions \nfollowing the main direction of the wind (Suresh et al., 2012). \nVisakhapatnam district is located in the eastern part of the Eastern Ghat \nMobile Belt (EGMB). The hills and rock bodies which are adjacent to beach \narea are composed of khondalites, hypersthene granites (charnockites), \ngarnetiferous granites (leptynites), quartzites, and pegmatites. These \nrocks are exposed on the beach in some places, such as Revupolavaram, \nPudimadaka, Yarada beach, and Bhimunipatnam.  \n \nVisakhapatnam coast exhibits many geomorphological features. These \nfeatures have been classified according to the formation processes (waves, \nsea level oscillation, etc.). Sandy beaches, dunes and rocky beach are the \nmain features that characterize the present study area (Jagannadha, \n2012). Red sediments (or Bad Lands), which are located 2 km south of \nBhimunipatnam (Gosthani River estuary), are the unique topography in \nour study area. These bad lands are recent deposits according to geological \ntimescale with a distinctive red colour. \n\n\n\n3. MATERIALS AND METHODS \n\n\n\n3.1 Sampling \n\n\n\nThe samples were collected during the pre-monsoon season (June, 2018) \nand post-monsoon season (January, 2019). A total of 82 representative \nsurficial samples (41 each season) from three environments viz. foreshore, \nberm, and dune were selected to determine the heavy mineral \nconcentrations. About 100 grams were taken from the bulk sample by \ncoining and quartering. Every sub-sample was washed with distilled water \nto remove salts and suspended impurities. Free salt samples were soaked \nwith HCl (1/10) for 12 hours to remove carbonate materials. Then the \nsamples were soaked in H2O2 to remove organic matter. Later, each sample \nwas soaked in SnCl2 to remove iron coating. The dry samples were \nsubjected to grain size analysis by standard Ro-Tap sieve shaker at \u00bd \u00d8 \nintervals of ASTM meshes (Hegde et al., 2006). \n\n\n\n3.2 Heavy Mineral Analysis \n\n\n\nIn order to separate heavy grains from other light grains, a heavy liquid \nnamed Bromoform (CHBr3, sp.gr = 2.89) was used in this process. For this, \ndifferent sizes of sieves were mixed to make the samples into two fractions \n(+60 coarse and +230 fine). Acetone was used to deodorize and remove \nthe traces of the used Bromoform from the heavy mineral grains. Taking \ninto account the fugitive character of acetone, hot air oven (60\u00b0C) was used \nto dry the separated heavy mineral grains. The weight percentage (wt.%) \nof the light and heavy minerals were calculated for each fraction. Franz \nisodynamic separator was used to separate the magnetic minerals, and the \nwt.% of these minerals has been calculated. About 200 to 300 heavy grains \nwere mounted on a thin glass slide by using Canada balsam. Then, these \nslides were studied under the Petrological microscope with mechanical \nstage. Ribbon counting methods was used in heavy minerals counting \n(Galehouse, 1969). The weight percentage (wt.%) for each mineral was \ncalculated by multiplying the occurrence number of respective minerals in \nthe slide with its specific gravities. \n\n\n\n3.3 Surface Microtextures Study \n\n\n\nScanning Electron Microscope (SEM) is one of the most reliable \ninstruments used to study the micro-size textures on the sand surface. \nFour locations, namely, Revupolavaram beach, Sarada river estuary, \nYarada beach and Gosthani river estuary have been chosen for this study. \nAll the grains have been identified before fixing them on SEM stage. Gold \ncoating is done for every grain to improve the imaging of the sample. The \ngrain samples were analysed with JEOL, model JSM-66101LV SEM at the \nAdvanced Analytical Laboratory, Andhra University. Microtextural \nfeatures were observed with reference to the classifications (Krinsley et \nal., 1962a, b; 1973; Vos et al., 2014). Twenty-two features have been \nidentified. Of these features, thirteen were a result of mechanical \nprocesses, four are of chemical origin, and five features have been formed \nas a result of interaction between chemical and mechanical processes. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 12-21 \n\n\n\n\n\n\n\n \nCite the Article: Ali Mohammad, E.N. Dhanamjayarao (2021). The Impact Of Seasonal Changes On Heavy Minerals Concentration From A Part Of East Coast Of India.  \n\n\n\nMalaysian Journal of Geosciences, 5(1): 12-21. \n \n\n\n\n\n\n\n\n4. RESULTS AND DISCUSSION \n\n\n\nThe sediment samples which are collected from the beach environments \nshow variations from one environment to the other, which reflects the \nenergy conditions of these environments. On the other hand, these \nchanges were also found during both seasons (pre and post-monsoon) \n(Tables 1 and 2). In general, the mean grain size of the beach sediments is  \n \n\n\n\nranging from medium to fine sand with an exception at Yarada beach area, \nwhere the sediments of the three environments show abrupt change of \ngrain size during both seasons. Sediments from foreshore environment of \nthe study area show increase of grain size during post-monsoon season, \nwhich indicates the increase of transporting medium energy to carry \ncoarse grains. However, berm samples consist of medium to fine sand, \nexcept for Yarada beach, where the sediments have more coarse grains.  \n\n\n\nTable 1: Textural parameters of pre- monsoon sediments from the beach area between Sarada and Gosthani rivers. \n\n\n\nLocation  \nName \n\n\n\nLocation No. \n\n\n\nPre-monsoon \n\n\n\nForeshore Berm Dune \n\n\n\nMean \nsize \n\n\n\nSorting Skewness Kurtosis \nMean \nsize \n\n\n\nSorting Skewness Kurtosis \nMean \nsize \n\n\n\nSorting Skewness Kurtosis \n\n\n\nRevupolavaram L1 \n1.8 \nMS \n\n\n\n0.604 \nMWS \n\n\n\n-0.054 \nSY \n\n\n\n0.892 \nPK \n\n\n\n1.679 \nMS \n\n\n\n0.449 \nWS \n\n\n\n0.073 \nSY \n\n\n\n1.004 \nMK \n\n\n\n2.321 \nFS \n\n\n\n0.412 \nWS \n\n\n\n-0.057 \nSY \n\n\n\n1.054 \nMK \n\n\n\nSarada estuary L2 \n1.732 \nMS \n\n\n\n0.485 \nWS \n\n\n\n0.03 \nSY \n\n\n\n0.958 \nMK \n\n\n\n1.729 \nMS \n\n\n\n0.47 \nWS \n\n\n\n0.051 \nSY \n\n\n\n0.953 \nMK \n\n\n\n- - - - \n\n\n\nRambilli L3 \n2.762 \nFS \n\n\n\n0.445 \nWS \n\n\n\n0.045 \nSY \n\n\n\n1.6 \nVLK \n\n\n\n2.002 \nFS \n\n\n\n0.518 \nMWS \n\n\n\n-0.141 \nCSK \n\n\n\n1.037 \nMK \n\n\n\n2.524 \nFS \n\n\n\n0.369 \nWS \n\n\n\n-0.061 \nSY \n\n\n\n1.128 \nLK \n\n\n\nPudimadaka L4 \n2.498 \nFS \n\n\n\n1.212 \nPS \n\n\n\n-0.456 \nVCSK \n\n\n\n0.911 \nMK \n\n\n\n2.698 \nFS \n\n\n\n0.45 \nWS \n\n\n\n0.104 \nFSK \n\n\n\n1.346 \nLK \n\n\n\n2.784 \nFS \n\n\n\n0.386 \nWS \n\n\n\n0.148 \nFSK \n\n\n\n1.457 \nLK \n\n\n\nMuthyalammapalem L5 \n2.003 \nFS \n\n\n\n0.767 \nMS \n\n\n\n-0.343 \nVCSK \n\n\n\n0.79 \nPK \n\n\n\n2.432 \nFS \n\n\n\n0.467 \nWS \n\n\n\n-0.165 \nCSK \n\n\n\n1.197 \nLK \n\n\n\n2.553 \nFS \n\n\n\n0.428 \nWS \n\n\n\n0.056 \nSY \n\n\n\n1.212 \nLK \n\n\n\nAppikonda L6 \n2.406 \nFS \n\n\n\n0.638 \nMWS \n\n\n\n-0.319 \nVCSK \n\n\n\n1.197 \nLK \n\n\n\n2.631 \nFS \n\n\n\n0.347 \nVWS \n\n\n\n-0.015 \nSY \n\n\n\n1.335 \nLK \n\n\n\n2.728 \nFS \n\n\n\n0.435 \nWS \n\n\n\n0.046 \nSY \n\n\n\n1.689 \nVLK \n\n\n\nYarada L7 \n0.737 \nFS \n\n\n\n0.434 \nWS \n\n\n\n0.027 \nSY \n\n\n\n0.909 \nMK \n\n\n\n1.122 \nMS \n\n\n\n0.692 \nMWS \n\n\n\n0.067 \nSY \n\n\n\n1.057 \nMK \n\n\n\n1.333 \nMS \n\n\n\n0.561 \nMWS \n\n\n\n0.206 \nFSK \n\n\n\n1.188 \nLK \n\n\n\nVizag Harbour L8 \n1.607 \nFS \n\n\n\n0.983 \nMS \n\n\n\n-0.203 \nCSK \n\n\n\n0.673 \nPK \n\n\n\n2.323 \nFS \n\n\n\n0.49 \nWS \n\n\n\n-0.155 \nCSK \n\n\n\n1.063 \nMK \n\n\n\n- - - - \n\n\n\nLawsons Bay L9 \n2.599 \nFS \n\n\n\n0.448 \nWS \n\n\n\n-0.169 \nCSK \n\n\n\n1.386 \nLK \n\n\n\n2.465 \nFS \n\n\n\n0.486 \nWS \n\n\n\n-0.179 \nCSK \n\n\n\n1.164 \nLK \n\n\n\n- - - - \n\n\n\nSagar Nagar L10 \n0.528 \nCS \n\n\n\n0.529 \nMWS \n\n\n\n0.181 \nFSK \n\n\n\n0.913 \nMK \n\n\n\n2.048 \nFS \n\n\n\n0.541 \nMWS \n\n\n\n0.067 \nSY \n\n\n\n0.893 \nPK \n\n\n\n2.078 \nFS \n\n\n\n0.53 \nMWS \n\n\n\n0.108 \nFSK \n\n\n\n0.932 \nMK \n\n\n\nRushikonda L11 \n0.956 \nCS \n\n\n\n0.654 \nMWS \n\n\n\n0.098 \nSY \n\n\n\n0.839 \nPK \n\n\n\n1.778 \nMS \n\n\n\n0.433 \nWS \n\n\n\n-0.027 \nSY \n\n\n\n1.067 \nMK \n\n\n\n1.971 \nMS \n\n\n\n0.489 \nWS \n\n\n\n0.123 \nFSK \n\n\n\n0.99 \nMK \n\n\n\nChepalappada L12 \n1.899 \nMS \n\n\n\n0.782 \nMS \n\n\n\n-0.229 \nCSK \n\n\n\n0.868 \nPK \n\n\n\n2.172 \nFS \n\n\n\n0.609 \nMWS \n\n\n\n0.061 \nSY \n\n\n\n0.936 \nMK \n\n\n\n2.179 \nFS \n\n\n\n0.609 \nMWS \n\n\n\n-0.079 \nSY \n\n\n\n0.937 \nMK \n\n\n\nRed bed L13 \n1.205 \nMS \n\n\n\n0.74 \nMS \n\n\n\n-0.171 \nCSK \n\n\n\n1.04 \nMK \n\n\n\n2.206 \nFS \n\n\n\n0.577 \nMWS \n\n\n\n-0.243 \nCSK \n\n\n\n0.878 \nPK \n\n\n\n2.309 \nFS \n\n\n\n0.427 \nWS \n\n\n\n-0.024 \nSY \n\n\n\n0.905 \nMK \n\n\n\nGosthani estuary L14 \n1.888 \nMS \n\n\n\n0.595 \nMWS \n\n\n\n0.016 \nSY \n\n\n\n1.061 \nMK \n\n\n\n2.665 \nFS \n\n\n\n0.516 \nMWS \n\n\n\n-0.054 \nSY \n\n\n\n1.216 \nLK \n\n\n\n- - - - \n\n\n\nAnnavaram L15 \n2.008 \nFS \n\n\n\n0.677 \nMWS \n\n\n\n0.024 \nSY \n\n\n\n0.881 \nPK \n\n\n\n2.54 \nFS \n\n\n\n0.44 \nWS \n\n\n\n-0.195 \nCSK \n\n\n\n1.17 \nLK \n\n\n\n2.139 \nFS \n\n\n\n0.511 \nMWS \n\n\n\n0.151 \nFSK \n\n\n\n0.92 \nMK \n\n\n\n* Note: Mws: Moderately well sorted, Ms: Moderately sorted, Ws: well sorted, Ps: poorly sorted, Sy: symmetrical, Fsk: fine skewed, Vfsk: Very fine skewed, \n\n\n\nCsk: Coarse skewed, Mk: Mesokurtic, Pk: Platykurtic, Lk: Leptokurtic, Vlk: Very leptokurtic. \n\n\n\nTable 2: Textural parameters of post- monsoon sediments from the beach area between Sarada and Gosthani rivers. \n\n\n\nLocation  \nName \n\n\n\nLocation \n No. \n\n\n\nPost-monsoon \n\n\n\nForeshore Berm Dune \n\n\n\nMean \nsize \n\n\n\nSorting Skewness Kurtosis \nMean \nsize \n\n\n\nSorting Skewness Kurtosis \nMean \nsize \n\n\n\nSorting Skewness Kurtosis \n\n\n\nRevupolavaram L1 \n1.814 \nMS \n\n\n\n0.638 \nMWS \n\n\n\n0.045 \nSY \n\n\n\n1.581 \nVLK \n\n\n\n2.135 \nFS \n\n\n\n0.459 \nWS \n\n\n\n0.227 \nFSK \n\n\n\n0.964 \nMK \n\n\n\n2.235 \nFS \n\n\n\n0.414 \nWS \n\n\n\n0.14 \nFSK \n\n\n\n0.88 \nPK \n\n\n\nSarada estuary L2 \n2.134 \nFS \n\n\n\n0.383 \nWS \n\n\n\n0.23 \nFSK \n\n\n\n0.925 \nMK \n\n\n\n2.121 \nFS \n\n\n\n0.42 \nWS \n\n\n\n0.39 \nVFSK \n\n\n\n0.966 \nMK \n\n\n\n- - - - \n\n\n\nRambilli L3 \n1.89 \nMS \n\n\n\n0.736 \nMS \n\n\n\n0.009 \nSY \n\n\n\n0.97 \nMK \n\n\n\n2.096 \nFS \n\n\n\n0.46 \nWS \n\n\n\n0.239 \nFSK \n\n\n\n0.976 \nMK \n\n\n\n2.151 \nFS \n\n\n\n0.621 \nMWS \n\n\n\n0.219 \nFSK \n\n\n\n1.182 \nLK \n\n\n\nPudimadaka L4 \n1.696 \nMS \n\n\n\n1.106 \nPS \n\n\n\n-0.06 \nSY \n\n\n\n0.924 \nMK \n\n\n\n2.674 \nFS \n\n\n\n0.588 \nMWS \n\n\n\n0.001 \nSY \n\n\n\n1.296 \nLK \n\n\n\n2.627 \nFS \n\n\n\n0.512 \nMWS \n\n\n\n0.108 \nFSK \n\n\n\n1.277 \nLK \n\n\n\nMuthyalammapalem L5 \n2.18 \nFS \n\n\n\n0.853 \nMS \n\n\n\n-0.263 \nCSK \n\n\n\n1.308 \nLK \n\n\n\n2.679 \nFS \n\n\n\n0.641 \nMWS \n\n\n\n0.013 \nSY \n\n\n\n1.502 \nVLK \n\n\n\n2.621 \nFS \n\n\n\n0.452 \nWS \n\n\n\n0.047 \nSY \n\n\n\n1.429 \nLK \n\n\n\nAppikonda L6 \n2.628 \nFS \n\n\n\n0.694 \nMWS \n\n\n\n0.017 \nSY \n\n\n\n1.201 \nLK \n\n\n\n2.844 \nFS \n\n\n\n0.456 \nWS \n\n\n\n0.207 \nFSK \n\n\n\n1.555 \nVLK \n\n\n\n2.797 \nFS \n\n\n\n0.491 \nWS \n\n\n\n0.194 \nFSK \n\n\n\n1.671 \nVLK \n\n\n\nYarada L7 \n1.224 \nMS \n\n\n\n0.721 \nMS \n\n\n\n-0.207 \nCSK \n\n\n\n1.103 \nMK \n\n\n\n1.152 \nFS \n\n\n\n0.73 \nMS \n\n\n\n-0.02 \nSY \n\n\n\n0.881 \nPK \n\n\n\n2.221 \nFS \n\n\n\n0.582 \nMWS \n\n\n\n0.196 \nFSK \n\n\n\n0.958 \nMK \n\n\n\nVizag Harbour L8 \n2.18 \nFS \n\n\n\n0.549 \nMWS \n\n\n\n0.016 \nSY \n\n\n\n0.874 \nPK \n\n\n\n2.221 \nFS \n\n\n\n0.588 \nMWS \n\n\n\n0.251 \nFSK \n\n\n\n1.206 \nLK \n\n\n\n- - - - \n\n\n\nLawsons Bay L9 \n1.733 \nMS \n\n\n\n0.729 \nMS \n\n\n\n0.181 \nFSK \n\n\n\n1.244 \nLK \n\n\n\n1.964 \nMS \n\n\n\n0.732 \nMS \n\n\n\n0.031 \nSY \n\n\n\n1.109 \nMK \n\n\n\n- - - - \n\n\n\nSagar Nagar L10 \n1.234 \nMS \n\n\n\n0.758 \nMS \n\n\n\n-0.242 \nCSK \n\n\n\n1.142 \nLK \n\n\n\n2.236 \nFS \n\n\n\n0.555 \nMWS \n\n\n\n0.13 \nFSK \n\n\n\n0.889 \nPK \n\n\n\n2.213 \nFS \n\n\n\n0.49 \nWS \n\n\n\n0.293 \nFSK \n\n\n\n0.949 \nMK \n\n\n\nRushikonda L11 \n1.109 \nMS \n\n\n\n0.803 \nMS \n\n\n\n-0.271 \nCSK \n\n\n\n0.762 \nPK \n\n\n\n1.756 \nMS \n\n\n\n0.411 \nWS \n\n\n\n0.093 \nSY \n\n\n\n1.389 \nLK \n\n\n\n2.132 \nFS \n\n\n\n0.388 \nWS \n\n\n\n0.557 \nVFSK \n\n\n\n0.966 \nMK \n\n\n\nChepalappada L12 \n1.924 \nMS \n\n\n\n0.576 \nMWS \n\n\n\n0.268 \nFSK \n\n\n\n0.898 \nPK \n\n\n\n2.086 \nFS \n\n\n\n0.449 \nWS \n\n\n\n0.344 \nVFSK \n\n\n\n0.957 \nMK \n\n\n\n2.352 \nFS \n\n\n\n0.592 \nMWS \n\n\n\n0.046 \nSY \n\n\n\n1.035 \nMK \n\n\n\nRed bed L13 \n1.702 \nMS \n\n\n\n0.481 \nWS \n\n\n\n0.104 \nFSK \n\n\n\n1.35 \nLK \n\n\n\n2.181 \nFS \n\n\n\n0.682 \nMWS \n\n\n\n0.132 \nFSK \n\n\n\n0.937 \nMK \n\n\n\n2.462 \nFS \n\n\n\n0.521 \nMWS \n\n\n\n-0.159 \nCSK \n\n\n\n0.818 \nPK \n\n\n\nGosthani estuary L14 \n2.209 \nFS \n\n\n\n0.554 \nMWS \n\n\n\n0.152 \nFSK \n\n\n\n0.925 \nMK \n\n\n\n2.829 \nFS \n\n\n\n0.459 \nWS \n\n\n\n0.069 \nSY \n\n\n\n1.436 \nLK \n\n\n\n- - - - \n\n\n\nAnnavaram L15 \n1.884 \nMS \n\n\n\n0.562 \nMWS \n\n\n\n0.212 \nFSK \n\n\n\n1.183 \nLK \n\n\n\n2.602 \nFS \n\n\n\n0.471 \nWS \n\n\n\n-0.117 \nCSK \n\n\n\n1.208 \nLK \n\n\n\n2.018 \nFS \n\n\n\n0.456 \nWS \n\n\n\n0.306 \nVFSK \n\n\n\n1.07 \nMK \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 12-21 \n\n\n\n\n\n\n\n \nCite the Article: Ali Mohammad, E.N. Dhanamjayarao (2021). The Impact Of Seasonal Changes On Heavy Minerals Concentration From A Part Of East Coast Of India.  \n\n\n\nMalaysian Journal of Geosciences, 5(1): 12-21. \n \n\n\n\n\n\n\n\nThe berm and dune sediments exhibit a slight decrease in the mean size \n(fine sand); this is due to the addition of fresh sediment at the river mouth \nand later drifting of sediments by monsoonal generated long shore \ncurrents. This behaviour was reported by Sastry et al. (1987), where he \ngave an explanation of the mechanism for the movement of sediments in \nthe beach area along the northern part of Visakhapatnam coast. In this \narea, foreshore micro-environment is considered as a high energy zone. \nThe sediments that are transported along the straight coastline tend to \nhave finer sand population, authenticate the trend of transport explained \n(Li and Komar, 1992). The sediments of this area are winnowed out the \nfine particles towards the berm by the actions of waves, which are very \nactive in this zone, especially during the SW monsoon period.  \n \nOn the other hand, wind plays an important role along the beach area, \nwhere the wind is generating normal waves, storm waves and long shore \ncurrents. During the action of the high waves (June to September), the fine \nparticles are carried upslope as suspended materials leaving behind the \nvery coarse particles, while the coarse and medium sand particles move \non the slope in rolling motion (Friedman and Sanders, 1978). Storm and \nhigh waves during monsoon period cause a great erosion of beach; \nconsequently, considerable quantities of beach sediments are transported \nas suspension by long shore currents. Thus, seasonal changes are quite \ncommon to observe in our study area as the changes on grain size are \nfollowing the general direction of long shore currents from South to North. \nMoriarty et al. (2008) have studied the effect of seasonal variation on \nsediments which are transported by rivers and deposited at the collision \nmargin area. This study revealed that the sediment budgets on beaches \nand continental slops are significantly associated with the seasonal input \nof sediments. This result can be used to describe the changes in grain size \nof input sediments along our study area, where the capacity of rivers \nvaries and changes the size and sediments budget.  \n \nThe seasonal sorting values from the coastal area under this study range \nfrom well to moderately sorted, where the values from foreshore range \nfrom 0.434\u00d8 to 1.212\u00d8 (av. 0.66\u00d8) during pre-monsoon (Table 1) and \nfrom 0.383\u00d8 to 1.106\u00d8 (av. 0.676\u00d8) during post-monsoon (Table 2). The \nsorting values from berm sediments range from 1.152\u00d8 to 2.844\u00d8 (av. \n2.238\u00d8) during post-monsoon and from 0.347\u00d8 to 0.692\u00d8 (av. 0.49\u00d8) \nduring pre-monsoon. Whereas the sorting values from dune environment \nvary from 0.38\u00d8 to 0.59\u00d8 (av. 0.48\u00d8) during post-monsoon and from \n0.36\u00d8 to 0.6\u00d8 (av. 0.46\u00d8) during pre-monsoon. Well to moderate degree \nof sorting indicates that sediments contain one or two modes in equal \n\n\n\namounts.  \n \nThe variations of the seasonal sorting from beach area is mainly due to the \nvariations on energy flow, where the area is under two types of wind \nsystems SW (June-September) and NE (October-January). Up till now, no \none have compared or even studied the beach area south of Yarada village \nwith the northern beach. Yadhunath et al. (2014) have studied the monthly \nchanges on the sediments\u2019 characteristics from Yarada beach. Their results \ncame to explain the relation between the seasonal changes and sediment \ncharacteristics. Thus, most of the analysed sediments are moderately well \nsorted and of medium size, which corresponds with our study from same \narea. This moderately well sorted nature of the sediments is due to the \ncontribution of coarser sediments from river or channel sources and \nmixing them with finer sands along the coast. The sand in Yarada beach is \ndumped on this area from two sources. The first source is from the south \nand north areas as this sand is transported to Yarada beach during SW and \nNE monsoons. The second source is the erosion of pre-existing rocks and \nthe hills which surrounds this area by the continuous wave action. \n\n\n\n4.1 Seasonal Distribution of Heavy Minerals from Beach \n\n\n\nEnvironments \n\n\n\nThe study of heavy mineral suites along the study area reveals that the \npredominant minerals are magnetite, ilmenite, garnet, sillimanite, rutile, \nzircon, and monazite. This study also shows that there is a correlation \nbetween the increase in the heavy mineral concentration and the decrease \nin the grain size of the sediments. The total heavy mineral (THM) weight \npercentage (wt.%) and the distribution of heavy minerals assemblage \nwere studied during both seasons, i.e. pre and post-monsoon seasons from \ndifferent environments.  \n\n\n\n4.2 Foreshore \n\n\n\nThe THM% during pre-monsoon ranges from 1.17 wt.% to 46.35 wt.% \n(av.11.48 wt.%). The weight percentage in the coarse fraction (+60) \nranges from 0.15 wt.% to 35.86 wt.% with an average of 4.84 wt.% (Table \n3), Ilmenite and monazite minerals show the highest concentration in the \ncoarse fraction (32%, each), followed by rutile and magnetite. In fine \nfraction (+230), the weight percentage is ranging from 1.91% to 78.02%, \nwhile the average value is 18.14%, which contains 28% ilmenite, 19% \nmagnetite, 18% sillimanite, and 16% garnet (see Supplementary 2). \n\n\n\n\n\n\n\nTable 3: Pre-monsoon concentration of heavy mineral from the beach sediments  between Sarada and Gosthani rivers. \n\n\n\nlocation Name. \nSample \nNo. \n\n\n\nPre-monsoon \n\n\n\nForeshore Berm Dune \n\n\n\nCoarse \nFraction \n\n\n\nFine \nFraction \n\n\n\nTHM% \nCoarse \nFraction \n\n\n\nFine \nFraction \n\n\n\nTHM% \nCoarse \nFraction \n\n\n\nFine \nFraction \n\n\n\nTHM% \n\n\n\nRevupolavaram L1/A 0.25 2.102 1.176 0.642 3.38 2.011 0.228 9.122 4.675 \n\n\n\nSarada estuary L2/A 0.302 2.282 1.292 0.514 1.154 0.834 - - - \n\n\n\nRambilli L3/A 0.34 10.782 5.561 0.502 17.438 8.97 0.42 16.498 8.459 \n\n\n\nPudimadaka L4/A 0.24 8.366 4.303 0.832 7.032 3.932 0.34 11.186 5.763 \n\n\n\nMuthyalammapalem L5/A 0.32 6 3.16 1.562 13.98 7.771 0.22 4.754 2.487 \n\n\n\nAppikonda L6/A 0.15 2.274 1.212 0.146 3.962 2.054 5.464 19.786 12.625 \n\n\n\nYarada L7/A 1.05 1.9098 1.48 9.434 85.032 47.233 4.724 57.51 31.117 \n\n\n\nVizag Harbour L8/A 1.682 4.754 3.128 35.292 59.442 47.37 - - - \n\n\n\nLawsons Bay L9/A 1.984 7.892 4.93 35.292 59.95 47.62 - - - \n\n\n\nSagar Nagar L10/A 0.286 7.082 3.68 15.254 56.472 35.86 20.852 55.518 38.18 \n\n\n\nRushikonda L11/A 3.43 24.716 14.07 5.998 12.652 9.32 11.256 42.83 27.04 \n\n\n\nChepalappada L12/A 15.866 14.45 25.16 44.392 77.128 60.76 22.25 51.272 36.76 \n\n\n\nRed bed L13/A 3.062 63.618 33.34 17.234 90.54 53.89 4.934 44.2 24.57 \n\n\n\nGosthani estuary L14/A 9.002 37.884 23.44 14.862 77.474 46.17 - - - \n\n\n\nAnnavaram L15/A 14.672 78.022 46.35 29.102 92.572 60.84 4.902 49.506 27.204 \n Min 0.15 1.9098 1.176 0.146 1.154 0.834 0.22 4.754 2.487 \n Max 35.866 78.022 46.35 44.392 92.572 60.84 22.25 57.51 38.18 \n Av. 4.842 18.1423 11.485 14.071 43.881 28.976 6.872 32.926 19.898 \n\n\n\nThe distribution of heavy minerals within foreshore sediments along the \nstudy area during post-monsoon shows increase from the southern sector \ntowards the northern sector. The total heavy mineral percentage in the \nforeshore sediments ranges from 1.58 wt.% to 79.85 wt.% with an average \n18.6 wt.% (Table 4). In the course (+60) fraction, the total heavy mineral \nweight percentage ranges from 0.016 wt.% to 62.23 wt.%. The average \ntotal heavy mineral weight percentage is 8.86 wt.%. Ilmenite is present \n\n\n\nwith average value 29%, followed by magnetite (19%). Rutile and \nmonazite are present in the coarse fraction with valuable ratios (18% and \n16%, respectively), where the highest concentration was observed in the \nnorthern sector (location 15). Moreover, in the fine fraction (+230), this \npercentage is ranging from 3.12 wt.% to 97.46 wt.% with an average of \n28.34 wt.%, which contains 34% sillimanite, 25% magnetite, and 12% \nilmenite (see Supplementary 4).\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 12-21 \n\n\n\n\n\n\n\n \nCite the Article: Ali Mohammad, E.N. Dhanamjayarao (2021). The Impact Of Seasonal Changes On Heavy Minerals Concentration From A Part Of East Coast Of India.  \n\n\n\nMalaysian Journal of Geosciences, 5(1): 12-21. \n \n\n\n\n\n\n\n\nTable 4: Post- monsoon concentration of heavy mineral from the beach sediments  between Sarada and Gosthani rivers.  \n\n\n\nlocation Name. \nSample \nNo. \n\n\n\nPost-monsoon \n\n\n\nForeshore Berm Dune \n\n\n\nCoarse \nFraction \n\n\n\nFine \nFraction \n\n\n\nTHM% \nCoarse \nFraction \n\n\n\nFine \nFraction \n\n\n\nTHM% \nCoarse \nFraction \n\n\n\nFine \nFraction \n\n\n\nTHM% \n\n\n\nRevupolavaram L1/A 0.526 4.268 2.397 0.254 9.662 4.958 0.27 9.13 4.7 \n\n\n\nSarada estuary L2/A 8.934 15.1 12.017 7.222 23.68 15.451 - - - \n\n\n\nRambilli L3/A 0.214 6.614 3.414 0.0163 12.492 6.25 0.36 11.628 5.99 \n\n\n\nPudimadaka L4/A 0.18 3.156 1.67 0.428 4.322 2.37 0.514 7.534 4.02 \n\n\n\nMuthyalammapalem L5/A 11.842 24.23 18.03 16.028 22.462 19.24 0.198 7.574 3.89 \n\n\n\nAppikonda L6/A 0.588 4.656 2.62 1.406 8.394 4.9 1.41 9.722 5.57 \n\n\n\nYarada L7/A 0.038 3.124 1.58 5.778 84.752 45.26 2.986 17.11 10.05 \n\n\n\nVizag Harbour L8/A 4.132 4.898 4.51 37.132 42.386 39.76 - - - \n\n\n\nLawsons Bay L9/A 0.818 6.118 3.47 9.398 73.354 41.38 - - - \n\n\n\nSagar Nagar L10/A 0.45 10.572 5.51 37.39 85.862 61.62 28.836 59.958 44.4 \n\n\n\nRushikonda L11/A 1.992 5.68 3.84 2.954 16.34 9.64 3.224 34.356 18.79 \n\n\n\nChepalappada L12/A 0.294 5.742 3.02 1.706 11.94 6.82 15.87 34.036 24.95 \n\n\n\nRed bed L13/A 1.366 29.08 15.22 12.464 88.856 50.66 5.606 77.584 41.59 \n\n\n\nGosthani estuary L14/A 5.246 17.79 11.51 53.162 91.506 72.33 - - - \n\n\n\nAnnavaram L15/A 16.406 45.668 31.03 62.234 97.466 79.85 3.414 33.38 18.4 \n Min 0.016 3.12 1.58 0.0163 4.322 2.37 0.198 7.534 3.89 \n Max 62.23 97.46 79.85 62.234 97.466 79.85 28.836 77.584 44.4 \n Av. 8.86 28.34 18.6 16.51 44.9 30.7 5.7 27.46 16.58 \n\n\n\n \nThe spatial distribution of heavy minerals within coarse fraction shows \nabnormal value at Chepalappada (location 12), where the wt.% value \nduring pre-monsoon is 15.866%. This might be due to the influence of \ngeographical situation of this area; where the waves remove the fine \nfraction leaving behind coarse grains. Moreover, the high value of heavy \nmineral wt.% in the Sarada River estuary and Muthyalammapalem \n(locations 2 and 5 respectively) during post-monsoon is due to high supply \nof coarse heavies by this river and other streams. In addition, the \nconcentration of heavy minerals within fine fraction reaches the highest \nvalues at Annavaram (location 15). Cheepurupalli et al. (2012) studied the \ndistribution of heavy mineral suites at Bhimunipatnam coast. They found \nthat the concentration of heavies increases from South to North, which is \ncompatible with our result. In addition to the above-mentioned, they also \nconcluded that the heavy minerals are associated with fine fraction and \nthe most abundant minerals are opaque, sillimanite and garnet, whereas \nthe rest minerals constitute only 15% of the total (Cheepurupalli et al., \n2012). These results also support our finding.  \n\n\n\n4.3 Berm  \n\n\n\nDuring pre-monsoon period, the total heavy mineral weight percentage \nvaries from 0.83 wt.% to 60.84 wt.% with an average of 28.97 wt.%. In the \ncoarse fraction (+60), the heavy mineral weight percentage varies \nbetween 0.14 wt.% and 44.39 wt.%, while the average is 14.07 wt.%. In \nthe fine fraction (+230), heavy minerals showed relatively very high \nconcentrations, where these values ranged between 1.154 wt.% to 92.57 \nwt.%, with an average value of about 43.88 wt.%. Ilmenite mineral is the \nmost abundant among the other heavy minerals (average 29%), where the \nhighest concentration was found in Location 15 (Annavaram).  \n  \nOn the other hand, the total heavy minerals in the berm environment in \nthe present study area during post-monsoon period have relatively high \npercentage compared to other environments. However, the average value \nof total heavy minerals during the post-monsoon season is 28.7 wt.%. The \nheavy minerals weight percentage within coarse (+60) and fine fractions \n(+230) is increasing from south to north along the study area, where the \nhighest values are in location 15 (Annavaram). \n \nIn the coarse fraction (+60), the average total heavy mineral weight \npercentage is 16.51 wt.% (see Supplementary 3). Garnet shows high \nconcentrations in the coarse fraction due to crystalline properties of this \nmineral. In the fine fraction (+230), the average total heavy mineral weight \npercentage is 44.9 wt.%. The concentration of magnetite during post-\nmonsoon period show high values compared to that during pre-monsoon \nperiod, where the highest values are found in the northern sector on both \nsides of the Gosthani River mouth. This leads us to the conclusion that in \nthe period after the monsoon, the sediment supply ratio increases, in \naddition to the influence of beach processes that ultimately lead to an \nincrease in the concentration of some types of minerals in the estuaries. \n\n\n\n\n\n\n\nFigure 2: A. black sand from Annavaram beach, B. Alternate layers of \n\n\n\nheavy and light minerals \n\n\n\nAlternate layers of light and dark sand (Figure 2) correspond to the \nseasonal changes, where the energy conditions osculate between low and \nhigh (Hegde et al., 2006). During high energy conditions, the high waves \nwhich reach the berm remove light minerals leaving the heavier ones. In \nthis case, we can call the action winnowing waves action. Whereas during \nlow energy condition, the waves roll back the earlier removed minerals \n(light minerals) and settle them down above the dark minerals. The \nrepetition of these actions for a long period of time produces the alternate \nlayers. On the other hand, the variations of wind energy during different \nseasons can also produce the same alternated layers in backshore and \ndunes.  \n\n\n\n4.4 Dune  \n\n\n\nThe heavy mineral distribution values from dune environment along the \ncoastal area show big variation between the coarse and fine fractions. In \ngeneral, the concentration of heavy minerals increases from south \ntowards north within dune sediments. This increase takes the same \ndirection during both seasons. The weight percentages of heavies for the \ntwo size fractions from dune environment during both seasons are given \nin tables 3 and 4. The concentration of heavy minerals increases during \nthe pre-monsoon season compared to that during the post-monsoon \nseason, where the total heavy minerals wt.% ranges between 2.49 wt.% to \n38.18 wt.% with an average of 19.9 wt.%. Magnetite and ilmenite show the \nhighest concentrations among other heavy minerals (28% and 26%, \nrespectively) during pre-monsoon period. On the other hand, the total \nheavy mineral in the dune sediments during the post-monsoon season \nranges between 3.89 wt.% and 44.4 wt.% with an average of 16.58 wt.%.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 12-21 \n\n\n\n\n\n\n\n \nCite the Article: Ali Mohammad, E.N. Dhanamjayarao (2021). The Impact Of Seasonal Changes On Heavy Minerals Concentration From A Part Of East Coast Of India.  \n\n\n\nMalaysian Journal of Geosciences, 5(1): 12-21. \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 3: Pre-monsoon spitial distribution of heavy minerals \n\n\n\nconcentration along the study area. \n\n\n\nIn general, dune environment receives its sediments from the winnowing \naction, which transports the sand grains from the beach and backshore \nenvironments when the wind direction is from sea side towards the land \n(Davidson-Arnott, 2019). On the other hand, when the wind is in the \nopposite direction (from land to sea), it works to move and transport the \nsand grains and other materials (silt and/or clay) from old dunes to new \ndunes (Friedman, 1961). However, the size and the type of minerals are \nassociated with the energy of the wind (Appa and Karuna Karudu, 2018; \nProdger et al., 2017). The spatial distribution charts of heavy minerals \nduring both seasons show some differences between coarse and fine \nfractions. In general, the concentration of these heavies shows relatively \nlow concentration in the southern sector, whereas this concentration \nincreases towards North. However, in coarse fraction the concentration of \nheavies shows low values in Annavaram (location 15). In fine fraction, this \nconcentration rises to reach relatively high values, which might be due to \nthe decrease in the wind energy to move coarse fraction from backshore \nand beach towards dune environment.  \n\n\n\n\n\n\n\nFigure 4: Post-monsoon spitial distribution of heavy minerals \n\n\n\nconcentration along the study area. \n\n\n\nFigures 3 and 4 show the spatial concentration of heavy minerals in coastal \nsediments during pre-monsoon and post-monsoon respectively. It is clear \nthat the concentration of total heavy minerals is increasing from south to \nnorth along the coastal region under study. This is due to many factors, \nwhere the rate of sediments\u2019 supply and the direction of currents are the \nmajor factors that control the distribution and concentration of theses \nminerals. On the other hand, the seasonal changes seem to have their own \nimpact on heavy minerals concentration. The concentration of heavy \nminerals is higher during the period after monsoon season. This is due to \nthe increase in the supply rate and the increase in selective operations in \nthe coastal region. On the other hand, the effect of seasonal changes \nappeared on the distribution of heavy mineral assemblages. In the coarse \nfraction, the concentrations of ilmenite, monazite, and rutile during pre-\nmonsoon period (Figure 5) are higher than that during post-monsoon \nperiod (Figure 6), while the concentrations of garnet, sillimanite, and \nmagnetite are higher during post-monsoon period. In the fine fraction, \nmagnetite and sillimanite show wide distribution range during pre-\nmonsoon season (Figure 7), whereas the concentration values of ilmenite, \nrutile, and zircon show higher values during post-monsoon season (Figure \n8). \n\n\n\n\n\n\n\nFigure 5: Pre-monsoon concentrations of heavy mineral assembleges in \n\n\n\nthe coarse fraction (+60). \n\n\n\n\n\n\n\nFigure 6: Post-monsoon concentrations of heavy mineral assembleges in \n\n\n\nthe coarse fraction (+60). \n\n\n\n\n\n\n\nFigure 7: Pre-monsoon concentrations of heavy mineral assembleges in \n\n\n\nthe fine fraction (+230). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 12-21 \n\n\n\n\n\n\n\n \nCite the Article: Ali Mohammad, E.N. Dhanamjayarao (2021). The Impact Of Seasonal Changes On Heavy Minerals Concentration From A Part Of East Coast Of India.  \n\n\n\nMalaysian Journal of Geosciences, 5(1): 12-21. \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 8: Post-monsoon concentrations of heavy mineral assembleges in \n\n\n\nthe fine fraction (+230). \n\n\n\n4.5 Surface Microtexture  \n\n\n\nThe surface microtextures of sand grains have been used for decades to \nidentify the diagnostic textures of particular environments (Itamiyaet al., \n2019). In the later stage, after the rock fragments are released from the \nparent rocks, many chemical and physical changes take place on the grain \nsurface to produce characteristic surface features. These features can be \nused as distinctive indicators to identify and attribute the various \nprocesses which occur during the sediments\u2019 journey from source to \ndeposition area. Literature concerning the grain surface microtexture is \nreplete with exhaustive laboratory investigations (Morton, 1991; Morton \nand Hallsworth, 1999; Dill, 2007; Costa et al., 2012; Bellanova et al., 2016; \nCosta et al., 2018).  \n \n\n\n\nTable 5: Identified microtextures and their abundance on heavy \nmineral grains from the study area \nLocation \nname \n\n\n\nHeavy \nMinerals \n\n\n\nMechanical \nOrigin \n\n\n\nMechanical / \nChemical Origin \n\n\n\nChemical \nOrigin \n\n\n\nRevupola\nvaram \n\n\n\nSillimanite VA A AB \n\n\n\nGarnet VA P C \n\n\n\nSarada \nestuary \n\n\n\nMonazite VA C C \n\n\n\nIlmenite C R A \n\n\n\nRutile VA R A \n\n\n\nKyanite VA P D \n\n\n\nYarada \n\n\n\nGarnet VA R A \n\n\n\nRutile C R VA \n\n\n\nZircon P AB A \n\n\n\nIlmenite VA R P \n\n\n\nGosthani \nestuary \n\n\n\nIlmenite A C C \n\n\n\nGarnet C P VA \n\n\n\nZircon C C A \n\n\n\nMonazite A P C \n\n\n\nNote: VA: very abundant (>75%), A: Abundant (75-50%), C: Common (50-\n25%), P: Present (25-5%), R: Rare (<5%), AB: Absent \n \nDepending on these investigations, the surface microtextures occur on \ndetrital minerals (heavy and/or light) due to three factors.  These factors \nare named depending on their formation origin, namely, mechanical, \nchemical and mechanical/ chemical origins (Table 5). In general, the \nmechanical and chemical marks on the grain surface are associated with \nthe crystal structure of the mineral and also with its cleavages. Thus, the \nminerals with low abrasion resistance degree are more vulnerable to \nattrition and chipping (Krinsley and Doornkamp, 1973). The chemical \nfeatures originate from the long/short interaction of grains with one or \nmore of the chemical agents such as sea water, rain water or interstitial \nwater. Moreover, the mechanical features are generally due to the collision \nbetween grains and with the riverbed rocks. These features occur during \ntransportation and also after deposition. \n \nWe have studied the surface microtextures from four locations chosen \nfrom the beach of our study area. These locations are Revupolavaram \nbeach, Sarada river estuary, Yarada beach and Gosthani river estuary. The \nsurface microtextures show dominance of mechanical features. Most \nfeatures are present as associations rather than in isolated forms. Garnet \ngrins from Revupolavaram beach show extremely variable and rough \nsurfaces. These features resulted from breaking the edges, impact-V pits \nand grooves (Figure 9, C and D). On the other hand, sillimanite grains show \nprismatic shape with smooth fracture surfaces with some solution bits as \n\n\n\nshown in Figure (9, A and B). From the above-mentioned results, we can \nconclude the importance of crystal structure of grains during \ntransportation in determining the final shape of the grain. \n\n\n\n\n\n\n\nFigure 9: surface microtextures of heavy minerals from Revupolavaram \n\n\n\nbeach .(A) and (B) Prismatic sillimanite grain shows conchoidal fractures, \n\n\n\nArcuate steps (red arrow) and the main feature on the grain surface is \n\n\n\nfracture plates. (C) and (D) irregular grains of garnet and sillimanite with \n\n\n\nnumerous mechanical features on their surfaces. \n\n\n\n\n\n\n\nFigure 10: Surface microtextures of heavy minerals from Sarada river \n\n\n\nestuary. (A) Sub-rounded  monazite grain shows small pits, Adhering \n\n\n\nparticles (red arrow) and curved scratches (white arrow. (B) Rounded \n\n\n\nilmenite grain with small pits and solution pits (arrow). (C) Rutile grain \n\n\n\nshows angular shape with some rounded edges and numerous conchoidal \n\n\n\nfractures, upturned plates and v-shape pits. (D) Kyanite grain shows \n\n\n\nelongated-angular shape with collision pits, arcuate steps (white arrow) \n\n\n\nand straight steps (red arrow). \n\n\n\n\n\n\n\nFigure 11: Surface microtextures of heavy minerals from Gosthani river \n\n\n\nestuary. (A) Ilmenite grain shows rounded shape with flakes features on \n\n\n\nthe surface and straight steps (arrow). (B) Angular garnet grain shows \n\n\n\ncrystalline overgrowth (red arrow), chemical solution and v-shape pits. \n\n\n\n(C) Sub-rounded zircon grain shows solution hole (red arrow) and big & \n\n\n\nsmall pits. (D) Sub-rounded monazite grain shows solution pits (arrow) \n\n\n\nand reworked conchoidal fractures.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 12-21 \n\n\n\n\n\n\n\n \nCite the Article: Ali Mohammad, E.N. Dhanamjayarao (2021). The Impact Of Seasonal Changes On Heavy Minerals Concentration From A Part Of East Coast Of India.  \n\n\n\nMalaysian Journal of Geosciences, 5(1): 12-21. \n \n\n\n\n\n\n\n\nStudied minerals from Sarada and Gosthani estuaries (Figure 10 and \nFigure 11) represent transported minerals through the river which are not \nsubjected yet to beach processes (i.e. waves and dissolution in salty \nwater). In this environment, the surface textures are depending mainly on \nthe stability of the minerals. Grains from both estuaries show rounded \n(Anhedral) to sub-rounded grains. The rounded shape and edges are \nresulted specifically from the rolling motion of grains during \ntransportation period. Many researchers have referred to the changes on \nthe mineral surface as a result of the mechanical actions during \ntransportation (Cardona et al., 2005; And\u00f2 et al., 2012; D\u2019Haen et al., 2012; \nCosta et al.,2013; G\u00e4rtner et al., 2017). These changes are abrasion or \nattrition, where the size of the grains gradually decreases with the \nincrease of the transportation distance. In additional to mechanical \nsurface textures, theses grains also show numerous chemical \nmorphologies, such as, solution pits, hollows and irregular solution \nsurfaces. Monazite from both estuaries show rounded edges, solution pits \nand curved grooves on its surface. The garnet grains show euhedral \nmorphology with conchoidal features. Crystalline overgrowth on garnet \ngrains is a characteristic feature on the grain surface. In all locations, \nIlmenite shows rounded morphology with solution pits. \n \nIn Yarada beach, which is considered an erosional area throughout various \nseasons (Ganesan and Raju, 2010), heavy mineral grains seem to be highly \naffected by chemical processes (Figure 12). Chemical textures are the \ndominant on the surface of these grains (solution pits and hollows). Garnet \ngrains show sub-rounded outlines. These rounded edges appear contrary \nto what we noticed on Sarada and Gosthani estuaries. Thus, we can \nattribute these features to the high impact of the wave actions. On the \nother hand, mechanical processes have formed lots of features on these \ngrain surfaces, such as, small pits, conchoidal fractures and scratches, \nwhich can be noticed on the garnet, rutile and zircon grains (Figure 12-A, \nB, C). \n\n\n\n\n\n\n\nFigure 12: Surface microtextures of heavy minerals from Yarada beach. \n(A) Garnet grain shows sub-angular surface with rounded edges, this grain \nshows conchoidal fractures, straight scratches (white arrow) and big size \ncurves (red arrow). (B) sub-rounded rutile grain shows big chemical hole \nand crystals growth (red arrow) and medium pits. (C) Sub-rounded zircon \ngrain shows straight grooves (arrow) and reworked conchoidal fractures. \n(D) Rounded ilmenite with numerous small pits and chemical solution. \n\n\n\n5. CONCLUSION \n\n\n\nSeasonal studies of modern sediments from a part of Visakhapatnam coast, \neast coast of India revealed that the sediment characteristics and heavy \nmineral concentrations in the study area are associated with fluctuations \nin seasonal monsoons. The grain size parameters of these coastal \nsediments decrease from pre monsoon to post monsoon. As the \nconcentration of heavy minerals are associated with size and sorting of the \nsediments, the grain size parameters also effect the distribution of heavy \nminerals. The total concentration of economic heavy minerals (such as \nmagnetite, ilmenite, garnet and zircon) varies in these two seasons. Pre \nmonsoon is characterized with relatively high concentration of heavy \nminerals when compared to post monsoon. The total concentrations also \nvary spatially, where the concentration of heavies increases from south to \nnorth along the study area.  \n \nIn addition to the seasonal changes, there are other factors, i.e. sediment \nsupply, selective sorting and circulation of currents along the shore that \naffect the distribution and concentration of these heavy minerals. Study of \nthe grain morphology and surface micro textures of heavy minerals \nrevealed the effect of the environment, i.e. energy and erosional processes \n\n\n\non the surface of heavy mineral grains. The grain microtextures of the \nheavy minerals from the Sarada and Gosthani estuaries show the \ndominance of mechanical features, as these grains were subjected to long \ndistance transport. The microtextures from the coastal area also shows the \neffect of mechanical and chemical erosion. Hence, the microtextural \nfeatures on the surface of heavy mineral grains can be used to distinguish \nbetween the coastal and fluvial environments. \n\n\n\nARTICLE HIGHLIGHTS \n\n\n\n\u2022 This paper deals with modern sediments along the coastal area of \n\n\n\nVisakhapatnam, East coast of India, and the seasonal changes in the \n\n\n\nsediment properties.  \n\n\n\n\u2022 Significant and potential concentrations of valuable heavy minerals \n\n\n\nhave been found within the present study area. \n\n\n\n\u2022 The main aim of this paper is to identify the effect of seasonal \n\n\n\nfluctuations on the concentration and distribution of heavy minerals \n\n\n\nin the beach region. \n\n\n\nACKNOWLEDGMENTS \n\n\n\nThis research did not receive any financial support from any governmental \nor private agency. All costs were provided by the author. The author would \nlike to thank Dr. Mai Mohamad for valuable recommendations.  \n\n\n\nCONFLICT OF INTEREST \n\n\n\nThe corresponding author states that there is no conflict of interest. \n\n\n\nREFERENCES \n\n\n\nAagaard, T., Kroon, A., Andersen, S., M\u00f6ller S\u00f8rensen, R., Quartel, S., \n\n\n\nVinther, N., 2005. Intertidal beach change during storm conditions; \n\n\n\nEgmond, The Netherlands. Marine Geology, 218, Pp. 65-80. \n\n\n\nAlbino, J., Suguio, K., 2010. Sedimentation processes and beach \n\n\n\nmorphodynamics active at the Doce River mouth, Esp\u00edrito Santo State, \n\n\n\nBrazil. Annals of the Brazilian Academy of Sciences, 82 (4), Pp. 1031-\n\n\n\n1044.  \n\n\n\nAnd\u00f2, S., Garzanti, E., Padoan, M., Limonta, M., 2012. Corrosion of heavy \n\n\n\nminerals during weathering and diagenesis: A catalog for optical \n\n\n\nanalysis. Sedimentary Geology, 280, Pp. 165-178. \n\n\n\nAppa, R.G., Karuna, K.T., 2018. Grain Size Studies of Coastal Sediments of \n\n\n\nVisakhapatnam to Bhimunipatnam, East Coast of India - Statistical \n\n\n\nApproach. International Journal of Engineering Science Invention, 7 (3), \n\n\n\nPp. 62-75. \n\n\n\nArmstrong, A.J.S., Lee, Y.I., Kasper, Z.J.J., Trejo, R.E., 2017. Mineralogy and \n\n\n\ngeochemistry of sands along the Manzanillo and El Carrizal beach areas, \n\n\n\nsouthern Mexico: implications for palaeo-weathering, provenance and \n\n\n\ntectonic setting. Geol. J., 52, Pp. 559\u2013582. \n\n\n\nBellanova, P., Bahlburg, H., Nentwig, V., Spiske, M., 2016. Microtextural \n\n\n\nanalysis of quartz grains of tsunami and non-tsunami deposits, A case \n\n\n\nstudy from Tir\u00faa (Chile). Sedimentary Geology, 343, Pp. 72-84. \n\n\n\nBoggs, S., 2006. Principles of sedimentology and stratigraphy. Upper \n\n\n\nSaddle River, N.J: Pearson Prentice Hall. \n\n\n\nBryan, K.R., Robinson, A., Briggs, R.M., 2007. Spatial and temporal \n\n\n\nvariability of titanomagnetite placer deposits on a predominantly black \n\n\n\nsand beach. Marine Geology, 236, Pp. 45\u201359. \n\n\n\nCardona, J.M., Mas, J.G., Bell\u00f3n, A.S., Dom\u00ednguez-Bella, S., L\u00f3pez, J.M., 2005. \n\n\n\nSurface textures of heavy-mineral grains: a new contribution to \n\n\n\nprovenance studies. Sedimentary Geology, 174 (3-4), Pp. 223-235. \n\n\n\nChauhan, O.S., 1995. Monsoon induced temporal changes in beach \n\n\n\nmorphology and associated sediment budget at central east coast of \n\n\n\nIndia; Journal of Coastal Research, 11, Pp. 767\u2013777. \n\n\n\nChauhan, R., Ramanathan, A.L., Adhya, T.K., 2014. Patterns of seasonal \n\n\n\nvariability in granulometric characteristics of Bhitarkanika Mangrove\u2013\n\n\n\nestuarine complex, East coast of India. Indian Journal of Geo-Marine \n\n\n\nSciences, 43 (6), Pp. 1083-1090. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 12-21 \n\n\n\n\n\n\n\n \nCite the Article: Ali Mohammad, E.N. Dhanamjayarao (2021). The Impact Of Seasonal Changes On Heavy Minerals Concentration From A Part Of East Coast Of India.  \n\n\n\nMalaysian Journal of Geosciences, 5(1): 12-21. \n \n\n\n\n\n\n\n\nCheepurupalli, N., AnuRadha, B., Reddy, K.S.N., Dhanamjaya Rao, E.N. \n\n\n\nDayal, A.M., 2012. Heavy Mineral distribution studies in different \n\n\n\nmicroenvironments of Bhimunipatnam coast, Andhra Pradesh, India. \n\n\n\nInternational Journal of Scientific and Research Publications, 2, Pp. 1-\n\n\n\n10. \n\n\n\nCosta, P.J., Andrade, C., Dawson, A.G., Mahaney, W.C., Freitas, M.C., Paris, R. \n\n\n\nTaborda, R., 2012. Microtextural characteristics of quartz grains \n\n\n\ntransported and deposited by tsunamis and storms. Sedimentary \n\n\n\nGeology, 275, Pp. 55-69.  \n\n\n\nCosta, P.J., Gelfenbaum, G., Dawson, S., La Selle, S., Milne, F., Cascalho, J., \n\n\n\nJaffe, B., 2018. The application of microtextural and heavy mineral \n\n\n\nanalysis to discriminate between storm and tsunami deposits. \n\n\n\nGeological Society, London, Special Publications, 456 (1), Pp. 167-190. \n\n\n\nCosta, P.J.M., Andrade, C., Mahaney, W.C., Da Silva, F.M., Freire, P., Freitas, \n\n\n\nM.C., Lopes, V., 2013. Aeolian microtextures in silica spheres induced in \n\n\n\na wind tunnel experiment: Comparison with aeolian quartz. \n\n\n\nGeomorphology, 180, Pp. 120-129. \n\n\n\nD\u2019Haen, K., Verstraeten, G., Degryse, P., 2012. Fingerprinting historical \n\n\n\nfluvial sediment fluxes. Progress in Physical Geography, 36 (2), Pp. 154-\n\n\n\n186. \n\n\n\nDavidson-Arnott, R., Bauer, B., Houser, C., 2019. Introduction to coastal \n\n\n\nprocesses and geomorphology. Cambridge University Press. \n\n\n\nDill, H.G., 2007. Grain morphology of heavy minerals from marine and \n\n\n\ncontinental placer deposits, with special reference to Fe\u2013Ti oxides. \n\n\n\nSedimentary Geology, 198 (1-2), Pp. 1-27. \n\n\n\nErgin, M., Karaka\u015f, Z.S., Tekin, E., 2018. Provenance discrimination among \n\n\n\nforeshore, backshore, and dune environments in the black sand beaches \n\n\n\nalong the Samanda\u011f/Hatay coasts, SE Turkey (E Mediterranean). \n\n\n\nArabian Journal of Geosciences, 11, Pp. 109-128. \n\n\n\nhttps://doi.org/10.1007/s12517-018-3429-2. \n\n\n\nErgin, M., Keskin, S., Do\u011fan, \u00dc.A., Kad\u0131o\u011flu, K.Y., Karaka\u015f, Z., 2007. Grain \n\n\n\nsize and heavy mineral distribution as related to hinterland and \n\n\n\nenvironmental conditions for modern beach sediments from the Gulfs \n\n\n\nof Antalya and Finike, eastern Mediterranean. Marine Geology, 240, Pp. \n\n\n\n185-196. \n\n\n\nFriedman, G.M., 1961. Distinction between dune, beach and river sands \n\n\n\nfrom textural characters. Journal of Sedimentary Petrology, 31, Pp. 514-\n\n\n\n529. \n\n\n\nFriedman, G.M., Sanders, J.E., 1978. Principles of sedimentology, Wiley, \n\n\n\nNew York. \n\n\n\nGalehouse, J.S., 1969. Counting Grain Mounts: Number Percentage vs. \n\n\n\nNumber Frequency. Journal of Sedimentary Research, 39 (2), Pp. 812-\n\n\n\n815. \n\n\n\nGandhi, M.S., Solai, A., Kanan, K., Baskar, K., Kaveri, S., Dhamodharan, V., \n\n\n\nRajamanickam, V., 2011. Post Tsunami Heavy Mineral Distribution \n\n\n\nBetween Cuddalore to Kanyakumari Along the Tamil Nadu Coast, India-\n\n\n\nA Review. INTECH Open Access Publisher. Pp. 189\u2013198. \n\n\n\nGanesan, P., Rajo, N.S.N., 2010. Beach profiling studies at Yarada beach, \n\n\n\nVisakhapatnam, East coast of India. NIO Technical Report, Goa, Pp. 1-\n\n\n\n42. \n\n\n\nG\u00e4rtner, A., Youbi, N., Villeneuve, M., Sagawe, A., Hofmann, M., Mahmoudi, \n\n\n\nA., Linnemann, U., 2017. The zircon evidence of temporally changing \n\n\n\nsediment transport\u2014the NW Gondwana margin during Cambrian to \n\n\n\nDevonian time (Aoucert and Smara areas, Moroccan Sahara). \n\n\n\nInternational Journal of Earth Sciences, 106 (8), Pp. 2747-2769. \n\n\n\nGarzanti, E., Ando, S., Vezzoli, G., 2009. Grain-size dependence of sediment \n\n\n\ncomposition and environmental bias in provenance studies. Earth \n\n\n\nPlanet Sci. Lett., 277, Pp. 422-432. \n\n\n\nGervais, M., Balouin, Y., Belon, R., 2012. Morphological response and \n\n\n\ncoastal dynamics associated with major storm events along the Gulf of \n\n\n\nLions Coastline, France Geomorphology, 143-144, Pp. 69-80. \n\n\n\nHegde, V.S., Shalini, G., Gosavi, K.D., 2006. Provenance of heavy minerals \n\n\n\nwith special reference to ilmenite of the Honnavar beach, central west \n\n\n\ncoast of India. Current Science, 91 (5), Pp. 644-648.  \n\n\n\nHossain, H.Z., Armstrong-Altrin, J.S., Jamil, A.H.M.N., Rahman, M.M., \n\n\n\nHern\u00e1ndez-Coronado, C.J., Ramos-V\u00e1zquez, M.A., 2020. Microtextures \n\n\n\non quartz grains in the Kuakata beach, Bangladesh: implications for \n\n\n\nprovenance and depositional environment. Arabian Journal of \n\n\n\nGeosciences, 13 (7), Pp. 1-12. \n\n\n\nHossain, Z.H., Tarek, M., Armstrong-Altrin, J.S., Monir, M.M.U., Ahmed, M.T., \n\n\n\nAhmed, S. I., Hernandez-Coronado, C.J., 2014. Microtextures of detrital \n\n\n\nsand grains from the Cox\u2019s Bazar beach, Bangladesh: Implications for \n\n\n\nprovenance and depositional environment. Carpathian Journal of Earth \n\n\n\nand Environmental Sciences, 9 (3), Pp. 187-197. \n\n\n\nIbrahim, M.M.M., Ibrahim S.I., Elhag A.M.H., Dafalla, M.S., 2015. Heavy \n\n\n\nminerals assemblage of sediments in Almanaqil Ridge, Gezira State, \n\n\n\nSudan. International Journal of Scientific and Research Publications, 5 \n\n\n\n(7), Pp. 1-9. \n\n\n\nItamiya, H., Sugita, R., Sugai, T., 2019. Analysis of the surface microtextures \n\n\n\nand morphologies of beach quartz grains in Japan and implications for \n\n\n\nprovenance research. Progress in Earth and Planetary Science, 6 (1), Pp. \n\n\n\n43-57. \n\n\n\nJagannadha, R.M., Greeshma, G.A.G., Avatharam, P., Anil, N.C., \n\n\n\nKarunaKarudu, T., 2012. Studies on Coastal Geomorphology along \n\n\n\nVisakhapatnam to Bhimunipatnam, East Coast of India. J. Ind. Geophys. \n\n\n\nUnion, 16 (4), Pp. 179-187. \n\n\n\nJagannadha, R.M., VenkataRamana, J., Venugopal, R., Chandra, R.M., 2005. \n\n\n\nGeochemistry and Ore mineralogy of Ilmenite from beach placers of the \n\n\n\nVisakhapatnam \u2013 Bhimunipatnam deposit, Andhra Pradesh. Journal of \n\n\n\nGeological Society of India, 66, Pp. 147-150. \n\n\n\nJarmalavicius, D., \u0160matas, V., Stankunavicius, G., Pupienis, D., \u017dilinskas, G., \n\n\n\n2016. Factors controlling coastal erosion during storm events. In: Vila-\n\n\n\nConcejo, A.; Bruce, E.; Kennedy, D. M., and McCarroll, R. J. (eds.), \n\n\n\nProceedings of the 14th International Coastal Symposium (Sydney, \n\n\n\nAustralia). Journal of Coastal Research, Special Issue, 75, Pp. 1112 - \n\n\n\n1116.  \n\n\n\nJoevivek, V., Chandrasekar, N., 2014. Seasonal impact on beach \n\n\n\nmorphology and the status of heavy mineral deposition\u2013central Tamil \n\n\n\nNadu coast, India. Journal of Earth System Science, 123 (1), Pp. 135-149. \n\n\n\nKaruna, K.T., 2019. Heavy Mineral distribution and provenance studies of \n\n\n\ncoastal sediments of Visakhapatnam Coast \u2013 statistical approach. \n\n\n\nJournal of The Indian Association of Sedimentologists, 36 (2), Pp. 64-85. \n\n\n\nKarunarathna, H., Pender, D., Ranasinghe, R., Short, A.D., Reeve, D.E., 2014. \n\n\n\nThe effect of storm clustering on beach profile variability. Marine \n\n\n\ngeology, 348, Pp. 103-112. \n\n\n\nKrinsley, D., Doornkamp, J.C., 1973. Atlas of Quartz Sand Surface Textures. \n\n\n\nCambridge University Press, Pp. 91. \n\n\n\nKrinsley, D.H., Takahashi, T., 1962a. The surface textures of sand grains, an \n\n\n\napplication of electron microscopy. Science, 135, Pp. 923-925. \n\n\n\nKrinsley, D.H., Takahashi, T., 1962b. The surface textures of sand grains, an \n\n\n\napplication of electron microscopy: glaciation. Science, 138, Pp. 1262-\n\n\n\n1264. \n\n\n\nLi, M.Z., Komar, P.D., 1992. Long shore grain sorting and beach placer \n\n\n\nformation adjacent to the Columbia River.  Journal of Sedimentary \n\n\n\nPetrology, 62, Pp. 429-441. \n\n\n\nMadhavaraju, J., Lee, Y.I., Armstrong-Altrin, J.S., Hussain, S.M., 2006. \n\n\n\nMicrotextures on detrital quartz grains of upper Maastrichtian-Danian \n\n\n\nrocks of the Cauvery Basin, Southeastern India: implications for \n\n\n\nprovenance and depositional environments. Geosciences Journal, 10 \n\n\n\n(1), Pp. 23-34. \n\n\n\nMagesh, N.S., Chandralekha, L.I., Chandrasekar, N., Kaliraj, S., 2014. \n\n\n\nInfluence of tidal cycle on heavy minerals variation in the Kottilpadu \n\n\n\nbeach, Kanyakumari coast, Tamil Nadu, India. Journal of Coastal \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 12-21 \n\n\n\n\n\n\n\n \nCite the Article: Ali Mohammad, E.N. Dhanamjayarao (2021). The Impact Of Seasonal Changes On Heavy Minerals Concentration From A Part Of East Coast Of India.  \n\n\n\nMalaysian Journal of Geosciences, 5(1): 12-21. \n \n\n\n\n\n\n\n\nSciences, 1 (1), Pp. 15-21.  \n\n\n\nMahadevan, C., Sriramadas, A., 1948. Monazite in the beach sands of \n\n\n\nVizagapatam district. In Proceedings of the Indian Academy of Sciences-\n\n\n\nSection A, 27 (4), Pp. 275-278. \n\n\n\nMahadevan, C., Sriramadas, A., 1948. Monazite in the beach sands of \n\n\n\nVizagapatam district. In Proceedings of the Indian Academy of Sciences-\n\n\n\nSection A, 27 (4), Pp. 275-278. \n\n\n\nMohammad, A., Murthy, P.B., Dhanamjayarao, E.N., Prasad, H., 2020. A \n\n\n\nstudy on textural characteristics, heavy mineral distribution and grain-\n\n\n\nmicrotextures of recent sediment in the coastal area between the \n\n\n\nSarada and Gosthani rivers, east coast of India. International Journal of \n\n\n\nSediment Research, 35 (5), Pp. 484-503. \n\n\n\nMoriarty, J.M., Kniskern, T., Harris, C.K., 2008. Effect of Seasonal Variation \n\n\n\non Sediment Transport and Deposition on a Collision Margin: The \n\n\n\nUmpqua River.  AGU Fall Meeting, San Francisco, California. Poster \n\n\n\nPresentation. \n\n\n\nMorton, A.C., 1991. Geochemical studies of detrital heavy minerals and \n\n\n\ntheir application to provenance research. In: Morton, A. C., Todd, P. D. \n\n\n\nW., Haughton, P. D. W., editors. Developments in sedimentary \n\n\n\nprovenance studies. Geological Society of London, Special Publication, \n\n\n\n57, Pp. 31- 46. \n\n\n\nMorton, A.C., Hallsworth, C.R., 1999. Processes controlling the composition \n\n\n\nof heavy mineral assemblages in sandstones. Sedimentary Geology, \n\n\n\n124, Pp. 3\u201329. \n\n\n\nMurali, K.N., Reddy, K.S.N., Ravi Sekhar, C.H., Bangakunaidu, K., \n\n\n\nGanapathirao, P., Reddy, G.V.R., 2016. Heavy mineral studies on Late \n\n\n\nQuaternary Red Sediments of Bhimunipatnam, Andhra Pradesh, East \n\n\n\nCoast of India. Journal Geological Society of India, 88, Pp. 637-647. \n\n\n\nNair, A.G., Suresh, B.D.S., Damodaran, K.T., Shankar, R., Prabhu, C.N., 2009. \n\n\n\nWeathering of ilmenite from Chavara deposit and its comparison with \n\n\n\nManavalakurichi placer ilmenite, southwestern India. Journal of Asian \n\n\n\nEarth Science, 34, Pp. 115\u2013122. \n\n\n\n\u00d6rg\u00fcn, Y., Alt\u0131nsoy, N., \u015eahin, S.Y., G\u00fcng\u00f6r, Y., G\u00fcltekin, A.H., Karahan, G., \n\n\n\nKarac\u0131k, Z., 2007. Natural and anthropogenic radionuclides in rocksand \n\n\n\nbeach sands from Ezine region (\u00c7anakkale), Western natolia, Turkey. \n\n\n\nAppl. Radiat. Isot., 65, Pp. 739\u2013747. \n\n\n\n\n\n\n\nProdger, S., Russell, P., Davidson, M., 2017. Grain\u2010size distributions on \n\n\n\nhigh\u2010energy sandy beaches and their relation to wave dissipation. \n\n\n\nSedimentology, 64 (5), Pp. 1289-1302. \n\n\n\nQuartel, S., Kroon, A., Ruessink, B.G., 2008. Seasonal accretion and erosion \n\n\n\npatterns of a microtidal sandy beach. Marine Geology, 250 (1\u20132), Pp. \n\n\n\n19\u201333. \n\n\n\nRezaye, F., Shivanna Rao, M.J., 2018. Studies on Geochemical and Heavy \n\n\n\nMineral Characteristics of Sediments of Gosthani River Estuary. Open \n\n\n\nJournal of Geology, 8, Pp. 263-277. \n\n\n\nSastry, A.V.R., Swamy, A.S.R., Prasada, R., 1981. Distribution of sands along \n\n\n\nVisakhapatnam - Bheemunipatnam beach. Indian Journal of Marine \n\n\n\nScience, 10, Pp. 369-370. \n\n\n\nSastry, A.V.R., Swamy, A.S.R., Vasudev, K., 1987. Heavy minerals of beach \n\n\n\nsands along Visakhapatnam\u2013Bhimunipatnam, East coast of India. \n\n\n\nIndian Journal of Marine Sciences, 16, Pp. 39-42. \n\n\n\nSrinivasalu, S., Jonathan, M.P., Thangadurai, N., Ram-Mohan, V., 2010. A \n\n\n\nstudy on pre-and post-tsunami shallow deposits off SE coast of India \n\n\n\nfrom the 2004 Indian Ocean tsunami: a geochemical approach. Natural \n\n\n\nHazards, 52 (2), Pp. 391-401. \n\n\n\nSuresh Gandhi, M., Raja, M., 2014. Heavy mineral distribution and \n\n\n\ngeochemical studies of coastal sediments between Besant Nagar and \n\n\n\nMarakkanam, Tamil Nadu, India. Journal of Radiation Research and \n\n\n\nApplied Sciences, 7 (3), Pp. 256\u2013268. \n\n\n\nSuresh, R.R.V., Patnaik, K.V.K.R.K., Ramesh, U.S. Misra, S.C., 2012. A Study \n\n\n\non The Seasonal Variability of Near-shore Waves Off Visakhapatnam, \n\n\n\nEast Coast Of India. International Journal of innovative Research & \n\n\n\nDevelopment, 1 (10), Pp. 373-389. \n\n\n\nVos, K., Vandenberghe, N., Elsen, J., 2014. Surface textural analysis of \n\n\n\nquartz grains by scanning electron microscopy (SEM): From sample \n\n\n\npreparation to environmental interpretation. Earth-Science Reviews, \n\n\n\n128, Pp. 93-104. \n\n\n\nYadhunath, E.M., Raju, N.S.N., Ganeasan, P., Gowthaman, R. Seelam, J.K., \n\n\n\n2014. Sediment characteristics at intertidal regions across Yarada \n\n\n\nbeach, East coast of India. Indian Journal of Geo- Marine Sciences, 43 \n\n\n\n(7), Pp. 1196-1201. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 08-17 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2023.08.17 \n\n\n\nCite The Article: K. N. Murali Krishna (2023). Sediment Charterstics, Organic Carbon and Calcium Carbonate in \nCore  Sediments of Krishna River Delta, East Coast of India. Malaysian Journal of Geosciences, 7(1): 08-17.\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2023.08.17\n\n\n\nSEDIMENT CHARTERSTICS, ORGANIC CARBON AND CALCIUM CARBONATE IN \nCORE SEDIMENTS OF KRISHNA RIVER DELTA, EAST COAST OF INDIA \n\n\n\nK. N. Murali Krishna \n\n\n\nSasi Institute of Technology and EngineeringRinggold ID 471415, Tadepalligudem, INDIA. \n*Corresponding Author Email: murali.dst2013@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 26 October 2022 \nRevised 06 November 2022 \nAccepted 11 December 2022 \nAvailable Online 17 January 2023\n\n\n\nThe present study is an attempt to understand sediment charterstics and distribution of  organic carbon and \ncalcium carbonate in four cores viz. Turumella, Inturu, Ponnapalli and Nizampatnam of the Krishna River \ndelta. Grain size data indicates that the  sediments in the study area are dominantly composed of sandy clay \nand silty clay. The average organic carbon in Turumella (1.69%), Ponnapalli (1.55%), Nizmpatnam (1.18%) \nand Inturu (1.14%). Organic matter controlled by vegetative roots and quick sedimentation and CaCO3 \npersentage related to institu authegenic shell fragments. Semi arid environmental conditions and pedogenic \nprocesses for long time exposure of mud flats and flood plains before deposition of other sediments on it. \nOrganic carbon in plants and soil undergo the primary degradation process driven by bacterial and fungal \naction, which alters their molecular composition before their input into riverine environment. The organic \ncarbon (OC) is often a good index for deciphering depositional environment. It plays a major role in \ncontrolling the redox potential of the sediments constituting the source material for petroleum. The first time \nin the Krishna River delta, where in shallow drilling was carried out upto the 160m depth undisturbed \nsamples have been used for the purpose. \n\n\n\nKEYWORDS  \n\n\n\nGrain size, Organic carbon, CaCO3, Krishna River delta. \n\n\n\n1. INTRODUCTION \n\n\n\nTextural characteristics of riverine sediments depend on the source area, \nmorphology of the river basin and hydrodynamics of the fluvial system; \nwhereas characteristics of the estuarine sediments are controlled by the \ncirculation pattern of the estuary which, in turn, is influenced by the fluvio-\nmarine process. Normally, the influence of the seasonal hydrodynamic \nconditions would reflect in the textural variations of sediments. Organic \ncarbon in river sediments is an integrator of terrestrial processes related \nto the lithology of river basins and its landscape (Longworth et al., 2007; \nBlanchi et al., 2007; Eckard et al., 2007). Terrestrial organic carbon is a \nmixture of allochthonous (vascular plants and soil) and autochthonous \n(riverine/estuarine phytoplankton) materials. Organic carbon in plants \nand soil undergo the primary degradation process driven by bacterial and \nfungal action, which alters their molecular composition before their input \ninto riverine environment. Several factors control the accumulation of \norganic matter in modern sediments i.e. rate of supply of organic matter \nto the depositional milieu and/or rate of preservation (Muller and Suess, \n1979; Demaison and Moore, 1980; Arthur et al, 1984; Tissot and Welte, \n1978).  \n\n\n\nSettling of organic matter is highest in areas where deposition of fine \ngrained sediment takes place with little or no activity like stagnated or \nsilled basin (restricted). The quantity of organic carbon (OC) in rocks is \nclosely related to sediment particle size (Trask et al., 1932; Trask, 1939). \nThis is because fine grained sediments retard the rate of diffusion of \noxygen and sulphate ions through the sedimentary coloumn, these ions \nare principal destroyer of organic matter. The OC content in the Viking \nshale Alberta indicated OC increased with decreasing grain size of \nsediments (Hunt, 1963). A similar study of carbonates showed the highest \nconcentration of OC in the lime muds and the lowest values in the skeletal \n\n\n\ngrains (Gehman, 1962). The organic carbon (OC) is often a good index for \ndeciphering depositional environment. It plays a major role in controlling \nthe redox potential of the sediments constituting the source material for \npetroleum. The amount of organic carbon in marine sediments reflects the \nsupply and preservation of organic materials from marine and terrestrial \nsources (Tissot et al., 1980; Summerhayes, 1981).  \n\n\n\nThe creation of anoxic layer within the water body by vertical mixing of \nwaters or rapid sediment inflow causes a favorable locale for organic \nmatter preservation.Variation in calcium carbonate (CaCO3) content is one \nof the unique characteristics of deltaic sediments. Preservation of CaCO3 is \ninfluenced by numerous parameters such as transportation time through \nthe water column, sea water and pore water chemistry, sedimentation \nrate, bio-turbidity and sediment mineralogy, climate and marine \nenvironment. Calcium carbonate can be studied as a climate control \nparameter. A group researhcers noticed biological productivity, water \ndepth, pressure, chemistry, turbulence; dilution by non carbonate \ncomponents could be considered the main factors influencing the \ncarbonate distribution (Kolla et al., 1976). The enrichment CaCO3 can be \noffset by desolution and dilution from non biogenic components. \nCalcareous organisms break down by physical, chemical, and biological \nerosion processes through a series of discrete sediment sizes (Perry et al., \n2011).  \n\n\n\nPotasznik and Szymczyk studied at River-lake systems comprise chains of \nlakes connected by rivers and streams that flow into and out of them \n(Potasznik and Szymczyk, 2015). The contact zone between a lake and a \nriver can act as a barrier, where inflowing matter is accumulated and \ntransformed. Calcium is natural component of surface water, and their \nconcentrations can be shaped by various factors, mostly the geological \nstructure of a catchment area. The details presented above clearly \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 08-17 \n\n\n\nCite The Article: K. N. Murali Krishna (2023). Sediment Charterstics, Organic Carbon and Calcium Carbonate in \nCore  Sediments of Krishna River Delta, East Coast of India. Malaysian Journal of Geosciences, 7(1): 08-17.\n\n\n\nillustrate that though a number of studies have been carried out primarily \nbased on surface features and samples from various subenvironemnts on \ndifferent aspects of Krishna Delta. There is a distinct gap in knowledge on \nsub-surface studies of Krishna Delta. Hence, in order to bridge this gap in \nscientific information, the present study incorporates cored subsurface \ninformation to study the evolution of Krishna Delta.   \n\n\n\n2. MATERIALS AND METHODS \n\n\n\n2.1 Study Area \n\n\n\nThe study area covers western part of a lower delta of Krishna River \nadjacent to Nizampatnam Bay. The cored holes to fall in the area bounded \nin between E 800 37' 40'' and 800 42' 03'' and N 150 54' 04'' and 160 06' 12' \n' (Figure 1). The Krishna River originates in the Western Ghats near \nMahabaleswar at an altitude of 1438m above MSL and cuts across a \nnumber of geological formations in the peninsular shield over a length of \n1400 km before it meets the Bay of Bengal. The Krishna River emerges out \n\n\n\nof the hilly terrain at Vijayawada and flows through its own deltaic \nplains/terrain for over 96km before it joins the sea. The Hamsaladivi \ndistributary branches out near Avanigadda 60km downstream from \nVijayawada and flows northeastward and joins the sea near \nMachlipatnam. The Golumuthapaya and Nadimeru distributaries branch \nout at downstream from Avanigadda flows northeastward and joins the \nsea. The main river flows southward and joins the sea at False Divi Point. \nThe Hamsaladivi is the oldest distributary and sediment contribution to \nthe modern delta is insignificant (Swamy, 1970). The rapid growth of the \ndelta, in particular the growth of narrow delta southward along the main \nriver has resulted in the evolution of Nizampatnam Bay. The Krishna River \nand its tributaries with a total drainage length of 25,345km drains an area \nof 258,948 km2 (Figure 2) and it has a total annual mean run-off of 55,764 \nmillion cusecs (Rao, 1979). Several multipurpose projects exist on the \nriver, one of them, the Nagarjuna Sagar is the world\u2019s highest gravity dam \nand has greatly reduced the sediment transport into the sea. The barrage \nat Vijayawada at the head of the delta is a water storage project to supply \nthe irrigation and drinking water needs of the delta. \n\n\n\nFigure 1: Location map of the study area \n\n\n\nFigure 2: Drainage map of the Krishna River basin \n\n\n\n2.2 Estimation of Organic Carbon and Calcium Carbonate (CaCO3) \n\n\n\nFrom the four sedimentary cores two hundred and thirty two samples \nhave been subjected for organic carbon and calcium carbonate analyses. \nOrganic carbon is determined by the process of titration between \npotassium dichromate and ferrous ammonium sulphate. This process is \nalso called Walkey-Black\u2019s method (Jackson, 1967; Gaudette et al., 1974). \nFor the estimation of organic carbon, 0.5 gm of powdered sample is taken \ninto a 500 ml conical flask. Then 10 ml of a 1N potassium dichromate is \nadded. 20 ml of concentrated H2SO4 is added to the above solution and \nkept the solution for about 20-30 minutes. Then 170 ml of distilled water \nis added to the solution, along with this 10 ml of phosphoric acid, 0.2 gm \nof sodium fluoride and 30 drops of di-phenyl amine indicator are added.  \n\n\n\nThe solution is titrated against 0.5N ferrous ammonium sulphate which is \ntaken in the burette. The end point of the titration is brilliant green. This \nprocess is also carried out with blank, to estimate the error. For estimation \nof CaCO3, 0.4 gm of sample is taken into 100 ml volumetric flask and 5 ml \nof 25% acetic acid is added to the sample. After 1-2 hours the solution is \nmade into 100 ml with distilled water and set aside for overnight. For the \nidentification of calcium carbonate another 5 ml of the solution is taken \ninto porcelain basin. 10 ml of distilled water, 2 ml of potassium hydroxide \nand 4-5 drops of P&R indicator are added and titrated against 0.02M \nEthylene Diamine Tetra Acetic acid (EDTA) until blue colour is formed \n(Muller, 1967). \n\n\n\nPercentage of CaCO3 = EDTA of CaCO3 value X 10 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 08-17 \n\n\n\nCite The Article: K. N. Murali Krishna (2023). Sediment Charterstics, Organic Carbon and Calcium Carbonate in \nCore  Sediments of Krishna River Delta, East Coast of India. Malaysian Journal of Geosciences, 7(1): 08-17.\n\n\n\n3. RESULTS \n\n\n\n3.1   Turumella Core \n\n\n\n3.1.1   Organic Carbon \n\n\n\nThe organic carbon content in the Turumella core sediments ranges from \n0.43 to 7.96% and with an average of 4.26% (Table 1). The sequence I \n(120-90m) is the bottom most part of the studied core contains fine \ngrained sand to silty sand with an average organic carbon content of \n\n\n\n1.36%. Sequence II (90-82m) is mainly sandy silt with an average organic \ncarbon content is 0.96%. Sequence III (79.50-59.65m) is mainly fine \ngrained sand except two samples at depth of 76.50-79.50m and 62.00-\n63.00m, which are medium grained sand and an average organic carbon \ncontent is 1.10%. Sequence IV (58.65- 30.30m) is sandy silt and silt with \nan average organic carbon content of 1.83%. Sequence V (26.10-20.10m) \nis fine grained sand to silty sand with an average organic carbon content \nis 4.86%. In sequence VI (18.10-12m) is mainly sandy silt with an average \norganic carbon content of 0.79%. In sequence VII (10.10-0m) is mainly silt \nwith an average organic carbon content of 1.55% (Figure 2). \n\n\n\nTable 1: Distribution Of Organic Carbon and CaCO3 In Turumella Core Sediments \n\n\n\nS. No. Depth (m) Mean grain size (\u0444) Organic carbon (%) CaCO3 (%) \n\n\n\nT-1 0.00-0.50 5.89 1.24 4.00 \n\n\n\nT-2 2.00-2.50 5.75 1.12 4.00 \n\n\n\nT-3 4.00-4.10 6.20 1.74 5.00 \n\n\n\nT-4 6.00-6.10 5.84 1.63 18.50 \n\n\n\nT-5 8.00-8.10 6.18 2.00 3.00 \n\n\n\nT-6 10.00-10.10 6.54 1.27 3.00 \n\n\n\nT-7 12.00-12.10 5.53 1.18 6.00 \n\n\n\nT-8 14.00-14.20 5.96 0.61 3.00 \n\n\n\nT-9 16.20-16.30 4.74 0.57 4.00 \n\n\n\nT-10 18.00-18.10 5.97 0.78 5.00 \n\n\n\nT-11 20.10-20.15 2.62 7.96 2.00 \n\n\n\nT-12 22.00-22.05 2.58 0.47 2.00 \n\n\n\nT-13 24.20-24.25 2.07 5.72 6.00 \n\n\n\nT-14 26.05-26.10 2.26 5.29 6.00 \n\n\n\nT-15 30.30-30.35 6.54 4.24 1.00 \n\n\n\nT-16 32.20-32.25 6.76 0.89 2.00 \n\n\n\nT-17 34.50-34.60 6.72 3.25 1.50 \n\n\n\nT-18 35.90-36.00 6.54 2.76 3.00 \n\n\n\nT-19 37.90-38.00 6.98 2.80 1.00 \n\n\n\nT-20 44.10-44.20 5.08 0.51 1.00 \n\n\n\nT-21 46.00-46.10 4.88 2.11 2.00 \n\n\n\nT-22 48.00-48.10 5.04 0.45 3.50 \n\n\n\nT-23 50.30-50.40 6.57 1.67 7.50 \n\n\n\nT-24 52.60-52.70 6.19 1.30 7.50 \n\n\n\nT-25 54.20-54.25 6.49 1.75 2.00 \n\n\n\nT-26 56.00-56.75 6.14 0.49 2.00 \n\n\n\nT-27 57.65-58.65 6.55 1.59 3.00 \n\n\n\nT-28 59.65-61.45 2.24 2.02 1.50 \n\n\n\nT-29 62.00-63.00 1.27 0.53 1.50 \n\n\n\nT-30 63.00-64.50 2.77 1.98 1.50 \n\n\n\nT-31 64.50-67.30 2.48 1.03 1.50 \n\n\n\nT-32 67.30-70.50 2.45 1.02 1.00 \n\n\n\nT-33 70.50-73.50 2.62 1.15 1.00 \n\n\n\nT-34 73.50-76.50 2.40 0.53 1.50 \n\n\n\nT-35 76.50-79.50 1.27 0.57 1.50 \n\n\n\nT-36 82.00-82.50 5.40 1.18 27.00 \n\n\n\nT-37 88.50-90.00 4.90 0.73 8.00 \n\n\n\nT-38 90.00-93.00 3.09 0.82 7.50 \n\n\n\nT-39 93.00-96.00 2.85 0.92 1.00 \n\n\n\nT-40 96.00-99.00 3.20 1.07 2.00 \n\n\n\nT-41 99.00-102.00 2.24 1.49 1.50 \n\n\n\nT-42 102.00-103.00 2.74 2.82 2.00 \n\n\n\nT-43 103.10-105.00 2.67 1.53 2.00 \n\n\n\nT-44 105.00-108.00 2.69 1.34 2.00 \n\n\n\nT-45 108.00-111.00 2.62 0.43 1.00 \n\n\n\nT-46 114.00-117.00 2.40 1.73 1.00 \n\n\n\nT-47 117.00-119.00 1.27 1.47 1.00 \n\n\n\nT-48 119.00-120.00 2.26 1.34 1.00 \n\n\n\nMin. 1.27 0.43 1.00 \n\n\n\nMax. 6.98 7.96 27.00 \n\n\n\nAv. 4.26 1.69 3.68 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 08-17 \n\n\n\nCite The Article: K. N. Murali Krishna (2023). Sediment Charterstics, Organic Carbon and Calcium Carbonate in \nCore  Sediments of Krishna River Delta, East Coast of India. Malaysian Journal of Geosciences, 7(1): 08-17.\n\n\n\n3.1.2   Calcium Carbonate  \n\n\n\nCalcium carbonate ranges from 1 to 27% in Turumella core sediments and \nan average CaCO3 is 3.68 %. (Table 1). The sequence I (120-90m) is bottom \nmost part of the studied core contains an average 2% of CaCO3. In \nsequence II (90-82m), an average CaCO3 content is 17.50%. In sequence III \n\n\n\n(79.50-59.65m), an average CaCO3 content is 1.3%. In sequence IV (58.65- \n30.30m), an average CaCO3 content is 2.85%. The sequence V (26.10-\n20.10m) contains 4% of CaCO3. In sequence VI (18.10-12m), an average \nCaCO3 content is 4.50%. In the upper part of the core is sequence VII \n(10.10-0m) contains with an average CaCO3 of 6.25% (Figure 3). \n\n\n\nFigure 3: Down core variation of mean grain size (\u03a6), percentage of organic carbon and CaCO3 in Turumella sediments \n\n\n\nThe average CaCO3 in Turumella core is 3.68%. Higher concentration of \nCaCO3 17.50% and 6.25% noted in sequence II and VII respectively. These \ntwo are flood plains composed of fine grained sand and silt with calcretes. \n\n\n\nIn sequence V and VI average CaCO3 concentration is 4% and 4.5% \nrespectively. These are river channel and estuarine channel sediments \ncomposed of medium sand and silt with shells. \n\n\n\n3.2   Inturu Core \n\n\n\n3.2.1   Organic Carbon \n\n\n\nFigure 4: Down core variation of mean grain size (\u03a6), percentage of organic carbon and CaCO3 in Inturu sediments \n\n\n\nThe organic carbon content in Inturu core sediments varies from 0.20 to \n2.27% with an average of 1.14% (Table 2). The sequence I (160- 50m) is \nthe bottom most part of studied core contains coarse grained sand to silty \nsand with an average organic carbon content of 1.21%. Sequence II (48.10-\n20m) is medium grained sand to sandy silt with average organic carbon \n\n\n\ncontent of 1.63%. Sequence III (18.25-6m) is sandy silt to clayey silt with \nan average organic carbon content of 1.10% and top most part of this core \nis sequence IV (4.50-0m) which contains sandy silt to clayey silt, mainly \nclayey silt with an average organic carbon content of 0.91% (Figure 4). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 08-17 \n\n\n\nCite The Article: K. N. Murali Krishna (2023). Sediment Charterstics, Organic Carbon and Calcium Carbonate in \nCore  Sediments of Krishna River Delta, East Coast of India. Malaysian Journal of Geosciences, 7(1): 08-17.\n\n\n\nTable 2: Distribution of Organic Carbon and Caco3 In Inturu Core Sediments \n\n\n\nS. No. Depth (m) Mean grain size (\u0444) Organic carbon (%) CaCO3 (%) \n\n\n\nI-1 0.00-0.50 5.87 0.45 4.00 \n\n\n\nI-2 2.00-2.50 5.86 0.91 6.50 \n\n\n\nI-3 4.00-4.50 5.84 1.36 2.50 \n\n\n\nI-4 6.00-6.50 5.13 0.91 4.00 \n\n\n\nI-5 8.00-8.50 5.08 1.36 3.00 \n\n\n\nI-6 10.00-11.00 5.08 0.45 2.50 \n\n\n\nI-7 12.00-12.75 5.77 0.91 1.50 \n\n\n\nI-8 14.00-14.50 5.76 1.36 5.00 \n\n\n\nI-9 16.00-16.50 5.77 0.91 4.00 \n\n\n\nI-10 18.00-18.25 5.78 1.82 2.50 \n\n\n\nI-11 20.00-22.50 1.35 0.60 4.00 \n\n\n\nI-12 24.00-24.50 1.01 0.91 1.00 \n\n\n\nI-13 26.00-28.00 1.07 1.36 1.50 \n\n\n\nI-14 30.00-30.50 1.14 0.45 2.00 \n\n\n\nI-15 32.00-32.50 1.04 0.91 2.00 \n\n\n\nI-16 34.00-34.50 1.07 0.91 0.50 \n\n\n\nI-17 36.00-36.25 1.06 0.95 3.50 \n\n\n\nI-18 38.00-38.10 1.07 0.85 1.50 \n\n\n\nI-19 40.00-42.10 1.06 0.98 2.00 \n\n\n\nI-20 44.00-44.50 1.43 0.87 1.50 \n\n\n\nI-21 46.00-46.10 3.95 1.36 2.00 \n\n\n\nI-22 48.00-48.10 4.26 1.36 1.00 \n\n\n\nI-23 50.00-52.00 2.18 0.91 3.00 \n\n\n\nI-24 54.00-54.25 2.16 1.36 3.50 \n\n\n\nI-25 56.00-56.10 2.63 1.36 0.50 \n\n\n\nI-26 58.00-58.50 2.59 1.82 2.50 \n\n\n\nI-27 62.00-62.10 3.83 1.82 2.00 \n\n\n\nI-28 64.00-64.50 4.28 2.27 1.00 \n\n\n\nI-29 66.00-66.50 2.56 1.82 1.00 \n\n\n\nI-30 68.00-68.25 2.56 1.36 1.50 \n\n\n\nI-31 70.00-70.10 2.54 2.27 2.50 \n\n\n\nI-32 72.00-72.10 2.52 0.91 1.50 \n\n\n\nI-33 74.00-74.25 2.29 0.45 1.50 \n\n\n\nI-34 78.00-78.10 1.52 0.45 3.00 \n\n\n\nI-35 80.00-80.50 1.90 0.98 2.25 \n\n\n\nI-36 82.00-82.50 2.06 1.82 3.50 \n\n\n\nI-37 84.00-84.10 1.98 0.86 4.00 \n\n\n\nI-38 86.00-86.50 2.30 1.36 2.50 \n\n\n\nI-39 88.00-88.25 2.07 2.27 3.00 \n\n\n\nI-40 90.00-90.50 1.91 0.98 4.50 \n\n\n\nI-41 92.00-92.50 1.91 0.65 2.00 \n\n\n\nI-42 94.00-94.50 1.90 0.55 3.50 \n\n\n\nI-43 96.00-96.50 1.84 0.45 3.00 \n\n\n\nI-44 98.00-98.25 1.83 0.54 4.00 \n\n\n\nI-45 100.00-100.50 2.59 1.82 4.50 \n\n\n\nI-46 102.00-102.50 2.66 1.36 1.50 \n\n\n\nI-47 104.00-104.10 2.57 0.91 3.00 \n\n\n\nI-48 106.00-106.50 2.59 1.82 1.50 \n\n\n\nI-49 108.00-108.50 2.57 1.36 1.50 \n\n\n\nI-50 110.00-110.50 1.91 0.52 2.50 \n\n\n\nI-51 112.00-112.50 1.91 0.85 3.00 \n\n\n\nI-52 114.00-114.50 1.89 0.82 2.00 \n\n\n\nI-53 116.00-118.00 2.01 1.36 1.50 \n\n\n\nI-54 120.00-122.00 2.65 2.27 2.50 \n\n\n\nI-55 126.00-128.00 2.40 1.82 1.50 \n\n\n\nI-56 130.00.134.00 1.49 0.36 2.50 \n\n\n\nI-57 138.00-138.50 1.45 0.87 2.00 \n\n\n\nI-58 140.00-142.00 0.53 0.20 2.00 \n\n\n\nI-59 146.00-146.50 0.74 1.00 2.00 \n\n\n\nI-60 150.00-152.00 1.02 0.78 1.50 \n\n\n\nI-61 154.00-154.50 1.38 0.99 2.00 \n\n\n\nI-62 156.00-156.50 2.38 1.82 2.00 \n\n\n\nI-63 158.00-160.00 2.42 1.38 1.96 \n\n\n\nMin. 0.53 0.20 0.50 \n\n\n\nMax. 5.87 2.27 6.50 \n\n\n\nAv. 2.60 1.14 2.46 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 08-17\n\n\n\nCite The Article: K. N. Murali Krishna (2023). Sediment Charterstics, Organic Carbon and Calcium Carbonate in \nCore  Sediments of Krishna River Delta, East Coast of India. Malaysian Journal of Geosciences, 7(1): 08-17.\n\n\n\n3.2.2   Calcium Carbonate \n\n\n\nCalcium carbonate content ranges from 0.50 to 6.50% in Inturu core \nsediments with an average of 2.46% (Table 2). The sequence I (160-50 m) \nis bottom most part of the studied core and it contains an average 2.36% \nof CaCO3. In sequence II (48.10-20m), an average CaCO3 content is 1.88%. \nThe sequence III (18.25-6m) contains an average 3.21% of CaCO3 and in \ntop most part of core is sequence IV (4.50-0m), contains an average CaCO3 \nof 4.33% (Figure 4). The average CaCO3 in Inturu core is 2.46%, but \nsequence IV and III have higher concentrations of CaCO3 is 4.33% and \n3.21% respectively. These are flood plain and mud flats. Flood plain \nsediments are silt with calcretes and mud flats sediments are composed \n\n\n\nsilt with shells. \n\n\n\n3.3   Ponnapalli Core \n\n\n\n3.3.1   Organic Carbon \n\n\n\nThe organic carbon content in Ponnapalli core sediments varies from 0.25 \nto 3.25% with an average 1.55% (Table 3). Sequence I (110-50m) is the \nbottom most part of studied core contains medium grained sand to silty \nsand with an average organic carbon content of 1.40%. Sequence II (48.10-\n0m) is coarse to fine grained sand with an average organic carbon content \nof 1.68 % (Figure 5).\n\n\n\nTable 3: Distribution of Organic Carbon and CaCO3 in Ponnapalli Core Sediments \n\n\n\nS. No. Depth (m) Mean grain size (\u0444) Organic Carbon (%) CaCO3 (%) \n\n\n\nP-1 0.00-0.50 2.13 1.75 18.00 \n\n\n\nP-2 2.00-2.25 1.77 2.25 15.00 \n\n\n\nP-3 4.00-4.10 1.92 2.00 10.00 \n\n\n\nP-4 6.00-6.25 1.99 2.50 18.00 \n\n\n\nP-5 8.00-8.25 1.89 2.75 20.00 \n\n\n\nP-6 10.00-10.50 1.77 2.50 20.00 \n\n\n\nP-7 12.00-12.25 1.77 0.25 5.00 \n\n\n\nP-8 14.00-14.50 1.35 1.50 10.00 \n\n\n\nP-9 16.00-16.10 0.95 1.25 8.00 \n\n\n\nP-10 18.00-18.10 1.49 3.25 5.00 \n\n\n\nP-11 20.00-21.00 1.04 1.25 6.00 \n\n\n\nP-12 22.00-22.10 2.28 0.25 8.00 \n\n\n\nP-13 24.00-24.10 2.39 1.00 4.00 \n\n\n\nP-14 26.00-26.25 2.63 0.50 3.00 \n\n\n\nP-15 28.00-28.50 1.67 0.75 5.00 \n\n\n\nP-16 30.00-30.25 2.19 1.50 4.00 \n\n\n\nP-17 32.00-32.50 2.00 1.75 6.00 \n\n\n\nP-18 34.00-34.50 1.77 2.25 9.00 \n\n\n\nP-19 36.00-36.10 1.93 1.75 15.00 \n\n\n\nP-20 38.00-38.10 2.18 2.75 9.00 \n\n\n\nP-21 40.00-40.25 2.42 0.50 10.00 \n\n\n\nP-22 42.00-42.50 2.70 1.25 6.00 \n\n\n\nP-23 44.00-44.50 2.11 1.75 5.00 \n\n\n\nP-24 46.00-46.10 2.06 2.50 4.00 \n\n\n\nP-25 48.00-48.10 1.73 2.25 3.00 \n\n\n\nP-26 50.00-50.25 2.31 2.00 3.00 \n\n\n\nP-27 52.00-52.10 3.26 2.25 10.00 \n\n\n\nP-28 54.00-54.25 1.98 2.75 2.00 \n\n\n\nP-29 56.00-57.00 2.35 3.25 6.00 \n\n\n\nP-30 58.00-58.25 1.67 2.75 4.00 \n\n\n\nP-31 60.00-60.10 1.85 2.25 2.00 \n\n\n\nP-32 62.00-62.50 1.93 2.50 3.00 \n\n\n\nP-33 64.00-64.10 1.51 1.00 7.00 \n\n\n\nP-34 66.00-66.50 1.98 0.50 4.00 \n\n\n\nP-35 68.00-68.50 2.47 1.75 5.00 \n\n\n\nP-36 70.00-70.25 1.72 2.25 2.00 \n\n\n\nP-37 72.00-72.25 1.75 1.50 3.00 \n\n\n\nP-38 74.00-74.50 1.69 1.00 3.00 \n\n\n\nP-39 76.00-76.50 2.07 0.25 10.00 \n\n\n\nP-40 78.00-78.10 1.62 2.00 4.00 \n\n\n\nP-41 80.00-80.15 1.92 0.25 6.00 \n\n\n\nP-42 82.00-82.15 1.67 1.00 3.00 \n\n\n\nP-43 84.00-84.25 2.63 0.75 5.00 \n\n\n\nP-44 94.00-95.00 2.42 0.25 3.00 \n\n\n\nP-45 96.00-97.50 2.31 0.50 5.00 \n\n\n\nP-46 98.00-99.00 1.85 0.25 4.00 \n\n\n\nP-47 100.00-100.50 1.98 1.00 4.00 \n\n\n\nP-48 102.00-102.50 2.47 1.25 5.00 \n\n\n\nP-49 104.00-104.25 1.49 1.50 3.00 \n\n\n\nP-50 106.00-110.00 1.77 0.25 5.00 \n\n\n\nMin. 0.95 0.25 2.00 \n\n\n\nMax. 3.26 3.25 20.00 \n\n\n\nAv. 1.97 1.55 6.71 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 08-17 \n\n\n\nCite The Article: K. N. Murali Krishna (2023). Sediment Charterstics, Organic Carbon and Calcium Carbonate in \nCore  Sediments of Krishna River Delta, East Coast of India. Malaysian Journal of Geosciences, 7(1): 08-17.\n\n\n\nFigure 5: Down core variation of mean grain size (\u03a6), percentage of organic carbon and CaCO3 in Ponnapalli sediment \n\n\n\n3.3.2   Calcium Carbonate \n\n\n\nCalcium carbonate content varies from 2 to 20% in Ponnapalli core \nsediments with an average of 6.71% (Table 3). Sequence I (110-50m) is \nthe bottom most part of studied core and it contains an average 4.44% of \n\n\n\nCaCO3 and in top most part of the core is sequence II (48.10-0m), with an \naverage CaCO3 content is 9.04 % (Figure 5). The average CaCO3 in \nPonnapalli core is 6.71%, but the sequence II have higher concentration of \nCaCO3 of 9.04%. The sequence is estuarine channel sediments, medium to \nfine sand with shells. \n\n\n\n3.4   Nizampatnam Core \n\n\n\n3.4.1   Organic Carbon \n\n\n\nFigure 6: Down core variation of mean grain size (\u03a6), percentage of organic carbon and CaCO3 in Nizampatnam sediment \n\n\n\nThe organic carbon content in Nizampatnam core sediments ranges from \n0 to 4.83% with an average of 1.18 % (Table 4). Sequence I (150-130m) is \nthe bottom most part of studied core contains mainly coarse grained sand \nand with an average organic carbon content of 0.62%. In sequence II \n(128.10-82m) mainly silty sand with an average organic carbon content of \n1.27%. Sequence III (80.20-76m) is mainly clayey silt with an average \norganic carbon content of 0.37%. Sequence IV (74.10-50m), is silty sand to \n\n\n\nclayey silt with an average organic carbon content of 1.87%. In sequence \nV (48.10-40m) is medium grained sand to silty sand with average organic \ncarbon content of 0.65 %. Sequence VI (38.10-10m) is sandy silt to clayey \nsilt with average organic carbon content of 1.10%. Sequence VII (8.40-0m) \nis very fine grained sand with an average organic carbon content of 1.16% \n(Figure 6). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 08-17 \n\n\n\nCite The Article: K. N. Murali Krishna (2023). Sediment Charterstics, Organic Carbon and Calcium Carbonate in \nCore  Sediments of Krishna River Delta, East Coast of India. Malaysian Journal of Geosciences, 7(1): 08-17.\n\n\n\nTable 4: Distribution of organic carbon and CaCO3 in Nizampatnam core sediments \nS.No Depth (m) Mean grain size (\u0444) Organic carbon (%) CaCO3 (%) \nN-1 0.00-0.50 3.27 1.32 2.50 \nN-2 2.00-2.30 3.65 1.80 2.00 \nN-3 4.00-4.30 3.45 0.47 3.00 \nN-4 6.00-6.50 3.20 1.37 4.50 \nN-5 8.00-8.40 3.18 0.84 10.00 \nN-6 10.00-10.30 6.16 0.91 4.50 \nN-7 12.00-13.10 6.18 0.52 2.50 \nN-8 14.00-14.10 6.16 0.04 2.00 \nN-9 16.00-16.10 6.15 2.49 6.00 \n\n\n\nN-10 18.00-18.10 5.64 0.91 5.00 \nN-11 20.00-20.10 5.62 0.14 5.00 \nN-12 22.00-22.10 5.62 0.40 15.00 \nN-13 24.00-24.10 5.63 0.66 3.00 \nN-14 26.00-26.10 4.92 0.11 3.00 \nN-15 28.00-28.10 4.83 0.42 2.00 \nN-16 30.00-31.10 5.21 2.34 2.00 \nN-17 32.00-32.10 5.22 3.96 4.00 \nN-18 34.10-34.80 5.89 1.79 7.00 \nN-19 36.00-36.10 5.89 0.78 7.00 \nN-20 38.00-38.10 5.87 0.96 2.50 \nN-21 40.00-40.10 1.11 0.58 18.00 \nN-22 42.00-42.20 1.15 0.46 7.00 \nN-23 44.50-45.00 2.17 0.38 6.00 \nN-24 46.00-46.10 1.79 1.20 2.50 \nN-25 48.00-48.10 2.06 0.61 3.00 \nN-26 50.00-50.10 6.01 4.83 3.00 \nN-27 52.00-52.10 5.98 1.38 3.50 \nN-28 54.00-54.10 5.38 2.69 2.50 \nN-29 56.00-56.10 5.42 0.00 4.50 \nN-30 58.00-58.10 5.37 2.47 3.00 \nN-31 60.00-60.10 6.26 1.03 2.50 \nN-32 62.00-62.10 5.85 0.83 1.50 \nN-33 63.30-63.40 5.81 1.52 20.50 \nN-34 66.00-66.10 5.85 3.19 2.50 \nN-35 68.00-68.10 5.97 1.22 18.50 \nN-36 70.00-70.10 5.85 3.83 1.00 \nN-37 72.00-72.10 3.90 1.15 1.50 \nN-38 74.00-74.10 3.83 0.20 0.50 \nN-39 76.00-76.10 6.13 0.61 2.00 \nN-40 78.00-78.10 6.12 0.10 1.50 \nN-41 80.10-80.20 6.15 0.40 1.00 \nN-42 82.00-83.20 2.76 0.35 2.50 \nN-43 84.00-84.20 2.79 0.25 2.50 \nN-44 86.00-86.20 2.69 1.67 1.00 \nN-45 88.00-88.20 3.09 2.96 1.50 \nN-46 90.00-90-20 3.30 0.43 2.00 \nN-47 92.00-92.20 3.34 1.63 2.00 \nN-48 94.00-94.20 3.27 0.42 3.00 \nN-49 96.00-96.10 3.72 2.02 3.50 \nN-50 98.00-98.20 4.04 1.84 2.00 \nN-51 100.00-100.10 3.97 2.50 3.00 \nN-52 102.00-102.10 3.77 2.19 2.00 \nN-53 104.00-104.10 3.64 0.87 3.00 \nN-54 106.00-106.10 3.02 1.72 2.00 \nN-55 108.00-108.10 3.10 1.15 3.00 \nN-56 109.75-110.00 3.15 2.36 4.00 \nN-57 112.00-112.10 4.42 0.60 3.00 \nN-58 114.00-114.10 3.66 0.36 4.00 \nN-59 116.00-116.10 3.88 1.95 4.00 \nN-60 118.00-118.10 3.76 0.90 3.00 \nN-61 120.00-120.10 3.70 0.77 4.00 \nN-62 126.00-126.10 3.70 0.69 4.00 \nN-63 128.00-128.10 3.66 0.30 3.00 \nN-64 130.00-130.10 0.60 0.90 3.50 \nN-65 132.00-133.00 0.62 0.85 1.00 \nN-66 135.00-138.00 0.63 0.13 1.00 \nN-67 138.00-141.00 0.93 0.60 2.00 \nN-68 142.00-143.00 0.52 0.52 2.50 \nN-69 144.00-145.00 0.55 0.85 2.94 \nN-70 145.00-148.00 0.60 0.60 3.36 \nN-71 148.00-150.00 0.76 0.50 0.84 \nMin. 0.52 0.00 0.50 \nMax. 6.26 4.83 20.50 \nAv. 3.96 1.18 3.91 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 08-17 \n\n\n\nCite The Article: K. N. Murali Krishna (2023). Sediment Charterstics, Organic Carbon and Calcium Carbonate in \nCore  Sediments of Krishna River Delta, East Coast of India. Malaysian Journal of Geosciences, 7(1): 08-17.\n\n\n\n3.4.2   Calcium Carbonate \n\n\n\nCalcium carbonate content ranges from 0.50 to 20.50% in Nizampatnam \ncore sediments with an average of 3.91% (Table 4). Sequence I (150-\n130m) is the bottom most part of studied core contains an average 2.14% \nof CaCO3. In sequence II (128.10-82m), an average CaCO3 content is 2.82%. \nIn sequence III (80.20- 76m), an average CaCO3 content is 1.50%. In \nsequence IV (74.10-50m), an average CaCO3 content is 5%. The sequence \nV (48.10-40m) contains an average 7.30% of CaCO3. In sequence VI (38.10-\n10m), an average CaCO3 content is 4.70% and in top most part of the core \nis sequence VII (8.40-0m), an average CaCO3 content is 4.40% (Figure 6). \nThe average CaCO3 in Nizampatnam core is 3.91% but sequences V, VI and \nVII have higher concentrations of CaCO3 7.30%, 4.70% and 4.40% \nrespectively. These are estuarine channel, mud flats and estuarine \nchannel. These sediments are composed medium sand to silt with shells. \n\n\n\n4. DISCUSSION\n\n\n\nTwo fluvial facies viz. (a) River channel sand and (b) flood plains were \nidentified based on their textural charterstics and occurrence of \nforaminiferal assemblages in the four cores of the study area. River \nchannel sand facies in Turumella, Ponnapalli and Nizampatnam cores do \nnot show any significant positive / negative correlation between mean \ngrain size and organic carbon content. It indicates that sediment grain size \nhas no relation in concentration of organic carbon content in these cores, \nbut these medium to fine grained sand have more organic carbon content \nit was not related to the grain size, it indicates that the principal \ncontribution of organic carbon is from terrestrial source and well \npreserved because of rapid sedimentation.  \n\n\n\nThe Inturu river channel deposit is mainly of medium to fine grain sand \nshows significant positive correlation between mean grain size and \norganic carbon content. It indicates that increase of mean grain size i.e. \nfiner the sediments organic carbon content increased. Flood plain \nsediments of Turumella and Nizampatnam have a positive correlation of \norganic carbon it indicates that the mean grain size of the sediment i.e. \nfiner the grain size organic carbon content increases. Increase of the \norganic carbon with decreasing of the grain size (increasing of clay \ncontent), it is indicated that co- sedimentation of particulate organic \nmatter with clay (Carter and Mitterer, 1978; Sheu and Presely, 1986). \n\n\n\nTwo estuarine facies viz. (a) mud flats (tidal flats) and (b) estuarine \nchannel sediments were identified in the studied four cores. Estuarine \nchannel sand of Turumella, Inturu and Nizampatnam cores and mud flat \nsediments of Turumella and Inturu shows positive correlation between \nmean grain size and organic carbon content. These sand are dominantly \nsilt, it indicates that increase of mean grain size i.e. finer the grain size \norganic carbon concentration increases. Positive correlation between clay \ncontent and organic carbon in estuarine facies of mud flats and estuarine \nchannel sediments indicate that the organic carbon co-precipitation along \nwith clay deposition. High concentration of organic carbon in a river \nchannel sand area due to (a) more land detritus from a drainage basin (b) \nmeandering stream banks (flood plain / levee plain) enriched with \nluxuriant fresh water plants /trees erosion with rapid sedimentation and \n(c) more biological activity (Sarma et al., 2011).\n\n\n\nThe positive correlation between CaCO3 and mean grain size sediments of \nflood plain and mud flat indicates that the finer the grain size, \nconcentration of CaCO3 increased. At only few depths in sequences of flood \nplain and mud flats have high concretions CaCO3 noted, in those sequences \nCaCO3 concretions of various sizes were noted. It indicates that the CaCO3 \nconcentration in these sediments is due to the presence of CaCO3 \nconcretions not because of co-precipitation of CaCO3 along with very fine \nsand and silt. A few horizons in mud flats also have high concentration \nCaCO3 because of shell material and CaCO3 concretions. Deposition of flood \nplain take place mostly by sheet floods during monsoon period.  \n\n\n\nDuring dry season major portion of the flood plain will be derived up in \nthe dry period. These concretions formed due to capillary rise in vadoze \nzone which indicates semi arid environmental conditions and pedogenic \nprocesses for long time exposure of mud flats and flood plains before \ndeposition of other sediments on it (Rao et al., 1993). In the deltaic \nenvironments, especially mud flats/tidal flats, preservation and \nconcentration of organic carbon and/or CaCO3 is mainly controlled by \nenvironmental conditions rather than the grain size of sediments. In mud \nflats /tidal flats calcretes and/or biological activity causes for high \nconcentration CaCO3.  \n\n\n\n5. CONCLUSIONS \n\n\n\nThe average CaCO3 in Turumella core is 3.68%, but in sequences II and VII \n\n\n\nhave higher concentration of CaCO3 i.e. 17.50% and 6.25% respectively. \nThese are flood plains composed of fine grained sand and silt with \ncalcretes of various sizes. In sequences V and VI the average CaCO3 \nconcentrations are 4% and 4.5% respectively. These are river channel and \nestuarine channel facies composed of medium sand and silt with shells. \n\n\n\nThe average CaCO3 in Inturu core is 2.46%, but sequences IV and III have \nhigher concentrations of CaCO3 i.e. 4.33% and 3.21% respectively. These \nare flood plain and mud flats. The CaCO3 contribute to flood plain is due to \ncalcretes and mud fltats are due to shelly material. \n\n\n\nThe average CaCO3 in Ponnapalli core is 6.71%, but in the sequence II, \nhigher concentration of CaCO3 is noted. This sequence is estuarine channel \nsediments with medium to fine sand with shells. \n\n\n\nThe average CaCO3 in Nizampatnam core is 3.91%, but sequences V, VI and \nVII have higher concentrations (9.04%) of CaCO3 i.e. 7.30%, 4.70% and \n4.40% respectively. These are estuarine channel, mud flats and estuarine \nchannel respectively. These sediments are composed of medium sand to \nsilt with shells. \n\n\n\nThe CaCO3 contribute to river channel, estuarine channel and mud flats are \nshelly material and flood plains are derived from calcretes. The occurrence \nof calcretes at various levels is the studied cores indicates pedogenic \nprocesses and/or long time gap in deposition of sediments.  \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nWe thank the authorities of Department of Geology,Andhra \nUniversity,Visakhapatnam, for providing lab facilities to carry out \nchemical analysis and DST Inspire, New Delhi for financial assistance to K. \nN. Murali Krishna (IF130683) in the form of SRF. \n\n\n\nREFERENCES \n\n\n\nArthur, M.A., Dean, W.E., Bottjer, D., and Scholle, P.A., 1984. Rhythmic \nbedding in Mesozoic-Cenozoic pelagic carbonate sequences; The \nprimary and diagenetic origion of milankovitch-like cycles, in \nBerger, A, imbrie, J., Hays, J.D, Kukla, G., and Saltzman, B., eds., \nMilankovitch and Climate, pt. J:Amsterdam, Reidel Publishing \ncompany, Pp. 191-222. \n\n\n\nBlanchi, T.S., Galler, J.J., Allison, M.A., 2007. Hydrodynamic sorting and \ntransport of terrestrially derived organic carbon in sediments of the \nMississippi and Atchafalaya Rivers. Estuar. Coast Shelf Sci., 73, Pp. \n211\u2013222. \n\n\n\nCarter, P.W., and Mitterer, R.M., 1978. Amino acid composition of organic \nmatter associated with carbonate and non carbonate sediments. \nGeochim. Cosmochim Acta., 42, Pp. 1231-1238. \n\n\n\nDemaison, G.J. and Moore, G.T., 1980. Anoxic environments and oil source \nbed genesis. Org. Geochem., 2, Pp. 9-31. \n\n\n\nEckard, R.S., Hernes, P.J., Bergamaschi, B.A., Stepanauskas, R., Kendall, C., \n2007. Landscape scale controls on the vascular plant component of \ndissolved organic carbon across a freshwater delta. Geochim. \nCosmochim. Acta, 71, Pp. 5968\u20135984. \n\n\n\nGaudette, H.E., Wilson, R.F., Lois, T., and David, W.P., 1974. An inexpensive \ntitration method for the determination of organic carbon in recent \nsediments. Jour. Sed. Petrol., 44, Pp. 249-253. \n\n\n\nGehman, H.M., 1962. Organic matter in limestones. Geochem. Cosmochim. \nActa, 26, Pp. 885-897. \n\n\n\nHunt, J.M., 1963. Geochemical data on organic matter in sediments. In V. \nBese (ed) Proceedings of 3rd international scientific conference on \nGeochemistry, microbiology and petroleum chemistry, October 8-\n13. Budapest; KULTURA 1, Pp. 394-412. \n\n\n\nJackson, M.L., 1967. Soil chemical analysis. New Delhi, Prentice-Hall of \nIndia. Pvt. Ltd., 498. \n\n\n\nKolla, V., David, G., Moore, and Curray, J.R., 1976. Recent bottom current \nactivity in the deep western Bay of Bengal. Mar. Geol., 21, Pp. 255-\n270. \n\n\n\nLongworth, B.E., Petsch, S.T., Raymond, P.A., Bauer, J.E., 2007. Linking \nlithology and land use to sources of dissolved and particulate \norganic matter in headwaters of a temperate passive-margin river \nsystem. Geochem. Cosmochim. Acta, 71, Pp. 4233\u20134250. \n\n\n\nMuller , G., 1967. Methods of sedimentary petrology . Schweizerbort, \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 08-17 \n\n\n\nCite The Article: K. N. Murali Krishna (2023). Sediment Charterstics, Organic Carbon and Calcium Carbonate in \nCore  Sediments of Krishna River Delta, East Coast of India. Malaysian Journal of Geosciences, 7(1): 08-17.\n\n\n\nVerlag, Stuttgart., Pp. 283. \n\n\n\nMuller, J.J., and Suess, E., 1979. Productivity, sedimentation rate, and \nsedimentary organic matter in the oceans \u2013 I. organic carbon \npreservation, Deep \u2013 sea Res., 26 (A), Pp. 1347-1362. \n\n\n\nPerry, C.T., Kench, P.S., Smithers, S.G., Riegl, B., Yamano, Hand O'Leary, M.J., \n2011. Implications of reef ecosystem change for the stability and \nmaintenance of coral reef islands. Global Change Biology, 17, Pp. \n3679-3696. \n\n\n\nPotasznik, A., and Szymczyk, S., 2015. Magnesium and calcium \nconcentrations in the surface water and bottom deposits of a river-\nlake system. Jour. Elem., 20 (3), Pp. 677-692. \n\n\n\nRao, P.N., Rao, A.T., Deva, V.D., and Purnachandra, R.K., 1993. Calcretes in \nthe red sediments of Visakhapatnam. Ind. Jour. Landscape systems \nand Ecological studies, 16, Pp. 92-100. \n\n\n\nSarma, V.V.S.S., Krishna, M.S., Prasad, V.R., Kumar, B.S.K., Naidu, S.A., Rao, \nG.D., Viswanadham, R., Sridevi, T., Kumar, P.P., and Reddy, N.P.C., \n2011. Distribution and source of particulate organic matter in the \nIndian monsoonl estuaries during monsoon, Jour. Geophy. Res. \nBiogeosci., 38, Pp. 1-15. \n\n\n\nSheu, D.D., and Presley, B.J., 1986. Variations of calcium carbonate organic \ncarbon and iron sulphides in anoxic sediment from the Orca basin, \nGulf of Mexico, Mar. Geol., 70, Pp. 103-118. \n\n\n\nSummerhayes, C.P., 1981. Organic facies of middle Cretaceous black shales \nin deep North Atlantic. Am. Assoc. Pet. Geol. Bull., 65, Pp. 2364-2380. \n\n\n\nTissot, B.P., and Welte, D.H., 1978. Petroleum formation and occurrence: \nSpringer - Verlag, Berlin, Pp. 538. \n\n\n\nTissot, B.P., Demaison, G., Masson, P., Deteil, J.R., and Combaz, A., 1980. \nPaleoenvironment and petroleum potential of the mid \u2013Cretaceous \nblack shales in the atlantic basin Bull. Am. Assoc. Pet. Geo., 64, Pp. \n2051 -2063. \n\n\n\nTrask, P.D., 1939. Organic content of recent Marine sediments. In: A \nsymposium on Recent Marine sediments, P.D. Trask (Editor), Tulsa, \nAmer. Assoc. Petrol. Geol., Pp. 428-453. \n\n\n\nTrask, P.D., Hammar H.E., and Wu, C.C., 1932. Does petroleum form in \nsediments at time of deposition. Bull. Am. Ass. Petrol. Geol., 14, Pp. \n51-63.\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 19-21 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2020.19.21 \n\n\n\nCite the Article: Mukrimah Abdullah, Mohd Parid Mamat, Abang Ahmad Abang Morni, Thanlany Kamri, Lim Hin Fui (2020). The Economic Impacts Of Rehabilitation Of \nSelabat Mudflats Nature Reserve, Kuching, Sarawak. Malaysian Journal of Geosciences, 4(1): 19-21. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2020.19.21 \n\n\n\nTHE ECONOMIC IMPACTS OF REHABILITATION OF SELABAT MUDFLATS NATURE \nRESERVE, KUCHING, SARAWAK  \n\n\n\nMukrimah Abdullaha*, Mohd Parid Mamata, Abang Ahmad Abang Mornib, Thanlany Kamric, Lim Hin Fuia \n\n\n\na Forest Research Institute Malaysia (FRIM), 52109 Kepong, Selangor. \nb Forest Department Sarawak, 93660 Kuching, Sarawak.  \nc Universiti Teknologi MARA Sarawak, 94300 Samarahan, Sarawak.  \n\n\n\n*Corresponding author email: mukrimah@frim.gov.my \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 05 January 2020 \nAccepted 10 February 2020 \nAvailable online 28 February 2020\n\n\n\nCoastal erosion is a concern to coastline management, and it affects the socio-economic well-being of local \ncommunities. The rehabilitation of Selabat Mudflats Nature Reserve (SMNR) in Sarawak is one of the most \nimpressive achievements of the \u201cMangrove and Suitable Tree Species Planting at the National Coastline \nProgram\u201d implemented by the Ministry of Natural Resources and Environment (NRE). The rehabilitation \nefforts began in 2009, where 20 hectares of Rhizophora mucronata were planted and followed by an \nadditional 1.7 hectares of Casuarina equisetifolia. Previously, this area was a sandy coastline where coastal \nerosion occurred every year causing local loss of land and properties. However, through conservation and \nrehabilitation efforts, this area was restored and is now able to generate more income for local communities \nas well as playing a protective role against coastal erosion caused by coastal waves and strong winds. In 2016, \nthis study was conducted to assess the economic impacts of SMNR rehabilitation. The findings from a sample \nof 42 households showed that the income generated for local communities around SMNR amounted to \nRM250, 320 per month or RM3, 003,840 per year. A non-user survey of 401 samples was also conducted, \nusing Contingent Valuation Method to estimate the economic value of SMNR rehabilitation. The result showed \nthe economic value of rehabilitation of SMNR was RM39 million for the year 2016 or RM195, 980 per hectare. \nThese findings showed that the function of rehabilitation and conservation of mangroves forests against \ncoastal erosion brings positive economic impacts. Rehabilitation and conservation of mangrove areas in \nMalaysia should be given more attention as global warming and rising sea level are going to have negative \nimpacts on coastal settlements. Wise decision-making in the utilisation and allocation of limited resources is \nimportant particularly in the context of conflict between conservation and other development that led to its \ndestruction.  \n\n\n\nKEYWORDS \n\n\n\nEconomic value, rehabilitation, coastal erosion.\n\n\n\n1. INTRODUCTION \n\n\n\nMalaysia has a coastline of 4800 kilometres and has a variety of different \ncharacters and ecosystems (Sharifah Mastura S.A, 1992). The National \nCoastal Erosion Study in 1985 stated that 30% of the country's coastline \nwas prone to coastal erosion. Coastal erosion is not only impacting on \nphysical aspects, but also on the economic and societal aspects of the local \ncommunity. Conservation activities and programs need to be undertaken \nto address this coastal erosion issue. Pantai Pasir Putih in Kuching \nSarawak is one of the conservation projects that have a positive impact on \nthe local community. The area was originally a sandy coastline eroded by \nwaves and strong winds. Locals in Kampung Pasir Putih lost land, property \nand most of them moved out to nearby village, Kg Selabat. In 2009, under \nthe Mangrove Tree Planting Program and Suitable Species in the National \nCoastline by the NRE this area is rehabilited. The area was later gazetted \nas Selabat Mudflats Nature Reserve (SMNR) in 2015, with a total area of \n199 hectares. Therefore, a study was conducted to assess the economic \nimpact of rehabilitation of mangrove at SMNR through the application of \nvaluation techniques based on the commodity market and non-market \n\n\n\nprice. This study was also in line with studied by Spurgeon (1998) that \nshowed how valuation can be used to support ecosystem rehabilitation \nand protection in coastal and marine habitats. \n\n\n\n2. MATERIALS AND METHODS \n\n\n\nDifferent approaches were used to obtain different types of research data. \nThe approaches are in form of interview, discussion, and survey. There are \ntwo types of data collected through this study, namely primary and \nsecondary data. Primary data involves Rapid Rural Appraisal (RRA) and \nsurveys on households / respondents. Secondary data involves collecting \ninformation from printed materials such as annual reports, books, \njournals and other related materials.  \n\n\n\n2.1 Socio-economic impacts \n\n\n\nFor this study, the approaches applied were Rapid Rural Appraisal (RRA) \nand survey. According to  Liswanti et.al (2012), RRA is a tool that enables \na quick assessment of the existing environment and the possible impacts \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 19-21 \n\n\n\n\n\n\n\n \nCite the Article: Mukrimah Abdullah, Mohd Parid Mamat, Abang Ahmad Abang Morni, Thanlany Kamri, Lim Hin Fui (2020). The Economic Impacts Of Rehabilitation Of \n\n\n\nSelabat Mudflats Nature Reserve, Kuching, Sarawak. Malaysian Journal of Geosciences, 4(1): 19-21. \n \n\n\n\n\n\n\n\nof the forest resource utilization and the other environmental services to \nthe local socio-economics livelihood. For survey, it involved households \nusing structured questionnaire (Figure 1). Questionnaire was constructed \ninto few sections covering demographic characteristics of the households, \nhousehold income sources, and their perception towards the SMNR \nconservation. A total of 42 households (71% from total households) from \nKampung Pasir Putih and Kampung Selabat were interviewed during the \nsurvey, conducted in October 2016. During the household interview, the \nrespondents were briefed on the objectives and purpose of the survey.  \n\n\n\n\n\n\n\nFigure 1: The structure/organization of questionnaire \n\n\n\n2.2 Economic benefits \n\n\n\nIn Malaysia, very few studies have been conducted to quantify the real \neconomic value and benefits of environmental goods and services. This \ncould possibly be due to the difficulty in measuring these values as it is not \ntraded in the marketplace (Mohd Parid et al. 2013). The Contingent \nValuation Method (CVM) is the most commonly used method, in which \nrespondents are directly asked on how much they are willing to pay for \nconserving the environmental goods and services (Mukrimah et. al. 2015). \nTherefore, for the purpose of this study CVM was used to estimate the \neconomic benefits of conserving SMNR. The approach of CVM was the \ndichotomous choice \u2013 double bounded format. The format gives the \nrespondents an opportunity to choose the amount of WTP. There were 5 \ndifferent bids given to different respondents randomly. The five bids were \nselected for use: RM2, RM5, RM10, RM15, and RM30. The exploration of \nwhether a person was willing to pay for conservation of SMNR was done \nusing Logistic and Ordinary Least Squares (OLS) models. These models \nwere chosen because of its ability to deal with a dichotomous dependent \nvariable and a well-established theoretical background. The respondents \nfor this study were randomly sampled by Department of Statistics \nMalaysia. A total 401 respondents were interviewed in 2016. \n\n\n\n3. RESULTS AND DISCUSSION \n\n\n\n3.1 Socio-economic impacts \n\n\n\n3.1.1 Impact towards households\u2019 income \n\n\n\nA survey was conducted at Kg Pasir Putih and Kg Selabat that used SMNR \ndirectly. Local incomes generated from SMNR mangrove areas show its \nability to generate income either in form of cash or income in-kind. Cash \nincome refers to income gain from the sales or business related to \nmangroves products, while income in-kind refers to mangrove resources \nconsumed as food sources by households. In 2016, the average monthly \nhousehold income was RM3, 404. The income level was higher than the \naverage household income for Rural Malaysia (RM3, 080) and lower than \nMalaysia (RM6, 141). The result also showed 17% of the total 225 \nhousehold members earned income from SMNR mangrove area. Of which, \nthe total income generated from the SMNR mangrove area was RM560 \n(16%) per month in 2016, where RM472 was cash income from the \nmangroves and RM88 was income in-kind (Figure 2). This figure was an \nincrease of 6% from 2009 to 2016.  \n\n\n\n\n\n\n\nFigure 2: Average monthly household income in Kg Pasir Putih and Kg \n\n\n\nSelabat \n\n\n\n3.1.2 Poverty rate \n\n\n\nThe extent of poverty among these local villagers could be seen from the \nincidence of poverty among the households in the village. Poverty in \nMalaysia is measured on the basis of a minimum expenditure level or the \npoverty line income (PLI) to separate the poor from non-poor\" \n(Government of Malaysia 1986a). In 2014, the per capita PLI for rural \nSarawak was RM240. Taking into consideration the rise in the consumers\u2019 \n\n\n\nprice index from 111.8 in 2014 to 114.8 in 2016, the per capita PLI was \ncalculated to be RM240 in 2016 and this was used to measure the \nincidence of poverty in the village studied. For both of these villages, the \npoverty rate was 4.8% equivalent to two households below the poverty \nline. The results of the analysis showed that if no income was derived from \nSMNR mangrove resources, the poverty rate increased to 9.5% in 2016.  \n\n\n\n3.1.3 Fishery value \n\n\n\nAccording to Donna (1999), mangrove ecosystems provide physical \nfactors that are important for the reproduction of many fish and \ninvertebrate species, where it serves as habitat, breeding area. In the \nSMNR, fisheries are one of local community\u2019s sources of income (direct use \nvalue).  The marketed value is the amount generated per month by each \nhousehold from the SMNR. Based on the survey results, estimated the \naverage income from SMNR mangrove areas generated by each household \nwere RM560 per month. By taking into account 447 total number of \ncoastal fishermen adjacent to SMNR, the direct use (fishery) of SMNR was \nestimated to be RM250, 320 per month or RM3,003,840 per year for 2016. \n\n\n\n3.2 Economic benefits \n\n\n\n3.2.1 Level of willingness-to-pay (WTP) \n\n\n\nAnalysis shows that 77% of respondents were willing to contribute \ntowards conservation of mangrove forests especially in SMNR through a \nconservation fund, while another 23% were not interested in contributing \n(protest bidder) for SMNR conservation. This latter category they felt that \nconservation activities should be funded by the government and claimed \nthey could not afford to pay any amount. Table 1 shows the summary of \nrespondents\u2019 willingness to pay for conserving SMNR mangrove. \n \n\n\n\nTable 1: Level of Willingness to pay \n\n\n\nWTP Level Percentage \n\n\n\nRM 1 - RM 5 16.6 \n\n\n\nRM 6 - RM 10 28.0 \n\n\n\nRM 11-RM 15 4.2 \n\n\n\nRM 16-RM 20 5.9 \n\n\n\nRM 21-RM 25 0.7 \n\n\n\nRM 26-RM 30 13.7 \n\n\n\n>RM 30 30.9 \n\n\n\nTotal 100 \n\n\n\n \nFrequency analysis shows the level of willingness to pay (WTP) on the \nconservation of SMNR mangrove area ranging from RM1.00 to RM200 \nannually. The result also shows that the average WTP was RM29.93 per \nyear. The findings show that the WTP rate of more than RM30 has the \nhighest frequency of 30.9%, followed by the WTP rate of RM6-RM10 by \n28%. Meanwhile, the WTP RM1-RM5 has a frequency of 16.6%. However, \nthe minimum WTP level is the WTP level of RM21-RM25, 0.7% only.  \n\n\n\n3.2.2 The CVM analysis \n\n\n\nThere are two approaches involved in estimating mean and median WTP, \nnamely the Logistic and the Linear (OLS) analysis (Table 2). The CVM \nanalysis by using Logistic model shows that the Bid price (InitialBid) is \nnegative and significant at the 1% significance level, which was as \nexpected and in line with the \"supply demand curve\" theory. The \ncoefficient for income class 3 (INC3) is positive and significant at the 10% \nlevel. From this analysis it implies that the respondents who are in the \nincome class 3 with income of RM3, 501-RM5, 000 influence the \nrespondent's response to the bid.  The results also show the variables of \nrespondents that had experience visiting SMNR (EXPvisit) and the price \nbid presented for the conservation of SMNR is positively correlated, \nindicating that respondents who visited SMNR are more likely to receive \nthe price bid and it is significant at the level of significance 1%. The R2 \nvalue is 0.36, 36% of the variance in the dependent variable is explained \nby the independent variable. \n \n \n\n\n\nCash\n\n\n\nIn-kind\n\n\n\n0 1000 2000 3000 4000\n\n\n\nT\ny\np\n\n\n\ne\n o\n\n\n\nf \nin\n\n\n\nc\no\n\n\n\nm\ne\n\n\n\nIncome (RM)\n\n\n\nTotal income\n\n\n\nIncome from mangrove\n\n\n\nSection A: Demographic profile of the household\u2019s \nSection B: Perception toward the SMNR conservation \n\n\n\nSection C: Sources of household\u2019s income \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 19-21 \n\n\n\n\n\n\n\n \nCite the Article: Mukrimah Abdullah, Mohd Parid Mamat, Abang Ahmad Abang Morni, Thanlany Kamri, Lim Hin Fui (2020). The Economic Impacts Of Rehabilitation Of \n\n\n\nSelabat Mudflats Nature Reserve, Kuching, Sarawak. Malaysian Journal of Geosciences, 4(1): 19-21. \n \n\n\n\n\n\n\n\nTable 2: CVM analysis result \n\n\n\nVariables \nCoefficient \n\n\n\nLogistic Model OLS Model \n\n\n\nInitialBID \n-0.053 0.977 \n\n\n\n(0.012)*** (0.076)*** \n\n\n\nINC3 \n1.780 4.399 \n\n\n\n(1.049)* (3. 494) \n\n\n\nINC4 \n-0.564 -4.494 \n\n\n\n(0. 547) (4. 328) \n\n\n\nEXPvisit \n-0.519 -4.619 \n\n\n\n(0. 444) (3. 064) \n\n\n\nConstant \n3.780 12.539 \n\n\n\n(0.484)*** (2.285)*** \n\n\n\n - 2Log likelihood 153.460  \n\n\n\nNumber of respondents (n) 307 307 \n\n\n\nCox and Snell R2 0.11  \n\n\n\nR2  0.36 \n\n\n\nF Value  42.16 \n\n\n\nNote: ***, **, * = Significance at 1%, 5%, 10% level. \n\n\n\n3.2.3 Economic benefits \n\n\n\nIn 2016, the estimated value of conservation benefits of SMNR mangrove \narea calculated based on the individual willingness to pay (WTP) \nmultiplied by the number of households in Kuching District. The annual \nconservation value or interest for SMNR is based on the calculated WTP \nmean from logistic and OLS models. There is a difference between mean \nWTP for different models. In the logistics model, WTP mean values are \nhigher than those given by the OLS model. In 2016, the economic benefits \nof SMNR conservation based on the willingness to pay (WTP) for the \nlogistics model is about RM39 million, while for OLS model the benefit is \nabout RM12 million. If there is a proposal to charge (i.e. in the form of tax) \nfor the SMNR conservation fund, the maximum amount found in this study \nis RM84.68 / year, this value may be used by the authorities to determine \nappropriate conservation fees. \n \n\n\n\nTable 3: Economic benefits of SMNR conservation \n\n\n\nNumber of \n\n\n\nhouseholds \n\n\n\nLogistic Model OLS Model \n\n\n\nWTP=RM84.68 WTP=26.11 \n\n\n\n460, 935 RM39, 031, 976 RM12,035,013 \n\n\n\n4. CONCLUSION \n\n\n\nThe high amount of economic benefits shows that the rehabilitation and \nconservation of mangroves to mitigate coastal erosion bring positive \neffects. The number of households generated from SMNR increase after \nrehabilitation. Their income from SMNR also rose. In general, the non-\nusers public is willing to SMNR fund. The conservation of mangroves in \nMalaysia should thus be given more attention, Economic valuation of \nenvironmental goods and services is very important  in decision making \nprocess will affects the use and allocation of limited resources, in \nparticular in the context of conflict between conservation and \ndevelopment. \n\n\n\nACKNOWLEDGMENT \n\n\n\nThis project was funded by JTRD, \u201cProgram Penanaman Pokok Bakau dan \nSpesies bersesuai di Pesisir Pantai Negara\u201d under Ministry of Natural \nResources and Environment (NRE). Special thanks go to Forest Research \nInstitute Malaysia (FRIM), Forest Department Sarawak (JHS) and UiTM \nSamarahan, Kuching Sarawak. \n\n\n\nREFERENCES \n\n\n\nDonna, J.N., 1999. Trade-offs of Mangrove Area Development in the \n\n\n\nPhilippines. Ecological Economics, 28, (2), 279-298.  \n\n\n\nEconomic Planning Unit Malatsua. 1985. National Coastal Erosion Study, \n\n\n\nFinal Report, Phase 1, Prepared by Stanley Consultant Inc and Others. \n\n\n\nLiswanti, N., Shantiko, B., Fripp, E., Mwangi, E., Laumonier, Y., 2012. \n\n\n\nPractical Guide for Socio-economic livelihood, land tenure and rights \n\n\n\nsurveys for Use in Collaborative Ecosystem-based Land Use Planing. \n\n\n\nCIFOR, Bogor, Indonesia. \n\n\n\nMohd Parid, M., Lim, H.F., Huda Farhana, M.M., Mukrimah, A., Tariq \n\n\n\nMubarak, H., 2013. Assessing the Conservation Value of Mangrove \n\n\n\nForest Ecosystem. Proceeding of Conference on Forestry and Forest \n\n\n\nProducts Research (CFFPR). \n\n\n\nMukrimah, A., Mohd Parid, M., Lim, H.F., Tariq Mubarak, H., 2015.  \n\n\n\nEconomic Analysis of Mangrove Forest: A Case of Delta Kelantan \n\n\n\nMangrove Forest (DKMF). The Malaysian Forester 79 (1-2), 203-211. \n\n\n\nSharifah Mastura S.A., 1992. The Coastal Zone In Malaysia. Processes, \n\n\n\nIssues and Management Plan. Bsckground Paper, Malaysian National \n\n\n\nConservation Strategy, Economic Planning Unit, Kuala Lumpur. \n\n\n\nSpurgeon, J.P.G., 1998. The socio-economic costs and benefits of coastal \nhabitat rehabilitation and creation. Marine Pollution Bullletin, 37 (8\u2013\n12), 373\u2013382. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 44-51 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2023.44.51 \n\n\n\n \nCite The Article: Olubusayo Akinyele Olatunji (2023). Palynofacies and Sedimentology  \n\n\n\nof Hb-001 Well. Malaysian Journal of Geosciences, 7(2): 44-51. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nREVIEW ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2023.44.51 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPALYNOFACIES AND SEDIMENTOLOGY OF HB-001 WELL \n\n\n\nOlubusayo Akinyele Olatunji* \n\n\n\nDepartment of Earth Sciences, Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria. \n*Corresponding Author Email: olubusayo.olatunji@aaua.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 11 January 2022 \nRevised 14 February 2022 \nAccepted 27 March 2023 \nAvailable online 31 March 2023 \n\n\n\n\n\n\n\nThe palynofacies and sedimentological analyses of the sedimentary succession of the HB-001 well were \ncarried out to establish biozones and the palaeodepositional environment. Ninety-six samples of ditch cutting \nwithin intervals of depth of 1250 -4011 m were analyzed. Most of the previous researchers established \nbiozones with alpha-numeric method. The use of palynomorphs and palynofacies analyses to delineate \npaleoenvironment of deposition has been applied to a limited extent in the Niger Delta Basin in Nigeria. The \nanalysis produced fairly diverse and moderately abundant pollen and spores with high recoveries of \npalynomaceral 1 and 2 and minimal retrievals of palynomaceral 3 and 4. The textural, lithologic, as well as \nwireline log data point out that the whole studied interval in the HB-001 well fit in to the Agbada Formation. \nLate Miocene age was assigned on the bases of the analysis of stratigraphic age range of indicative \npalynological markers for example Zonocostites ramonae, Verrucatosporites sp., Laevigatosporites sp., \nMonoporites annulatus, Pachydermites diederixi and Psila,tricolporites crassus. Two assemblage zones were \nestablished in the well with the use of the International Stratigraphic Guide for the biozones establishment. \nThe two recognized palynostratigraphic zones are Cyperaceaepollis sp. - Nympheaepollis clarus, and \nStereisporites sp Zones. Lower delta plain, pro-delta and delta front depositional environments have been \ndeciphered for well with the use of the palynofacies association, palyno-ecological groupings, and \nsedimentological features. Bodies of sand that signify sub-environments inside these settings are deposited \nin sequences. Every sequence begins with a transgressive stage, after that considerable regression. The \npalyno-ecological groupings of the retrieved palynomorph taxa revealed that the well intervals studied were \ndeposited under alternating wet and dry paleoclimatic settings. The zones of dry climate presented high \naccount of montane and savannah taxa and low incidence of rainforest, mangrove and freshwater taxa. The \nzones of wet climate signified increased account of rainforest, freshwater and mangrove taxa and lower \nincidences of montane and savannah taxa. \n\n\n\nKEYWORDS \n\n\n\nPalynofacies; Biozones; Formation; Palaeodepositional; Sedimentological  \n\n\n\n\n\n\n\n1.   INTRODUCTION \n\n\n\nPrecise biozonation and paleoenvironmental analyses within a field or \nbasin have posed a great challenge to hydrocarbon exploration. Therefore, \ndetailed biozonation and paleoenvironmental analyses are necessary for \nsuccessful exploration. Alpha-numeric biozonation has been utilized by \nsome Niger Delta Basin workers, and several other researchers have also \nestablished their biozones using the same technique. Thus, a uniform \nbiozonation scheme in accordance with the global stratigraphic guidelines \nfor the creation of biozones is necessary. A group researchers gave details \nof the use of palynofacies in examining the depositional paleoenvironment \n(Tyson, 1995; Durugbo and Uzodimma, 2013; Chukwuma-Orji et al., \n2017). There are few published publications on the integration of \npalynofacies, pollen of spores biostratigraphy and biozonation, \nsedimentology of the Paleogene-Neogene Niger Delta. This research was \nrequired due to the necessity for an integrated strategy utilizing \npalynofacies, sedimentology, and wireline logs in the exploration of \npetroleum for a more positive conclusion. \n\n\n\nSome researchers were the first to contribute to our understanding of \nmicroflora in Nigeria (Van Hoeken-Klinkenberg, 1964; 1966; Clarke, \n1966). Peregrinipollis nigericus was discovered in the Upper Tertiary Niger \nDelta (Clarke, 1966). In addition, Clarke and Frederiksen discovered and \n\n\n\ndescribed eight new species of pollen in the sediments of Paleogene-\nNeogene strata in Nigeria, assignable to three new genera: Marginipollis, \nAreolipollis, and Nummulipollis (Clarke and Frederiksen, 1968). They came \nto the conclusion that the present families Acanthacea and Lecythidaceae \nare connected to these forms. A group researchers produced the most \nthorough addition to our understanding of the palynology of the Niger \nDelta (Germeraad et al., 1968). The palynomorph assemblages of the \nTertiary deposits from three tropical regions\u2014parts of South America, \nAsia, and Africa (Nigeria)\u2014were the foundation for their study. They \nidentified three primary zones: Pan-tropical, Trans-Atlantic, and \nIntracontinental based on chosen zonal marker species. \nPalynostratigraphic and paleoenvironmental studies of the eastern Niger \nDelta basin were conducted (Ajaegwu et al., 2012). The zonation and \ndating of the studied sections were made possible by the diagnostic \npalynomorphs that were found.  \n\n\n\nThe examined section's age of Late Miocene to Early Pliocene was \ndetermined using the First Appearance Datum (FAD) of Nymphaeapollis \nclarus and an increase in Monoporites annulatus. The interpretation of the \npaleoenvironment of the strata that were penetrated by the well, \ndemonstrated that the overall environment ranged from coastal to \nmarginal marine. According to Olajide analysis of the strata in the offshore \nNiger Delta that were reached by the CHEV-2 well, the sediments were \n\n\n\n\nmailto:olubusayo.olatunji@aaua.edu.ng\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 44-51 \n\n\n\n\n\n\n\n \nCite The Article: Olubusayo Akinyele Olatunji (2023). Palynofacies and Sedimentology  \n\n\n\nof Hb-001 Well. Malaysian Journal of Geosciences, 7(2): 44-51. \n \n\n\n\ndeposited at the time of the Miocene age (Olajide, 2013). The stratigraphic \nranges of Psilatricolporites crassus, Relitricolporites irregularis, \nZonocostites ramonae, Echitricolporis spinosus, Monoporites annulatus, \nFoveotricolporites crassiexius, Multiareolites formosus, Psilatricolporites \noperculatus, Podocarpus milanjianus and a number of other marker species \nwere utilized to distinguish five informal palyonological zones in the \nresearch area. Oloto identified two pollen and spore zones and four \ndinocysts correspondingly in the Niger Delta Igbomotoru-1 well \npalynological studies (Oloto, 2014). The zonation of pollen and spore is \nmade up of Magnastriatites howardi and Verrucatosporites usmensis zones. \nThe zones are subdivided into the Upper Miocene, Lower Pliocene, and \nUpper Pliocene to Pleistocene ages. Combined maceral, palynologic, and \nsedimentological data reveals that deposition took place in a marine \nenvironment that got gradually shallower up section, near to the shore. \n\n\n\n2.   THE LOCATION OF STUDY WELL  \n\n\n\nThe Basin of the Niger Delta is situated between latitudes 4\u00b0 and 6\u00b0N and \nlongitudes 3\u00b0 and 9\u00b0E in Southern Nigeria. HB-001 well lies within \nlatitudes 4\u00ba 08' 48.6\" N and longitudes 5\u00ba 58' 40.5\" E in the shallow \noffshore of the Western Niger Delta (Figure 1). \n\n\n\n2.1   Geology of Niger Delta Basin \n\n\n\nThe Niger Delta is Nigeria's most significant sedimentary basin from the \nperspective of sediment bulk and thickness. Likewise, the region is crucial \n\n\n\nfrom an economic perspective as well because of its oil deposits, which \naccount for a sizable portion of the nation's foreign exchange earnings. \nThere is general agreement that the current Niger Delta was built on an \noceanic crust from pre-continental drift, which suggests that NE Brazil had \na significant impact on the current Niger Delta (Stoneley, 1966). Other \ngeological and geographical observations include the existence of a series \nof linear, subdued, and alternately negative and positive anomalies \nunderneath the Niger Delta, which interpreted as seafloor spreading \nlineation (Burke et al., 1971; Mascle, 1976). \n\n\n\nThree major sequences of sedimentation have been recognized in the \nNiger Delta in addition to other sections of the southern Nigerian \nSedimentary Basin (Murat, 1972; Short and Stauble, 1967). These are the: \n\n\n\ni. Lower Cretaceous to Santonian Cycle (oldest); \n\n\n\nii. Campanian to Paleocene Cycle; and \n\n\n\niii. Paleocene/ Lower Eocene to Date Cycle (youngest) \n\n\n\nThe majority of the delta's expansion can be attributed to the third \nsedimentary period, which started in the Paleocene/Early Eocene. The \nyoungest (Tertiary) sedimentary cycle's thick sequence of rocks encloses \nthe region where the Niger Delta oil province and its lucrative oilfields are \nlocated. The Niger Delta exhibits the three primary depositional \nenvironments typical of deltaic settings (marine, paralic, and continental) \n(Murat, 1972; Evamy et al., 1978).  \n\n\n\n\n\n\n\nFigure 1: Location Map of the study area in the superficial offshore depobelt of the Niger Delta (Samuel, 2009; Oluwajana, 2019) \n\n\n\n3.   MATERIALS AND METHODS \n\n\n\nNinety-six (96) samples of ditch cutting and wireline logs within intervals \nof depth of 1250 - 4011 m provided by Shell Production and Development \nCompany, Nigeria were employed for the analyses. The laboratory \nanalyses and sample preparation for the study were performed at Crystal \nAge Limited, Lagos, Nigeria. The descriptions of lithology were done \nmostly by examining the signatures of the gamma-ray log, the ditch \ncuttings samples physical examination, along with microscopic analysis of \nthe washed samples. Low and high values of gamma-ray logs indicate \nsandstone and shale lithologies, respectively (Olayiwola and Bamford, \n2016). The upward coarsening and fining signatures of the gamma-ray \noutlines described by were utilized in this research (Sneider et al., 1978; \nBeka and Oti, 1995). The standard acid palynological technique of \nadministration of sample for palynomorphs and palynofacies was used.  \n\n\n\nTen grammes (10g) of every sample were treated using thirty millilitres \n(30ml) of 10% hydrochloric (HCl) acid in a plastic beaker and kept in a \nfume cupboard until the next day. Subsequent to decantation of the acid, \n\n\n\nthe samples were cleansed with distilled water and the carbonates were \nsieved out. This was trailed by adding thirty millilitres (30 ml) of 40% \nhydrofluoric (HF) acid so as to remove the inorganic silicate from the \nsamples. The samples were washed with distilled water thrice and then \nsieved by means of 5 and 10\u03bcm mesh sieves. Under a light-transmitted \nOlympus CX41 microscope at magnifications of x40 and x25 and using \nrelevant literature for description based on shape, size, structure, \nsculpture, and aperture, the prepared slides for palynofacies and \npalynomorphs were examined, identified, and counted (Erdtman, 1952; \nGonzalez-Guzman, 1967; Germeraad et al., 1968; Knaap, 1971; Legoux, \n1978; Adegoke et al., 1978; Jan du Chene et al.,1978; Salard-Cheboldaeff, \n1975, 1976; 1978; Salami, 1983; Agip, 1987). \n\n\n\n4.   RESULTS AND DISCUSSION  \n\n\n\n4.1   Lithologic Description and Sedimentological Analyses \n\n\n\nSand and shale units alternate within the lithology, indicating rapid \ncoastline progradation. In the bottom of the well, the grain size is \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 44-51 \n\n\n\n\n\n\n\n \nCite The Article: Olubusayo Akinyele Olatunji (2023). Palynofacies and Sedimentology  \n\n\n\nof Hb-001 Well. Malaysian Journal of Geosciences, 7(2): 44-51. \n \n\n\n\npredominantly fine to medium, with occasional coarse to granule-sized \ngrains. At the top of the well, the grain size is predominately fine to \nmedium, with occasional coarse to granule-sized grains. The majority of \nthe sands have sub-angular to sub-rounded edges, are infrequently \nrounded, and are often weakly to fairly sorted. Ferruginous debris, \ncarbonaceous detritus, glauconite pellets, shell pieces, and pyrites \npredominate among the accessory minerals, with sporadic occurrences of \nmica flakes (Figure 2). The combination of wire line log motifs, \nlithologic/textural attributes and the circulation of the accessory \n\n\n\nmaterials were used to make sedimentological deduction. As a results of \nthe deductions, the Agbada Formation was able to be assigned to the entire \nexamined section (1250\u20134011 m) of the HB\u2013001 well. These criteria \nallowed for the identification of two major lithofacies sequences within \nthe well section: The Marine Paralic and Paralic Lithofacies Sequences \n(Figure 2). The majority of the sands in the Paralic Lithofacies Sequences \nare quartzose, slightly feldsparhic, fine to medium in size, infrequently \nvery coarse to coarse in size, and granular. Sands are typically weakly to \nwell-sorted and are intercalated with relatively thick layers of shales.  \n\n\n\n\n\n\n\nFigure 2: Sedimentological and Lithologic chart of HB-001 well \n\n\n\nThe Marine Paralic Lithofacies sequences are primarily composed of \nshales with intercalations of relatively thin sand. Sands are primarily \nquartzose, somewhat feldsparthic, fine to medium-grained, sporadically \ncoarse to very coarse-grained, and moderately to well-sorted between \n2573 and 3559 meters. Below 3559 m, sands are quartzose, fine to \nmedium, intermittently coarse-grained as well as well sorted. \n\n\n\n4.2   Palynofacies \n\n\n\nThe diverse palynomorph taxa in addition to the varieties of \npalynomacerals recovered from the examined intervals are shown in \nfigure 3. Moderately varied and fairly abundant palynomorphs were \nrecognized. There is a high retrieval of palynomaceral 1 and 2 with \npalynomaceral 3 and 4 appearing at low occurrences.  \n\n\n\n4.2.1   Palynomaceral 1 (PM 1) \n\n\n\nThe retrieved palynomaceral 1 (Alganite) from this research looked to be \ndense, opaque, structureless, irregular in shape, and orange brown to dark \nbrown in color. It is diverse, of higher plant origin, and some of it is the \n\n\n\nresult of exudation activities, like the plant detritus gelification in \nsediments. Palynomaceral 1 comprises irregularly shaped resinous \ncortical materials, tiny, medium, and large sizes of flora debris, and \ncompounds that resemble humic gel. \n\n\n\n4.2.2   Palynomaceral 2 (PM 2) \n\n\n\nAs of this research, palynomaceral 2 (Exinites) is an irregularly shaped, \nbrown-orange structural substance. It contains algal debris, platy-like \nstructural plant materials, a trace amount of humic gels, and resinous \ncompounds. Being thinner than Palynomaceral 1, it is more buoyant than \nPalynomaceral 1. \n\n\n\n4.2.3   Palynomaceral 3 (PM 3) \n\n\n\nThe retrieved palynomaceral 3 (Vitrinite) from this investigation has a \ncolor ranging from white to brown, is rather thin, has an irregular shape, \nis translucent, and includes stomata dispersed throughout. Degraded \naqueous plant material and structured plant material, primarily of \ncuticular origin. It floats higher than Palynomaceral 2. \n\n\n\nWell Name : HB-1\nInterval : 1250m - 4012m SEDIMENTOLOGY CHART\n\n\n\nScale : 1:7000 AUTHOR: OLATUNJI, OLUBUSAYO AKINYELE HB-1\nChart date: 27 January 2020 DEPT. OF GEOLOGY, FEDERAL UNIVERSITY OF TECHNOLOGY, MINNA.\n\n\n\nCRYSTAL AGE LIMITED\n\n\n\nLAGOS\n\n\n\nD\nep\n\n\n\nth\n (\n\n\n\nm\n)\n\n\n\n    1250m\n\n\n\n    1300m\n\n\n\n    1350m\n\n\n\n    1400m\n\n\n\n    1450m\n\n\n\n    1500m\n\n\n\n    1550m\n\n\n\n    1600m\n\n\n\n    1650m\n\n\n\n    1700m\n\n\n\n    1750m\n\n\n\n    1800m\n\n\n\n    1850m\n\n\n\n    1900m\n\n\n\n    1950m\n\n\n\n    2000m\n\n\n\n    2050m\n\n\n\n    2100m\n\n\n\n    2150m\n\n\n\n    2200m\n\n\n\n    2250m\n\n\n\n    2300m\n\n\n\n    2350m\n\n\n\n    2400m\n\n\n\n    2450m\n\n\n\n    2500m\n\n\n\n    2550m\n\n\n\n    2600m\n\n\n\n    2650m\n\n\n\n    2700m\n\n\n\n    2750m\n\n\n\n    2800m\n\n\n\n    2850m\n\n\n\n    2900m\n\n\n\n    2950m\n\n\n\n    3000m\n\n\n\n    3050m\n\n\n\n    3100m\n\n\n\n    3150m\n\n\n\n    3200m\n\n\n\n    3250m\n\n\n\n    3300m\n\n\n\n    3350m\n\n\n\n    3400m\n\n\n\n    3450m\n\n\n\n    3500m\n\n\n\n    3550m\n\n\n\n    3600m\n\n\n\n    3650m\n\n\n\n    3700m\n\n\n\n    3750m\n\n\n\n    3800m\n\n\n\n    3850m\n\n\n\n    3900m\n\n\n\n    3950m\n\n\n\n    4000m\n\n\n\nL\nith\n\n\n\no\nst\n\n\n\nra\nti\n\n\n\ng\nra\n\n\n\np\nh\n\n\n\ny\n\n\n\n1250 \n\n\n\n4011 \n\n\n\nA\nG\n\n\n\nB\nA\n\n\n\nD\nA\n\n\n\nF\nor\n\n\n\nm\nat\n\n\n\nio\nn\n\n\n\nGamma Log\n(API)0 150\n\n\n\nL\nith\n\n\n\no\nlo\n\n\n\ng\ny\n\n\n\nNeutron Porosity\n(c/s)1.5 45\n\n\n\nC\nh\n\n\n\nro\nn\n\n\n\nos\ntr\n\n\n\nat\nig\n\n\n\nra\nph\n\n\n\ny\n\n\n\n1250 \n\n\n\n4011 \n\n\n\nLa\nte\n\n\n\n M\nio\n\n\n\nce\nne\n\n\n\nP\ner\n\n\n\nio\nd\n\n\n\n/E\np\n\n\n\no\nch\n\n\n\nPREDOMINANTLY\nQUARTZOSE,SLIGHTLY\nFELDSPARTHIC,FINE TO\nMEDIUM,OCC. COARSE TO VERY\nCOARSE-GRAINED AND\nGRANULAR,POORLY TO WELL\nSORTED,SUBANGULAR TO\nROUNDED SANDS WITH\nGREY,DARK GREY TO BROWNISH\nGREY,PLATY TO FLAGGY AND\nMOD.HARD TO HARD SHALES.\n\n\n\nDORMINANTLY\nQUARTZOSE,SLIGHTLY\nFELDSPARTHIC, FINE TO\nMEDIUM-GRAINED,OCC. COARSE\nTO VERY COARSE-GRAINED,MOD.\nTO MOD.WELL\nSORTED,SUBANGULAR TO\nSUBROUNDED SANDS WITH\nGREY,DARK GREY TO LIGHT\nGRAY,SOLTY,PLATY TO\nFLAGGY,MOD. HARD TO HARD\nSHALES.\n\n\n\nQUARTZOSE, FINE TO\nMEDIUM-GRAINED,\nOCCASIONALLY\nCOARSE-GRAINED, WELL\nSORTED, SUBANGULAR TO\nSUDROUNDED SANDS WITH\nGREY, DARK GREY TO BROWNISH\nGREY, PAPERY TO FLAGGY,\nMODERATELY HARD TO HARD\nSHALES.\n\n\n\nComments\n\n\n\nCOASTAL/SHORELINE DEPOSITS\nOF DISTRIBUTARY CHANNEL AND\nMARINE SHALES.\n\n\n\n---------------------------------------------\n\n\n\nCOASTAL/SHORELINE DEPOSITS\nOF SUBAQEOUS CHANNEL AND\nMARINE SHALES.\n\n\n\n---------------------------------------------\n\n\n\nCOASTAL/SHORELINE DEPOSITS\nOF BARRIER BAR/FOOT,\nSUBAQEOUS CHANNEL, BARRIER\nBAR AND MARINE SHALES.\n\n\n\n---------------------------------------------\n\n\n\nCOASTAL/SHORELINE DEPOSITS\nOF BARRIER BAR, SUBAQEOUS\nCHANNEL AND MARINE SHALES.\n\n\n\n---------------------------------------------\n\n\n\nCOASTAL/SHORELINE DEPOSITS\nOF OFFSHORE BAR, SUBAQEOUS\nCHANNELS AND MARINE SHALES.\n\n\n\n---------------------------------------------\n\n\n\nCOASTAL/SHORELINE / SHELF\nDEPOSITS OF OFFSHORE BAR,\nBARRIER BAR AND MARINE\nSHALES.\n\n\n\nPaleoenviroments\n\n\n\n*1\n\n\n\nFe\nrr\n\n\n\nug\nin\n\n\n\nou\ns \n\n\n\nM\nat\n\n\n\ner\nia\n\n\n\nls\nG\n\n\n\nla\nu\n\n\n\nco\nni\n\n\n\nte\n P\n\n\n\nel\nle\n\n\n\nts\nC\n\n\n\nar\nb\n\n\n\non\nac\n\n\n\neo\nus\n\n\n\n D\ne\n\n\n\ntri\ntu\n\n\n\ns\nS\n\n\n\nhe\nll \n\n\n\nfra\ngm\n\n\n\nen\nts\n\n\n\n.\nP\n\n\n\nyr\nite\n\n\n\ns \n\"\n\n\n\nM\nic\n\n\n\na \nFl\n\n\n\nak\nes\n\n\n\nMinerals\n\n\n\nL\nith\n\n\n\no\nfa\n\n\n\nci\nes\n\n\n\n S\neq\n\n\n\nu\nen\n\n\n\nce\nP\n\n\n\nA\nR\n\n\n\nA\nLI\n\n\n\nC\nM\n\n\n\nA\nR\n\n\n\nIN\nE\n\n\n\n P\nAR\n\n\n\nAL\nIC\n\n\n\nS\nch\n\n\n\nem\ne\n\n\n\nA\nft\n\n\n\ner\n H\n\n\n\nar\nd\n\n\n\nen\nbo\n\n\n\nl e\nt \n\n\n\nal\n. (\n\n\n\n19\n98\n\n\n\n)\n\n\n\n5.6 Ma SB\n\n\n\n5.6 Ma SB\n\n\n\n5.0 Ma MFS\n\n\n\n5.0 Ma MFS\n\n\n\n5.6 Ma SB\n\n\n\n6.0 Ma MFS\n\n\n\n6.7 Ma SB\n\n\n\n7.4 Ma MFS\n\n\n\n6.7 Ma SB\n\n\n\n7.4 Ma MFS\n\n\n\n8.5 Ma SB\n\n\n\nS\nY\n\n\n\nS\nTE\n\n\n\nM\nS\n\n\n\n T\nR\n\n\n\nA\nC\n\n\n\nT\nS\n\n\n\n5.6 Ma SB\n\n\n\n5.6 Ma SB\n\n\n\n5.0 Ma MFS\n\n\n\n5.0 Ma MFS\n\n\n\n5.6 Ma SB\n\n\n\n6.0 Ma MFS\n\n\n\n6.7 Ma SB\n\n\n\n7.4 Ma MFS\n\n\n\n6.7 Ma SB\n\n\n\n7.4 Ma MFS\n\n\n\n8.5 Ma SB\n\n\n\nD\nep\n\n\n\nth\n (\n\n\n\nm\n)\n\n\n\n    1250m\n\n\n\n    1300m\n\n\n\n    1350m\n\n\n\n    1400m\n\n\n\n    1450m\n\n\n\n    1500m\n\n\n\n    1550m\n\n\n\n    1600m\n\n\n\n    1650m\n\n\n\n    1700m\n\n\n\n    1750m\n\n\n\n    1800m\n\n\n\n    1850m\n\n\n\n    1900m\n\n\n\n    1950m\n\n\n\n    2000m\n\n\n\n    2050m\n\n\n\n    2100m\n\n\n\n    2150m\n\n\n\n    2200m\n\n\n\n    2250m\n\n\n\n    2300m\n\n\n\n    2350m\n\n\n\n    2400m\n\n\n\n    2450m\n\n\n\n    2500m\n\n\n\n    2550m\n\n\n\n    2600m\n\n\n\n    2650m\n\n\n\n    2700m\n\n\n\n    2750m\n\n\n\n    2800m\n\n\n\n    2850m\n\n\n\n    2900m\n\n\n\n    2950m\n\n\n\n    3000m\n\n\n\n    3050m\n\n\n\n    3100m\n\n\n\n    3150m\n\n\n\n    3200m\n\n\n\n    3250m\n\n\n\n    3300m\n\n\n\n    3350m\n\n\n\n    3400m\n\n\n\n    3450m\n\n\n\n    3500m\n\n\n\n    3550m\n\n\n\n    3600m\n\n\n\n    3650m\n\n\n\n    3700m\n\n\n\n    3750m\n\n\n\n    3800m\n\n\n\n    3850m\n\n\n\n    3900m\n\n\n\n    3950m\n\n\n\n    4000m\n\n\n\nBase Lithology\n\n\n\nshale/mudstone\n\n\n\nsandy mudstone\n\n\n\nargillaceous sandstone\n\n\n\nIGD Boundary Key\nPossible\n\n\n\nProbable\n\n\n\nConfident\n\n\n\nUnconformable\n\n\n\n? ?Unconformable\n\n\n\nf Fault\n\n\n\n?f ?Fault\n\n\n\nDefault Abundance Scheme\nPresent ( 1  )\n\n\n\nRare ( 2  )\n\n\n\nCommon ( 5  )\n\n\n\nAbundant ( 15  )\n\n\n\nSuper Abundant ( 50  )\n\n\n\n+ Present outside count \n\n\n\nText Keys\n*1 Semi-quantitative, (Default Abundance Scheme)\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 44-51 \n\n\n\n\n\n\n\n \nCite The Article: Olubusayo Akinyele Olatunji (2023). Palynofacies and Sedimentology  \n\n\n\nof Hb-001 Well. Malaysian Journal of Geosciences, 7(2): 44-51. \n \n\n\n\n4.2.4   Palynomaceral 4 (PM 4) \n\n\n\nThe palynomaceral 4 (Inertnite) found in this study is made up of opaque, \nblade-shaped components that range in color from black to dark brown \n\n\n\nand have no discernible structure. Compressed humic gels, charcoal, and \ngeothermally fused material make up this substance. Its substance is \nformed like a blade or a needle. \n\n\n\n\n\n\n\nFigure 3: Palynofacies and palynomorphs distribution chart of HB-001 well \n\n\n\nPalynomaeceral 4 has a blade-like or equidimensional form and is \nresistant to deterioration. As a result, they are frequently moved over \ngreat distances. In an environment with strong energy, PM 4 \npredominates. \n\n\n\n4.3   Palynostratigraphic Zonation and Biochronology of HB-001 Well \n\n\n\nThe Stereisporites sp. assemblage zone and the Cyperaceaepollis sp. \u2013 \nNympheaepollis clarus assemblage zone were recognized as two \nassemblage zones in the HB-001 well (1250 - 4011 m). The palynoflora \nassemblages of marker and associate marker species identified in the well \nwere used to achieve this. Based on the determined age range of marker \nand related marker species that defined the body of strata, the assemblage \nbiozones were created (Murphy and Salvador, 1999). The two (2) \nsubzones subdivision of the assemblage zones are the Nympheaepollis-\nEchitriletes pliocenicus subzone (1250 - 2896 m) and the Cyperaceaepollis-\nElaeis guineensis subzone (2896- 3833 m). \n\n\n\nCyperaceaepollis sp.- Nympheaepollis clarus Assemblage Zone \n\n\n\nDepth: 1250 -3833 m \n\n\n\nAge: Late Miocene \n\n\n\nEchitricolporites spinosus zone of P860 palynological subzone, and P850-\nP840 palynological subzone are comparable palynological subzones \n(Germeraad et al., 1968; Evamy et al., 1978). With a succession of shale \nand argillaceous sandstones that is roughly 2583 m thick, this assemblage \nbiozone is the youngest in age. The interval was marked by the palynoflora \nmarker species Cyperaceaepollis sp., Nympheaepollis clarus, Echitriletes \npliocenicus, and Elaeis guineensis. This assemblage zone has the \nstratigraphically relevant ranges of the important marker species \nCyperaceaepollis sp. and Nympheaepollis clarus. It is possible that the true \ntop of the zone is stratigraphically higher than the first sample, which was \nexamined at 1250 m, based on the presence of Cyperaceaepollis sp. (1387 \nm), which is close to the measured top of the well. The base occurrence of \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 44-51 \n\n\n\n\n\n\n\n \nCite The Article: Olubusayo Akinyele Olatunji (2023). Palynofacies and Sedimentology  \n\n\n\nof Hb-001 Well. Malaysian Journal of Geosciences, 7(2): 44-51. \n \n\n\n\nCyperaceaepollis sp., designated at 3833 m, establishes the lower limit. \n\n\n\nAbundant prevalences of Verrucatosporites sp., Monoporites annulatus, \nZonocostites ramonae, Laevigatosporites sp., Psilatricolporites crassus and \nPachydermites diederixi quantitatively typified the biozone. Two (2) \nsubzones are recognizable inside this assemblage zone and discussed \nbelow: \n\n\n\nNympheaepollis clarus - Echitriletes pliocenicus subzone \n\n\n\nDepth: 1250 - 2896 m \n\n\n\nAge: Late Miocene (Messinian-Tortonian) \n\n\n\nThe base of the subzone is indicated by the location of Nympheaepollis \nclarus, which has been determined to be at 2896 m. The Zonocostites \nramonae, Verrucatosporites sp., and Retitricolporites irregularis are \nabundant in the Nympheaepollis clarus-Echitriletes pliocenicus subzone, \nwhile Peregrinipollis nigericus and Echitricolporites spinosus are scarce to \nuncommon and Sapotaceae and Nympheaepollis clarus are both frequently \nfound. This subzone connects with the P860 subzone and the \nEchitricolporites spinosus palynological zone (Evamy et al., 1978; \nGermeraad et al., 1968). \n\n\n\nCyperaceaepollis sp. - Elaeis guineensis subzone \n\n\n\nDepth: 2896 - 3833 m \n\n\n\nAge: Late Miocene (Tortonian) \n\n\n\nThe base occurrence of Nympheaepollis clarus at 2896 m marks the top of \nthis subzone. The Cyperaceaepollis sp. base occurrence, designated at 3833 \nm, serves as a marker for the base. This subzone was further characterized \nby the presence of Elaeis guineensis, the sparse prevalence of \nMagnastriatites howardii and Echiperiporites estelae, and the reduced \nabundance of Verrucatosporites sp. and Acrostichum aureum. This subzone \nis related to the P850-P840 (undifferentiated) subzone of and the \nEchitricolporites spinosus palynological zone described (Evamy et al., \n1978; Germeraad et al., 1968).  \n\n\n\nStereisporites sp.  Assemblage Zone \n\n\n\nDepth: 3833- 4011 m \n\n\n\nAge: Late Miocene (Tortonian) \n\n\n\nThis zone has been linked to the P830 palynological subzones and the \nEchitricolporites spinosus zone (Evamy et al., 1978; Germeraad et al., \n1968). The presence of marker species like Stereisporites sp., an \nabundance of Corylus sp., and Pachydermites diederixi are characteristics \nof this assemblage zone. \n\n\n\n4.4   Palaeoenvironment of Deposition \n\n\n\nPaleoenvironment of deposition is used to infer the periodic changes in \nthe depositional environment over geologic time. Understanding different \n\n\n\ndepositional settings and the characteristics of their reservoirs, such as \nporosity, permeability, and architecture, requires the interpretation of \npaleodepositional environments. Quantitative variations of the palyno-\necological groups of the investigated palynomorphs; they include \nBotryococus braunii, Cyperaceaepollis sp., Pachydermites diederixi, and \nZonocostites ramonae (Table 1). \n\n\n\nFreshwater swamp taxa have the highest representation of all recovered \npalynomorphs, according to the examined well, which is followed by \nmangrove and savannah taxa (Figure 4) The well contains the least \namount of taxa from the montane, swamp, and rain forest. Throughout the \ncoastal plain, erosion and incision occur when the sea level drops. Both \nfreshwater wetlands and mangrove swamp taxa will have less of an \nimpact. Thus, it is anticipated that coastal swamp species will be scarce in \nthe early stages of sea level decrease and that well-drained fluvial habitats \nwill boost pollen levels (Morley, 1995 and Adojoh et al., 2015). Taxa from \nthe savannah and mountains spread widely. \n\n\n\nUpper delta plain forms as a result of sea level rise and is regulated by \nfreshwater and alluvial wetland (Adojoh et al., 2015). When the \nfreshwater and rainforest swamps get larger and the sea level declines, the \namount of mangrove pollen will increase (Figure 5) (Morley, 1995 and \nRull, 2002). The lack of dinocysts and poorly sorted palynomacerals 1 and \n2 are characteristics of coastal habitats, as are frequent to abundant fungal \nspore occurrences. \n\n\n\n\n\n\n\nFigure 4: Palyno-ecological groups (%) of palynomorphs from HB-001 \nwell \n\n\n\nSmall to medium organic matter that is well sorted, common to abundant \npalynomacerals 1 and 2, some needle- to lath-shaped palynomacerals 4, \nand the presence of foraminiferal linings and dinocysts are all indicators \nof the maritime environment (Oyede, 1992). Ferruginous materials, shell \npieces, and carbonaceous detritus are the index minerals that are \nfrequently found in shallow water habitats. Deeper water conditions can \nbe identified with indicator minerals including mica flakes, pyrites, and \ngluaconites pellets (Selley, 1978). For the examined intervals of the HB-\n001 well, lower delta plain to delta front and prodelta environment within \ncoastal deltaic to shallow marine environment of deposition were inferred \n(Figure 5 and Table 2) \n\n\n\n\n\n\n\nFigure 5: General Correlation between Palaeovegetation, Palaeoecology, Eustasy and Climate in the Tropical Setting (Adojoh et al., 2015 and Chukwuma-\nOrji et al., 2017) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 44-51 \n\n\n\n\n\n\n\n \nCite The Article: Olubusayo Akinyele Olatunji (2023). Palynofacies and Sedimentology  \n\n\n\nof Hb-001 Well. Malaysian Journal of Geosciences, 7(2): 44-51. \n \n\n\n\nTable 1: Palyno-ecological Groups of Palynomorphs from some selected depths in HB-001 Well \n\n\n\n7 \nMONTANE \n\n\n\nTAXA \nRAINFOREST SWAMP TAXA FRESHWATER SWAMP TAXA \n\n\n\nMANGROVE \nTAXA \n\n\n\nS\na\n\n\n\nm\np\n\n\n\nle\n d\n\n\n\ne\np\n\n\n\nth\n (\n\n\n\nm\ne\n\n\n\nte\nr)\n\n\n\n/T\na\n\n\n\nx\na\n\n\n\n\n\n\n\nP\no\n\n\n\nly\np\n\n\n\no\nd\n\n\n\nia\nce\n\n\n\no\np\n\n\n\no\nri\n\n\n\nte\ns \n\n\n\nsp\n/P\n\n\n\nte\nri\n\n\n\ns \nsp\n\n\n\n\n\n\n\nC\ny\n\n\n\np\ner\n\n\n\na\nce\n\n\n\na\np\n\n\n\no\nli\n\n\n\ns \nsp\n\n\n\n\n\n\n\nC\no\n\n\n\nry\niu\n\n\n\ns \nsp\n\n\n\n\n\n\n\nN\nu\n\n\n\nm\nu\n\n\n\nli\np\n\n\n\no\nll\n\n\n\nis\n n\n\n\n\neo\ng\n\n\n\nen\nio\n\n\n\nu\ns \n\n\n\nP\no\n\n\n\nd\no\n\n\n\nca\nrp\n\n\n\nu\ns \n\n\n\nm\nil\n\n\n\na\nn\n\n\n\nji\na\n\n\n\nn\nu\n\n\n\ns \n\n\n\nF\nu\n\n\n\nn\ng\n\n\n\na\nl \n\n\n\nsp\no\n\n\n\nre\n \n\n\n\nC\nh\n\n\n\nen\no\n\n\n\np\no\n\n\n\nd\nip\n\n\n\no\nll\n\n\n\nis\n s\n\n\n\np\n \n\n\n\nC\na\n\n\n\nn\nth\n\n\n\niu\nm\n\n\n\nid\nit\n\n\n\nes\n s\n\n\n\np\n \n\n\n\nM\no\n\n\n\nn\no\n\n\n\np\no\n\n\n\nri\nte\n\n\n\ns \na\n\n\n\nn\nn\n\n\n\nu\nla\n\n\n\ntu\ns \n\n\n\nE\nch\n\n\n\nip\ner\n\n\n\nip\no\n\n\n\nri\nte\n\n\n\ns \nes\n\n\n\nte\nla\n\n\n\ne \n\n\n\nP\nro\n\n\n\nte\na\n\n\n\nci\nd\n\n\n\nit\nes\n\n\n\n s\np\n\n\n\n\n\n\n\nS\nte\n\n\n\nir\nes\n\n\n\np\no\n\n\n\nri\nte\n\n\n\ns \nsp\n\n\n\n\n\n\n\nT\no\n\n\n\nta\nl \n\n\n\nS\na\n\n\n\nv\na\n\n\n\nn\nn\n\n\n\na\n t\n\n\n\na\nxa\n\n\n\n\n\n\n\nA\nln\n\n\n\nip\no\n\n\n\nll\nen\n\n\n\nit\nes\n\n\n\n v\ner\n\n\n\nu\ns \n\n\n\nP\no\n\n\n\nd\no\n\n\n\nca\nrp\n\n\n\nid\nit\n\n\n\nes\n s\n\n\n\np\n \n\n\n\nT\no\n\n\n\nta\nl \n\n\n\nM\no\n\n\n\nn\nta\n\n\n\nn\ne \n\n\n\nT\na\n\n\n\nxa\n \n\n\n\nR\net\n\n\n\nib\nre\n\n\n\nv\nit\n\n\n\nri\nco\n\n\n\nlp\no\n\n\n\nri\nte\n\n\n\ns \np\n\n\n\nro\ntr\n\n\n\nu\nd\n\n\n\nen\ns \n\n\n\nP\na\n\n\n\nch\ny\n\n\n\nd\ner\n\n\n\nm\nit\n\n\n\nes\n d\n\n\n\nie\nd\n\n\n\ner\nix\n\n\n\ni \n\n\n\nP\ner\n\n\n\neg\nri\n\n\n\nn\nip\n\n\n\no\nll\n\n\n\nis\n n\n\n\n\nig\ner\n\n\n\nic\nu\n\n\n\ns \n\n\n\nS\na\n\n\n\np\no\n\n\n\nta\nce\n\n\n\na\ne \n\n\n\nR\net\n\n\n\nit\nri\n\n\n\nco\nlp\n\n\n\no\nri\n\n\n\nte\ns \n\n\n\nir\nre\n\n\n\ng\nu\n\n\n\nla\nri\n\n\n\ns \n\n\n\nE\nla\n\n\n\nei\ns \n\n\n\ng\nu\n\n\n\nin\nee\n\n\n\nn\nsi\n\n\n\ns \n\n\n\nP\nra\n\n\n\ned\na\n\n\n\np\no\n\n\n\nll\nis\n\n\n\n f\nle\n\n\n\nxi\nb\n\n\n\nil\nis\n\n\n\n\n\n\n\nT\no\n\n\n\nta\nl \n\n\n\nR\na\n\n\n\nin\nfo\n\n\n\nre\nst\n\n\n\n t\na\n\n\n\nxa\n \n\n\n\nS\ntr\n\n\n\nia\ntr\n\n\n\nic\no\n\n\n\nlp\no\n\n\n\nri\nte\n\n\n\ns \nca\n\n\n\nta\ntu\n\n\n\nm\nb\n\n\n\nu\ns \n\n\n\nG\nem\n\n\n\nm\na\n\n\n\nm\no\n\n\n\nn\no\n\n\n\np\no\n\n\n\nri\nte\n\n\n\ns \nh\n\n\n\nia\nn\n\n\n\ns \n\n\n\nC\nra\n\n\n\nss\no\n\n\n\nre\ntu\n\n\n\nri\nle\n\n\n\nte\ns \n\n\n\nv\nen\n\n\n\nra\na\n\n\n\nd\nsh\n\n\n\no\no\n\n\n\nv\nen\n\n\n\ni \n\n\n\nL\na\n\n\n\nev\nig\n\n\n\na\nto\n\n\n\nsp\no\n\n\n\nri\nte\n\n\n\ns \nsp\n\n\n\n\n\n\n\nP\ned\n\n\n\nia\nst\n\n\n\nru\nm\n\n\n\n s\np\n\n\n\n\n\n\n\nB\no\n\n\n\nty\no\n\n\n\nco\ncc\n\n\n\nu\ns \n\n\n\nB\nra\n\n\n\nu\nn\n\n\n\nii\n \n\n\n\nV\ner\n\n\n\nru\nca\n\n\n\nto\nsp\n\n\n\no\nri\n\n\n\nte\ns \n\n\n\nsp\n \n\n\n\nM\na\n\n\n\ng\nn\n\n\n\na\nst\n\n\n\nri\na\n\n\n\nti\nte\n\n\n\ns \nh\n\n\n\no\nw\n\n\n\na\nrd\n\n\n\ni \n\n\n\nA\ncr\n\n\n\no\nst\n\n\n\nic\nh\n\n\n\niu\nm\n\n\n\n a\nu\n\n\n\nre\nu\n\n\n\nm\n \n\n\n\nT\no\n\n\n\nta\nl \n\n\n\nfr\nes\n\n\n\nh\nw\n\n\n\na\nte\n\n\n\nr \nsw\n\n\n\na\nm\n\n\n\np\n t\n\n\n\na\nxa\n\n\n\n\n\n\n\nZ\no\n\n\n\nn\no\n\n\n\nco\nst\n\n\n\nit\nes\n\n\n\n r\na\n\n\n\nm\no\n\n\n\nn\na\n\n\n\ne \n\n\n\nP\nsi\n\n\n\nla\ntr\n\n\n\nic\no\n\n\n\nlp\no\n\n\n\nri\nte\n\n\n\ns \ncr\n\n\n\na\nss\n\n\n\nu\ns \n\n\n\nT\no\n\n\n\nta\nl \n\n\n\nM\na\n\n\n\nn\ng\n\n\n\nro\nv\n\n\n\ne \nta\n\n\n\nxa\n \n\n\n\n125\n0 \n\n\n\n        28    2\n8 \n\n\n\n    1  3    4    20   7   2\n7 \n\n\n\n18  18 \n\n\n\n184\n4 \n\n\n\n2        1   1 4       4    4   1 12   4   1\n7 \n\n\n\n10  10 \n\n\n\n202\n7 \n\n\n\n1  1      12    1\n4 \n\n\n\n            2  11  2 1   1\n6 \n\n\n\n17  17 \n\n\n\n262\n1 \n\n\n\n 2       1    3        1   1    4  2    6 13  13 \n\n\n\n280\n4 \n\n\n\n  6         2 8 1  1            11   3   1\n4 \n\n\n\n23  23 \n\n\n\n301\n0 \n\n\n\n        3 1  1 5  1 1              4 6 1 6 \n1\n7 \n\n\n\n\n\n\n\n360\n4 \n\n\n\n1            1 1 1 2            5  8 6   \n1\n9 \n\n\n\n6  6 \n\n\n\n381\n0 \n\n\n\n  3          3        1   1      7   2 9 8  8 \n\n\n\n401\n1 \n\n\n\n  14      3    \n1\n7 \n\n\n\n              13  18 2   \n3\n3 \n\n\n\n14 1 15 \n\n\n\nThe HB-001 well's intervals between 1250 and 1768 meters were thought to have been deposited in a \nforeshore-like environment known as the lower delta plain (Figure 5 and Table 2). The criteria for \ninterpretations are: \n \nThe intervals are characterized by high incidence of taxa of freshwater swamp, afterward mangrove, savanna \nand rainforest swamp taxa amid nominal existence of montane taxa (Adojoh et al., 2015; Olayiwola and \nBamford, 2016). Zonocostites ramonae, Sapotaceoidaepollenites sp., Retitricolporites irregularis, \nPsilatricolporites crassus, Verrucatosporites sp., Acrostichum ahereum and Laevigatosporites sp. are the \nexemplars of the recorded taxa. Abundant freshwater algae, Botryococcus brunii was discovered throughout \nthese intervals, which indicates a significant inflow of freshwater; Poorly sorted, small to big palynomacerals I \nand II are frequently to frequently found, together with tiny to frequently occurring, small to medium sized PM \nIII and IV (Oyede, 1992). The sands display funnel- and cylinder-shaped Gamma ray log patterns, which point \nto lower deltaic plain distributary mouth and distributary channel bar deposits (Sneider et al., 1978). Pebbly \nto fine-grained sands that have been weakly sorted and with modest shale intercalations make up the majority \nof the lithology. \n\n\n\nTable 2: Depositional Environment in HB-001 Well \n\n\n\nHB-001 well intervals (m) Inferred environment of deposition \n\n\n\n1250 \u2013 1768 Delta plain (lower delta plain/foreshore \n\n\n\n1768 \u2013 2573 Subaqueous delta plain (delta front/upper shoreface) \n\n\n\n2573 \u2013 4011 \nSubaqueous delta plain (delta front to prodelta/lower shoreface to \n\n\n\nproximal offshore) \n\n\n\nThe sequence contains abundant accessory minerals, with shell fragments, carbonaceous detritus, mica flakes, \nand ferruginous elements predominating. Glauconite pellets indicate shallow water clastic inflow (Selley, \n1978). \n \nThe intervals of the HB-001 well between 1768 and 2573 m were thought to have been deposited in a delta \nfront environment. This corresponds to upper shoreface (Figure 4). These deductions are justified by:\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 44-51 \n\n\n\n\n\n\n\n \nCite The Article: Olubusayo Akinyele Olatunji (2023). Palynofacies and Sedimentology  \n\n\n\nof Hb-001 Well. Malaysian Journal of Geosciences, 7(2): 44-51. \n \n\n\n\nSavanna taxa are more common during the intervals, which are composed \nprimarily of abundant freshwater marsh and mangrove swamp taxa. Taxa \nfrom the montane and rainforest swamps are barely represented. \nExamples of taxa found in the intervals include Botryococcus braunii, \nSapotaceoidae pollenites, Monoporites ramonae, Psilatrocolporites crassus, \nRetribrevitricolporites protrudens, and Monoporites annulatus; \n\n\n\nPoorly sorted, small to big palynomacerals I and II that are frequently to \noccasionally present, and few palynomacerals III and IV (Oyede, 1992): \n\n\n\nThe main funnel, cylinder, and smaller bell-shaped motifs in the log \ncharacteristics of the intervals suggest deposition in the delta front and \nrange from barrier bars, distributary channels, and tidal channel (Sneider \net al., 1978 and Beka and Oti, 1995). The shale is moderately firm, grey, \nplaty to flaggy, ranging in color from dark grey to brownish grey. \nUncommon to common ferruginous minerals and infrequent to abundant \ncarbonaceous detritus indicate shoreface deposition (Selley, 1978). \n\n\n\nThe lowermost portions of the HB-001 well, the intervals 2573 - 4011 m, \nwere designated to be deposited in a delta front to prodelta depositional \nenvironment. The similar environment is the lower shoreface to proximal \noffshore (Figure 5). These standards govern this inference: \n\n\n\nAt the intervals with increased prevalence of rainforest swamp species, \nthere is a strong dominance of freshwater swamp and mangrove swamp \ntaxa. Savanna and montane taxa are less prevalent, implying proximate \noffshore (Adojo et al., 2015; Olayiwola and Bamford, 2016): \n\n\n\nPalynomacerals I and II are small to large, somewhat sorted, common to \nabundant, and have widespread palynomacerals IV (Oyede, 1992): \n\n\n\nThe sands are sub-angular to sub-rounded, milky white, fine to medium \ngrained, intermittently very coarse to coarse grained, moderately to highly \nsorted, and fine to medium in texture. The shale is silty, platy to flaggy, \nsomewhat hard, and dark grey to grey in color. Guide minerals are \nsubjugated by ferruginous materials carbonaceous detritus and glauconite \npellets which indicates barrier bar/floor source (Selley, 1978). The \nmultiserrate upward coarsening, cylinder, and fining upward outlines are \nbarrier bars, subaqueous channel, and tidal channel deposits, according to \nthe sand's log characteristics. The sediments of those intervals are \ndeposited down in coastal deltaic to inner shelf settings. \n\n\n\n5.   CONCLUSIONS \n\n\n\nPalynofacies, sedimentological and paleoenvironmental analyses were \nconducted in sedimentary sections penetrated in HB-001 well, with the \nusage of samples from ditch cutting and wireline logs given by Shell \nProduction and Development Company. Palynomacerals 1 and 2 were \nfound in large sizes, and palynomacerals 3 and 4 were found infrequently \nto frequently. Rapid coastal progradation is revealed by the observed \nlithological alternation of sand and shale units. Ferruginous materials, \nglauconite pellets, carbonaceous detritus, shell fragments, and pyrites are \nthe most prevalent index minerals and accessories, with sporadic \noccurrences of mouse flakes. The whole interval examined in the HB-001 \nwell is believed to represent a part of the Agbada Formation based on the \nlithologic, textural, and Gamma Ray Log data. Two palynostratigraphic \nzones were delineated in the well using the worldwide stratigraphic guide. \nThe stratigraphic age range of the recovered diagnostic marker species \nimplies Late Miocene age for the examined periods. With the help of \npalyno-ecological groupings, palynofacies associations, and \nsedimentological features, the lower delta plain, delta front, and pro-delta \nenvironments of deposition have been effectively explained. The \nrecovered palynomorph taxa's palyno-ecological groupings demonstrated \nthat the examined well intervals were formed under a cycle of wet and dry \npaleoclimate conditions. \n\n\n\nREFERENCES \n\n\n\nAdegoke, O.S., Jan du Chene, R.E., Agumanu, A.E., and Ajayi, P.O., 1978. \nPalynology and age of the Kerri-Kerri Formation, Nigeria. Rev. Esp. \nMicropaleontol., 10 (2), Pp. 267-283. \n\n\n\nAdojoh, O., Lucas, F.A., and Dada, S., 2015. Palynocycles, Palaeoecology and \nSystems Tracts Concepts: A Case Study from the Miocene Okan-1 \nwell. Niger Delta Basin, Nigeria. Applied Ecology and Environmental \nSciences, 3 (3), Pp. 66-74 \n\n\n\nAgip, 1987. Palynomorphs of Niger Delta. Agip publication, 44 plates. \n\n\n\nAjaegwu, N.E., Odoh, B.I., Akpunonu, E.O., Obiadi, I.I., and Anakwuba, E.K., \n2012. Late Neocene to Early Pliocene palynostratigraphy and \npalaeonvironments of ANE. 1 well, Eastern Niger Delta. Nigeria \n\n\n\nJournal of Mining and Geology, 48 (1), Pp. 31-43. \n\n\n\nBeka, F.T., and Oti, M.N., 1995. The Distal Offshore Niger Delta:  Frontier \nProspects of a Mature Petroleum Province, In: M. N. Oti and G. \nPostma (Eds), Geology of Deltas Rotterdam, A. A,Balkema presspp., \nPp. 237 \u2013 241. \n\n\n\nBurke, K.C., Dessauvagie, T.F.J., and Whiteman, A.J., 1971. Opening of the \nGulf of Guinea and Geological history of the Benue depression and \nNiger Delta. Nature Physical Sciences, 233 (38), Pp. 51-55. \n\n\n\nChukwuma-Orji, J.N., Okosun, E.A., Goro, I.A., Waziri, S.H., 2017. \nPalynofacies, sedimentology and palaeoenvironment evidenced by \nstudies of IDA-6 well, Niger Delta, Nigeria. Palaeoecology of Africa, \n34, Pp. 87-105. \n\n\n\nClarke, R.T., 1966. Peregrinipollis nigericus, a new palynomorph from the \nUpper Tertiary of Nigeria. Grana. Palynol., 6, Pp. 545-546. \n\n\n\nClarke, R.T., and Frederiksen, N.O., 1968. Some new sporomorphs from the \nUpper Tertiary of Nigeria. Grana. Palynol., 8, Pp. 210-224. \n\n\n\nDurugbo, E.U., and Uzodimma, E., 2013. Effects of lithology on \npalynomorph abundance in wellsX1 and X2 from the western Niger \ndelta. Nigeria. International Journal of Geology, Earth and \nEnvironmental Sciences, 3, Pp. 170-179. \n\n\n\nErdtman, G., 1952. Pollen morphology and plant taxonomy \u2013 Angiosperms, \nAlmqvist and Wiksell, Stockholm, Pp. 539. \n\n\n\nEvamy, B.D., Haremboure, J., Kemerling, W.A., Molloy, F.A., and Rowlands, \nP.H., 1978. Hydrocarbon habitat of Tertiary Niger Delta, American \nAssociation of Petroleum Geologists Bulletin, 62, Pp. 1-39. \n\n\n\nGermeraad, J.H., Hoppings, C.A., and Muller, J., 1968. Palynology of Tertiary \nSediments from Tropical areas. Review of Palaeobotany and \nPalynology, 6 (3), Pp. 189-348. \n\n\n\nGonzalez-Guzman, A.E., 1967. A palynological study of the Upper Los \nCuervos and Mirador Formations (Lower and Middle Eocene, Tibu \narea, Columbia). Thesis, Univ. Amsterdam, Pp. 68. \n\n\n\nJan du Chene, R.E., Onyike, M.S., and Sowunmi, M.A., 1978. Some new \nEocene pollen of the Ogwashi-Asaba Formation Southeastern \nNigeria, Rev. Esp. Micropal., 10 (2), Pp. 285-322. \n\n\n\nKnaap, W.A., 1971. A montane pollen species from the Upper Tertiary of \nthe Niger Delta. Journal of Mining and Geology, 6, Pp. 23-29. \n\n\n\nLegoux, O., 1978. Quelques especes de pollen caracteristiques du Neogene \ndu Nigeria. Bulletin Centers Recherche Exploration-Production \nd\u2019Elf-Aquitaine, 2, Pp. 265-317. \n\n\n\nMascle, J.R., 1976. Submarine Niger Delta. Structural Framework. Journal \nof Nig. Geol. Metal Soc., Pp. 12-28. \n\n\n\nMorley, R.J., 1995. Tertiary stratigraphic palynology in Southeast Asia: \nCurrent statue and new directions. Geological Society Malaysia \nBulletin, Pp. 1-36. \n\n\n\nMurat, R.C., 1972. Stratigraphy and Paleogeography of the Cretaceous and \nLower Tertiary in Southern Nigeria. First Conference on African \nGeology Proceedings Ibadan, Nigeria, Ibadan University Press, Pp. \n251-266. \n\n\n\nMurphy, M.A., and Salvador, A., 1999. International stratigraphic guide \u2013 \nAn abridged version, International sub commission on the \nstratigraphic classification of IUGS, International Commission on \nStratigraphy, Special Episodes, 22 (4), Pp. 255 -272. \n\n\n\nOlajide, F.A., 2013. Palynology of Late Miocene to Pliocene Agbada \nFormation, Niger Delta Basin, Nigeria. Elixir Geoscience, 56, Pp. \n13370-13373. \n\n\n\nOlayiwola, M.A., and Bamford, M.K., 2016. Petroleum of the Deep: \nPalynological proxies for paleoenvironment of deep offshore upper \nMiocene-Pliocene sediments from Niger delta, Nigeria. \nPalaeontologia Africana, 50, Pp. 31-47. \n\n\n\nOloto, I.N., 2014. Palynological Study of Igbomotoru \u20131 Well, Central \nCoastal Niger Delta, Nigeria. International Journal of Scientific and \nTechnology Research, 3 (2), Pp. 287-294. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 44-51 \n\n\n\n\n\n\n\n \nCite The Article: Olubusayo Akinyele Olatunji (2023). Palynofacies and Sedimentology  \n\n\n\nof Hb-001 Well. Malaysian Journal of Geosciences, 7(2): 44-51. \n \n\n\n\nOluwajana, O.A., 2019. 2D seismic interpretation and evaluation of Middle \nMiocene source rocks within Agbada Formation, Coastal Swamp \ndepobelt, Niger Delta Basin, Nigeria. Global Journal of Geological \nScience, 17 (2), Pp. 97-103. \n\n\n\nOyede, A.C., 1992. Palynofacies in deltaic stratigraphy. Nigerian \nAssociation of Petroleum Explorationist Bulletin, 7, Pp. 10-16. \n\n\n\nRull, L.J., 2002. Marine benthic algae of Namibia, Scientia Marina, 66 \n(Suppl.), Pp. 5-256. \n\n\n\nSalami, M.B., 1983. Some Late Cretaceous and Early Tertiary Pteridophytic \nspores from the Southern Nigeria Sedimentary Basin. Rev. \nEspanola. Micropaleont., 15, Pp. 252-272. \n\n\n\nSalard \u2013 Cheboldaeff, M., 1976. A propos de la microflore cryptogamique \nTertiaire de basin sedimentaire coties du Cameroon, Rev. \nMicropaleont., 18 (2), Pp. 97-116. \n\n\n\nSalard \u2013 Cheboldaeff, M., 1978. Sur la palynoflore Maestrichtienne et \nTertiaire du basin sedimentaire littoral du Cameroun, Pollen et \nSpores, 20, Pp. 215 \u2013 260. \n\n\n\nSalard-Cheboldaeff, M., 1975. Quelques grains de pollen peripores \nTertiares de Cameroon, Rev. Micropaleont, 17 (4), Pp. 182-190. \n\n\n\nSamuel, O., 2009. Integrated Evaluation of CO2 Risk in Niger Delta \n\n\n\nReservoirs: A Critical Value Driver for HPHT Prospects. SPDC \ninternal unpublished report. \n\n\n\nSelley, R.C., 1978. Concepts and Methods of Subsurface Facies Analysis. \nAAPG Education Course Notes. Series 9, Pp. 86.  \n\n\n\nShort, K.C., and Stauble, A.J., 1967. Outline of geology of Niger Delta. \nAmerican Association of Petroleum Geologists Bulletin, 51, Pp. 761-\n771. \n\n\n\nSneider, R.M., Tinker, C.N., and Merkel, L.D., 1978.  Deltaic Environment, \nReservoir Types and their Characteristics. Journal of Petroleum \nTechnology, Pp. 538 \u2013 1546. \n\n\n\nStoneley, R., 1966. The Niger Delta region in the light of the theory of \ncontinental drift. Geological Magazine, 105, pp. 185-39 \n\n\n\nTyson, R., 1995. Sedimentary organic matter: organic facies and \npalynofacies. Chapman and Hall, London, New York. \n\n\n\nVan Hoeken-Klinkenberg, P.M.J., 1964. A palynological investigation of \nSome Upper Cretaceous sediments in Nigeria. Pollen et spores, 6, \nPp. 209-231. \n\n\n\nVan Hoeken-Klinkenberg, P.M.J., 1966. Maastrichtian, Paleocene and \nEocene pollen and spores from Nigeria. Leidse. Geol. Mededel., 38, \nPp. 37-48.\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 33-42 \n\n\n\nCite The Article: Chinedu S. Orji, Etim D. Uko, Iyeneomie Tamunobereton-ari (2019). Permeability-Porosity Trends In Cawc Reservoir Sands In The Niger Delta Nigeria, \nUsing Well-Log Data. Malaysian Journal Of Geosciences, 3(2): 33-42. \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 01 April 2019 \nAccepted 03 May 2019 \nAvailable online 09 May 2019\n\n\n\nABSTRACT\n\n\n\nReservoir characteristics analysis in the onshore Cawthorne Channel (CAWC) oil field, Niger Delta is here \npresented. The aim of the research was to assess reservoir properties and their relationships. A suite of \ngeophysical logs comprising gamma ray, resistivity, neutron and density logs from eight wells were used in \nthe analysis. Three reservoirs sands were delineated and linked across all eight wells. The litho-stratigraphy \ncorrelation section revealed that each of the sand units spreads over the field are differs in thickness with \nsome units occurring at greater depth than their adjacent unit, that is possibly an evidence of faulting. The \nresults show volume of shale values range from 11% to 17% indicating that the fraction of shale in the \nreservoirs is quite low. The total porosity of the reservoirs ranges from 0.22 to 0.39 indicating a very good \nreservoir quality and reflecting probably well sorted coarse-grained sandstone reservoirs. The permeability \nof the reservoirs ranges from 288 mD to 1250mD and this suggests good reservoir horizons. The hydrocarbon \nsaturation of the reservoirs ranges from 0.59 to 0.71 indicating that the proportion of void spaces occupied by \nwater is low consequently high hydrocarbon production. Sand-shale lithology was calculated, with sandstone \nvolume decreasing with increasing depth, while shale volume increases with depth. Porosity and permeability \nshowed decreasing trend with depth for both sandstone and shale units in all wells with few exceptions. This \ncould be as a result of low compaction by overburden pressure from overlying rocks. Plot of lithology versus \ndepth reveals that shale lithology increases with depth, while sandstone decreases. Lithology versus porosity \nplots show an inverse relationship between permeability and shale volume and direct relationship between \npermeability and volume of sand. Lithology versus permeability shows that permeability and shale volume \nhave an inverse relationship whereas permeability and volume of sand have a direct relationship. Permeability \ndecreases exponentially with decrease in porosity in rock matrix made up of intercalation of sandstone and \n\n\n\nshale. The modelled equation of permeability and porosity is given by K = 0.053e32.934\u0424. This implies that in \nthe absence of core and well-log data, permeability can be estimated using only porosity data. The results of \nthis work can be used as an exploration tool for the identification of prospective areas and also for feasibility \nstudies during an appraisal activity. \n\n\n\n KEYWORDS \n\n\n\nPorosity, Permeability, Reservoir, Lithology, Nigeria\n\n\n\n1. INTRODUCTION \n\n\n\nFormation evaluation is used to understand the geology of the wellbore \nat high resolution and also to estimate the producible hydrocarbon \nreservoir. Formation evaluation is still a challenge in many fields because \nof the complexity of the reservoir environment subsequent diagenesis \neffect. Once formation evaluation is performed on the reservoir, it is \ncrucial to pay attention to the location of the possible reservoir zone in \nthe drilled section, determination of fluid type (gas, oil, water) present in \nthe pore space, saturation level, and the mobility of the fluids across the \nconnected pore space of the rock. To better achieve such information, it \nis important to have a good understanding of porosity (total, primary, \neffective porosity), water saturation computation, pay thickness and \nselection of cut offs. The aim of this process is to economically establish \nthe existence of producible reservoirs. In this study, various well logs \nwhich include gamma ray, neutron, spontaneous potential, resistivity and \ndensity logs were analyzed and interpreted in order to define lithologic \nunits of prospective zones, differentiating between hydrocarbon bearing \nand non-hydrocarbon bearing zone(s), and to investigate the relationship \n\n\n\nbetween the petrophysical properties. \n\n\n\n2. LOCATION AND GEOLOGY OF THE STUDY AREA\n\n\n\nThe Cawthorne Channel oil field is an onshore field in the coastal swamp \ndepositional belt of the Niger Delta. Figure 1 shows the Cawthorne \nChannel oil field [1]. Its coordinates are Latitude 4\u00b026'56.5\" north and \nLongitude 7\u00b05'1.8\" east. The study area covers an area of 1,035 square \nkilometres and includes the Alakiri, Cawthorne Channel, Krakama, and \nBuguma Creek fields and related facilities. Comprising part mangrove \nswamp, the concession covers 1,035 km2. Geologically, the study area lies \nin the eastern part of the Cenozoic Niger Delta (a typical wave and tidally \ndominated delta) where the main reservoirs are the sandstones of the \nheterolithic Agbada Formation (Eocene to Recent) deposited within \ndelta-front, delta-topset, and fluvial-deltaic environments. The seismic \ndata across the concession defines three mega structural trends; \nNorthern (Alakiri, Buguma Creek, Orubiri and Asaritoru), Central \n(Krakama, Cawthorne Channel and Awoba) and Southern (no discoveries \nbut at least one very good prospect identified. \n\n\n\nMalaysian Journal of Geosciences (MJG) \nDOI : http://doi.org/10.26480/mjg.02.2019.33.42 \n\n\n\nRESEARCH ARTICLE \n\n\n\nPERMEABILITY-POROSITY TRENDS IN CAWC RESERVOIR SANDS IN THE NIGER \nDELTA NIGERIA, USING WELL-LOG DATA \n\n\n\nChinedu S. Orji, Etim D. Uko, Iyeneomie Tamunobereton-ari \n\n\n\nDepartment of Physics, Rivers State University, PMB 5080, Port Harcourt, Rivers State, Nigeria. \n\n\n\n*Corresponding Author Email: cstephenorji@gmail.com, e_uko@yahoo.com, tamunoberetonari@yahoo.com\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in \nany medium, provided the original work is properly cited. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\n\nmailto:cstephenorji@gmail.com\n\n\nmailto:e_uko@yahoo.com\n\n\nmailto:tamunoberetonari@yahoo.com\n\n\n\n\n\n\n  \nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 33-42 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Chinedu S. Orji, Etim D. Uko, Iyeneomie Tamunobereton-ari (2019). Permeability-Porosity Trends In Cawc Reservoir Sands In The Niger Delta Nigeria, \nUsing Well-Log Data. Malaysian Journal Of Geosciences, 3(2): 33-42. \n\n\n\n\n\n\n\nFigure 1: Map of the Niger Delta Showing Study Area [2] \n \n \n3.  MATERIALS AND METHODS \n\n\n\n \nThe data consist of well log suite which include calliper log, resistivity log, \nlithology logs (spontaneous log and gamma ray log), and porosity logs \n(sonic, neutron and density logs) from eight wells (CAWC 009, 013, 017, \n021, 022, 023, 041 and 044). However, due to the gaps and null values in \nsome wells, not all were used in the analysis. These data were analysed \nusing Schlumberger Petrel. The methodology to estimate quantitative \npetrophysical properties from wireline log data using various rock \nphysics models has the following stages: Well log preparation and editing, \ndelineation of reservoir beds and well log correlation, petrophysical \nproperties estimation and cluster analysis. \n \n3.1 Delineation of Reservoir Beds \n \nThis is the process of determining reservoir zones with considerable \nhydrocarbon saturation. Logs respond to different lithologies. The \ngamma ray (GR) log is particularly useful for defining shale beds as well \nas the Spontaneous Potential (SP) log. The GR log reflects the proportion \nof shale and, in many regions, can be used quantitatively as a shale \nindicator. \n \n3.2 Litho-stratigraphy correlation \n \nA horizon represents an isochronous geologic time surface. It is the \ninterface between two different rocks layers. It is associated with \ncontinuous and reliable reflection on the sections that appear over a large \narea. In order to perform a log analysis, it is necessary to pick the various \nzones of interest. In this study, selection of values was made on a \nconsistent basis from day to day to assist reproducibility of results. \n \n3.3 Computation of Petrophysical Properties \n \n3.3.1 Volume of shale (Vsh) \n \nDresser proposed a new approach as a result of empirical correlation \nwhere the relationship changes according to the age or volume content of \nthe formation. Younger rocks (Tertiary), unconsolidated [3]: \n \nVsh = 0.083 (23.7IGR \u2013 1)                                                                       (1) \nVsh = Volume of shale  \nIGR = Gamma-ray index \n \nThe gamma-ray index can be obtained from the linear equation: \n  \n\n\n\n                                                                  (2) \n \n  \nWhere IGR = Gamma-ray index; GR (log) = Gamma-ray reading from the log; \nGR (min) = Gamma-ray sand line; GR (max) = Gamma-ray shale line. \n \n3.3.2 Total Porosity (\u03a6t) and Effective Porosity (\u03a6eff) \n \nIn this work, the density log was used for the determination of the \nporosity by applying the equation [4]. Total Porosity was calculated from \ndensity porosity log using the equation: \n  \n\n\n\n                                                                       (3) \n \n  \nWhere \ud835\ude31ma matrix density which is taken to be 2.65g/cc for sandstones \n[3]; \ud835\ude31b = Bulk density read directly from the log; \ud835\ude31f = the fluid density \nwhich is taken to be 1 for gas and 0.87 for oil. Effective Porosity is usually \nbased \n  \non an adjustment of total porosity by means of estimated shale volume \n(content) [3]: \n \n\n\n\n                                                                         (4)  \n \n \nwhere eff = effective porosity; T = total porosity; sh = log reading in \na shale zone and Vsh = volume of shale. \n \n3.3.3 Determination of Water Saturation (Sw) \n \nWater Saturation is mathematically expressed as: \n \n\n\n\n                                                                  (5) \n  \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 33-42 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Chinedu S. Orji, Etim D. Uko, Iyeneomie Tamunobereton-ari (2019). Permeability-Porosity Trends In Cawc Reservoir Sands In The Niger Delta Nigeria,  \n Using Well-Log Data. Malaysian Journal Of Geosciences, 3(2): 33-42. \n\n\n\nwhere Sw = Water saturation; a = Tortuosity factor; m = Cementation \nfactor; n = Saturation exponent; \u03a6 = Porosity of the formation; Rt = Deep \nresistivity of the formation. \n \n3.3.4 Determination of Hydrocarbon Saturation (Shc) \n \nThe hydrocarbon saturation was deduced from water saturation by the \nfollowing relationship: \n \nShc = 1 \u2013 Sw                                                                                            (6) \n \n3.3.5 Determination of Permeability (K) \n \nThe permeability values for the observed reservoirs were calculated \nusing the equation after method [5].  \n \nK1/2 = 250 x \u03a63/Swirr                                                                       (7) \n \nwhere K = Permeability; \u03a6 = Porosity; Swir = Irreducible water \nSaturation. \n \n3.4 Cluster Analysis \n \n\n\n\nCross plot analysis was carried out to determine the rock \nproperties/attributes that better discriminate the reservoir [6,7]. The \ngoal of rock physics analysis is to determine the feasibility of \ndiscriminating between reservoir fluids and lithology of rock formations. \nVarious cross plots were carried out. They include: depth versus \nlithology, depth versus porosity, depth versus permeability, lithology \nversus porosity, lithology versus \npermeability and porosity versus permeability. \n \n4.  RESULTS AND DISCUSSION \n\n\n\n \n4.1 Reservoir Identification \n \nThe wells display a shale/sand/shale sequence which is characteristic of \nthe Niger delta formation (Figures 2 to 10). The wells were analysed in \nterms of lithology from gamma ray log. Shale lithologies were defined by \nthe high gamma ray value. Shale lithologies cause the deflection of \nresistivity to the far left due to its high conductive nature. It obvious that \nfor every reservoir, porosity is generally high, signatures for volume of \nshale are low, water saturation are low while permeability are high as \nseen in the curves. This validates the expected log signatures of the \nproperties of a productive reservoir. \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 2: CAWC 9 Composite Well Logs \n \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 3: CAWC 13 Composite Well Logs \n  \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 33-42 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Chinedu S. Orji, Etim D. Uko, Iyeneomie Tamunobereton-ari (2019). Permeability-Porosity Trends In Cawc Reservoir Sands In The Niger Delta Nigeria,  \n Using Well-Log Data. Malaysian Journal Of Geosciences, 3(2): 33-42. \n\n\n\n\n\n\n\nFigure 4: CAWC 17 Composite Well Logs \n \n \n \n\n\n\n\n\n\n\nFigure 5: CAWC 21 Composite Well Logs \n \n \n\n\n\n\n\n\n\n \nFigure 6: CAWC 22 Composite Well Logs \n\n\n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 33-42 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Chinedu S. Orji, Etim D. Uko, Iyeneomie Tamunobereton-ari (2019). Permeability-Porosity Trends In Cawc Reservoir Sands In The Niger Delta Nigeria,  \n Using Well-Log Data. Malaysian Journal Of Geosciences, 3(2): 33-42. \n\n\n\n\n\n\n\nFigure 7: CAWC 23 Composite Well Logs \n \n\n\n\n \n \nFigure 8: CAWC 41 Composite Well Log  \n \n \n\n\n\n \n \nFigure 9: CAWC 44 Composite Well Logs \n \n4.2 Lithologic Correlation \n \nLithologic units were identified on the logs and correlated across the \nwells. Lithology was interpreted based on gamma ray log signatures. \nDetailed observation of gamma ray logs shows progressive alternation of \nsand and shale. The stratigraphic cross-sections produced show a general \nlateral continuity of the lithologic units across the field. These surfaces \nwere identified based on the abrupt change in well log properties \u2013 \ngamma-ray, resistivity and density among others. Three zones of interest \n(Sand A, Sand B and Sand C) were delineated and correlated across all \neight wells. The litho-stratigraphy correlation section revealed that each \nof the sand units spreads over the field and differs in thickness with some \nunits occurring at greater depth than their adjacent unit that is possibly \n\n\n\nan evidence of faulting. The shale layers were observed to increase with \ndepth along with a corresponding decrease in sand layers. This pattern in \nthe Niger delta indicates a transition from Benin to Agbada Formation. \n  \n\n\n\n \n  \n \nFigure 10: Lithologic Correlation Panel across CAWC Field for all Wells \n \n4.3 Petrophysical Properties Evaluation \n \nThe computations were done using relevant equations and results \nobtained are presented in Tables 1 to 8. The wells are CACW 9, CACW 13, \nCACW 21, CACW 22, CACW 23, CACW 41 and CACW 44. After the wells \nwere delineated, petrophysical properties were evaluated for each \nreservoir. The results obtained for the entire reservoirs are thus \nanalysed. \n \nThe volume of shale was calculated from gamma ray index and the values \nrange from 11% to 17% indicating that the fraction of shale in the \nreservoirs is quite low. The inference is the reservoir has a large volume \nof sand deposit than shale, therefore, hydrocarbon saturated. These \nreservoirs are good reservoir with high oil saturation at irreducible water \nsaturation, because volume of shale values is low from 11% to 17%, \nwhich means that the sand body in all the reservoirs is high and there will \nbe high rate of free flow of hydrocarbon in all the reservoirs as \ncorroborated by their permeability values. \n \nThe total porosity of the reservoirs was estimated from density log \n(RHOB) using porosity formula and these values ranges from 0.22 to 0.39 \nindicating a very good reservoir quality and reflecting probably well \nsorted coarse-grained sandstone reservoirs with minimal cementation. \nThe permeability of the reservoirs ranges from 288 md to 1250 Md. This \nimplies that the permeability varies from very good to excellent and \nsuggests that these are good (exploitable) reservoir horizon. For a rock \nto be considered as an exploitable hydrocarbon reservoir without \nstimulation, its permeability must be greater than approximately 100 md \n(however, depending on the nature of the hydrocarbon - gas reservoirs \nwith lower permeabilities are still exploitable because of the lower \nviscosity of gas with respect to oil). This is as a result of very good to \nexcellent sand quality.  \n \nThe hydrocarbon saturation of the reservoirs ranges from 0.59 to 0.71 \nindicating that the proportion of void spaces occupied by water is low \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 33-42 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Chinedu S. Orji, Etim D. Uko, Iyeneomie Tamunobereton-ari (2019). Permeability-Porosity Trends In Cawc Reservoir Sands In The Niger Delta Nigeria,  \n Using Well-Log Data. Malaysian Journal Of Geosciences, 3(2): 33-42. \n\n\n\nconsequently high hydrocarbon saturation and high hydrocarbon \nproduction. These results imply that the reservoir is highly porous and \npermeable. It also contains high hydrocarbons that is very viable for \nproduction. The curves of the various calculated petrophysical properties \nnamely; NTG, Gamma Ray Index, Volume of Shale, Total/Effective \nPorosity, Water Saturation and Permeability for the wells studied are \n\n\n\npresented in figures 4.18 to 4.26. It obvious that for every reservoir \nporosity are generally high, signatures for volume of shale are low, water \nsaturation values are low while permeability values are high as seen in \nthe curves. This validates the expected log signatures of the properties of \na productive reservoir. \n \n\n\n\n \nTable 1: Average petrophysical properties for CACW 9 \n\n\n\n \nReservoir Top Base Volume \u03a6T \u03a6eff Sw Sh K \n\n\n\nName (ft) (ft) of (frac) (frac) (frac) (frac) (mD) \n\n\n\n   Shale      \n\n\n\n   (frac)      \n\n\n\n \nSand A 8680 8920 0.1607 0.2921 0.2485 0.2876 0.7124 1164.88 \n\n\n\nSand B 9324 9680 0.1644 0.2838 0.2401 0.3018 0.6982 970.72 \n\n\n\nSand C 10060 10390 0.1183 0.2668 0.2374 0.3151 0.6849 799.65 \n\n\n\n \nTable 2: Average petrophysical properties for CACW 13 \n\n\n\n \n \nReservoir Name Top (ft) Base (ft) Volume of  Shale \n\n\n\n(frac) \n\n\n\n\u03a6T \n \n\n\n\n(frac) \n\n\n\n\u03a6eff (frac) Sw (frac) Sh (frac) K (mD) \n\n\n\nSand 1 8600 8820 0.1244 0.2446 0.2174 0.3494 0.6506 535.55 \n\n\n\nSand 2 8980 9316 0.1598 0.2640 0.2267 0.3311 0.6689 840.08 \n\n\n\nSand 3 9740 10040 0.1293 0.2365 0.2101 0.3748 0.6252 528.59 \n\n\n\n \nTable 3: Average petrophysical properties for CACW 17 \n\n\n\n \n \nReservoir Top \n\n\n\n\n\n\n\n(ft) \n\n\n\nBase \n\n\n\n\n\n\n\n(ft) \n\n\n\nVolume \u03a6T \u03a6eff Sw Sh K \n\n\n\n\n\n\n\n(mD) \n\n\n\nName   of \n\n\n\n\n\n\n\nShale \n\n\n\n(frac) (frac) (frac) (frac)  \n\n\n\n   (frac)      \n\n\n\nSand 1 8530 8715 0.1002 GAPS GAPS GAPS GAPS GAPS \n\n\n\nSand 2 8840 9190 0.1552 0.2670 0.2294 0.3226 0.6774 793.90 \n\n\n\nSand 3         9610                   9970              0.1115 -                            -                              -                            -                               - \n\n\n\n \nTable 4: Average petrophysical properties for CACW 21 \n\n\n\n \n \nReservoir \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName \n\n\n\nTop (ft) \n\n\n\nMD \n\n\n\nBase (ft) \n\n\n\nMD \n\n\n\nVolume \n\n\n\n\n\n\n\nof \n\n\n\n\u03a6T \n \n\n\n\n\n\n\n\n(frac) \n\n\n\n\u03a6eff \n \n\n\n\n\n\n\n\n(frac) \n\n\n\nSw \n \n\n\n\n\n\n\n\n(frac) \n\n\n\nSh \n \n\n\n\n\n\n\n\n(frac) \n\n\n\nK (mD) \n\n\n\n   Shale \n\n\n\n\n\n\n\n(frac) \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 33-42 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Chinedu S. Orji, Etim D. Uko, Iyeneomie Tamunobereton-ari (2019). Permeability-Porosity Trends In Cawc Reservoir Sands In The Niger Delta Nigeria,  \n Using Well-Log Data. Malaysian Journal Of Geosciences, 3(2): 33-42. \n\n\n\nSand 1 8640 8820 0.0884 0.2652 0.2427 0.3167 0.6833 761.07 \n\n\n\nSand 2 9195 9500 0.1562 0.2392 0.2056 0.3635 0.6365 449.02 \n\n\n\nSand 3 9960 10210 0.1031 0.2483 0.2251 0.3442 0.6558 595.14 \n\n\n\n \nTable 5: Average petrophysical properties for CACW 22 \n\n\n\n \n  \nReservoir Name Top (ft) Base (ft) Volume of  Shale \n\n\n\n(frac) \n\n\n\n\u03a6T \n \n\n\n\n(frac) \n\n\n\n\u03a6eff (frac) Sw (frac) Sh (frac) K (mD) \n\n\n\nSand 1 9590 9825 0.10556 0.27026 0.24239 0.3069 0.6931 707.294 \n\n\n\nSand 2 9290 9510 0.17036 0.22577 0.19124 0.38617 0.61383 348.464 \n\n\n\nSand 3 9650 10310 - - - - - 223.004 \n\n\n\n \nTable 6: Average petrophysical properties for CACW 23 \n\n\n\n \n \nReservoir \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName \n\n\n\nTop \n\n\n\n(ft) MD \n\n\n\nBase \n\n\n\n(ft) MD \n\n\n\nVolume \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nof \n\n\n\n\u03a6T \n \n\n\n\n\n\n\n\n\n\n\n\n(frac) \n\n\n\n\u03a6eff \n \n\n\n\n\n\n\n\n\n\n\n\n(frac) \n\n\n\nSw \n \n\n\n\n\n\n\n\n\n\n\n\n(frac) \n\n\n\nSh \n \n\n\n\n\n\n\n\n\n\n\n\n(frac) \n\n\n\nK (mD) \n\n\n\n   Shale \n\n\n\n\n\n\n\n(frac) \n\n\n\n\n\n\n\nSand 1 8750 9080 0.12511 0.26217 0.23054 0.3185 0.681498 649.339 \n\n\n\nSand 2 9380 9650 0.17876 0.21918 0.18515 0.41001 0.589986 319.946 \n\n\n\nSand 3 9960       10650 - - - - -                  288.968 \n\n\n\n \nTable 7: Average petrophysical properties for CACW 41 \n\n\n\n \n \nReservoir Top Base  \n\n\n\nVolume \n\n\n\n\n\n\n\n\u03a6T \n\n\n\n\n\n\n\n\u03a6eff \n\n\n\n\n\n\n\nSw \n\n\n\n\n\n\n\nSh \n\n\n\n\n\n\n\n (ft) (ft)       \n\n\n\n        K \n\n\n\nName   of (frac) (frac) (frac) (frac)  \n\n\n\n        (mD) \n\n\n\n   Shale      \n\n\n\n   (frac)      \n\n\n\nSand 1 9380 9760 0.09077 0.24214 0.22145 0.34566 0.654343 449.6013 \n\n\n\nSand 2 9923 10120 0.1262 0.27863 0.24511 - - 998.1756 \n\n\n\nSand 3 10310 11020 0.12449 0.2291 0.20333 0.37552 0.624484 353.97 \n\n\n\n               \nTable 8: Average petrophysical properties for CACW 44 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nReservoir \n\n\n\nTop Base  \n\n\n\nVolume \n\n\n\n\n\n\n\n\u03a6T \n\n\n\n\n\n\n\n\u03a6eff \n\n\n\n\n\n\n\nSw \n\n\n\n\n\n\n\nSh \n\n\n\n\n\n\n\n (ft) (ft)       \n\n\n\n        K \n\n\n\nName   of (frac) (frac) (frac) (frac)  \n\n\n\n        (mD) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 33-42 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Chinedu S. Orji, Etim D. Uko, Iyeneomie Tamunobereton-ari (2019). Permeability-Porosity Trends In Cawc Reservoir Sands In The Niger Delta Nigeria,  \n Using Well-Log Data. Malaysian Journal Of Geosciences, 3(2): 33-42. \n\n\n\n   Shale \n\n\n\n\n\n\n\n(frac) \n\n\n\n\n\n\n\nSand 1 9560 9930 0.0773 0.2872 0.2661 0.2920 0.7080 1250.37 \n\n\n\nSand 2 10020 10140 0.1132 0.2822 0.2530 0.3135 0.6865 1205.63 \n\n\n\nSand 3 10360 10720 0.1284 0.2403 0.2156 0.4022 0.5978 754.01 \n\n\n\n4.4 Cluster Analysis \n \n4.4.1 Delineation of Reservoirs \n \nThe top and base of the identified reservoirs of interest for Wells CACW \n9, CACW 13, CACW 21, CACW 22, CACW 23, CACW 41 and CACW 44 are \nshown in Table 1-8. Three reservoirs each were delineated for each well, \nwhich adds up to twenty-four reservoirs. The wells display a \nshale/sand/shale sequence which is characteristic of the Niger delta \nformation. The wells were analyzed in terms of lithology from gamma ray \nlog. Shale lithologies were defined by the high gamma ray value. Shale \nlithologies cause the deflection of resistivity to the far left due to its high \nconductive nature. Regions showing low gamma ray, high resistivity are \nmapped as sand lithologies. \n \n4.4.2 Porosity-Depth Relations \n \nPorosities decreases as the depth increases in all the reservoirs except for \na few exceptions (Figure 11 show all wells). This could be as a result of \nlow compaction by overburden pressure from overlying rocks or more \nnon- interconnected pores spaces in the well. In the Niger Delta, shale \nlithology increases with depth, while sandstone decreases. Our \nobservation confirms the results of porosity is lost with increasing depth \nof burial [8-10]. \n \n4.4.3 Permeability-Depth Relations \n \nFigure 12 shows permeability-depth cross-plots. There is a normal linear \ndecrease of permeability with an increase in depth, but within the Sand A \nand C of CAWC 9, there is an increase of permeability with an increase in \ndepth as shown in the trend line of the depth-permeability cross-plot. \nThis indicates an excellent permeability which is a property of highly \nprolific reservoirs. \n \n4.4.4 Depth- Lithology Relations \n \nThe percentages of sandstones and shales (inferred to be lithology) were \nestimated using gamma ray logs. The graph of depth against volume of \nshale and sand were plotted and is shown in Figure 13. Clean sands (sand \nstones) are delineated as log signatures increasing towards the sand-line \nthat is low. Hence, from the plots we can that the reservoirs are composed \nof sand stones with small pockets of shale. These plots show normal \nporosity decrease with depth. In the Niger Delta, shale lithology increases \nwith depth, while sandstone decreases. Our observation confirms the \nresults of shale lithology increases with increasing depth of burial [8-10]. \n \n3.4.5 Lithology-Porosity Relations \n \nFigure 14 is Lithology versus Total Porosity cross-plots for all wells. \nThere is generally an inverse relationship between permeability and \nshale volume and direct relationship between permeability and volume \nof sand. Comparing both lithologies (Shale bed and sandstone bed) to the \ntrend relationship, it was observed that approximately at same depth; \nshale is denser than sandstone; because shale undergoes plastic \ncompaction or deformation while sandstone undergoes elastic \ncompaction or deformation [11]. Also, our results show that shale \nporosity decreases with increase in depth [12-16]. \n  \n4.4.6 Lithology-Permeability Relations \n \nFigure 15 is the plot of permeability versus lithology for all wells. It is \nobserved that permeability and shale volume have an inverse \nrelationship whereas permeability and volume of sand have a direct \nrelationship. The rate of shale compaction decreases with increase in \nburial [12-16]. This may be caused by decreasing shale permeability and \nincreasing water viscosity, thus increasing rate of fluid expulsion with \nincreasing compaction. \n \n4.4.7 Porosity-Permeability Relations \n \n\n\n\nPermeability decreases exponentially with decrease in porosity in rock \nmatrix made up of intercalation of sandstone and shale as demonstrated \nin Figure 16. The modelled Equation of permeability and porosity is given \nby: K = 0.053e32.934\u0424. This implies that in the absence of core and well-\nlog data, permeability can be estimated using only porosity information. \n \n\n\n\n\n\n\n\nFigure 11: A Plot of Total Porosity versus Depth for all wells \n  \n\n\n\n\n\n\n\nFigure 12: A Plot of Permeability versus Depth for all wells \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 33-42 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Chinedu S. Orji, Etim D. Uko, Iyeneomie Tamunobereton-ari (2019). Permeability-Porosity Trends In Cawc Reservoir Sands In The Niger Delta Nigeria,  \n Using Well-Log Data. Malaysian Journal Of Geosciences, 3(2): 33-42. \n\n\n\n\n\n\n\nFigure 13: Total Porosity Versus lithology for all wells \n \n\n\n\n\n\n\n\nFigure 14: Permeability versus Lithology for all wells \n  \n\n\n\n\n\n\n\nFigure 15: Depth-Lithology for all wells \n \n\n\n\n\n\n\n\nFigure 16: Permeability versus Porosity for all wells \n \n\n\n\n5.  CONCLUSION \n \nReservoir property study and assessment have been successfully done \nusing well log data in Cawthorne Channel oil field, Onshore, Niger Delta \nto assess reservoir properties and their connection. Three zones of \ninterest (Sand A, Sand B and Sand C) were delineated and correlated \nacross all eight wells. The litho-stratigraphy correlation section revealed \nthat each of the sand units spreads over the field and differs in thickness \nwith some units occurring at greater depth than their adjacent unit that \nis possibly an evidence of faulting. The petrophysical parameters \ncalculated include total/effective porosity, water/hydrocarbon \nsaturation, permeability, net-to-gross and volume of shale. Also, graphs \nwere plotted to investigate the relationship between the petrophysical \nproperties in the cross-plot space. The results obtained show volume of \nshale values range from 11% to 17% indicating that the fraction of shale \nin the reservoirs is quite low. The total porosity of the reservoirs ranges \nfrom 0.22 to 0.39 indicating a very good reservoir quality and reflecting \nprobably well sorted coarse-grained sandstone reservoirs with minimal \ncementation. \n \nThe permeability of the reservoirs ranges from 288 mD to 1250 mD. This \nimplies that the permeability varies from very good to excellent and \nsuggests that these are good (exploitable) reservoir horizon. The \nhydrocarbon saturation of the reservoirs ranges from 0.59 to 0.71 \nindicating that the proportion of void spaces occupied by water is low \nconsequently high hydrocarbon saturation and high hydrocarbon \nproduction. These results imply that the reservoir is highly porous and \npermeable. It also contains high hydrocarbons that is very viable for \nproduction. Sand-shale lithology was calculated, with sandstone volume \ndecreasing with increasing depth, while shale volume increases with \ndepth. \n \nPorosity and permeability showed decreasing trend with depth for both \nsandstone and shale units in all wells with few exceptions. This could be \nas a result of low compaction by overburden pressure from overlying \nrocks or more non-interconnected pores spaces in the well. Plot of \nlithology versus depth reveals that shale lithology increases with depth, \nwhile sandstone decreases. Lithology versus porosity plots show an \ninverse relationship between permeability and shale volume and direct \nrelationship between permeability and volume of sand. Lithology versus \npermeability shows that permeability and shale volume have an inverse \nrelationship whereas permeability and volume of sand have a direct \nrelationship. Permeability decreases exponentially with decrease in \nporosity in rock matrix made up of intercalation of sandstone and shale. \nThe modelled Equation of permeability and porosity is given by: K = \n0.053e32.934\u0424. This implies that in the absence of core and well-log data, \npermeability can be estimated using only porosity information. The \nresults of this work can be used as an exploration tool for the \nidentification or evaluation of prospective areas, locations and also for \nfeasibility studies during an appraisal activity. This study also has proven \nthat the estimation of petrophysical data is a key factor for effective \nproductivity of hydrocarbons. \n \nACKNOWLEDGEMENT \n \nThe authors are thankful to Nigeria National Petroleum Company (NNPC) \nfor the permission given to us to obtain data from The Shell Petroleum \nDevelopment Company (SPDC) Nigeria Limited. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 33-42 \n   \n\n\n\n\n\n\n\n\n\n\n\nCite The Article: Chinedu S. Orji, Etim D. Uko, Iyeneomie Tamunobereton-ari (2019). Permeability-Porosity Trends In Cawc Reservoir Sands In The Niger Delta Nigeria,  \n Using Well-Log Data. Malaysian Journal Of Geosciences, 3(2): 33-42. \n\n\n\nREFERENCES \n \n[1] Short, K.C., Stauble, A.J. 1967. Outline of geology of Niger Delta. \nAmerican Association of Petroleum Geologists Bulletin, 51, 761-779. \n \n[2] Nton, M.E., Esan, T.B. 2010. Sequence Stratigraphy of EMI Fields, \nOffshore Eastern Niger Delta, Nigeria. European Journal of Scientific \nResearch, 44 (1), 115 \u2013 132. \n \n[3] Dresser, A. 1982. Well logging and interpretation techniques, the \ncourse for home study. Dresser Industries Inc, Houston. \n \n[4] Schlumberger, 1989. Log Interpretation, Principles and Application: \nSchlumberger Wireline and Testing, Houston, Texas, pp. 21-89. \n \n[5] Wyllie, M.R.J., Rose, W.D. 1989. Some theoretical considerations \nrelated to the quantitative evaluation of the physical characteristics of \nreservoir rock from electric log data. Trans AIME 189, 105p. \n \n[6] Singh, N.P. 2019. Permeability prediction from wireline logging and \ncore data: a case study from Assam- Arakan basin. Journal of Petroleum \nExploration and Production Technology, 9 (1), 97\u2013305. \n \n[7] Omudu, L.M., Ebeniro, J.O. 2007. Cross-Plot and Descriptive Statistics \nfor Lithology and Fluid Discrimination: A Case Study from Onshore Niger \nDelta: Presented at the Annual Meeting of Nigerian Association of \nPetroleum Explorationists (NAPE), Abuja, Nigeria. \n[8] Friedman, J.H., Sanders, J.E. 1978. Principles of Sedimentology J. Wiley \n& sons, New York. \n \n[9] Blatt, H., Middleton, G., Murray, R. 1980. Origin of Sedimentary Rocks, \n\n\n\n2nd edition, Printice Hall, Inc., New Jersey, pp 782. \n \n[10] Selly, R.C. 1980. Introduction to Sedimentology, 2nd edition \n(Academic Press, London), pp. 475. \n \n[11] Tamunosiki, D., Han Ming, G.U., Liping, W., Uko, E.D., Warmate, T. \n2014. Petrophysical Characteristics of Coastal Swamp Depobelt \nReservoir in the Niger Delta Using Well-Log Data. Journal of Applied \nGeology and Geophysics, 2 (2), 76-85. \n \n[12] Magara, K. 1980. Comparism of Porosity depth relationships of shale \nand sandstone. Journal of Petroleum Geology, 3 (2). \n \n[13] Onuoha, C., Uko, E.D., Tamunobereton-ari, I. 2018. Determination of \nLithology and Pore-Fluid of A Reservoir in Parts of Niger Delta Using \nWell-Log Data. Journal of Applied Physics, 10 (2), 71- 82. \n \n[14] Uko, E.D., Alabraba, M.A., Idahosa, L., Tamunosiki, D. 2017. Porosity-\nPermeability Relationship in the North-West Niger Delta Basin, Nigeria. \nWorld Journal of Applied Science and Technology, 9 (2), 150 \u2013 159. \n \n[15] Uko, E.D., Dieokuma, T., Gu, H., Ming, I., Tamunobereton-ari, I., \nEmudianughe, J.E. 2015. Porosity Modelling of the South-East Niger Delta \nBasin, Nigeria. International Journal of Geology, Earth and Environmental \nSciences, 4 (1), 49-60. \n \n[16] Boaca, T., Malureanu, I. 2017. Determination of oil reservoir \npermeability and porosity from resistivity measurement using an \nanalytical model. Journal of Petroleum Science and Engineering, 157, \n884- 893 \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 31-38 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2023.31.38 \n\n\n\n\n\n\n\n Cite the Article: Chukwunweike C. Amadi, Etim D. Uko, Charles O. Ofoegbu, Adepelumi A. Adekunle, Olatunji S. Ayanninuola  (2023).  \nAppraisal Of the Abaji-Abuja (Nigeria) ML 2.25 Earth-Tremor Of 10th January 2020. Malaysian Journal of Geosciences, 7(1): 31-38.  \n\n\n\n\n\n\n\n\n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2023.31.38 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAPPRAISAL OF THE ABAJI-ABUJA (NIGERIA) ML 2.25 EARTH-TREMOR OF 10th \nJANUARY 2020 \n\n\n\nChukwunweike C. Amadia, Etim D. Ukob*, Charles O. Ofoegbuc, Adepelumi A. Adekunled, Olatunji S. Ayanninuolad \n\n\n\na Department of Physics, Nassarawa State University, PMB 1022, Keffi, Nigeria. \nb Department of Physics, Rivers State University, PMB 5080, Port Harcourt, Nigeria. \nc Institute of Geosciences and Earth Resources, Nasarawa State University, PMB 1022, Keffi, Nigeria. \nd Earthquake & Space Weather Laboratory, Department of Geology, Obafemi Awolowo University, Ile-Ife, Nigeria. \n*Corresponding Author Email: e_uko@yahoo.com  \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 01 January 2023 \nRevised 03 February 2023 \nAccepted 06 March 2023 \nAvailable online 09 March 2023 \n\n\n\n This research aims at determining the epicentre, magnitude and energy dissipated during the earth-tremor \nwhich took place at Abaji-Abuja (Nigeria) on 10th January 2020 at 13:46:20 UTC. The event was recorded by \nVolksmeter II VMII-2RU broadband Seismographs located at the Earthquake and Space Weather laboratory, \nObafemi Awolowo University, Ife, Osun state, Nigeria. The computed P-wave and S-wave arrival times and lag \ntimes were 35.8seconds, 36.1seconds and 33.3seconds recorded by three Seismographs Ife (1), Ife (2) and \nIfe (3) respectively. The WinQuake software was used for the analysis. Epicentre distances computed were \n330.3 km, 333.5 km and 303.2 km from the three seismograms, using trilateration method. The results gave \nthe epicentre location at 8049'N and 6047'E (Gurdi town, Yaba District, Abaji LGA, Abuja, Nigeria),focal depth \nof 5 km, and average epicentral distance of 322.3km from Ile-Ife. The tremor has a Local Magnitude of 2.25 \nMLand Coda Magnitude of 3.7Md, and the dissipated energy of 149.6 \u00d7 10-12 KJ. The implication of these results \nis that a local magnitude of 2.25 ML of 149.6 \u00d7 10-12KJ of energy serves as an indicator to the future occurrence \nof another earthquake within the said region. Necessary precautionary measures should be taken when \ncarrying out geological and construction works within the Abaji-Abuja environs. The massive rock blasting \nand quarrying must have reactivated the faults within the area. \n\n\n\nKEYWORDS \n\n\n\nSeismic, Epicentre, Focal Depth, Trilateration, WinQuake, Abaji, Abuja, Nigeria. \n\n\n\n1. INTRODUCTION  \n\n\n\nAn earthquake results from the rapid release of stored elastic strain in the \nlithosphere and takes the form of sudden movement of portions of the \nearth\u2019s crust along faults (Gibson and Sandiford, 2013; Ugwu and Onuoha, \n2010). This energy propagates as seismic waves capable of shaking the \nearth leading to earthquake. The earth is always in the state of shaking. \nAccording to Plate Tectonic theory, lithospheric plates are in constant \nhorizontal motion relative to each other plates and to the axis of earth\u2019s \nrotation, and earthquake occur at plate margins (Villaverde, 2009). \nEarthquakes occur where there is enough stored energy.  Once the build-\nup is large enough, the rocks move suddenly, rupturing and releasing the \nstored energy producing earthquakes. The seismic energy radiates as \nelastic strain seismic waves accompanied by frictional heat and the \ncracking or fracture energy of the rock. Prior to earthquake event, some \ncommon warnings may be ground bulges and/or tilts, foreshocks, gas \nemissions, changes in water level in wells and sometimes accompanied \nwith changes in the water taste as well and unusual behaviour of animals \n(Cicerone et al., 2009).  \n\n\n\nSince 1933 when the first tremor was felt in Nigeria, over thirty-nine (39) \nevents have been reported (Adepelumi et al., 2008). Several investigators \nhave adduced the main causes of earth tremors in Nigeria to intraplate \ntremors activities, regional stress drop, and zone of weakness in the crust \nor transfer of stress from plate boundaries (Afegbua et al., 2011; Tsalha et \n\n\n\nal., 2015; Akpan et al., 2015; Nwankwoala and Orji, 2018; Adepelumi et al., \n2010; Eze et al., 2011; Osagie, 2008). Of these 39 seismic events reported \nso far in Nigeria, less than 20% were recorded instrumentally and \nanalysed. The overwhelming majority were reported historically without \nanalysing the tremor attributes of magnitude, energy and possible causes. \nThus, the main thrust of this work is to analyse the Abaji-Abuja (Nigeria) \nseismic tremor event recorded on 10th January 2020 in order to estimate \nthe epicentre, magnitude and the energy released during the event. The \nresults of this research would aid the Federal Government of Nigeria, oil \nand gas, mining, construction and environmental sectors to properly plan \ntowards minimal damage in the case of a future turbulent seismic event; \nand to monitor fault systems within the said region, probably locations of \nmaximum impact, and Nigeria\u2019s seismic building codes. \n\n\n\n2. TECTONO-GEOLOGICAL SETTING OF NIGERIA AND THE STUDY \nAREA  \n\n\n\n2.1   Location and Geologic Structure \n\n\n\nNigeria is located at the Gulf of Guinea on the west coast of Africa. It \noccupies an area of 923,76 8km2. It is bordered by Chad on the NE, by \nCameroon on the E, by the Atlantic Ocean (Gulf of Guinea) on the S by Benin \n(formerly Dahomey) on the W, and by Niger on the NW and N. The study \narea, Abaji, is situated near Abuja the Federal Capital Territory (FCT), \nNigeria with geographical coordinates of 8\u00b028'0\"N, and 6\u00b0 57' 0\"E (Figure \n\n\n\n\n\n\n\n\nCite the Article: Chukwunweike C. Amadi, Etim D. Uko, Charles O. Ofoegbu, Adepelumi A. Adekunle, Olatunji S. Ayanninuola  (2023).  \nAppraisal Of the Abaji-Abuja (Nigeria) ML 2.25 Earth-Tremor Of 10th January 2020. Malaysian Journal of Geosciences, 7(1): 31-38.  \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 31-38 \n\n\n\n\n\n\n\n\n\n\n\n1). The surface area of the Nigerian state lies predominantly on the \nbasement complex which is overlain by Cretaceous and Tertiary \nsediments that are distributed over the seven sedimentary basins of the \ncountry (Obaje, 2009). About two-thirds of Nigeria is underlain by the \nPrecambrian basement complex rocks comprising of gneisses, migmatites, \nschist, various metamorphic rocks and granites.  \n\n\n\nFracture zones which are defined as major lines of weakness in Earth\u2019s \ncrust due to the movement of continental or oceanic plates are present \nwithin the country\u2019s geology (Olujide and Udoh, 1989; Britt, 2012). Major \nfracture zones in Nigeria are clearly depicted in Figure 2. These fault \n\n\n\nsystems have been linked to the structural trends of the Benue Trough and \ntransverse several states in Nigeria, extending for several kilometres \n(Elueze, 1990; Eze, 2011; Ofoegbu 2019). The Nigerian geological \nframework is believed to be located within the mobile belt of Africa \nbetween the West African Craton and the Congo Craton (Figure 1). There \nare also older granites which originated from the Pan-African orogeny \n(Olujide and Udoh, 1989). The Pan African orogeny which occurred at \nabout 600\u00b1100Ma was the last major deformation and metamorphism \nexperienced within the belt with slight side effect on the adjacent craton \n(Turner, 1971). \n\n\n\n\n\n\n\nFigure 1: Map Nigeria showing area of study. \n\n\n\n\n\n\n\nFigure 2: Tectonic map superimposed on geographic map of Nigeria showing the major fault/fractured lines (Ayodeji and Baruah, 2016) \n\n\n\n2.2    Fault and Fracture Zones in Nigeria \n\n\n\nFirst, the possible fault systems were inferred based on the spatial \ndistribution of the earth tremors of Yola-Dambata, Akka-Jushi and Warri-\nIjebu Remo systems (Afegbua et al., 2011). Most of these fault systems are \ntrending North-West\u2013South-East. The other assertion which was the \nearlier theory revealed that the tremors occurred in the inland extension \nof the North-East \u2013 South-West originating from the Atlantic Ocean and \nthat possibly causes the activities along the Ijebu-Ode and Ibadan axis \nwhich is inferred to be associated with the Ifewara-Zungeru fracture \nsystems (Adepelumi, et al., 2008; Afegbua, 2011; Eze et al., 2011; Tsalha et \nal., 2015).  \n\n\n\nThe Romanche Fracture Zone which is located along the northern part of \nthe Gulf of Guinea is hypothesized to be linked with the structural trends \nof the basement west of the Benue Trough (Emery et al., 1975; Burke, \n1969). The Chain and Charcot fracture zones are regarded as offshore \ntransform faults which have continental extensions. The Chain Fracture \n\n\n\nZone, extends near the Niger Delta while the Charcot Fracture Zone, forms \na volcanic relief (Wright, 1976). The St Paul\u2019s Fracture Zone \ncounterbalances the axis of the Mid-Atlantic Ridge about 560 km in an EW \ndirection (Francis et al., 1978). The Calabar Flank is characterized by \ncrustal block faulting trending in the NW-SE direction (Ukpong et al., 2018; \nNyong and Ramanathan, 1985). The sedimentary basin was controlled by \nvertical movements of faulted blocks notably the Ituk High and the Ikang \nTrough and extends to the Cameroon Volcanic ridge (Murat, 1972; Nyong, \n1995). The Benin Hinge Line is a major fault structure that marks the \nwestern limit of the Niger Delta basin (Minapuye et al., 2018). \n\n\n\nThe stresses that build up around plate boundaries could travel towards \nthe centre of the plates triggering intraplate tremors especially in pre-\nexisting faults. A group researchers attribute the seismicity majorly to \nregional stress and zone of weakness in the crust or transfer of stress from \nplate boundaries, considering the fact that the coastal area of Nigeria lies \nin close proximity to the boundary between the African plate and South \nAmerican plate implied that some of the tremors that occurred in the \n\n\n\n\n\n\n\n\nCite the Article: Chukwunweike C. Amadi, Etim D. Uko, Charles O. Ofoegbu, Adepelumi A. Adekunle, Olatunji S. Ayanninuola  (2023).  \nAppraisal Of the Abaji-Abuja (Nigeria) ML 2.25 Earth-Tremor Of 10th January 2020. Malaysian Journal of Geosciences, 7(1): 31-38.  \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 31-38 \n\n\n\n\n\n\n\n\n\n\n\ncoastal areas of Nigeria could have been possibly initiated by this process \n(Adepelumi et al., 2008; Eze et al., 2011). The stresses built up around \nplate boundaries could travel towards the centre of the plate, triggering \nintraplate tremors especially in pre-existing faults. The presence of \ninhomogeneities and zones of weakness created by previous episodes of \nmagmatic intrusion can also aid in the tremor creation.  \n\n\n\nSome scholars have suggested that the possible triggering mechanism of \nthe Nigeria\u2019s earth tremors might be associated with the Oceanic \n(Romanche and Chain) fracture zones (Anifowose 2006; Ajakaiye et al., \n1984). The presence of the fractures zones which prominently traverse \nthe Western half of the country has been pointed out by to be responsible \nfor the seismic (Ajakaiye et al., 1987; Odeyemi, 1989; Elueze, 1990; \nOfonime, 2010). The important fault systems in Nigeria as reported by is \nthe Ifewura-Zungeru, the Yola-Dambata; Akka-Jushi, and Warri-Ijebu \nRemo fault systems (Tsahla et al., 2015). The Ifewurazungeru fault is \nbelieved to be limited to the Atlantic fracture system, the Romanche Fault \nSystem and is the longest linear feature within the Precambrian basement \ncomplex of Nigeria. It is a 250km trending NE-SW mega structure \n(Adepelumi et al., 2008; Olujide, 1989). \n\n\n\nIt stretches from East of Ijebu-Ode in the South through Ifewara, Iwaraja, \nOkumesi to the Basin of River Niger, South of Lafiagi to Zungeru and \n\n\n\nbeyond to Kalangai in North-Western Nigeria (Anifowose et al., 2006). A \ngroup researchers reports that the many NW-SE trending faults along the \nIjebu-Ode - Ibadan-Oyo Axis could be where the earthquakes could have \noriginated and is linked to the Mid- Atlantic transform fractures zones in \nthe Gulf of Guinea (Ajakaiye et al., 1987). Regional analyses of Landsat data \nfor Nigeria by Ananaba suggest a complex network of fractures or \nlineaments with dominant directions of northeast-southwest, northwest-\nsoutheast and north-south (Ananaba, 1991). The results confirm fractures \nin the oceanic crust off West Africa and lineaments in Guinea (Neev et al., \n1982). It was also suggested that these lineaments, when extrapolated \ntowards the West Africa coast, have a one-to-one relationship with four \nmajor Fracture zones in the continental margins of West Africa, namely St. \nPaul\u2019s, Romanche, Chain and Charcot fracture zones (Figure 3).  \n\n\n\n3. MATERIALS AND METHODS \n\n\n\n3.1   The 10th January 2020 Abaji-Abuja Seismic Event  \n\n\n\nOn the 10th day of January 2020, Abaji in the Federal capital territory of \nNigeria, experienced an earth tremor at 13:46:20 UTC which is 14:46:20 \nWAT (Nigerian Time). This tremor was picked and recorded by \nseismometers at the Earthquake and Space Weather Laboratory in \nObafemi Awolowo University, Ife, Osun State (Figure 4). The WinQuake \n3.4.2 software was used for the analysis of the seismograms. \n\n\n\n\n\n\n\nFigure 3: Drainage map of Nigeria showing dam sites and inferred megalineaments (Ananaba, 1991) \n\n\n\n \n(a) \n\n\n\n \n(b) \n\n\n\n \n(c) \n\n\n\nFigure 4: a) Ife-1 Data with Ambient noise; b) Ife-2 Data with Ambient noise; c) Ife-3 Data with Ambient noise \n\n\n\n\n\n\n\n\nCite the Article: Chukwunweike C. Amadi, Etim D. Uko, Charles O. Ofoegbu, Adepelumi A. Adekunle, Olatunji S. Ayanninuola  (2023).  \nAppraisal Of the Abaji-Abuja (Nigeria) ML 2.25 Earth-Tremor Of 10th January 2020. Malaysian Journal of Geosciences, 7(1): 31-38.  \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 31-38 \n\n\n\n\n\n\n\n\n\n\n\n3.2   Data Filtering \n\n\n\nThe noise frequencies inherent in the data were filtered out (Figure 5). The \nfrequency of the signals recorded in station A (Ife-1) is between 0.0030 HZ \n\u2013 12.48 HZ, station B (Ife-2) is between 0.0031 HZ \u2013 12.50HZ and station C \n(Ife-3) is between 0.0010 HZ \u2013 12.42 Hz. \n\n\n\n3.3   Computation of Arrival Times and Epicentre \n\n\n\nThe first arrivals of P-wave and S-wave shown in Figure 6 assisted in \ndeducing the travel path of the waves, and epicentre. The symbols are \nexplained in Table1. \n\n\n\nThe trilateration method was used to locate the epicentre of the event \nwhich was placed at Latitude 8.816o and longitude 6.797o coordinates. \nTrilateration method was used (Yu et al., 2019). Figures 7 and 8 shows the \nepicentre in the earth tremor events recorded. \n\n\n\n3.4   Determining the Magnitude and Energy Released \n\n\n\nThe local magnitude, ML, of the earth-tremor was computed using formula  \n\n\n\n\n\n\n\n(Langston, et al., 1998): \n\n\n\n( ) crbrLogaALogML +++=\n \n\n\n\n(1) \n\n\n\nWhere ML = local magnitude; A = maximum ground displacement (46mm); \nthis is obtained from measuring the highest amplitude recorded on the \nseismogram; a = geometric spreading (0.776); r = hypocentral distance \n(5km); b = the attenuation (0.342); c = base level (\u22121.66). This gives a local \nmagnitude ML value of 2.25 ML. The Coda Magnitude Md, which is the \nduration from the start of the event signal to its decay was calculated to be \n3.7 Md. The energy released by the tremor was calculated using Richter-\nGutenberg magnitude-energy relation (Gutenberg and Richter, 1956): \n\n\n\nLog E = 11.8 + 1.5M (2) \n\n\n\nWhere Log = log to base 10; E = energy released in ergs; M = Richter \nmagnitude = 2.25. \n\n\n\n\uf05b \uf05d( )25.25.18.1110 +=E\n \n\n\n\n= 149.6 \u00d7 10-12 KJ (3) \n\n\n\n\n\n\n\n\n\n\n\n(a) \n\n\n\n\n\n\n\n(b) \n\n\n\n \n(c) \n\n\n\nFigure 5: a) Ife-1 Data after removal of ambient; b) Ife-2 Data after removal of ambient noise noise (Filtered using the IIR filter, 1.0Hz High Pass) \n(Filtered using the IIR filter, 1.5Hz High Pass); c) Ife-3 Data after removal of ambient noise (Filtered using the IIR filter,  0.5Hz High Pass) \n\n\n\n\n\n\n\n\n\n\n\n(a) \n\n\n\n\n\n\n\n(b) \n\n\n\n \n(c) \n\n\n\nFigure 6: a) Arrival times in the Ife-1 Seismogram; b) Arrival times in the Ife-2 Seismogram; c) Arrival times in the Ife 3 Seismogram. \n\n\n\n\n\n\n\n\nCite the Article: Chukwunweike C. Amadi, Etim D. Uko, Charles O. Ofoegbu, Adepelumi A. Adekunle, Olatunji S. Ayanninuola  (2023).  \nAppraisal Of the Abaji-Abuja (Nigeria) ML 2.25 Earth-Tremor Of 10th January 2020. Malaysian Journal of Geosciences, 7(1): 31-38.  \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 31-38 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable 1: Appellation of Seismic Phases Identified after Analysis (Ota, 1990) \n\n\n\nSymbol Meaning \n\n\n\nP Direct compressional wave travelling through the upper crust. \n\n\n\nS Direct shear wave travelling through the upper crust \n\n\n\nPg Compressional wave that travelled through the granitic layer of the crust \n\n\n\nSg Shear wave that travelled through the granitic layer of the crust \n\n\n\nPb Compressional wave that travelled along (just beneath) the Conrad discontinuity layer \n\n\n\nPP P wave reflected once in the earth surface \n\n\n\nSS S wave reflected once in the earth surface \n\n\n\n \nTable 2: The arrival times of P and S waves and Epicentre distances of the Stations \n\n\n\nStation \nP-Wave Arrival \n\n\n\nTime \nS-Wave Arrival Time Lag Time \n\n\n\nEpicentre \nDistance \n\n\n\nP-Wave Travel \nTime \n\n\n\nS-Wave Travel \nTime \n\n\n\nOrigin Time \n\n\n\nIfe-1 (A) 13:46:34.5 13:47:10.3 00:00:35.8 330.3km 00:00:47.8 00:01:23.6 13:45:46.7 \nIfe-2 (B) 13:46:34.3 13:47:10.4 00:00:36.1 333.5km 00:00:48.1 00:01:24.3 13:45:46.1 \nIfe-3 (C) 13:46:34.8 13:47:08.1 00:00:33.3 303.2km 00:00:47.4 00:01:20.7 13:45:47.4 \n\n\n\n\n\n\n\n\n\n\n\nFigure 7: Trilateration method showing the epicenter, epicenter coordinates (Lat. 8.816, long. 6.797) and stations. \n\n\n\n\n\n\n\nFigure 8: Epicentre location superimposed on the global map. \n\n\n\n4. RESULTS AND DISCUSSION \n\n\n\nThe geologic structure overlying Abaji, FCT Municipal Council, Abuja, \nNigeria dissipated seismic energy on 10thJanuary, 2020 at 13:45:46 UTC. \nThis seismic energy waves were picked up and recorded on seismograms \nand the WinQuake software was used for the analysis of this data. The \nepicentre of the 10th January 2020 seismic event, Abaji-Abuja, Federal \nCapital Territory, FCT (Figure 3) falls under the Guinea savannah belt of \nNigeria and is underlain by two major rock formations (Longpia et al., \n2013). Abaji-Abuja area falls majorly within the Patti formation of the \nsedimentary basin which is connected to the Nupe sandstone of the Upper \nbasin and extends southwards towards the lower Niger basin (Jatau et al., \n2013). Ojo and Ajakaiye study have shown the presence of rift structures \n\n\n\nwithin the area (Ojo and Ajakaiye, 1989; Oyawoye, 1972; McCurry, 1976). \n\n\n\nThe frequency contents of the seismograms were determined using the \nFast Fourier Transform (FFT) which gave 0.0030 HZ \u2013 12.48 HZ, 0.0031 HZ \n\u2013 12.50 HZ and 0.0010 HZ \u2013 12.42 HZ respectively. High pass filter set to \n1.0HZ, 1.5HZ and 0.5HZ respectively was applied to the seismograms using \nthe Infinite Impulse Response (IIR) filter in order to remove noise \ncomponent of the data. The phases determined on the seismograms \nrevealed that the event was a shallow event with a focal of 5 km ascribed \nto it. The epicentre location was determined using the trilateration \nmethod. According to trilateration is defined as the process of determining \nabsolute or relative locations of points by measurement of distances, using \nmostly geometry of circles; while in the triangulation method, the points \n\n\n\n\n\n\n\n\nCite the Article: Chukwunweike C. Amadi, Etim D. Uko, Charles O. Ofoegbu, Adepelumi A. Adekunle, Olatunji S. Ayanninuola  (2023).  \nAppraisal Of the Abaji-Abuja (Nigeria) ML 2.25 Earth-Tremor Of 10th January 2020. Malaysian Journal of Geosciences, 7(1): 31-38.  \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 31-38 \n\n\n\n\n\n\n\n\n\n\n\nof interest are usually computed based on the measured angles only (Uren \nand Price, 1985). Trilateration locates a point by measuring \u201cdistances\u201d \nand Triangulation tries to do same by measuring \u201cangles\u201d. In this study the \nformer was used to locate the epicentre and not the later. \n\n\n\nThe derived coordinates of the Abaji-Abuja event is 8048\u201957.6\u201dN, \n6047\u201949.2\u201dE. When inputted into Google map, the coordinates were tied to \na location called Gurdi in Yaba district of Abaji local government area of \nAbuja (Federal Capital Territory) - see Figure 8. The local magnitude 2.25 \nML and coda magnitude 3.7 Mdwere computed in order to ascertain the \neffects of the earth tremor on the environment. Amount of energy released \nduring the event was also calculated and a value of 149.6 \u00d7 10-12 KJ was \nobtained. The analysis of the seismogram carried in this work placed the \nlocal magnitude at 2.25 ML. Magnitude of this value classifies the \nearthquake as a micro earthquake (earth tremor), which might not be felt \nby humans most especially during the peak of the day\u2019s activities \n13:45:46.7 when the event occurred. As minor vibrations felt within the \nregion could be written off easily by the locals as farming, road, quarrying \n\n\n\nor construction machineries vibrations. \n\n\n\nThe Total Magnetic Intensity (TMI) anomaly map reveals that the study \narea is characterized by magnetic bodies which intensities vary between -\n48 and 108nT. Prominent anomalies trend in the NE-SW, E-W, and NW-SE \ndirections. These anomalies are likely to be the cause of the magnetic \nresponses from the underlying linear geological structures (Figure 9). We \ndeduced that the reactivation of the faults in the area combined with \nanthropogenic quarrying and possibly water extraction activities might \nhave triggered the earth tremor in the area on that day (Madrigal, 2008; \nPeltz and Saunders, 2017; Okeke, 2018). The limitations of the study \ninclude Available travel time graphs failed to accurately calculate the \ndistance of the event; this was probably due to the little lag time derived \nfrom the seismogram. This resulted in inaccuracies in distance calculation \nwhen trying to manually calculate the distance using travel time graph. \nThe seismogram from station Ife-2 was submerged with lots of ambient \nnoise; this resulted in difficulties in filtering and picking of the P and S \nwave arrival times. \n\n\n\n\n\n\n\nFigure 9: The inferred lineament map of Abaji area (Adepelumi, 2020) \n\n\n\n5. CONCLUSION \n\n\n\nAs this study\u2019s interpretation of the Abaji-Abuja earth tremor has revealed, \nthe occurrence of this events is attributable to the fault lines present; \npredominantly the Romanche fault line which transverses the region and \nalso the presence and reactivation of fault lines within Abaji-Abuja \nenvirons. These fault lines could be attributed to the pan African orogeny \nand its reactivation was as a result of man-made activities of dam \nconstructions, heavy mining activities, drilling borehole clusters, injection \nof carbon dioxide and wastewater into the ground. The computed P-wave \nand S-wave arrival times and lag times were 35.8seconds, 36.1seconds \nand 33.3seconds respectively. Epicentre distances were 330.3km, \n333.5km and 303.2km from the three seismograms using trilateration \nmethod resulted in the epicentre location at 8049'N and 6047'E with a focal \ndepth of 5km. The result revealed that the seismic event has a Local \nMagnitude of 2.25MLand Coda Magnitude of 3.7Mdand the energy \ndissipated was 149.6 \u00d7 10-12KJ.  \n\n\n\nNigeria is not located within plate boundaries as such the country could, \nat first thought, be classified as a seismically passive country. However, \nrecords of seismic events within the region have forced a rethink on that \nposition. Nigeria\u2019s location on one of the mobile belts of Africa and the \nmajor fault lines from the seismically active gulf of Guinea i.e. South \n\n\n\nAtlantic Ocean and the Cameroun volcanic line that transverse through \nvarious states in Nigeria namely; Kwara, Niger, Kaduna, Kano, Jigawa, \nOgun, Oyo, Osun, Ekiti, Kogi, Bauchi, Ondo, Nasarawa, Plateau, Bayelsa, \nImo, Enugu, Benue, Taraba, Katsina and Abuja (Federal Capital Territory \nof Nigeria) have been inferred to be the cause of the seismic events felt in \nthe region.  \n\n\n\nThese fault lines could be attributed to the pan African orogeny and its \nreactivation was as a result of man-made activities, in this study a massive \nrock blasting and quarrying was responsible for the reactivation of these \nfaults. The assertion that Nigeria is a seismically passive country should be \ndiscarded as events of various magnitudes have proved otherwise. The \n2.25ML local magnitude earth tremor in Abaji-Abuja which released 149.6 \n\u00d7 10-12KJ of energy serves as an indicator to the future occurrence of \nanother earthquake within the said region. The region is no longer \ndormant, as such, necessary precautionary measures should be taken \nwhen carrying out geological or construction works within the Abaji-\nAbuja environs. In this study, it is deduced that, massive rock blasting and \nquarrying must have possibly reactivated the faults that traverse the \nAbaji-Abuja area. \n\n\n\nThe assertion that Nigeria is a seismically passive country should be \ndiscarded as events of various magnitudes have proved otherwise. The \n\n\n\n\n\n\n\n\nCite the Article: Chukwunweike C. Amadi, Etim D. Uko, Charles O. Ofoegbu, Adepelumi A. Adekunle, Olatunji S. Ayanninuola  (2023).  \nAppraisal Of the Abaji-Abuja (Nigeria) ML 2.25 Earth-Tremor Of 10th January 2020. Malaysian Journal of Geosciences, 7(1): 31-38.  \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 31-38 \n\n\n\n\n\n\n\n\n\n\n\n2.25 ML local magnitude earth tremor in Abaji-Abuja serves as an indicator \nto the future occurrence of another earthquake within the said region as \nthe fault system within the region is no longer dormant as such necessary \nprecautionary measures should be taking when carrying out geological or \nconstruction works within the Abaji-Abuja environs. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe authors are very grateful to Earthquake and Space Weather \nLaboratory, Obafemi Awolowo University, Ile-Ife, Osun State Nigeria for \nsupplying thedata used in this study. \n\n\n\nREFERENCES \n\n\n\nAdepelumi, A., Ako, B., Olorunfemi, J., Awoyemi, M., and Falebite, D., 2008. \nIntegrated Geophysical Mapping of the Ifewara Transcurrent Fault \nSystem, Nigeria. Journal of African Earth Sciences, 52 (4-5), Pp. 161-\n166. \n\n\n\nAdepelumi, A.A., 2020. Preliminary interpretation of the January 10, 2020 \nearth-tremor occurrence at Abaji, FCT Municipality with ML = 3.8. \nEarthquake & Space weather laboratory, Department of Geology, \nObafemi Awolowo University, Ile-Ife, Osun State, Nigeria. A Paper \npresented to Earthquake & Space weather laboratory, Department \nof Geology, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria, \nPp. 1-8. \n\n\n\nAfegbua, K.U., Yakubu, T.A., Akpan, O.U., Dauda, D., and Usifoh, S.E., 2011. \nTowards an integrated seismic hazard monitoring in Nigeria using \ngeophysical and geodetic techniques. International Journal of the \nPhysical Sciences, 6 (28), Pp. 6385-6393. \n\n\n\nAjakaiye, D.E., D.H. Hall, T.W., Millar, P.J.T., and Verheijen, Ojo, S.B., 1986. \nAeromagnetic anomalies and tectonic trends in and around the \nBenue Trough, Nigeria. Nature, 319 (54), Pp. 582 \u2013 584.  \n\n\n\nAjakaiye, D.E., Hall, D.H., and Miller, T., 1984. Interpretation of \nAeromagnetic Data across the Central Crystalline Shield of Nigeria, \nin press. Geophysical Journal. \n\n\n\nAkpan, A.E., Ilori, A.O., and Essien, N.U., 2015. Geophysical investigation of \nObot Ekpo Landslide site, Cross River State, Nigeria. Journal of \nAfrican Earth Sciences, Pp. 154-167. \n\n\n\nAnanaba, S.E., 1991. Dam Sites and Crustal Megalineaments in Nigeria, ITC \nJournal, 1, Pp. 26-29. \n\n\n\nAnifowose, A.Y.B., Odeyemi, I.B., and Borode, A.M., 2006. The Tectonic \nSignificance of the Ifewara- ZungeruMegastruture in Nigeria, in \nTeme, S.C. and Ezeigbo, C.U. (Eds.), Proceedings of the 1st \nInternational Workshop on Geodesy & Geodynamics, Centre of \nGeodesy & Geodynamics, Toro, Nigeria, Pp. 17-28. \n\n\n\nAyodeji, E.A., and Baruah, S., 2016. A GIS-based Tectonic Map of Nigeria. J. \nAssam Sc. Soc., 57, (1 & 2), Pp. 108-114. \n\n\n\nBritt, L., 2012. Fracture stimulation fundamentals. Journal of Natural Gas \nScience and Engineering,  8, Pp. 34-51.  \n\n\n\nBurke, K., 1969. Seismic areas of the Guinea coast where Atlantic fracture \nzones reach Africa. Nature, 222, Pp. 655-657. \n\n\n\nCicerone, R.D., John, E.A., and Britton, J., 2009. A Systematic Compilation of \nEarthquake Precursors, Tectonophysics, 476, Pp. 371-396. \n\n\n\nElueze, A.A., 1990. Geological setting of Proterozoic schist belts and \nrelationship to crustal movement. In: Ofoegbu, C.O., Osemekhain, \nJ.E.A, Ekwelibe, P., Ezeife, N., Ejezie, S. and Obiaa, T. I. Proceedings of \nthe 5th International Seminar and Exhibition on Earthquake \nPrognostics. National Technical Committe on Earthquake \nPhenomena., Pp. 108-114, Lagos. \n\n\n\nEmery, K., Uchupi, O., Phillips, J., Brown, C., and Mascle, J., 1975. \nContinental margins off west Africa: Angola to SierraLeone. America \nAssociation of Petroleum Geologists Bulletin, 59, Pp. 2209-2265. \n\n\n\nEze, C.L., Sunday, V., Ugwu, S., Uko, E.D., and Ngah, S., 2011. Mechanical \nModel for Nigeria Intraplate Earth Tremors. . Earth Observation, \nPort-Harcourt. IEEE Oceanic Engineering Society. \n\n\n\nFrancis, T., Porter, I., and Lilwal, R., 1978. Microearthquakes near the \neastern end of St Paul\u2019s Fracture Zones. Geophysical Journal of the \nRoyal Astronomical Society, Pp. 201-217. \n\n\n\nGibson, G., and Sandiford, M., 2013. Seismicity and Induced Earthquakes, \nMelbourne Energy Institute, University of Melbourne. \n\n\n\nGutenberg, B., Richter, C.F., 1956. Earthquake magnitude, intensity, \nenergy, and acceleration: (Second paper). Bulletin of the \nSeismological Society of America, 46 (2), Pp. 105\u2013145. \n\n\n\nJatau, B.S., Lazarus, G., and Oleka, B.A., 2013. Geoelectric Drilling Of Part Of \nAbaji And Environs Abaji Area Council ederal capital Territory \nAbuja North-Central Nigeria. International Research Journal of \nNatural Sciences, 1 (2), Pp. 1-10. \n\n\n\nLangston, C.A., Brazier, R., Nyblade, A.A., and Owens, T.J., 1998. Local \nMagnitude Scale and Seismicity Rate for Tanzania, East Africa. \nBulletin of the Seismological Society of America, 88, Pp. 712-721. \n\n\n\nLongpia, C.B., Ashano, E.C., and Pam, B.G., 2013. Geophysical Investigation \nfor Engineering Site Development arond Galadima-Aleita Area, \nAbuja, North Central Nigeria. Journal of Applied Geology and \nGeophysiocs, 1 (6), Pp. 01-11. \n\n\n\nMcCurry, P., 1976. The geology of the Precambrian to Lower Paleozoic \nrocks of Northern Nigeria. In C.A. Kogbe (Ed). Geology of Nigeria, Pp. \n15-39. Elizabethan Press. Lagos. \n\n\n\nMinapuye, I.O., Soronnadi-Ononiwu, G.C., Prince, S.M., and Oduah, A.A., \n2018. Tectono-Stratigraphy of Benin Basin, West Africa. American \nScientific Research Journal for Engineering, Technology, and \nSciences, 48 (1), Pp. 1-16. \n\n\n\nMurat, R.C., 1972. Stratigraphy and paleogeography of the Cretaceous and \nLower Tertiary in Southern Nigeria. In: Dassauvagie, T. F. J and \nWhiteman, A. J. (Eds.). Africa Geology, Geology Department, \nUniversity of Ibadan, Nigeria, Pp. 251-266. Ibadan. \n\n\n\nNeev, D., Hall, J.K., and Saul, J.M., 1982. The pelusiummegashear system \nacross Africa and associated lineament swarms. J. Geophys Res., 87, \nPp. 1015-1030. \n\n\n\nNwankwoala, H., and Orji, O., 2018. An Overview of Earthquakes & \nTremors in Nigeria: Occurances, Distribution and Implications for \nMonitoring. International Journal of Geology and Earth Sciences, 4 \n(4), Pp. 56-76. \n\n\n\nNwankwoala, H.O., 2018. Geoethics: Georisk Management for a Safer and \nmore Resilient Society. A Lead Paper Presented at the First Nigeria \nConference of the International Association for Promoting \nGeoethics (IAPG), held between 18 \u2013 19 October 2018 at the Faculty \nof Law Auditorium\u201d, Rivers State University, Port Harcourt, Nigeria. \n\n\n\nNyong, E.E., 1995. Cretaceous sediments in the Calabar Flank. In: B. N. \nEkwueme; E.E. Nyong and S.W Petters (Eds.). Geological Excursion \nguidebook to the Oban Massif, Calabar Flank and Mamfe \nEmbayment, Southeastern Nigeria. Nigeria Mining and Geosciences \nSociety 31st Annual Conference, pp. 14-25. Calabar. \n\n\n\nNyong, E.E., and Ramanathan, R., 1985. A record of oxygen-deficient \npaleoenvironments in the Cretaceous of the Calabar Flank, SE \nNigeria. Journal of Africa earth Science, 3 (4), Pp. 455-460. \n\n\n\nObaje, N., 2009. Geology and Mineral Resources of Nigeria. Lecture Notes \nin Earth Sciences, Berlin Springer Verlag, Pp. 120 doi = \n10.1007/978-3-540-92685-6. \n\n\n\nOjo, S.B., and Ajakaiye, D.E., 1989. Preliminary Interpretation of Gravity \nMeasurements in the Middle Niger Basin Area, Nigeria. In C. A. \nKogbe, Geology of Nigeria, 2nd Edition Pp. 347-358. Lagos: \nElizabethan Publishing Company. \n\n\n\nOkeke, C., 2018. Boreholes drill holes into future of environment. \nRetrieved December 2019, from Daily Trust: \nhttps://www.dailytrust.com.ng/boreholes-drill-holes-into-future-\nof-environment.html. \n\n\n\nOlujide, P.O., and Udoh, A.N., 1989. Preliminary Comments on the Fracture \nSystems of Nigeria. National Seminaron Earthquakes in Nigeria; \nAjakaiye, D.E., Ojo, S.B. and Daniyan, M.A. (Eds.), Pp. 97-109. \n\n\n\nOta, K., 1990. Anatomy of Seismograms. Amsterdam: Elesevier Science B.V. \n\n\n\nOyawoye, M.O., 1972. The Basement Complex of Nigeria (In) African \nGeology (T.F.J. Dessauvagie and A. J. Whiteman, Eds., pp 67-99. \nIbadan Univ. Press. \n\n\n\n\n\n\n\n\nCite the Article: Chukwunweike C. Amadi, Etim D. Uko, Charles O. Ofoegbu, Adepelumi A. Adekunle, Olatunji S. Ayanninuola  (2023).  \nAppraisal Of the Abaji-Abuja (Nigeria) ML 2.25 Earth-Tremor Of 10th January 2020. Malaysian Journal of Geosciences, 7(1): 31-38.  \n\n\n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(1) (2023) 31-38 \n\n\n\n\n\n\n\n\n\n\n\nPeltz, A., and Saunders, N., 2017. Six ways oil and gas development can \ncontaminate land and water and what to do about it. Retrieved \nJanuary 10, 2020, from Enviromental Defense Fund: \nhttp://blogs.edf.org/energyexchange/2017/12/29/six-ways-oil-\nand-gas-development-can-contaminate-land-and-water-and-what-\nto-do-about-it/. \n\n\n\nTsalha, M.S., Lar, U.A., Yakubu, T.A., Uma, R.A.K., and Duncan, D., 2015. The \nReview of the Historical and Recent Seismic Activity in Nigeria. \nIOSR, Journal of Applied Geology and Geophysics, e-ISSN; 2321-\n0990, 3 (1), Pp. 48-56. \n\n\n\nTurner, D.C., Macleod, W.N., Buchanan, M.S., 1971. The Geology of Jos \nPlateau\u201d. G.S.N. Bulletin, 32 (2), Pp. 107.  \n\n\n\nUgwu, S.A., and Onuoha, K.M., 2010. An Analysis of Earthquakes Focal \n\n\n\nDepths in Africa (1900-2000), The IUP Journal of Earth Sciences, 4 \n(2), Pp. 42-52. 2016 Social Sciences Electronics Publishing. Inc., \nSocial Science Research Network. \n\n\n\nUren, J., Price, W.F., 1985. Triangulation and Trilateration. In: Surveying \nfor Engineers. Palgrave, London. https://doi.org/10.1007/978-1-\n349-07355-9_6. \n\n\n\nVillaverde, R., 2009. Fundamental Concepts of Earthquake Engineering, \nCRC Press, London, New York. \n\n\n\nWright, J., 1976. Fracture systems in Nigeria and initiation of fracture \nzones in the south Atlantic. Tectonophysics, Pp. 43-47. \n\n\n\nYu, Z., Wenfei, L., Xionghui, L., and Xu, Z., 2019. Single-Camera \nTrilateration. Applied Sciences, 1, Pp. 29. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 38-42 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2020.38.42 \n\n\n\nCite the Article: Jewel E. Thomas (2020). Seismo - Ionospheric Induced Perturbations Prior To The September 28, 2007 M7.5 Northern Mariana U.S.A. Geoquake From \nGps, Tec And Demeter Data. Malaysian Journal of Geosciences, 4(1): 38-42. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2020.38.42 \n\n\n\nSEISMO - IONOSPHERIC INDUCED PERTURBATIONS PRIOR TO THE SEPTEMBER \n28, 2007 M7.5 NORTHERN MARIANA U.S.A. GEOQUAKE FROM GPS, TEC AND \nDEMETER DATA \n\n\n\nJewel E. Thomas \n\n\n\nGeophysics Research Group, Department of Physics, Akwa Ibom State University, Ikot Akpaden, Mkpat Enin, Akwa Ibom State, Nigeria. \n*Corresponding Author Email: jewelemem@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 10 June 2020 \nAccepted 14 July 2020 \nAvailable online 24 July 2020\n\n\n\nData from DEMETER (IAP and ISL sensors) and GPS (TEC), were used to decipher variations of electron \ndensity, electron temperature and ion density within the seismogenic zone of a seismic event that occurred \non September 28, 2007 at Northern Mariana U.S.A, through statistical analysis.  The study revealed both pre \nand post ionospheric perturbations from both sets of data. The observed anomalous variations were screened \nfor false alarm using the geomagnetic indices of kp and Dst. It was observed that the abnormal TEC on -10, -\n7, -3 and -2 days occurred under quiet geomagnetic conditions while all pre-seismic (-15, -10, - 9 -7 days) \nionospheric variations from the DEMETER data were also obtained during quiet geomagnetic conditions \nsuggesting them to be seismo-ionospheric induced perturbations. Interestingly, the perturbations on -10 and \n-7 days were simultaneously observed from both GPS and DEMETER datasets under quiet geomagnetic \nionospheric conditions offering a strong pointer to the impending geo-quake.  \n\n\n\nKEYWORDS \n\n\n\nGeomagnetic indices, Seismogenic zones, and ionospheric perturbations.\n\n\n\n1. INTRODUCTION \n\n\n\nOn September 28, 2007, a magnitude 7.5 geo-quake occurred in Northwest \nof Farallon de Pajaros, Northern Mariana Islands which is south of Japan \nresulting from oblique reverse faulting at intermediate depth nearly 260 \nkm below the North Pacific Ocean and almost 300 km west of the Mariana \nTrench, marking where the Pacific plate begins its subduction beneath the \nPhilippine Sea plate. Focal mechanism solutions indicate that oblique \nrupture occurred on either a northwest- or east-northeast-striking, \nmoderately dipping reverse fault. Of these two possible fault orientations, \nfinite-fault modeling of globally recorded seismic data is more consistent \nwith slip on the east-northeast-striking fault. At the location of the \nearthquake, the Pacific plate moves to the west with respect to the \nPhilippine Sea plate at a velocity of about 40 mm/yr. The earthquake \nrepresents the release of stress resulting from the distortion of the Pacific \nplate at depth (Hayes et al., 2017). This was just one out of the 2270 \nearthquakes that occurred in 2007 and due to its depth, no damage was \nrecorded.  \n\n\n\nEarthquake is among the natural disaster that greatly impacts the Earth\u2019s \nsurface which has proven to be dangerous to human life and properties. \nAs a result of the complex nature of earthquake-preparation processes, \nfinding accurate earthquake precursors from abnormal geophysical \nsignals is still a world-class problem. Similarly, seismic activity is one of \nthe causes of daily ionospheric inconsistency. This is due to the fact that \nthe coupling between the anomalous generated electric field in the \nearthquake preparation zone at the ground surface and the one generated \nin ionosphere causes the ion drift that resulted in the modification of the \nionosphere formation. However, to carefully forecast the geo-quake using \n\n\n\nthe ionosphere, these questions must be address. (i)Is there any change in \nthe ionosphere due to pre-seismic event and aftermath event? (ii) Having \nunderstood that there are different mechanisms responsible for the \nplasma distribution in the ionospheric layers, what are the basic \nionospheric drivers responsible for each stages of precursor, aftermath \nand during the earthquake? (iii) is there any significant on latitudinal \ndependence of the ionospheric precursor, during and aftermath of \nearthquake in the ionosphere?  \n\n\n\nThere is complexity with Earthquake physics. The occurrence of geo-\nquake is linked with the earth\u2019s crust dynamics. Due to the acoustic driven \nmechanisms in the atmosphere, the origin of the waves generated at the \nsurface of the Earth penetrates into the ionosphere, thereby triggering the \nredistribution of the neutral gases in the ionosphere before the \nearthquakes (Pulinets and Boyarchuk, 2004; Pulinets and Davidenko, \n2014; Ryu et al., 2014). A lot has been widely reported on the disturbances \nand the underlying mechanisms that cause the seismio-ionospheric \ncoupling activities (Ondoh, 2008; Namgaladze et al., 2009; Kuo et al., \n2011). Nevertheless, the electric and geomagnetic fields, neutral winds \nand diffusion mechanisms, as well as the acoustic gravity wave that \nemanated from the lower altitude of the atmosphere, are the main drivers \nof low- and mid-latitude ionospheric dynamics during and after the \nearthquake preparation (Heelis, 2004).  \n\n\n\nVarious techniques and measurements had been earlier employed in the \nstudy of seismo-ionospheric precursor but notably among them is the \nstatistical seismo-ionospheric analysis. The ionospheric earthquake \nprecursor has been linked through various mechanisms of the formation \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 38-42 \n\n\n\nCite the Article: Jewel E. Thomas (2020). Seismo - Ionospheric Induced Perturbations Prior To The September 28, 2007 M7.5 Northern Mariana U.S.A. Geoquake From \nGps, Tec And Demeter Data. Malaysian Journal of Geosciences, 4(1): 38-42. \n\n\n\nof the ionosphere and in the atmosphere, which are valid for different \nionospheric disturbances (Parrot et al., 2016; Surkov 2015). A study \nreported that the onset of the irregularities in the morphology of the \nionosphere is of course close to the future epicenter (7-15 days) (Parrot et \nal., 2016). These irregularities were brought about by gravity waves that \nemanated from the activation of the fault where the permeability changes \nand where aerosols and gas including radon can appear (Surkov, 2015). \nThis leads to the ionization of air molecules, which are responsible for the \nre-modification of the ionosphere. Then, different effects can occur: \ngrowth of air temperature, formation of temperature and pressure \nanomalies, anomalies in Outgoing Long wave infrared Radiation (OLR), \nredistribution of electric charges in the Earth's atmospheric system and \nthen in the ionosphere due to the global electric circuit and electric field \nirregularities in the connection action. \n\n\n\nFrom the above discussion, seismic precursors are still much debatable \nand there was no satellite mission for the detection of EQ associated \nionospheric abnormalities afore the DEMETER satellite (Parrot, 2009). \nDEMETER (Detection of Electromagnetic Emission Transmitted from \nEarthquake Region) was a micro-satellite (130 kg mass) placed on an \nalmost polar orbit with a low altitude (710 km) providing a global \ncoverage of active seismic regions (Cussac et al., 2006). From a study the \nmain scientific objectives of DEMETER were the variations in the \nionosphere as a result of EQ induced electromagnetic activity and \nanthropogenic activities (Parrot, 2009). This paper studied seismo- \nionospheric anomalies from both DEMETER and GPS TEC data over the EQ \npreparation zone (i.e. M7.5, N, Mariana U. S. A. seismic event of September \n28, 2007).  \n\n\n\n2. DATA \n\n\n\n2.1 DEMETER data \n\n\n\nDEMETER was a low-altitude satellite which was launched in June 2004 \non a polar and circular orbit that measures electromagnetic waves and \nplasma parameters around the world apart from in the auroral zones. Its \noriginal elevation of ~710 km was decreased to ~660 km at the end of \n2005. This was the first satellite dedicated mainly to record seismo-\nelectromagnetic effects on the ionosphere. It had six scientific payloads. \nEach of them offered long-time and continuously high-quality data to \nallow performing meaningful statistical studies with a much larger \nnumber of recorded events in comparison with previous ones. Detailed \ndescription of the DEMETER have been given by a lot of researchers \n(Cussac et al., 2006; Lebreton et al., 2006; Berthelier et al., 2006; Mei and \nParrot, 2013; Ibanga et al., 2017). Of the six experiments in DEMETER, only \ntwo experiments (Plasma Analyser (IAP) and Langmuir Probe (ISL)) were \nused in this study.  \n\n\n\nThe experiment IAP recorded the ion density (total ion density being the \nsum of H+, He+ and O+) and seismic activities that took place during the \nsatellite's lifetime with a 4 s time resolution. The electron density and \nelectron temperature data used in this paper were measured by the ISL \nexperiment of the satellite. DEMETER recorded many seismic events. \nFigure 1, is an example of an event as recorded by it. This relates to the \ngeo-quake of September 28, 2007 at 13:38:57 UTC having a magnitude of \n7.5 and a focal depth of 260 km. Its geographic coordinates were 22.0130N \nand 142.6680E. From the top to the bottom panels, the top panel gives the \nheader frame that includes date, orbit number, involved institutes, date \nand version of quick-look creation. The second panel is the ISL sweep \nspectrograph with the version of the onboard and ground processing \nframe.  \n\n\n\nThe spectrogram of the Langmuir probe measures the sweep voltage in \nvolt and collected current in log (nA). Parameters deduced from ISL \nmeasurements (ISL current and potentials) are displayed in the third \npanel. To this, the version of onboard and ground processing software are \ngiven at the top right side of the panel, as well as currents and potentials \n(Vf \u2013 floating potentials in V, \u0278s \u2013 potential in V(-\u0278s is displayed) and Ie- \nelectron current in nA (log (Ie) is displayed). The bottom panel specifies \nthe satellite closest approach of past and future EQ epicenters that are \nwithin 2000 km from the DEMETER orbit. The Y -axis represents the \ndistances D between the epicenters and the satellite, from 750 up to 2000 \nkm.  \n\n\n\nThe symbols are filled square for post-seismic events, filled triangle for \npre-seismic events. The scale on the right represents the time interval \nbetween the EQs and the DEMETER orbit with a graduation from >30 days \nup to a [0\u20136 h] interval. The empty symbols have similar significations \nexcept that they are related to the magnetically conjugated points of the \nepicenters (the distance D is then the distance between these magnetically \n\n\n\nconjugated points of the epicenters and the satellite). The symbol sizes \ncorrespond to EQs of magnitude [5\u20136], [6\u20137], and [>7]. At 00:52:00 UTC \nthe red triangle indicates the closest approach to the epicenter of this EQ. \n\n\n\nFigure 1: DEMETER orbit that recorded the anomalous variations on the \n\n\n\nearthquake day. \n\n\n\n2.2 GPS data \n\n\n\nThe Global Positioning System (GPS) satellites are now primary sensors \nused to determine signatures linked with natural hazards such as \nearthquake. GPS is a group of satellite that orbits the earth two times per \nday at an elevation of about 20,000 km. The enormous network of GPS \nreceivers (a few thousands all over the planet) elucidates simultaneous \ncoverage in universal scale with high temporal resolution. The GPS \nsatellites transmit two frequencies of signals (LI = 1575.42 MHz and L2 = \n1227.60 MHz). These GPS receivers are able to detect ionospheric TEC \nperturbations caused by surface-generated Rayleigh, acoustic and gravity \nwaves. TEC can be used to estimate spatial sizes and temporal dynamics \nof pre-earthquake ionospheric effects in any seismogenic region.  From a \nlist of International Global navigation satellite system service (IGS) station \ncode, the IGS station within the radius of the earthquake preparation zone \n(1678.80 km) at GUUG was selected.  \n\n\n\nRequest was made for the observation file within the studied time frame. \nApplying the RINEX Gopi software, total electron content (TEC) was \ncalculated from the observation data. TEC variations are often explored for \nseismo- ionospheric precursors due to TEC data global analysis, \ncontinuous observation and satisfactory time- and space resolution plus \nenormous amount of the data available. This method has been used to \nclassify variations from geo-quake (Calais and Minster, 1995). A group \nresearchers have used a statistical approach with TEC data from TOPEX \u2013 \nPOSEIDON to investigate connections between ionospheric perturbations \nand seismic event (Zaslavski et al., 1998). In other study, researchers \nequally used a statistical technique to obtain the ionospheric TEC from \ndata measured by a network of the GPS in Taiwan (Liu et al., 2002). \n\n\n\n2.3 Geomagnetic data \n\n\n\nLocal but significantly large-scale fluctuations in atmospheric electricity \nover seismically active zones afore the seismic event are transmitted to \nthe ionosphere by means of a large-scale electric field. From the \npenetration of this electric field into the ionosphere, electron \nconcentration irregularities are detected when the region affected has an \narea with a diameter larger than 200 km2 (Dobrovolsky et al., 1979). \nNevertheless, the disparities in the ionospheric strictures are not only \nfrom earthquakes as there are numerous possibilities of ionospheric \ndistresses that can originate from other sources such as solar activity, \nacoustic gravity waves, traveling ionospheric disturbances, plasma \ndynamics, and large meteorological phenomena etc. Subsequently, the \ndetected parameters may exhibit variations in the absence of seismic \nactivity; hence, it is hard to segregate pre-seismic ionospheric phenomena \nfrom the ionospheric turbulences due to the solar-terrestrial activities \n(Ondoh, 2008).  \n\n\n\nThus, to differentiate the seismo-ionospheric perturbations from \ngeomagnetic instabilities, the geomagnetic indices Dst and Kp were \nchecked. The Kp index monitors the planetary activity on a universal scale \nwhile the Dst index registers the equatorial ring current variations \n(Mayaud, 1980). The ionospheric influence of a geomagnetic storm has a \nglobal effect being observed all over the world while, the seismogenic \nimpact is observed only by places with distance less than 2000 km from \nthe potential epicenter (Pulinets et al., 2003). Data of Dst and Kp are used \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 38-42 \n\n\n\nCite the Article: Jewel E. Thomas (2020). Seismo - Ionospheric Induced Perturbations Prior To The September 28, 2007 M7.5 Northern Mariana U.S.A. Geoquake From \nGps, Tec And Demeter Data. Malaysian Journal of Geosciences, 4(1): 38-42. \n\n\n\nto estimate the effects of geomagnetic activity. Dst data with time \nresolution of 1 hour are used to describe the geomagnetic activity in the \nmiddle and low latitude region. Moderate magnetic storm occurs when the \nDst values are less than \u201350 nT while great geomagnetic storm occurs \nwhen the Dst data surpass \u2013100 nT (Xinzhi et al., 2014). Kp data with time \nresolution of 3 hours indicate the global geomagnetic activity. Kp value \nspans from 0 (low activity) to 9 (strong activity). Kp index is less than 3 \nwhen the geomagnetic activity is quiet (Rostoker, 1972). \n\n\n\n3. MATERIAL AND METHOD \n\n\n\nThe M7.5 North Mariana earthquake of September 28, 2007 at an \nepicenter of 22.0130N and 142.668 0E by 13:38: 57 UTC (11:38:57 LT) at a \nfocal depth of 260km was selected for this study. Orbits closet to the \nepicenter (at a resolution of 200 for longitude and 100 for latitude) were \nselected 30 days prior and 10 days post the geo-quake. This time period \nallowed enough time to carefully monitor the ionospheric plasma \nparameter from its unperturbed to perturb state enhancing separation of \nseismic anomalies from the background of natural variations, with the \nexpectation of the former to appear at the end of the period. Different time \nintervals such as two months to five days have been chosen to monitor the \nionosphere but principally, reports on seismo-electromagnetic variations \nare observed three weeks or less to the earthquake day (P\u00ed\u0161a et al., 2011; \nRong et al., 2008; Parrot, and Li, 2012).  \n\n\n\nThe total ion density (Oxygen, Hydrogen and Helium ions from the IAP \nSensor), electron density and electron temperature (from the ISL Sensor) \nwere obtained by downloading data files from the DEMETER website. Data \nfrom each orbit were available in two modes (survey and burst modes) but \nonly the burst mode data was utilized in this research. The middle and the \ninter-quartile range of the data were employed to find their upper and \nlower limits in order to differentiate seismic variances from the \nbackground of regular variations. A reference value k was selected to be \n2.1. Any perturbations outside these bounds were anomalous. These \ninvolved computation of upper and lower boundaries, median value and \ninter-quartile range using Eqs. (1) \u2013 (3) below: \n\n\n\nIQRkMxhigh \u2022+=\n  (1) \n\n\n\nIQRkMxlow \u2022\u2212=    (2) \n\n\n\nIQR\n\n\n\nMx\nDxk\n\n\n\nIQR\n\n\n\nMx\nkxxx highlow\n\n\n\n\u2212\n=\uf03c\n\n\n\n\u2212\n\uf03c\u2212\uf0de\uf03c\uf03c ;\n\n\n\n  (3)  \n\n\n\nHere x, xhigh, xlow, M, IQR and Dx are parameter values, upper bound, lower \n\n\n\nbound, middle of the data, inter-quartile range and differential of  x \n\n\n\ncorrespondingly. Thus, if the absolute value of Dx is more than k, (i.e., \n\n\n\n\u2502Dx\u2502>k) (\u2502Dx\u2502 > k), then the behaviour of x is assumed to be anomalous. \n\n\n\nSimilar statistical methods have been used by many researchers to isolate \n\n\n\nbackground perturbations from seismo- induced perturbations (Liu et al., \n\n\n\n2004, Pulinets and Boyarchuks, 2004; Akhoondzadeh et al., 2010, Ibanga \n\n\n\net al., 2017).  However, the detected anomaly had to be crisscrossed with \n\n\n\ngeomagnetic indices each day to isolate geomagnetic induced \n\n\n\nperturbations from seismo-induced variations. The radius (R) of the \n\n\n\nearthquake preparation zone was determined from Eq. (4) given \n\n\n\n(Dobrovolsky et al., 1979): \n\n\n\nR= 10 0.43M   (4)\n\n\n\nwhere M is the magnitude of the earthquake. From Eq. (4), it is obvious \nthat the preparation zone is proportional to the geo-quake\u2019s magnitude.  \n\n\n\n4. RESULTS \n\n\n\nResults of DEMETER data analysis for the Mariana Island, U.S.A. \nearthquake (28 September, 2007) is presented in two dimensional plots \n(figure 2). The earthquake day (0) is represented as vertical dotted line. \nThe lilac horizontal lines indicate the upper and lower bounds \n(M\u00b12.0*IQR). The orange horizontal lines indicate the median value (M). \nThe x-axis represents the day relative to the earthquake day. The y-axis \nrepresents (i) electron density derived from the measurements of the ISL \nexperiment; (ii) electron temperature derived from the measurements of \nthe ISL experiment and (iii) total ion density derived from the \nmeasurements of the IAP experiment during (a) night and (b) morning. \nTEC anomaly plot obtained when |DTEC| > 3, Kp < 3 and Dst > \u221220 (nT).  is \npresented in figure 3. The TEC anomaly is represented as a function of the \nUniversal Time Coordinate (UTC) and number of days relative to the main \nshock. Correspondingly, the contour plots of kp and Dst geomagnetic \n\n\n\nindices are shown in figure 4a and 4b. All detected anomalies from the \nthree datasets are clearly shown in Table 1. \n\n\n\nFigure 2: Results of DEMETER data analysis for the Mariana Island, U.S.A. \n\n\n\nearthquake (28 September, 2007). \n\n\n\nThe earthquake day is represented as vertical dotted line. The green \nhorizontal lines indicate the upper and lower bounds (M\u00b12.0*IQR). The \nred horizontal line indicates the median value (M).The x-axis represents \nthe day relative to the earthquake day. The y-axis represents (i) electron \ndensity derived from the measurements of the ISL experiment; (ii) \nelectron temperature derived from the measurements of the ISL \nexperiment and (iii) total ion density derived from the measurements of \nthe IAP experiment during (a) night and (b) morning. \n\n\n\nFigure 3: TEC anomaly plot detected based on: |DTEC| > 3, Kp < 3and Dst \n\n\n\n> \u221220 (nT). \n\n\n\n(a) kp index  (b)Dst index \n\n\n\nFigure 4: Results of analysis of variations of kp (a) and Dst (b) \n\n\n\ngeomagnetic indices respectively. The y-axis represents the Time (UTC) \n\n\n\nwhile the x-axis represents day relative to earthquake. \n\n\n\n(iii))         (a) (b) \n\n\n\nM+2.0*IQR \nM+2.0*IQR \n\n\n\nM+2.0*IQR \n\n\n\nM+2.0*IQR \n\n\n\nM+2.0*IQR \n\n\n\nM+2.0*IQR \n\n\n\nM-2.0*IQR M-2.0*IQR \n\n\n\nM-2.0*IQR \n\n\n\nM-2.0*IQR M-2.0*IQR \n\n\n\nM \nM-2.0*IQR \n\n\n\nM \n\n\n\nM \n\n\n\nM \n\n\n\nM \n\n\n\nM \n\n\n\nEarthquake day \n\n\n\nEarthquake day \n\n\n\nEarthquake day \n\n\n\nEarthquake day \n\n\n\nEarthquake day \n\n\n\nEarthquake day \n\n\n\nMagnetic activity \n\n\n\nMagnetic activity \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 38-42 \n\n\n\nCite the Article: Jewel E. Thomas (2020). Seismo - Ionospheric Induced Perturbations Prior To The September 28, 2007 M7.5 Northern Mariana U.S.A. Geoquake From \nGps, Tec And Demeter Data. Malaysian Journal of Geosciences, 4(1): 38-42. \n\n\n\nTable 1: A table showing detected anomalies from all dataset. Days are \n\n\n\nrelative to EQ (Day 0) \n\n\n\n5. DISCUSSION\n\n\n\nSeismo-ionospheric induced perturbations have been widely studied \nusing GPS and DEMETER data. In order to differentiate geomagnetic \nvariations from seismogenic sources, the Dst and kp geomagnetic indices \nwere checked alongside with the studied data. From the GPS data where \nthe TEC was derived, a total of 22 anomalies were detected from 12 days \n(Table 1). These anomalous TEC were criss- crossed with both Dst and kp \ngeomagnetic indices on each day. From the result, it was revealed that \nthese geomagnetic indices were active on -26, -25, -22, -5, -1, 0, +1 and +8 \ndays. However, the perturbations on -10, -7, and 3 and -2 days prior to the \nseismic event happened in quiet geomagnetic conditions. The TEC \nanomaly plot obtained when |DTEC| > 3, Kp < 3 and Dst > \u221220 (nT) (Figure \n3) displayed these variations, Similarly, DEMETER data displayed 8 \nunusual variations from 7days.  \n\n\n\nThe three investigated ionospheric plasma parameters (total ion density, \nelectron density and temperature) were all perturbed within the studied \nperiod. On -15days, the electron density from ISL device unveiled a \nconspicuously high value of 6.65 in the morning orbit (Figure 2i (b)). \nCorrelating this result with figures 4a and 4b and by virtual inspection, it \nis clearly seen that there was no geomagnetic activity from both indices. \nFrom the same ISL sensor, the electron temperature was perturbed on -10 \nand -9 days (figure 2ii (a)) with values of 2.24 and 1.50 in quiet \ngeomagnetic conditions as portrayed by fig 4a and 4b. The IAP sensor that \nrecorded the total ion density revealed a striking abnormal variation in the \nnight time orbit measurement 7 days afore (figure 2iii (a)) the geo-quake \nhaving a numerical value of 6.56.  \n\n\n\nThis is consistent with the reports which showed that the efficiency of the \nanomalous electric field penetration into the ionosphere at night is higher \nthan in daytime Cross checking this with geomagnetic indices of kp and \nDst (figure 4a and 4b), it was observed that this perturbation occurred in \ngeomagnetically quiet conditions (Pulinets and Boyarchuk, 2004; Ibanga \net al., 2017). However, the investigation revealed that the perturbations \non the earthquake day (0) and after the seismic event (+2 and +6 days) \n(figure 2i (b) happened in active geomagnetic conditions (fig 4a and 4b). \nA correlation of the anomalies from both GPS and DEMETER revealed that \nboth data sets were simultaneously disturbed on -10 and -7 days before \nthe September 28, 2007 earthquake under quiet geomagnetic conditions. \nThis is agreement with the reports of Parrot et al. (2016) that the onset of \nthe irregularities in the morphology of the ionosphere is of course close to \nthe future epicenter (7-15 days). \n\n\n\n6. CONCLUSION \n\n\n\nSeismo ionospheric induced perturbations prior to the September 28, \n2007 M7.5 Northern Mariana U.S.A. Geo-quake from GPS TEC and \nDEMETER data have been investigated.  The study revealed both pre and \npost (from -26 days to 6 days) ionospheric perturbations from GPS and \nDEMETER data. The observed anomalous variations were screened for \nfalse alarm using the geomagnetic indices of kp and Dst. The abnormal TEC \non -10, -7, -3 and -2 days occurred under quiet geomagnetic conditions. All \npre-seismic ionospheric variations from the DEMETER data were obtained \nin quiet geomagnetic conditions. Interestingly, the perturbations on -10 \nand -7 days were simultaneously observed in both GPS and DEMETER \ndatasets under quiet geomagnetic ionospheric conditions. This result is in \nagreement with that reported by many researchers reported that the \nonset of the irregularities in the morphology of the ionosphere is of course \nclose to the future epicenter (7-15 days). Nevertheless, it is important to \nnote that the ionosphere displays complex behaviour even under quiet \ngeomagnetic condition and the measured parameters may sometimes \ndisplay variations in quiet seismic condition that can be associated with \n\n\n\nother unknown factors. The seismo ionospheric anomalies reported in this \npaper novel are promising for the short-term prediction. However, for \nfurther studies, attention has to be paid to further investigation leading to \na very precise regional model of quiet time for ionosphere to classify \nseismic precursors from the background of daily variations. \n\n\n\nACKNOWLEDGEMENTS  \n\n\n\nThe author is very grateful to the IGS community for providing the GIM-\nTEC data, the USGS, and Kyoto University for providing EQ details and \ngeomagnetic storm data, respectively. Further thanks to the \nadministration of DEMETER satellite for open access data. \n\n\n\nREFERENCES \n\n\n\nAkhoondzadeh, M., Parrot, M., Saradjian, M.R., 2010. Electron and ion \n\n\n\ndensity variations and before strong earthquakes (M>6.0) using \n\n\n\nDEMETER and GPS data. Natural Hazards Earth System Sciences, 10, Pp. \n\n\n\n7-18. \n\n\n\nBerthelier, J.J., Godefroy, M., Leblanc, F., Seran, E., Peschard, D., Gilbert, P., \n\n\n\nArtru, J., 2006. IAP, the thermal plasma analyzer on DEMETER. \n\n\n\nPlanetary and Space Science, 54, Pp. 487\u2013501. \n\n\n\nCalais, E., Minster, J.B., 1995. GPS detection of ionospheric perturbations \n\n\n\nfollowing the January 17, 1994, Northridge earthquake. Geophysical \n\n\n\nResearch Letters, 22, Pp. 1045\u20131048. \n\n\n\nCussac, T., Clair, M., Ultre\u00b4-Guerard, P., Buisson, F., Lassalle-Balier, G., Ledu, \n\n\n\nM., Elisabelar, C., Passot, X., Rey, N., 2006. The Demeter microsatellite \n\n\n\nand ground segment. Planetary and Space Science, 54, Pp. 413\u2013427. \n\n\n\nDobrovolsky, I.R., Zubkov, S.I., Myachkin, V. I., 1979. Estimation of the size \n\n\n\nof earthquake preparation zones. Pure Applied Geophysics, 117, Pp. \n\n\n\n1025\u20131044. \n\n\n\nHayes, G.P., Myers, E.K., Dewey, J.W., Briggs, R.W., Earle, P.S. Benz, H.M., \n\n\n\nSmoczyk, G,M., Flamme, H.E., Barnhart, W.D., Gold, R.D., Furlong, K.P., \n\n\n\n2017. Tectonic summaries of magnitude 7 and greater earthquakes \n\n\n\nfrom 2000 to 2015: U.S. Geological Survey  \n\n\n\nHeelis, R.A., 2004. Electrodynamics in the low and middle latitude \n\n\n\nionosphere: A tutorial. J.  Atmos. Sol. Terr. Phys., 66, Pp. 825\u2013838, \n\n\n\ndoi: 10.1016/j.jastp.2004.01.034. \n\n\n\nIbanga, J.I., Akpan, A.E., George, N.J., Ekanem, A.M., George, A.M., 2017. \n\n\n\nUnusual  ionospheric variations before the strong Auckland \n\n\n\nIslands, New Zealand earthquake of  30th September, 2007. NRIAG \n\n\n\nJournal of Astronomy and Geophysics, \n\n\n\nhttps://doi.org/10.1016/j.nrjag.2017.12.007 \n\n\n\nKuo, C.L., Huba, J.D., Joyce, G., Lee, L.C., 2011. Ionosphere plasma bubbles \n\n\n\nand density  variations induced by pre-earthquake rock currents \n\n\n\nand associated surface charges. J  Geophys. Res.: Space Phys., Pp. \n\n\n\n116. https://doi.org/10.1029/ 2011JA016628 \n\n\n\nLebreton, J.P., Stverak, S., Travnicek, P., Maksimovic, M., Klinge, D., \n\n\n\nMerikallio, S., Lagoutte, D., Poirier, B., Kozacek, Z., Salaquarda, M., 2006. \n\n\n\nThe ISL Langmuir Probe  experiment and its data processing \n\n\n\nonboard DEMETER: scientific objectives, description  and first \n\n\n\nresults. Planet. Space Sci., 54 (5), Pp. 472\u2013486. \n\n\n\nLiu, J.Y., Chuo, Y.J., Pulinets, S.A., Tsai, H.F., Zeng, X., 2002. A study on the \n\n\n\nTEC  perturbations prior to the Rei-Li, Chi-Chi and Chai-Yi \n\n\n\nearthquakes, In \u201cSeismo- Electromagnetics: Iithosphere-\n\n\n\nAtmosphere-Ionosphere Coupling\u201d, Eds Hayakawa, M.  and \n\n\n\nMolchanov O.A., Terra Scientific Publishing Company, Tokyo, Pp. 297-\n\n\n\n301. \n\n\n\nLiu, J.Y., Chuo, Y.J., Shan, S.J., Tsai, Y.B., Chen, Y.I., Pulinets, S.A., Yu, S.B., \n\n\n\n2004. Pre- earthquake ionospheric anomalies registered by \n\n\n\ncontinuous GPS TEC  measurements, Ann.  Geophys., 22, Pp. \n\n\n\n1585\u20131593. http://www.anngeophys.net/22/1585/2004 \n\n\n\nMayaud, P.N., 1980. Derivation, Meaning and use of geomagnetic indices, \n\n\n\nGeophysical Monograph 22, American Geological Union, Washington \n\n\n\nDC. \n\n\n\n\nhttps://doi.org/10.1016/j.nrjag.2017.12.007\n\n\nhttp://www.ann/\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 38-42 \n\n\n\nCite the Article: Jewel E. Thomas (2020). Seismo - Ionospheric Induced Perturbations Prior To The September 28, 2007 M7.5 Northern Mariana U.S.A. Geoquake From \nGps, Tec And Demeter Data. Malaysian Journal of Geosciences, 4(1): 38-42. \n\n\n\nMei, L., Parrot, M., 2013. Statistical Analysis of an ionospheric parameter \n\n\n\nas a base for earthquake prediction. Journal of Geophysical Research \n\n\n\n(Space Physics), 118 (6), Pp. 3731-3739. \n\n\n\nNamgaladze, A.A., Klimenko, M.V., Klimenko, V.V., Zakharenkova, I.E., \n\n\n\n2009. Physical mechanism and mathematical simulation of ionosphere \n\n\n\nearthquake precursors observed in total electron content. \n\n\n\nGeomagnetism and Aeronomy, 49, Pp. 252\u2013262. \n\n\n\nOndoh, T., 2008. Investigation of precursory phenomena in the \n\n\n\nionosphere, atmosphere and groundwater before large earthquakes of \n\n\n\nM >6.5. Advance Space Research, 43, Pp. 214\u2013223. \n\n\n\nParrot, M., 2009. Anomalous seismic phenomena: view from space. In: \n\n\n\nHayakawa, M. (Ed.), Electromagnetic Phenomena Associated with \n\n\n\nEarthquakes. Transworld Research Network, Kerala, Pp. 205\u2013233. \n\n\n\nParrot, M., Li, M., 2012. The use of an ionospheric parameter for \n\n\n\nearthquake prediction,paper NH44A-04, AGU Fall Meeting, San-\n\n\n\nFrancisco, 3-7 December, Ionosphere Coupling. Advances in Space \n\n\n\nResearch, 26 (8), Pp. 1209\u20131218. \n\n\n\nParrot, M., Tramadol, V., Liu, T.J.Y., Pulinets, S., Ouzounov, D., Genzno, N., \n\n\n\nLisi, M., Hattori, K., Namgaladze, A., 2016. Atmospheric and Ionospheric \n\n\n\ncoupling phenomena related to large earthquakes. Natural Hazards and \n\n\n\nEarth System Sciences https://doi.org/10.5194/nhess-2016-172 \n\n\n\nP\u00ed\u0161a, D., Parrot, M., Santol\u00edk, O., 2011. Ionospheric density variations \n\n\n\nrecorded before the 2010 Mw 8.8 earthquake in Chile. Journal of \n\n\n\nGeophysical Research, 116, Pp. A08309; doi:10.1029/2011JA016611 \n\n\n\nPulinents, S., Boyarchuk, K., 2004. Ionospheric precursor of earthquakes. \n\n\n\nSpringer Berlin Heidelberg New York, ISBN: 3-540-20839-9. \n\n\n\nPulinets, S., Davidenko, D., 2014. Ionospheric precursors of earthquakes \n\n\n\nand Global Electric Circuit, Advances in Space Research doi: \n\n\n\nhttp://dx.doi.org/10.1016/j.asr.2013.12.035 \n\n\n\nPulinets, S.A., Legen, A.D., Gaivoronskaya, T.V., Depuev, V.K., 2003. Main \n\n\n\nphenomenological features of ionospheric precursors of strong \n\n\n\nearthquakes. Journal of Atmospheric Solar and Terrestrial Physics, 65, \n\n\n\nPp. 1337\u20131347. \n\n\n\nRong, Z., Feng, J., Xin-yan, O., 2008. Ionospheric perturbations before Pu\u2019er \n\n\n\nearthquake observed on DEMETER. Acta Seismologica Sinica, 21 (1), Pp. \n\n\n\n77 -81. \n\n\n\nRostoker, G., 1972. Geomagnetic Indices. Reviews of Geophysics and Space \n\n\n\nPhysics, 10 (4), Pp. 935-950. \n\n\n\nRyu, K., Lee, E., Chae, J.S., Parrot, M., Pulinets, S., 2014. Seismo-ionospheric \n\n\n\ncoupling appearing as equatorial electron density enhancements \n\n\n\nobserved via DEMETER electron density measurements, J. Geophys. \n\n\n\nRes. Space Physics, 119, Pp. 8524\u20138542. doi:10.1002/2014JA020284 \n\n\n\nSurkov, V.V., 2015. Pre- Seismic variations of atmospheric radon activityas \n\n\n\na possible reason for abnormal atmospheric effects, Ann. Geophysics, \n\n\n\n58 (5), Pp. A05554. \n\n\n\nXinzshi, W., Junhui, J., Dongjie, Y., Fuyang, K., 2014. Analysis of Ionospheric \n\n\n\nVTEC disturbances before and after the Yutian Ms 7.3 earthquake in \n\n\n\nXinjiang Uygur Autonomous Region. Geodesy and Geodynamics, 5 (3), \n\n\n\nPp. 8 \u2013 15. \n\n\n\nZaslavski, Y., Parrot, M., Blanc, E., 1998. Analysis of TEC measurements \n\n\n\nabove active seismic regions. Physics of the earth and Planetary \n\n\n\nInteriors, 105, Pp. 219-228.\n\n\n\n\nhttps://doi.org/10.5194/nhess-2016-172\n\n\nhttp://dx.doi.org/10.1016/j.asr.2013.12.035\n\n\n\n\n\n\n\n"
"\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 01-05 \n\n\n\nCite the article: Norzanah Abd Rahman, Zamali Tarmudi, Munirah Rossdy, Fatihah Anas Muhiddin (2017). Flood Mitigation Measures Using Intuitionistic Fuzzy \nDematel Method. Malaysian Journal Geosciences, 1(2) : 01-05. \n\n\n\n  ARTICLE DETAILS \n\n\n\n ARTICLE HISTORY: \n\n\n\nReceived 12 May2017  \nAccepted 12 July 2017  \nAvailable online 10 September 2017 \n\n\n\nKEYWORDS:\n\n\n\nFlood mitigation measures, \nIntuitionistic Fuzzy DEMATEL \nmethod, Sensitivity Analysis\n\n\n\nABSTRACT\n\n\n\nFlood is a natural disaster induced by climate change that resulted in the losses of lives, damages to property, and \ndisrupts the daily activities of local community. Thus, the flood mitigation measures are developed to reduce the \nimpact of flood in our country. The aim of this paper is; to propose IF-DEMATEL method and deal with the \nuncertainty of input data set of flood mitigation measures, and to validate it using sensitivity analysis. Here, this \nmethod is used for flood mitigation measures comprise drainage improvements, barriers, wet flood proofing, dry \nflood proofing, elevation, relocation, and acquisition. A numerical example from the flood control project selection \nproposed by a researcher which was adopted to show the applicability of the proposed method. The result shows \nthat the flood mitigation measures are placed based on their priority. Although the rank of flood mitigation \nmeasures is sensitive to changes based on the weight of criteria but the best measures is remaining unchanged. \n\n\n\n1. INTRODUCTION \n\n\n\nFlooding is known as the most reported natural disaster worldwide induced \nby natural phenomenon and human activities [1-3]. Flood causes significant \nlosses in terms of lives [4], effects on human health [5], kills the plants [6], \ndamages to property (tangible and intangible) [7], and disrupts the daily \nactivities of local community [4].   \n\n\n\nAs a response to these loses, the government concentrated on managing the \nfloodwaters based on the flood management strategies framework to lessen \nthe flood problems [8]. The strategies are the establishing appropriate and \nworkable institutions for implementing flood control works and flood relief \noperations, carrying out river basin studies, and implement flood mitigation \nmeasures (structural, non-structural measure, and contingency) [9,10]. The \nflood mitigation measure is a long-term effort to lessen the impact of disaster \nby managing the effects, rather than trying to avert it totally [8]. Almost, every \ncountry has their own strategies to manage the flood, as in the United States, \nthey executed the flood damage reduction projects under consideration of \nlarge-scale and small-scale capital projects, ecosystem-based projects, land-\nuse management, and flood warning and preparedness [11].  \n\n\n\nHowever, the adaption of flood mitigation measures can be complex and \ndifficult since it involves uncertainty of the future changes and limiting \nfactors. Thus, bottomless consideration on possible significance factors and \nlimitation are needed. For instance, Devesh Sharma, [12] emphasized that the \nmeasure is considered based on lesson learned from previous flood events, \nfuture scenarios (climatic, socio-economic development) and local condition \n(topography, weather, population, area, land-use, institutional set-ups, and \nso on). In previous pilot studies, the researchers applied the qualitative \napproach such as participatory approach that require a contribution of the \npeople or community in decision making process [13-16]. Likewise, 26 River \nBasin Studies in Malaysia has been carried out for river flood areas to draw \nup the suitable flood maps and practical projects [17]. \n\n\n\nHowever, this qualitative approach cannot cope with the uncertainty in \nfuture changes such as impossibility to predict future human behavior in \nterms of population change, social and economic development, \neffectiveness of the climate mitigation policy, and adaptation to climate  \n\n\n\nchange impacts [18]. Therefore, a mathematical method such as Fuzzy \nMulti-Criteria Decision Making (FMCDM) are needed to model an algorithm \nthat can solve uncertainty, multiple and conflicting criteria aspect in the \ndecision-making process [19]. In previous studies, it has been combined \nwith the hydrological model, Geographical Information Systems (GIS), and \nArtificial Intelligence (AI) to solve spatial problems in flood environment. \nFor example, Nirupama and Slobodan, [20] used a Spatial Fuzzy \nCompromise Programming (SFCP) in the GIS environment to select the best \nstrategies in floodplain management strategies. Costa et al., [21] proposed \n(Measuring Attractiveness by Categorical Based Evaluation Technique) \nMACBETH to evaluate flood control options for the water catchment. \nMoreover, a type-2 fuzzy Technique for Order Preference by Similarity to \nan Ideal Solution (TOPSIS) method is applied to select the best alternatives \namong several flood controls which are reservoir, dikes, and channel \nimprovement as well as diversion scheme [22]. Recently, Banihabib and \nLaghabdoost, [23] investigated flood management alternatives based on \nsustainable development criteria (SDC) using an Elimination ET Choice \nTranslating Reality (ELECTRE-III). \n\n\n\nIn this paper, we want to prioritize the flood mitigation measures using \nIntuitionistic Fuzzy Decision-Making Trial and Evaluation Laboratory (IF-\nDEMATEL) method. This method will be used to deal with the uncertainty \nof input data set of flood mitigation measures. We believe that IF-DEMATEL \nmethod is suitable approach to assist the decision makers (DMs) to make \nchoice in flood mitigation measure that involves uncertainty, multiple and \ncomplex in nature. In addition, this method has proven to possess an \nexcellent result by establishing a contextual relationship among criteria \n[24-26]. In this paper, we used a sensitivity analysis (SA) to validate our \nmethodology.  A numerical example is presented in this paper by adopting \n[22] to show the applicability and practicality of our proposed method.\n\n\n\nThus, the remainder of this paper is presented as follows: After a brief \nintroduction in section I, section II will present the theoretical concepts that \ncomprise preliminaries of the IFS, DEMATEL, and proposed methodology \n(IF-DEMATEL). Section III portrays the application of the IF-DEMATEL \ntogether with the result of a numerical example, before the conclusion \nmade in section IV.  \n\n\n\n2. THE THEORITICAL CONCEPT AND PROPOSED METHODOLOGY\n\n\n\n2.1 Preliminaries \n\n\n\nContents List available at RAZI Publishing \n\n\n\nMalaysian Journal of Geosciences Journal \nHomepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/ \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (online)\n\n\n\nFLOOD MITIGATION MEASURES USING INTUITIONISTIC FUZZY DEMATEL \nMETHOD \nNorzanah Abd Rahman*, Zamali Tarmudi, Munirah Rossdy, Fatihah Anas Muhiddin \nDepartment of Mathematics, Faculty of Computer and mathematical Sciences, Universiti Teknologi MARA (UiTM), Locked Bag 71, 88997 Kota \nKinabalu, Sabah, Malaysia.\n*Corresponding Author e-mail: norzanah.abdrahman@gmail.com \n\n\n\nhttps://doi.org/10.26480/mjg.02.2017.01.05\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\nhttps://doi.org/10.26480/mjg.02.2017.01.05\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 01-05 \n\n\n\nCite the article: Norzanah Abd Rahman, Zamali Tarmudi, Munirah Rossdy, Fatihah Anas Muhiddin (2017). Flood Mitigation Measures Using Intuitionistic Fuzzy \nDematel Method. Malaysian Journal Geosciences, 1(2) : 01-05. \n\n\n\n2 \n\n\n\nIn this section, basic definitions of both method are briefly explained \n\n\n\nfor the references proposes.  \n\n\n\nIntuitionistic fuzzy set (IFs) is a generalization of fuzzy set that has \n\n\n\nan ability to degree the uncertainty in decision maker choice of alternatives \n\n\n\n(Atanassov, 1986). \n\n\n\nDefinition 1:  \nLet a set X be fixed. An IFs in   in X is defined as an object of the following \nform \n\n\n\n}|)(),(,{ XxxvxxA AA \uf0ce\uf0f1\uf0e1\uf03d \uf06d .    (1) \n\n\n\nWhere the functions: ]1,0[: \uf0aeXA\uf06d  and ]1,0[: \uf0aeXvA\n define \n\n\n\nthe degree of membership and the degree of non-membership of the \n\n\n\nelement Xx\uf0ce , respectively, and for every Xx\uf0ce :\n\n\n\n1)()(0 \uf0a3\uf02b\uf0a3 xvx AA\uf06d\n\n\n\nDefinition 2:  \n\n\n\nThe value of )()(1)( xvxx AAA \uf02d\uf02d\uf03d \uf070\uf070  is called the degree of \n\n\n\nnon-determinacy (or uncertainty) of the element Xx\uf0ce to the IFs A.\n\n\n\nTheorem 1:  \n\n\n\nSuppose \uf028 \uf029'\n\n\n\n4,3',\n'\n\n\n\n2,\n'\n\n\n\n1;4,3,2,1\n\n\n\n~\n\n\n\nppppppppP \uf03d be a Trapezoidal \n\n\n\nIntuitionistic Fuzzy Number (TrIFN) in X. When\n\n\n\n3'4'\n\n\n\n3'\n\n\n\n,\n\n\n\n1'2'\n\n\n\n2'\n,\n\n\n\n34\n\n\n\n4\n, \n\n\n\n12\n\n\n\n1\n\n\n\npp\n\n\n\npx\n\n\n\npp\n\n\n\nxp\n\n\n\npp\n\n\n\nxp\n\n\n\npp\n\n\n\npx\n\n\n\n\uf02d\n\n\n\n\uf02d\n\n\n\n\uf02d\n\n\n\n\uf02d\n\n\n\n\uf02d\n\n\n\n\uf02d\n\n\n\n\uf02d\n\n\n\n\uf02d\n\n\n\nXpppppppp \uf0ce\uf0a3\uf0a3\uf0a3\uf0a3\uf0a3\uf0a3\uf0a3 4'43'322'11' , the expected value is \n\n\n\ncalculated as follows  \n\n\n\n\uf028 \uf029'\n\n\n\n43'\n'\n\n\n\n2\n\n\n\n'\n\n\n\n14321\n8\n\n\n\n1\n)\n\n\n\n~\n\n\n\n( ppppppppPEV \uf02b\uf02b\uf02b\uf02b\uf02b\uf02b\uf02b\uf03d (2)\n\n\n\n2.2 DEMATEL Method \n\n\n\nDEMATEL method is one of the popular technique that can visualize the \nrelationship of effect and cause group in digraph by establishing the worthy \nand important of factors and sub-factors [27]. The basic steps are: \n\n\n\nForming the direct-relation (average) matrix using equation below \n\n\n\nnxn\n\n\n\np\n\n\n\nija\np\n\n\n\nA ][\uf03d  (3) \n\n\n\nWhere,  \n\n\n\n\uf0e5\n\uf03d\uf03d\n\n\n\np\nh nxnija\n\n\n\np\nnxnija 1 ][\n\n\n\n1\n][\n\n\n\nNormalizing the initial direct relation matrix using equation (4) \n\n\n\n\uf0e5\n\uf03d\n\n\n\n\uf0e5\n\uf03d\n\n\n\n\uf03d\nn\ni ija\n\n\n\nn\nj ija\n\n\n\np\nA\n\n\n\nD\n\n\n\n1 }1 max,max{max\n\n\n\n   (4) \n\n\n\nAssuming that the power of matrix Dm (m-direct influence) would converge \nto zero matrix. The total relation matrix, T can be obtained by following \nformula: \n\n\n\n1\n)(  \n\n\n\n)(\n1\n\n\n\n)(  \n\n\n\n)\n1\n\n\n\n...\n32\n\n\n\n1(  \n\n\n\n...\n32\n\n\n\n]0[lim\n\n\n\n\uf02d\n\uf02d\uf03d\n\n\n\n\uf02d\n\uf02d\n\n\n\n\uf02d\uf03d\n\n\n\n\uf02d\n\uf02b\uf02b\uf02b\uf02b\uf03d\n\n\n\n\uf02b\uf02b\uf03d\uf0e5\uf0a5\uf03d\n\n\n\n\uf03d\n\uf0a5\uf0ae\n\n\n\nDID\n\n\n\nm\nDIDID\n\n\n\nm\nDDDDD\n\n\n\nm\nDDDDm iDT\n\n\n\nnxn\n\n\n\nm\nD\n\n\n\nm\n\n\n\n   (5) \n\n\n\nWhere, \n I is an identity matrix. \n\n\n\n3. THE PROPOSED METHODOLOGY\n\n\n\nIn this research, we modified the IF-DEMATEL proposed by Razieh \nKeshavarzfard and Ahmad Makui, [26] as shown in the figure 1. \n\n\n\nIn step 1, 2, and 3, p respondents are chosen to make the sets of \npairwise comparisons between the criteria based on triangular IF \nlinguistic phrases. Here, we used trapezoidal IF linguistic phrases which \nare Very High Influence (VH), High Influence (H), Medium influence (M), \nLow influence (L) and No Influence (No) as shown in Table 1. \n\n\n\nFigure 1: IF-DEMATEL method \n\n\n\nTable 1: The Trapezoidal Intuitionistic Fuzzy Linguistic Scale \nLinguistic \nphrases \n\n\n\nTrIFN Expected \nvalues \n\n\n\nVH \uf028 \uf029 )1,1,1,1(,1,1,1,1 1.00 \n\n\n\nH \uf028 \uf029 \uf028 \uf0299,10.7,0.8,0.,9,10.7,0.8,0. 0.85 \n\n\n\nM \uf028 \uf029 \uf028 \uf0295,0.70.2,0.4,0.,5,0.60.3,0.4,0. 0.45 \n\n\n\nL \uf028 \uf029 \uf028 \uf0290.30,0.1,0.2,,0.30,0.1,0.2, 0.15 \n\n\n\nN \uf028 \uf029 )0,0,0,0(,0,0,0,0 0.00 \n\n\n\nThe sets of pairwise comparisons of each respondent\n)(~ h\n\n\n\nA is the direct-\nrelation IF matrix of expert h. \n\n\n\n\uf0fa\n\uf0fa\n\uf0fa\n\uf0fa\n\uf0fa\n\uf0fa\n\n\n\n\uf0fb\n\n\n\n\uf0f9\n\n\n\n\uf0ea\n\uf0ea\n\uf0ea\n\uf0ea\n\uf0ea\n\uf0ea\n\n\n\n\uf0eb\n\n\n\n\uf0e9\n\n\n\n\uf03d\n\n\n\n0   ...  \n)(\n\n\n\nn2 \n\n\n\n~\n\n\n\na \n)(\n\n\n\nn1 \n\n\n\n~\n\n\n\na\n\n\n\n\n\n\n\n)(\n\n\n\n2n \n\n\n\n~\n\n\n\na    ... 0 \n\n\n\n~\n)(\n\n\n\n21\n\n\n\n)(\n\n\n\n1n \n\n\n\n~\n\n\n\na      \n)(\n\n\n\n12 \n\n\n\n~\n\n\n\na   0\n\n\n\n~\n)(\n\n\n\nhh\n\n\n\nhh\na\n\n\n\nhh\n\n\n\nh\nA\n\n\n\n\uf04f\uf04d\n\n\n\n; h=1,2,\u2026, p    (6)  \n\n\n\n)\n'\n\n\n\n,\n'\n\n\n\n,\n'\n\n\n\n;,,(\n\n\n\n~\n)(\n\n\n\nijzijyijxijzijyijx\nh\n\n\n\nija \uf03d   (7) \n\n\n\nIn step 4, the matrix \n)(~ h\n\n\n\nA is normalized using equation (8) \n\n\n\n)\n\n\n\n'\n\n\n\n,\n\n\n\n'\n\n\n\n,\n\n\n\n'\n\n\n\n;,,)(\n\n\n\n~\n\n\n\n(\n\n\n\n)(\n~)(\n\n\n\n~\n\n\n\ns\n\n\n\nijz\n\n\n\ns\n\n\n\nijy\n\n\n\ns\n\n\n\nijx\n\n\n\ns\n\n\n\nijz\n\n\n\ns\n\n\n\nijy\n\n\n\ns\n\n\n\nijx\n\n\n\ns\n\n\n\nija\n\n\n\ns\n\n\n\nh\n\n\n\nA\n\n\n\nh\n\n\n\nB \uf03d\uf03d   (8) \n\n\n\nWhere, ni\nn\n\n\n\nj\nijzs \uf0a3\uf0a3\uf0e5\n\n\n\n\uf03d\n\uf03d 1    \n\n\n\n1\n)\n\n\n\n'\nmax(  In step 5, we modified \n\n\n\nthis step to calculate the average matrix of \n\n\n\n~\n\n\n\nB from eq. (9) to equation \n(10). \n\n\n\np\n\n\n\np\n\n\n\nC BBB /)\n\n\n\n)(~)2(~)1(~\n\n\n\n(\n\n\n\n~\n\n\n\n...\uf0c5\uf0c5\uf0c5\uf03d   (9) \n\n\n\n\uf0e5\n\uf03d\n\n\n\n\uf0c5\uf0c5\uf0c5\n\n\n\n\uf03d\np\n\n\n\nh h\nw\n\n\n\np\n\n\n\nB\np\n\n\n\nwBwBw\n\n\n\nC\n\n\n\n1\n\n\n\n)(\n~\n\n\n\n...\n\n\n\n)2(\n~\n\n\n\n2\n\n\n\n)1(\n~\n\n\n\n1\n~\n\n\n\n  (10) \n\n\n\nStep 1: Selecting the \nrespondents\n\n\n\nStep 2: Developing the \ncriteria and designing \nthe fuzzy linguistic \n\n\n\nscale.\n\n\n\nStep 3: Generating the \nassessments of \n\n\n\nrespondents\n\n\n\nStep 4: Normalizing \nthe direct-relation \n\n\n\nfuzzy matrix.\n\n\n\nStep 5: Establishing \nand analyzing the \nstructural model.\n\n\n\nStep 6: Producing a \ncasual diagram\n\n\n\nStep 7: Validating the  \nproposed method\n\n\n\nStep 8: Making \nDecision\n\n\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 01-05 \n\n\n\nCite the article: Norzanah Abd Rahman, Zamali Tarmudi, Munirah Rossdy, Fatihah Anas Muhiddin (2017). Flood Mitigation Measures Using Intuitionistic Fuzzy \nDematel Method. Malaysian Journal Geosciences, 1(2) : 01-05. \n\n\n\n3 \n\n\n\nWhere, hw is the importance of hth respondents based on the importance \n\n\n\nweights of respondents that presented in the Table 2. \nTable 2: The Importance Weight of Respondents \n\n\n\nLinguisti\nc \n\n\n\nVariable \n\n\n\nFuzzy \nNumbers \n\n\n\nMean of Fuzzy \nNumbers\n\n\n\nhw\n\n\n\nRespondent \n\n\n\nVery \nHigh, VH \n\n\n\n(0.7,0.9,1) 0.8667 \n\n\n\nHigh, H (0.5,0.7,0.9) 0.7 1 \nMedium, \n\n\n\nM \n(0.3,0.5,0.7) 0.5 2 \n\n\n\nLow, L (0.1,0.3,0.5) 0.3 3 \nVery \n\n\n\nLow, VL \n(0,0.1,0.3) 0.13333 \n\n\n\nThen, IF number is converted into crisp value using Theorem 1 (eq. 2) before \n\n\n\nthe total direct relation \n~\n\n\n\nD is computed using equation (11) as follows: \n\n\n\n1\n)\n\n\n\n~\n\n\n\n(\n\n\n\n~~\n\n\n\n D\n\uf02d\n\n\n\n\uf02d\uf03d CIC  (11) \n\n\n\nIn step 6, we produce a casual diagram by calculating the sum of rows and \n\n\n\nsum of columns are separately denoted as vector \n~\n\n\n\niD and vector\n~\n\n\n\niR . The\n\n\n\nhorizontal axis vector or prominence, )\n\n\n\n~~\n\n\n\n( iRiD \uf02b represents how much \n\n\n\nimportance the criterion has. The vertical axis or relation, )\n\n\n\n~~\n\n\n\n( iRiD \uf02d divides \n\n\n\nthe criteria into cause group (negative values) and effect group (positive \nvalues). The causal diagram can be acquired by mapping the dataset of the\n\n\n\n))\n\n\n\n~~\n\n\n\n(),\n\n\n\n~~\n\n\n\n(( iRiDiRiD \uf02d\uf02b . \n\n\n\nThe importance of criteria is calculated by the following equation: \n\n\n\n2\n1\n\n\n\n}\n2\n\n\n\n)\n\n\n\n~~\n\n\n\n((,\n2\n\n\n\n)\n\n\n\n~~\n\n\n\n{( iRiDiRiDi \uf02d\uf02b\uf03d\uf077  (12)  \n\n\n\nThe importance of any criterion can be normalized as follows: \n\n\n\n\uf0e5\n\uf03d\n\n\n\n\uf03d\nn\ni i\n\n\n\ni\n\n\n\niW\n\n\n\n1\uf077\n\n\n\n\uf077  (13) \n\n\n\nIn step 7, the SA is performed using different weight of respondent 1. \n\n\n\nFinally, in step 8, the flood mitigation measures can be prioritized based on \nthe importance of criteria that are computed in step 6. \n\n\n\n4. APPLICATION \n\n\n\nFor illustration example, the criteria of case study from [22] was adopted for \nflood mitigation measures (see Table 3).  \n\n\n\nStep 1: Three respondents from Department of Irrigation and Drainage (DID) \nis selected based on their knowledge and skill on the case study. \nStep 2: The respondents are required to evaluate four criteria (C) of seven \nflood mitigation measures (A) based on her/his opinion (see Table 3 and 4). \n\n\n\nTable 3: The Criteria of Flood Mitigation Measures \n\n\n\nC Remarks \n\n\n\nC1 \n\n\n\nProject cost: Operations and maintenance cost, project \nbenefits, reliability economic parameter \n\n\n\nC2 Social acceptability: Effect on demographic, effect on \ninfrastructure, recreation activity \n\n\n\nC3 Environmental aspect: Water quality, nature \nconservation, soil impact, landscape. \n\n\n\nC4 Technical: Lifetime, adaptability, level of protection, \ntechnical complexity, flexibility \n\n\n\nSources: Nurnadiah Zamri [22] \n\n\n\nTable 4: The Flood Mitigation Measures \nA Function \nA1 Drainage improvement \nA2 Barriers \nA3 Wet Flood Proofing \n\n\n\nA4 Dry Flood Proofing \nA5 Elevation \nA6 Relocation \nA7 Acquisition \n\n\n\nStep 3: The assessment data of respondent 1 is gathered as in Table 5. \n\n\n\nTable 5: The Assessment Data of Respondent 1 for A1 \n\n\n\nC1 C2 C3 C4 \n\n\n\nC1 0 VH L L \n\n\n\nC2 H 0 M H \n\n\n\nC3 L H 0 L \n\n\n\nC4 M H L 0 \n\n\n\nStep 4: The direct-relation matrix is defined from the assessment data of \nrespondent. The normalized initial direct-relation matrix is calculated \nusing eq. (8). \n\n\n\nStep 5: The average of normalize direct-relation matrix is computed using \neq. (10). The total relation matrix is obtained using eq. (11) as shown in \ntable 6. \n\n\n\nTable 6: The Total Relation Matrix for  \n\n\n\nA1 \n\n\n\nStep 6: The causal diagram is obtained as in Figure 2. \n\n\n\nFigure 2: Total Causal relationship \nStep 7: The sensitivity analysis will be performed for the IF-DEMATEL \nmethod using different weight of respondent 1 which is 0.5 (Medium) as \nshown in Table 7. \n\n\n\nTable 7: The Importance Criteria \n\n\n\nC \nImportance \nof Criteria \n\n\n\n(actual) \n\n\n\nImportance \nof Criteria \n\n\n\n(SA) \n\n\n\nA1C1 8 2 \n\n\n\nA1C2 28 21 \n\n\n\nA1C3 2 4 \n\n\n\nA1C4 7 18 \n\n\n\nA2C1 11 11 \n\n\n\nA2C2 27 28 \n\n\n\nA2C3 1 1 \n\n\n\nA2C4 9 9 \n\n\n\nA3C1 16 12 \n\n\n\nA3C2 24 25 \n\n\n\nA3C3 4 3 \n\n\n\nA3C4 18 13 \n\n\n\nA4C1 20 19 \n\n\n\nA4C2 26 26 \n\n\n\nA1C1\n\n\n\nA1C2\n\n\n\nA2C3\n\n\n\nA1C4\n\n\n\nA2C1\n\n\n\nA2C2\n\n\n\nA2C3\n\n\n\nA2C4\n\n\n\nA3C1\n\n\n\nA3C2\n\n\n\nA3C3\n\n\n\nA3C4\n\n\n\nA4C1\nA4C2\n\n\n\nA4C3\n\n\n\nA4C4\n\n\n\nA5C1\n\n\n\nA5C2\n\n\n\nA5C3\n\n\n\nA5C4\n\n\n\nA6C1\n\n\n\nA6C2\n\n\n\nA6C3\n\n\n\nA6C4\n\n\n\nA7C1A7C2\n\n\n\nA7C3\n\n\n\nA7C4\n\n\n\n-0.2\n\n\n\n-0.15\n\n\n\n-0.1\n\n\n\n-0.05\n\n\n\n0\n\n\n\n0.05\n\n\n\n0.1\n\n\n\n0.15\n\n\n\n0.2\n\n\n\n0.25\n\n\n\n0 0.5 1 1.5 2\n\n\n\nD\n-R\n\n\n\nD+R\n\n\n\nC1 C2 C3 C4 \n\n\n\nC1 1.11382 -0.37218 0.011118 -0.06232 \nC2 -0.288598 1.26171 -0.26702 -0.28579 \nC3 0.0059143 -0.28259 1.06899 -0.02956 \n\n\n\nC4 -0.091084 -0.3165 0.025255 1.10031 \n\n\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 01-05 \n\n\n\nCite the article: Norzanah Abd Rahman, Zamali Tarmudi, Munirah Rossdy, Fatihah Anas Muhiddin (2017). Flood Mitigation Measures Using Intuitionistic Fuzzy \nDematel Method. Malaysian Journal Geosciences, 1(2) : 01-05. \n\n\n\n4 \n\n\n\nA4C3 17 16 \n\n\n\nA4C4 6 6 \n\n\n\nA5C1 19 20 \n\n\n\nA5C2 25 27 \n\n\n\nA5C3 19 17 \n\n\n\nA5C4 5 7 \n\n\n\nA6C1 15 15 \n\n\n\nA6C2 23 24 \n\n\n\nA6C3 21 22 \n\n\n\nA6C4 12 14 \n\n\n\nA7C1 13 10 \n\n\n\nA7C2 22 23 \n\n\n\nA7C3 10 8 \n\n\n\nA7C4 3 5 \n\n\n\nStep 8: Finally, the flood mitigation measures is prioritize based on the mean \nof the importance criteria obtained using eq. (12-13) as presented in Table 8. \n\n\n\nTable 8: The rank of measures \nMeasure Mean of importance of criteria Rank \n\n\n\nA1 0.1472 2 \nA2 0.1470 3 \nA3 0.1422 4 \nA4 0.1382 6 \nA5 0.1396 5 \nA6 0.1373 7 \nA7 0.1486 1 \n\n\n\n5. CONCLUSION \n\n\n\nFinding revealed that, the environmental aspect of barriers (A2C3) has the \n\n\n\nhighest score in )\n\n\n\n~~\n\n\n\n( iRiD \uf02b as shown in Figure 2. This indicate that it has the \n\n\n\nrelative significance of the flood mitigation measures. The project cost of wet \n\n\n\nflood proofing (A3C1) are the most influenced by other criteria as it )\n\n\n\n~~\n\n\n\n( iRiD \uf02d\n\n\n\nscore negative among other criteria in the effect group. The importance of \ncriteria is sensitive to change of the importance weight of respondent, \nhowever the highest importance of criteria is remaining unchanged (see \nTable 7). Overall, we must consider both prominence and relation ranking \nand according to Figure 2, environmental aspect of acquisition (A7C3) is more \nsignificance than (A2C3). In this paper, we have determined that acquisition is \nthe priority of flood mitigation measures which has the highest importance \ncriteria which is 0.1486 followed by drainage improvement, barriers, wet \nflood proofing, elevation, dry flood proofing, and relocation (see Table 8). \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThis research was supported by Grant from Malaysian of Higher Education \n(MOHE) for \u201cBifuzzy Set Refinement\u201d code: 600-RMI/RAGS 5/3 (148/2014). \n\n\n\nREFERENCES \n\n\n\n[1] Department of Irrigation and Drainage [DID]. 2007. Flood and \ndrought management in Malaysia. pp. 1-39. Retrieved from \nhttp://www.met.gov.my. \n\n\n\n[2] Water and Energy Consumer Association of Malaysia [WECAM]. \n2013. Malaysia: Flood mitigation and adaptation. pp. 1-14. Retrieved \nfrom http://www.wecam.org.my. \n\n\n\n[3] Lawal, D.U., Matori, A. N., Hashim, A.M., Yusof, K.W., and Chandio, I.A. \n2012. Natural flood influencing factors: A case study of Perlis, Malaysia. \nInternational Conference on Civil, Offshore and Environmental \nEngineering (12-14 June 2012). Kuala Lumpur. \n\n\n\n[4] Umar, C., M. 2007. Disaster mitigation support and management in \nMalaysia, 1-38.  Prime Minister\u2019s Department Malaysia, Malaysia. \n\n\n\n[5] Torti, J. 2012. Floods in Southeast Asia: A health priority. School of \nPublic Health, University of Alberta, Edmonton, Alberta, Canada, 2 (2), \ndoi: 10.7189/jogh.02.020304. \n\n\n\n[6] Eni, Devalsam, I., Atu, Joy, E., Oko, Comfort, O., Ekwok, and Innocent. \n2011. Flood and its impact on farmlands in Itigidi, Abi local government \n\n\n\narea, cross river state, Nigeria. International Journal of Humanities and \nSocial Science, 1 (9), 98-104. \n\n\n\n[7] Alan, W., Donald, H., Tom, B., Amy, T., and Andrew, B. 2007. Exploring \nthe social impacts of flood risk and flooding in Scotland. Blackwell\u2019s \nBookshop, South Bridge, Edinburgh. \n\n\n\n[8] Department of Irrigation and Drainage [DID]. 2009. Volume 1: Flood \nmanagement. Retrieved from www. \nhttp://forum.mygeoportal.gov.my/smanre/aduan/Volume1_Flood%20 \nManagement.pdf. \n\n\n\n[9] Hiew, K. L. 1996. Flood mitigation and flood risk management in \nMalaysia. Proceeding of International Workshop on Floodplain Risk \nManagement, 11, pp. 205-216. \n\n\n\n[10] Natural Resources Office Sabah. 1994. Water resources master \nplan: Final report-overview, 1-75. \n\n\n\n[11] Lauren, D. 2013. Proactive flood and drought management: A \nselection of applied strategies and lessons learned from around the \nUnited States, 1-63. American Water Resources Association, Virginia. \n\n\n\n[12] Devesh Sharma. 2012. Situation analysis of flood disaster in South \nand Southeast Asia \u2013 a need of integrated approach. International Journal \nof Science, Environment and Technology, 1 (3), 167-173. \n\n\n\n[13] Ramsbottom. 2005a. Sustainable flood mitigation strategies pilot \nstudy 1: community flood mitigation activities. Report EX 5129. HR \nWallingford. \n\n\n\n[14] Ramsbottom. 2005b. Sustainable flood mitigation strategies pilot \nstudy 2: community flood mitigation activities. Report EX 5130. HR \nWallingford. \n\n\n\n[15] Mathias, S., Morgan De Dapper, Chris, M. M. & Antonio, Y. 2011. \nParticipatory approach for integrated basin planning with focus on \ndisaster risk reduction: The case of the Limpopo River. Water, 3, 737-763. \ndoi: 10.3390/w3030737. \n\n\n\n[16] Vojinovic, Golub, D., Weesakul, S., Keerakamolchai, W., Hirunsalee, \nS., Meesuk, V., Abbott, M. 2014. Merging quantitative and qualitative \nanalyses for flood risk assessment at heritage sites, the case of Ayutthaya, \nThailand. 11th International Conference on Hydroinformatics [HIC \n2014], New York City, USA. \n\n\n\n[17] Chia, C. W. 2004. Managing Flood Problems in Malaysia. Buletin \nIngenieur 22, June-August 2004: Board of Engineers Malaysia, 38-43. \nKuala Lumpur. \n\n\n\n[18] Ali Chavoshian and Kuniyoshi Takeuchi (Eds.). 2011. Large-scale \nfloods report book: lessons learned and best practices for flood disaster \nmanagers and policy makers. ICHARM Book Series, 1, 1-205. \n\n\n\n[19]Jie, L., Guangquan, Z. D. R., and Fengjie, W. 2006. Multi-objective \ngroup decision making: methods, software and applications with fuzzy \nset technique. London: Imperial College Press. \n\n\n\n[20] Nirupama, and Slobodan, P. S. 2002. A spatial fuzzy compromise \napproach for flood disaster management (Doctoral thesis, The Institute of \nCatastrophic Loss Reduction, London, Ontario). Retrieved from \nhttp://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1004&context=wrrr. \n\n\n\n[21] Costa, C. A. B. E., Silva, P. A. D., Coria, F. N. 2003. Multicriteria \nevaluation of flood control measures: The case of Ribeira Do Livramento. \nThe London School of Economics and Political Science, London. \n\n\n\n[22] Zamri, N., Abdullah, L., Hitam, M.S., Noor, N.M.M., Jusoh, A. 2013. A \ntype-2 multi criteria decision making approach for flood control project \nselection. Australian Journal of Basic and Applied Sciences, 7 (10), 180-\n191. \n\n\n\n[23] Banihabib, M. E., and Laghabdoost, A. A. 2014. Multicriteria decision-\nmaking for flood management based on sustainable development criteria. \nInternational Environmental Modelling and Software Society (7th \nInternational Congress on Environment Modelling and Software). San \nDiego, USA. \n\n\n\n[24] Hui, X., Wanchun, D., Yonghe, S., and Yuanwei, D. 2013. Dynamic \nDEMETEL group decision approach based on intuitionistic fuzzy number. \n\n\n\n\nhttp://www.met.gov.my/\n\n\nhttp://www.wecam.org.my/\n\n\n\n\n\n\nMalaysian Journal Geosciences (MJG) 1(2) (2017) 01-05 \n\n\n\nCite the article: Norzanah Abd Rahman, Zamali Tarmudi, Munirah Rossdy, Fatihah Anas Muhiddin (2017). Flood Mitigation Measures Using Intuitionistic Fuzzy \nDematel Method. Malaysian Journal Geosciences, 1(2) : 01-05. \n\n\n\n5 \n\n\n\nTelkomnika, 12 (4), 1064-1072. doi: 10:12928/TELKOMNIKA.v12i4.787. \n\n\n\n[25] Govindan, Khodaverdi, R., and Vafadarnikjoo, A. 2015. Intuitionistic \nfuzzy based DEMATEL method for developing green practices and \nperformances in a green supply chain. Expert Systems with Applications, 42 \n(2015), 7207\u20137220. doi: 10.1016/j.eswa.2015.04.030. \n\n\n\n[26]  Keshavarzfard, R., and Makui, A. 2015. An IF-DEMATEL-AHP based on \ntriangular intuitionistic fuzzy numbers (TIFNs). Decision Science Letters, 4 \n(2015), 237-246. \n\n\n\n[27] Falatoonitoosi, E., Ahmed, S., amd Sorooshian., S. 2014. Expanded \nDEMATEL for determining cause and effect group in bidirectional \nrelations. The Scientific World Journal, 2014 (103846), 1-7. \ndoi:10.1155/2014/10384. \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geoscien  ces 2(1) (2018) 38-41 \n\n\n\nCite the Article: Ismail Abd Rahim, Junaidi Asis, Mohamed Ali Yusuf Mohd Husin (2018). Comparison of Different Type of Friction Angle in Kinematic \nAnalysis. Malaysian Journal of Geosciences, 2(1) : 38-41. \n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nThe kinematic analysis is a method in determining the types of failure modes for a rock slope. This analysis is \nreferring to the motion of bodies without reference to the forces that cause them to move and depending on the \nrelationship between slope geometry and internal friction angle of discontinuity plane or failure. The selection of \nfriction angle type for kinematic analysis is an important aspect in term of cost, availability and reliability of testing, \nequipment and result. Then, kinematic analysis has been conducted by using the peak, basic and conventional \nfriction angles values from triaxial test, tilt test and assumption, respectively for ten (10) selected slopes. Finally, \nthe cheaper, most available and reliable result was shown by the basic friction angle and recommended for \nkinematic analysis. \n\n\n\nKEYWORDS \n\n\n\nBasic friction angle, Tilt testing, Crocker Formation, Kinematic analysis.\n\n\n\n1. INTRODUCTION \n\n\n\nSlope stability analysis has been around for a long time for soil slopes such \nas limit equilibrium method to analyses the soil slope stability which was \nadapted to rock slope stability analysis. Methods such as kinematic \nanalysis, finite element analysis and limit equilibrium analysis are widely \nused.  \n\n\n\nMarkland and Hocking developed a test for identifying important pole \nconcentrations of discontinuities. Kinematical analysis refers to the \nmotion of bodies without reference to the forces that cause them to move \nand depending on the relationship between slope geometry and internal \nfriction angle of discontinuity plane or failure. Kinematic analysis is based \non Markland\u2019s test which is a technique to estimate the relative stability of \nthe body and the potential rock slope failure based on stereonet (plane, \nwedge, toppling failures) [1].  \n\n\n\nMarkland\u2019s test shows that, a plane failure is likely to occur when a \ndiscontinuity dips in the same direction (within 20\u00b0) as the slope face, at \nan angle gentler than the slope angle but greater than the friction angle \nalong the failure plane; a wedge failure may occur when the line of \nintersection of two discontinuities, forming the wedge-shaped block, \nplunges in the same direction as the slope face and the plunge angle is less \nthan the slope angle but greater than the friction angle along the planes of \nfailure; a toppling failure may result when a steeply dipping discontinuity \nis parallel to the slope face (within 10\u00b0) and dips into it [1]. \n\n\n\nThere are few types of usage for friction angles in kinematic analysis such \nas peak, residual, conventional and basic friction angles of discontinuities. \nThe peak, residual and basic friction angle can be estimated from the \ntriaxial test, uniaxial compressive test, direct shear test and tilt testing. The \npeak friction angle is a common input in estimating the mode of failure as \nwell as the conventional friction angle (30o) as used by many researchers. \nThe mode of failures by the usage of conventional friction angle in \nkinematic analysis for the Crocker, Temburung and Trusmadi formations \nare wedge, planar and toppling failures [2-12].  \n\n\n\nThere is some aspect in selecting the type of friction angle that might be \nconsidered before conducting kinematic analysis. The cost of estimating \nfriction value; the availability of testing equipment and the reliability of \nthe mode of failure or result. In order to identify the safest and cost \neffective type of friction angle for kinematic analysis, ten (10) rock cut \n\n\n\nslopes of the Crocker formation around Menggatal-Tuaran area, Sabah \n(Figure 1 and 2) are selected.  \n\n\n\nThe study area is mostly underlain by the Crocker formation of Late \nEocene-late Early Miocene ages and Quaternary Alluvium along the river \nand tributaries and low land area. The Crocker formation is a turbidite of \ndeep sea deposit. This formation consists of interbed sandstone, siltstone \nand shale units. Bouma sequence and sole mark can be found in some beds. \nThe thickness of rock unit differs from one outcrop to another.  The \nformation is highly folded and faulted to form a thrust-fold system in \nSabah. The alluvium is observed in the stream bed and originated from \ndifferent rocks around the study area. These deposits consist of gravel, \nsand, silt, clay and other materials.  \n\n\n\n2. MATERIAL AND METHOD \n\n\n\nGenerally, the methodology of this study consists of field study, laboratory \ntest and data analysis. Field study includes geological mapping and rock \nsampling. The rock samples were taken from slope E because of \nweathering grade (grade I, fresh rock) and all selected slopes consists of \nsame fine greywacke lithic sandstone [3]. Some of rock sample (hand \nspecimen) is prepared for thin section before examined under polarized \nmicroscope (Nikon Axio-lab 10) for petrographic study. Discontinuities \nsurvey is conducted as an observation and measurement studies on given \nrock cut slope which involved discontinuities quantification based on \nISRM before kinematic analysis [13]. The types of discontinuities \nparameters considered were the types, strike and dip of the \ndiscontinuities. The method used for discontinuities survey is random \nsurvey method and the data gathered were jotted down in a data sheet. \n\n\n\nThe orientation data of the discontinuities were then pole plotted on the \nstereogram to determine the types of discontinuity planes or sets via Dips \n7.0 software [2]. Finally, the planes or sets of discontinuities analyzed \nkinematically to identify the modes of failure such as planar, wedge, \ntoppling or circular failures [14].  \n\n\n\nThe rock core and block samples preparation for triaxial testing and tilt \ntesting are conducted in laboratory test, respectively. The rock samples \ncollected for this study are rock blocks (at least 15mm x 10mm x 20mm \ndimension) for core and block samples. Samples of sandstone from \nCrocker formation were prepared in the form of fresh clean sawn surfaces \nobtained using a diamond core bit and saw.  \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : https://doi.org/10.26480/mjg.01.2018.38.41\n\n\n\nCOMPARISON OF DIFFERENT TYPE OF FRICTION ANGLE IN KINEMATIC ANALYSIS \n\n\n\nIsmail Abd Rahim*, Junaidi Asis, Mohamed Ali Yusuf Mohd Husin\n\n\n\nNatural Disaster Research Centre (NDRC), Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah \n*Corresponding Author Email: arismail@ums.edu.my \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \n\n\n\nCODEN : MJGAAN \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\nmailto:arismail@ums.edu.my\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 38-41\n\n\n\nCite the Article: Ismail Abd Rahim, Junaidi Asis, Mohamed Ali Yusuf Mohd Husin (2018). Comparison of Different Type of Friction Angle in Kinematic Analysis. \nMalaysian Journal of Geosciences, 2(1) : 38-41. \n\n\n\nThe samples were cut with perfectly straight by carefully preparing and \nusing polishing machine and sufficiently large slabs to come out complete \ncontact. Occasionally, the contact occurs in a small zone in such a way that \nthe upper slab rotates around an axis located in the center of the reduced \ncontact zone. \n\n\n\nTriaxial test was conducted according to ISRM in Hoek cell as shown in \nFigure 3 [15]. The tilt test is also following ISRM but the samples types and \narrangement [16]. The test is called as \u2018square type' which performed on \nsquare base slabs with 50mm x 50mm x 20mm dimensions (Figure 4).  \n\n\n\nFigure 1: The selected slopes. Note: A-slope 1; B- slope 2; C- slope 3; D- \n\n\n\nslope 4; E- slope 5; F- slope 6; G- slope 7; H- slope 8; I- slope 9; J- slope 10. \n\n\n\nA detailed description of the procedures used for performing tilt tests as \n\n\n\nfollows. \n\n\n\na) The specimens were cut according to the indicated dimensions using \n\n\n\ndiamond core drill bits and saws. \n\n\n\nb) The lower specimens were placed on the plane-tilting platform in the \n\n\n\nhorizontal position and secured in place. \n\n\n\nc) The upper specimens were placed on the fixed specimens in the\n\n\n\nhorizontal position. \n\n\n\nd) All the samples are marked to monitor the movement and rotation \n\n\n\nduring tilting process. \n\n\n\ne) The platform was progressively tilted at the rate of 0.4 mm/s until \n\n\n\nthe upper specimens began to slide, and the tilt angle of the platform\n\n\n\nwas recorded. Only tests corresponding to displacements of at least\n\n\n\n10% of the sample length were taken into account. \n\n\n\nf) Each test was repeated for five (5) times.\n\n\n\ng) Results were calculated as the mean of the results for all the \n\n\n\nrepetitions of each test. \n\n\n\nFigure 2: The slopes location and stereonet plots of Markland test. \n\n\n\nFigure 3: The coring machine, polishing and Hoek cell for triaxial test. \n\n\n\nFigure 4: The tilt testing of square types samples. \n\n\n\n3. RESULT \n\n\n\nThe result and average peak friction angle and basic friction angle by \n\n\n\ntriaxial test and tilt testing are shown in Table 1 and 2, respectively. The \n\n\n\naverage peak friction angle and basic friction angle by triaxial test and tilt \n\n\n\ntesting are 44o and 24o, respectively.  But, for this study, the well-known \n\n\n\nconventional frictional angle of 30o is also used in kinematic analysis as \n\n\n\ncomparison. The results of kinematic analysis for these three (3) types of \n\n\n\nfriction angle are shown in Table 3 and Figure \n\n\n\nTable 1: The peak friction angle value by triaxial test. \n\n\n\nSandstone \nConfining \nPressure \n(MPA) \n\n\n\nStress \n(MPA) \n\n\n\nStrain \n(%) \n\n\n\nPeak \nfriction \nangle, \u03c6 \n(degree) \n\n\n\nFine DJ1A1 1.0 42.92 1.11 37 \nDJ1A2 2.0 64.57 1.97 \nDJ1B1 1.0 150.66  1.46  51 \nDJ1B2 2.0 171.70  2.13 \nDJ1B3 4.0 176.59 2.39 \n\n\n\nAverage 44 \n\n\n\n39\n\n\n\n\n\n\n\n\nMalaysian Journal of Geoscie n ces 2(1) (2018) 38-41\n\n\n\n\n\n\n\nCite the Article: Ismail Abd Rahim, Junaidi Asis, Mohamed Ali Yusuf Mohd Husin (2018). Comparison of Different Type of Friction Angle in Kinematic Analysis. \nMalaysian Journal of Geosciences, 2(1) : 38-41. \n\n\n\nThe result of kinematic analysis shows that the modes of failure are wedge, \ntoppling and planar failures. There is no potential mode of failure in \nkinematic analysis by using peak friction angles, but the wedge failure has \n\n\n\nbeen found by conventional friction angles. The three modes of failure i.e. \nwedge, toppling and planar are identified as potential by using both \nconventional and basic friction angle.  \n\n\n\nTable 2: The basic friction angle value by tilt testing. \n\n\n\nSample \nBasic friction \n\n\n\nangle, \u0278b \nTests with rotation, sliding \n\n\n\nor resettlement (%) \nSample \n\n\n\nBasic friction \nangle, \u0278b \n\n\n\nTests with rotation, sliding \nor resettlement (%) \n\n\n\nDJ1A 30 0 DJ1G 21 0 \n\n\n\nDJ1B 30 0 DJ1H 21 0 \n\n\n\nDJ1C 28 1 DJ1I 21 0 \n\n\n\nDJ3D 25 0 DJ1J 21 0 \n\n\n\nDJ1E 26 1 DJ1K 23 0 \n\n\n\nDJ1F 23 0 DJ1L 23 1 \n\n\n\nAVERAGE 24 25 \n\n\n\nTable 3: Kinematic analysis result for three (3) type\u2019s friction angle. \n\n\n\nSlope  \n\n\n\n(face = \n\n\n\nS/D) \n\n\n\nFriction \n\n\n\nangle \nDC (S/D) Failure \n\n\n\nDC or DC \n\n\n\nintersection \n\n\n\nSlope  \n\n\n\n(face = \n\n\n\nS/D) \n\n\n\nFriction \n\n\n\nangle \nDC Failure \n\n\n\nDC or DC \n\n\n\nintersection \n\n\n\n1 \n\n\n\n(25/55) \n\n\n\nPeak B (79/58) \n\n\n\nJ1 (324/86) \n\n\n\nJ2 (30/19) \n\n\n\nJ3 (295/60) \n\n\n\nJ4 (282/21) \n\n\n\nJ5 (331/86) \n\n\n\n-  - \n\n\n\n6 \n\n\n\n(120/70) \n\n\n\nPeak \n\n\n\nB (20/38) \n\n\n\nJ1 (150/73) \n\n\n\nJ2 (71/82) \n\n\n\nJ3 (183/53) \n\n\n\n- - \n\n\n\nC W BJ2 C \nW \n\n\n\nW \n\n\n\nBJ1 \n\n\n\nJ1J2 \n\n\n\nBasic \n\n\n\nW \n\n\n\nW \n\n\n\nW \n\n\n\nBJ2 \n\n\n\nBJ1 \n\n\n\nJ1J2 \n\n\n\nBasic \n\n\n\nW \n\n\n\nW \n\n\n\nW \n\n\n\nBJ1 \n\n\n\nJ1J2 \n\n\n\nBJ3 \n\n\n\n2 (333/85) \n\n\n\nPeak B (60/68) \n\n\n\nJ1 (253/70) \n\n\n\nJ2 (64/30) \n\n\n\nJ3 (153/75) \n\n\n\n-  - \n\n\n\n7 \n\n\n\n(152/50) \n\n\n\nPeak B (40/60) \n\n\n\nJ1 (110/75) \n\n\n\nJ2 (332/70) \n\n\n\nJ3 (215/50) \n\n\n\n- - \n\n\n\nC -  - C - - \n\n\n\nBasic W BJ1 Basic W BJ3 \n\n\n\n3 \n\n\n\n(100/75) \n\n\n\nPeak \nB (199/75) \n\n\n\nJ1 (45/58) \n\n\n\nJ2 (324/85) \n\n\n\nJ3 (250/25) \n\n\n\nW J1J2 \n\n\n\n8 \n\n\n\n(225/68) \n\n\n\nPeak \nB (39/66) \n\n\n\nJ1 (288/71) \n\n\n\nJ2 (162/23) \n\n\n\nJ3 (266/34) \n\n\n\n- - \n\n\n\nC -  - C - - \n\n\n\nBasic \nW \n\n\n\nW \n\n\n\nJ1J2 \n\n\n\nBJ1 \nBasic \n\n\n\nW \n\n\n\nW \n\n\n\nW \n\n\n\nJ1J2 \n\n\n\nJ1J3 \n\n\n\nJ2J3 \n\n\n\n4 \n\n\n\n(40/40) \n\n\n\nPeak B (225/75) \n\n\n\nJ1 (45/58) \n\n\n\nJ2 (324/85) \n\n\n\nJ3 (250/25) \n\n\n\n-  - \n\n\n\n9 \n\n\n\n(209/77) \n\n\n\nPeak B (35/78) \n\n\n\nJ1 (217/22) \n\n\n\nJ2 (36/75) \n\n\n\nJ3 (123/77) \n\n\n\n- - \n\n\n\nC -  - C - - \n\n\n\nBasic T B Basic \nP \n\n\n\nT \n\n\n\nJ1 \n\n\n\nB \n\n\n\n5 \n\n\n\n(266/72) \n\n\n\nPeak \n\n\n\nB (25/50) \n\n\n\nJ1 (123/34) \n\n\n\nJ2 (70/55) \n\n\n\nJ3 (255/30) \n\n\n\n-  - \n\n\n\n10 \n\n\n\n(115/84) \n\n\n\nPeak \n\n\n\nB (48/78) \n\n\n\nJ1 (76/35) \n\n\n\nJ2 (205/60) \n\n\n\nJ3 (190/40) \n\n\n\n- - \n\n\n\nC \nP \n\n\n\nT \n\n\n\nJ3 \n\n\n\nB \nC \n\n\n\nW \n\n\n\nW \n\n\n\nBJ1 \n\n\n\nBJ2 \n\n\n\nBasic \nP \n\n\n\nT \n\n\n\nJ3 \n\n\n\nB \nBasic \n\n\n\nW \n\n\n\nW \n\n\n\nW \n\n\n\nW \n\n\n\nW \n\n\n\nBJ1 \n\n\n\nBJ2 \n\n\n\nJ1J3 \n\n\n\nBJ3 \n\n\n\nJ2J3 \n\n\n\nNote: S/D-strike/dips; C-conventional; DC-discontinuity; W-wedge; P-planar; T-toppling \n\n\n\n4. DISCUSSION \n\n\n\nIn order to highlight the safest and cost-effective type of friction angle for \nkinematic analysis, there is few aspects must be considered. First, the cost \nfor conducting the testing in the determination of friction angles. Second, \nthe availability of testing equipment and finally, the reliability of the result. \n\n\n\nIn this study, the peak and basic friction angle have been obtained by \ntriaxial and tilt tests, respectively. The triaxial test is using the costly \u2018Hoek \n\n\n\ncell\u2019 machine compare to tilt testing. This shows that, the cost for basic \nfriction angle is cheaper than peak friction angle. The availability of testing \nequipment is depending on their price, where the cheaper tilting machine \ncan be found in many laboratories compares to Hoek cell. This has showing \nthat the basic friction angle is easier to recover compare to peak friction \nangle. The price and availability for conventional friction angle is ignore \nbecause it is just an assumption or without any testing.  \n\n\n\nThere are huge differences in the mode of failure from kinematic analisis \nfor these three (3) types of friction angle as shown in Table 3. The \n\n\n\n40\n\n\n\n\n\n\n\n\nMalaysian Journal of Geoscie n ces 2(1) (2018) 38-41\n\n\n\nCite the Article: Ismail Abd Rahim, Junaidi Asis, Mohamed Ali Yusuf Mohd Husin (2018). Comparison of Different Type of Friction Angle in Kinematic Analysis. \nMalaysian Journal of Geosciences, 2(1) : 38-41. \n\n\n\nexpensive and difficult to recover peak friction angle is not showing any \npotential mode of failure except the wedge failure in slope 3. This means, \nthe selected slopes are kinematically stable and doesn\u2019t need protection \nand stabilization measures or cost in term of slope design. \n\n\n\nThe most commonly used conventional friction angle has been showing \npotential wedge, toppling and planar failures except in slope 2, 3, 4, 7, 8 \ndan 9. The slopes are partly stable or 60% of the slope is stable and \nwithout protection and stabilization measures. This shows that, the usage \nof conventional friction angle is moderately recommended because the \nvalue is only assumption and questionable in representing real friction \nangle value for a discontinuity plane in the rock mass. But, it is suitable as \nan alternative in early design, low cost, fast or temporary projects. \n\n\n\nThe wedge, toppling and planar mode of failures are also potential \nkinematically when using basic friction angles for the selected slopes. This \nshows that the slopes are considered unstable. The potential mode of \nfailure by conventional and peak friction angle is includes in this simple \nand cheap basic friction angle as shown in slope 1, 3, 5, 6 and 10.  The \nresults can be interpreted as comprehensive and reliable in term of safety \nand cost. For the purposes of safety, the more identified potential mode \nfailure is better than less, even though the cost for stabilization and \nprotection are high. Investing high cost for protection and stabilization \nmeasures in construction phase are better than repairing and \nreconstructing the slopes in the future.   \n\n\n\n5. CONCLUSION \n\n\n\nThe modes of failure for selected slopes by kinematic analyses are wedge, \ntoppling and planar failures. The basic friction angle is recommended for \nkinematic analysis due to its operational cost, availability of testing \nequipment and reliability of result.   \n\n\n\nREFERENCES \n\n\n\n[1] Hoek, E., Bray, J. 1981. Rock Slope Engineering. (3rd Edition). Institute \n\n\n\nof Mining and Metallurgy, London, UK. \n\n\n\n[2] Rocscience, I. 2004. DIPS Version 7.0 Software for Graphical and \n\n\n\nStatistical Analysis of Orientation Data. Toronto, Ontario, Canada. \n\n\n\nwww.rocscience.com. \n\n\n\n[3] Rahim, I.A. 2015. Geomechanical classification scheme for \n\n\n\nheterogeneous Crocker Formation in Kota Kinabalu, Sabah: An update. \n\n\n\nBulletin of Geological Society of Malaysia, 61, 85-89. \n\n\n\n[4] Rahim, I.A. 2014. Slope stability assessment of the Temburung \n\n\n\nFormation along Beaufort-Tenom railway, Sabah. Borneo Science, 34, 11 \u2013 \n\n\n\n19.  \n\n\n\n[5] Rahim, I.A. 2013. The stability of Temburung Formation in Beaufort \n\n\n\narea, Sabah. Prosiding Seminar Bencana Alam 2013 (BENCANA2013), 3-4 \n\n\n\nDecember 2013, Auditorium Perpustakaan, Universiti Malaysia Sabah, \n\n\n\nKota Kinabalu, 62-63. \n\n\n\n[6] You, L.K., Rahim, I.A. 2017. Application of the GSI system for slope \n\n\n\nstability studies on selected slopes of the Crocker Formation in Kota \n\n\n\nKinabalu area, Sabah. Geological Behaviour, 1 (1), 10-12. \n\n\n\n[7] Rahim, I.A., Usli, M.N.R. 2017. Slope stability study around kampung \n\n\n\nKuala Abai, Kota Belud, Sabah, Malaysia. Malaysian Journal of Geosciences, \n\n\n\n1 (1), 38-42. \n\n\n\n[8] Rahim, I.A., Musta, B. 2015. The stability of metasedimentary rock in \n\n\n\nRanau, Sabah, Malaysia. Proceeding of the 2nd International Conference \n\n\n\nand 1st Joint Conference on Geoscience Challenge for Future Energy and \n\n\n\nEnvironment Sustainability, 29 September 2015, Luxton Hotel, Bandung, \n\n\n\nIndonesia.  \n\n\n\n[9] Rahim, I.A., Tahir, S., Musta, B., Omang, S.A.K. 2010. Slope Stability \n\n\n\nEvaluation of selected rock cut slope of Crocker Formation in Kota \n\n\n\nKinabalu, Sabah. Proceeding of the 3rd Southeast Asian Natural Resources \n\n\n\nand Environmental Management (SANREM 2010), 3-5, Promenade Hotel, \n\n\n\nKota Kinabalu, Sabah. \n\n\n\n[10] Rahim, I.A., Tahir, S.H., Musta, B., Roslee, R. 2006. Slope Stability \n\n\n\nAssessment of the Crocker Formation in the Telipok, Sabah. Prosiding \n\n\n\nPersidangan the Southeast Asian Natural Resources and Environmental \n\n\n\nManagement (SANREM 2006). 21-22, Le Meridian Hotel, Kota Kinabalu.  \n\n\n\n[11] Zaki, M.M.M., Tahir, S.H., Rahim, I.A., Yatim, A.N.M. 2012. Rock cut \n\n\n\nslope stability evaluation of the Crocker Formation along jalan UMS, Kota \n\n\n\nKinabalu, Sabah. Borneo Science 30, 32-39. \n\n\n\n[12] Roslee, R., Rahim, I.A., Omang, S.K.S. 2009. Geological factors \n\n\n\ncontributing to the landslide hazard occurrences in the Trusmadi \n\n\n\nFormation slopes, Sabah, Malaysia. Abstrak of the Eleventh Regional \n\n\n\nCongress on Geology, Mineral and Energy Resources of Southeast Asia \n\n\n\n(GEOSEA 2009), 8-10 Jun 2009, Hotel Istana, Kuala Lumpur. \n\n\n\n[13] ISRM. 1978. Suggested method for quantitative description of \n\n\n\ndiscontinuities in rock masses. Int. Journal of Rock Mech., Mining Sc. and \n\n\n\nGeomechanics Abstracts, 15, 319-368. \n\n\n\n[14] Wyllie, D.C., Mah, C.W. 2004. Rock Slope Engineering. (4th Edition). \n\n\n\nTaylor & Francis Group, UK. \n\n\n\n[15] ISRM. 2007. The Complete ISRM Suggested Methods for Rock \n\n\n\nCharacterization, Testing and Monitoring: 1974-2006. In: Ulusay, R. & \n\n\n\nHudson. J. A. (Ed.). Commission on Testing Methods International Society \n\n\n\nfor Rock Mechanics (ISRM). Elsevier, 627. \n\n\n\n[16] Alejano, L.R., Gonzalez, J., Muralha, J. 2012. Comparison of Different \n\n\n\nTechniques of Tilt Testing and Basic Friction Angle Variability Assessment. \n\n\n\nRock Mechanic and Rock Engineering, 45, 1023\u20131035.  \n\n\n\n41\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 19-28 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2022.19.28 \n\n\n\n\n\n\n\n Cite the Article: Casmed Charles Amadu, Sampson Owusu, Gordon Foli, Blestmond A. Brako, Samuel K. Abanyie  (2022). Comparison of Ordinary Kriging (OK) and \nInverse Distance Weighting (IDW) Methods for the Estimation of a Modified Palaeoplacer Gold Deposit: A Case Study of the Teberebie Gold Deposit, SW Ghana. \n\n\n\nMalaysian Journal of Geosciences, 6(1): 19-28. \n \n\n\n\n\n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2022.19.28 \n\n\n\n\n\n\n\n\n\n\n\nCOMPARISON OF ORDINARY KRIGING (OK) AND INVERSE DISTANCE WEIGHTING \n(IDW) METHODS FOR THE ESTIMATION OF A MODIFIED PALAEOPLACER GOLD \nDEPOSIT: A CASE STUDY OF THE TEBEREBIE GOLD DEPOSIT, SW GHANA \n \nCasmed Charles Amadua*, Sampson Owusub, Gordon Folic, Blestmond A. Brakoc, Samuel K. Abanyied \n \n\n\n\naDepartment of Earth Science, Faculty of Earth, and Environmental Sciences, CK Tedam University of Technology and Applied Sciences, P. O. Box \n24, Navrongo, Ghana \nbMineral Resources Department, Gold Fields Tarkwa Mine, P. O. Box 26, Tarkwa, Ghana \ncDepartment of Geological Engineering, Kwame Nkrumah University of Science and Technology (KNUST), PMB, University Post Office; Kumasi, \nGhana \ndUniversity for Development Studies, P O Box TL 1350, Tamale \n*Corresponding author E-mail: camadu@uds.edu.gh  \n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 01 December 2021 \nAccepted 08 January 2022 \nAvailable online 04 February 2022 \n\n\n\n The study described in this paper involves the application of a conventional resource estimation method, \ninverse distance weighting (IDW), and univariate geostatistical technique, ordinary kriging (OK) to the gold \ngrades data from the modified palaeoplacer Teberebie gold deposit, in Ghana. The deposit consists of 4 \nlayered well-defined orebodies referred to as A reef, CDE reef, F24 reef and G reef at the mine environment. \nSimple, reliable, and adequately accurate resource/reserve estimation are essential to mining operations. \nData used for the research were collected by diamond and reverse circulation (RC) drilling. A total of 19353 \none-meter composite samples, consisting of 18962 RC chip samples from 695 RC drill holes, and 391 diamond \ndrill core samples from 11 DD holes. Samples were analysed by atomic absorption spectrometry (AAS) for \ngold (Au). Descriptive statistical treatment was conducted on grade values for the reefs. To analyse for spatial \nstructure of Au mineralisation, experimental downhole, and several horizontal directional semi-variograms \nwere computed, and models fitted. Ore reserves were estimated by OK and IDW methods, and results of the \nvarious reefs compared. Regression analysis of estimated results indicate that, the inverse distance square \n(ID2) model produced estimates that compared well with the OK model in all the ore zones. It is therefore, \nappropriate to use ID2 as an alternative estimation method to the OK method for purposes of mine planning \nand grade control.  \n\n\n\nKEYWORDS \n\n\n\nGeostatistics, palaeoplacer gold deposit, ordinary kriging, inverse distance weighting, and regression \nanalysis. \n\n\n\n1. INTRODUCTION  \n\n\n\nGoldfields Ghana Limited\u2019s Tarkwa Mine is located in the Western Region \nof Ghana, about 300 km, west of Accra, Ghana\u2019s capital. The mine has been \nin operation since 1993 and consists of several low to moderately rich gold \npalaeoplacer deposits located within the Tarkwaian Group, close to \nTarkwa township. Mining is done from several open pit operations using \nconventional trucks and backhoe excavator method. Operations are \noptimised by Dispatch Fleet Management System (DFMS). The company \nhas as one of its core policies, the concept of continuous improvement in \nits resource/reserve estimation, aimed at establishing reliable ore \nreserves estimates for making decisions about future investments (Daya, \n2015). \n\n\n\nImplementation of an accurate and reliable estimation method is an \nimportant aspect in resource/reserve estimation (Shahbeik et al., 2014). \nNumerous approaches for mineral resources estimation are generally \n\n\n\ncategorised into 2 main groups: (1) conventional/traditional methods, and \n(2) geostatistical methods (Isaaks and Srivastava, 1989; Rossi and \nDeutsch, 2014; Silva and Almeida, 2017). The conventional methods use \nsections and plan maps, while geostatistical approach, involves a \ncomputer-driven two- and three-dimensional (2D and 3D) approach to \nestimate grade and tonnage of the deposit. It is based on the theory of \nregionalized variables (ReV) proposed (Matheron, 1971). \n\n\n\nDepending on the method of selecting auxiliary blocks, and the manner of \ncomputing average grades, conventional methods can further be classified \ninto, inverse distance weighting (IDW), polygonal methods, triangular \nmethods, method of sections, block matrices, and contour methods \n(Sinclair and Blackwell, 2002). IDW and geostatistical techniques such as \nordinary kriging (OK) are extensively employed for ore grade estimation \n(Tahmasebi and Hezarkhani 2010). Geostatistical methods, recognizes, \nbased on the theory of ReV, that, grade values in a mineral deposit are \nspatially correlated with one another (Matheron, 1971). \n\n\n\n\nabout:blank\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 19-28 \n\n\n\n\n\n\n\n \nCite the Article: Casmed Charles Amadu, Sampson Owusu, Gordon Foli, Blestmond A. Brako, Samuel K. Abanyie  (2022). Comparison of Ordinary Kriging (OK) and \n\n\n\nInverse Distance Weighting (IDW) Methods for the Estimation of a Modified Palaeoplacer Gold Deposit: A Case Study of the Teberebie Gold Deposit, SW Ghana. \nMalaysian Journal of Geosciences, 6(1): 19-28. \n\n\n\n\n\n\n\n\n\n\n\nThe underlying tool of geostatistical analysis is the semi-variogram (Wang \net al., 2017; Amadu et al., 2021). Constructed semi-variograms allow the \ndetermination of the structural characteristics of the regionalized earth \nphenomenon. The range of estimation methods evolved from geostatistics \nis termed as \u201ckriging\u201d, which generically describes a family of generalised \nleast-squares regression algorithms for estimating ReVs (Daya, 2015). In \nterms of the purpose of kriging, two general classes can be identified, first, \nis the global in-situ reserve estimation (estimation without the imposition \nof mining and economic factors), and secondly, the estimation of \nrecoverable reserves. Recoverable estimation takes into consideration the \nportion of the deposit that is technically recoverable when a cut-off grade \nis applied to selective mining units (SMUs): for example, in particular \nselective mining operations where various cut-off grade values are \nimposed (Annels, 1991; Sinclair and Blackwell, 2002). \n\n\n\nThe selection of a resource/reserve estimation method depends on factors \nsuch as geology, ease of implementation of the method, kind of mining \noperation, robustness of the estimation method, accuracy and precision \n(Annels, 1991; JORC, 2012; Rossi and Deutsch, 2014). A group researchers \nnoted that, improvement in grade estimation and mining methods leads to \nprofitable mining and increase of the life of mine (LOM) (Baldwin et al., \n2014). This paper is intended to investigate and evaluate the OK and IDW \napproaches to determine their relative merits in estimating the grade and \ntonnage of the modified palaeoplacer Teberebie gold deposit, on the basis \nof current mine operational demands. To preserve confidentiality, Au \ngrades have been multiplied by a factor. \n\n\n\n2. GEOLOGICAL SETTING AND MINERALISATION \n\n\n\nGold occurs in Ghana, principally from two major epigenetic gold-forming \nevents, from: (1) palaeoplacer hematite and magnetite quartz-pebble \nconglomerates of Tarkwaian System, and (2) shear-hosted orogenic gold \nof the Birimian Supergroup (Pigois et al., 2003; Perrouty et al., 2012; \nFougerouse et al, 2013; Hirdes and Nunoo, 1994). The Tarkwaian, is within \nthe Ashanti Belt in the Tarkwa syncline, which unconformably overlies the \nBirimian Supergroup (Feybesse et al, 2006; Allibone et al., 2002) (Figures \n1). \n\n\n\n \n(a) \n\n\n\n \n(b) \n\n\n\nFigure 1: (a- Top) Geology map of southern Ghana showing the location \nof Tarkwa; (b- Bottom) Geological map of the Tarkwa syncline (Oberth\u00fcr \n\n\n\net al, 1997; Pigois et al., 2003) \n\n\n\nThe Tarkwaian Group is divided into four units (Table 1), in succession \nfrom the base as: the Kawere Group; the Banket Series, the Tarkwa \nPhyllite; and the Huni Sandstone. \n\n\n\nTable 1: The divisions of the Tarkwaian System (Kesse, 1985) \n\n\n\nGroup Series \nThickness \n\n\n\nin (m) \nComposite lithology \n\n\n\nTarkwaian \n\n\n\nHuni \nsandstone \n\n\n\nand \n(Dompim \nphyllite) \n\n\n\n1370 \nSandstones, grits, \n\n\n\nquartzites with bands \nof phyllites \n\n\n\nTarkwaian \nphyllites \n\n\n\n120- 400 \n\n\n\nHuni sandstone \ntransitional beds, \n\n\n\ngreen and greenish \ngrey chloritic and \nsericite phyllites \n\n\n\nBanket \nseries \n\n\n\n120-160 \n\n\n\nTarkwa phyllite \ntransitional beds, \n\n\n\nsandstones, quartzites, \ngrits, breccias and \n\n\n\nconglomerates \n\n\n\nKawere \nGroup \n\n\n\n250-700 \nQuartzites, grits \n\n\n\nconglomerates and \nphyllites \n\n\n\nRecent investigations suggest the gold deposits within the Banket Series \nderived from a yet to be established source that is older than Birimian \nshear-hosted deposits (Hirdes and Nunoo, 1994; Fougerouse et al, 2013; \nPigois et al., 2003). The Banket conglomerates gold deposit underwent \ntectonic deformation and metamorphic processes (greenschist facies). A \ngroup researcher reports, deformation and metamorphism changed a \nsignificant amount of the original features of the Tarkwaian rocks (Greer \net al., 1988; Pigois et al., 2003). The focus of this study is on the Teberebie \ndeposit, where the deposits exist as a modified palaeoplacer deposits, \nwhere, the geology is dominated by rocks of the Banket Series, bounded \nby barren footwall and hanging wall quartzites (Klemd et al., 1993; Pigois \net al., 2003). The series consists of a sequence of mineralised auriferous \nreefs interlayered with barren immature quartzite units. A total of 9 reefs \n(Figure 2) have been identified within the mine, named for the purpose of \nidentification as: AFa, AFc, A1, A3, B2, C, E, F2, and G (Karpeta, 2000; SRK, \n2004). \n\n\n\n\n\n\n\nFigure 2: Tarkwa ore deposit model (Karpeta, 2000) \n\n\n\nThe reefs are usually lens-shaped, up to 400 m long and between 10 to 80 \nm wide (Karpeta, 2000). The units thicken to the west (Figure 3). \nInterpretation of the sedimentology and structure of the Tarkwaian, based \non current flow parameters analysis, suggest a flow from the east and \nnorth-east (Strongen, 1988). The deposit is situated to the south most part \nof the Takwa Mine concession and occurs within the Banket Series of the \nTarkwaian System (Figure 2). Exploration drill core logging and \ngeophysical surveys, reveal the orebodies in the area trend generally NE-\nSW exceeding a strike length of 1.2 km, and dips to the east, between 12 \u00ba \n- 18\u00ba (Karpeta, 2000; SRK, 2004). \n\n\n\nIn terms of mineralisation, economic concentrations of gold is restricted \nto within the silicified interstitial matrix between conglomerate clasts \n(Klemd et al., 1993). Pebble assemblage consists of white and smoky \nquartz, cherts, lithic fragments, quartzite and shale. Gold (Au) occurs \ngenerally as native (Klemd et al., 1993). The grades range between 0.9 and \n2.4 g/t (Pigois et al 2003). Accessory oxide minerals occurring in the ore \ninclude goethite, magnetite, rutile and ilmenite. Sulphides are occasionally \npresent, usually associated with intrusive rocks or quartz veining. The \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 19-28 \n\n\n\n\n\n\n\n \nCite the Article: Casmed Charles Amadu, Sampson Owusu, Gordon Foli, Blestmond A. Brako, Samuel K. Abanyie  (2022). Comparison of Ordinary Kriging (OK) and \n\n\n\nInverse Distance Weighting (IDW) Methods for the Estimation of a Modified Palaeoplacer Gold Deposit: A Case Study of the Teberebie Gold Deposit, SW Ghana. \nMalaysian Journal of Geosciences, 6(1): 19-28. \n\n\n\n\n\n\n\n\n\n\n\nsulphide minerals present include pyrite, chalcopyrite, pyrrhotite, \nsphalerite and galena. \n\n\n\n3. MATERIALS AND METHODS \n\n\n\n3.1 Borehole data and data processing \n\n\n\nThe portion of the Teberebie deposit used in this study was explored by \ndiamond drilling (DD), reverse circulation (RC) drilling, photogeological \nand geophysical interpretation, and field mapping. The data set is made of \na total of 19353 one-metre composite samples, consisting of 18962 RC \nchip samples from 695 RC holes and 391 diamond drill core samples from \n11 DD holes. While the RC holes consisted consistently of 1 m composites, \nthe DD hole cores were sampled with sample lengths respecting changes \nin lithological contacts. Samples obtained were assayed by Atomic \nAbsorption Spectrometry (AAS) method for gold (Au), at the SGS Ghana \nLimited laboratories, Tarkwa. Coring was carried out using LTK46 (core \ndiameter 35.6 mm) and BQ (core diameter 36.5 mm) equipment. \n\n\n\nFor the RC holes, MPD1500 (Rod diameter 32.60 mm) was used. The \naverage depth of a drill hole was 85 m. All holes were drilled at vertical \nangle, extending over a strike length of 1.3 km (52 sections). Drill holes \nintersected a number of sedimentary rock units. DD core recovery of 90 % \nand over provided information on lithology. DD holes were exploratory in \nnature, at selected locations and less regular. Drilling grid for the RC drill \nholes was 25 \u00d7 25 m (Figure 3), although in some places additional \nboreholes were drilled for further information. \n\n\n\n\n\n\n\nFigure 3: Borehole location plan \n\n\n\nFor the purpose of orebody modelling, and in order to obtain a reliable \ngeometry and grade of the orebodies within the study area, ASCII files of \ncollars to the drill holes, assay and mapping surveys were developed, \nsaved in comma-delimited formats and imported into Gemcom Surpac \nsoftware (Anon, 1998). Geological interpretation was carried out based on \ngold (Au) grade values, structural and lithological information from \ndiamond drill core and RC chip logs. Geological interpretation of folding, \nfaulting and litho-stratigraphic units were hand performed on vertical \nsection plots developed in Y-Z plane at separations of 25 m. In this study, \nfocus was on the limits between Eastings: 10750 \u2013 11400, Northings: 7400 \n\u2013 8700, and Elevation: (150) - (-150) m. This area was selected for this \nstudy because, the area showed well defined ore reefs and had adequate \nsample values, which is relevant for geostatistical and other techniques \nused in ore reserve estimation (Annels, 1991; Rossi and Deutsch, 2014). \n\n\n\n3.2 Delineation of ore zones \n\n\n\nBased on the intersections of drill holes within the study area on the \nvarious layers of rock units, zoning was carried out. This was carried out \nfrom the base of the hole to the top, using reefs characteristics such as: \nthickness, pebble characteristics and assemblages and grade distribution. \nThe reef zones identified at the Teberebie area include A, CDE, F24, and G. \nAn example of zoning is shown in Table 2. \n\n\n\nTable 2: Example of zone information \n\n\n\nHole ID \nDepth from \n\n\n\n(m) \nDepth to (m) Zone \n\n\n\nDEP18 0 4 OVB \n\n\n\nDEP18 4 10 HW \n\n\n\nDEP18 10 13 G \n\n\n\nDEP18 13 15 F5 \n\n\n\nDEP18 15 21 F24 \n\n\n\nDEP18 21 23 F1 \n\n\n\nDEP18 23 30 CDE \n\n\n\nDEP18 30 38 B \n\n\n\nDEP18 38 46 A \n\n\n\nDEP18 46 50 FW \n\n\n\nInformation from zones were used to digitize ore outlines that are \ndistinguished by colour codes (Figure 4a), followed by a 3-D wireframe \nmodel (Figure 4b), with the Surpac software. \n\n\n\n\n\n\n\nFigure 4: (a) Section 7625N of the Study Area and (b) 3D wireframe \nsolids generated \n\n\n\nThe segments defined in each section were linked to their corresponding \nsegments in the other sections, to form a three-dimensional (3D) \nwireframe of the mineralisation extending over the strike length from \n7400N to 8700N. The 3D wireframe solids were validated to ensure \ntriangles forming the solids were not overlapping. Output files created \nfrom the digitisation were used as the ore boundary string files and saved \nin Surpac. The assay data, bounded by the ore boundary string files were \nselected and used for: 1) assessing the continuity of ore and waste layers, \n2) statistical and geostatistical analysis, and 3) defining the wireframe and \nblock modelling. \n\n\n\n3.3 Statistical analysis on data \n\n\n\nBorehole sample comprising of DD and RC samples were analyzed by AAS \nmethod, and grades reported in g/t or ppm. DD samples were of unequal \nlengths. They were thus constrained and composited to 1 m lengths, as it \nis important to work with samples of equal support (Isaaks and Srivastava, \n1989; Daya, 2015). Statistical compatibility of DD and RC data sets were \nverified using the F \u2013 and t \u2013 tests as suggested (Al-Hassan and Annels, \n1994; Al-Hassan and Boamah, 2015). There was no significant difference \nstatistically, between DD core and RC chip sample types. They were thus \ncombined and used for further statistical analysis. The determination of \nunivariate statistics is a fundamental step in the resource/reserve \nevaluation, irrespective of whatever estimation method is to be employed \n(Arthur and Annels, 1994; Glacken et al., 2001). Statistical analysis \nprovides evidence of the distribution of the data, while Frequency \ndistribution analysis and defines sub-populations, and indicates distinct \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 19-28 \n\n\n\n\n\n\n\n \nCite the Article: Casmed Charles Amadu, Sampson Owusu, Gordon Foli, Blestmond A. Brako, Samuel K. Abanyie  (2022). Comparison of Ordinary Kriging (OK) and \n\n\n\nInverse Distance Weighting (IDW) Methods for the Estimation of a Modified Palaeoplacer Gold Deposit: A Case Study of the Teberebie Gold Deposit, SW Ghana. \nMalaysian Journal of Geosciences, 6(1): 19-28. \n\n\n\n\n\n\n\n\n\n\n\ngeological domains that are treated separately during evaluation (Rossi \nand Deutsch, 2014; Rezaei et al., 2019). Separate descriptive statistical \ntreatments were conducted for the reefs. \n\n\n\n3.4 Block modelling \n\n\n\nThe deposit block models (BMs), describe the 3D volumes of the orebodies \nwith relatively small-sized blocks known as cells. Block modelling is \ncarried out by grouping data and object features into a single space (Gibbs, \n1992). Information regarding features in the BM is referenced through its \nintersection with spatial objects like digital terrain models (DTMs), drill \ndata, 3D models of geologic features, plane surfaces, etc. Creation of BMs \nuse coordinate systems to locate relevant attributes or properties (Rossi \nand Deutsch, 2014). The orebodies are represented by series of arbitrary \nsolids. An unconstrained BM of the study area was generated, and the 3Ds \nof orebodies and their relative positions in space visualised in Surpac. \n\n\n\nA constraint was applied in the form of imposing the mineralised zone \nwireframes, as suggested (Gibbs, 1992): \n\n\n\n1. Creation of empty block model; \n\n\n\n2. Addition of geologic codes for lithology, degree of oxidation, \nalteration etc; \n\n\n\n3. Addition of constraints, such as structural information (e. g. faults); \nand \n\n\n\n4. Filling the model with numeric and character attribute records, such \nas mineralization type, degree of oxidation, and alteration, etc. \n\n\n\nThe orebodies are divided into fixed-size blocks and dimensions \ndetermined using sample spacing, grade variability, dip of deposit, \nplanned mining bench heights and other engineering considerations \n(Francois-Bongarcon and Guibal 1982; Al-Hassan and Boamah 2015). A \nuser block size of 10 x 10 x 3 m corresponding to half the average drill hole \ninterval was adopted. Block model parameters used, and the model \ngenerated is shown in Table 3, Figure 5, respectively. \n\n\n\nTable 3: Block model parameters \n\n\n\nBLOCK MODEL NAME: Teb_0610_10m_Project \n\n\n\nBlock Model Geometry \n\n\n\nMin. Coordinate Y = 7300 X = 10650 Z = -150 \n\n\n\nMax. Coordinate Y = 8600 X = 11410 Z = 150 \n\n\n\nUser Block size Y = 10 X = 10 Z = 3 \n\n\n\nMin. Block size Y = 10 X = 10 Z = 3 \n\n\n\nAttribute Description \n\n\n\nok Ordinary Kriging \n\n\n\nau_id4 IDW to the power 4 \n\n\n\nau_id3 IDW cubed \n\n\n\nau_id2 IDW Squared \n\n\n\nau_id IDW Squared to the power 1 \n\n\n\nMaterial type \nOre and waste of material \nconsidered \n\n\n\nSG Specific Gravity of material \n\n\n\nConstrains used Description \n\n\n\nReef Con. Ore zones within the solids \n\n\n\nTopo Con. \nDTM of the topography of the \narea \n\n\n\n\n\n\n\nFigure 5: Block model of Study Area \n\n\n\nThe BM for the study area was validated by visual examination (in \nsections) of color-coded drill hole assay values and intersections. \n\n\n\n3.5 Reserve estimation \n\n\n\n3.5.1 Variography for ordinary kriging (OK) \n\n\n\nOK method call for quantification of the spatial correlation structure, by \nsemi-variogram modelling (Annels, 1991; Lee et al., 2011; Gol et al., 2017; \nKang et al., 2019). Composited drillhole data within the individual \nwireframes of the reefs were used for variography, in accordance to a \nstudy and a series of variograms in several directions of mineralisation \nwere calculated, using the equation (Michel, 1982; Webster and Oliver, \n2007; Wang et al., 2017): \n\n\n\n \n(1) \n\n\n\nwhere,  defines the experimental variogram,  the \n\n\n\nvalue of sample grade at point ; Z ( grade of sample at \n\n\n\ndistant h from point  and, = count of sample pairs. \n\n\n\nTo determine the nugget variance C0, for the four reefs, downhole direction \nspherical semi-variograms were computed, on the basis that it has the \nclosest sampling interval, which is the shortest lag spacing of 1 m. \nDownhole variograms depicted single structure spherical models (Figure \n6). These were generated using the Geostatistics variogram modeling in \nSurpac, and the derived parameters shown in Table 4. \n\n\n\n\uf07b \uf07d\uf0e5\n=\n\n\n\n+\u2212=\nn\n\n\n\n1i\nhiZ()iZ(\n\n\n\n2\n\n\n\n2n\n\n\n\n1\n(h)\u03b3* xx\n\n\n\n( )h\u03b3* =)xiZ(\n\n\n\nxi =+h)xi\nxi n\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 19-28 \n\n\n\n\n\n\n\n \nCite the Article: Casmed Charles Amadu, Sampson Owusu, Gordon Foli, Blestmond A. Brako, Samuel K. Abanyie  (2022). Comparison of Ordinary Kriging (OK) and \n\n\n\nInverse Distance Weighting (IDW) Methods for the Estimation of a Modified Palaeoplacer Gold Deposit: A Case Study of the Teberebie Gold Deposit, SW Ghana. \nMalaysian Journal of Geosciences, 6(1): 19-28. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 6: Fitted experimental downhole semi-variograms for the reefs: \n(A) A-Reef, (B) CDE-Reef, (C) F-24 Reef, and (D) G-Reef. \n\n\n\nTo detect anisotropy in the horizontal plane, variograms were computed \nin various directions, separated by successive clockwise rotations of 30\u00ba \nfrom the north. Experimental variograms were calculated in 12 to 14 \ndirections for Au grade values within each of the reefs for the following \npurposes (Isaaks and Srivastava, 1989): \n\n\n\n\u2022 Quantify the variability of data sets with respect to spatial \ndistribution \n\n\n\n\u2022 Determine the ranges in the principal directions, \n\n\n\n\u2022 determine the existence or otherwise of anisotropy. \n\n\n\n\u2022 Define mathematical equations that represents fully, the grade \nvariations of the orebodies. \n\n\n\nA lag distance h, of 25 m, as the average drillhole spacing, and angular \ntolerance of 22.5o were used for the horizontal semi-variograms search, \nfor appropriate variogram or structural models to be fitted to the \nexperimental variograms (Wang et al., 2017). \n\n\n\nTable 4: Downhole semi-variogram model parameters of the various reefs on Azimuth of (0o) \n\n\n\nReef Lag Spac. (m) Plunge/Dip (\u00ba) Spread Spread Limit CO C a (m) \n\n\n\nA  -90 25 100 0.450842 0.450842 1.70 \n\n\n\nCDE  -90 25 100 0.89000 1.390166 2.15 \n\n\n\nF24  -90 25 100 0.247006 0.343113 1.643 \n\n\n\nG  -90 25 100 0.670232 0.895638 1.125 \n\n\n\nCo represents nugget variance, \u2018a\u2019 is range and C is the spatial variance\n\n\n\n3.5.2 Verification of variograms through cross validation \n\n\n\nAccuracy the various variogram models were done by cross validation \nwith point kriging, after the convention (Isaaks and Srivastava 1989). The \nGeostatistics\u2013Variogram Validation menu in Surpac was used to carry out \nthe validation process. Figure 7 shows examples of the scatter plot of the \nactual versus the estimated value using OK. \n\n\n\n\n\n\n\nFigure 7: Scatter plot of actual on kriged values, (A) A reef, (B), CDE reef \n\n\n\nRegression values were used to assess the agreement between predicted \nand actual values. The linear regression parameters for the four reefs \n(Table 5), approximate to those expected for perfect correlation (Davis, \n1986). The models were thus considered to satisfactorily characterize the \nspatial variability of the Au grades for the orebodies. \n\n\n\nTable 5: Regression equations of actual on kriged values for the ore \nzones \n\n\n\nReef Linear equation \nCorrelation coefficient \n\n\n\n(R) \n\n\n\nA \nActual = 0.0.419+0.8488* \n\n\n\nEstimate \n0.900 \n\n\n\nCDE \nActual = 0.3008+0.8808* \n\n\n\nEstimate \n0.9370 \n\n\n\nF24 \nActual = 0.3004+0.8291* \n\n\n\nEstimate \n0.8533 \n\n\n\nG \nActual = 0.2769+0.8321* \n\n\n\nEstimate \n0.9122 \n\n\n\n3.5.3 Reserve estimation using OK method \n\n\n\nHaving established spatial continuity of gold mineralisation for the various \nreefs by variogram analyses and modelling, local grade estimation was \nmodelled using OK and IDW methods. Grade values within the resource \nwireframe were used for estimation. Kriging, in general, is defined as a \nminimum variance estimator (Matheron, 1971; Sinclair and Blackwell, \n2002; Shahbeik et al., 2014). The average block grades were estimated by \nweighting samples according to derived parameters from the fitted semi-\nvariogram models (Section 3.5.1). Tonnage estimation for all blocks were \ndomained by rock type, degree of weathering and mean rock bulk density, \n\u03c1 values for each domain, and computed as, the product of average \nthickness of reef, plan area of the block (reef), and the tonnage factor or \nbulk density, 2.80 t/m3. \n\n\n\n3.5.4 Reserve estimation using IDW method \n\n\n\nIDW approach is probably one of the oldest spatial prediction techniques \n(Shepard, 1968) that employs a weighting factor, based on an exponential \ndistance function to each sample within a defined search neighbourhood \nabout the central point of a block (Annels, 1991; Harman et al., 2016). \nSample values within the neighbourhood are weighted by the inverse of \nthe distance of the sample from this central point, and raised to a power \n\u2018n\u2019, and computed as: (Harman et al., 2016): \n\n\n\nIDW = \n\n\n\n\n\n\n\n(2) \n\uf0e5\n\n\n\n\uf0e5\n\n\n\n=\n\n\n\n=\nn\n\n\n\n1i\nn\ni\n\n\n\nn\ni\n\n\n\ni\n\n\n\n*\nB\n\n\n\nd\n\n\n\n1\n\n\n\nd\n\n\n\n1\nZ\n\n\n\nZ\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 19-28 \n\n\n\n\n\n\n\n \nCite the Article: Casmed Charles Amadu, Sampson Owusu, Gordon Foli, Blestmond A. Brako, Samuel K. Abanyie  (2022). Comparison of Ordinary Kriging (OK) and \n\n\n\nInverse Distance Weighting (IDW) Methods for the Estimation of a Modified Palaeoplacer Gold Deposit: A Case Study of the Teberebie Gold Deposit, SW Ghana. \nMalaysian Journal of Geosciences, 6(1): 19-28. \n\n\n\n\n\n\n\n\n\n\n\nwhere: ZB\n* is the estimated variable of the block (of grade, thickness, \n\n\n\naccumulation etc.) \n\n\n\nZi is the value of the sample at location i \n\n\n\ndi is the separation distance from point i, to the point of reference, and, n \nis the power index. \n\n\n\nIn this study, different weighting powers, 1, 2, 3, and 4 were employed. \nBench by bench OK model grades were compared with that of IDW powers \nof 1, 2, 3, and 4 model grades. \n\n\n\n4. RESULTS AND DISCUSSIONS \n\n\n\n4.1 Delineation of the ore zones \n\n\n\nThe orebodies are well developed, strikes generally NE\u2013SW and dips 12 \u00ba-\n18\u00ba E. \n\n\n\n4.2 Sample data distribution analysis \n\n\n\nHistograms of raw data and logarithms of grades for the separate reefs are \nshown in Figure 8. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 8: Plots of the histograms of the raw data and logarithms of \ngrades for the separate reefs: (A) Frequency distributions of Au grades \n\n\n\nfor A reef, (B) Logarithms of grade for A reef, (C) Frequency distributions \nof Au grades for CDE reef, (D) Logarithms of grade for CDE reef, (E) \nFrequency distributions of Au grades for F24 reef, (F) Logarithms of \n\n\n\ngrade for CDE reef, (G) Frequency distributions of Au grades for G reef, \n(H) Logarithms of grade for G reef \n\n\n\nFigure 8, the distribution is positively skewed, typical of gold grades, and \nshow quite similar population distribution for the different reefs, hence \nthe population can be described as a single population (Davis, 1986). Plots \nof log-transformed values (Figure 8 B, D, F and H), and probability plots of \nthe raw Au grades show a one-parameter lognormal and unimodal global \ndistribution. This is indicative of a single main mineralization style, \nresulting from sporadic sediments depositional processes accompanied \nby syngenetic gold mineralization. The probability graph (Figure 9) shows \na distinct sub-population, and inflexion, e.g., at about 7.5 g/t for the A reef, \nwhich are outliers (Rossi and Deutsch, 2014). \n\n\n\n\n\n\n\nFigure 9: Example of probability plots, the case for A Reef \n\n\n\nResults of statistical analysis carried out on the raw data for the reefs are \npresented in Table 6. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 19-28 \n\n\n\n\n\n\n\n \nCite the Article: Casmed Charles Amadu, Sampson Owusu, Gordon Foli, Blestmond A. Brako, Samuel K. Abanyie  (2022). Comparison of Ordinary Kriging (OK) and \n\n\n\nInverse Distance Weighting (IDW) Methods for the Estimation of a Modified Palaeoplacer Gold Deposit: A Case Study of the Teberebie Gold Deposit, SW Ghana. \nMalaysian Journal of Geosciences, 6(1): 19-28. \n\n\n\n\n\n\n\n\n\n\n\nTable 6: Statistical parameters of Au samples, minimum (Min.), maximum \n(Max.), standard deviation (SD) and coefficient of variation (CoV) for the four \n\n\n\nore zones \n\n\n\nOre \nzone \n\n\n\nValues \nMin. \n(g/t) \n\n\n\nMax. \n(g/t) \n\n\n\nMean \n(g/t) \n\n\n\nVariance \n(g/t)2 \n\n\n\nSD \n(g/t) \n\n\n\nCoV \n\n\n\nA \nReef \n\n\n\n6555 0.01 9.00 1.230 0.902 0.95 0.772 \n\n\n\nCDE \nReef \n\n\n\n5753 0.01 19.9. 2.005 2.528 1.59 0.795 \n\n\n\nF24 \nReef \n\n\n\n4464 0.01 7.89 1.118 0.615 0.784 0.696 \n\n\n\nG \nReef \n\n\n\n2641 0.01 11.00 2.059 1.665 1.290 0.626 \n\n\n\nFrom the summary statistics, the CDE reef and G reef are showing \nrelatively higher mean grade with high variance and standard deviations \n(SD) compared to those of the A and F24 reefs. This is probably resulting \nfrom depositional history and source of the gold mineralization were \nunder favorable environment of formation conditions, sediments and \nsedimentary rocks act selectively as enriched elements potential economic \nzones (Deutsch, 2010). The number of Au samples differ for the reefs \nbecause, there is differences in the thickness of the reefs. The A reef is the \nthickest, while the G reef is the thinnest. The CDE recorded the highest \ngrade of 19.9 g/t and coefficient of variation (CoV) of 0.795. This probably \nis why the reef has the highest variance and standard deviation (SD). CoV \nis an expression of the relative variation of the data, and expresses the \ndegree of homogeneity of the distribution (Davis, 1986). CoVs for the reefs \nshow close differences (Table 6), this may be an indication of similar \ngeological and geochemical processes in their formation. Generally, data \nsets with CoV of less than 1, produce reasonable variogram models \n(Annels, 1991; Deutsch, 2010). In this study, the CoVs are found to be less \nthan 1 for all the reefs. \n\n\n\n4.3 Ore reserve estimation \n\n\n\n4.3.1 Variogram analysis \n\n\n\nAs mentioned in Section 3.5.1, to determine the nugget variance (nugget \neffect), C0, for the various reefs, downhole spherical semi-variograms were \ncomputed. C0 values obtained were 0.45, 0.89, 0.25, and 0.67 for the A reef, \n\n\n\nCDE reef, F24 reef, and G reef respectively. Directional experimental semi-\nvariograms in this study, in all cases of the ore zones were fitted with two \nstructure spherical models. Examples of variograms generated for some \ndirections are shown in Figure 10. Table 7 is model parameters obtained \nfor directional experimental variograms for the A reef (presented as an \nexample of parameters generated for the reefs). \n\n\n\n\n\n\n\nFigure 10: Examples of directional variograms: (A) A along 60\u00ba, (B) CDE \nalong 100\u00ba \n\n\n\nTable 7: A reef directional spherical semi-variogram parameters \n\n\n\nAzi. \n\n\n\n(\u00ba) \nDirection CO C1 C2 a1 a2 \n\n\n\nMajor/Semi \nmajor ratio \n\n\n\nMajor/Minor \nratio \n\n\n\n0  0.450842 0.035739 0.035739 12.83 58.628   \n\n\n\n30  0.450842 0.420617 0.015961 16.71 53.875   \n\n\n\n60 Major 0.450842 0.403515 0.048064 34.30 195.161 1.6915 114.7331 \n\n\n\n90  0.450842 0.397422 0.027352 11.62 85.199   \n\n\n\n120  0.450842 0.344493 0.082192 31.41 99.679   \n\n\n\n150 Semi-major 0.450842 0.342148 0.083957 25.68 115.379   \n\n\n\n180  0.450842 0.335964 0.088483 24.85 83.833   \n\n\n\n210  0.450842 0.283046 0.127976 24.99 80.303   \n\n\n\n240  0.450842 0.271529 0.147762 11.73 33.472   \n\n\n\n270  0.450842 0.271529 0.147762 10.30 33.116   \n\n\n\n300  0.450842 0.399124 0.037568 18.62 35.849   \n\n\n\n330  0.450842 0.382503 0.020441 9.706 62.229   \n\n\n\nThe general equation for this nested model agrees with those predicted \n(Sinclair and Blackwell, 2002; Journel and Huijbregts, 1978). It is \nexpressed as: \n\n\n\n\n\n\n\n(3) \n\n\n\n\n\n\n\nwhere, C0 = nugget variance, C1 and C2 = spatial variance of first and second \nstructure respectively, a1 and a2 = range of first and second structure, h = \ndistance separating pairs of sample values. C0 represents the random \nportion of the variability of the regionalised variable, i. e the variogram \nvalue \u03b3(h) at a distance of zero (i.e. when \u2018h\u2019 equal to zero). It is partly an \nexpression of the variability between samples at, or very close to zero \ndistance apart and partly the presence of sampling errors (Annels, 1991). \nFrom plots of variograms and parameters obtained from variogram \nmodels, it was observed variograms differed with different directions, \nsince different ranges and sills were obtained for different directions, an \nindication of anisotropy (Sinclair and Blackwell, 2002). \n\n\n\nTwo major horizontal directions were worth noting, N60\u00baE showed \nmaximum continuity ot mineralisation, which is along strike, and an \nazimuth of 150\u00ba which is across strike, indicated a semi-major continuity. \nRanges along strike were, 34.30 m, 32.516 m, 26.74 m and 33.44 m for the \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 19-28 \n\n\n\n\n\n\n\n \nCite the Article: Casmed Charles Amadu, Sampson Owusu, Gordon Foli, Blestmond A. Brako, Samuel K. Abanyie  (2022). Comparison of Ordinary Kriging (OK) and \n\n\n\nInverse Distance Weighting (IDW) Methods for the Estimation of a Modified Palaeoplacer Gold Deposit: A Case Study of the Teberebie Gold Deposit, SW Ghana. \nMalaysian Journal of Geosciences, 6(1): 19-28. \n\n\n\n\n\n\n\n\n\n\n\nA reef, CDE reef, F24 reef, and G reef respectively, indicating drill hole \nintervals are within the ranges in the strike direction of the orebodies. The \nshortest range was in the downhole direction, which reflect thickness of \nthe reefs. They ranged from 1.13 to 2.20 m. This agrees with observations \nmade for layer sedimentary mineralisation (Hayward et al., 2005). \n\n\n\n4.3.2 Comparison of estimated grades \n\n\n\nTable 8 presents grades and tonnages estimated by OK and IDW methods. \n\n\n\nTable 8: Comparison of estimated grades using OK and ID2 methods \n\n\n\nOK ID2 \n\n\n\nReef Vol.(m3) Tonnes (t) \nAve. grade \n\n\n\n(g/t) \nReef Vol. (m3) Tonnes (t) \n\n\n\nAve. grade \n(g/t) \n\n\n\nA 3716700 9849255 1.233 A 3716700 9849255 1.233 \n\n\n\nCDE 3122700 8275155 2.042 CDE 3122700 8275155 2.046 \n\n\n\nF24 2892000 7663800 1.095 F24 2892000 7663800 1.096 \n\n\n\nG 1694700 4490955 2.088 G 1694700 4490955 2.084 \n\n\n\nA number of criteria for comparing estimation methods exist, they include \nanalysis of the correlation between estimates, and the use of grade and \ntonnage curves (Ravenscroft, 1992). To avoid arbitrary weighting \napproach using IDW method, results produced by various weighting \npowers (1, 2, 3, and 4) were compared to those produced by OK method. \nGoodness - of - fit statistics showed that, inverse distance squared, ID2 \ncompared well with OK as compared to IDW- 1, IDW-3 and IDW- 4 (Figure \n11 and Table 9). \n\n\n\n\n\n\n\nFigure 13: Comparison of estimated grades: (A) OK, ID, ID2, ID3, and ID4 \nmodel for A reef, (B) OK, ID, ID2, ID3, and ID4 model for CDE reef, (C) OK \n\n\n\nand ID2 model for A reef, (D) OK, and ID2 model for CDE reef. \n\n\n\nTable 9: Results of regression analysis of block-by-block OK against \nID2 model grades \n\n\n\nOre zones Power index \nCorrelation coefficient \n\n\n\n(R) \n\n\n\nA 2 0.934 \n\n\n\nCDE 2 0.947 \n\n\n\nF24 2 0.912 \n\n\n\nG 2 0.917 \n\n\n\nThe regression parameters (Tables 9) indicate the ID2 model compared \nstrongly well with the OK model in all the four reefs and is therefore a \nsatisfactorily accurate estimation alternative method to OK for mine \nplanning and grade control for the Teberebie deposit. Generally, \nconventional methods such as IDW have some drawbacks in the accuracy \nof reserve estimation (Daya, 2015). Errors in estimating thickness of \norebodies can occur when assuming that the thickness/grade of a block is \nequal to the thickness of grade of a single point about which the block has \nbeen drawn. However, this problem is minimized in a situation of \nrelatively uniform thickness as in with this study area. Prior to grade \ninterpolation, OK method is preceded by the determination of spatial \nstructure of the mineralization by construction of semi-variograms (Lee et \nal., 2011). This make OK lengthier and much complex compared to IDW. \nThe selection of one estimation approach or a combination of approaches \nby an exploration/mining company can be a matter of familiarity, ease of \nemployment, or peculiar usefulness. \n\n\n\n5. CONCLUSION \n\n\n\nThe main objective of mineral resource/reserve estimation is to help in \ndeciding whether a mineral deposit is worth mining, and to guide in the \nmine planning and operations. The fundamental focus is for economic \ndecisions, and the appropriateness of those decisions depend on the \naccuracy of resource/reserve estimation. It can therefore be concluded \nthat both OK and inverse distance square, ID2 can be employed to reliably \nmodel and estimate the modified palaeplacer gold deposit. In this study, \nthe vertical downhole and directional variogram models of gold grades of \nthe deposits showed two-structure spherical models, with a nugget effect \nranging from 0.24 to 0.89 m, and maximum range in the along strike \ndirection of 34.30 m for A reef. Variography results in different directions \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 19-28 \n\n\n\n\n\n\n\n \nCite the Article: Casmed Charles Amadu, Sampson Owusu, Gordon Foli, Blestmond A. Brako, Samuel K. Abanyie  (2022). Comparison of Ordinary Kriging (OK) and \n\n\n\nInverse Distance Weighting (IDW) Methods for the Estimation of a Modified Palaeoplacer Gold Deposit: A Case Study of the Teberebie Gold Deposit, SW Ghana. \nMalaysian Journal of Geosciences, 6(1): 19-28. \n\n\n\n\n\n\n\n\n\n\n\nshow that, the ore deposit is anisotropic. Ore reserves were evaluated \nusing OK and IDW methods, and regression analysis of estimated results \nindicate the inverse square, ID2 model compared strongly well with the OK \nmodel in all the ore zones. ID2 model is therefore, appropriate to be used \nas an alternative resource/reserve evaluation method to the OK method \nfor mine planning and grade control for the for the Teberebie deposits. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe authors this paper are grateful to the Management of Gold Fields \nGhana Limited (GGL), Tarkwa Mine, for their permission to use the data. \n\n\n\nREFERENCES \n\n\n\nAl- Hassan, S., Annels, A.E., 1994. Geostatistical Evaluation of Manganese \nOxide Resources at Nsuta Mine, In Whateley, M.K.G and Harvey, P.K \n(Ed) 1994, Case Histories and Methods in Mineral Resources \nEvaluation, Geol. Soc. Sec. Pub. No., 79, Pp. 157-169. \n\n\n\nAl-Hassan, S., Boamah, E., 2015. Comparison of Ordinary Kriging and \nMultiple Indicator Kriging Estimates of Asuadai Deposit at Adansi \nGold Ghana Limited, Ghana Min. J., 15 (2), Pp. 42\u201349. \n\n\n\nAllibone, A.H., McCuaig, T.C., Harris, D., Etheridge, M., Munroe, S., Byrne, D., \nAmanor, J., Gyapong, W., 2002. Structural controls on gold \nmineralization at the Ashanti gold deposit, Obuasi, Ghana: Society of \nEconomic Geologists Special Publication, 9, Pp. 29. \n\n\n\nAmadu, C.C., Foli, G., Kissi-Abrokwa, B. and Akpah, S., 2021. Geostatistical \nApproach for The Estimation of Shear-Hosted Gold Deposit: A Case \nStudy of The Obuasi Gold Deposit, Ghana. Malaysian Journal of \nGeosciences, 5 (2), Pp. 76-84. \n\n\n\nAnnels, A.E., 1991. Mineral deposit evaluation - A Practical Approach. \nChapman and Hall, London, Pp. 436. \n\n\n\nAnon. 1998. Gemcom Surpac Reference Manual. Gemcom Software \nInternational Inc., Gemcom, Vancouver, Canada, Pp. 5-103. \n\n\n\nArthur, J., Annels, A.E., 1994. The application of geostatistical techniques \nto in-situ resource estimation in the sand and gravel industry, hi: \nWhateley, M. K. G. & Harvey. P. K. (eds) Mineral Resource Evaluation \nII: Methods and Case Histories. Geological Society. London, Special \nPublications, 79, Pp. 67-86. \n\n\n\nBaldwin, J.T., Lew, J.H., Whitman, M.F., 2014. Overview, in Mineral \nResource and Ore Reserve estimation \u2013 The AusIMM Guide to Good \nPractice, second edition, (The Australasian Institute of Mining and \nMetallurgy Melbourne), Pp. 3-12. \n\n\n\nDavis, J.C., 1986. Statistics and Data Analysis in Geology, John Wiley & Sons, \nNew York. Pp. 646. \n\n\n\nDaya, A.A., 2015. Ordinary kriging for the estimation of vein type copper \ndeposit: A case study of the Chelkureh, Iran, Journal of Mining and \nMetallurgy, 51A (1), Pp. 1 \u2013 14. \n\n\n\nDeutsch, J.L., 2010. Fitting probability plot to identify multiple population \nand outliers. CCG Annual Report, Pp. 12. \n\n\n\nFeybesse, J.L., Billa, M., Guerrot, C., Duguey, E., Lescuyer, J., Milesi, J.P., \nBouchot, 2006. The Paleoproterozoic Ghanaian province. Geodynamic \nmodel and ore controls, including regional stress modeling. \nPrecambrian Res., 149, Pp. 149-196. \n\n\n\nFougerouse, D., Micklethwaite, S., Ulrich, S., Miller, J., and McCuaig, T.C., \n2013. Multistage mineralization of the Obuasi giant gold deposit, \nGhana: Biennial SGA Meeting, 12th, 12\u201315 August 2013, Uppsala, \nSweden, Proceedings, 3, Pp. 1105\u20131108. \n\n\n\nFrancois-Bongari~on, D., Guibal, D., 1982. Algorithms for parameterising \nreserves under different geometrical constraints. 17th Symposium on \nthe applicaiton of computers and operations research in the mineral \nindustries (APCOM). AIME, New York, Pp. 297-310. \n\n\n\nGibbs, B.L., 1992. A Practical Guide to Special Estimation and Contouring. \nGibbs Association Ltd. Colorado, Pp. 43. \n\n\n\nGlacken, I., Snowden, D., and Edwards, A., 2001. Mineral Resource \nEstimation, Mineral Resource and Ore Reserve Estimation\u2014The \nAusimm Guide to Good Practice, The Australasian Institute of Mining \nand Metallurgy, Melbourne, 23 (1), Pp. 189\u2013198. \n\n\n\nGol, C., Bulut, S., Bolat, F., 2017. Comparison of different interpolation \nmethods for spatial distribution of soil organic carbon and some soil \nproperties in the Black Sea backward region of Turkey. Journal of \nAfrican Earth Sciences, 134, Pp. 85-91. \n\n\n\nGreer, I.R.., Netherway, D.G., Pertzel, B.A., 1988. The geology and \nexploration of the Detchikroum and Mpeasem concessions, Tarkwa \nBasin, Ghana. In: International conference and workshop on geology \nand exploration in Ghana. 75th Anniversary of the Ghana Geol. Surv. \nDept., Pp. 11. \n\n\n\nHarman, B.I., Koseoglu, H. and Yigit, C.O., 2016. Performance evaluation of \nIDW, Kriging and multiquadric interpolation methods in producing \nnoise mapping: A case study at the city of Isparta, Turkey. Applied \nAcoustics, 112, Pp. 147-157. \n\n\n\nHayward, C.L., Reimold, W.U., Gibson, R.L., Robb, L.J., 2005. Gold \nmineralization within the Witwatersrand Basin, South Africa: \nEvidence for a modified placer origin, and the role of the Vredefort \nimpact event. In: McDonald I, Boyce AJ, Butler IB, Herrington RJ, and \nPolya DA (eds.) Mineral Deposits and Earth Evolution. Geological \nSociety Special Publication, 248: Pp. 31\u201358. London: The Geological \nSociety of London. \n\n\n\nHirdes, W., Nunoo, B., 1994. The Proterozoic paleoplacers at Tarkwa Gold \nMine, SW Ghana: sedimentology, mineralogy, and precise age dating \nof the Main Reef and West Reef and bearing on the investigations on \nthe source area aspects. In: Oberthu\u00a8r T (ed) Metallogenesis of \nselected gold deposits in Africa. Geol Jahrb D100, Pp. 247\u2013311. \n\n\n\nIsaaks, E.H., Srivastava, R.M., 1989. Applied Geostatistics. Oxford \nUniversity Press, New York, Pp. 561. \n\n\n\nJoint Ore Reserves Committee (JORC). 2012. Australasian Code for \nReporting of Exploration Results, Mineral Resources and Ore \nReserves\u2014The JORC Code; Joint Ore Reserves Committee of the \nAustralasian Institute of Mining and Metallurgy, Australian Institute of \nGeoscientists and Minerals Council of Australia: Melbourne, Australia, \nPp. 44. \n\n\n\nKang, K., Qin, C., Lee, B., Lee, I., 2019. Modified screening-based kriging \nmethod with cross-validation and application to engineering design.\u201d \nApplied Mathematical Modelling, 70, Pp. 626-642. \n\n\n\nKarpeta, W.P., 2000. A Review of the Geology, Mining and Exploration of \nthe Tarkwa Mine Area, Pp. 140. \n\n\n\nKesse, G.O., 1985. The mineral and rock resources of Ghana. Rotterdam: \nA.A. Balkema, Pp. 610. \n\n\n\nKlemd, R., Hirdes, W., Olesch, M., Oberthur, T., 1993. Fluid inclusions in \nquartz pebbles of gold bearing Tarkwaian conglomarates as a guide to \ntheir provenance area. Miner. Deposita 28, Pp. 334-343. \n\n\n\nLee, W., Kim, D., Chae, Y., Ryu, D., 2011. Probabilistic evaluation of spatial \ndistribution of secondary compression by using kriging estimates of \ngeo-layers. Eng. Geol., 122, Pp. 239-248. \n\n\n\nMatheron, G., 1971. The theory of regionalized variables and its \napplications. Les cahiers du Centre de Morphologie Mathematique, \nFontainebleau, No. 5, Pp. 211. \n\n\n\nMichel, D., 1982. Geostatistical ore reserve estimation. New York: Elsevier \nScientific Publishing C. \n\n\n\nMilesi, J.P., Ledru, P., Feybesse, J.L., Dommanget, A., Marcoux, E., 1992. \nEarly Proterozoic ore deposits and tectonics of the Birimian orogenic \nbelt, West Africa. Precambrian Res., 58, Pp. 305\u2013344. \n\n\n\nOberth\u00fcr, T., Weiser, T., Amanor, J.A., Chryssoulis, S.L., 1997. Mineralogical \nsiting and distribution of gold in quartz veins and sulfide ores of the \nAshanti mine and other deposits in the Ashanti belt of Ghana: genetic \nimplications. Mineralium Deposita, 32, Pp. 2-15. \n\n\n\nPerrouty, S., Aill\u00e8res, L., Jessell, M.W., Baratoux, L., Bourassa, Y., Crawford, \nB., 2012. Revised Eburnean geodynamic evolution of the gold-rich \nsouthern Ashanti belt, Ghana, with new field and geophysical evidence \nof pre-Tarkwaian deformations: Precambrian Research, 204\u2013205, Pp. \n12\u201339. \n\n\n\nPigois, J.P., Groves, D.I., Fletcher, I.R., McNaughton, N.J., Snee, L.W., 2003. \nAge constraints on Tarkwaian palaeoplacer and lodegold formation in \nthe Tarkwa-Damang district, SW Ghana. Mineralium Deposita, 38, Pp. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 19-28 \n\n\n\n\n\n\n\n \nCite the Article: Casmed Charles Amadu, Sampson Owusu, Gordon Foli, Blestmond A. Brako, Samuel K. Abanyie  (2022). Comparison of Ordinary Kriging (OK) and \n\n\n\nInverse Distance Weighting (IDW) Methods for the Estimation of a Modified Palaeoplacer Gold Deposit: A Case Study of the Teberebie Gold Deposit, SW Ghana. \nMalaysian Journal of Geosciences, 6(1): 19-28. \n\n\n\n\n\n\n\n\n\n\n\n695-714. \n\n\n\nRavenscroft, P.J., 1992. Recoverable reserve estimation by conditional \nsimulation. Geological Society, London, Special Publications, 63, Pp. \n289-298. \n\n\n\nRezaei, A., Hassani, H., Fard-Mousavi, S.B., Jabbari, N., 2019. Evaluation of \nheavy metals concentration in Jajarm Bauxite deposit in Northeast of \nIran using environmental pollution indices. Malaysian Journal of \nGeosciences, 3 (1), Pp. 12\u201320. https://doi.org/10.26480/ \nmjg.01.2019.12.20 \n\n\n\nRossi, M.E., Deutsch, C.V., 2014. Mineral Resource Estimation, Springer \nScience Business Media, Dordrecht, Pp. 332. \n\n\n\nShahbeik, S., Afzal, P., Moarefvand, P., Qumarsy, M., 2014. Comparison \nbetween ordinary kriging (OK) and inverse distance weighted (IDW) \nbased on estimation error. Case study: Dardevey iron ore deposit, NE \nIran. Arab J Geosci., 7, Pp. 3693\u20133704. \n\n\n\nShepard, D., 1968. A two-dimensional interpolation function for \nirregularly spaced data. In Proceedings of the 1968 23rd ACM National \nConference, New York, NY, USA, Pp. 517\u2013524. \n\n\n\nSilva, D., Almeida, J., 2017. Geostatistical methodology to characterize \nvolcanogenic, massive and stockwork ore deposits. Minerals, 7 (12), \n\n\n\nPp. 238. \n\n\n\nSinclair, A.J., Blackwell, G.H., 2002. Applied Mineral Inventory Estimation, \nCambridge, Kluwer Academic Publishers, Pp. 330-337. \n\n\n\nSRK Consulting. 2004. An Independent Technical Report on the Tarkwa \nGold Mine, Ghana, Pp. 98. \n\n\n\nStrongen, P., 1988. The structure and Sedimentology of the Tarkwaian and \nits relevance to gold mining, exploration and development. Ext Abstr. \nInt. Conf. and Workshop on Geology of Ghana with special emphasis \non Gold. Accra, Ghana. \n\n\n\nTahmasebi, P., Hezarkhani, A., 2010. Application of adaptive neuro-fuzzy \ninference system for grade estimation; case study, Sarcheshmeh \nporphyry copper deposit, Kerman, Iran. Australian Journal of Basic \nand Applied Sciences, 4, Pp. 408\u2013420. \n\n\n\nWang, Y., Akeju, O.V., Zhao, T., 2017. Interpolation of spatially varying but \nsparsely measured geo-data: a comparative study. Eng. Geol., 231, Pp. \n200\u2013217. \n\n\n\nWebster, R., and Oliver, M.A., 2007. Geostatistics for Environmental \nScientists. John Wiley and Sons, West Sussex, England. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2023.61.80 \n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2023.61.80 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAQUIFER SYSTEMS CHARACTERIZATION FOR GROUNDWATER MANAGEMENT IN \nILE-OLUJI, SOUTHWESTERN NIGERIA, USING MCDA GIS-BASED AHP \n\n\n\nO.O. Falowo*, Y. Akindureni, O.C. Babalola \n\n\n\nFederal University of Technology Akure, Ondo State, Nigeria \nDepartment of Civil Engineering Technology, Rufus Giwa Polytechnic, Owo, Ondo State, Nigeria \n*Corresponding Author Email: oluwanifemi.adeboye@yahoo.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 23 May 2023 \nRevised 01 June 2023 \nAccepted 14 July 2023 \nAvailable online 17 July 2023 \n\n\n\n\n\n\n\nMulticriteria decision analysis for groundwater potential mapping utilizing analytical hierarchical process of \nsix hydrogeologic parameters including aquifer layer thickness, aquifer layer resistivity,  overburden \nthickness, transverse resistance, transmissivity, and coefficient of anisotropy; in relation to groundwater \nyield was carried out in Ile Oluji, Southwestern Nigeria. The aim was to develop groundwater potential map \nusing calculated groundwater potential index values (GWPIV). The obtained GWPIV which ranged from 1.53 \n(granite) \u2013 3.50 (migmatite) with an average of 2.18 suggestive of moderate groundwater potential (90 % of \nthe study area). The low potential zone (10 %) are observed sporadically in the central and northwestern \nparts. All the geological units recorded overlapping hydrogeologic properties. The longitudinal unit \nconductance recorded regional average of 0.219876 mhos. Therefore the protective capacity of groundwater \nsystem in the study area is weak, and relatively less-weaker in granite environment; and in northwestern and \ncentral parts. Nevertheless, the water table aquifer (accounts for 80%) and the fracture basement \n(constitutes 20%, frequently occurring in gneissic environment) are the water bearing units, with average \noverburden thickness in migmatite, granite, and gneiss  24.3 m, 24.5 m, and 27.9 m respectively. The average \ncoefficient of anisotropy (1.12); hydraulic conductivity (0.37 m/d), transmissivity 6.86 m2/d (migmatite: 7.17 \nm2/d, granite: 7.14 m2/d, and gneiss: 6.02 m2/d). Hence gneissic offered both thick weathered layer and \nfractured aquifer. Empirical model for plot of formation factor and hydraulic conductivity in migmatite, \ngranite, and gneiss, showed positive correlations in descending order as: granite (0.3778), migmatite \n(0.1057), and gneiss (0.0641).  \n\n\n\nKEYWORDS \n\n\n\nGroundwater Yield, Aquifer Properties, Hydrogeologic, Geographic Information System, Borehole Section  \n\n\n\n\n\n\n\n1.   INTRODUCTION \n\n\n\nAn aquifer is a geological entity capable of storing and delivering \nsignificant quantities of water for a variety of uses (Fetter, 2007). \nGroundwater is defined as underground fresh water that can be extracted \nfor domestic, agricultural, and commercial uses. Many shallow and deep \naquifers/groundwater bodies have been investigated, and significant \nlevels of contaminants have been discovered. Thus, assessment of \ngroundwater supply has become an important and critical task for current \nand future groundwater quality management (Falowo and Daramola, \n2023; Cosgrove and Loucks, 2015; Bayewu et al., 2018; Sajeena et al., \n2014). This is so, because of special characteristics of groundwater, as they \nare not easily degraded or exhausted like surface water, abundant aquifers \nare an important source of water supply in terms of quality and \navailability. They are generally available, reliable, and simple to use \n(Fetter, 2007). Groundwater quality is influenced by the quality of \nrecovered water, atmospheric rainfall, freshwater surface water, and \nsubsurface geochemical processes. Changes in the origin and makeup of \nrecharged water, as well as hydrologic and human variables, can also \nresult in occasional changes in ground water quality (Sameer et al., 2021; \nGao et al., 2018; Falowo et al., 2017). A region's geology has a major \ninfluence on the mineral content of water and its environs. The chemical \ncomposition of the nearby sediments and subsoil alters the nature of \n\n\n\ngroundwater. Modern civilization and growth, as well as the frequent \nrelease of industrial effluent, domestic sewage, and solid garbage dumps, \nall contribute to groundwater pollution. When groundwater becomes \npolluted, it poses a threat to human health, economic development and \nsocial prosperity (Falowo et al., 2017; Ting, 1993; Omer, 2018; Alley and \nLeake, 2004; Bayewu et al., 2018; (Falowo and Daramola, 2023; Cosgrove \nand Loucks, 2015; Sajeena et al., 2014). \n\n\n\nGroundwater accounts for 98% of the world's fresh water, as a result, the \nsustainable provision of groundwater supplies for current and future \nneeds is of regional to global significance (Mandel and Shiftan, 1981; \nKaranth, 1987). Groundwater research includes all activities that lead to \nthe identification of aquifers or underground pools from which water can \nbe obtained in adequate quantity and quality for the intended purpose \n(Harvill and Bell, 1986; Mohamaden, 2016). Groundwater resources are \ncritical to the longevity and viability of human life on Earth, as they are \nused for drinking, municipal, domestic, industrial, and agricultural reasons \n(Lewis, 1989; Akinrinade and Olabode, 2015; Harb et al., 2010). It exists \non earth and is irregularly distributed in time and space (Akinrinade and \nAdesina, 2016; Chaanda and Alaminiokuma, 2020). However, global \ndevelopment and population increase have put an excessive burden on the \nenvironment resulting in overexploitation and uncontrolled bore well \nsinking (Oyegoke et al., 2020). Groundwater sustainability and \nadministration is becoming an important subject of debate at the United \nNations Congress as a consequence of the Millennium Goals (Falowo and \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nDaramola, 2023; Cosgrove and Loucks, 2015; Fetter, 2007; Adagunodo et \nal., 2018; Akanbi, 2016; Bayewu et al., 2018). \n\n\n\nGeophysical studies, borehole recording, hydrogeological measurement, \ngeologic mapping, and pumping tests are some of the most effective \nmethods of evaluating an ecosystem without meddling with the \nhydrogeological system. However, the geophysical method involving the \nvertical electrical sounding (VES) method has been frequently used in \ngroundwater exploration to identify the geoelectric parameters in terms \nof thickness and resistivity of the subsurface layers, as well as their \nhydrogeologic properties. Hence, geophysical method combining \nelectrical resistivity has demonstrated to be efficient and cost-effective \n(Bayewu et al., 2018; Falowo, 2022; Adagunodo et al., 2018; Aina et al., \n2019). Overburden thickness/depth to bedrock, fissure structure, and \ntopography can all aid in identifying the ideal location for a borehole. \nGroundwater studies, mineral mining, engineering and environmental \nstudies, investigative geology, and archaeological research all benefit from \nelectrical's versatility (Robinson and Coruh, 1988; Telford et al., 1990). \nBecause it identifies the vertical variation with depth of electrical \nresistance (resistivity) at a particular location, vertical electrical resistivity \nis the most frequently used technique for groundwater exploration. \nPassing an electric current (dc or ac) through current electrodes into the \nground and detecting the potential difference between potential \nelectrodes is the technique (Telford et al., 1990). \n\n\n\nBorehole water failures and a lack of high-quality water have distressed \nthe people of Ile Oluji in the last ten years as the population has increased \ntremendously (Tartiyus et al., 2015) The establishment of the Federal \nPolytechnic Institute in the area has attracted small and medium-sized \nbusinesses/industry, as well as increased individual property ownership. \nAs a consequence, the region's existing groundwater supply infrastructure \nhas been put under undue pressure, and it calls for productive exploration \nand exploitation of groundwater, which is one of the essential natural \nresource required for existence of life in the world. Aquifer thickness, as \nwell as the extent and degree of interconnection of pore spaces within the \naquifer substance, are known to influence the calculation of groundwater \nsupplies (Freeze and Cherry, 1979; Bell, 2007). The region is part of the \nBasement Complex, which is distinguished by weathered and fractured \nrocks that are susceptible to surface or near-surface pollutants because \nthey frequently appear at shallow levels. As a result, effective groundwater \nexcavation in a basement topography necessitates a thorough knowledge \nof the hydrogeological features of the aquifer units in connection to their \nvulnerability to environmental pollution, as well as an evaluation of their \nprotective capacity.  \n\n\n\nAs a result, the sole aim of this study is to gain more understanding on the \nhydrogeological system in Ile Oluji, in order to define aquifers and target \npollution-free groundwater to ensure the resource's long-term use. This \nresearch objectives included assessing the area's groundwater potential, \n\n\n\n determining the aquifer protective capacity of the overlying rocks, \nparticularly its isolation from pollution, and suggesting appropriate \ngroundwater placement locations. Furthermore, the findings of this \nresearch are anticipated to add to the creation of a practical control and \nmitigation strategy for the resource's future use in household, commercial, \nand farming applications. Furthermore, this will supplement the \ngovernment's efforts in groundwater management strategies, especially \nthe avoidance of groundwater quality degradation. The study employed \ndirect (geological mapping, geomorphology, groundwater level \nmeasurement and pumping test) and indirect method using geoelectric \nmethod and its derived parameters to obtain the objectives of this study. \nThe obtained data from both investigations were subjected to multi-\ncriteria decision technique utilizing analytical hierarchy process (AHP). \nAHP is one of the most widely used multi-criteria choice techniques \ndescribed in the literature and entails creating a number of pair-wise \ncomparison grids that compare the criteria to one another (Saaty, 1980). \nThe comparison is carried out to determine a rating or weight for each \ncriterion; this rating describes the degree to which each criterion \ncontributes to the general goal (Saaty, 2006). AHP is capable of recording \nboth subjective and objective evaluation measures, as well as providing a \nhelpful method for checking the coherence of the evaluation measures and \noptions proposed by specialists or decision makers, thereby decreasing \ndecision-making prejudice (Vargas, 1990). \n\n\n\n1.1   Location and Geology of the Study Area \n\n\n\nIle Oluji is the study location, which is situated between 704800 m and \n708800 m East and 793350 m and 809900 m North (Figure 1). It is \nbordered by Ipetu \u2013 Ijesa, Ondo East/West, Ifetedo, Okeigbo, and Ifedore \nlocal government areas. The area is characterized by Otasun Hills, Ikeji \nhills, Okurughu, Oni river and Awo rivers (Adebawore et al., 2017).  It has \na geographical area of 600 km2 and a population of approximately 300,000 \nindividuals. The landscapes of Ile-Oluji can be classified into three types: \nplains, undulating slopes, and river valleys. The mountains, on the other \nhand, dominate the landscape. The town is surrounded by many granite \nboulders, including Ota-Ororo, Ota-Akoko, Ota-Didu, Ota-Upote, and \nIguruguru (Adebawore et al., 2017). The town serves as the headquarters \nof Ile-Oluji/Okeigbo Local Government. Ile Oluji is an agravian town, it is \none of the largest producers and exporters of Cocoa production in Nigeria.  \nCassava, yam, maize and oil palm are the major crops cultivated by farmers \nin the town. The major manufacturing company in the town is Cocoa \nProducts Ile Oluji Limited. The Federal Polytechnic, Ile Oluji is a major \ntertiary institution in the town while Gboluji Grammar school is the major \nhigh school in the area, and this school happens to be one of the oldest \nsecondary school in Nigeria. The area is within the tropical rain forest with \ndistinct wet and dry seasons. The annual rainfall varies between 1400 mm \nand 1800 mm. The mean temperature is 27\u00b0C and varies from 24.5\u00b0C in \nJuly to 29.5\u00b0C in February (Iloeje, 1981)  \n\n\n\n1.2   Geology \n\n\n\n\n\n\n\nFigure 1: Location map of the Study Area on map of Ondo State and Nigeria \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nThe study area is underlain by impermeable Precambrian basement \nmaterials. (Figure 2). Granites, quartzite, and migmatite-gneiss were \namong the local geological rock types identified from specimens. Quartzite \n(ridges) and granite gneiss are the most common, with granite gneiss \noccurring as intruding, low-lying formations. Field examination reveals \nthe presence of joints, fractures, or fissures within the bedrock. As a result, \nthere is a greater likelihood of these characteristics at greater depth, as \nthis is one of the basement complex's peculiarities (i.e. fault, incipient \njoints, and fracture systems) that are a result of continuous \ntectonic/orogenic processes. In a typical basement environments, the \nfractured zone and weathered layer are the main aquiferous components. \nOften, difficulty are experienced in deciphering prolific aquifer in the \nbasement and define its geometry, hence accurate knowledge of \nhydrogeological properties of the aquifer units and its susceptibility to \nenvironmental contamination is very important. The rocks mapped in the \nstudy area are granite, gneiss, migmatite (Figure 3).  \n\n\n\nThe granitic rocks are rich in quartz, feldspar, and accessory mica \n(muscovite, biotite), amphiboles (hornblende), augite, hyperstene, \nmagnetic, apatite, garnet, and tourmaline (Obaje, 2009).  Their texture \nranged from medium to coarse grained, while some are porphyritic \n(Figure 3a). The gneisses are megascopically crystalline foliated \nmetamorphic rocks. They are characterized with mineral segregation into \nlayers or bands of contrasting colour, texture and composition. Its \ncommon minerals are mica, feldspar, hornblende and quartz. The texture \nis medium to coarse with poor mineral arrangement. The gneisses show \nbands of micaceous minerals alternating with bands of equidimensional \nminerals like feldspar, quartz (Figure 3b).  The migmatite are mixed rocks \nthat consist of intimately associated members of igneous rock (granitic \nrock) and metamorphic (gneisses) groups, they are widespread in the \nstudy area. \n\n\n\n\n\n\n\nFigure 2: Geological map of (a) Nigeria and (b) Ondo State showing the study area, which falls within the Southwestern Basement Complex Nigeria with \nmigmatite being the predominant rock unit. (0Modified after NGSA, 2006) \n\n\n\n\n\n\n\nFigure 3: Surface exposure/outcrops of (a) granite (b) gneiss, and migmatite observed in the study area \n\n\n\n1.3   Landuse and Soil \n\n\n\nThe landuse/land cover of the study area in Figure 4a is primarily built-\nup, with tree plantation and agricultural practices common in the town's \noutskirts, though few plantations are observed within the town; and the \n\n\n\nsoil type in the area is ferric luvisols. (Figure 4b). The luvisols are soils with \npronounced textural difference within the soil profile, with the top horizon \ndrained of clay and clay buildup in a subsurface \"Argic\" horizon. Luvisols \nhave high activity clays throughout and no sudden textural shift, whereas \nferric luvisols have ferric characteristics. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\n\n\n\n\nFigure 4: (a) The land use/land cover of the study area which is predominantly built up area (b) Soil map of Southwestern Nigeria, with the study area \nfalling on Ferric Luvisols. Modified after Living Atlas, 2020; and FAO/DSMW, 2020 respectively) \n\n\n\nThe terrain analysis involving digital elevation model (DEM), hill shade, \nslope, and aspect (direction of slope) was produced using Quantum \nGeographic information system (QGIS) software. The elevation raster \ndata/shape file was acquired from USGS website (earthexplorer-USGS) \nusing SRTM 1 Arc Second Elevation file, and launched in QGIS and modified \nusing SAGA tool. The drainage channel, stream network or catchment area, \nbasin analysis were done using the processed DEM and the DEM was filled \nusing the Wang and Liu tool under terrain analysis hydrology. The stream \nnetwork was created using Shrahler order in SAGA analysis tool. Figure 6 \nshowed the processed DEM, hill shade, slope and aspect maps of the study \narea. The DEM map showed variation of low and highlands, the highlands \nare trending in northwest \u2013 southeast direction. The higher elevations are \ngenerally remarkable across the area (Figure 5a), while larger \nuplands/hills are noticeable on the hill shade (Figure 5b). Noticeable area \nof wide space of lowlands are observed in southwestern, and \nnorthwestern parts. This implies that there possibility of movement of \nwater towards these locations (discharge area), while highlands/uplands \nforming the watershed. The slope is generally uniform varying from 6.5 to \n89.9 degrees (Figure 5c). The aspect which is the direction of the slope \n\n\n\nfrom 39 to 359 degrees (Figure 5d).  \n\n\n\nThe drainage network and catchment area is shown in Figure 6. The area \nis well drained by few river channels (Figure 6a), with large catchment \nespecially at the northern part (Figure 6b). The drainage basin fall with the \nlow basin (Figure 7a) with generally low flow direction (Figure 7b), and \nlow flow connectivity (Figure 7c).  \n\n\n\n2.   MATERIALS AND METHODS \n\n\n\nWater resource management and planning are critical components of a \nnation's economic and social growth. Hydrogeological prospecting \ntypically entails the creation of hydrogeological maps, the chemical \nanalysis of water, the drilling of wells/boreholes, observation of the water \ntable, the delineation of groundwater bodies, and the determination of \ntheir type through pumping tests (Bell, 2007; Brassington, 1988; Asaad et \nal., 2004). Figure 8 depicts the data acquisition map, while the techniques \nused in this research are as follows: \n\n\n\n\n\n\n\nFigure 5: Maps of the digital elevation model (DEM), hill shade, slope, and aspect developed from Quantum geographic information system for the study \narea \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\n\n\n\n\nFigure 6: Maps showing the (a) drainage network (b) catchment boundaries for two catchment points at the northern and southern parts \n\n\n\n\n\n\n\nFigure 7: Maps showing the study area\u2019s (a) drainage basin type (b) degree of its flow connectivity (c) flow direction \n\n\n\n2.1   Direct Investigation \n\n\n\nThis involved geological mapping of outcrops, geomorphology of the area, \nand groundwater level measurement/pumping test in wells/boreholes. \nBoreholes drilling was carried out to examine the lithology by sampling, \ngrain porosity, permeability, thickness, and determination of \nhydrogeological parameters such as transmissivity, and hydraulic \nconductivity (Gogoi, 2013; Halford et al., 2006; Johnson, 2005). The \ngeology of the area was the major criterion that was considered or \ndetermined the number of boreholes that was drilled and location. The \nhydrogeological investigation includes static water level and hydraulic \nhead determination from fifty eight open wells across all geological units \nin the area. \n\n\n\nGroundwater levels in fifty eight water wells were monitored early in the \nmorning before abstraction, and the results were used to compute \nhydraulic heads. The rate of abstraction, operation time/season, depth, \nand locations were all noted. The steady water level, hydraulic head, \nborehole depth, water column thickness, and other measurements were \ntaken using a geographic positioning system and steel tape with the lower \nend marked with carpenter's chalk to allow readings to be taken from the \nimmersed section. To guarantee accuracy, two measurements were \nobtained at each well/borehole site, and the average values were \ncalculated whenever there was a difference. The readings were used to \ncalculate the thickness of the vadose zone across the region because the \n\n\n\ndepth to the static water level is deemed an estimate of the interface \nbetween the vadose and phreatic zones in a non-confined aquifer \nenvironment. \n\n\n\nHydraulic parameters from pumping was used to determine the rate of \nflow, drawdown, transmissivity and storativity (Gogoi, 2013; Halford et al., \n2006; Johnson, 2005; Adeleke et al., 2015). The test was carried out in \nJanuary 2023. The distance from the pumping wells varies from 15 to 50 \nm and conducted between 1 to 6 hours at pumping rate of 1.125 m3/day. \nBefore the pumping process started, the initial groundwater level was \nrecorded (ho) at time t equal to zero, and drawdown were recorded at an \ninterval of 5 min until 120 min when it was observed that further increase \nin pumping didn\u2019t give any corresponding increase in groundwater level, \ntherefore after taking recording at 60 min, the next was taken at 90 min \nand 120 min, and process terminated and the wells get recharged diffusely \nand/or localization. There are three methods to replenish a well: spread, \nindirect, and focused. Diffuse recharge is a form of direct recharge that \nhappens when rainfall percolates through the open zone of the earth to the \nwater table. Small depressions, joints, or cracks can supply concentrated \nreplenishment. Indirect replenishment is provided by mappable features \nsuch as rivers, canals, and lakes. The wetted tape method was used to \nmeasure total depth, water column height, and static water level. It was \ninexpensive and simple (but time consuming). To calculate the \ngroundwater elevation, the depth to groundwater was subtracted from the \nelevation acquired at the surface geo-referenced point. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\n\n\n\n\nFigure 8: Data acquisition map for the study showing different locations where data were collected from the wells, VESs, and boreholes\n\n\n\n2.2   Indirect Investigation \n\n\n\nThis method encompasses geoelectric methods, the Ohmega resistivity \nmeter was used to conduct the electrical resistivity survey technique using \nthe VES method. Fifty VES points were collected using the Schlumberger \nelectrode setup, with half-current electrode separations varying from 1 m \nto 150 m. The perceived resistivity values measured were the combination \nof the resistance received from the resistivity meter and a geometrical \ncomponent based on the electrode spacing used. The obtained data were \nplotted against half electrode spacing on a bi-logarithmic graph sheet, and \nthen submitted to partial curve matching and computer iterative modeling \n(1-D forward modeling) with resist software. Information from existing \nboreholes was utilized for correlation with the VES data. From the results \nof the VES, the reflection coefficient (Rc; equation 1), fracture contrast (Fc; \nequation 2), traverse resistance (T; equation 3), aquifer formation factor \n(FM; equation 4) were determined to assess the potentiality of the aquifer \nsystem in the area. Longitudinal unit conductance (LC; equation 5) and \nthickness of the vadose zone determined from the wells were used to \ndetermine the vulnerability of aquifer to pollution. \n\n\n\n\ud835\udc45\ud835\udc50 =  \n(\ud835\udf0c\ud835\udc5b\u2212 \ud835\udf0c)(\ud835\udc5b\u22121)\n\n\n\n\ud835\udf0c\ud835\udc5b+ \ud835\udf0c(\ud835\udc5b\u22121)\n                                                                            (1) \n\n\n\n\ud835\udc39\ud835\udc50  = \n\ud835\udf0c\ud835\udc5b\n\n\n\n\ud835\udf0c\ud835\udc5b\u22121\n                                                                                                 (2) \n\n\n\n\ud835\udc47 = \u2211 \ud835\udf0c\ud835\udc56\u210e\ud835\udc56\n\ud835\udc5b\n\ud835\udc56=1                                                                                                 (3) \n\n\n\n\ud835\udc34\ud835\udc39\ud835\udc40 =  \n\ud835\udc4e\ud835\udc63\ud835\udc52\ud835\udc5f\ud835\udc4e\ud835\udc54\ud835\udc52 \ud835\udc4e\ud835\udc5e\ud835\udc62\ud835\udc56\ud835\udc53\ud835\udc52\ud835\udc5f \ud835\udc64\ud835\udc4e\ud835\udc61\ud835\udc52\ud835\udc5f  \ud835\udc5f\ud835\udc52\ud835\udc60\ud835\udc56\ud835\udc60\ud835\udc61\ud835\udc56\ud835\udc63\ud835\udc56\ud835\udc61\ud835\udc66\n\n\n\n\ud835\udc5f\ud835\udc52\ud835\udc60\ud835\udc56\ud835\udc60\ud835\udc61\ud835\udc56\ud835\udc63\ud835\udc56\ud835\udc61\ud835\udc66 \ud835\udc5c\ud835\udc53 \ud835\udc64\ud835\udc4e\ud835\udc61\ud835\udc52\ud835\udc5f \ud835\udc4e\ud835\udc61 \ud835\udc60\ud835\udc56\ud835\udc61\ud835\udc52\n                                    (4) \n\n\n\n\ud835\udc3f\ud835\udc36 =  \u2211\n\u210e\ud835\udc56\n\n\n\n\ud835\udf0c\ud835\udc56\n\n\n\n\ud835\udc5b\n\ud835\udc56                                                                                                          (5) \n\n\n\nwhere Rc is reflection coefficient,  \ud835\udf0c\ud835\udc5b is the layer resistivity of the nth layer, \n\ud835\udf0c(\ud835\udc5b \u2212 1) is the layer resistivity overlying the nth layer, T is traverse \nresistance, \ud835\udf0c and h are resistivity and thickness of the nth layer \nrespectively,  \u210e\ud835\udc56 and  \ud835\udf0c\ud835\udc56 are the thickness and resistivity of \ud835\udc5b\ud835\udc61\u210e layer \nrespectively. The longitudinal resistivity (\ud835\udf0c\ud835\udc59), transverse resistivity (\ud835\udf0c\ud835\udc61), \nand coefficient of anisotropy (\ud835\udf06) are used in assessing overall aquifer \npotentiality (Olatunji et al., 2022) using geoelectrical parameters of \nresistivity and thickness, hence equations 6 and 8 were used.  \n\n\n\n\ud835\udf0c\ud835\udc59 =  \u2211\n\u210e\ud835\udc56\n\n\n\n\ud835\udc46\ud835\udc56\n\n\n\n\ud835\udc5b\n\ud835\udc56                                                                                                  (6) \n\n\n\n\ud835\udf0c\ud835\udc61 =  \u2211\n\ud835\udc47\ud835\udc56\n\n\n\n\u210e\ud835\udc56\n\n\n\n\ud835\udc5b\n\ud835\udc56                                      (9) \n\n\n\n\u03bb =  \u221a\n\ud835\udf0c\ud835\udc61\n\n\n\n\ud835\udf0c\ud835\udc59\n                                                                                                                  (10) \n\n\n\n3.   RESULTS AND DISCUSSION \n\n\n\n3.1   Indirect Method \n\n\n\nThe summary of the VES is presented in Table 1, while a typical geologic \nsection prepared for VESs 15, 19, 27, 28, and 45 in SW \u2013 NE direction, is \nshown in Figure 9. The curve types (Figure 10) obtained from the study \narea varied from three layer curve (H), four layer curves (KH, HK, and QH), \nand five layer curve (HKH), and six layer curve (KHKH). The H curve type \nis the most preponderant (34 %) followed by KH (24 %), HKH (14 %), QH \n(14 %), HK (8 %), KHKH (6 %). This implies that the area is generally made \nof high resistive topsoil, underlain by high conductive weathered layer, \nand basement rock. These curve types are prolific curve types that suggest \nsubsurface geoelectric configurations apparently favorable for \ngroundwater occurrence, especially in the Basement Complex of Nigeria \n(Falowo and Daramola, 2023; Bayewu et al., 2018; Akanbi, 2016; Gao et \nal., 2018). \n\n\n\nFrom the Table 1, topsoil has resistivity ranging from 82 \u2013 652 ohm-m \n(avg. 279 ohm-m) and thickness varying from 0.5 \u2013 1.5 m (avg. 0.97 m) and \ncomposed of clay, sandy clay and clayey sand. The subsoil is characterized \nwith resistivity ranging from 53 \u2013 589 ohm-m (avg. 265 ohm-m) and have \nsame composition as the topsoil, with thickness ranging from 2.1 to 10.5 \nm (avg. 5.20 m). The weathered layer has resistivity ranging between 38 \nohm-m and 751 ohm-m (avg. 212 ohm-m), while resistivity range of 145 \u2013 \n163 ohm-m is the most widespread (Figure 11a) denoting sandy clay \nwater bearing unit, and resistivity in the range of 38 \u2013 145 ohm-m  is \nextensive in the central and north eastern parts. These indicated a sandy \nclay weathered layer; the thickness ranged from 4.6 m and 38.7 m (avg. \n17.5 m); while the spatial distribution map (Figure 11b) showed thickness \nrange of 10 \u2013 17 m being preponderant. The fractured basement/partly \nweathered/fresh basement has resistivity of 338 \u2013 6550 ohm-m (avg. \n1435 ohm-m), the depths to this rock varied from 9.9 \u2013 39.6 m (avg. 22.4 \nm). Of the total number of fractured aquifer delineated (20 %), the \nmigmatite recorded 20 %, granite 10 %, and gneiss 70 %. Consequently, \nthe topsoil, subsoil, and weathered layer are generally composed of sandy \nclay material, which formed the overburden can be regarded as an \naquitard. Typical section shown in Figure 6 are characterized by topsoil \n(99 \u2013 199 ohm-m), subsoil (302 \u2013 413 ohm-m), weathered layer (84 \u2013 251 \nohm-m), fractured basement/partly weathered/fresh basement (398 \u2013 \n3212 ohm-m). The relief of the basement is rugged.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nTable 1: VES Interpretation Results \n\n\n\nEast North \nElev. \n\n\n\n(m) \n\n\n\nVES \n\n\n\nNO. \n\n\n\nResistivity (Ohms-meter)  Thickness (m)  Depth (m)  Curve \n\n\n\nType \ud835\udf0c1 \ud835\udf0c2 \ud835\udf0c3 \ud835\udf0c4 \ud835\udf0c5 \ud835\udf0c6 \u210e1 \u210e2 \u210e3 \u210e4 \u210e5 \ud835\udc511 \ud835\udc512 \ud835\udc513 \ud835\udc514 \ud835\udc515 \n\n\n\n706136 793908 256 1 458 201 1210    1.0 18.5    1 19.5    H \n\n\n\n706199 793972 257 2 652 229 1003    0.6 16.5    0.6 17.1    H \n\n\n\n706350 793953 256 3 428 112 994    0.8 22.6    0.8 23.4    H \n\n\n\n706539 793917 259 4 329 85 778    0.8 17.9    0.8 18.7    H \n\n\n\n706371 794384 266 5 201 470 110 885   1.1 5.9 29.5   1.1 7 36.5   KH \n\n\n\n706230 794540 266 6 145 351 82 751   0.9 3.9 19.8   0.9 4.8 24.6   KH \n\n\n\n706512 794521 267 7 102 315 108 898 217 1023 1.2 3.7 5.9 9.9 14.7 1.2 4.9 10.8 20.7 35.4 KHKH \n\n\n\n706382 794714 266 8 551 99 614 225 898  0.8 6.3 4.6 18.1  0.8 7.1 11.7 29.8  HKH \n\n\n\n706486 794897 260 9 361 523 188 858 91 1223 0.9 2.8 10.5 6.8 17.3 0.9 3.7 14.2 21 38.3 KHKH \n\n\n\n706251 795960 262 10 233 357 65 1425   0.9 2.3 27.2   0.9 3.2 30.4   KH \n\n\n\n706533 795584 264 11 189 421 147 1236   1.3 4.1 17.0   1.3 5.4 22.4   KH \n\n\n\n706533 795740 265 12 345 72 1101    1.1 18.7    1.1 19.8    H \n\n\n\n706805 796464 254 13 312 65 568    0.8 22.6    0.8 23.4    H \n\n\n\n706659 796757 263 14 82 53 38 655   0.5 3.3 12.3   0.5 3.8 16.1   QH \n\n\n\n706444 797243 259 15 99 413 118 3212   0.9 5.6 26.8   0.9 6.5 33.3   KH \n\n\n\n706544 797462 252 16 128 520 213 1002   1.0 10.5 19.2   1 11.5 30.7   KH \n\n\n\n706763 797600 256 17 241 158 92 998   1.2 5.7 14.4   1.2 6.9 21.3   QH \n\n\n\n706716 797701 256 18 305 193 102 689   1.2 6.3 17.9   1.2 7.5 25.4   QH \n\n\n\n706528 797609 252 19 187 410 251 1356   0.6 4.0 13.5   0.6 4.6 18.1   KH \n\n\n\n706288 797114 268 20 201 88 806    0.9 20.5    0.9 21.4    H \n\n\n\n706235 797050 269 21 362 132 1455    0.8 16.8    0.8 17.6    H \n\n\n\n706079 797334 257 22 446 144 521 97 936  0.8 2.1 9.8 16.2  0.8 2.9 12.7 28.9  HKH \n\n\n\n706356 797343 252 23 354 222 751 123 1330  0.9 3.3 12.3 15.7  0.9 4.2 16.5 32.2  HKH \n\n\n\n706361 797233 262 24 319 195 470 122 1114  0.9 2.9 7.7 16.8  0.9 3.8 11.5 28.3  HKH \n\n\n\n706366 797930 259 25 229 87 999    0.8 23.2    0.8 24    H \n\n\n\n706225 797820 255 26 310 45 1652    1.2 16.5    1.2 17.7    H \n\n\n\n706638 798113 264 27 199 84 2356    1.4 18.7    1.4 20.1    H \n\n\n\n706727 798470 263 28 175 302 201 852   1.1 6.3 18.9   1.1 7.4 26.3   KH \n\n\n\n706450 798305 267 29 502 322 612 108 1232  0.9 3.8 10.3 14.8  0.9 4.7 15 29.8  HKH \n\n\n\n706382 799606 272 30 156 98 57 911   0.6 6.9 18.5   0.6 7.5 26   QH \n\n\n\n706324 799551 269 31 195 120 68 1102   0.9 7.1 19.6   0.9 8 27.6   QH \n\n\n\n706345 798928 260 32 314 132 458 110 2250  1.2 2.5 8.9 19.4  1.2 3.7 12.6 32  HKH \n\n\n\n706350 798498 258 33 445 80 2378    1.1 18.2    1.1 19.3    H \n\n\n\n705906 797508 255 34 329 89 1468    1.3 23.4    1.3 24.7    H \n\n\n\n705613 796537 258 35 498 120 877    0.9 22.2    0.9 23.1    H \n\n\n\n705760 796620 259 36 214 403 182 2444   1.1 7.4 18.3   1.1 8.5 26.8   KH \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nTable 1: Continued  \n\n\n\nTable 1: VES Interpretation Results \n\n\n\nEast North \nElev. \n\n\n\n(m) \n\n\n\nVES \n\n\n\nNO. \n\n\n\nResistivity (Ohmns-meter)  Thickness (m)  Depth (m)  Curve \n\n\n\nType \ud835\udf0c1 \ud835\udf0c2 \ud835\udf0c3 \ud835\udf0c4 \ud835\udf0c5 \ud835\udf0c6 \u210e1 \u210e2 \u210e3 \u210e4 \u210e5 \ud835\udc511 \ud835\udc512 \ud835\udc513 \ud835\udc514 \ud835\udc515 \n\n\n\n705984 797050 261 37 205 81 801    0.8 22.5    0.8 23.3    H \n\n\n\n705849 797032 258 38 222 419 90 3358   1.4 5.4 19.2   1.4 6.8 26   KH \n\n\n\n705896 796409 259 39 474 221 6550    0.5 15.5    0.5 16    H \n\n\n\n705676 796427 258 40 188 369 102 1616   0.8 3.4 13.7   0.8 4.2 17.9   KH \n\n\n\n706758 796977 266 41 112 589 174 4122   1.5 2.2 9.8   1.5 3.7 13.5   KH \n\n\n\n706303 798104 265 42 477 214 509 115 2750  1.1 8.8 19.4   1.1 9.9 29.3   HKH \n\n\n\n706434 796015 261 43 94 218 144 998 58 3696 1.3 7.7 12.3 8.2 13.8 1.3 9 21.3 29.5 43.3 KHKH \n\n\n\n706570 796565 258 44 232 152 93 750   0.7 6.9 18.6   0.7 7.6 26.2   QH \n\n\n\n706350 796720 267 45 168 85 445 398   0.9 6.9 13.4   0.9 7.8 21.2   HK \n\n\n\n706533 796876 274 46 86 45 690    0.9 38.7    0.9 39.6    H \n\n\n\n706528 796922 274 47 314 112 555 410   1.1 9.9 19.2   1.1 11 30.2   HK \n\n\n\n706194 794833 262 48 186 92 480 338   1.2 5.4 18.7   1.2 6.6 25.3   HK \n\n\n\n706444 799139 267 49 411 147 621 448   1.2 3.6 14.9   1.2 4.8 19.7   HK \n\n\n\n706784 799386 269 50 159 121 94 661   0.8 3.3 21.2   0.8 4.1 25.3   QH \n\n\n\n\n\n\n\n\n\n\n\nFigure 9: Geologic Section/Profile along the selected VES point established in the study area \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\n\n\n\n\nFigure 10: The frequency chart of the obtained Curve Types \n\n\n\nThe overburden thickness of the study area ranged from 13.5 \u2013 43.3 m \nwith regional average of 25.3 m, with migmatite, granite, and gneiss, \nrecorded averages of 24.3 m, 24.5 m, and 27.9 m respectively; hence the \noverburden thickness is relatively higher in granite gneiss environment. \nThe spatial variation of the overburden thickness is shown in Figure 11c, \noverburden thickness in the range of 13.5 to 24.1 m is the most dominant, \nwhich can regarded as moderate/thick weathering profile. In the \nbasement complex of southwestern Nigeria, overburden thickness above \n15 m is usually considered thick and prolific for groundwater \naccumulation, hence the study area is highly favourable to groundwater \npropensity. Therefore priority most be given to those zones during \ngroundwater development scheme in the area. Although the nature of the \noverburden in terms of resistivity, transmissivity, hydraulic conductivity, \n\n\n\nand hydraulic gradient are also important parameters in deciphering \noverall water yield of the weathered layer (Falowo et al., 2020).  \n\n\n\nTable 2 showed the data calculated for aquifers\u2019 units\u2019 characteristics, \n\n\n\nhydraulic characteristics, and longitudinal unit conductance values for the \n\n\n\nstudy area. The geology of the area where the VESs were conducted are \n\n\n\nmigmatite (constitutes 32 % of the VES area), granite (42 %), and gneiss \n\n\n\n(25 %), with the weathered layer (80%) being the major aquifer, and \n\n\n\nfractured basement (20 %) is the minor aquifer.  The estimated formation \n\n\n\nfactor (FM) ranged from 1.79 (granite) \u2013 1.96 (gneiss) - 3.50 (migmatite) \n\n\n\nand average value of 2.38. Formation factor has good positive correlation \n\n\n\nwith groundwater yield, therefore migmatite showed better tendency. \n\n\n\nFigure 12a showed low FM across the area (less than 10), however \n\n\n\nrelatively high are common in the southern part, and low values in the \n\n\n\nnorthern part. This indicates low groundwater yield according to Table 8. \n\n\n\nThe estimated hydraulic conductivity (K) obtained ranged from 0.22 \u2013 \n\n\n\n0.55 m/d (0.37 m/d) which suggests clay sand and corroborates the VES \n\n\n\nresult; while the geological units recorded: migmatite (0.22 \u2013 0.55 m/d; \n\n\n\n0.39 m/d avg.), granite (0.26 \u2013 0.42 m/d; 0.35 m/d avg.), and gneiss (0.25 \n\n\n\n\u2013 0.51 m/d; 0.36 m/d avg. The transmissivity (T) varied between 3.49 to \n\n\n\n15.61 m2/d (6.86 m2/d avg.), while the respective geologic units ranged \n\n\n\nfrom migmatite: 4.13 \u2013 10.21 m2/d (7.17 m2/d avg.), granite: 3.49 \u2013 15.61 \n\n\n\nm2/d (7.14 m2/d avg.), and gneiss: 3.80 \u2013 9.20 m2/d (6.02 m2/d avg.). The \n\n\n\nspatial distribution of K and T in Figure 12b & c showed general values of \n\n\n\nK in the range of 0.35 to 0.38 m/d; while T distinguished the area into two \n\n\n\nzones i.e. zone with T greater than 10 (in central and northwester parts) \n\n\n\nand southern and northeastern part where Tis less than 10. However \n\n\n\nusing the criteria in Table 8, the average T value fall within low group since \n\n\n\nthe values are less than 10 m2/d.  \n\n\n\n\n\n\n\n\n\n\n\nFigure 11: Spatial Distribution Map of (a) Resistivity of the weathered layer (b) Thickness of the weathered layer (c) Overburden thickness across the \nstudy area \n\n\n\nThe fracture contrast and reflection coefficient ranged between 0.18 \u2013 \n63.72 (12.97 avg.) and -0.17 to 0.97 (0.747 avg.). The average FC and RC \nvalues obtained for migmatite are 8.34 and 0.62; granite: 14.23 and 0.79; \ngneiss: 16.64 and 0.83. The fracture contrast and reflection coefficient \nhave strong relationship with groundwater yield, as high FC implies high \ngroundwater potential; and low RC indicates high groundwater yield. The \nspatial distribution map of RC and FC is shown in Figure 13, the RC and FC \nvalues in the range of 0.6850 \u2013 0.9699 (Figure 13a) and 2.93 to 61.07 \n(Figure 13b) are the most prominent, although relatively high values of FC \nand low values of RC are seen at sporadic spots in each of the southern, \ncentral, and northern parts. These spotted locations are high groundwater \n\n\n\nyield or propensity zones characterized with weathered or partly \nweathered/fracture basement groundwater saturated aquifer. The \ntransverse (PT) and longitudinal (PL) resistivity ranged from 42.44 \u2013 \n521.59 (191.93 avg.) and 41.07 \u2013 383.25 (148.0 avg.) respectively, while \nthe geologic units recorded average values of 211.22 and 165.29 \n(migmatite); 157.38 and 128.95 (granite); and 224.0 and 157.50 (gneiss). \nThe Zordy coefficient of anisotropy or electrical anisotropy (\u03bb) ranged \nbetween 1 and 1.55 (avg. 1.12); while averages of 1.11, 1.09, and 1.19 were \nrecorded for migmatite, granite, and gneiss respectively, and from all these \nresults, the gneiss showed best groundwater prolificacy. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nTable 2: Summary of aquifer units\u2019 characteristics, hydraulics characteristics, derived geoelectric properties, and vulnerability \n\n\n\nVES \n\n\n\nNo. \n\n\n\nAquifer Unit Overburden \n\n\n\nThickness (m) \nS ( -1) T ( m2) K (m/day) T (m2/day) Fc Rc Fm PT PL \u03bb \n\n\n\nResistivity (\u03a9m) Thickness (m) Geology Type \n\n\n\n1 201 18.5 Migmatite Weathered Layer 19.5 0.0942 4177 0.55 10.21 6.02 0.72 3.50 214.18 206.96 1.02 \n\n\n\n2 229 16.5 Migmatite Weathered Layer 17.1 0.0730 4170 0.25 4.13 4.38 0.63 3.50 243.84 234.33 1.02 \n\n\n\n3 112 22.6 Migmatite Weathered Layer 23.4 0.2037 2874 0.22 4.97 8.88 0.80 3.50 122.80 114.90 1.03 \n\n\n\n4 85 17.9 Granite Weathered Layer 18.7 0.2130 1785 0.26 4.65 9.15 0.80 1.79 95.44 87.79 1.04 \n\n\n\n5 110 29.5 Granite Weathered Layer 36.5 0.2862 6239 0.41 12.23 8.05 0.78 1.79 170.93 127.53 1.16 \n\n\n\n6 82 19.8 Granite gneiss Weathered Layer 24.6 0.2588 3123 0.25 5.02 9.16 0.80 1.96 126.95 95.06 1.16 \n\n\n\n7 217 14.7 Granite gneiss Fracture Aquifer 35.4 0.1569 14005 0.26 3.80 4.71 0.65 1.96 305.52 225.61 1.16 \n\n\n\n8 225 18.1 Granite gneiss Fracture Aquifer 29.8 0.1530 7961 0.26 4.77 0.37 0.60 1.96 267.16 194.74 1.17 \n\n\n\n9 91 17.3 Migmatite Fracture Aquifer 38.3 0.2617 11172 0.55 9.55 13.44 0.86 3.50 250.59 146.33 1.31 \n\n\n\n10 65 27.2 Granite gneiss Weathered Layer 30.4 0.4288 2799 0.27 7.30 21.92 0.91 1.96 92.07 70.90 1.14 \n\n\n\n11 147 17 Migmatite Weathered Layer 22.4 0.1323 4471 0.27 4.65 8.41 0.79 3.50 199.59 169.36 1.09 \n\n\n\n12 72 18.7 Granite Weathered Layer 19.8 0.2629 1726 0.28 5.20 15.29 0.88 1.79 87.17 75.31 1.08 \n\n\n\n13 65 22.6 Granite Weathered Layer 23.4 0.3503 1719 0.41 9.37 8.74 0.79 1.79 73.44 66.81 1.05 \n\n\n\n14 38 12.3 Granite Weathered Layer 16.1 0.3920 683 0.28 3.49 17.24 0.89 1.79 42.44 41.07 1.02 \n\n\n\n15 118 26.8 Granite Weathered Layer 33.3 0.2498 5564 0.29 7.73 27.22 0.93 1.79 167.10 133.32 1.12 \n\n\n\n16 213 19.2 Granite Weathered Layer 30.7 0.1181 9678 0.29 5.63 4.70 0.65 1.79 315.23 259.85 1.10 \n\n\n\n17 92 14.4 Granite gneiss Fracture Aquifer 21.3 0.1976 2515 0.51 7.28 10.85 0.83 1.96 118.06 107.81 1.05 \n\n\n\n18 102 17.9 Granite Weathered Layer 25.4 0.2121 3408 0.30 5.34 6.75 0.74 1.79 134.16 119.77 1.06 \n\n\n\n19 251 13.5 Granite Weathered Layer 18.1 0.0667 5141 0.30 4.10 5.40 0.69 1.79 284.02 271.16 1.02 \n\n\n\n20 88 20.5 Migmatite Weathered Layer 21.4 0.2374 1985 0.31 6.32 9.16 0.80 3.50 92.75 90.13 1.01 \n\n\n\n21 132 16.8 Migmatite Weathered Layer 17.6 0.1295 2507 0.55 9.28 11.02 0.83 3.50 142.45 135.93 1.02 \n\n\n\n22 97 16.2 Granite gneiss Fracture Aquifer 28.9 0.2022 7336 0.31 5.08 0.19 0.81 1.96 253.85 142.93 1.33 \n\n\n\n23 123 15.7 Granite gneiss Fracture Aquifer 32.2 0.1614 12220 0.32 5.00 10.81 0.83 1.96 379.49 199.47 1.38 \n\n\n\n24 122 16.8 Granite gneiss Fracture Aquifer 28.3 0.1718 6521 0.32 5.43 9.13 0.80 1.96 230.43 164.74 1.18 \n\n\n\n25 87 23.2 Granite Weathered Layer 24 0.2702 2202 0.41 9.62 11.48 0.84 1.79 91.73 88.84 1.02 \n\n\n\n26 45 16.5 Granite Weathered Layer 17.7 0.3705 1115 0.33 5.42 36.71 0.95 1.79 62.97 47.77 1.15 \n\n\n\n27 84 18.7 Migmatite Weathered Layer 20.1 0.2297 1849 0.33 6.23 28.05 0.93 3.50 92.01 87.52 1.03 \n\n\n\n28 201 18.9 Migmatite Weathered Layer 26.3 0.1212 5894 0.34 6.39 4.24 0.62 3.50 224.11 217.04 1.02 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nTable 2: Continued \n\n\n\nTable 2: Summary of aquifer units\u2019 characteristics, hydraulics characteristics, derived geoelectric properties, and vulnerability \n\n\n\nVES \n\n\n\nNo. \n\n\n\nAquifer Unit Overburden \n\n\n\nThickness (m) \nS ( -1) T ( m2) K (m/day) T (m2/day) Fc Rc FM PT PL \u03bb \n\n\n\nResistivity (\u03a9m) Thickness (m) Geology Type \n\n\n\n29 108 14.8 Granite Fracture Aquifer 29.8 0.1675 9577 0.39 5.79 0.18 0.84 1.79 321.39 177.95 1.34 \n\n\n\n30 57 18.5 Granite Weathered Layer 26 0.3988 1824 0.34 6.35 15.98 0.88 1.79 70.17 65.19 1.04 \n\n\n\n31 68 19.6 Granite Weathered Layer 27.6 0.3520 2360 0.35 6.83 16.21 0.88 1.79 85.52 78.41 1.04 \n\n\n\n32 110 19.4 Migmatite Fracture Aquifer 32 0.2186 6917 0.35 6.85 0.24 0.91 3.50 216.16 146.41 1.22 \n\n\n\n33 80 18.2 Granite gneiss Weathered Layer 19.3 0.2300 1946 0.51 9.20 29.73 0.93 1.96 100.80 83.92 1.10 \n\n\n\n34 89 23.4 Migmatite Weathered Layer 24.7 0.2669 2510 0.36 8.38 16.49 0.89 3.50 101.63 92.55 1.05 \n\n\n\n35 120 22.2 Migmatite Weathered Layer 23.1 0.1868 3112 0.36 8.07 7.31 0.76 3.50 134.73 123.66 1.04 \n\n\n\n36 182 18.3 Migmatite Weathered Layer 26.8 0.1241 6548 0.37 6.74 13.43 0.86 3.50 244.34 216.04 1.06 \n\n\n\n37 81 22.5 Granite Weathered Layer 23.3 0.2817 1987 0.41 9.33 9.89 0.82 1.79 85.26 82.72 1.02 \n\n\n\n38 90 19.2 Granite Weathered Layer 26 0.2325 4301 0.37 7.17 37.31 0.95 1.79 165.44 111.81 1.22 \n\n\n\n39 221 15.5 Granite Weathered Layer 16 0.0712 3663 0.38 5.86 29.64 0.93 1.79 228.91 224.75 1.01 \n\n\n\n40 102 13.7 Granite Weathered Layer 17.9 0.1478 2802 0.38 5.25 15.84 0.88 1.79 156.56 121.12 1.14 \n\n\n\n41 174 9.8 Granite gneiss Weathered Layer 13.5 0.0734 3169 0.51 4.95 23.69 0.92 1.96 234.74 183.80 1.13 \n\n\n\n42 509 19.4 Granite gneiss Weathered Layer 29.3 0.0815 12283 0.39 7.53 23.91 0.92 1.96 419.20 359.33 1.08 \n\n\n\n43 58 13.8 Granite gneiss Fracture Aquifer 43.3 0.3807 12556 0.39 5.43 63.72 0.97 1.96 271.49 113.73 1.55 \n\n\n\n44 93 18.6 Granite gneiss Weathered Layer 26.2 0.2484 2941 0.40 7.41 8.06 0.78 1.96 112.25 105.47 1.03 \n\n\n\n45 445 13.4 Migmatite Weathered Layer 21.2 0.1166 6701 0.55 7.40 0.89 -0.06 3.50 316.07 181.75 1.32 \n\n\n\n46 45 38.7 Granite Weathered Layer 39.6 0.8705 1819 0.40 15.61 15.33 0.88 1.79 45.93 45.49 1.00 \n\n\n\n47 555 19.2 Migmatite Weathered Layer 30.2 0.1265 12110 0.41 7.84 0.74 -0.15 3.50 401.00 238.75 1.30 \n\n\n\n48 480 18.7 Migmatite Weathered Layer 25.3 0.1041 9696 0.41 7.73 0.70 -0.17 3.50 383.24 243.02 1.26 \n\n\n\n49 621 14.9 Granite Weathered Layer 19.7 0.0514 10275 0.41 6.18 0.72 -0.16 1.79 521.59 383.25 1.17 \n\n\n\n50 94 21.2 Granite Weathered Layer 25.3 0.2578 2519 0.42 8.87 7.03 0.75 1.79 99.58 98.12 1.01 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\n\n\n\n\nFigure 12: Spatial map of (a) Formation factor (b) Hydraulic conductivity (c) Transmissivity across the study area \n\n\n\n\n\n\n\nFigure 13: Spatial distribution map of (a) Reflection coefficient (b) Fracture contrast (c) Coefficient of Anisotropy across the study area \n\n\n\nThe traverse resistance ranged from 683 - 14005 \u2126m2 (avg. 5129 \u2126m2). \nThe average values estimated for different geological units in the area: \nmigmatite, granite, and gneiss are 5418 \u2126m2, 3828 \u2126m2, and 6875 \u2126m2 \nrespectively with gneiss having the highest value (Table 2). The spatial \ntransverse resistance values are shown in Figure 14a, lower values less \nthan 5000 ohm-m2 are areas with low yield/potential, values ranging from \n5000 \u2013 10000 ohm-m2 are moderate groundwater potential area, while \n\n\n\nvalues above 10000 are high prospect zones. Hence the area range from \nlow \u2013 moderate in equal proportion by areal extent. Aquifer transmissivity \nand traverse resistance have recorded positive correlation coefficient, \nhence transmissivity increases as the transverse resistance increases \n(Falowo, 2022). Consequently, the north central and portion of mid \nsouthern part showed moderate tendency. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\n\n\n\n\nFigure 14: Distribution of (a) Transverse resistance (b) Longitudinal unit conductance (c) Groundwater potential index values \n\n\n\nTable 3: Summary of Well Information and Sample Locations \n\n\n\nEast (m) North (m) Borehole No. Elevation (m) Total Depth (m) SWL (m) Geology Present State \n\n\n\n706617 799222 BH-1 267 38 22 Migmatite Functioning \n\n\n\n706356 798287 BH-2 267 42 19 Granite Functioning \n\n\n\n705608 796574 BH-3 258 45 22 Migmatite Functioning \n\n\n\n706533 795932 BH-4 262 39 20 Gneiss Functioning \n\n\n\n706664 797820 BH-5 257 48 26 Granite-Gneiss Functioning \n\n\n\n\n\n\n\n \nFigure 15: Borehole sections showing the various geologic units observed from borehole cuttings across three geological environments of migmatite, \n\n\n\ngranite, and gneiss \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nTable 4: Summary of Well Information from two geological units \n\n\n\nEast North Well. No Elevation (m) TD (m) SWL (m) WC (m) HH (m) K (m/day) T (m2/day) Geology \n\n\n\n706397 799634 W-1 272 8.2 4.5 3.7 267.5 0.41 1.53 Granite \n\n\n\n706497 799451 W-2/VES 49 269 12.3 7.5 4.8 261.5 0.41 1.99 Granite \n\n\n\n706664 799551 W-3 271 14.5 8.2 6.3 262.8 0.41 2.61 Granite \n\n\n\n706774 799570 W-4/VES50 271 6.5 3.2 3.3 267.8 0.41 1.37 Granite \n\n\n\n706617 799304 W-5 268 9.5 5.5 4 262.5 0.41 1.66 Granite \n\n\n\n706455 799029 W-6 266 8.7 3.9 4.8 262.1 0.41 1.99 Granite \n\n\n\n706674 799203 W-7 267 10.4 6.2 4.2 260.8 0.41 1.74 Granite \n\n\n\n706554 798406 W-8 263 12.7 5.8 6.9 257.2 0.41 2.86 Granite \n\n\n\n706298 798965 W-9 261 9.8 6.3 3.5 254.7 0.41 1.45 Granite \n\n\n\n706063 798910 W-10 258 7.8 4.3 3.5 253.7 0.41 1.45 Granite \n\n\n\n706340 798553 W-11 256 13.3 5.6 7.7 250.4 0.41 3.19 Granite \n\n\n\n706293 798104 W-12/VES 25 264 15.1 8.2 6.9 255.8 0.41 2.86 Granite \n\n\n\n706146 797820 W-13 255 8.6 5.2 3.4 249.8 0.51 1.72 Granite Gneiss \n\n\n\n705943 797609 W-14 255 9.5 3.7 5.8 251.3 0.41 2.40 Granite \n\n\n\n706037 797380 W-15/VES 37 256 8.2 5.3 2.9 250.7 0.41 1.20 Granite \n\n\n\n706162 797417 W-16 254 12.2 6.8 5.4 247.2 0.55 2.98 Migmatite \n\n\n\n706350 797343 W-17/VES 20 252 14.6 5.6 9 246.4 0.55 4.97 Migmatite \n\n\n\n706309 797261 W-18/VES 21 259 8.8 2.5 6.3 256.5 0.55 3.48 Migmatite \n\n\n\n706329 797178 W-19 266 6.7 3.4 3.3 262.6 0.51 1.67 Granite Gneiss \n\n\n\n706199 797178 W-20 263 11.3 7.2 4.1 255.8 0.51 2.07 Granite Gneiss \n\n\n\n706654 798150 W-21 265 14.9 6.8 8.1 258.2 0.51 4.09 Granite Gneiss \n\n\n\n706518 797407 W-22 252 9.2 3.8 5.4 248.2 0.51 2.73 Granite Gneiss \n\n\n\n706727 797719 W-23/VES 17 256 8.0 5.5 2.5 250.5 0.51 1.26 Granite Gneiss \n\n\n\n706486 797188 W-24 262 11.4 7.4 4 254.6 0.51 2.02 Granite Gneiss \n\n\n\n706497 797233 W-25 259 9.6 5.2 4.4 253.8 0.51 2.22 Granite Gneiss \n\n\n\n706549 797325 W-26 255 7.4 3.6 3.8 251.4 0.51 1.92 Granite Gneiss \n\n\n\n706659 797462 W-27 254 12.8 7.9 4.9 246.1 0.51 2.48 Granite Gneiss \n\n\n\n706716 797508 W-28 255 10.9 6.5 4.4 248.5 0.55 2.43 Migmatite \n\n\n\n706596 797243 W-29 259 13.3 8.1 5.2 250.9 0.55 2.87 Migmatite \n\n\n\n706674 797316 W-30 257 8.5 4.4 4.1 252.6 0.55 2.26 Migmatite \n\n\n\n706742 797426 W-31 256 9.9 3.6 6.3 252.4 0.55 3.48 Migmatite \n\n\n\n706606 797123 W-32 264 8.7 3.5 5.2 260.5 0.55 2.87 Migmatite \n\n\n\n706727 797133 W-33 263 9.2 5.2 4 257.8 0.55 2.21 Migmatite \n\n\n\n706789 797059 W-34 263 8.6 4.3 4.3 258.7 0.41 1.78 Granite \n\n\n\n706732 796922 W-35/VES 14 268 9.7 6.5 3.2 261.5 0.41 1.33 Granite \n\n\n\n706669 796748 W-36 262 10.7 6.9 3.8 255.1 0.41 1.58 Granite \n\n\n\n705587 796519 W-37 257 8.7 3.3 5.4 253.7 0.41 2.24 Granite \n\n\n\n705571 796775 W-38/VES 35 257 6.5 4.0 2.5 253 0.55 1.38 Migmatite \n\n\n\n705666 796400 W-39 257 9.8 4.9 4.9 252.1 0.41 2.03 Granite \n\n\n\n706079 796620 W-40 257 10.8 7.2 3.6 249.8 0.41 1.49 Granite \n\n\n\n706413 796574 W-41/VES 33 261 7.7 4.6 3.1 256.4 0.51 1.57 Granite Gneiss \n\n\n\n706465 796656 W-42 264 9.5 4.8 4.7 259.2 0.41 1.95 Granite \n\n\n\n706502 796647 W-43 263 10.5 5.6 4.9 257.4 0.55 2.71 Migmatite \n\n\n\n706324 795914 W-44/VES 8 263 12.3 7.7 4.6 255.3 0.55 2.54 Migmatite \n\n\n\n706622 795465 W-45/VES 9 265 9.4 6.2 3.2 258.8 0.55 1.77 Migmatite \n\n\n\n706727 795456 W-46/VES 32 264 7.6 5.5 2.1 258.5 0.55 1.16 Migmatite \n\n\n\n706403 794815 W-47 262 8.9 3.9 5 258.1 0.51 2.53 Granite Gneiss \n\n\n\n706288 794796 W-48/VES 6 263 13.8 7.9 5.9 255.1 0.51 2.98 Granite Gneiss \n\n\n\n706507 794494 W-49 267 11.5 6.6 4.9 260.4 0.51 2.48 Granite Gneiss \n\n\n\n706465 794934 W-50 259 12.6 8.1 4.5 250.9 0.51 2.27 Granite Gneiss \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nTable 4: Continued \n\n\n\nTable 4: Summary of Well Information from two geological units \n\n\n\nEast North Well. No Elevation (m) TD (m) SWL (m) WC (m) HH (m) K (m/day) T (m2/day) Geology \n\n\n\n706539 795199 W-51 264 10.2 5.8 4.4 258.2 0.51 2.22 Granite Gneiss \n\n\n\n706225 793990 W-52/VES 44 257 8.5 4.7 3.8 252.3 0.51 1.92 Granite Gneiss \n\n\n\n706141 793926 W-53 257 8.9 6.2 2.7 250.8 0.51 1.36 Granite Gneiss \n\n\n\n706105 796894 W-54 265 9.0 6.1 2.9 258.9 0.51 1.47 Granite Gneiss \n\n\n\n706376 796794 W-55 270 8.5 4.2 4.3 265.8 0.51 2.17 Granite Gneiss \n\n\n\n706340 796739 W-56 267 10.5 5.9 4.6 261.1 0.51 2.32 Granite Gneiss \n\n\n\n706277 796693 W-57 264 8.7 4.7 4 259.3 0.41 1.66 Granite \n\n\n\n706612 796830 W-58 270 7.6 3.8 3.8 266.2 0.41 1.58 Granite \n\n\n\n3.2   Direct Method \n\n\n\nThe information from the boreholes is presented in Table 3, with total \ndepth ranging from 38 (migmatite) \u2013 48 m (gneiss) and an average of 42 \nm, showed SWL ranging from 19 (granite) \u2013 26 m in gneiss (avg. 22.0 m).  \nHydraulic information was taken from fifty five wells, and some of the \nwells coincided with the location of the VES\u2019s stations, as presented in \nTables 5 and 6. This information showed that the area has a thick \noverburden thickness/depth of weathering and corroborates the obtained \ngeoelectric data i.e. 22.0 m. The sections of the boreholes are presented in \nFigure 15.  The cuttings were visually inspected in their natural state or \ncondition during drilling. The geologic units observed from the sites \ninvestigated (within migmatite, granite, and gneiss environments) \ncomprised clay, sandy clay, clayey sand (which graded to sand or clayey \nmaterial in many places), clay-sand mixture, and fresh basement rock. The \nthickness of the clay topsoil delineated under BHs-02 \u2013 04 ranged from 1.1 \n\u2013 5.7 m; the sandy clay was observed in all the boreholes with thickness \n\n\n\nrange of 7.6 m (BH-03) to 23.2 m (BH-05); clayey sand has thickness \nvariation of 1.2 m (BH-03) to 15.5 m (BH-04); clay-sand mixture has \nthickness varying from 3.3 m (BH-02) to 23.5 m (BH-03). The clay-sand \nmixture is the main water bearing units, which constitute the weathered \nlayer. The depth to basement rock ranged between 33.8 \u2013 44.1 m. The \nupper 10 m of the sections are dominated by clay, sandy clay, and clayey \nsand; while sandy clay being the most dominant soil. The SWL is deep \nranging from 18.5 \u2013 24.6 m. The structural features possibly fractures \nobserved in BHs-01, 05, and 06 agreed with the fractured zone delineated \non the geoelectric data with depths range of 25 \u2013 38.5 m and average depth \nof 29.8 m. This range of values overlap and within the 32.0 \u2013 43.5 m \ndelineated in the sections. This zone and weathered layer are the main \nwater bearing units in the area. The depth to the basement ranged from \n33.5 m for borehole 03 (migmatite) \u2013 44.4 m for borehole 05 (gneiss). This \nsupports the overburden thickness of 13.5 to 43.3 m recorded in VES \nresults for gneiss. Hence gneissic offered both thick weathered layer and \nfractured aquifer. \n\n\n\n\n\n\n\nFigure 16: Typical pumping test curves for wells across the three geological units (a) W-23 -gneiss (b) W-35 \u2013 granite (c) W-46 - migmatite (d) W-53 \u2013 \ngneiss \n\n\n\nThe data acquired from fifty eight (58) open wells in Table 4, showed total \ndepth of well investigated ranging from 6.5 (granite; W-4 & 38) \u2013 15.1 m \nin granite W-12 (avg. 10.1 m). The water column which is \nstorage/reservoir potential of the wells ranged from 2.1 m in W-46 \n(migmatite) to 9.0 m in W-17 migmatite (avg. 4.5 m) in migmatite rocks. \nThe SWL varied from 2.5 m in migmatite (W-18) to 8.2 m in W-3&12 \ngranite (avg. 5.5 m), with corresponding hydraulic head of 246.1 \u2013 267.8 \nm above the seal level (avg. 256.0 m). The thickness of the vadose zone \nwhich corresponds to the static water level (SWL) is generally moderate \ni.e. above 5 m, which is capable of providing average daily consumption \n\n\n\nfor domestic needs. The hydraulic conductivity, transmissivity, and \nstorativity was obtained from the pumping test, with typical pumping test \ndata/curves is shown in Figure 15 and Table 5. The K ranged from 0.41 \n(granite) to 0.55 m/d in migmatite (0.48 m/d avg.), transmissivity 1.16 \n(W-46; migmatite) \u2013 4.97 m2/d (W-17; migmatite) with average value of \n2.18 m2/d. The storativity (Sr) varied from 0.0057 in W-02 (gneiss) to \n0.0227 in W-01 (granite). The regression model (equation 11) was used to \npredict the storativity especially in locations where pumping test was not \ncarried. The empirical relationship between transmissivity and storativity, \ngives high positive linear correlation of 0.7076. Storativity is the product \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nof the specific storage and the aquifer thickness. Consequently, the average \nstorativity (Sr) or coefficient of storativity is greater than 0.005 which \nsuggests an unconfined aquifer. The results of the well hydraulics \ncorrelate well with those estimated for the VES locations which delineated \n80 % of the study area to be weathered aquifer. \n\n\n\nStorativity = 0.0095 (x) + 0.0437                                 (11) \n\n\n\nWhere x is transmissivity \n\n\n\nTable 5: Typical field data obtained during the pumping test for some wells \n\n\n\nTime (min) \nW-23 W-35 W-46 W-53 \n\n\n\nDrawdown (m) GWL (m) Drawdown (m) GWL (m) Drawdown (m) GWL (m) Drawdown (m) DWL (m) \n\n\n\n0 0 5.5 0 6.5 0 5.5 0 4 \n\n\n\n5 0.21 5.71 0.38 6.88 0.25 5.75 0.2 4.2 \n\n\n\n10 0.3 5.8 0.59 7.09 0.45 5.95 0.46 4.46 \n\n\n\n15 0.49 5.99 0.77 7.27 0.59 6.09 0.8 4.8 \n\n\n\n20 0.7 6.2 0.84 7.34 0.7 6.2 1.07 5.07 \n\n\n\n25 0.79 6.29 0.9 7.4 0.87 6.37 1.32 5.32 \n\n\n\n30 1.2 6.7 0.95 7.45 1.01 6.51 1.56 5.56 \n\n\n\n35 1.31 6.81 1.06 7.56 1.11 6.61 1.63 5.63 \n\n\n\n40 1.38 6.88 1.14 7.64 1.19 6.69 1.67 5.67 \n\n\n\n45 1.42 6.92 1.2 7.7 1.26 6.76 1.7 5.7 \n\n\n\n50 1.53 7.03 1.28 7.78 1.31 6.81 1.71 5.71 \n\n\n\n60 1.54 7.04 1.33 7.83 1.33 6.83 1.71 5.71 \n\n\n\n90 1.54 7.04 1.4 7.9 1.35 6.85 1.71 5.71 \n\n\n\n120 1.54 7.04 1.4 7.9 1.35 6.85 1.71 5.71 \n\n\n\nT (m2/min) 0.19 0.19 0.22 0.19 \n\n\n\nS 0.0227 0.0057 0.01392 0.01395 \n\n\n\n \n3.3 Hydrogeological Parameters Modeling and Groundwater \nPotential Mapping \n\n\n\nEmpirical model (Table 6) was developed for the three geological units \n(migmatite, granite, and gneiss), by plotting formation factor on x-axis and \nhydraulic conductivity on y-axis. All the rock units showed positive \ncorrelations in descending order as: granite (0.3778), migmatite (0.1057), \nand gneiss (0.0641). The modeling of the water bearing unit potentiality \nzones was done using groundwater potential index values (GWPIV) to \nproduce groundwater potential map in Figure 14c and the obtained values \nfor all rated parameters are shown in Table 8. The GWPIV has proved been \nidentified as one of the veritable tools in groundwater assessment and \nmanagement. Therefore, in this study, multi-criteria decision analysis \n(MCDA) was done using analytical hierarchy process (AHP) by Saaty \n(1980). The AHP is a theory of measurement for dealing with \nquantification and/or intangible criteria that has found rich applications \nin decision theory, conflict resolution and in models of the brain (Vargas, \n1990). The decision applications of the AHP are carried out in two phases: \nhierarchic design and evaluation using paired comparisons. The normal \nprocedure for AHP was involved in this study by prioritizing the \nhydrogeologic parameters according to their importance in groundwater \naccumulation; then the parameters are pair-wise in a matrix form using \nSaaty (1980) scale of importance, where 1, 3, 5, 7, and 9 are equal, \nmoderate, strong, very strong, and extreme importance respectively; \nwhile 2, 4, 6, and 8 are intermediate values; and 1/3, 1/5, 1/7, and 1/9 are \nvalues for inverse comparison.  \n\n\n\nThe average values of the relative values in every row are determined, and \nthis gives the criteria weights; consequently the consistency ratio was \ndetermined by multiplying the pair-wise comparison matrix with criteria \nweights calculated for each row, and the average value determined to \nobtain the weighted sum value. Thereafter, the weighted sum value is \ndivided by criteria weights to obtain consistency ratio for each of the rows. \nHence, the average consistency ratio is obtained as Lambda (\u03bbmax). \n\n\n\nTable 6: Empirical Relationship between hydraulic conductivity and \nformation factor for different water bearing formations derived \n\n\n\ngeological units \n\n\n\nS/Nos. Geological units \nExponential \n\n\n\nEquation \nCorrelation \ncoefficient \n\n\n\n1 Migmatite y = 0.6373e-0.041x 0.1057 \n\n\n\n2 Granite y = 0.8816e-0.422x 0.3778 \n\n\n\n3 Gneiss y = 0.8387e-0.2590x 0.0641 \n\n\n\nThe next is the determination of consistency index (CI) using equation 12. \nIt is the consistency index of a pairwise comparison matrix which is \ngenerated randomly, random index depends on the number of elements \nwhich are compared as shown in Table 6.  \n\n\n\n\ud835\udc36\ud835\udc3c =  \n\ud835\udefe\ud835\udc5a\ud835\udc4e\ud835\udc65\u2212\ud835\udc5b\n\n\n\n\ud835\udc5b\u22121\n                                                                                                                                              (12) \n\n\n\nWhere n is the number of parameters compared. \n\n\n\nThe criteria weight was tested to know its accuracy, reliability, credibility, \nand consistency (Saaty, 2008, 1990) in predicting groundwater yield in the \nstudy area by dividing the CI with random consistency index value \nobtained in Table 7 (Saaty, 2006) and the resulted value (0.095 obtained \nfrom this study) must be less than 0.10, which is the rule of the process. \nThe obtained weights (w) were used to rate the parameters accordingly \n(Table 8) as AQT - aquifer layer thickness (0.07), AQR - aquifer layer \nresistivity (0.10), OVT - overburden thickness (0.16), TR - transverse \nresistance (0.19), TMY - transmissivity (0.26), CoA - coefficient of \nanisotropy (0.22). In generating groundwater potential index values \n(GWPIV), the rating (r) obtained from AHP was multiplied with the \nweights (which varied from 1 to 5) based on their degree of relevance in \ngroundwater storage and utilization (Table 8), and were summed up \n(equations 13-14). \n\n\n\n\ud835\udc3a\ud835\udc4a = \ud835\udc53(\ud835\udc34\ud835\udc44\ud835\udc47, \ud835\udc34\ud835\udc44\ud835\udc45, \ud835\udc42\ud835\udc49\ud835\udc47, \ud835\udc47\ud835\udc45, \ud835\udc47\ud835\udc40\ud835\udc4c, \ud835\udc36\ud835\udc42\ud835\udc34)                                 (13) \n\n\n\nTherefore the GWPIV was determined using the expression below: \n\n\n\nGWPIV = \ud835\udc34\ud835\udc44\ud835\udc47\ud835\udc64\ud835\udc34\ud835\udc44\ud835\udc47\ud835\udc5f + \ud835\udc34\ud835\udc44\ud835\udc45\ud835\udc64\ud835\udc34\ud835\udc44\ud835\udc45\ud835\udc5f + \ud835\udc42\ud835\udc49\ud835\udc47\ud835\udc64\ud835\udc42\ud835\udc49\ud835\udc47\ud835\udc5f + \ud835\udc47\ud835\udc45\ud835\udc64\ud835\udc47\ud835\udc45\ud835\udc5f +\n\ud835\udc47\ud835\udc40\ud835\udc4c\ud835\udc64\ud835\udc47\ud835\udc40\ud835\udc4c\ud835\udc5f + \ud835\udc36\ud835\udc42\ud835\udc34\ud835\udc64\ud835\udc36\ud835\udc42\ud835\udc34\ud835\udc5f                                                                         (14) \n\n\n\nThematic layers of the parameters were generated using QGIS software; \nand the same software was used to do the classification, and produced the \nGWPIV and subsequent groundwater potential map. The GWPIV was \nranked as very low: 0.0 \u2013 1.0; low: 1.0 \u2013 2.0; moderate: 2.0 \u2013 3.0, high: 3.0 \n\u2013 4.0, and very high greater than 4. Thus the GWPIV ranged from granite \n1.53 (VES 14; weathered aquifer) \u2013 migmatite 3.50 (VES 47; weathered \naquifer) with an average of 2.18 indicating moderate groundwater \npotential. The developed groundwater potential map (Figure 11c) \ndistinguished the area into two major potential zones, with prominent \nmoderate zone constituting 90 % of the study area. The low potential zone \nare observed sporadically in the area and constitute 10 % of the study \narea, with notable places of occurrence included central and northwestern \nparts. However, the longitudinal unit conductance (LUC) recorded values \nranging from 0.0514 \u2013 0.8705 mhos (with regional average of 0.219876 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nmhos); while migmatite recorded 0.0730 \u2013 0.2669 mhos (0.1641 mhos \navg.); granite (0.0514 \u2013 0.8705 mhos; 0.2678 mhos avg.); and gneiss \nrecorded 0.0734 \u2013 0.4288 mhos (0.2111 mhos avg.). Using Table 10, the \n\n\n\ngroundwater system in the study area is weak, although the northwest and \ncentral parts appear less weak, as shown in spatial distribution map of LUC \nin Figure 14b. \n\n\n\nTable 7: Random Consistency Index Table for number of parameter (N) and corresponding random value (Saaty, 2006) \n\n\n\nN 1 2 3 4 5 6 7 8 9 10 \n\n\n\nRV 0 0 0.52 0.89 1.11 1.25 1.35 1.40 1.45 1.49 \n\n\n\nTable 8: Probability rating, normalized weight for different classes of parameters used in deriving the GWPIV \n\n\n\n Parameter Range Weight Remark Rating \n\n\n\n1 \nAquifer Layer Thickness \n\n\n\n(m) \n\n\n\n0 \u2013 5 1 Very Low \n\n\n\n\n\n\n\n0.07 \n\n\n\n6 \u2013 10 \n\n\n\n11 \u2013 15 \n\n\n\n16 - 20 \n\n\n\n2 \n\n\n\n3 \n\n\n\n4 \n\n\n\nLow \n\n\n\nModerate \n\n\n\nHigh \n\n\n\n>20 5 Very High \n\n\n\n2 \n\n\n\n\n\n\n\nAquifer Layer Resistivity \n(ohm-m) \n\n\n\n1 \u2013 100  (Clay) 1 Very Low \n\n\n\n\n\n\n\n0.10 \n\n\n\n101 \u2013 250 (Sandy clay) 2 Low \n\n\n\n251 \u2013 350 (Clayey sand) 3 Moderate \n\n\n\n351 \u2013 750 (Sand/Fractured aquifer) 5 Very High \n\n\n\n3 \nOverburden Thickness \n\n\n\n(m) \n\n\n\n1 \u2013 10 1 Low \n\n\n\n\n\n\n\n0.16 \n\n\n\n11 \u2013 20 2 Medium \n\n\n\n21 \u2013 30 \n\n\n\n>30 \n\n\n\n3 \n\n\n\n5 \n\n\n\nHigh \n\n\n\nVery High \n\n\n\n4 \nTransverse Resistance \n\n\n\n(ohm-m2) \n\n\n\n1 \u2013 5000 1 Low \n \n\n\n\n0.19 \n5001 \u2013 10000 3 Fair \n\n\n\n>10000 5 High \n\n\n\n5 \n\n\n\n\n\n\n\nTransmissivity \n\n\n\n(m2/d) \n\n\n\n1 \u2013 10 1 Low \n \n\n\n\n0.26 \n11 \u2013 20 3 Moderate \n\n\n\n>20 5 High \n\n\n\n6 Coefficient of Anisotropy \n\n\n\n1.1 \u2013 1.15 \n\n\n\n1.15 \u2013 1.19 \n\n\n\n1.19 \u2013 1.25 \n\n\n\n1.25 \u2013 1.30 \n\n\n\n1.30 \u2013 2.0 \n\n\n\n1 \n\n\n\n2 \n\n\n\n3 \n\n\n\n4 \n\n\n\n5 \n\n\n\nVery Low \n\n\n\nLow \n\n\n\nModerate \n\n\n\nHigh \n\n\n\nVeryHigh \n\n\n\n\n\n\n\n0.22 \n\n\n\nTable 9: Summary of the obtained values for the seven parameters pair-wise and resulted GWPIV \n\n\n\nEast Aquifer VES No. AQR AQT OVT TR T COA GWPIV GWPIV (%) GWP \n\n\n\nMigmatite WL 1 0.2 0.28 0.32 0.2 0.78 0.44 2.22 44.0 Moderate \n\n\n\nMigmatite WL 2 0.2 0.28 0.32 0.2 0.26 0.44 1.7 33.7 Low \n\n\n\nMigmatite WL 3 0.2 0.35 0.48 0.2 0.26 0.44 1.93 38.2 Low \n\n\n\nGranite WL 4 0.1 0.28 0.32 0.2 0.26 0.44 1.6 31.7 Low \n\n\n\nGranite WL 5 0.2 0.35 0.8 0.6 0.78 0.66 3.39 67.1 High \n\n\n\nGranite gneiss WL 6 0.1 0.28 0.48 0.2 0.26 0.66 1.98 39.2 Low \n\n\n\nGranite gneiss FB 7 0.2 0.21 0.48 1 0.26 0.44 2.59 51.3 Moderate \n\n\n\nGranite gneiss FB 8 0.1 0.28 0.48 0.6 0.26 0.22 1.94 38.4 Low \n\n\n\nMigmatite FB 9 0.1 0.28 0.8 1 0.26 0.66 3.1 61.4 High \n\n\n\nGranite gneiss WL 10 0.1 0.35 0.8 0.2 0.26 0.44 2.15 42.6 Moderate \n\n\n\nMigmatite WL 11 0.2 0.28 0.48 0.2 0.26 0.44 1.86 36.8 Low \n\n\n\nGranite WL 12 0.1 0.28 0.32 0.2 0.26 0.44 1.6 31.7 Low \n\n\n\nGranite WL 13 0.1 0.35 0.48 0.2 0.26 0.44 1.83 36.2 Low \n\n\n\nGranite WL 14 0.1 0.21 0.32 0.2 0.26 0.44 1.53 30.3 Low \n\n\n\nGranite WL 15 0.2 0.35 0.8 0.6 0.26 0.44 2.65 52.5 Moderate \n\n\n\nGranite WL 16 0.2 0.28 0.8 0.6 0.26 0.44 2.58 51.1 Moderate \n\n\n\nGranite gneiss FB 17 0.1 0.21 0.48 0.2 0.26 0.44 1.69 33.5 Low \n\n\n\nGranite WL 18 0.2 0.28 0.48 0.2 0.26 0.44 1.86 36.8 Low \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nTable 9: Summary of the obtained values for the seven parameters pair-wise and resulted GWPIV \n\n\n\nEast Aquifer VES No. AQR AQT OVT TR T COA GWPIV GWPIV (%) GWP \n\n\n\nGranite WL 19 0.3 0.21 0.32 0.6 0.26 0.44 2.13 42.2 Moderate \n\n\n\nMigmatite WL 20 0.1 0.35 0.48 0.2 0.26 0.44 1.83 36.2 Low \n\n\n\nMigmatite WL 21 0.2 0.28 0.32 0.2 0.26 0.44 1.7 33.7 Low \n\n\n\nGranite gneiss FB 22 0.1 0.28 0.48 0.6 0.78 0.66 2.9 57.4 Moderate \n\n\n\nGranite gneiss FB 23 0.2 0.28 0.8 1 0.26 0.66 3.2 63.4 High \n\n\n\nGranite gneiss FB 24 0.2 0.28 0.48 0.6 0.26 0.44 2.26 44.8 Moderate \n\n\n\nGranite WL 25 0.1 0.35 0.48 0.2 0.26 0.44 1.83 36.2 Low \n\n\n\nGranite WL 26 0.1 0.28 0.32 0.2 0.26 0.44 1.6 31.7 Low \n\n\n\nMigmatite WL 27 0.1 0.28 0.48 0.2 0.26 0.44 1.76 34.9 Low \n\n\n\nMigmatite WL 28 0.2 0.28 0.48 0.6 0.26 0.44 2.26 44.8 Moderate \n\n\n\nGranite FB 29 0.2 0.28 0.48 0.6 0.26 0.66 2.48 49.1 Moderate \n\n\n\nGranite WL 30 0.1 0.28 0.48 0.2 0.26 0.44 1.76 34.9 Low \n\n\n\nGranite WL 31 0.1 0.28 0.48 0.2 0.26 0.44 1.76 34.9 Low \n\n\n\nMigmatite FB 32 0.2 0.28 0.8 0.6 0.26 0.44 2.58 51.1 Moderate \n\n\n\nGranite gneiss WL 33 0.1 0.28 0.32 0.2 0.26 0.44 1.6 31.7 Low \n\n\n\nMigmatite WL 34 0.1 0.35 0.48 0.2 0.26 0.44 1.83 36.2 Low \n\n\n\nMigmatite WL 35 0.2 0.35 0.48 0.2 0.26 0.44 1.93 38.2 Low \n\n\n\nMigmatite WL 36 0.2 0.28 0.48 0.6 0.26 0.44 2.26 44.8 Moderate \n\n\n\nGranite WL 37 0.1 0.35 0.48 0.2 0.26 0.44 1.83 36.2 Low \n\n\n\nGranite WL 38 0.1 0.28 0.48 0.2 0.26 0.44 1.76 34.9 Low \n\n\n\nGranite WL 39 0.2 0.28 0.32 0.2 0.26 0.44 1.7 33.7 Low \n\n\n\nGranite WL 40 0.2 0.21 0.32 0.2 0.26 0.44 1.63 32.3 Low \n\n\n\nGranite gneiss WL 41 0.2 0.14 0.32 0.2 0.26 0.44 1.56 30.9 Low \n\n\n\nGranite gneiss WL 42 0.5 0.28 0.48 1 0.26 0.44 2.96 58.6 Moderate \n\n\n\nGranite gneiss FB 43 0.1 0.21 0.8 1 0.26 0.88 3.25 64.4 High \n\n\n\nGranite gneiss WL 44 0.1 0.28 0.48 0.6 0.26 0.44 2.16 42.8 Moderate \n\n\n\nMigmatite WL 45 0.5 0.21 0.48 0.6 0.26 0.66 2.71 53.7 Moderate \n\n\n\nGranite WL 46 0.1 0.35 0.8 0.2 0.78 0.44 2.67 52.9 Moderate \n\n\n\nMigmatite WL 47 0.5 0.28 0.8 1 0.26 0.66 3.5 69.3 High \n\n\n\nMigmatite WL 48 0.5 0.28 0.48 0.6 0.26 0.66 2.78 55.0 Moderate \n\n\n\nGranite WL 49 0.5 0.21 0.32 1 0.26 0.44 2.73 54.1 Moderate \n\n\n\nGranite WL 50 0.1 0.35 0.48 0.2 0.26 0.44 1.83 36.2 Low \n\n\n\nTable 10: Longitudinal unit conductance and corresponding \nprotective rating (Falowo, 2022) \n\n\n\nTotal Longitudinal unit \n\n\n\nConductance (mhos) \n\n\n\nRating of overburden\u2019s \n\n\n\naquifer protective capacity \n\n\n\n<0.10 Poor \n\n\n\n0.1 \u2013 0.49 Weak \n\n\n\n0.5 \u2013 0.99 Moderate \n\n\n\n1.0  - 4.99 Good \n\n\n\n5.0 \u2013 10.0 Very good \n\n\n\n>10.0 Excellent \n\n\n\n4.   CONCLUSION \n\n\n\nHydrogeologic studies has been carried out in Ile Oluji, Ondo State, \nSouthwestern Nigeria using multi-criteria decision analysis using \ngeographic information system supported analytical hierarchy process on \nsix hydrogeologic/geoelectric parameters comprising aquifer layer \nthickness, aquifer layer resistivity,  overburden thickness, transverse \nresistance, transmissivity, and coefficient of anisotropy. These parameters \nwere used to estimate the GWPIV, which was lower in granite, and higher \nin migmatite. The average value of GWPIV suggested moderate \ngroundwater potential constituting 90 % of the study area. The low \npotential zone (10 % of the study area) are observed sporadically in the \n\n\n\ncentral and northwestern parts. The longitudinal unit conductance \nrecorded weak regional average of 0.219876 mhos, suggestive of high \ngroundwater system vulnerability to pollution in the study area, and \nrelatively less-weaker in granite, while the northwest and central parts \nappear less weak. Nonetheless, the water table aquifer (accounts for 80%) \nand the fracture basement (constitutes 20%, frequently occurring in \ngneissic environment). The average overburden thickness is high in gneiss \nand lesser in migmatite, and granite terrain.  \n\n\n\nACKNOWLEDGEMENTS \n\n\n\nThe author is grateful to TETFund, Nigeria (under the Institution Based \nResearch) Nigeria. Special appreciation to all students of especially Higher \nNational Diploma students of Civil Engineering Technology Department, \nfor the assistance rendered during data acquisition. \n\n\n\nREFERENCES \n\n\n\nAdagunodo, M.K., Sunmonu, L.A., Aizebeokhai, A.P., Oyeyemi, K.D., \nAbodunrin, F.O., 2018. Groundwater Exploration in Aaba \nResidential Area of Akure, Nigeria. Front. Earth Science, 66(6), Pp. \n1\u201312, DOI:10.3389/feart.2018.00066. \n\n\n\nAdebawore, A.A., Akinyeye, R. O.,  Awokunmi, E. E., Ayodele, O., \nOlanipekun, E. O., et al., 2017. Seasonal variations of Heavy Metals \nin Water Samples from Selected Hand-Dug Wells Close to Petrol \nStations in Ile-Oluji, Ondo State, Nigeria. J. of Physical and Chemical \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nSciences.V5I3. DOI: 10.15297/JPCS.V5I3.05 \n\n\n\nAdeleke, O.O., Makinde, V., Eruola, A.O., Dada, O.F., Ojo, A.O., Aluko, T.J. \n2015. Estimation of Groundwater Recharges in Odeda Local \nGovernment Area, Ogun State, Nigeria using Empirical Formulae. \nChallenges, 6, Pp. 271\u2013281. \n\n\n\nAina, J.O., Adeleke O.O., Makinde V., Egunjobi, H.A., Biere, P.E. 2019. \nAssessment of Hydrogeological Potential and Aquifer Protective \nCapacity of Odeda, Southwestern Nigeria. RMZ \u2013 M and G, Vol. 66, \nPp. 199\u2013210 \n\n\n\nAkanbi, O.A. 2016. Use of vertical electrical geophysical method for spatial \ncharacterisation of groundwater potential of crystalline crust of \nIgboora area, southwestern Nigeria. International Journal of \nScientific and Research Publications, 6(3), Pp. 399\u2013406. \n\n\n\nAkanbi, O.A. 2017. Hydrogeological Characterisation of Crystalline \nBasement Aquifers of part of Ibarapa Area, Southwestern Nigeria. \nPh. D. Thesis. Ibadan-Nigeria: University of Ibadan 2017; Pp. 312. \n\n\n\nAkinrinade, O. J., Olabode, O.P. 2015. Integrated geophysical approach to \naquifer delineation in crystalline basement environment. \nInternational Journal of Scientific and Engineering Research \n(IJSER), France, 6(10), Pp. 107\u2013127. \n\n\n\nAkinrinade, O.J., Adesina, R.B. 2016. Hydrogeophysical investigation of \ngroundwater potential and aquifer vulnerability prediction in \nbasement complex terrain \u2013 A case study from Akure, Southwestern \nNigeria. RMZ \u2013 M&G, Vol. 63 | Pp. 55\u2013066. DOI: 10.1515/rmzmag-\n2016-0005 \n\n\n\nAlley, W.M., Leake, S.A., 2004. The journey from safe yield to sustainability. \nGroundwater, 42(1): 12-16. https://doi.org/10.1111/j.1745-\n6584.2004.tb02446.x \n\n\n\nAssaad, F., LaMoreaux, P.E., Hughes, T. 2004. Field Methods for Geologists \nand Hydrogeologists. Springer, Berlin. \n\n\n\nBayewu, O.O., Oloruntola, M.O., Mosuroa, G.O., Laniyana, T.A., Ariyo, S.O., \nFatoba, J.O. 2018. Assessment of groundwater prospect and aquifer \nprotective capacity using resistivity method in Olabisi Onabanjo \nUniversity campus, Ago-Iwoye, Southwestern Nigeria. NRIAG \nJournal of Astronomy and Geophysics, 7, pp. 347\u2013360. \n\n\n\nBell, F.G. 2007. Engineering Geology, Second Edition. Elsevier, 581pp. \n\n\n\nBrassington, R. 1988. Field Hydrogeology. Wiley, Chichester, 926pp. \n\n\n\nChaanda, M.S., Alaminiokuma, G.I. 2020. Hydrogeophysical Investigation \nfor Groundwater Resource Potential in Masagamu, Magama area, \nFractured Basement Complex, North-Central Nigeria. Malaysian \nJournal of Geosciences, 4(2): 43-47. \nDOI:10.26480/mjg.02.2020.43.47 \n\n\n\nCosgrove, W.J., Loucks, D.P. 2015. Water Management: Current and Future \nChallenges and Research Directions. Water Resources Research, \n51(6), pp. 4823\u20134839. \n\n\n\nFalowo, O.O. 2022. Modeling of hydrogeological parameters and aquifer \nvulnerability assessment for groundwater resource potentiality \nprediction at Ita Ogbolu, Southwestern Nigeria. Modeling Earth \nSystems and Environment, 21 pp. \nhttps://doi.org/10.1007/s40808-022-01490-8 \n\n\n\nFalowo, O.O., Akindureni, Y., Ojo O. 2017. Groundwater Assessment and Its \nIntrinsic Vulnerability Studies Using Aquifer Vulnerability Index \nand GOD Methods. International Journal of Energy and \nEnvironmental Science, Vol. 2, No. 5, 2017, pp. 103-116. doi: \n10.11648/j.ijees.20170205.13 \n\n\n\nFalowo, O.O., Daramola, A.S. 2023. Geo-Appraisal of groundwater resource \nfor sustainable exploitation and management in Ibulesoro, \nSouthwestern Nigeria. Turkish Journal of Engineering 2023, 7(3), \n236-258, DOI: 10.31127/tuje.1107329 \n\n\n\nFalowo, O.O., Ojo, O.O., Daramola, A.S. 2020. Groundwater Resource \nAssessment by Hydraulic Properties Determination for Sustainable \nPlanning and Development in Central Part of Ondo State, Nigeria. \nEnvironmental and Earth Sciences Research Journal Vol. 7, No. 1, \nMarch, 2020, pp. 1-8 https://doi.org/10.18280/eesrj.070101 \n\n\n\nFAO/DSMW, 2020. Digital Soil Map of the World at 1,500,000 Scale \n\n\n\n(Geonetwork). Food and Agricultural Organization \nhttps://www.fao.org/soils-portal/data-hub/soil-maps-and-\ndatabases/faounesco-soil-map-of-the-world/en/ Accessed 19th \nJanuary, 2023. \n\n\n\nFetter, C.W. 2007. Applied hydrogeology. 2nd edition. USA: Merrill \nPublishing Company. \n\n\n\nFreeze, R.A., Cherry, J.A. 1979. Groundwater. Prentice-Hall, Englewood \nCliffs, New Jersey \n\n\n\nGao, Q., Shang, Y., Hassan, M., Jin, W., Yang, P. 2018. Evaluation of a \nWeathered Rock Aquifer using ERT Method in South Guangdong, \nChina. Water. 10:1-22. https://doi.org/10.3390/w10030293 \n\n\n\nGogoi, U., 2013. Determination of aquifer parameters of the shallow \naquifers of Barak valley, Assam, India. Int. Bulletin Water Resources \nDev., 1: 1-13. \n\n\n\nHalford, K.J., Wright, W.D., Schreiber, R.P., 2006. Interpretation of \nTransmissivity estimates from single well pumping aquifer tests. \nGround Water, 44: 467- 471. DOI: 10.1111/j.1745-\n6584.2005.00151.x \n\n\n\nHarb, N., Haddad, K., Farkh, S., 2010. Calculation of transverse resistance \nto correct aquifer resistivity of groundwater saturated zones: \nimplications for estimating its hydrogeological properties, \nLebanese Science Journal, 11(1), Pp. 105\u2013115. \n\n\n\nHarvill, Bell, F.G., 1986. Groundwater Resource Development, \nButterworks, London.  \n\n\n\nIloeje, N.P. 1981. A New Geography of Nigeria. Longman Publisher Nigeria, \npp. 201. \n\n\n\nJohnson, T. 2005. A measure of well performance, well problems and \naquifer Transmissivity. WRD Technical Bulletin, 2: Pp. 1-2. \n\n\n\nKaranth, K.R. 1987. Groundwater Assessment, Development and \nManagement, Tata McGraw Hill Pub. Co. Ltd., New Delhi.  \n\n\n\nKruseman, G.P., de Ridder, N.A., 1991. Analysis and Evaluation of Pumping \nTest Data. International Institute for Land Reclamation and \nImprovement/ILRI. Wageningen, The Netherlands. \n\n\n\nLewis, M A., 1989. Water: In Earth Science Mapping For Planning, \nDevelopment AndConservation. Mccall, J and Marker, B; Graham \nand Trotman (Editors). \n\n\n\nLiving Atlas, 2020. A-10 m Resolution map of earth\u2019s; and surface from \n2020, developed by Impact Observatory. \nhttps://livingatlas.arcgis.com/landcover/ Accessed on 19th \nJanuary 2023. \n\n\n\nMandel, S., Shiftan, Z.L., 1981. Groundwater Resources \u2013 Investigation and \nDevelopment, Academic Press, Inc., New York  \n\n\n\nMohamaden, M.I., 2016. Delineating groundwater aquifer and subsurface \nstructures by using geoelectrical data: case study (Dakhla Oasis, \nEgypt). NRIAG J Astron Geophys 5(1): Pp. 247\u2013253. https \n://doi.org/10.1016/j.nrjag .2016.05.001 \n\n\n\nNigeria Geological Survey Agency (NGSA), 2006. Published by the \nAuthority of the Federal Republic of Nigeria. \n\n\n\nNwosu, L.I., Ekine, A.S., Nwankwo, C.N., 2013. Evaluation of Groundwater \nPotential from Pumping Test Analysis and Vertical Electrical \nSounding Results: Case Study of Okigwe District of Imo State \nNigeria. The Pacific Journal of Science and Technology, Volume 14. \nNumber 1,Pp.  536 \u2013 548. \nhttp://www.akamaiuniversity.us/PJST.htm \n\n\n\nObaje, N.G., 2009. Geology and Mineral Resources of Nigeria. Springer- \nVerlag, Berlin, 218p. \n\n\n\nOlatunji, S., Issa, U., Ajadi, J., 2022. Groundwater Potential Evaluation in \nParts of Southwestern Nigeria Using Dar-Zarouk Parameters. \nLAUTECH Journal of Civil and Environmental Studies Volume 8, \nIssue 1; March, 2022, Pp. 8. DOI: 10.36108/laujoces/2202.80.0150 \n\n\n\nOmer, A.M., 2018. Sustainability Criteria for Water Resource Systems: \nSustainable Development and Management. Int. J. Adv. Res. Water \nResc. Hydr. Engi., 1(1&2):Pp. 1-19. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 61-80 \n\n\n\n\n\n\n\n \nCite The Article: O.O. Falowo, Y. Akindureni, O.C. Babalola (2023). Aquifer Systems Characterization for Groundwater Management \n\n\n\n in Ile-Oluji, Southwestern Nigeria, Using MCDA Gis-Based AHP. Malaysian Journal of Geosciences, 7(2): 61-80. \n \n\n\n\nOyegoke, S.O., Adebanjo, A.S., Fayomi, O.O., Obot, O., 2020. Groundwater \nProspecting using combined Vertical Electrical Sounding and \nBorehole Performance. LAUTECH Journal of Civil and \nEnvironmental Studies Volume 4, Issue 1; March 2020. \n\n\n\nRobinson, E.S., Coruh, C., 1988. Basic Exploration Geophysics, John Wiley \nand Sons, New York. \n\n\n\nSaaty, I.T., 1980. The analytical hierarchy process, New York, McGraw-Hill \nInternational. \n\n\n\nSaaty, I.T., 1990. How to make a decision: the analytic hierarchy process, \n  \n\n\n\nSaaty, I.T., 2006. Rank from comparisons and from ratings in the \nanalytical/network processes. European Journal of Operational \nResearch, Vol. 168, Pp. 557-570. \n\n\n\nSaaty, T.L., 2008. There is no Mathematical validity for using Fuzzy \nNumber Crunching in the Analytic Hierarchy Process, Unpublished \nreport. \n\n\n\nSajeena, S., Abdul Hakkim, V.M., Kurien, E.K., 2014. Identification of \nGroundwater Prospective Zones using Geoelectrical and \nElectromagnetic Surveys. International Journal of Engineering \nInventions, 3(6), Pp. 17\u201321. \n\n\n\nSameer, S.M., Mustafa, A.S., Al-Somaydaii, J.A., 2021. Study of the \nSustainable Water Resources Management at the Upper Euphrates \nBasin, Iraq. International Journal of Design and Nature and \nEcodynamics Vol. 16, No. 2, April, 2021, Pp. 203-210. \nhttps://doi.org/10.18280/ijdne.160210 \n\n\n\nTartiyus, E.H., Mohammed, I.D., Amade, P., 2015. Impact of Population \nGrowth on Economic Growth in Nigeria (1980\u20132010). Journal of \nHumanities and Social Science (IOSR-JHSS), 20(4), Pp. 115\u2013123. \nDOI: 10.9790/0837-2045115123.  \n\n\n\nTelford, W.M., Gedart, L.P., Sheriff, R.E., 1990. Applied Geophysics. \nCambridge University Press: London, UK. 726. \n\n\n\nTing, C.S., 1993. Groundwater resources evaluation and management \nstudies for the Pingtung Plain, Taiwan. National Science Council, \nNational Pingtung Polytechnic Institute and Faculty of Earth \nSciences, Free University, Amsterdam, Pp. 87. \n\n\n\nVargas, L.G. 1990. An overview of the analytical hierarchy process and its \napplications. European Journal of Operational Research, 48: Pp. 2-\n8. \n\n\n\nYang, Ch.Ch., Chen, B.Sh., 2004. Key quality performance evaluation using \nFuzzy AHP, Journal of the Chinese Institute of Industrial Engineers, \nVol. 21, No. 6, Pp. 543-550. \n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geoscien  ces 2(1) (2018) 09-17 \n\n\n\nCite the Article: Rodeano Roslee, Felix Tongkul (2018).Engineering Geological Assessment (EGA) on Slopes Along The Penampang to Tambunan Road, Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 09-17. \n\n\n\nARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 12 November 2017  \nAccepted 12 December 2017  \nAvailable online 1 January 2018 \n\n\n\nABSTRACT\n\n\n\nThis study focused on the engineering geological investigation of slope failures along Penampang to Tambunan road, \napproximately 12th km to 101th km from Kota Kinabalu city, Sabah, Malaysia. The area is underlain by the Crocker \nFormation (Late Eocene to Early Miocene age) and the Quaternary Deposits (Recent age). These rock units show \nnumerous lineaments with complex structural styles developed during several regional Tertiary tectonic activities. \nThe tectonic complexities influenced the physical and mechanical properties of the rocks, resulting in a high degree \nof weathering and instability. The weathered materials are unstable and may experience sliding due to by high pore \npressure and intensively geomorphological processes. In this study, a total of 31 selected critical slope failures were \nstudied and classified into two main groups: rock slope and soil slope. Failures in soil slopes (including embankments) \nare 21 (67 %) whereas 10 of all failures (33 %) of rock slope. Soil slope failures normally involved large volumes of \nfailed material as compared much rock slopes, where the failures are mostly small. Of the 21 failures in soil slopes, 15 \n(71 %) are embankment failures making them 48 % of all types of failures. Physical and mechanical properties of 84 \nsoil samples indicated that the failure materials mainly consist of poorly graded to well graded materials of clayey \nloamy soils, which characterized by low to intermediate plasticity content (9 % to 28 %), containing of inactive to \nnormal clay (0.34 to 1.45), very high to medium degree of swelling (5.63 to 13.85), variable low to high water content \n(4 % to 22 %), specific gravity ranges from 2.57 to 2.80, low permeability (9.66 X 10-3 to 4.33 X 10-3 cm/s), friction \nangle (\uf066) ranges from 7.70\u02da to 29.20\u02da and cohesion (C) ranges from 3.20 KPa to 17.27 KPa. The rock properties of 10 \nrock samples indicated that the point load strength index and the uniaxial compressive strength range classified as \nmoderately week. Kinematics slope analyses indicates that the variable potential of circular, planar, wedges and \ntoppling failures modes as well as the combination of more than one mode of aforementioned failure. Rock and soil \nslopes stability analysis indicates that the factor of safety value as unsafe (0.52 to 0.98). Engineering geologic \nevaluation of the study area indicates that the slope failures took place when rock and soil materials were no longer \nable to resist the attraction of gravity due to a decrease in shear strength and increase in the shear stresses due to \ninternal and external factors. Internal factors involve some factors change in either physical or chemical properties of \nthe rock or soil such as topographic setting, climate, geologic setting and processes, groundwater condition and \nengineering characteristics. External factors involve increase of shear stress on slope, which usually involves a form \nof disturbance that is induced by man includes removal of vegetation cover, induced by vehicles loading and artificial \nchanges or natural phenomenon such as tremors. Development planning has to consider the hazard and \nenvironmental management program. This engineering geological study may play a vital role in slope stability \nassessment to ensure the public safety. \n\n\n\nKEYWORDS \n\n\n\nEngineering Geology, Kinematics Analysis, Slope Stability Analysis, Sabah & Malaysia.\n\n\n\n1. INTRODUCTION \n\n\n\nThis paper deals with the engineering geological investigation study of 31 \nselected critical slopes with the aims of analysis the physical and \nmechanical properties of soil and rock, calculate the factor of safety for \nslopes and to evaluate the main factors contributing to slope failures. The \nstudy area is located the stretch between 12th km to 101th km from Kota \nKinabalu city, Sabah to the town of Tambunan in Sabah, which connecting \nthe lowland areas of the west coast to the interior regions of Sabah, \nMalaysia. The study area is bounded by longitudes line 116o 15\u2019 to 116o 30\u2019 \nE and latitudes line 05o 42\u2019 to 05o 55\u2019 N (Figures. 1 & 2). The 89 km length \nstudy area, crosses over 90 % rugged mountainous terrain with a different \nof elevation exceeding 1000 m. Part of this highway is constructed across \nthe steep slopes of the Crocker Range, creating problems of slope and \nstability especially during periods of intense rainfall. Since it\u2019s opening in \n1980, the problem of slope stability has adversely affected the use of the \nhighway. The Public Work Department of Malaysia (JKR) authority has \nstarted a program of repairing and rehabilitation of slope failures since \n1990 to improve the highway. This work is still going on today. \n\n\n\n2. METHODOLOGY \n\n\n\nSeveral classifications can be used to describe slope failures. For this study \nin the topics, the types of slope failures were classified according to the \nproposals of a group researcher [1]. In this system, slope failures are \nclassified into two main groups: soil slope failures and rock slope failures. \nSoil slope failures were divided into slides (T1), slumps (T2), flows (T3), \ncreep (T4) and complex failures (T5) whereas rock slope failures were \ndivided into circular (B1), planar (B2) and wedge failures (B3) together \nwith toppling (B4). In this study, only failures with volume exceeding 10 \nm3 were considered, since failures involving smaller volume did not \ngenerally affect the road users. On the basis, the slope failure was divided \ninto three groups: small (10 \u2013 50 m3), Medium (50 \u2013 500 m3) and Large (> \n500 m3). For each slope failures that were studied (Figure 2), type of \nfailures, the geometry of the slope, geological background characteristics, \nweathering characteristics, ground water condition, discontinuity \ncharacteristics, physical and mechanical of the sliding materials and an \ninterpretation of the factors causing the failure based on field observations \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : https://doi.org/10.26480/mjg.01.2018.09.17\n\n\n\nENGINEERING GEOLOGICAL ASSESSMENT (EGA) ON SLOPES ALONG THE \nPENAMPANG TO TAMBUNAN ROAD, SABAH, MALAYSIA \n\n\n\nRodeano Roslee1,2*, Felix Tongkul 1,2 \n1 Natural Disaster Research Centre (NDRC), Universiti Malaysia Sabah \n2 Faculty of Science and Natural Resources, Universiti Malaysia Sabah \n*Corresponding Author Email: rodeano@ums.edu.my\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \n\n\n\nCODEN : MJGAAN \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 09-17\n\n\n\nCite the Article: Rodeano Roslee, Felix Tongkul (2018).Engineering Geological Assessment (EGA) on Slopes Along The Penampang to Tambunan Road, Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 09-17. \n\n\n\nwere recorded. Soil and rock samples from the study area were collected \nduring field mapping for detailed laboratory analysis.  \n\n\n\nThe laboratory works such as classification tests (grain size, atterberg \n\n\n\nlimit, shrinkage limit, specific gravity and water content), permeability \ntest, consolidated isotropically undrained (CIU) test, rock uniaxial \ncompressive strength and point load test were carried out in compliance \nand accordance to British Standard Code and ISRM [2-6].  \n\n\n\nFigure 1: Location of the study area [7]. \n\n\n\nFor the soil slopes stability analysis, using the \u201cSLOPE/W\u201d software was \ndone successfully to determine susceptibility of the slopes to shallow non-\ncircular slides based on the determination of factor of safety values, which \nare common in the study area [8]. The advantage of these methods is that \nin its limit equilibrium calculations, forces and moments on each slice is \nconsidered.  \n\n\n\nDiscontinuity orientation data has been collected from ten (10) selected of \n\n\n\nrock slope failure by random method. For each rock slope failures that \nwere studied, the geometry of the slope, dip direction and dip value, \npersistence, roughness, unevenness, aperture, infilling material, water \ncondition, weathering, geological background characteristics, engineering \nproperties of the sliding materials and an interpretation of the factors \ncausing the failure were recorded. Determination of discontinuities sets, \ncritical discontinuities plane, potential mode and rock slope stability \nanalysis has been performed by RockPack III program [9].  \n\n\n\nFigure 2: Location map of the slopes \n\n\n\n Philippine Sea Plate \n\n\n\nMolusca \n\n\n\nCaroline Plate \n\n\n\nEurasian Plate \n\n\n\nSunda Subduction \n\n\n\n  Study Area \n\n\n\n10\n\n\n\n\n\n\n\n\nMalaysian Journal of Geoscie n ces 2(1) (2018) 009-17\n\n\n\n\n\n\n\nCite the Article: Rodeano Roslee, Felix Tongkul (2018).Engineering Geological Assessment (EGA) on Slopes Along The Penampang to Tambunan Road, Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 09-17. \n\n\n\n3. LOCAL GEOLOGY AND ENGINEERING GEOLOGY CHARACTERISTICS\n\n\n\nThe geology of the study area is made up of sedimentary rock of the \nCrocker Formation (Late Eocene age) and Quaternary Alluvium Deposits. \nTable 1 shows the composite stratigraphic column of rock units with their \nwater bearing and engineering properties. The effect of faulting activity \ncan be observed on the lithologies of the study area. This was confirmed \nby the existence of transformed faulted material consisting of angular to \nsub angular sandstone fragments, with fine recrystallined quartz along the \njoint planes, poorly sorted sheared materials and marked by the \noccurrence of fault gouge with fragments of subphyllite and slickensided \nsurfaces.  \n\n\n\nEngineering works may involves excavating, removing of the existing \noverburden soil and weathered rocks, filling of lowland and cutting of hill \nslope. These processes exposed the rock and soil in the study area to \nweathering and erosion. The slope materials become weak and loss its \nengineering properties. Moreover, the weathered products have high \ncontent of clay which may lower the rock strength and its engineering \nproperties, approaching that of soil [10]. The cutting of hill slope, dumping \nof the stream within or along the slope and removing trees and vegetation \nfrom the hill slope reduces the stability of the slope. As a result, the hill \n\n\n\nslope becomes critical / unstable. The layered nature of the sandstone, \nsiltstone and shale may constitute possible sliding surfaces (Table 1). The \nsandstone-shale contact is easily accessible by water and such contact \nseepage may weaken the shale surface and cause slides and falls within \nthe formations. Interbedded sandstone, siltstone and shale may also \npresent problems of settlement and rebound. The magnitude, however, \ndepends on the character and extent of shearing in the shale. The strength \nof the sandstone will also depend on the amount and type of cement-\nmatrix material occupying the voids. The sandstones are compacted and \nin grain to grain contact with each other. Instead of chemical cement (vein) \nor matrix, the pores are filled by finer-grained sands to silt-sized materials \nor squeezed rock fragments. The absence of chemical cement reduces the \nstrength of the sandstone especially when it is weathered or structurally \ndisturbed. The shale units have an adequate strength under dry conditions \nbut lose this strength when wet [10]. During the rainy season, the shale \nbecomes highly saturated with water which increases the water pressure \nand reduces resistances to sliding and falling especially within the \nsandstones-shale contact. This condition, in addition to varying amounts \nof bitumen and levels of degradation, makes shale unpredictable and \nunsuitable for road construction sites. Its unstable nature can be remedied \nby proper management of soaking and draining of water from the rock or \nalong the sandstone-shale contact. \n\n\n\nTable 1: Local Stratigraphic Column and their Water Bearing and Engineering Properties \n\n\n\n4. GEOHYDROLOGIC AND HYDROGEOLOGY\n\n\n\nThe study area and its surrounding areas are controlled by heavy drainage \n\n\n\nsystem of different patterns (e.g. Trellis and Parallel) (Figure 2). The \n\n\n\nregion has a high drainage density, being the cradle and origin of major \n\n\n\nrivers in the study area. Structurally, a number of linear river segments \n\n\n\nbelong to different watershed systems indicate the existence of major \n\n\n\ntectonic fractures. The structural control of the river tributaries of the area \n\n\n\nis evidenced by the physical characteristics of sedimentary rocks; highly \n\n\n\nfractured areas and less competent shale beds. The sedimentary rocks are \n\n\n\nmore intensely dissected by fault zones than the ultrabasic rocks. \n\n\n\nGroundwater occurs and moves through interstices or secondary pore \n\n\n\nopenings in the rock formations. Such openings can be the pore spaces \n\n\n\nbetween individual sedimentary grains, open joints and fractures in hard \n\n\n\nrocks or solution and cavernous opening in brecciated layers and \n\n\n\ncataclasites. The direction of groundwater movement is generally under \n\n\n\nthe influence of gravity. The rock formations exhibit a high degree of \n\n\n\nweathering and covered by thick residual soil that extends to more than \n\n\n\n30 meters in thickness. The weathered materials are weak and caused \n\n\n\nslope failures due to high fractured porosity and high pore pressure \n\n\n\nsubjected by both shallow and deep groundwater. \n\n\n\nCalculation of the groundwater balance helps to show the amount of rain \n\n\n\nwater available for surface run-off and deep percolation. The Thornwaite\u2019s \n\n\n\nmethod is used to compute for the potential evapotranspiration \n\n\n\n(evaporation & transpiration). This method is selected because it needs \n\n\n\nonly the temperature and rainfall data. Analysis from Table 2 shows that \n\n\n\nduring the earlier (January to March) and middle (June to September) part \n\n\n\nof the year, the potential evapotranspiration is higher than rainfall. \n\n\n\nTheoretically, therefore all the precipitation that fall over the area during \n\n\n\nthis period will be lost through evapotranspiration, leaving no excess \n\n\n\nwater available for run-off and deep percolation. It is only in the months \n\n\n\nof April, May and later months of the year (October to December), that \n\n\n\nexcess rainfall becomes available for run-off and deep percolation. Result \n\n\n\nshow the high amount of evapotranspiration, reduced the water available \n\n\n\nfor run-off and deep percolation to a more 40 mm per year. One would \n\n\n\nhave to note in mind that the average surface temperature of the study \n\n\n\narea is lower than the temperature used in the computation of Table 2. \n\n\n\nTherefore, the true evapotranspiration could be significantly lower \n\n\n\nallowing more surface sun-off and deep percolation. \n\n\n\nAge Rock Formation Unit General Character \nWater-Bearing \n\n\n\nProperties \nEngineering \nProperties \n\n\n\nQuaternary Alluvium \n- \n\n\n\nUnconsolidated gravel, sand and silt \nwith minor amounts of clay deposited \nalong the rivers or streams and their \ntributaries. Includes natural levee and \nflood plain deposit. \n\n\n\nGravelly and sandy, \nportions are highly \npermeable and yield \nlarge quantities of \nwater. Important to \ngroundwater \ndevelopment. \n\n\n\nGenerally, poorly \nconsolidated. Hence not \nsuitable for heavy \nstructures and \nsubsidence under \nheavy load. \n\n\n\nLate Eocene to \nEarly Miocene \n\n\n\nCrocker \nFormation \n\n\n\nShale \n\n\n\nThis unit is composed of two types of \nshale red and grey. It is a sequence of \nalteration of shale with siltstone of \nvery fine. \n\n\n\nIt has no significant to \ngroundwater \ndevelopment due to its \nimpermeable \ncharacteristic. \n\n\n\nVery dangerous site for \nheavy structures and \nthe main causes of mass \nmovement. \n\n\n\nInterbedded \nShale-\n\n\n\nSandstone \n\n\n\nIt is a sequence of interlayering of \npermeable sandstone with \nimpermeable shale. The permeability \nof this unit is quite variable. \nGroundwater in this unit tends to be \nunder semi-confined to confined \nsystem. \n\n\n\nLittle importance to \ngroundwater provides \nsome water but not \nenough for \ngroundwater \ndevelopment. \n\n\n\nDangerous site for \nheavy structures and \nhigh potential for mass \nmovement. \n\n\n\nSandstone \n\n\n\nLight grey to cream colour, medium to \ncoarse -grained and some time pebbly. \nIt is highly folded, faulted, jointed, \nfractured occasionally cavernous, \nsurfically oxidized and exhibits \nspheriodal weathering. \n\n\n\nImportance to \ngroundwater. \n\n\n\nGood site for heavy \nstructures with careful \ninvestigation. Stable \nfrom mass movement \nand provide some \nmodification like \nclosing of continuous \nstructure. \n\n\n\n11\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 09-17\n\n\n\nCite the Article: Rodeano Roslee, Felix Tongkul (2018).Engineering Geological Assessment (EGA) on Slopes Along The Penampang to Tambunan Road, Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 09-17. \n\n\n\nTable 2: Groundwater Balance for the year 2016 \n\n\n\nMonth Jan. Feb March April May June July \nAugus\n\n\n\nt \nSept. Oct. Nov. Dec. Total \n\n\n\nTemperature 26.80 27.04 27.51 28.11 28.11 27.95 29.81 27.51 27.34 27.33 27.08 26.86 - \n\n\n\nMonthly \nTemperature \nIndex \n\n\n\n12.68 12.89 13.23 13.64 13.67 13.56 13.43 13.24 13.12 13.00 12.93 12.75 158.14 \n\n\n\nStandard \nPotential \nEvapotrans- \npiration (mm) \n\n\n\n138.0\n5 \n\n\n\n141.9\n6 \n\n\n\n141.6\n8 \n\n\n\n143.8\n9 \n\n\n\n144.0\n7 \n\n\n\n143.3\n0 \n\n\n\n143.5\n5 \n\n\n\n141.67 \n141.0\n\n\n\n6 \n140.7\n\n\n\n2 \n140.0\n\n\n\n7 \n139.2\n\n\n\n6 \n1700.2\n\n\n\n8 \n\n\n\nCorrection \nFactor  \n\n\n\n1.02 0.93 1.04 1.02 1.06 1.04 1.06 1.06 1.02 1.03 1.00 1.02 - \n\n\n\nEvapotrans- \npiration \nPotential after \ncorrection \n\n\n\n141.8\n3 \n\n\n\n131.3\n1 \n\n\n\n146.6\n4 \n\n\n\n146.7\n7 \n\n\n\n152.7\n1 \n\n\n\n148.3\n2 \n\n\n\n152.1\n6 \n\n\n\n149.46 \n143.1\n\n\n\n8 \n144.9\n\n\n\n4 \n139.3\n\n\n\n7 \n141.3\n\n\n\n5 \n1738.0\n\n\n\n4 \n\n\n\nRain (mm) \n117.6\n\n\n\n0 \n127.6\n\n\n\n0 \n109.2\n\n\n\n0 \n153.8\n\n\n\n0 \n166.6\n\n\n\n0 \n130.9\n\n\n\n0 \n119.7\n\n\n\n0 \n138.10 \n\n\n\n119.7\n0 \n\n\n\n145.3\n0 \n\n\n\n147.0\n0 \n\n\n\n153.0\n0 \n\n\n\n1628.5\n0 \n\n\n\nDifference \n-ve -24.23 -3.71 -37.44 - - -17.42 -32.46 -11.36 -23.48 - - - -150.10 \n\n\n\n+ve - - - 7.03 13.89 - - - - 0.36 7.63 11.65 - \n\n\n\nTrue \nEvapotrans- \npiration before \ncorrection \n\n\n\n117.6\n0 \n\n\n\n127.6\n0 \n\n\n\n109.2\n0 \n\n\n\n146.7\n7 \n\n\n\n152.7\n1 \n\n\n\n130.9\n0 \n\n\n\n119.7\n0 \n\n\n\n138.10 \n119.7\n\n\n\n0 \n144.9\n\n\n\n4 \n139.3\n\n\n\n7 \n141.3\n\n\n\n5 \n1587.9\n\n\n\n4 \n\n\n\nYearly soil moisture deficiency = 150.1 \nTrue yearly evapotranspiration  = 1587.94 \nWater available for seepage and run-off = 4.56 mm \n\n\n\n5. SLOPE STABILITY ANALYSIS\n\n\n\nIn this study, a total of 31 selected critical slope failures were studied and \n\n\n\nclassified into two main groups: soil slope and rock slope. Failures in soil \n\n\n\nslopes (including embankments) are 21 (67 %) whereas 10 of all failures \n\n\n\n(33 %) of rock slope. Soil slope failures normally involved large volumes \n\n\n\nof failed material as compared much rock slopes, where the failures are \n\n\n\nmostly small. Of the 21 failures in soil slopes, 15 (71 %) are embankment \n\n\n\nfailures making them 48 % of all types of failures. \n\n\n\nResults of a detailed analysis of soil slope stability are presented in Table \n\n\n\n3. Considering cut slopes, all the major lithologies are involved showing \n\n\n\nthat this type of failure is not mostly controlled by lithology. The failure \n\n\n\nvolume scale involved generally small to large in size possibly endangering \n\n\n\nroad users. In term of weathering grades, the materials that underwent \n\n\n\nfailure were in the ranges from grade IV to VI (Figures 3 to 6). Weathering \n\n\n\nis the main factor causing failure with the depth of weathering influencing \n\n\n\nthe volume of material that fails. It appears that grade IV to grade V \n\n\n\nmaterials actually failed with the overlying grade VI material sliding or \n\n\n\nslumping down together with this material during failure. Physical and \n\n\n\nmechanical properties of 84 soil samples indicated that the failure \n\n\n\nmaterials mainly consist of poorly graded to well graded materials of \n\n\n\nclayey loamy soils, which characterized by low to intermediate plasticity \n\n\n\ncontent (9 % to 28 %), containing of inactive to normal clay (0.34 to 1.45), \n\n\n\nvery high to medium degree of swelling (5.63 to 13.85), variable low to \n\n\n\nhigh water content (4 % to 22 %), specific gravity ranges from 2.57 to 2.80, \n\n\n\nlow permeability (9.66 X 10-3 to 4.33 X 10-3 cm/s), friction angle ( ) \n\n\n\nranges from 7.70\u02da to 29.20\u02da and cohesion (C) ranges from 3.20 KPa to \n\n\n\n17.27 KPa. Soil slopes stability analysis indicates that the factor of safety \n\n\n\nvalue as unsafe (0.56 to 0.98). The presence of ground water, slope angle, \n\n\n\nremoval of vegetation cover, lack of proper drainage system, artificial \n\n\n\nchanging, climatological setting, geological characteristics and material \n\n\n\ncharacteristics are additional factors contributing to the failures.\n\n\n\nTable 3: Analysis results of soil slope failures \n\n\n\nType of failure Shallow slide (T1 \u2013a) Deep slide (T1 \u2013 b) \nMultiple Slump \n\n\n\n(T2-b) \nEarth Flow (T3-a) \n\n\n\nLocation (km) KM 12 KM 35 KM 61 KM 17 KM 45 KM 26 KM 82 KM 15 KM 88 \n\n\n\nSlope SS1 SS5 SS9 SS3 SS6 SS4 SS13 SS2 SS17 \n\n\n\nGeological Formation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \n\n\n\nLithology Sediment Sediment Sediment Sediment Sediment Sediment Sediment Sediment Sediment \nWeathering grade IV to VI IV to VI IV to VI IV to VI IV to VI IV to VI IV to VI IV to VI IV to VI \n\n\n\nVolume (1) Small Large Medium Large Large Small Small Small Medium \nSand (%) 76 \u2013 78 60 \u2013 63 65 \u2013 68 45 \u2013 48 68 \u2013 70 21 \u2013 24 44 \u2013 45 55 \u2013 58 62 \u2013 63 \nSilt (%) 13 \u2013 14 10 \u2013 14 8 \u2013 12 13 \u2013 16 16 \u2013 18 54 \u2013 58 28 \u2013 33 9 \u2013 13 10 \u2013 13 \n\n\n\nClay (%) 22 \u2013 24 26 \u2013 28 22 \u2013 24 38 \u2013 40 15 \u2013 16 20 \u2013 22 20 \u2013 23 31 \u2013 33 22 \u2013 26 \nLiquid limit (%) 37 \u2013 39 27 \u2013 29  27 \u2013 30 31 \u2013 33 28 \u2013 32 31 \u2013 34 27 \u2013 31 25 \u2013 29 33 \u2013 38 \nPlastic limit (%) 21 \u2013 23 15 \u2013 16 12 \u2013 14 16 \u2013 19 17 \u2013 18 12 \u2013 14 14 \u2013 17  15 \u2013 18 16 \u2013 19 \n\n\n\nPlasticity index (%) 15 \u2013 18 12 \u2013 15 13 \u2013 16 15 \u2013 17 15 \u2013 17 19 \u2013 22 17 \u2013 19 14 \u2013 16 17 \u2013 20 \n\n\n\nLiquidity index (%) \n- 0.05 to \n\n\n\n- 0.03 \n-0.39 to \n- 0.37 \n\n\n\n0.02 to \n0.04 \n\n\n\n- 0.11 to \n- 0.09 \n\n\n\n- 1.56 to \n- 1.53 \n\n\n\n- 0.35 to \n- 0.30 \n\n\n\n0.02 to \n0.05 \n\n\n\n- 0.18 to \n- 0.14 \n\n\n\n0.09 to \n0.15 \n\n\n\nClay activity \n0.98 \u2013 \n1.00 \n\n\n\n0.40 \u2013 \n0.48  \n\n\n\n0.34 \u2013 \n0.40 \n\n\n\n0.41 \u2013 \n0.45 \n\n\n\n0.66 \u2013 \n0.75 \n\n\n\n0.98 \u2013 \n0.99 \n\n\n\n0.78 \u2013 \n0.85 \n\n\n\n0.46 \u2013 \n0.52 \n\n\n\n0.42 \u2013 \n0.47 \n\n\n\n12\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 09-17\n\n\n\nCite the Article: Rodeano Roslee, Felix Tongkul (2018).Engineering Geological Assessment (EGA) on Slopes Along The Penampang to Tambunan Road, Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 09-17. \n\n\n\nShrinkage limit (%) \n8.45 \u2013 \n8.50 \n\n\n\n5.79 \u2013 \n5.88 \n\n\n\n6.10 \u2013 \n6.35 \n\n\n\n7.28 \u2013 \n7.55 \n\n\n\n7.04 \u2013 \n7.65 \n\n\n\n8.68 \u2013 \n8.89 \n\n\n\n7.98 \u2013 \n8.25 \n\n\n\n6.34 \u2013 \n677 \n\n\n\n7.98 \u2013 \n8.33 \n\n\n\nMoisture content (%) 20 \u2013 22 10 \u2013 14 10 \u2013 12 11 \u2013 14 7 \u2013 10 5 \u2013 8 14 \u2013 19 12 \u2013 14 10 \u2013 14 \n\n\n\nSpecific gravity \n2.65 \u2013 \n2.68 \n\n\n\n2.63 \u2013 \n2.64 \n\n\n\n2.76 \u2013 \n2.78 \n\n\n\n2.60 \u2013 \n2.62 \n\n\n\n2.73 \u2013 \n2.77 \n\n\n\n2.60 \u2013 \n2.62 \n\n\n\n2.66 \u2013 \n2.67 \n\n\n\n2.68 \u2013 \n2.72 \n\n\n\n2.74 \u2013 \n2.80 \n\n\n\nPermeability (cm/s)  \n(X 10-3) \n\n\n\n8.54 7.55 9.15 6.39 8.28 3.32 9.08 7.98 7.40 \n\n\n\nCohesion, C (kN/m2) 7.20 7.31 6.78 9.50 6.27 5.13 12.29 12.54 3.20 \nFriction angle (o) 26.30 29.20 28.90 25.50 22.90 7.70 17.30 9.30 21.00 \nFactor of Safety 0.87 0.97 0.95 0.78 0.76 0.65 0.68 0.89 0.91 \n\n\n\nMain factors causing failures SA, W, V, GWL, M, C, G, OBV, DS, EC and AC \n\n\n\nTable 3: (Cont\u2019d) Analysis results of soil slope failures \n\n\n\nType of failure Debris Flow (T3 \u2013 b) Creep (T4)  Complex failure (Slide Flow) (T5 \u2013 a) \nLocation (km) KM 48 KM 75 KM 83 KM 92 KM 50 KM 76 KM 85 KM 100 \n\n\n\nSlope SS7 SS10 SS14 SS18 SS8 SS11 SS15 SS20 \n\n\n\nGeological Formation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nCrocker \n\n\n\nFormation \nLithology Sediment Sediment Sediment Sediment Sediment Sediment Sediment Sediment \n\n\n\nWeathering grade IV to VI IV to VI IV to VI IV to VI IV to VI IV to VI IV to VI IV to VI \nVolume (1) Medium Medium Large Medium Medium Medium Medium Medium \n\n\n\nSand (%)  54 \u2013 58 20 \u2013 23 44 \u2013 45 68 \u2013 70 59 \u2013 61 63 \u2013 65 36 \u2013 39 39 \u2013 42 \nSilt (%)  21 \u2013 23 52 \u2013 55 16 \u2013 19 12 \u2013 16 6 \u2013 10 5 \u2013 12 22 \u2013 26 18 \u2013 20 \n\n\n\nClay (%)  20 \u2013 22 20 \u2013 22 32 \u2013 35 18 \u2013 22  30 \u2013 33 32 \u2013 36 38 \u2013 40 40 \u2013 43 \nLiquid limit (%) 31 \u2013 33 28 \u2013 32 39 \u2013 41 28 \u2013 30 26 \u2013 30  28 \u2013 31 41 \u2013 44 41 \u2013 43 \nPlastic limit (%) 13 \u2013 16 13 \u2013 15 16 \u2013 19 16 \u2013 20 10 \u2013 14 15 \u2013 18 22 \u2013 24 23 \u2013 25 \n\n\n\nPlasticity index (%)  18 \u2013 20 15 \u2013 19 20 \u2013 23 9 \u2013 12 12 \u2013 18 12 \u2013 16 17 \u2013 19 18 \u2013 20 \n\n\n\nLiquidity index (%)  \n- 0.02 to - \n\n\n\n0.01 \n- 0.33 to -\n\n\n\n0.25 \n0.14 to \n\n\n\n0.18 \n- 0.62 to -\n\n\n\n0.58 \n- 0.88 to -\n\n\n\n0.85 \n- 0.68 to \n\n\n\n\u2013 0.60 \n- 0.84 to - \n\n\n\n0.78 \n- 0.85 to -\n\n\n\n0.83 \n\n\n\nClay activity \n0.87 \u2013 \n0.91 \n\n\n\n1.00 \u2013 \n1.11  \n\n\n\n0.53 \u2013 \n0.55 \n\n\n\n0.48 \u2013 \n0.50 \n\n\n\n0.38 \u2013 \n0.39 \n\n\n\n0.47 \u2013 \n0.50 \n\n\n\n0.43 \u2013 \n0.49 \n\n\n\n1.43 \u2013 \n1.45 \n\n\n\nShrinkage limit (%)  \n8.53 \u2013 \n9.12 \n\n\n\n9.16 \u2013 \n9.86 \n\n\n\n8.84 \u2013 \n9.98 \n\n\n\n5.63 \u2013 \n6.53 \n\n\n\n5.63 \u2013 \n6.66 \n\n\n\n7.51 \u2013 \n7.95 \n\n\n\n7.98 \u2013 \n8.65 \n\n\n\n8.45 \u2013 \n9.26 \n\n\n\nMoisture content (%)  13 \u2013 15 6 \u2013 10 22 \u2013 25 7 \u2013 12 4 \u2013 8 4 \u2013 8 9 \u2013 12 7 \u2013 11 \n\n\n\nSpecific gravity \n2.61 \u2013 \n2.63 \n\n\n\n2.61 \u2013 \n2.64 \n\n\n\n2.60 \u2013 \n2.62 \n\n\n\n2.72 \u2013 \n2.77 \n\n\n\n2.66 \u2013 \n2.68 \n\n\n\n2.57 \u2013 \n2.58 \n\n\n\n2.64 \u2013 \n2.68 \n\n\n\n2.62 \u2013 \n2.69 \n\n\n\nPermeability (cm/s)  \n(X 10-3) \n\n\n\n5.41 5.60 5.66 9.66 8.78 7.83 4.33 5.58 \n\n\n\nCohesion, C (kN/m2) 11.43 17.27 7.76 9.62 10.40 9.82 12.80 15.47 \nFriction angle (o) 11.29 23.70 27.70 21.20 24.50 29.50 21.50 22.30 \nFactor of Safety 0.85 0.56 0.88 0.89 0.98 0.78 0.58 0.63 \n\n\n\nMain factors causing failures SA, W, V, GWL, M, C, G, OBV, DS, EC and AC \n\n\n\nTable 3: (Cont\u2019d) Analysis results of soil slope failures \n\n\n\nType of failure Complex failure (Slump flow) (T5 \u2013 b) \n\n\n\n  Location (km) KM 77 KM 87 KM 96 KM 101 \n\n\n\nSlope SS12 SS16 SS19 SS21 \n\n\n\nGeological Formation \nCrocker \n\n\n\nFormation \nCrocker Formation Crocker Formation \n\n\n\nCrocker \nFormation \n\n\n\nLithology Sediment Sediment Sediment Sediment \n\n\n\nWeathering grade IV to VI IV to VI IV to VI IV to VI \n\n\n\nVolume (1) Large Large Large Large \n\n\n\nSand (%)  48 \u2013 51 46 \u2013 50 46 \u2013 47 48 \u2013 51 \n\n\n\nSilt (%)  18 \u2013 22 18 \u2013 20 10 \u2013 13 7 \u2013 11 \n\n\n\nClay (%)  26 \u2013 30 30 \u2013 33 36 \u2013 38 38 \u2013 42 \n\n\n\nLiquid limit (%) 31 \u2013 33 35 \u2013 37 46 \u2013 49 29 \u2013 32 \n\n\n\nPlastic limit (%) 14 \u2013 16 22 \u2013 25 19 \u2013 21 13 \u2013 17 \n\n\n\nPlasticity index (%)  17 \u2013 19 13 \u2013 15 27 \u2013 28 18 \u2013 21 \n\n\n\nLiquidity index (%)  - 0.26 to - 0.22 - 1.08 to - 1.05 - 0.41 to - 0.38 0.18 to 0.20 \n\n\n\nClay activity 0.62 \u2013 0.68 0.35 \u2013 0.40 0.69 \u2013 0.77 0.38 \u2013 0.47 \n\n\n\nShrinkage limit (%)  7.98 \u2013 8.12 6.10 \u2013 7.33 12.68 \u2013 13.85 8.22 \u2013 10.54 \n\n\n\nMoisture content (%)  9 \u2013 12 8 \u2013 10 6 \u2013 10 14 \u2013 17 \n\n\n\nSpecific gravity 2.60 \u2013 2.62 2.64 \u2013 2.65 2.58 \u2013 2.60 2.65 \u2013 2.68 \n\n\n\n13\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 09-17\n\n\n\nCite the Article: Rodeano Roslee, Felix Tongkul (2018).Engineering Geological Assessment (EGA) on Slopes Along The Penampang to Tambunan Road, Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 09-17. \n\n\n\nPermeability (cm/s)  \n(X 10-3) \n\n\n\n7.81 7.61 4.62 8.47 \n\n\n\nCohesion, C (kN/m2) 10.40 10.36 11.43 8.53 \n\n\n\nFriction angle (o) 24.50 18.50 11.29 20.45 \nFactor of Safety 0.87 0.95 0.79 0.92 \n\n\n\nMain factors causing failures SA, W, GWL, M, C, G, DS and AC \n\n\n\nNote: (1) Volume: small (10 \u2013 50 m3), Medium (50 \u2013 500 m3) and Large (> 500 m3) and (2) Discontinuity (D), Slope angle (SA), Weathering (W), \nVegetation (V), Groundwater level (GWL), Material characteristics (M), Climatological setting (C), Geological characteristics (G), Over burden or vibration \n(OBV), Drainage system (DS), Embankment construction (EC) and Artificial changing (AC) \n\n\n\nFigure 3: Shallow slide (T1 \u2013 a) at KM 35 (SS5) shows the failure \n\n\n\nmovement are starting to move into several discrete blocks through the \n\n\n\ndevelopment of transverse cracks  \n\n\n\nFigure 4: Embankment failure in the form of a deep slide (T1 \u2013 b) at KM \n\n\n\n45 (SS6) \n\n\n\nFigure 5: Earth flow (T3-a) showing the settling soil block suffers from \n\n\n\nfracturing, stumping or flowing considerable lateral movement along \n\n\n\nthe basal mobile zone is common, as is upheaval of the terrain down \n\n\n\nslope of the failure (Location: KM 88 (SS17)  \n\n\n\nFigure 6: Complex failure (slump flow) (T5 \u2013 b) at KM96 (SS19) shows the \n\n\n\nfailure movement are starting to move into several discrete blocks \n\n\n\nthrough the development of transverse cracks  \n\n\n\nTable 3 shows the results of a detailed analysis of rock slope failures. \nAlthough rock slope failures contributed only 33 % (10 failures) of the \ntotal failures, they involved large volume of weathered and brecciated \nrocks (Figures 7 & 8). The main factor contributing to rock slope failures \nwas the orientation and intensity of discontinuity planes. That is why rock \nslope failures occur most frequently along the highway on sedimentary \nrocks, which were highly brecciated and fractured. Generally, the failed \nmaterial underwent only moderately to completely weathering (grade III \nto V). The rock properties characterization for 10 rock samples indicated \nthat point load strength index ranges from 0.33 MPa to 0.52 MPa \n(moderately week) and uniaxial compressive strength range from 7.81 \nMPa to 12.57 MPa (moderately week). Kinematics slope analyses indicates \nthat the variable potential of circular, planar, wedges and toppling failures \nmodes as well as the combination of more than one mode of \naforementioned failure (Figure 9). Rock slopes stability analysis indicates \nthat the factor of safety value as unsafe (0.52 to 0.95). Other factors \ncontributing to rock slope failure are the presence of groundwater, \nclimatological setting, joints filling material, high degree of rock fracturing \ndue to shearing, steep of slope angle, high intensive of faulting and folding \nactivities and locating at the fault zones area. \n\n\n\n14\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 09-17\n\n\n\nCite the Article: Rodeano Roslee, Felix Tongkul (2018).Engineering Geological Assessment (EGA) on Slopes Along The Penampang to Tambunan Road, Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 09-17. \n\n\n\nTable 4: Analysis results of rock slope failures\n\n\n\nLocation \n(km) \n\n\n\nSlope \nGeological \nformations \n\n\n\nLithology \nWeathering \n\n\n\ngrade \n\n\n\nSlope face \norientation \n\n\n\n(o) \nVolume (1) \n\n\n\nMajor \nDiscontinui-\n\n\n\nties (o) \n\n\n\nIntersect \ninvolved \n\n\n\nCritical Release Potential Possible \n\n\n\nPoint load \nstrength \n\n\n\nindex,  \n\n\n\nIS (50) (MPa) \n\n\n\nUniaxial \ncompressive \n\n\n\nstrength \ncorrelation, \nUCS = 24 IS \n\n\n\n(50) (MPa) \n\n\n\nFactor \nof \n\n\n\nSafety \n\n\n\nMain \nfactors \ncausing \nfailures \n\n\n\n(2) \n\n\n\nMitigation \nmeasure \n\n\n\nKM \n21 \n\n\n\nRS1 \nCrocker \n\n\n\nFormation \nSediment III to V 110/74 Large \n\n\n\nJ1=049/44, \nJ2=164/42, \nJ3=250/34, \nJ4=305/27, \nJ5=343/51,  \nJ6=111/30  \n& J7=210/36 \n\n\n\nJ6 J6 J1, J2, J5 & J7 Plane Circular \n\n\n\n0.33 7.81 0.88 \n\n\n\nD, SA, W, \nGWL, MC, \n\n\n\nWR, CR, BF,\nD, GC & AC \n\n\n\nRB, RD, S & \nRHD \n\n\n\nJ4 J4 - Toppling - \n\n\n\nKM \n29 \n\n\n\nRS2 \nCrocker \n\n\n\nFormation \nSediment III to IV 190/60 Large \n\n\n\nJ1=055/53, \nJ2=172/47, \nJ3=090/68,  \nJ4=211/62    & \nJ5=309/27 \n\n\n\nJ3 X J4 \nJ2 X J3 \n\n\n\nJ3 J1, J2, J4 & J5 \n\n\n\nWedge - 0.42 10.11 0.74 \n\n\n\nJ2 X J4 J2 J3 & J4 \n\n\n\nKM \n38 \n\n\n\nRS3 \nCrocker \n\n\n\nFormation \nSediment III to IV 050/65 Medium  \n\n\n\nJ1=016/29, \nJ2=071/39, \nJ3=301/67, \nJ4=168/67, \nJ5=240/70,  \nJ6=138/75  \n& J7=115/37 \n\n\n\nJ2 X J6 \n\n\n\nJ2 X J4 X J7 \n\n\n\nJ6 \n\n\n\nJ4 \n\n\n\nJ3 & J5 \n\n\n\nJ2 & J6 \nWedge Circular \n\n\n\n0.42 9.88 0.92 \n\n\n\nJ5 J5 \nJ1, J3, J4, J6 & \n\n\n\nJ7 \n- Toppling \n\n\n\nKM \n41 \n\n\n\nRS4 \nCrocker \n\n\n\nFormation \nSediment III to IV 308/75 Medium \n\n\n\nJ1=154/45,  \nJ2=062/47  \n& J3=303/35  \n\n\n\nJ3 J3 J1 & J2  Plane - 0.41 9.74 0.65 \n\n\n\nKM \n54 \n\n\n\nRS5 \nCrocker \n\n\n\nFormation \nSediment III to IV 275/70 Large \n\n\n\nJ1=096/17,  \nJ2=161/17  \n& J3=270/63 \n\n\n\nJ3 J3 J1 & J2 Plane - 0.47 11.37 0.77 \n\n\n\nKM \n56 \n\n\n\nRS6 \nCrocker \n\n\n\nFormation \nSediment III to IV 125/78 Medium \n\n\n\nJ1=063/53, \nJ2=174/46, \nJ3=041/74, \nJ4=214/61, \nJ5=306/29,  \nJ6=266/54  \n& J7=163/68 \n\n\n\nJ3 X J7 \nJ1 X J3 \nJ1 X J2 \nJ2 X J3 \n\n\n\nJ3 \nJ1 \nJ2 \nJ2 \n\n\n\nJ4 & J5 \nJ1, J3 & J5 \nJ1, J4 & J7 \nJ1, J3 & J7 \n\n\n\nWedge Circular \n\n\n\n0.48 11.64 0.89 \n\n\n\nJ6 J6 J1, J2, J3 & J7 - Toppling \n\n\n\nKM \n72 \n\n\n\nRS7 \nCrocker \n\n\n\nFormation \nSediment III to IV 150/60 Large \n\n\n\nJ1=023/25, \nJ2=148/47, \nJ3=300/65    & \nJ4=224/48 \n\n\n\nJ2 X J4 J4 J1, J2 & J3 Wedge - \n\n\n\n0.47 11.27 0.52 \nJ2 J2 J1, J3 & J4 Plane - \n\n\n\nJ3 J3 J1 & J4 - Toppling \n\n\n\nKM \n74 \n\n\n\nRS8 \nCrocker \n\n\n\nFormation \nSediment III to IV 010/68 Large \n\n\n\nJ1=145/34, \nJ2=306/66, \nJ3=014/76    & \nJ4=064/71 \n\n\n\nJ2 X J4 J2 J3 & J4 Wedge - \n\n\n\n0.52 12.57 0.95 \n\n\n\nJ3 J3 J1, J2 & J4 - Plane \n\n\n\nKM \n90 \n\n\n\nRS9 \nCrocker \n\n\n\nFormation \nSediment III to V 025/78 Large \n\n\n\nJ1=171/45, \nJ2=066/68, \nJ3=301/66, \nJ4=018/76, \nJ5=241/75,  \nJ6=339/75  \n& J7=307/30 \n\n\n\nJ2 X J3 \nJ2 X J6 \n\n\n\nJ3 \nJ2 \n\n\n\nJ2 & J6 \nJ4 & J6 \n\n\n\nWedge Circular \n\n\n\n0.47 11.34 0.68 \n\n\n\nJ4 J4 \nJ1, J2 & J5 \nJ3, J4 & J6 \n\n\n\nPlane - \n\n\n\nKM \n98 \n\n\n\nRS1\n0 \n\n\n\nCrocker \nFormation \n\n\n\nSediment III to IV 055/70 Medium \n\n\n\nJ1=052/42, \nJ2=141/41, \nJ3=302/71    & \nJ4=247/73 \n\n\n\nJ1 X J3 \nJ1 X J2 \n\n\n\nJ3 \nJ2 \n\n\n\nJ1, J2 & J4 \nJ1, J3 & J4 \n\n\n\nWedge - \n0.44 9.95 0.76 \n\n\n\nJ1 J1 J2, J3 & J4 Plane - \n\n\n\n\u2022 Note (Cont\u2019d) \n\n\n\n(1) Volume: small (10 \u2013 50 m3), Medium (50 \u2013 500 m3) and Large (> 500 m3) \n(2) Discontinuity (D), Slope angle (SA), Weathering (W), Groundwater level (GWL), Material characteristics (MC), Weak rock (WR), Crushed rock (CR), \nBlocks and fragments (BF), Debris (D), Geological characteristics (GC) and artificial changing (AC) \n(3) Rock bolt (RB), Rock dowel (RD), Shotcrete (S) & Rock horizontal drainage (RHD) \n\n\n\nFigure 7: Circular failure (crushed) (B1 \u2013 b) at East KM 21 (RS1) shows \n\n\n\nthe surface weakness (revealing) within the structure, which composed of \n\n\n\ncoherent crushed rocks on the gently sloping discontinuities \n\n\n\nFigure 8: Wedge failure (blocks and fragments) (B3 \u2013 a) at KM 29 (RS2) \nindicates the form of discontinuities provides intersecting sheets to form \nyield stepped surfaces or boundary vertical joints \n\n\n\n15\n\n\n\n\n\n\n\n\nMalaysian Journal of Geoscie n ces 2(1) (2018) 09-17\n\n\n\n\n\n\n\nCite the Article: Rodeano Roslee, Felix Tongkul (2018).Engineering Geological Assessment (EGA) on Slopes Along The Penampang to Tambunan Road, Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 09-17. \n\n\n\n6. DISCUSSION \n\n\n\nFigure 9: Streoplots and view of rock slope failures in the study area \n\n\n\nThe steep topography terrain in the study area is naturally slope instability \nprone areas. Drainage systems in this steep rugged terrain are \ncharacterised by short and rapid flowing streams. These fast-moving \nbodies of water, causes surface erosion and gulling on the slopes. Surface \nerosion removes the necessary top soil to sustain vegetation cover. This \nfurther exposes the slope and weakens the strength of the slope materials. \nThe steep terrain also poses real problems for infrastructural \ndevelopments, like road construction. Due to this reason, construction of \nroads across the mountain range would definitely involve slope cutting \nand building of fill slopes.  \n\n\n\nThe geology of the Crocker Formation does not benefit slope stability. The \ninterbedded sandstone-shale and shale unit lithologies of the Crocker \nFormation weather, rapidly when exposed to the elements. This is \nespecially through along many man-made slope cuts. Weathering changes \nthe sandstone and shale into fine clayey materials. Determined indirectly \nfrom their clay activity values some of these clay minerals are suspected \nto be in-active and normal clay [11]. These two types of clay minerals \nwhen interacting with water would expand and lubricate rock joints and \nother discontinuities. The highly fractured nature of the lithology also \ncontributes to slope instability. Fractured rock masses have much lower \nshear strength compared to the original fresh rock. The orientation of the \ndiscontinuities and its relation to the geometry and strike of the slope, \nhave a direct influence on the occurrence of rock slope failures. Due to the \ninfluence of the regional tectonic forces the rock joints in the study area \nare predominantly orientated at a Northeast - Southwest and Northwest \u2013 \nSoutheast direction. It has been determined that slopes that strike \nnorthward, westward, and South \u2013 Southeast show a higher probability to \nfails. Therefore, as a matter of precaution, cutting the slopes at these strike \ndirections, if possible should be avoided.  \n\n\n\nThe climate of the study area is very much the same as the other parts of \nSabah, Malaysia. The condition of a warm and moist tropical climate \ninduces rapid weathering of the lithology forming thick weathering \n\n\n\nprofiles. High annual rainfall is accumulated during periods of rainfall and \ndrizzling which occur daily in this high mountainous region. The high \namount of rainfall helps to sustain the moist and wet nature of the slope \nthroughout most of the day. During the storm\u2019s period of high rainfall \nintensity, the rate of water infiltration to the soil would not be sufficient, \nthe excess water would accumulate to form surface run off. Calculation on \nthe groundwater balance shows that the area experiences high \nevapotranspiration, leaving only a small amount of water available for run \noff during most periods of the year [12]. During intense storms, the \namount of run off and deep percolation greatly exceeds the \nevapotranspiration. Excessive surface run off causes rapid surface erosion, \nwhich blocks the limited existing man-made drainage. As the result of \nblocked and insufficient drainage the excess water overflows the drains \nand on to the road surface. The excess water would also accumulate on \ncertain parts of the road and fill slope. At several locations where the huge \ndrainage pipes are buried underneath the road, subsidence is seen to \noccur. Where the outlet of the drainage pipe (just below road level) directs \nthe water down slope, erosion and undercutting of the fill slope is seen to \noccur. This shows a weakness in the drainage design. The drainage pipes \nshould be extended to the nearest natural drainage.  \n\n\n\nRemoval of vegetation cover on the slope will seriously reduce the slope \nstability. The natural vegetation on the slope cuts have almost all been \nremoved. It has been with limited success replaced by secondary trees and \ngrasses. The lost of the original anchoring buttress roots is irreplaceable. \nThe younger plants have found it hard to grow on the thin layer of soil on \nthe surface of slope cuts. Removal of vegetation also disrupts the \nhydrological cycle, by allowing more infiltration and reducing the removal \nof groundwater by transpiration. More infiltration and less transpiration \nwould cause the soil materials to be more saturated with water. This \nweakens the strength of the slope directly. Another important \ncontribution to slope failure is the anthropological factor. Man is solely \nresponsible in the faulty design and construction of unstable slope cuts. In \nthe study area man\u2019s road construction is responsible for much of the \n\n\n\nfailure of fill slopes. Due to the rugged topography sections of the road were built across natural drainage valleys. Without proper drainage, the \n\n\n\n16\n\n\n\n\n\n\n\n\nMalaysian Journ al of Geosciences 2(1) (2018) 09-17\n\n\n\nCite the Article: Rodeano Roslee, Felix Tongkul (2018).Engineering Geological Assessment (EGA) on Slopes Along The Penampang to Tambunan Road, Sabah, Malaysia. \nMalaysian Journal of Geosciences, 2(1) : 09-17. \n\n\n\nfill slope blocks the path of water flow. The retention of water at these \nslopes, caused subsidence and tension cracks to appear on the road \nsurface [11]. If left unchecked, total failure would occur at these sections \nof road. Lack of maintenance is another real problem; which slopes are left \nwithout proper care until landslides have occurred. Other contributing \nanthropological factor includes the heavy traffic flow, illegal deforestation \nof slopes and irresponsible slope land development. \n\n\n\nFactor of safety (FOS) provides away for an engineering assessment of \nslope stability. Slope stability analyses were conducted for various \nconditions such as the existence of tension cracks and variation of shear \nstrength parameters in order to study the dominant factors causing the \nslope failure. Rock and soil slopes stability analysis indicates that the FOS \nvalue as unsafe (0.52 to 0.98). The location of slip circles also tallies well \nwith the slope failures as observed in the field. This confirms that the \nmobilised strength during the slope failure is very close to the subsoil peak \nstrength parameter interpreted from the laboratory strength test results. \nAny rise in groundwater profile would certainly further reduce the FOS. In \ndealing with risk of slope failure, level of awareness and mitigation \nmeasures must be increased when there is obvious climatic change \nespecially on rainy season. \n\n\n\n7. CONCLUSIONS \n\n\n\nIn light of available information, the following conclusions may be drawn \nfrom the present study: \n\n\n\n1. A total of 31 selected critical slope failures were studied. Failures in \nsoil slopes (including embankments) are 21 (67 %) whereas 10 of all \nfailures (33 %) of rock slope. Soil slope failures normally involved \nlarge volumes of failed material as compared much rock slopes, \nwhere the failures are mostly small. Of the 21 failures in soil slopes, \n15 (71 %) are embankment failures making them 48 % of all types of \nfailures. \n\n\n\n2. Physical and mechanical properties of 84 soil samples indicated that \nthe failure materials mainly consist of poorly graded to well graded \nmaterials of clayey loamy soils, which characterized by low to \nintermediate plasticity content (9 % to 28 %), containing of inactive \nto normal clay (0.34 to 1.45), very high to medium degree of swelling \n(5.63 to 13.85), variable low to high water content (4 % to 22 %), \nspecific gravity ranges from 2.57 to 2.80, low permeability (9.66 X 10-\n\n\n\n3 to 4.33 X 10-3 cm/s), friction angle (\uf066) ranges from 7.70\u02da to 29.20\u02da \nand cohesion (C) ranges from 3.20 KPa to 17.27 KPa. \n\n\n\n3. The rock properties of 10 rock samples indicated that the point load \nstrength index and the uniaxial compressive strength range classified \nas moderately week. Kinematics slope analyses indicates that the \nvariable potential of circular, planar, wedges and toppling failures \nmodes as well as the combination of more than one mode of \naforementioned failure. \n\n\n\n4. Rock and soil slopes stability analysis indicates that the factor of \nsafety value as unsafe (0.52 to 0.98). \n\n\n\n5. Engineering geologic evaluation of the study area indicates that the \nslope failures took place when rock and soil materials were no longer \nable to resist the attraction of gravity due to a decrease in shear \n\n\n\nstrength and increase in the shear stresses due to internal and \nexternal factors. Internal factors involve some factors change in \neither physical or chemical properties of the rock or soil such as \ntopographic setting, climate, geologic setting and processes, \ngroundwater condition and engineering characteristics. External \nfactors involve increase of shear stress on slope, which usually \ninvolves a form of disturbance that is induced by man includes \nremoval of vegetation cover, induced by vehicles loading and \nartificial changes. \n\n\n\nREFERENCES \n\n\n\n[1] Komoo, I. 1985. Pengelasan Kegagalan Cerun di Malaysia. Jurnal Ilmu \nAlam, 14-15. \n\n\n\n[2] British Standard BS 5930. 1981. Site Investigation. London: British \nStandard Institution. \n\n\n\n[3] British Standard BS 1377. 1990. Methods of Test for Soils for Civil \nEngineering Purposes. London: British Standard Institution. \n\n\n\n[4] ISRM. 1979a. Suggested methods for determining water content, \nporosity, density, absorption and related properties, and swelling and \nslake-durability index properties. ISRM Commission on Standardization of \nLaboratory and Field Tests. International Journal of Rock Mechanics and \nMining Sciences, 16, 141-156. \n\n\n\n[5] ISRM. 1979b. Suggested methods for determining the uniaxial \ncompressive strength and deformability of rock materials. ISRM \nCommission on Standardization of Laboratory and Field Tests. \nInternational Journal of Rock Mechanics and Mining Sciences, 16, 135-140. \n\n\n\n[6] ISRM. 1985. Suggested methods for determining point load strength. \nISRM Commission on Standardization of Laboratory and Field Tests. \nInternational Journal of Rock Mechanics and Mining Sciences, 22 (2), 51-\n60. \n\n\n\n[7] Puspito, N.T. 2004. Geophysical Hazards in Indonesia. International \nSeminar on the Active Geosphere. Institut Teknologi Bandung. Indonesia. \n\n\n\n[8] Geoslope International Ltd. 2002. Slope/W User\u2019s guide for slope \nstability analysis. Version 5., Calgary, Alta, Canada. \n\n\n\n[9] Watts, C.F. 2003. Rockpack III for Windows. ROCK Slope Stability \nAnalysis Package. User\u2019s Manual. Radford Computerized University, \nRadford, Virginia  \n\n\n\n[10] Roslee, R., Tahir, S., Omang, S.A.K.S. 2006. Engineering Geology of the \nKota Kinabalu Area, Sabah, Malaysia. Bulletin of the Geological Society of \nMalaysia, 52, 17-25. \n\n\n\n[11] Edward Voo, L.Z. 1999. Slope Stability Analysis Along Kota Kinabalu \n\u2013 Tambunan Road, Sabah. M. Sc. Thesis, University Malaysia Sabah \n(Unpublished). \n\n\n\n[12] Faisal, M.M., Tahir, S., Zan, E.V.L. 1998. Preliminary report on slope \nstability of the Kota Kinabalu Tambunan Road, Sabah. Borneo Science, 4, \n11-26. \n\n\n\n17\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 101-108 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2023.101.108 \n\n\n\n \nCite The Article: Abdullahi, Aliyu Itari, Iliyasu, Abdullahi Yerima, Umar, Nuhu Degree, Abdullahi, Saidu (2023). Geoelectrical  \n\n\n\nAssessment of Groundwater Potential of Keana Area Northcentral Nigeria. Malaysian Journal of Geosciences, 7(2): 101-108. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2023.101.108 \n\n\n\n\n\n\n\n\n\n\n\nGEOELECTRICAL ASSESSMENT OF GROUNDWATER POTENTIAL OF KEANA AREA \nNORTHCENTRAL NIGERIA \n\n\n\nAbdullahi, Aliyu Itaria, Iliyasu, Abdullahi Yerimab, Umar, Nuhu Degreec, Abdullahi, Saidud* \n\n\n\na Department of Geology, University of Nigeria, Nsukka, Nigeria. \nb Department of Geology, University of Maiduguri, Nigeria \nc Department of Geology, Federal University of Lafia, Nigeria \nd Department of Geology, Federal University Zamfara, Nigeria \n*Corresponding Author Email: saiduabdullahi@fugusau.edu.ng  \n\n\n\nThis is an open access journal distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 09 June 2023 \nRevised 18 July 2023 \nAccepted 28 August 2023 \nAvailable online 01 September 2023 \n\n\n\n This work was undertaken to unravel the hydro geophysical characteristics of Keana metropolis Nasarawa \nState, northcentral Nigeria. This study aims to assess and determine the sustainability of groundwater \nresources in Keana Town by using geoelectric techniques to collect data and analyze subsurface \ncharacteristics that can assist identify suitable sites for groundwater extraction. Once accomplished, it will \ngive useful information for groundwater resource management and contribute to Keana's long-term growth. \nFifteen (15) Vertical Electrical Sounding (VES) data with a maximum electrode spacing of (AB/2) of 200 \nmeters were acquired using the Schlumberger electrode configuration.  The VES data were interpreted using \nthe conventional partial curve matching technique to obtain initial model parameters, which were used as \ninput for computer iterative modelling using the IPI2WINTM software. The study revealed three (3) to six (6) \ngeo-electric layers with underlying indurated sandstone and shale beds in some areas. In the study area, the \napparent resistivity of the aquifer ranges from 32.4 to 407 m, with a depth ranging from 30 to 120m. \nInformation extracted from iso-resistivity models and geoelectric cross sections revealed sandy strata with \nan exception around the northern portion i.e., around Federal Government Girls College Keana (FGGC) where \na thick layer of shale is envisaged, extending to over 150 m depth with an average apparent resistivity value \nof 35\u03a9 m. Thus, making the section fair to poor groundwater potential. However, this research has aided in \ndelineating the groundwater potential of the area into three distinct zones. \n\n\n\nKEYWORDS \n\n\n\nAquifer, Geo-electric, Groundwater, Keana, Resistivity \n\n\n\n \n1. INTRODUCTION \n\n\n\nThe availability of safe and potable water in an environment is a veritable \nindex of a tremendous role in the development and growth of a \ncommunity. Over the years Keana as a municipal has witnessed an \nincrease in the population of various groups of people. In most cases, the \ninhabitants of the area live on subsistence farming and rely on perennial \nstreams to provide them with water for their domestic needs. Borehole \nprojects have been undertaken by private organizations, communities, \nand individuals to have feasible portable water. Several boreholes and \nwells have failed due to a lack of reasonable quantity of underground \nwater in some areas. This has posed a serious challenge to some residents \nof the Keana community about these unproductive boreholes in some \nareas. The citing of several of these projects was inaccurate; some of these \nproject\u2019s function seasonally, while others have been abandoned. Due to \nthe lack of detailed geophysical surveys in the Keana area, that could have \nidentified aquifers and groundwater potential zones. Additionally, there is \nlittle understanding of the geology of the study area. \n\n\n\nIt is known that certain rock properties vary greatly with water content, \nwhich is why geophysical methods are used to determine groundwater \naquifers. The pores in soils or eroded/fragmented rocks, (water bearing \nrock), are where groundwater can be found, therefore groundwater is \ncrucial to human survival since it is used extensively in agriculture, \nsanitization, residential, and industrial processes (Umar et al., 2019). \n\n\n\nThere is a specific resistivity range in these rock formations and \nsediments, a given medium's electrical resistivity is influenced by \nproperties including particle size, water content, and porosity. Rock \nresistance is controlled by porosity, which typically reduces as resistivity \nrises, and vice versa (Uchenna, 2013). Several authors have therefore \ndelineated aquifers and estimated aquifer hydraulic parameters using \nsurface geophysical methods in different parts of the world (Ekwe et al., \n2012; Onyekeru, 2010).  \n\n\n\nHence, the unique application of geoelectric investigation to assess the \ngroundwater potential in Keana Town also lies in the identification and \ninterpretation of geoelectric parameters to provide valuable insights into \nthe underground aquifers, their characteristics and their potential for \nsupplying adequate and safe drinking water to the local community. No or \nlittle research work has been carried out on the groundwater prospect of \nthe study area, but few studies have been done on the quality status of \ngroundwater in the area (Chukwu, 2008; Amadi et al., 1989). Therefore, \nKeana town, like many other communities, relies extensively on \ngroundwater as a key source of drinking water as well as for a variety of \nagricultural and industrial applications. Assessing the groundwater \npotential in this area is critical for sustaining the water supply and \nsatisfying local population needs. The geoelectric examination technique \nused in the study provide useful insights into subsurface properties such \nas aquifer presence, depth, and yield or quantity of groundwater reserves.  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 101-108 \n\n\n\n\n\n\n\n \nCite The Article: Abdullahi, Aliyu Itari, Iliyasu, Abdullahi Yerima, Umar, Nuhu Degree, Abdullahi, Saidu (2023). Geoelectrical  \n\n\n\nAssessment of Groundwater Potential of Keana Area Northcentral Nigeria. Malaysian Journal of Geosciences, 7(2): 101-108. \n\n\n\n\n\n\n\nThis data assists in the optimal planning and management of water \n\n\n\nresources, ensuring their long-term use. More importantly, the discoveries \n\n\n\nof this research can improve broader scientific knowledge and \n\n\n\nunderstanding of groundwater exploration techniques in similar \n\n\n\ngeological settings. It can also serve as an overview for researchers and \n\n\n\nprofessionals engaged in groundwater evaluation and development \n\n\n\nprojects not only in Keana Town but also in other regions facing similar \n\n\n\ndifficulties. The findings from the study can help local governments and \n\n\n\norganizations in charge of water resource management make decisions. \n\n\n\nThe investigation's findings can help lead to the development of \n\n\n\nappropriate approaches and regulations for the protection, conservation, \n\n\n\nand equitable distribution of groundwater resources.  \n\n\n\nHence, using geoelectric investigations to assess Keana Town's \n\n\n\ngroundwater potential has an important significance for ensuring \n\n\n\nsustainable water supplies, supporting scientific knowledge, facilitating \n\n\n\neffective resource management, and guiding decision-making processes \n\n\n\nfor the benefit of the local community and the broader region. It has \n\n\n\ntherefore become necessary to study the groundwater potentials of the \n\n\n\narea for proper planning and execution of water projects. This paper \n\n\n\nattempts to highlight some of the hydrogeological parameters that could \n\n\n\nbe useful in this direction. The results obtained would also add to the \n\n\n\nscanty hydrogeological information in the study area.  \n\n\n\n1.1   Regional Geology \n\n\n\nOne of the most notable geologic features in West Africa is the Nigerian \n\n\n\nBenue Trough. It stretches over an area of 800 km, with a length that \n\n\n\ntrends NNE-SSW from the Niger Delta to the Lake Chad basin's southwest, \n\n\n\nand a width that varies from 130 to 250 km (Obaje, 2009). Due to the large \n\n\n\nregional extent, studies in the Trough are often divided geographically \n\n\n\n(though arbitrarily) into upper, middle and lower regions. In the Middle \n\n\n\nBenue Trough, six Upper Cretaceous lithogenic Formations (Asu River \n\n\n\nGroup, Keana Formation, Awe Formation, Ezeaku Formation, Awgu \n\n\n\nFormation and Lafia Formation) comprise the stratigraphic succession \n\n\n\nafter Obaje, 2009 (Figure 1). The Asu River Group consists of Albian Arufu, \n\n\n\nUomba and Gboko Formations (Offodile, 1976; Nwajide, 1990). These are \n\n\n\noverlain by the Cenomanian-Turonian Keana and Awe Formations and \n\n\n\nfollowed by the Ezeaku Formation which shares a common boundary with \n\n\n\nthe Konshisha River Group and the Wadata Limestone in the Makurdi area. \n\n\n\nThe Late Turonian-Early Santonian coal-bearing Awgu Formation lies \n\n\n\nconformably on the Ezeaku Formation. The Middle Benue Trough's \n\n\n\nsedimentation was terminated by the Campano-Maastrichtian Lafia \n\n\n\nFormation, and in the Tertiary, widespread volcanic activity took over \n\n\n\n(Obaje, 2009). \n\n\n\n\n\n\n\nFigure 1: Stratigraphic succession in the Middle Benue Trough (Modified \nafter Obaje, 2009) \n\n\n\n1.2   Local Geology \n\n\n\nA good understanding of the geology of the study areas is necessary for a \n\n\n\nthorough assessment of the characteristics of the sub-surface rocks and \n\n\n\nformation fluid. Available information indicates that the Keana area falls \n\n\n\nwithin the Middle Benue Trough, underlain by the following geological \n\n\n\nsequence; Asu River Group, Ezeaku, Keana, Awe and Awgu Formations and \n\n\n\nfinally the Lafia Sandstone. The sedimentary Formations listed above are \n\n\n\nunderlain by the Basement complex of Precambrian age. The Keana \n\n\n\nFormation overlies the Awe Formation, the contact between the two being \n\n\n\nvariously described as gradational and unconformably (Offodile, 1976; \n\n\n\n1984; Reyment and Offodile, 1976). Thickly bedded, cross-bedded, fine to \n\n\n\nextremely coarse-grained, occasionally conglomeratic, gritty arkosic \n\n\n\nsandstone and bands of shale with an inferred fluvial or deltaic origin \n\n\n\nmake up the majority of the Keana Formation and Offodile and Reyment \n\n\n\ndescribed the Keana Formation as in some places lying below beds \n\n\n\nreferred to as the Ezeaku Formation and elsewhere interfingering with \n\n\n\nthem (Keana et al., 1976; Murat, 1972; Offodile, 1976).  \n\n\n\nAlthough not directly dated, the Keana Formation has generally been \n\n\n\nregarded as late Albian to Cenomanian and represents the southern part \n\n\n\nof a fluvial-deltaic system discharging into the receding sea. Its literal \n\n\n\nequivalent to the north is the \u201cMuri sandstone\u201d (Cratchley and Jones, 1965; \n\n\n\nBenkhelil et al., 1989). To the South, the Keana Formation passes laterally \n\n\n\ninto Makurdi Formation (Nwajide, 1985; Benkhelil et al., 1989). Keana \n\n\n\nFormation is a good aquifer but it's limited. The sandstone near the core \n\n\n\nof the Keana anticline is hard and less permeable than the one in the \n\n\n\nsynclinal area. However, Keana together with Ezeaku Formations form a \n\n\n\nvery thick productive aquifer when encountered in a borehole (Figure 2). \n\n\n\n\n\n\n\nFigure 2: Geological Map of the Study Area \n\n\n\n2.   STUDY LOCATION \n\n\n\nThe study area is located in Keana local Government Areas in the south-\n\n\n\neastern part of Nasarawa State. The study area is approximately 12.4 km2 \n\n\n\nin size, bounded by latitudes 8007'53.00\" N to 8009'56\" N and longitudes \n\n\n\n08047'2.30\" E to 08048'47.10\" E. The area is accessible by the Lafia-Obi \n\n\n\nRoad down to local communities of Benue State and to other localities \n\n\n\nwithin a minor road and lots of footpaths. The research area shares the \n\n\n\nsame two (2) main and distinct seasons as northcentral Nigeria, which are \n\n\n\nthe wet season, which typically lasts from March to October, and the dry \n\n\n\nseason, which commonly lasts from November to February. Vegetation of \n\n\n\nthe areas is of the guinea savannah type, with dense (gallery) forests \n\n\n\nfringing some of the rivers (Figure 3). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 101-108 \n\n\n\n\n\n\n\n \nCite The Article: Abdullahi, Aliyu Itari, Iliyasu, Abdullahi Yerima, Umar, Nuhu Degree, Abdullahi, Saidu (2023). Geoelectrical  \n\n\n\nAssessment of Groundwater Potential of Keana Area Northcentral Nigeria. Malaysian Journal of Geosciences, 7(2): 101-108. \n\n\n\n\n\n\n\n\n\n\n\nFigure 3: Location and Drainage map showing VES points of the Study Area \n\n\n\n3.   MATERIAL AND METHODS \n\n\n\nThe geophysical method adopted for delineating the depth of aquiferous \nzones of the study areas is the electrical resistivity technique of Vertical \nElectrical Sounding (VES). A total of fifteen (15) VES were carried out in \nthe study areas. The Schlumberger configuration was adopted with a \ncurrent electrode spread (AB/2) of 200m while the potential electrode \nseparation (MN/2) was maintained between 0.5 and 20m (Figure 4). The \nresistance values obtained at each measurement are multiplied by a \ngeometric factor appropriate to the electrode spacing. An interpretation of \nthe curve using appropriate software gives an  estimated  thickness  based \n\n\n\n on the resistivity values of the subsurface strata encountered. The VES \ncurves were quantitatively interpreted by partial curve matching and \ncomputer iteration techniques based on linear filter theory using \nIPI2winTM computer software. Although various geophysical techniques \nare commonly employed for groundwater investigation, electrical \nresistivity is the most unique for its ability to detect an increase in the \nconductivity of an aquifer that results from increases in porewater (Loke, \n1999). This method can also be used to determine the nature, geometry \nand thickness of geological formations (Telford et al., 1977; Oteri, 1977). \n\n\n\n\n\n\n\nFigure 4: Principle of resistivity measurement with a four-electrode array (Kn\u00f6del et al., 2007) \n\n\n\nFurthermore, several indices were utilized to estimate the groundwater \npotential in Keana town. These indices aided in the analysis of geoelectric \ndata and the drawing of conclusions concerning groundwater supply. The \nfollowing indices were evaluated in the study; Resistivity values, the \nresistivity of subsurface materials provides information about their water-\nbearing capacity. It can also be used to calculate the depth and thickness \nof subsurface layers. Differences in apparent resistivity can aid in the \nidentification of potentially water-bearing strata. Lower resistivity \nreadings frequently indicate the presence of potential aquifers, while \nelevated readings may indicate marginalized groundwater conditions. \nLithology, the geological composition of the area has a considerable impact \non groundwater potential. Different rock types and formations can have \nvaried water-holding capacities, permeability, and porosity, influencing \ngroundwater availability. Geoelectric sections and sounding curves, these \nprovide vital information about the subsurface layers' characteristics by \nvisualizing resistivity data obtained from geophysical surveys. These \nillustrations aid in the interpretation of probable groundwater-bearing \nformations. The synthesis and review of these indices aid in analyzing the \n\n\n\ngroundwater potential in Keana town, as well as providing vital \ninformation for water resource management and planning. \n\n\n\n4.   RESULTS AND DISCUSSION \n\n\n\n4.1   Geoelectrical Characteristics \n\n\n\nThe results of the data obtained from the field are characterized and \npresented in form of tables, sounding curves, geo-electric sections and \ncontour maps. Thus, subjecting this data to qualitative and quantitative \nanalysis has enabled the classification of the VES data into curve types. \nThis is because the shape of a VES curve depends on the number of layers \nin the subsurface, the thickness of each layer, and the ratio of the resistivity \nof the layers (Schwarz, 1988). The classification ranged from simple three \nelectrical layers to six-layered curves arising from the layer resistivity \ncombinations. The field curves obtained includes; H, A, Q, HK and HQ types, \nwith the H-type being the dominant (see Table 1). The typical VES curve \ntypes are shown in Figure 5. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 101-108 \n\n\n\n\n\n\n\n \nCite The Article: Abdullahi, Aliyu Itari, Iliyasu, Abdullahi Yerima, Umar, Nuhu Degree, Abdullahi, Saidu (2023). Geoelectrical  \n\n\n\nAssessment of Groundwater Potential of Keana Area Northcentral Nigeria. Malaysian Journal of Geosciences, 7(2): 101-108. \n\n\n\n\n\n\n\nTable 1: Summary Modelling of Vertical Electrical Sounding Results \n\n\n\nVES Points Layers Number Resistivity (Ohm) Thickness (m) Depth (m) Curve Types Lithology \n\n\n\n \n1 \n \n\n\n\n1 927 2.6 2.6  \nQ \n \n\n\n\nTop Soil \n\n\n\n2 32 58.7 61.3 Shale \n\n\n\n3 32.4 Infinity Infinity Sandy Shale \n\n\n\n2 \n\n\n\n1 274 1.09 1.09 \n\n\n\nQ \n\n\n\nTop Soil \n\n\n\n2 20.1 17.6 18.6 Shale \n\n\n\n3 38.3 Infinity Infinity Sandy Shale \n\n\n\n3 \n\n\n\n1 1063 2.893 2.893 \n\n\n\nQ \n \n\n\n\nTop Soil \n\n\n\n2 38.36 3.098 5.991 Lateritic clay \n\n\n\n3 427.8 5.188 11.18 Sandstone \n\n\n\n4 60.05 Infinity Infinity Sands (Aquifer) \n\n\n\n4 \n \n\n\n\n1 297 9.26 9.26 \n\n\n\nH \n\n\n\nTop Soil \n\n\n\n2 132 38 47.3 Lateritic Soils \n\n\n\n3 4071 6.23 53.5 Sandstone \n\n\n\n4 64336 26.5 80 Sandstone \n\n\n\n5 4077 Infinity Infinity Sands (Aquifer) \n\n\n\n5 \n \n\n\n\n1 132 9.58 9.58 \n\n\n\nHK \n \n\n\n\nTop Soil \n\n\n\n2 210 12 21.6 Lateritic Soil \n\n\n\n3 21.4 28.9 50.6 Sands \n\n\n\n4 1621 49.475 100 Sands (Aquifer) \n\n\n\n5 8475 Infinity Infinity Sandstone \n\n\n\n6 \n \n\n\n\n1 84.5 1.04 1.04 \n\n\n\nH \n\n\n\nTop Soil \n\n\n\n2 44.2 17.1 18.1 Lateritic Clay \n\n\n\n3 164 11.1 29.2 Sands \n\n\n\n4 812 40.8 70 Sandstone \n\n\n\n5 145 Infinity Infinity Sands (Aquifer) \n\n\n\n7 \n\n\n\n1 506 2.12 2.12 \n\n\n\nQ \n\n\n\nTop Soil \n\n\n\n2 44.8 7.14 9.27 Lateritic Soil \n\n\n\n3 300 9.52 18.8 Sands \n\n\n\n4 13.2 23.9 42.7 Sandy clay \n\n\n\n5 564 57.3 100 Sands (Aquifer) \n\n\n\n6 5329 Infinity Infinity Sandstone \n\n\n\n8 \n\n\n\n1 120 6.2 6.2 \n\n\n\nH \n\n\n\nTop Soil \n\n\n\n2 802 3.49 9.69 Lateritic Soil \n\n\n\n3 88.9 10.8 20.5 Sandy Clay \n\n\n\n4 476 26.4 46.9 Sandstone \n\n\n\n5 72.3 90.4 137 Sands (Aquifer) \n\n\n\n6 985 Infinity Infinity Sandstone \n\n\n\n9 \n\n\n\n1 51.8 2.13 2.13 \n\n\n\nH \n\n\n\nTop Soil \n\n\n\n2 74.2 35.7 37.8 Lateritic clay \n\n\n\n3 31762 33.8 71.7 Sandstone \n\n\n\n4 600 Infinity Infinity Sands (Aquifer) \n\n\n\n10 \n\n\n\n1 22.5 3.19 3.19 \n\n\n\nH \n\n\n\nTop Soil \n\n\n\n2 208 10.5 13.6 Lateritic Soil \n\n\n\n3 431 47.6 61.2 Sandstone \n\n\n\n4 53 Infinity Infinity Sands (Aquifer) \n\n\n\n11 \n\n\n\n1 2428 1.59 1.59 \n\n\n\nH \n\n\n\nTop Soil \n\n\n\n2 24.75 5.791 7.381 Lateritic Soils \n\n\n\n3 221 10.38 17.76 Sandstone \n\n\n\n4 158.8 33.57 51.33 Sands (Aquifer) \n\n\n\n5 183.8 Infinity Infinity Sands (Aquifer) \n\n\n\n12 \n \n\n\n\n1 70.1 3.42 3.42 \n\n\n\nA \n\n\n\nTop Soil \n\n\n\n2 36.1 5.09 8.51 Lateritic clay \n\n\n\n3 1952 9.44 17.9 Sandstone \n\n\n\n4 131 34.5 52.4 Sands (Aquifer) \n\n\n\n5 1302 Infinity Infinity Sandstone \n\n\n\n13 \n\n\n\n1 93.2 4.39 4.39 \n\n\n\nH \n \n \n\n\n\nTop Soil \n\n\n\n2 78 7.21 11.6 Lateritic clay \n\n\n\n3 46.6 15 26.6 Sandy clay \n\n\n\n4 332 53.3 79.9 Sands (Aquifer) \n\n\n\n5 5768 Infinity Infinity Sandstone \n\n\n\n14 \n\n\n\n1 321 1.48 1.48 \nH \n \n \n\n\n\nTop Soil \n\n\n\n2 137 14.4 15.9 Lateritic clay \n\n\n\n3 485 45.6 61.5 Sands (Aquifer) \n\n\n\n4 12.1 Infinity Infinity Shale \n\n\n\n15 \n\n\n\n1 366 2.12 2.12 \n\n\n\nQH \n \n\n\n\nTop Soil \n\n\n\n2 17.8 14.6 16.8 Lateritic clay \n\n\n\n3 153 24.4 41.1 Sands (Aquifer) \n\n\n\n4 2.16 Infinity Infinity Shale \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 101-108 \n\n\n\n\n\n\n\n \nCite The Article: Abdullahi, Aliyu Itari, Iliyasu, Abdullahi Yerima, Umar, Nuhu Degree, Abdullahi, Saidu (2023). Geoelectrical  \n\n\n\nAssessment of Groundwater Potential of Keana Area Northcentral Nigeria. Malaysian Journal of Geosciences, 7(2): 101-108. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 5: A typical VES curve type from the Study area; (a&b) H - Curve Type (VES 6&9)  Main market (c&d) A - Curve Type (VES 14&12) Osana\u2019s \nPalace \n\n\n\n4.2   Aquifer Resistivity and Depth  \n\n\n\nAquifer apparent resistivity and depth across the study area have been \ndetermined from VES data and are presented in the form of contour maps \n(Figure 6). The minimum aquifer apparent resistivity is around 30 \u03a9m \nnear FGGC while the maximum apparent resistivity is about 4077 \u03a9m near \nthe main market and taxi park among others. The mean aquifer apparent \nresistivity in the study area is approximately 569.9 \u03a9m (Figure 6a). Depth \n\n\n\nto aquifers has been deduced from sounding results, indicating that the \nwater-bearing zones are shallower in areas around the southern part (i.e., \nOsana\u2019s palace, GGSS etc.) with a depth of 30 m and much deeper in areas \naround central apart (i.e., Keana main market and motor park etc.) with a \ndepth varying from 75 to 120 m.  Whereas, areas around FGGC Keana have \na depth greater than 120m which is exceptionally deep compared to other \nadjoining areas (Figure 6b). \n\n\n\n\n\n\n\nFigure 6: (a) A plot of Aquifer Apparent Resistivity in \u03a9m (b) A plot of Depth to aquifer layers of the Study area in meters. \n\n\n\n4.3   Iso-Resistivity Model  \n\n\n\nIso-Resistivity Model across the Study Area was calculated and presented \nin table 2. An iso-resistivity map is a qualitative interpretative tool which \nshows possible variations in resistivity at the given electrode spacing and \ndoes not give the true resistivities of a definite geo-electric layer (Uchenna \net al., 2013). Contour maps of the iso-resistivity values at specific depth \nintervals of AB/2 equal to 10 m, 20 m, 40 m, 60 m, 80 m, 100 m, 140 m and \n200 m were generated (Figure 7). The contour maps revealed a continuous \ndifference of resistivity values with depth, suggesting a high resistive \nmaterial at a greater depth of 200 m, with an average apparent resistivity \nof 231.07 \u03a9m. These were inferred as coarse grain sandstone, and a lower \nresistive material at a shallower depth of 10 \u2013 20 m, also with an average \n\n\n\napparent resistivity of 71.567 - 71.667 \u03a9m referred to as fine to medium \ngrain sandstone. So, at an intermediate depth of 80 \u2013 100 m, having an \naverage apparent resistivity of 134.3 \u2013 131.8 \u03a9m correspond to conductive \nmaterials inferred as medium grain sands, the water-bearing unit. \n\n\n\nA similar trend is maintained in all the iso-resistivity plots from AB/2 \nequal to 10 m, 20 m, 40 m, 60 m, 80 m, 100 m, 140 m and 200 m, revealing \nvery low resistivity values in the northern and southern part i.e., areas \naround FGGC Keana and GGSS Keana to a depth of over 200 m. However, \nfrom the above findings, it was deduced that the extreme northern section \nof the study area may likely appear to be unproductive of groundwater to \na depth less than 150 m, this is because the area is suspected to be \nunderlain by bands of shale of Keana Formation. (Figure 7). \n\n\n\na b\n\n\n\nc d\n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 101-108 \n\n\n\n\n\n\n\n \nCite The Article: Abdullahi, Aliyu Itari, Iliyasu, Abdullahi Yerima, Umar, Nuhu Degree, Abdullahi, Saidu (2023). Geoelectrical  \n\n\n\nAssessment of Groundwater Potential of Keana Area Northcentral Nigeria. Malaysian Journal of Geosciences, 7(2): 101-108. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFigure 7: Iso-Resistivity Contour Map of the Study Area (a) AB/2=10m (b) AB/2=40m (c) AB/2=80m (d) AB/2=140m \n\n\n\n4.4   Geo-Electric Models \n\n\n\nInterpretations from VES carried out around the study area (Table 1) were \nused to generate geo-electric models or cross sections with three (3) major \nprofiles  which  include; A-AI, B-BI, and  C-CI were  taken  for  interpretation \n\n\n\n. Four to six distinct geo-electric layers representative of the sub-surface \nlithology in the study area were noted. However, two of the cross-sections \ncovering the main habitable areas are presented below and were used to \ninfer the groundwater potentials of the study area (Figure 8). \n\n\n\nTable 2: An Iso-resistivity value across the Study Area \n\n\n\nVES NO AB/2 (m)=10 AB/2 (m)=20 AB/2 (m)=40 AB/2 (m)=60 AB/2 (m)=80 AB/2 (m)=100 AB/2 (m)=140 AB/2 (m)=200 \n\n\n\n1 19 21 17 14 17 21 20 15 \n\n\n\n2 17 16 17 17 22 23 22 13 \n\n\n\n3 180 117 122 87 73.5 71 50 34 \n\n\n\n4 205 151 186.5 180 219.5 307 377 415 \n\n\n\n5 71 92 107.5 132 101.5 44.5 61 143 \n\n\n\n6 38 47 83 107 152 141 183 38 \n\n\n\n7 36.5 43 58.5 83 105 133 210 33 \n\n\n\n8 44 31 96 500 190.5 156 30 230 \n\n\n\n9 47 62 88 139 136.5 154 233 254 \n\n\n\n10 31 53 79 111 143 143 215 236 \n\n\n\n11 55 64 91.5 118 126.5 134.5 163 250 \n\n\n\n12 67.5 96 160.5 222 239.5 254 317 367 \n\n\n\n13 77 80 100 118 133.5 123 345 453 \n\n\n\n14 154 160 200 236 297 243 690 960 \n\n\n\n15 31.5 42 60 62 57.5 29 27 25 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 101-108 \n\n\n\n\n\n\n\n \nCite The Article: Abdullahi, Aliyu Itari, Iliyasu, Abdullahi Yerima, Umar, Nuhu Degree, Abdullahi, Saidu (2023). Geoelectrical  \n\n\n\nAssessment of Groundwater Potential of Keana Area Northcentral Nigeria. Malaysian Journal of Geosciences, 7(2): 101-108. \n\n\n\n\n\n\n\n\n\n\n\nFigure 8: (a) Interpretative Cross-Section along B-BI Profile line (b) \nInterpretative Cross-Section along C-CI Profile line of the Study Area. \n\n\n\nProfile B-BI is 800 meters long and is oriented north to southward of the \nstudy area. Which cut across the opposite police station, former savanna \nbank, motor park, main market and taxi park areas. The section revealed \nFive to Six lithologic units with least conductive (coarse sandstone) layer \nforming the basal unit but also occur at some shallow depth in some areas, \nhaving resistivity values between 427.8 \u2013 64336 \u03a9m; this is overlain by \nthe sands (medium grain) unit with resistivity ranging from 60.05 \u2013 4077 \n\u03a9m; this unit is also overlain by the sandstone (medium to fine grain) with \nresistivity ranging from 21.4 \u2013 4071 \u03a9m; this is overlain by the lateritic \nunit with resistivity ranging from 38.36 \u2013 210 \u03a9m; Overlying this layer is \nthe Topsoil unit with resistivity ranging from 84.5 to 1063 \u03a9m (Figure 8a). \n\n\n\nProfile C-CI is 700 km long and trends southwest to northeastward of the \nstudy area cutting across Government Girls Secondary School Keana \n(GGSS), Obene primary school and close to Osana\u2019s palace. The section also \nrevealed the occurrence of four to six geologic units with the sandstone \n(coarse grain) layer at the base, though missing at VES 14 with the \nemergence of a shale unit with a resistivity of 12.1\u03a9m, has resistivity \nvalues ranging from 985 to 5768 \u03a9m; this is overlain by the saturated sand \nunit with resistivity ranging from 72.3 to 485 \u03a9m; overlying this is another \nsandy unit, also missing at VES 14, has resistivity ranging from 46.6 to \n1952 \u03a9m; the sandy unit is overlain a lateritic soil having resistivity \nranging from 36.1 to 802 \u03a9m; overlying this, is the topsoil layer with \nresistivity ranging from 70.1 to 93.2 \u03a9m (Figure 8b). \n\n\n\n5.   CONCLUSIONS \n\n\n\nIn groundwater research, the electrical resistivity sounding technique is \nfrequently used and has found critical applications all over the world. The \ngeoelectric examination in Keana area showed the existence of subsurface \ngeological formations that are conducive to groundwater storage. The \nfindings reveal the presence of prospective aquifers, which could serve as \nviable groundwater supplies. Numerous groundwater potential zones \nexist, allowing for more efficient water resource management and \ndistribution. So, this present study has helped map out zones for drilling \nproductive boreholes in the study area. The VES analysis reveals that the \nCentral part i.e., areas around VES 4 to 10 (central primary school, motor \npack, main market etc.) and the Southern parts i.e., areas around VES 11 \nto 15 (which encompasses Osana\u2019s Palace, Obene Primary School, White \nHouse and GGSS Keana), of the study delineate aquiferous zones of the \nKeana area on an average depth of 100 m to 70 m as high to moderate yield \nis envisaged. Whereas the northern parts i.e., areas around VES 1 and 2 \n(FGGC), would have serious underground water problems. So, boreholes \ndrilled in these areas that are shallower than 100 m may be unproductive. \n\n\n\nYet, this recommendation does not supersede geophysical studies before \ndrilling and the need to have a geologist at the site during drilling. It is \ntherefore hoped that the results of this study will be invaluable to the \nplanning of water supply schemes within the area. Additionally, the \ngeoelectric analysis in Keana Town showed promising groundwater \npotential zones with appreciable depths and adequate water quantity for \nvarious applications. As a result, this contributes to a better understanding \nof aquifer properties and the long-term sustainability of water resources.  \n\n\n\nACKNOWLEDGEMENT \n\n\n\nThe authors are thankful to the entire staff of Lifewaters and Associate, \nNasarawa State Lafia, for their immense support and guidance throughout \nthis research work. Many thanks to the editor and reviewers for their help \nin re-shaping and refining the quality of this research article. \n\n\n\nAUTHORS\u2019 CONTRIBUTIONS \n\n\n\nAll authors contributed to the study conception and design. Material \npreparation, data collection and analysis were performed by Abdullahi \nAliyu Itari, Iliyasu, Abdullahi Yerima, Umar, Nuhu Degree and Abdullahi, \nSaidu respectively. The first draft of the manuscript was written by Umar, \nNuhu Degree and all authors commented on previous versions of the \nmanuscript. All authors read and approved the final manuscript. \n\n\n\nCOMPETING INTEREST \n\n\n\nThe authors declare that they have no competing interests. \n\n\n\nFUNDING SOURCES \n\n\n\nThis research did not receive any specific grant from funding agencies in \nthe public, commercial, or not-for-profit sectors. \n\n\n\nREFERENCES \n\n\n\nAmadi, P.A., Ofoegbu, C.O., and Morrison, T., 1989. Hydrogeochemical \nassessment of groundwater quality in parts of the Niger Delta, \nNigeria. Environmental Geology and Water Sciences, 14, Pp. 195-202. \n\n\n\nBenkhelil, J., Dainelli, P., Ponsard, J.F., Popoff, M., and Saugy, L., 1988. The \nBenue Trough: wrench-fault-related basin on the border of the \nequatorial Atlantic. In Developments in Geotectonics, 22, Pp. 787-\n819. \n\n\n\nChukwu, G.U., 2008. Water quality assessment of boreholes in Umuahia \nSouth Local Government Area of Abia State, Nigeria. Pac J Sci Technol, \n9 (2), Pp. 592-598. \n\n\n\nCratchley, C.R., and Jones, G.P., 1965. An interpretation of the geology and \ngravity anomalies of the Benue Valley, Nigeria. Overseas Geological \nSurvey, Geophysical paper, (1), Pp. 1-25. \n\n\n\nEkwe, A.C., and Opara, A.I., 2012. Aquifer transmissivity from surface geo-\nelectrical data: A case study of Owerri and environs, southeastern \nNigeria. Journal of the Geological Society of India, 80, Pp. 123-128. \n\n\n\nKn\u00f6del, K., Lange, G., Voigt, H.J., Seidel, K., and Lange, G., 2007. Direct \ncurrent resistivity methods. Environmental geology: handbook of \nfield methods and case studies, Pp. 205-237. \n\n\n\nLoke, M.H., 1999. Electrical imaging surveys for environmental and \nengineering studies. A practical guide to, 2, Pp. 70. \n\n\n\nMurat, R.C., 1972. Stratigraphy and palaeogeography of the Cretaceous \nand Lower Tertiary in Southern Nigeria. African geology, 1 (1), Pp. \n251-266. \n\n\n\nNwajide, C.S., 1990. Cretaceous sedimentation and palaeogeography of the \ncentral Benue Trough. The Benue. Trough Structure and Evolution \nInternational Monograph Series, Braunschweig, Pp. 19-38. \n\n\n\nObaje, N.G., and Obaje, N.G., 2009. The Benue trough. Geology and Mineral \nResources of Nigeria, Pp. 57-68. \n\n\n\nOffodile, M.E., 1976. The geology of the middle Benue, Nigeria. \nPalaeontological Institute, University Uppsala, Special Publication, 4, \nPp. 1-166. \n\n\n\nOffodile, M.E., 1984. The geology and tectonics of Awe brine field. Journal \nof African Earth Sciences, 2 (3), Pp. 191-202. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 7(2) (2023) 101-108 \n\n\n\n\n\n\n\n \nCite The Article: Abdullahi, Aliyu Itari, Iliyasu, Abdullahi Yerima, Umar, Nuhu Degree, Abdullahi, Saidu (2023). Geoelectrical  \n\n\n\nAssessment of Groundwater Potential of Keana Area Northcentral Nigeria. Malaysian Journal of Geosciences, 7(2): 101-108. \n\n\n\n\n\n\n\nOffodile, M.E., and Reyment, R.A., 1976. Stratigraphy of the Keana-Awe \narea of the middle Benue, Nigeria\u2016, university of Uppsala. Bull. \nGeology Inst, 7, Pp. 36-66. \n\n\n\nOnyekuru, S., 2010. Depositional Patterns of Upper Cretaceous Sediments \nin the Southeastern Part of Anambra Basin, Southeastern Nigeria,\u201d \nUnpublished Ph.D. Thesis, Federal University of Technology, Owerri, \nPp. 280. \n\n\n\nOteri, A.U., 1977. Application of surface geophysics in hydrogeology. \nUniversity of London and Diploma Imperial College, London, Pp. 94. \n\n\n\nSchwarz, S.D., 1988. Application Of Geophysical Methods to Groundwater \nExploration in The Rolt River Basin, Washington State. In 1st EEGS \nSymposium on the Application of Geophysics to Engineering and \n\n\n\nEnvironmental Problems, Pp. cp-214. European Association of \nGeoscientists & Engineers. \n\n\n\nTelford, W.M., King, W.F., and Becker, A., 1977. VLF mapping of geological \nstructure (Vol. 25). Minister of Supply and Services Canada. \n\n\n\nUchenna, U., Alexander I.O., Theophilus T.E., Frank D.I., Alexander I.O., and \nEdwin N.W., 2013. Delineation of shallow aquifers of Umuahia and \nenvirons, Imo River Basin, Nigeria, using geo-sounding data. Journal \nof Water Resource and Protection. \n\n\n\nUmar, N.D., and Igwe, O., 2019. Geo-electric method applied to \ngroundwater protection of a granular sandstone aquifer. Applied \nWater Science, 9, Pp. 1-14. \n\n\n\n \n\n\n\n\n\n"
"\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 13-19 \n\n\n\nCite the article: Rodeano Roslee, Alvyn Clancey Mickey, Norbert Simon, Mohd. Norazman Norhisham (2017). Landslide Susceptibil ity Analysis (Lsa) Using Weighted \nOverlay Method (Wom) Along The Genting Sempah To Bentong Highway, Pahang . Malaysian Journal Geosciences, 1(2) : 13-19\n\n\n\n  ARTICLE DETAILS \n\n\n\nARTICLE HISTORY: \n\n\n\nReceived 12 May2017  \nAccepted 12 July 2017  \nAvailable online 10 September 2017 \n\n\n\nKEYWORDS:\n\n\n\nLandslide Susceptibility Analysis \n(LSA) \nWeighted Overlay Method (WOM) \nPahang\n\n\n\nABSTRACT\n\n\n\nThis study focused on the Landslide Susceptibility Analysis (LSA) of the Karak highway, which link the Genting \nSempah to Bentong area, Pahang. The physical relief of the study area is largely flat to undulating and moderately \nrough to steep mostly. The aims of this study are to identify the landslide prone area and to produce the Landslide \nSusceptibility Level (LSL) map using Weighted Overlay Method (WOM) integrated with Geographic Information \nSystem (GIS) and remote sensing techniques. Landslide locations were identified in the study area from imagery \nand aerial photograph interpretations followed by field work observation. The topographic, geologic data and \nsatellite images were collected, processed and constructed into a spatial database using image processing. The \nfactors that influence landslide occurrences such as slope gradient, slope aspect, topographic curvature and \ndistance from drainage were retrieved from the topographic database. Geomorphology, lithology and geological \nstructure were generated from the geologic database; whereas land use and soil types from SPOT satellite data \nimage. Several areas are considered as susceptible, such as areas of Ladang Fook Who, Kg. Temiang, Ladang Ng \nChin Siu, Kemajuan Tanah Genting Pandak, Kg. Lentang, Kg. Baharu Bt. Tinggi and Ladang Pandak. To avoid or \nminimize the landslide occurrences, development planning has to consider the hazard and environmental \nmanagement program. This engineering geological study may play a vital role in Landslide Risk Management \n(LRM) to ensure the public safety.\n\n\n\n1. INTRODUCTION \n\n\n\nFlooding Landslide is among the major geohazards in Malaysia. As with \nflooding, tsunami, siltation and coastal erosion, these have repeatedly \noccurred in the region with disastrous effect. Landslide is a general term \nfor a variety of earth processes by which large masses of rock and earth \nmaterial spontaneously move downward, either slowly or quickly by \ngravitation [1]. Such earth processes become geologic hazards when \ntheir direct interaction with the material environment is capable of \ncausing significant negative impact on a human\u2019s wellbeing.  \n\n\n\nLandslide processes take place when the slope materials are no longer \nable to resist the force of gravity. This decrease in shear resistance \nresulting in landslide is due to either to internal or external causes. \nInternal causes involve some change in either the physical or the \nchemical properties of the rock or soil or its water content. External \ncauses, which lead to an increase in shear stress on the slope usually, \ninvolve a form of disturbance that may be either natural or induced by \nman. With the growth of human population and the expansion of the \nscope of human\u2019s activities in Malaysia, we find ourselves increasingly in \nconflict along steeply area [2]. A landslide zoning provides information \non the susceptibility of the terrain to slope failures and can be used for \nthe estimation of the loss of fertile soil due to slope failures (in agriculture \nareas), the selection of new construction sites and road alignments (in \nurban or rural areas) and the preparation of landslide prevention, \nevacuation and mitigation plans. Natural hazard mapping concerns not \nonly delineation of pas occurrences of natural hazards such as landslide, \nbut it also includes predicting such occurrences [3]. \n\n\n\nIn the literature, there are four different approaches to the analysis of \nLSL: landslide inventory-based probabilistic, heuristic (which can be \ndirect geomorphological mapping, or indirect qualitative map \ncombination), statistical (bivariate or multivariate statistics) and \ngeotechnical approach [4-6]. LSL analysis using probabilistic models  \n\n\n\nwere published by some researcher [7,8]. Most of the above studies have \nbeen conducted using the regional landslide inventories derived from \naerial photographs and remotely sensed images.  \n\n\n\nThe heuristic approach is considered to be useful for obtaining \nqualitative LSL maps for large areas in a relatively short time. It does not \nrequire the collection of geotechnical data, although detailed \ngeomorphological mapping is essential. The qualitative approach is \nbased on expert opinion and the susceptible areas are categorized by \nsuch terms as \u201cvery high\u201d, \u201chigh\u201d, \u201cmoderate\u201d, \u201clow\u201d and \u201cvery low\u201d. The \nincreasing popularity of Geographic Information Systems (GIS) has led \nto many studies, mainly using indirect susceptibility-mapping \napproaches [9]. As a consequence, fewer investigations use GIS in \ncombination with a heuristic approach, either geomorphological \nmapping, or index overlay mapping and analytical hierarchy process \n[10-20].  \n\n\n\nStatistical analyses are popular because they provide a more \nquantitative analysis and can examine the various effects of each factor \non an individual basis. Statistical analyses of LSL can include bivariate \nand multivariate methods. The bivariate methods, are a modified form of \nthe qualitative map combination with the exception that weights are \nassigned based upon statistical relationships between past landslides \nand various factor maps; alternatively, these statistics can be used to \ndevelop decision rules [21]. The main difference among the specific \nbivariate methods is the manner in which the weights are produced. \nDifferent methods have been proposed, including: general instability \nindex, frequency index, surface percentage index, statistical index \nmethod, weighting factor, certainty factor, conditional analysis, weights \nof evidence, landslide susceptibility analysis, and information value \nmethod [22-72]. These indices measure, directly or in a weighted form, \nthe relative or absolute abundance of landslide area or number in \ndifferent terrain categories. This information is then used by the \n\n\n\nContents List available at RAZI Publishing \n\n\n\nMalaysian Journal of Geosciences \nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/ \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (online)\n\n\n\nLANDSLIDE SUSCEPTIBILITY ANALYSIS (LSA) USING WEIGHTED \nOVERLAY METHOD (WOM) ALONG THE GENTING SEMPAH TO \nBENTONG HIGHWAY, PAHANG \nRodeano Roslee*1,2, Alvyn Clancey Mickey3, Norbert Simon4, Mohd. Norazman Norhisham1 \n\n\n\n1\nFaculty of Science and Natural Resources, University Malaysia Sabah,UMS Road, 88400 Kota Kinabalu, Sabah, Malaysia \n\n\n\n2Natural Disaster Research Centre (NDRC), Universiti Malaysia Sabah, 88400, Kota Kinabalu, Sabah, Malaysia. \n3Mineral and Geosciences Department of Malaysia (Sabah), Locked Bag 2042, Jalan Penampang , 88999, Kota Kinabalu, Sabah, Malaysia. \n4Faculty of Science and Technology, University Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia \n*\nCorresponding author email: rodeano@ums.edu.my.\n\n\n\nhttps://doi.org/10.26480/mjg.02.2017.13.19\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\nmailto:rodeano@ums.edu.my\n\n\nhttps://doi.org/10.26480/mjg.02.2017.13.19\n\n\n\n\n\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 13-19 \n\n\n\nCite the article: Rodeano Roslee, Alvyn Clancey Mickey, Norbert Simon, Mohd. Norazman Norhisham (2017). Landslide Susceptibil ity Analysis (Lsa) Using Weighted Overlay \nMethod (Wom) Along The Genting Sempah To Bentong Highway, Pahang . Malaysian Journal Geosciences, 1(2) : 13-19\n\n\n\n14\n\n\n\ninvestigator to establish susceptibility levels. Meanwhile, multivariate \nmethods have been used for LSL. The prominent techniques used in \nmultivariate methods are: multiple linear regression analysis, \ndiscriminant analysis, and logistic regression analysis [73-104]. When \nmany factors are available, to reduce the number of variables and to limit \ntheir interdependence, principal component analysis (PCA) is an option \n[105,106]. More advanced methods employ a variety of classifications \ntechniques such as fuzzy systems, artificial neural networks (ANN), \nexpert systems and Factor Analysis Model [107-109].  \n\n\n\nVarious approaches to geotechnical analysis for LSL have been \ndeveloped. Some of the earliest studies in a GIS environment were \ncarried out [110-115]. Their use of a GIS environment made it possible \nto extend the conventional, site specific deterministic model into larger \nareas, where the spatial distribution of input parameters is taken into \naccount. However, a study observed that geotechnical approaches for \nLSA in a GIS environment have not been checked with traditional \nmethods of analysis, neither have they been validated with results of \nlandslide monitoring [116,117]. Comprehensive studies concerning \nregional slope stability assessment supported by deterministic \napproaches in a GIS environment have also been carried out. Some \nresearcher combines a slope stability model (Stability INdex MAPping, \nSINMAP) with a steady-state hydrology model in selected watersheds of \nnorthern Vancouver Island, British Columbia and in the central \nhighlands of Honduras, respectively [118]. High attention should be paid \nto the accuracy and variability associated with the input parameters. \nSimilar examples of regional modeling and prediction of shallow \nlandslides using a transient rainfall infiltration model in combination \nwith slope stability calculation (Transient Rainfall Infiltration and Grid-\nbased Regional Slope-stability analysis; TRIGRS) were applied for the \nSeattle area, Washington State, USA and the Umbria Region, central Italy \n[119]. The TRIGRS model predicts a larger area of instability than the \narea that actually failed, mainly due to uncertainty in soil thickness, local \nvariation in soil properties, and Digital Elevation Model (DEM) errors. \n\n\n\n2. STUDY AREA \n\n\n\nThe study area, located along the Karak highway, which link the \nGenting Sempah to the Bentong, Pahang. It is bounded by longitude \nline E 101o 45\u2019 to E 101o 55\u2019 and latitude line N 03o 20\u2019 to N 03o \n25\u2019 (Figure 1 & 2). The physical relief of this study area is largely flat \nto undulating and moderately rough to steep mostly and has \naltitude ranging from mean sea level to 1317m. Mt Kolam Berengga is \nthe highest peak. \n\n\n\n  Figure 1: Location of study area in Pahang State \n\n\n\nFigure 2: Landslide locations area \n\n\n\n3. MATERIALS AND METHODOLOGY\n\n\n\nIn LSA, data were provided and stored into a spatial database. The \n\n\n\nanalysis was carried out based on eight attributes: slope gradient, slope \naspect, lithological, soil types, geologic structural, geomorphology \nsetting, drainage and land use. All of these factor attributes was \nextracted and analysis based on the knowledge of weightage overlay. \nEach given weightage on the attributes was summed altogether and \nreclassified to generate a landslide susceptibility map. Lastly this LSL \nmap needed to be verified. In this study, all attributes factor are \nconsider equal important.  \n\n\n\nA key assumption using probabilistic model, weightage overlay \napproach, is that the potential of landslides will be comparable to the \nactual frequency of landslides based on the attributes factor. These \nweightage on the attributes are subject to the combination degree of \nlandslide occurrences. Landslide susceptible areas are observed and \ndetected by the imagery and aerial photograph interpretations followed \nby fieldwork verification. For this study, detail landslide history areas \nwere reviewed and acting as a control factor. By given topographic \ndatabase, the digital elevation model (DEM) with 20 m resolutions, \nslope gradient and slope aspect maps were produced. Using the \ntopographic database also, the distance from drainage and lineament \n(geologic structural) were calculated. The buffer interval used for \ndistance calculation was in 50 m range and presented to a raster map. \nAll the attribute factors were given as weightage accordingly to their \ncriteria and priority (Table 1). All the calculated and extracted \nweightage were converted to raster map for analysis. Using the \nweightage overlay approach, the spatial relationships between each \nlandslide-factor were analysed. The entire factor\u2019s rating (weightage) \nwas summed to produce LSL map. Finally, a ground checking was \nconducted on field to verify the LSL map (Figure 3).  \n\n\n\nTable 1: Attribute weightage \n\n\n\nGeomorphology (GGM_DESC) \n\n\n\nWt Description \n\n\n\n4 \n\n\n\n10 \n\n\n\nDenudational hill \n\n\n\nStructure denudational hill \n\n\n\nSoil (AST_SERIES) \n\n\n\nWt Description \n\n\n\n4 \n\n\n\n10 \n\n\n\n6 \n\n\n\nRenggam \n\n\n\nSteepland \n\n\n\nTelemong \n\n\n\nRiver (DISTANCE) \n\n\n\nWt Meters \n\n\n\n10 \n\n\n\n8 \n\n\n\n6 \n\n\n\n4 \n\n\n\n2 \n\n\n\n1 \n\n\n\n50 \n\n\n\n100 \n\n\n\n150 \n\n\n\n200 \n\n\n\n250 \n\n\n\n1000 \n\n\n\nStructure/ Lineament (Distance) \n\n\n\nWt Meters \n\n\n\n10 \n\n\n\n8 \n\n\n\n6 \n\n\n\n4 \n\n\n\n2 \n\n\n\n1 \n\n\n\n50 \n\n\n\n100 \n\n\n\n150 \n\n\n\n200 \n\n\n\n250 \n\n\n\n10000 \n\n\n\nLand use (ALU_DESC) \n\n\n\nWt Description \n\n\n\n8 \n\n\n\n4 \n\n\n\n3 \n\n\n\n1 \n\n\n\n5 \n\n\n\n2 \n\n\n\n6 \n\n\n\n7 \n\n\n\nUrban & associated areas \n\n\n\nRubber \n\n\n\nOrchard \n\n\n\nNatural forest \n\n\n\nScrub \n\n\n\nBush \n\n\n\nMixed horticulture \n\n\n\nRecreational areas \n\n\n\nLitology (LITO_TYPE) \n\n\n\nWt Description \n\n\n\n10 \n\n\n\n4 \n\n\n\nSchist \n\n\n\nAcid intrusive (undifferentiated) \n\n\n\n\n\n\n\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 13-19 \n15\n\n\n\nFigure 3: Flowchart of methodology \n\n\n\n4. GEOLOGICAL BACKGROUND \n\n\n\nThe study area is essentially made up of granitic rocks as the main \nunderlying geology (Figure 4). The granite body is postulated to be \nTriassic in age which is part of the Main Range Granite [28]. Ong \ndescribed termed it as the Gombak Granite which consist mainly of \ncoarse to medium grained biotite muscovite granite, fine to medium \ngrained tourmaline granite, pegmatite and greisen [89]. Beside the \ngranite body, older rock formations are the Hawthornden Formation of \nMiddle-Upper Silurian in age and co-exist with the Kuala Lumpur \nLimestone Formation, although the latter is postulated to be younger \nand lies unconformably above the former. The Hawthornden Formation \ncomprises mainly of phyllite, slate and schist, whereas the Kuala \nLumpur Limestone has been metamorposed and recrystalised to form \ncoarse grained white to pale coloured marble.  \n\n\n\nThe alteration process of the granite country rock and the formation \nof the quartz dyke were believed to take place during the Post-Triassic \nera. There is no certain age given to the Quartz Reef except that it \nis younger than the surrounding Triassic granite (Figure 5). \nHowever, from radioactive dating of K?Ar of two generations of \nmuscovite in quartz reef sample from the Seri Gombak area it is \nbelieved to be as old as Mid-Cretaceous to Jurassic. More than \nhalf geomorphological landforms of the state comprises of alluvial \nplain and fluvial landforms whereas the others were occupied by \ndenudational landforms namely residual hill, structural hill \ndenudational hill etc. Landslide features were found and recorded \nat several localities especially at the newly developed hilly area. \nThere have been several landslides occurrences in this surrounding \narea recently (Figure 2). \n\n\n\nFigure 4: Granitic rocks in the   Figure 5: Quartz reef at Taman  \n\n\n\n      study area        Melawati Area \n\n\n\n5. RESULTS AND DISSCUSSION\n\n\n\n5.1 Causes of landslide in the study area \n\n\n\nThe main factors causing of landslide in the study area include \npreparatory mechanism and triggering factors: \n\n\n\n5.1.1 Preparatory mechanisms \n\n\n\nCite the article: Rodeano Roslee, Alvyn Clancey Mickey, Norbert Simon, Mohd. Norazman Norhisham (2017). Landslide Susceptibil ity Analysis (Lsa) Using Weighted Overlay \nMethod (Wom) Along The Genting Sempah To Bentong Highway, Pahang . Malaysian Journal Geosciences, 1(2) : 13-19\n\n\n\nPreparatory mechanisms are cumulative events, which prepare the \nslope for failure but do not necessarily produce movement. These \nincludes the geology, slope gradient, elevation, soil geotechnical \nproperties, vegetation cover, long \u2013 term drainage system / pattern and \nweathering. The study of the preparatory or conditioning factors should \nbe based on a systematic inventory conducive to the creation of a \ndatabase, which will allow the quantification of the relationship \nbetween slope failure and the geological and geomorphological \ncharacteristics of the terrain.  \n\n\n\n5.1.2 Triggering factors \n\n\n\nTriggering factors or variables are which shift the slope from a \nmarginally stable to an unstable state and thereby initiating failure in \nan area of given susceptibilities such as heavy rainfall and tremors. \nThese variables can change over a short time span and are thus very \ndifficult to estimate. If triggering variables are not taken into account \nthe term susceptibility may be employed to define the likelihood of the \nlandslide event occurrence. Susceptibility to failure is determined by \nthe geological structure and lithology of the slope, hydrogeological \nconditions and the stage of morphological of the study area. \n\n\n\n5.2 Application of Weightage Overlay Method (WOM) and \nLandslide Susceptibility Level (LSL) map  \n\n\n\nLandslide occurrence is determined from landslide related factor and \nthe future landslide can occur in the same condition with past landslide. \nBased on the assumption using probability method, the relationship \nbetween areas with landslide occurrences and landslide related factors \ncould be distinguished from the relationship between area without \noccurrences of landslide and landslide related factors. To present the \ndistinction quantitatively, the weightage overlay method was used for \nthis study. \n\n\n\nThe analysis and calculation processes in the analysis and modelling \npart were similar for all the parameter maps. To avoid longer time for \ndoing the calculation and redundant task, the scripts or batch files as \nshown in Tab. 1 were used in the analysis. The weightage value shows \nthat the most causative factor that influenced landslide occurrences is \nslope gradient. Figs. 6 to 9 show the weightage value polygon to \nland use, distance from drainage, distance from lineament, soil \nlithology and geomorphology.  \n\n\n\nFive classes of LSL were adopted: very low (10 %), low (50 %), \nmedium (15 %), high (15 %) and very high (10 %) (Figs. 6 to 9). The \nvery low to low LSL reflects the probability of occurrence of landslides \nare very limited even with existence strong triggering factors, such as \nheavy rainfall and tremendous land use changes. On the other \nhand, moderate LSL means that, some landslide will be generated \nunder the influence of intense triggering factors whereas the high to \nvery high hazard means a considerable number of landslides will \noccur even with the presence of weak triggering factor. In the study \narea, most of the high to very high LSL area are elongated along the \nhilly terrain area in the eastern part of the state. \n\n\n\nFigure 6: Landslide hazard zoning map of the study area \n\n\n\n\n\n\n\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 13-19 \n\n\n\nCite the article: Rodeano Roslee, Alvyn Clancey Mickey, Norbert Simon, Mohd. Norazman Norhisham (2017). Landslide Susceptibil ity Analysis (Lsa) Using Weighted Overlay \nMethod (Wom) Along The Genting Sempah To Bentong Highway, Pahang . Malaysian Journal Geosciences, 1(2) : 13-19\n\n\n\n16\n\n\n\nFigure 7: Landslide hazard zoning map at Ladang Perting Pandak Baharu \narea. \n\n\n\nFigure 8: Landslide hazard zoning map at Kemajuan Tanah Genting \nPandak area \n\n\n\nFigure 9: Landslide hazard zoning map at Ladang Pandak \n\n\n\n6. CONCLUSION\n\n\n\nIn light of available information, the following conclusions may be \ndrawn from the present study: \n\n\n\na. Engineering geologic evaluation of the study area indicates that \nthe landslide took place when slope materials are no longer able \nto resist the attraction of gravity due to a decrease in shear \nstrength and increase the shear stresses resulting landslide, which \nis due to preparatory mechanism and triggering factors. \nPreparatory mechanisms are cumulative events, which prepare \nthe slope for failure but do not necessarily produce movement. \nThese include the geology, slope gradient, elevation, soil \ngeotechnical properties, vegetation cover, long \u2013 term drainage \nsystem / pattern and weathering. Triggering factors or variables \nare which shift the slope from a marginally stable to an unstable \nstate and thereby initiating failure in an area of given \nsusceptibilities such as heavy rainfall and tremors. \n\n\n\nb. High (15 %) to very high (10 %) LSL means a considerable number \nof landslides will occur even with the presence of weak triggering \nfactor. Mostly these areas have been totally or partially cleared for \nutilized for other associated infrastructure developments. High to \nvery high LSL is not so suitable for development and would \nencounter high geotechnical constraints, requires intensive site \ninvestigations and thus would incur high development costs. \n\n\n\nc. LSL maps are useful to planners and engineers for choosing \nsuitable locations to implement developments. Although the \nresults can be used as a basic data to assist slope management and \nland use planning, the methods used in the study area only valid \n\n\n\nfor generalized planning and assessment purposes, and may be \nless useful at the site-specific scale where local geological and \ngeographic heterogeneities prevail. \n\n\n\n7. RECOMMENDATION\n\n\n\nLandslide occurrences have been the most critical issues in Malaysia. \nFrequency, size and impact the community kept on increasing. \nLandslide incidents mostly are due to human activities. Therefore by \nplanning, management and proper construction, this landslide problem \ncan be avoided. Settlement of the landslide issues required multi-\npurpose approach and long term planning. Cooperation among \ngovernment agencies such as local universities, Department of Public of \nWork (DPW), Department of Irrigation and Drainage (DID), Department \nof Natural Environmental (DNE), Department of Mineral and \nGeosciences (DMG), Department of Meteorological (DM) and local \nauthorities are needed at all levels and should be continuously. \nResearch therefore needs to have integration approach and need to be \nperformed systemically. In spite of that geology discipline can \ncontribute at all levels of avoidance and control of this geohazards \noccurrences. \n\n\n\n8. REFERENCES \n\n\n\n[1] Akg\u00fcn, A., and Bulut, F. 2008. GIS-based landslide susceptibility \nfor Arsin-Yomra (Trabzon, North Turkey) region. Environmental \nGeology, 51,1377\u20131387. \n[2] Aleotti, P. and Chowdhury, R. 1999. Landslide hazard assessment: \nsummary review and new perspectives. Bulletin of Engineering \nGeology and the Environment, 58 (1), 21-44. \n\n\n\n[3] Arora, M.K., Das Gupta, A.S., and Gupta, R.P. 2004. An artificial \nneural network approach for landslide hazard zonation in the \nBhagirathi (Ganga) valley, Himalayas. International Journal of \nRemote Sensing, 25 (3), 559\u2013572. \n[4] Atkinson, P., and Massari, R. 1998. Generalised linear modelling \nof susceptibility for landsliding in the Central Apennines, Italy. \nComputers and Geosciences, 24 (4), 373-385. \n\n\n\n[5] Ayalew, L., Yamagishi, H., and Ugawa, N. 2005. Landslide \nsusceptibility mapping using GIS-based weighted linear combination, \nthe case in Tsugawa area of Agano River, Niigata prefecture, Japan. \nLandslides, 1 (1), 73-81. \n[6] Baeza, C., and Corominas, J. 2001. Assessment of shallow \nlandslide susceptibility by means of multivariate statistical \ntechniques. Earth Surface Processes and Landforms, 26 (12), 1251-\n1263. \n[7] Barredo, J.I., Benavides, A., Hervas, J. and Van Westen, C.J. 2000. \nComparing heuristic landslide hazard assessment techniques using \nGIS in the Tirajana basin, Gran Canaria Island, Spain. International \nJournal of Applied Earth Observation and Geoinformation, 2 (1), 9-23. \n[8] Bernknopf, R.L., Campbell, R.H., Brookshire, D.S. and Shapiro, C.D. \n1988. A probabilistic approach to landslide hazard mapping in \nCincinnati, Ohio, with application for economical evaluation. Bulletin \nof Engineering Geology and the Environment, 25, 39\u201356. \n[9] Bignell, D.J., and Snelling, N.J. 1977. Geochronology of the \nMalaysian Granites. Overseas, Geology and Mineral Resources, Vol. \n47, 72p \n[10] Binaghi, E., Luzi, L., Madella, P., and Rampini, A. 1998. Slope \ninstability zonation: a comparison between certainty factor and fuzzy \nDempster-Shafer approaches. Natural hazards, 17, 77-97. \n[11] Bonham-Carter, G.F. 1996. Geographic Information Systems for \ngeoscientists, modelling with GIS. Oxford: Pergamon Press. \n[12] Brabb, E.E. 1984. Innovative approaches to landslide hazard \nand risk mapping. In: Proc 4th Int Symp on Landslides. Canadian \nGeotechnical Society, Toronto, vol 1, pp 307\u2013324. \n[13] Cardinali, M., Carrara, A., Guzzetti, F. and Reichenbach, P. 2002. \nLandslide hazard map for the upper Tiber river basin. CNR Gruppo \nNazionale per la Difesa dalle Catastrofi Idrogeologiche Publication n. \n2116, scale 1:100,000 \n[14] Carrara, A. 1978. Considerazioni sulla cartografia applicata alla \nstabilit\u00e0 dei versanti. Seminario Sottoprogetto Fenomeni Franosi, \nMarch 1978, Bari. 11 hlm. \n[15] Carrara, A. 1982. Cartografia tematica, stoccaggio ed \nelaborazione dati. Convegno Conclusivo Progetto Finalizzato \n\n\n\n\n\n\n\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 13-19 \n\n\n\nCite the article: Rodeano Roslee, Alvyn Clancey Mickey, Norbert Simon, Mohd. Norazman Norhisham (2017). Landslide Susceptibil ity Analysis (Lsa) Using Weighted Overlay \nMethod (Wom) Along The Genting Sempah To Bentong Highway, Pahang . Malaysian Journal Geosciences, 1(2) : 13-19\n\n\n\n17\n\n\n\nConservazione del Suolo, Relazione Generale, Sottoprogetto \nFenomeni Franosi, 9-10 June 1982, Rome. hlm. 265-281. \n[16] Carrara, A. 1983. Multivariate models for landslide hazard \nevaluation. Mathematical Geology, 15 (3), 403-426. \n[17] Carrara, A., Cardinali, M. and Guzzetti, F. 1992. Uncertainty in \nassessing landslide hazard and risk. ITC Journal, 2, 172-183. \n[18] Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V. and \nReichenbach, P. 1991. GIS techniques and statistical models in \nevaluating landslide hazard. Earth Surface Processes and Landforms, \n16 (5), 427-445. \n[19] Carrara, A., Cardinali, M., Guzzetti, F. and Reichenbach, P. 1995. \nGIS technology in mapping landslide hazard. Geographical \ninformation systems in assessing natural hazards. Kluwer Academic \nPublishers, Dordrecht. The Netherlands, 135-175. \n[20] Carrara, A., Crosta, G. and Franttini, P. 2003. Geomorphological \nand historical data in assessing landslide hazard. Earth Surface \nProcesses and Landforms 28, 1125\u20131142. \n[21] Cevik, E. And Topal, T. 2003. GIS-based landslide susceptibility \nmapping for a problematic segment of the natural gas pipeline, \nHendek (Turkey). Environmental Geology, 44, 949\u2013962. \n[22] Champatiray, P. K., Dimri, S., Lakhera, R. C. and Sati, S. 2007. \nFuzzy-based method for landslide hazard assessment in active \nseismic zone of Himalaya. Landslides, 4, 101\u2013111. \n[23] Champatiray, P.K. and Bhan, S.K. 1998. Remotely sensed and \nancillary data integration techniques for landslide hazard zonation. \nIn: Tripathy NK, Bajpai VN (eds) Remote sensing in geoscience. Anmol \nPublisher, New Delhi, pp 245\u2013260. \n[24] Champatiray, P.K. 1996. Landslide hazard zonation using fuzzy \nlogic and probability analysis in western Himalayas. Project report \nunder IIRS-ITC programme, internal publication. ITC, Netherlands. \n[25] Champatiray, P.K. 2004. GIS based landslide modelling. In: \nNagarajan R (ed) Landslide disaster: assessment and monitoring. \nAnmol Publications, New Delhi, pp 81\u201396. \n[26] Chi, K.H., Park, N.W. and Chung, C.J. 2002. Fuzzy logic \nintegration for landslide hazard mapping using spatial data from \nBoeun, Korea. Symposium on Geospatial Theory, Processing and \nApplications. Ottawa 2002. 6 hlm. \n[27] Chung, C.F., and Fabbri, A.G. 1993. Representation of \ngeoscience information for data integration. 29th International \nGeology Conferrence. Kyoto, Japan. 11 hlm. \n[28] Chung, C.F. and Fabbri, A.G. 2001. Prediction models for \nlandslide hazard zonation using a fuzzy set approach. Dlm. Marchetti, \nM., Rivas, V. (pnyt.). Geomorphology & Environmental Impact \nAssessment. Balkema Publishers, 31-47. \n[29] Chung, C.F. and Fabbri, A.G. 1998. Three Bayesian prediction \nmodels for landslide hazard. In: Buccianti, R., Potenza, R., and Nardi, \nG. (eds). Proceedings of International Association for Mathematical \nGeology 1998 Annual Meeting (IAMG 98). Ischia, Italy. October 3-7, \n1998. hlm. 204-211. \n[30] Chung, C.F. and Leclerc, Y. 1994. A quanititative technique for \nzoning landslide hazard. International Association for Mathematical \nGeology Annual Conference, Mont Tremblant, Qu\u00e9bec, 87-93. \n[31] Chung, C.F., Fabbri, A.G. and Van Westen, C.J. 1995. Multivariate \nregression analysis for landslide hazard zonation. Dlm. Carrera, A., \nand Guzzetti, F. (pnyt.). Geographical information systems in \nassessing natural hazards. Kluwer Academic Publishers, Dordrecht, \nThe Netherlands, 107-133. \n[32] Clerici, A., Perego, S., Tellini, C. and Vescovi, P. 2002. A \nprocedure for landslide susceptibility zonation by the conditional \nanalysis method. Geomorphology, 48, 349-364. \n[33] Corominas, J. and Santacana, N. 2003. Stability analysis of the \nVallcebre translational slide, Eastern Pyrenees (Spain) by means of a \nGIS. Nat Hazards (Dordr), 30 (3), 473\u2013485. \n[34] Crosta, G.B. and Dal Negro, P. 2003. Observations and \nmodelling of soil slip-debris flow initiation processes in pyroclastic \ndeposits: the Sarno 1988 event. Natural Hazards and Earth System \nSciences, 3 (1-2), 53-69. \n[35] Dai, F.C. and Lee C.F. 2002. Landslide characteristics and slope \ninstability modeling using GIS, Lantau Island, Hong Kong. \nGeomorphology, 42, 213-228. \n[36] Dai, F.C. and Lee, C.F. 2003. A spatiotemporal probabilistic \nmodelling of storm-induced shallow landsliding using aerial \nphotographs and logistic regression. Earth Surface Processes and \nLandforms, 28 (5), 527-545. \n[37] Dai, F.C., Lee, C.F. and Xu, Z.W. 2001. Assessment of landslide \nsusceptibility on the natural terrain of Lantau Island, Hong Kong. \nEnvironmental Geology, 40 (3), 381-391. \n\n\n\n[38] Davis, T.J. and Keller, C.P. 1997. Modelling uncertainty in \nnatural resource analysis using fuzzy sets and Monte Carlo \nsimulation: slope stability prediction. International Journal of \nGeographical Information Systems, 11 (5), 409-434. \n[39] Dietrich, W.E., Reiss, R., Hsu, M.L. and Montgomery, D.R. 1995. \nA process-based model for colluvial soil depth and shallow \nlandsliding using digital elevation data. Hydrological Process, 9, 383-\n400. \n[40] Duman, T., \u00c7an, T., Emre, \u00d6., Ke\u00e7er, M., Do\u011fan, A., Ate\u015f, \u015e. and \nDurmaz, S. 2005. Landslide inventory of southwestern Anatolia, \nTurkey. Engineering Geology, 77, 99\u2013114. \n[41] Dymond, J.R., Jessen, M.R. and Lovell, L.R. 1999. Computer \nsimulation of shallow landsliding in New Zealand hill country. \nInternational Journal of Applied Earth Observation and \nGeoinformation, 1 (2), 122-131. \n[42] Ercanoglu, M. and Gokceoglu, C. 2002. Assessment of landslide \nsusceptibility for a landslide-prone area (North of Yenice, NW \nTurkey) by fuzzy approach. Environmental Geology, 41, 720\u2013730. \n[43] Ercanoglu, M., Gokceoglu, C. and Van Asch, T.H.W.J. 2004. \nLandslide susceptibility zoning north of Yenice (NW Turkey) by \nmultivariate statistical techniques. Nat. Hazard, 32, 1\u201323. \n[44] Gokceoglu, C., Sonmez, H., and Ercanoglu, M. 2000. \nDiscontinuity controlled probabilistic slope failure risk maps of the \nAltindag (settlement) region in Turkey. Engineering Geology, 55, 277-\n296. \n[45] Gomez, H. and Kavzoglu, T. 2005. Assessment of shallow \nlandslide susceptibility using artificial neural networks in Jabonosa \nRiver Basin, Venezuela. Engineering Geology, 78, 11 \u201327. \n[46] Grosevski, P.V., Gessler, P. and Foltz, R.B. 2000. Spatial \nprediction of landslide hazard using discriminant analysis and GIS. \nGIS in the Rockies 2000 Conference and Workshop, applications for \nthe 21st Century. Denver, Colorado. \n[47] Guzzetti, F., Carrarra, A., Cardinali., M., and Reichenbach, P. \n1999. Landslide hazard evaluation: a review of current techniques \nand their application in a multi-scale study, Central Italy. \nGeomorphology, 31, 181-216. \n[48] Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M. and \nArdizzone, F. 2005. Landslide hazard assessment in the Staffora basin, \nNorthern Italian Apennines. Geomorphology, 72, 272-299. \n[49] He, Y.P., Xie, H., Cui, P., Wei, F.Q., Zhong, D.L. and Gardner, J.S. \n2003. GIS-based hazard mapping and zonation of debris flows in \nXiaojiang Basin, southwestern China. Environmental Geology, 45, \n286-293. \n[50] Jade, S. and Sarkar, S. 1993. Statistical models for slope \ninstability classification. Engineering Geology, 36, 91-98. \n[51] Jasmi, A.T. 2003. Probabilistic landslide susceptibility analysis\nand verification using GIS and remote sensing data at Penang, \nMalaysia. Bulletin of Geological Society of Malaysia, 46, 173-179. \n[52] Jasmi, A.T. 2004. Landslide hazard zoning mapping using \nremote sensing and GIS techniques. Proceeding of Malaysia-Japan \nSymposium on Geohazards and Geoenvironmental Engineering-\nrecent advances, 115-120. \n[53] Juang, C.H., Lee, D.H. and Sheu, C. 1992. Mapping slope failure \npotential using fuzzy sets. Journal of Geotechnical Engineering ASCE, \n118 (3), 475-493. \n[54] Kanungo, D.P. Arora, M.K., Gupta, R. P. and Sarkar, S. 2008. \nLandslide risk assessment using concepts of danger pixels and fuzzy \nset theory in Darjeeling Himalayas. Landslides, 5,407\u2013416. \n[55] Kobashi. S. and Suzuki, M. 1988. Hazard index for the judgment \nof slope stability in the Rokko Mountain region. In: Proc. \nINTERPRAEVENT, 1988, Graz, Austria, 1, 223\u201323. \n[56] Lan, H.X., Zhou, C.H., Wang, L.J., Zhang, H.Y. and Li, R.H. 2004. \nLandslide hazard spatial analysis and prediction using GIS in the \nXiaojiang watershed, Yunnan, China. Engineering geology, 76, 109\u2013\n128. \n[57] Lee, S, and Min, K. 2001. Statistical analysis of landslide \nsusceptibility at Yongin, Korea. Environmental Geology, 40, 1095-\n1113. \n[58] Lee, S., Choi, J,. and Min, K. 2002b. Landslide susceptibility \nanalysis and verification using the Bayesian probability model. \nEnvironmental Geology, 46, 120-131. \n[59] Lee, S., Chwae, U., and Min, K. 2002a. Landslide susceptibility \nmapping by correlation between topography and geological \nstructure: the Janghung area, Korea. Geomorphology, 46, 49-162. \n[60] Lee, S., and Choi, J. 2004. Landslide susceptibility mapping \nusing GIS and the weight-of-evidence model. International Journal of \nGeographical Information Science, 18, 789\u2013814. \n\n\n\n\n\n\n\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 13-19 \n\n\n\nCite the article: Rodeano Roslee, Alvyn Clancey Mickey, Norbert Simon, Mohd. Norazman Norhisham (2017). Landslide Susceptibil ity Analysis (Lsa) Using Weighted Overlay \nMethod (Wom) Along The Genting Sempah To Bentong Highway, Pahang . Malaysian Journal Geosciences, 1(2) : 13-19\n\n\n\n18\n\n\n\n[61] Lee, S. and Pradhan, B. 2007. Landslide hazard mapping at \nSelangor, Malaysia using frequency ratio and logistic regression \nmodels. Landslides, 4, 33-41. \n[62] Lee, S., and Sambath, T. 2006. Landslide susceptibility mapping \nin the Damrei Romel area, Cambodia using frequency ratio and \nlogistic regression models. Environmental Geology, 50, 847\u2013855. \n[63] Lee, S. and Talib, J.A. 2005. Probabilistic landslide susceptibility \nand factor effect analysis. Environmental Geology, 47, 982\u2013990. \n[64] Lee, S. 2005. Application and cross-validation of spatial logistic \nmultiple regression for landslide susceptibility analysis. Geosciences, \n9 (1), 63-71. \n[65] Lee, S. 2004. Application of likelihood ratio and logistic \nregression models to landslide susceptibility mapping using GIS. \nEnvironmental Management, 34 (2), 223-232. \n[66] Lee, S., Ryu, J.H., Lee, M.J. and Won, J.S. 2003b. Landslide \nsusceptibility analysis using artificial neural network at Boeun, Korea. \nEnvironmental Geology, 44, 820\u2013833. \n[67] Lee, S., Ryu, J.H., Lee, M.J. and Won, J.S. 2006. The application of \nartificial neural networks landslide susceptibility mapping at \nJanghung, Korea. Mathematical Geology, 38, 199-220. \n[68] Lee, S., Ryu, J.H., Min, K. And Won J.N. 2004b. Landslide \nsusceptibility analysis using GIS and artificial neural network. Earth \nSurface Processes and Landforms, 28 (12), 1361-1376. \n[69] Lee, S., Ryu, J.H., Min, K. and Won, J.S. 2003a. Landslide \nSusceptibility Analysis using GIS and Artificial neural network. Earth \nSurface Processes and Landforms, 27, 1361\u20131376. \n[70] Lee, S., Ryu, J.H., Won, J.S. and Park, H.J. 2004a. Determination \nand application of the weights for landslide susceptibility mapping \nusing an artificial neural network. Engineering Geology, 71, 289\u2013302, \n[71] Lohnes, R.A. and Handy, R.L. 1968. Slope angles in friable loess. \nGeology journal, 76 (3), 247-258. \n[72] Luzi, L. and Pergalani, F. 1999. Slope instability in static and \ndynamic conditions for urban planning: the \"Oltre Po Pavese'case \nhistory (Regione Lombardia \u2013 Italy). Natural hazards, 20, 57-82. \n[73] McClelland, D.E., Foltz, R.B., Wilson, W.D., Cundy, T.W., \nHeinemann, R., Saurbier, J.A. and Schuster, R.L. 1997. Assessment of\nthe 1995 & 1996 floods and landslides on the Clearwater National \nForest, Part I: landslide assessment. A report to the Regional Forester, \nNorthern Region, U.S. Forest Service. December 1997. 52 hlm. \n[74] Mehrotra, G.S., Sarkar, S. and Dhramaraju, R. 1991. Landslide \nhazard assessment in Rishikesh Tehri area, Garhwal Himalaya, India. \nIn: Bell DH (ed) Landslides. Balkema, Rotterdam, pp 1001\u20131007. \n[75] Mezughi, T., Akhir, J,M., Rafek, A.G. and Abdullah, I. 2012a. \nAnalytical hierarchy process method for mapping landslide \nsusceptibility to an area along E-W Highway (Gerik-Jeli) Malaysia. \nAsian Journal of Earth Sciences, 5 (1), 13-24. \n[76] Montgomery, D.R. and Dietrich, W.E. 1994. A physically-based \nmodel for the topographic control on shallow landsliding. Water \nresources research, 30, 1153-1171. \n[77] Nagarajan, R., Roy, A., Kumar, R.V., Mukherjee, A., and Khire, \nM.V. 2000. Landslide hazard susceptibility mapping based on terrain \nand climatic factors for tropical monsoon regions. Bulletin of \nEngineering Geology and the Environment, 58, 275-287. \n[78] Naithani, A.K., Prasad, C., Bisht, M.P.S. and Kumari, G. 1997. \nLandslide zonation and geoenvironmental appraisal along main \ncentre thrust zone in Mandakini Valley, Garhwal Himalaya, India. \nHimalayan Geology Journal, 18, 135\u2013143. \n[79] Neuh\u00e4user, B. and Terhorst, B. 2007. Landslide susceptibility \nassessment using \u201cweights-of-evidence\u201d applied to a study area at the \nJurassic escarpment (SW-Germany). Geomorphology, 86, 12-24. \n[80] Neuland, H. 1976. A prediction model of landslips. Catena 3, \n215\u2013230. \n[81] Ohlmacher, G. C. and Davis, J. C. 2003. Using multiple logistic \nregression and GIS technology to predict landslide hazard in \nnortheast Kansa, USA; Engineering Geology, 69, 331\u2013343. \n[82] Okimura, T. and Kawatani, T., 1986. Mapping of the potential \nsurface-failure sites on granite mountain slopes. In: Gardiner V (ed.). \nInt Geomorp Part I. Wiley, New York, 121\u2013138. \n[83] Oztekin, B. and Topal, T. 2005. GIS-based detachment \nsusceptibility analyses of a cut slope in limestone, Ankara\u2013Turkey. \nEnvironmental geology, 49, 124\u2013132. \n[84] Pachauri, A.K. and Pant, M. 1992. Landslide hazard mapping \nbased on geological attributes. Engineering Geology, 32, 81\u2013100. \n[85] Pachauri, A.K., Gupta, P.V. and Chander, R. 1998. Landslide zoning \nin a part of the Garhwal Himalayas. Environmental Geology, 36 (3-4), \n325-334. \n[86] Pack, R.T., Tarboton, D.G. and Goodwin, C.N. 1998. The SINMAP \n\n\n\napproach to terrain stability mapping. In: Proceedings of 8th \nCongress of the International Association of Engineering Geology, \nVancouver, British Columbia, Canada. hlm. 1157-1165. \n[87] Parise, M. and Jibson, R.W. 2000. A seismic landslide \nsusceptibility rating of geologic units based on analysis of \ncharacteristics of landslides triggered by the 17 January, 1994 \nNorthridge, California earthquake. Engineering Geology, 58, 251-270. \n[88] Pistocchi, A., Luzi, L. and Napolitano, P. 2002. The use of \npredictive modeling techniques for optimal exploitation of spatial \ndatabases: a case study in landslide hazard mapping with expert \nsystem-like methods. Environmental Geology, 41, 765\u2013775. \n[89] Reger, J.P. 1979. Discriminate analysis as a possible tool in \nlandslide investigations. Earth Surface Processes and Landforms, 4, \n267-273. \n[90] Rodeano, R. 2004. Study of Mass Movement along Bundu \nTuhan to Kundasang Highway, Sabah, Malaysia. MSc Thesis. \nUnpublished. Universiti Malaysia Sabah. \n[91] Rodeano, R., Jamaluddin, T.A., and Talip, M.A. 2012a. \nIntergration of GIS using GEOSTAtistical Interpolation Techniques \n(Kriging) (GEOSTAINT-K) in deterministic model for landslide \nsusceptibility analysis (LSA) at Kota Kinabalu, Sabah, Malaysia. ISSN \n1916-9787. Journal of Geography and Geology, 4 (1), 18-32. \n[92] Salciarini, D., Godt, J.W., Savage, W.Z., Conversini, P., Baum, R.L. \nand Michael, J.A., 2006. Modeling regional initiation of rainfall-\ninduced shallow landslides in the eastern Umbria Region of central \nItaly. Landslides, 3 (3), 181\u2013194. \n[93] Santacana, N., Baeza, B., Corominas, J., De Paz, A. and Marturi\u00e1, \nJ. 2003. A GIS-Based multivariate statistical analysis for shallow \nlandslide susceptibility mapping in La Pobla de Lillet area (Eastern \nPyrenees, Spain). Natural Hazards, 30 (3), 281-295. \n[94] Sarkar, S., Kanungo, D.P. and Mehrotra, G.S. 1995. Landslide \nhazard zonation: a case study in Garhwal Himalaya, India. Mountain \nResearch and Development, 15 (4), 301\u2013309. \n[95] Soeters, R. and Van Westen, C.J. 1996. Slope instability \nrecognition analysis and zonation. Dlm. Turner, K.T., Schuster, R.L. \n(pnyt.), Landslide: investigation and mitigation. Spec Rep 47. \nTransportation Research Board, National Research Council, \nWashington, DC. hlm. 129\u2013177. \n[96] Spiegelhalter, D.J. 1986. Uncertainty in expert systems, in \nartificial intelligence and statistics. Addison Wessey, Reading, MA, \npp17\u201355. \n[97] Sreemal, P.S., Chmpati Ray, P.K. and Srivastav, S.K. 2003. \nRemote sensing and GIS based method and software customization \nfor landslide hazard assessment along Silchar\u2013Shillong Highway, \nNortheast India. Tropical Agricultural Research, 15, 316\u2013326. \n[98] S\u00fczen, M.L., and Doyuran, V. 2004. A comparison of the GIS \nbased landslide susceptibility assessment methods: multivariate \nversus bivariate. Environmental Geology, 45, 665\u2013679. \n[99] Tangestani, H.M. 2003. Landslide susceptibility mapping using \nthe fuzzy gamma operation in a GIS, Kakan catchment area, Iran. Map \nIndia Conference, Disaster Management. 6 hlm. \n[100] Tangestani, H.M. 2004. Landslide susceptibility mapping using \nthe fuzzy gamma approach in a GIS, Kakan catchment area, southwest \nIran. Australian Journal of Earth Sciences, 51 (3), 439 \u2013 450. \n[101] Temesgen, B., Mohammed, M.U. and Korme, T. 2001. Natural \nhazard assessment using GIS and remote sensing methods, with \nparticular reference to the landslides in the Wondogenet area, \nEthiopia. Physics and Chemistry of the Earth Part C (26), 665\u2013675. \n[102] Terlien, M.T.J., van Asch, T.W.J. and van Westen, C.J. 1995. \nDeterministic modelling in GIS-based landslide hazard assessment. \nDlm. Carrara, A., and Guzzetti, F. (pnyt.). Geographical information \nsystems in assessing natural Hazards. Kluwer Academic Publishing: \nThe Netherlands. hlm. 57-77. \n[103] Thiery, Y., Malet, J.P., Sterlacchini, S., Puissant, A. and Maquaire, \nO. 2007. Landslide susceptibility assessment by bivariate methods at \nlarge scales: Application to a complex mountainous environment. \nGeomorphology, 92 (1), 18. \n[104] Thiery, Y., Sterlacchini, S., Malet, J.P., Puissant, A. and Maquaire, \nO. 2004. Strategy to reduce subjectivity in landslide susceptibility \nzonation by GIS in complex mountainous environments. In: Toppen, \nF., Prastacos, P. (eds), Proc. of AGILE 2004: 7th AGILE Conference on \nGeographic Information Science. 29th April \u2013 1st May 2004, \nHeraklion, Greece: 623-634. \n[105] Uromeihy, A. and Mahdavifar, M.R. 2000. Landslide hazard \nzonation of the Khorshrostam area, Iran. Bulletin of Engineering \nGeology and the Environment, 58 (3), 207-213. \n[106] van Westen, C.J. and Getahun, F.L. 2003. Analyzing the \n\n\n\n\n\n\n\n\n Malaysian Journal Geosciences (MJG) 1(2) (2017) 13-19 \n\n\n\nCite the article: Rodeano Roslee, Alvyn Clancey Mickey, Norbert Simon, Mohd. Norazman Norhisham (2017). Landslide Susceptibil ity Analysis (Lsa) Using Weighted Overlay \nMethod (Wom) Along The Genting Sempah To Bentong Highway, Pahang . Malaysian Journal Geosciences, 1(2) : 13-19\n\n\n\n19\n\n\n\nevolution of the Tessina landslide using aerial photographs and \ndigital elevation models. Geomorphology, 54, 77\u201389. \n[107] Van Westen, C.J. and Lulie Getahun, F. 2003. Analyzing \nthe evolution of the Tessina landslide using aerial photographs \nand digital elevation models. Geomorphology, 54 (1-2), 77-89. \n[108] van Westen, C.J. and Soeters, R. 1996. GISSIZ: \n\n\n\nGeographic Information Systems in Slope Instability Zonation Part 1: \nTheory (Vol. Version 2.). \n[109] Van Westen, C.J. 1992. Medium scale landslide hazard \n\n\n\nanalysis using a PC based GIS: a case study from Chinchina. In: \nAlzate JB (ed). Proc. 1st Simposio Internacional sobre Sensores \nRemotes y Sistemas de Informacion Geografica (SIG) para el \nEstudo de Riesgos Naturales, Bogota, Colombia. Instituto Geografico \nAgustin Codazzi, Bogota, vol 2, 20 pp. \n\n\n\n[110] van Westen, C.J. 1994. GIS in landslide hazard zonation: \nA review, with examples from the Andes of Colombia. Dlm. Price, \nM.F. and Heywood, D.I. (pnyt.). Mountain Environments and \nGeographic Information Systems. Taylor and Francis Publishers. \nhlm.135-165. \n[111] van Westen, C.J. 1997. Statistical landslide hazard \n\n\n\nanalysis. ILWIS 2.1 for Windows application guide. ITC Publication. \nEnschede, 73\u201384. \n[112]  \n\n\n\n[112] van Westen, C.J., 1993. GISSIZ. Training package for \ngeograophic infromation systems in slope instability zonation. Part 1: \ntheory. UNESCO\u2014ITC Project on \u201cGeo-information for \nenvironmentally sound management of natural resources\u201d. Enschede, \nNetherlands. \n\n\n\n[113] Van Westen, C.J., Rengers, N. and Soeters, R. 2003. Use of \ngeomorphological information in indirect landslide susceptibility \nassessment. Nat. Hazard., 30, 399\u2013419. \n[114] Varnes, D. J. 1978. Slope movement types and process. In Dikau, \nR., Brunsden, D. John Wiley and Sons. \n[115] Varnes, D.J. 1984. Landslide Hazard Zonation: A review of \nprinciples and practice. Commission on landslides of the IAEG, \nUNESCO. Natural Hazards, 3, 61p \n[116] Wieczorek, G.F. 1996. Landslide triggering mechnanisms. Dlm. \n\n\n\nA. And Turner, Landslides investigation and mitigation. Special report \n247 (pnyt.). Washington: National Academy Press. hlm. 76. \n\n\n\n[117] Yalcin, A. and Bulut, F. 2007. Landslide susceptibility mapping \nusing GIS and digital photogrammetric techniques: a case study from \nArdesen (NE-Turkey). Nat. Hazards, 41, 201\u2013226. \n[118] Yin, K.J. and Yan, T.Z. 1988. Statistical prediction model for \nslope instability of metamorphosed rocks. Proc. 5th International \nSymposium on Landslides. Lausanne, Switserland. Vol. 2, 1269-1272. \n[119] Zaitchik, B.F., van Es, H.M. and Sullivan, P.J., 2003. Modeling \nslope stability in Honduras: parameter sensitivity and scale of \naggregation. Soil Science Society of America Journal, 67 (1), 268\u2013278. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 35-40 \n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2021.35.40 \n\n\n\nCite the Article: Norazah Arjuna, Azlan Adnan, Nabilah Abu Bakar, Nabila Huda Aizon, Noor Sheena Herayani Harith (2021). 2-Dimensional Ground Response Analysis: \nA Review. Malaysian Journal of Geosciences, 5(1): 35-40. \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nREVIEW ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2021.35.40 \n\n\n\n2-DIMENSIONAL GROUND RESPONSE ANALYSIS: A REVIEW \n\n\n\nNorazah Arjunaa*, Azlan Adnanb, Nabilah Abu Bakarc, Nabila Huda Aizond, Noor Sheena Herayani Harithe \n\n\n\na,b,dSchool of Civil Engineering, Faculty of Engineering, University Teknologi Malaysia, 81310 Skudai, Johor, Malaysia \ncDepartment of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia \neFaculty of Engineering, Universiti Malaysia Sabah (UMS), Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia \n*Corresponding Author email: norazaharjuna@gmail.com \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 07 April 2021 \nAccepted 11 May 2021 \nAvailable online 03 June 2021\n\n\n\nEarthquake is one of the natural disasters that is caused by ground shaking in soil. Ground response analysis \nis conducted to obtain the ground motion acceleration on soil surface. Conventional 1-D ground response \nanalysis often suggests that soils are horizontally layered, with little consideration for heterogeneous \ndistribution of soil properties. In this study, literature on 2-D ground response analysis studies has been study \nas it covers vertically and horizontally waves. Therefore, researcher works were presented in numerical \nmodelling as substantial parameters for studies in near-surface structure. Besides, aspects for future research \nin the area 2-Dimensional Ground Response Analysis are included. The paper contributes to the under- \nstanding of 2-Dimensional Ground Response Analysis for the application of seismic risk mitigation.  \n\n\n\nKEYWORDS \n\n\n\nGround Response Analysis, microzonation, numerical method.\n\n\n\n1. INTRODUCTION \n\n\n\nGround response analysis is used to emphasize the microzonation maps in \n\n\n\na particular region. Different regions will have different microzonation \n\n\n\nmaps as different subsurface data is required. Subsurface data which \n\n\n\ninclude local geology and the geotechnical condition, cause wave \n\n\n\npropagation. This is important for assessing the performance of the \n\n\n\nalgorithm liquefication hazard, and determination of the earthquake-\n\n\n\ninduced forces. Moreover, the analysis led to instability of earth and earth-\n\n\n\nretaining structures. It will also be important to determine the fault \n\n\n\nrupture model from the source of an earthquake, the propagation of stress \n\n\n\nwaves to top of bedrock beneath the specific site, and to determine the \n\n\n\nground surface motion below ideal conditions. The shaking of the ground \n\n\n\nat a specific location is attributed to the impact of the earthquake \n\n\n\noccurrence occurring at that location, according to ground response \n\n\n\nresearch (Kramer, 1996). The intensity and magnitude of an earthquake \n\n\n\nare determined by the site's position and ground characteristics. It is \n\n\n\nnecessary to evaluate the ground shaking for that specific location to \n\n\n\nassess the seismic hazards. Any site's ground motion speed can be \n\n\n\nmeasured in terms of peak ground acceleration (PGA) and the geological \n\n\n\ncharacteristics of the ground position and the input ground motion data \n\n\n\nwill determine the PGA values (Shukla D., and Solanki C.H., 2021). \n\n\n\nIt is crucial to examine the mechanism involved in the propagation of \n\n\n\nstress waves from point of an earthquake which the delivering across the \n\n\n\nearth on particular site. Then, these are considered in determining \n\n\n\nwhether the soils above the bedrock influence ground surface motion. \n\n\n\nFollowing the ground response study, it is essential to take the following \n\n\n\nsteps. (1) collection of data, (2) develop numerical model (3) perform \n\n\n\nnumerical analysis and (4) result interpretation.  Shear wave velocity, \n\n\n\ndamping, soil depth and type of soil are input data that are needed to \n\n\n\nperform the analysis. The input data were divided into four groups \n\n\n\n(Yoshida, 2018), geological or topological configuration such as soil \n\n\n\nprofiles and cross-sectional shape, mechanical properties such as elastic \n\n\n\nmodulus and Poisson\u2019s ratio, input earthquake motion and parameters to \n\n\n\ncontrol the flow of the computer program or the method of the analysis. \n\n\n\nThe basic approach to begin the study is with input data, geological or \n\n\n\ntopological configuration in category 1. Category 2 mentioned above, as \n\n\n\nwell as the soil's mechanical properties including elastic modulus and \n\n\n\nPoisson\u2019s ratio are inserted. Moreover, to proceed with the analysis, input \n\n\n\nearthquake motion must also be obtained. Last category is parameters \n\n\n\ninput to control the method of analysis such as linear, equivalent linear or \n\n\n\nnon-linear analysis. Figure 1 shows the steps required for the ground \n\n\n\nresponse (see to: (Yoshida, 2018) for guidance). \n\n\n\nFigure 1: Steps for seismic ground response analysis (Yoshida, 2018) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 35-40 \n\n\n\nCite the Article: Norazah Arjuna, Azlan Adnan, Nabilah Abu Bakar, Nabila Huda Aizon, Noor Sheena Herayani Harith (2021). 2-Dimensional Ground Response Analysis: \nA Review. Malaysian Journal of Geosciences, 5(1): 35-40. \n\n\n\nThe process starts with a soil boring investigation (1), followed by the \n\n\n\ntranslation of data from the compiled soil boring log (2) into soil profile \n\n\n\nmodelling (3). Soil profile modelling separates the soil into four categories: \n\n\n\nsand, silt, clay, and bedrock. Obtaining an appropriate amount of soil \n\n\n\nmechanical properties can be a challenging job. There are insufficient \n\n\n\nresults to establish if the mechanical and in situ properties data are \n\n\n\ncorrect, necessitating a laboratory test. There are two methods for \n\n\n\nevaluating the elastic modulus (5): on-site estimation using the to measure \n\n\n\non site using the wave velocity (4) and using empirical equations based on \n\n\n\nother field measurements (6). The wave velocity can be obtained from the \n\n\n\nSPT N-value (7). \n\n\n\nIn addition, nonlinear soil parameters (12), is one of the mechanical \n\n\n\nproperties (11) that can be obtained directly from laboratory test (10) by \n\n\n\nusing undisturbed samples taken from site (9). This was based on previous \n\n\n\nexperience within the research team which had found focused on \n\n\n\nempirical equations or prior knowledge (14). Other parameters such as \n\n\n\nphysical property (13) based on plasticity index may be important. \n\n\n\nConversion from test data is required to represent the material property \n\n\n\n(12) by the empirical equations proposed. The next step is to obtain the \n\n\n\nvalues of the computer program's parameters in (8). Stress-strain \n\n\n\nrelationship are conveyed by means of a mathematical formula in many \n\n\n\ncomputer programs with determination of coefficients value. Damping \n\n\n\ncharacteristic (15) and earthquake motion (16) is compulsory for the \n\n\n\nearthquake response analysis (17). The results must be evaluated after the \n\n\n\nanalysis is complete (18).  \n\n\n\nAs shown in Figure 2, obtaining the modulus reduction curve, shear wave \n\n\n\nvelocity, and damping-strain curve are necessary to determine the \n\n\n\ndynamic site characterization. Dynamic site characterization is also \n\n\n\nincluded in mechanical properties category data mentioned before \n\n\n\n(Yoshida, 2018). From the dynamic site data, selection of rock motion is \n\n\n\nrequired to proceed in ground response analysis. The result of this \n\n\n\nanalysis is summarised in site-specific design spectra. \n\n\n\nFigure 2: Site specific ground response analysis (Govindaraju L. et al. \n\n\n\n2004) \n\n\n\nThe dimensionality of the model where incoming shear waves propagate \n\n\n\nfrom the underlying bedrock can be divided into three categories: one-\n\n\n\ndimensional (1-D), two-dimensional (2-D), and three-dimensional (3-D) \n\n\n\nshear wave propagation methods. \n\n\n\nFor flat or gently sloping sites with parallel material boundaries, the 1-D \n\n\n\napproach in ground response analysis is useful. Consequently, such \n\n\n\nsituations are normal used in geotechnical earthquake practice. \n\n\n\nFurthermore, the 1-D method is recommended as many commercial \n\n\n\nprograms with different soil models are applicable in personal computers, \n\n\n\nand it is proven this methodology survived by real earthquakes using the \n\n\n\n1-D design in structures (Govindaraju L. et al. 2004). In addition, (Phillips \n\n\n\nC. and Hashash Y., 2009), 1-D ground response analysis methods are \n\n\n\ncommonly used to measure the effect of soil deposits on propagated \n\n\n\nground motion. Besides (Shukla D. and Solanki C. H., 2021) and (Mazlina \n\n\n\nM. et al., 2021) also using site\u2019s soil profile with the 1-D ground response \n\n\n\nanalysis to hazards contribution. \n\n\n\nAssumption for 1-D ground response analysis (Govindaraju L. et al. 2004) \n\n\n\nwhich are all boundaries are horizontal, soil and bedrock are assumed to \n\n\n\nextend infinitely in the horizontal direction (half-sphere) and because of \n\n\n\nthe decrease in velocities of surface deposits, inclined incoming seismic \n\n\n\nrays are reflected in a near-vertical direction. As a result, shear waves \n\n\n\npropagating vertically from the underlying bedrock are unlikely to have \n\n\n\ncaused the observed shift in the soil deposit's response. \n\n\n\nIn general, the use of 1-D equivalent linear wave propagation models may \n\n\n\nbe unadvisable when the lateral soil spatial variation is not homogeneous \n\n\n\nand the underlying bedrock interface is obviously variable (Chen G. et al \n\n\n\n2015). Available evidence shows that the dynamic response of the soil is \n\n\n\nclassified as a linear action under low levels of strain to determine the \n\n\n\namplification of seismic waves. However, for higher stress-strain levels, \n\n\n\nlaboratory testing of soil samples reveals a nonlinear relationship that \n\n\n\nreflects the nonlinear nature of the soil response. (M. Hosseini et al 2010). \n\n\n\nNevertheless, 2-D method of analysis is dependent on bedrock depth. \n\n\n\nMicrotremor array measurements are used to estimate if the boreholes \n\n\n\nare not deep enough to hit bedrock, the seismic bedrock depth. The data \n\n\n\nfrom microtremor array studies was combined with topographical \n\n\n\nproperties and geological section to obtain 2-D shear wave velocity, \n\n\n\naccording to studies by (M. E. Hasal and R. Iyisan, 2014). Furthermore, \n\n\n\n(Pehlivan M et al., 2012) found the effect of horizontal soil property \n\n\n\nvariability on the ground response can be evaluated using 2-D site \n\n\n\nresponse analysis with properties that differ both vertically and \n\n\n\nhorizontally. In the frequency or time domains, it can be solved using \n\n\n\ndynamic finite-element analysis. Two- or three-dimensional mapping may \n\n\n\nbe used for sloping or irregular ground surfaces, as well as embedded \n\n\n\nstructures. Dynamic finite element analysis (R. B. Jishnu et al 2013) is \n\n\n\nwidely used to solve such problems. However, this is a fundamentally \n\n\n\ndifficult problem for 1-D analysis as PGA values obtained can be less \n\n\n\nconservative depending on the site and earthquake ground motion data, \n\n\n\nnecessitating 2-D analysis. \n\n\n\nIn addition, the presence of a soft soil valley and/or a hill should contribute \n\n\n\nto the acceptance of 2-D or 3-D numerical schematizations, likely due to \n\n\n\nthe focalization of seismic waves at the valley's ground surface and at the \n\n\n\ncrest, respectively (A. Amorosi et al 2018). Moreover, (Reddy M. V. R. K. et \n\n\n\nal., 2021) current study investigate the ground reaction of pond ash \n\n\n\nobtained from Odisha in one-dimensional (1-D), two-dimensional (2-D), \n\n\n\nand three-dimensional (3-D) dimensions under various earthquake \n\n\n\nmotions. \n\n\n\n2. 2-DIMENSIONAL GROUND RESPONSE STUDIES \n\n\n\nMany researchers use ground response analysis to upgrade the knowledge \n\n\n\nfor seismological and structural behaviour. 2-D ground response analysis \n\n\n\nis preferred for problems, in which 1-D is significantly larger than others \n\n\n\nsuch as earth dams, tunnels, cantilever retaining wall etc., (P. Nautiyal et \n\n\n\nal 2019). Besides, 2-D analysis requires certain conditions such as sloping \n\n\n\nor irregular ground surface, the presence of heavy structures or stiff, \n\n\n\nembedded structures, or walls and tunnel (S. L. Kramer 1996). \n\n\n\nThe effect of local geology in the change of seismic wavefield at a recording \n\n\n\nsite is called site effects which local geology contains of surface topography \n\n\n\nand surface sedimentary site. Parameters used to describe the behaviour \n\n\n\nof site effects are the geometry of soil stratigraphy (thickness and lateral \n\n\n\ndiscontinuities), the shape of topographic relief and the dynamic, physical \n\n\n\nand mechanical properties of soil and rock materials (A. Ansal 2004). \n\n\n\nIn this paper, 5 categories of studies can be summarized in 2-D ground \n\n\n\nresponse analysis studies such as the study of site effects, development of \n\n\n\nseismic microzonation, seismic wave propagation in soil, seismic \n\n\n\nresponse, and the study of edge effect. \n\n\n\n2.1 Study site effects \n\n\n\nThe effects on ground motion as seismic waves interact with the complex \n\n\n\ngeological system in the first 100 metres or so of the earth's crust are \n\n\n\nreferred to as the site effect. Studies from (A. Cipta et al. 2018) use 2-D \n\n\n\nground response analysis to analyse the effect of site amplification and \n\n\n\nbasin resonance. Ground motion is amplified by basin structure and depth \n\n\n\nat different locations, depending on the depth of the basin, distance from \n\n\n\nthe source, distance from the basin edge, and the magnitude of the \n\n\n\nearthquake. Moreover (M. Tapia et al. 2006) who critically discussed that \n\n\n\n1-D numerical analysis result for basin effects can underestimate the site \n\n\n\namplification effects thus 2-D or 3-D ground analysis is required to obtain \n\n\n\nmore accurate results. In addition, (P. P. Capilleri et al. 2018) presented \n\n\n\nthe 2-D ground response analysis can consider both stratigraphic and \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 35-40 \n\n\n\nCite the Article: Norazah Arjuna, Azlan Adnan, Nabilah Abu Bakar, Nabila Huda Aizon, Noor Sheena Herayani Harith (2021). 2-Dimensional Ground Response Analysis: \nA Review. Malaysian Journal of Geosciences, 5(1): 35-40. \n\n\n\ntopographic effects for the amplification on the ground. Dams, bridges, \n\n\n\nindustrial facilities, residential areas, and source locations need seismic \n\n\n\nwave amplification. Seismic waves disperse and reflect at the surface, at \n\n\n\nthe layer interface, and around topographic anomalies, amplifying the \n\n\n\neffects of earthquakes (M. Bararpour et al. 2016). Furthermore, (H. Reda \n\n\n\net al. 2016) investigated 2-D ground response analysis to verify the \n\n\n\npresence of local site effects by comparing simulated versus transfer \n\n\n\nfunction. (R. Iyisan and H. Khanbabazadeh 2013) studied analytical \n\n\n\nmethods for examining 2-D and 3-D dynamic behaviour is the general \n\n\n\nmethod among researchers to reduce the receivers used in alluvial valley \n\n\n\nin order to assess site effects during earthquakes. The parameters most \n\n\n\noften used for description of site effect analysis are site and soil \n\n\n\ncharacterization. \n\n\n\n2.2 Develop seismic microzonation \n\n\n\nSeveral researchers (C. Lacave et al. 2008), (M. Tapia et al. 2006) have \n\n\n\nstudied 2-D ground response analysis to obtain seismic microzonation \n\n\n\nstudy for a particular area. In addition, (A. Cipta et al. 2018) used data from \n\n\n\nearthquake that occurred on January 11th, 1963 as the maximum plot to \n\n\n\nobtain the surface peak ground acceleration and spectral acceleration \n\n\n\nvalues of Catania (Italy). Both PGA value and spectral acceleration can be \n\n\n\nobtained from the seismic microzonation data. \n\n\n\n2.3 Study seismic wave propagation in soil \n\n\n\nThe seismic wave propagation in a heterogeneous medium can be studied \n\n\n\nusing a 2-D ground response analysis (C. Du and G. Wang 2015). In this \n\n\n\nresearch, the vertically incident plane wave is input through a \n\n\n\ndisplacement boundary, and the soil shear modulus is modelled as a \n\n\n\nspatially random field with correlation distances in both horizontal and \n\n\n\nvertical directions. The effect of amplification factors is then investigated. \n\n\n\nMeanwhile, to evaluate the response of a valley to SH waves, (N. \n\n\n\nTheodoulidis et al. 2018) use 2D ground response analysis. \n\n\n\n2.4 Study basin effect \n\n\n\n(D. Komatitsch and J-P. Vilotte 1998) conducted 2-D dynamic analysis on \n\n\n\nthe basin to study the edge effect on the spatial variation of surface ground \n\n\n\nmotion. The main aspects of the studies are the superposition of two \n\n\n\nweakly interacting effects: the shape of the surface topography and the \n\n\n\nshape of the sedimentary basin for this incident wavelength. However, the \n\n\n\neffects of the basin structure are constrained. (R. Iyisan and H. \n\n\n\nKhanbabazadeh 2013) too studied the impact of basin edge on the \n\n\n\ndynamic behaviour of the basin by using a variety of bedrock inclinations \n\n\n\nthat are chosen, ranging from gentler 10 and 20 slopes to steeper 30 and \n\n\n\n40 slopes at the valley. By focusing on the earthquake response \n\n\n\nexamination of the basin that is laterally confined and in the form of filled \n\n\n\nsediment, (B. Ozaslan et. al., 2021) study presents the effects of \n\n\n\nheterogeneities in both vertical and lateral directions on the local seismic \n\n\n\nresponse. Moreover, (Peyman Ayoubi, et al., 2021) use an elastic medium \n\n\n\nexposed to vertically propagate SV plane waves. They also examine the \n\n\n\nresults of basin geometry and material properties using idealized basin \n\n\n\nshapes. \n\n\n\n2.5 Seismic response \n\n\n\n(A. Cipta et al. 2018) and (A. Pagliaroli et al. 2018) use 2-D ground \n\n\n\nresponse analysis to study the basin effects that influence the seismic \n\n\n\nresponse. \n\n\n\nA summary of 2-D ground response studies is given in Table 1 below. Many \n\n\n\nresearchers use 2-D ground response studies to investigate the effects of \n\n\n\nsoil amplification and seismic response. Most of the research in this 2-D \n\n\n\nground response studies are aimed at peak ground acceleration, \n\n\n\namplification, site effects and transfer function.\n\n\n\nTable 1: Summary from Previous Studies \n\n\n\nAuthor \n\n\n\nInput Output \n\n\n\nS\nP\n\n\n\nT\n D\n\n\n\na\nta\n\n\n\n\n\n\n\nS\nh\n\n\n\ne\na\n\n\n\nr w\na\n\n\n\nv\ne\n\n\n\n v\ne\n\n\n\nlo\ncity\n\n\n\n\n\n\n\nB\na\n\n\n\nsin\n \n\n\n\nS\ntra\n\n\n\ntig\nra\n\n\n\np\nh\n\n\n\nic a\nn\n\n\n\nd\n \n\n\n\nto\np\n\n\n\no\ng\n\n\n\nra\np\n\n\n\nh\nic \n\n\n\nP\ne\n\n\n\na\nk\n\n\n\n g\nro\n\n\n\nu\nn\n\n\n\nd\n \n\n\n\na\ncce\n\n\n\nle\nra\n\n\n\ntio\nn\n\n\n\n (P\nG\n\n\n\nA\n) \n\n\n\nA\nm\n\n\n\np\nlifica\n\n\n\ntio\nn\n\n\n\n\n\n\n\nS\ne\n\n\n\nism\nic re\n\n\n\nsp\no\n\n\n\nn\nse\n\n\n\n\n\n\n\nS\nite\n\n\n\n e\nffe\n\n\n\ncts \n\n\n\nT\nra\n\n\n\nn\nsfe\n\n\n\nr F\nu\n\n\n\nn\nctio\n\n\n\nn\n \n\n\n\n(C. Lacave et al. 2008) \n\n\n\n(M. Tapia et al. 2006)  \n\n\n\n(A. Cipta et al. 2018) \n\n\n\n(C. Du and G. Wang 2015) \n\n\n\n(N. Theodoulidis et al. 2018) \n\n\n\n(A. Pagliaroli et al. 2018) \n\n\n\n(P. P. Capilleri et al. 2018) \n\n\n\n(H. Reda et al. 2016) \n\n\n\n(D. Komatitsch and J-P. Vilotte 1998) \n\n\n\n(R. Iyisan and H. Khanbabazadeh \n2013) \n\n\n\n(B. Ozaslan et. al., 2021) \n\n\n\n(Peyman Ayoubi, et al., 2021) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 35-40 \n\n\n\n\n\n\n\n \nCite the Article: Norazah Arjuna, Azlan Adnan, Nabilah Abu Bakar, Nabila Huda Aizon, Noor Sheena Herayani Harith (2021). 2-Dimensional Ground Response Analysis: \n\n\n\nA Review. Malaysian Journal of Geosciences, 5(1): 35-40. \n \n\n\n\n\n\n\n\nSince placing enough receivers in an alluvial valley to determine site \n\n\n\neffects during earthquakes is costly, analytical methods for evaluating 2-D \n\n\n\nand 3-D dynamic activity of the sites have increased in popularity among \n\n\n\nthe researchers (R. Iyisan and H. Khanbabazadeh 2013). \n\n\n\n3. NUMERICAL METHOD IN 2-D ANALYSIS \n\n\n\nGround response analysis is required to replace the physical observation \n\n\n\non site. However, further investigation is necessary to explore the \n\n\n\ngeotechnical investigations. Overall, this work offers a successful to the \n\n\n\nsubstantial parameters for numerical modelling studies in near-surface \n\n\n\nstructure. \n\n\n\nBased on literature review by (J. F. Semblat 2011) five methods can be \n\n\n\nidentified, which are Finite Difference Method, Finite Element Method, The \n\n\n\nSpectral Element Method, The Boundary Element Method and Discrete \n\n\n\nWavenumber Method. \n\n\n\n3.1 Finite Difference Method \n\n\n\nUsing this approach, partial differential equations can be calculated \n\n\n\ndirectly under any scenario. It can approximate by linear combinations of \n\n\n\nfunction values at the grid points, which are replaced with a set of discrete \n\n\n\nequations, called finite-difference equations. The finite-difference method \n\n\n\nis typically represented on a regular grid; therefore, it is seldom used for \n\n\n\nirregular CAD geometries, but regular rectangular or block-shaped \n\n\n\nmodels. It is the most commonly used measure to model seismic wave \n\n\n\npropagation in an elastic media. Studies by (M. Tapia et al. 2006), the \n\n\n\npropagation of seismic waves in the 2-D cross-section of the valley can be \n\n\n\nmodelled using the finite-difference method. However, the method is \n\n\n\naccurate in elastodynamics but apply to simple geometries (J. F. Semblat \n\n\n\n2011). Beside, (M. E. Hasal and R. Iyisan 2014) reported that for modelling \n\n\n\nseismic wave propagation in an elastic medium, the finite difference \n\n\n\napproach usually employs a uniform mesh. It is simple and \n\n\n\nstraightforward to use, but it falls short of simulating complex boundary \n\n\n\nconditions such as surface topography, subsurface geometry, and sloping \n\n\n\nbedrock. \n\n\n\nFurthermore, Finite Difference Method could be carried out for \n\n\n\ntopographical structure site response review (M. Kamalian et al 2006). It \n\n\n\ncan solve nonlinear wave propagation problems in the time domain by \n\n\n\ncompletely formulating the numerical method. \n\n\n\n\n\n\n\nFigure 3: 2-D Finite Difference Method Model (F. A. F. Lopez et al 2015) \n\n\n\nA finite-difference method model (FLAC) as shown in Figure 3 was used \n\n\n\nby (F. A. F. Lopez et al 2015). Absorbing boundaries were used at the sides \n\n\n\nof the model under consideration for the seismic waves. FLAC engage a \n\n\n\nspecial lateral boundary known as free field, in which these lateral \n\n\n\nboundaries are coupled to the free field mesh through viscous damping \n\n\n\ndashpots which simulate absorbing boundaries. However, studied by \n\n\n\n(Carolina Volpini et. al., 2019) due to geometric scattering of waves, a 2-D \n\n\n\nmodel with the same dimensions and material properties would normally \n\n\n\noverestimate the soil's dynamic stiffness and radiation damping. \n\n\n\nIn addition, (J. Miksat et al 2010) found that to model 3-D amplification \n\n\n\neffects inside the basin, a finite-difference approach was used. They \n\n\n\ndiscovered that shallow earthquakes produce more powerful surface \n\n\n\nwaves than deep earthquakes, and that computational modelling can \n\n\n\nmeasure frequency-dependent site amplifications for the Taipei basin. \n\n\n\n3.2 Finite Element Method \n\n\n\nThe finite-element method (FEM) is a computational method that divides \n\n\n\na model into small, finite-sized geometrically simple components. Finite-\n\n\n\nelement mesh is formed by combining all these basic shapes. Partial \n\n\n\ndifferential equations describe a system of field equations mathematically \n\n\n\nand these equations are formulated for each element. Each element \n\n\n\napproximates a simple function such as a linear or quadratic polynomial, \n\n\n\nwith a finite number of degrees of freedom (DOFs). Sparse matrix solvers \n\n\n\nare the solution for combination of all elements. \n\n\n\nThe finite element method is capable to deal with complex geometries and \n\n\n\nnumerous heterogeneities (even for inelastic constitutive models but has \n\n\n\nseveral difficulties such as numerical dispersion and numerical damping. \n\n\n\nIt is very useful for modelling complex geometry and boundary conditions \n\n\n\nbecause it allows irregular mesh with elements of various sizes and \n\n\n\ngeometries to be used. (J. F. Semblat 2011). \n\n\n\nFEM has been shown to be effective in solving problems with bounded \n\n\n\ndomains, particularly when inhomogeneities and nonlinear effects must \n\n\n\nbe considered. For domains with infinite extensions, regular finite element \n\n\n\ndiscretization produces wave reflections at the edges of the FE mesh, \n\n\n\nwhich can only be partially prevented in some cases by using so-called \n\n\n\ntransmit discretization. The disadvantage of FE being formulated in \n\n\n\ntransformed spaces, cannot be used in nonlinear dynamic analysis (M. \n\n\n\nKamalian et al 2006). \n\n\n\nSeveral finite element softwares are capable of modelling geotechnical \n\n\n\nengineering problems where it can be used to analyse structures such as \n\n\n\nretaining walls, slopes, embankment dams, etc. In finite element method, \n\n\n\nthe region to be analysed is divided into several elements connected at \n\n\n\ntheir command nodal points. A finite element mesh used in the seismic \n\n\n\nanalysis. By means of finite element method, it can calculate each element \n\n\n\nin horizontal and vertical movements of each nodal point at each stage in \n\n\n\nthe analysis. \n\n\n\nQUAD4M software spread P and/or SV waves with vertical incidence. (A. \n\n\n\nPagliaroli et al. 2018) and (S. Amoroso et al. 2018) performed QUAD4M \n\n\n\nfinite element to model the vertical incident (SV) in plane shear waves. \n\n\n\nThey concluded that by adding viscous dampers at the bottom of the mesh \n\n\n\nwhere the input is applied in terms of shear stress history, QUAD4M can \n\n\n\nmodel an elastic foundation. Side boundaries, on the other hand, are \n\n\n\nperfectly reflecting; therefore, to minimize the effect of artificially \n\n\n\nreflected waves, side boundaries were extended around 500m in both \n\n\n\ndirections from the basin's edges. \n\n\n\nMeanwhile QUAKE/W has a finite element approach in which the \n\n\n\ngoverning motion equation for dynamic response of a system can be \n\n\n\nexpressed as: [ M]{ \u00fc}+ [C ]{ \u00f9}+[K]{ u } ={F } Where; [M] is mass matrix, \n\n\n\n[C] is damping matrix, [K] is stiffness matrix, {F} is vector of loads, {\u00fc} is \n\n\n\nnodal acceleration vector,{ \u00f9 } is nodal velocity vector, {u} is nodal \n\n\n\ndisplacement vector. (M. E. Hasal and R. Iyisan 2014) and (M. Bararpour \n\n\n\net al 2016) used QUAKE/W 2-D analysis, to obtain the maximum absolute \n\n\n\nhorizontal acceleration values at the surface. (M. Bararpour et al 2016) \n\n\n\nperformed PLAXIS to their model based on the method defined by Lysmer \n\n\n\nand Kuhlmeyer 1969, viscous adsorbent boundaries have been \n\n\n\nimplemented. They concluded that the amplification factors given by the \n\n\n\nanalysis are greater than the amplification factors given by Italian code. \n\n\n\n3.3 The Spectral Element Method \n\n\n\nThe spectral element method has been increasingly studied to analyze 2-\n\n\n\nD and 3-D wave propagation in linear media with a good accuracy due to \n\n\n\nits spectral convergence properties (J. F. Semblat 2011). (J. Miksat et al \n\n\n\n2010) too used same approach to build a representation of the Taipei \n\n\n\nbasin's ground motions. Another study by (A. Cipta et al. 2018) applied to \n\n\n\ninvestigate seismic wave interaction with 3-D structure of the Georgia \n\n\n\nBasin, British Columbia, Canada. (C. Du and G. Wang 2015) used \n\n\n\nSPECFEM2D to resemble a viscoelastic medium. \n\n\n\n3.4 The Boundary Element Method \n\n\n\nFor dynamic analysis of linear elastic bounded and unbounded media, the \n\n\n\nBoundary Element Method (BEM) is capable of producing realistic \n\n\n\nnumerical method. As the discretization is done, only on the boundary, \n\n\n\nresulting in smaller mesh systems of equations for wave propagation \n\n\n\nmatter. For scattered waves in topographical systems, the outgoing waves \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 35-40 \n\n\n\n\n\n\n\n \nCite the Article: Norazah Arjuna, Azlan Adnan, Nabilah Abu Bakar, Nabila Huda Aizon, Noor Sheena Herayani Harith (2021). 2-Dimensional Ground Response Analysis: \n\n\n\nA Review. Malaysian Journal of Geosciences, 5(1): 35-40. \n \n\n\n\n\n\n\n\nacross infinite domains are useful. As a consequence, when using this \n\n\n\napproach to solve problems with semi-infinite domains, there is no need \n\n\n\nto model the far field. \n\n\n\nSimilar work has also been pursued by (M. Kamalian et al 2006) who \n\n\n\nstudied the seismic response of canyons and alluvial basins using a time-\n\n\n\ndomain 2D Boundary Element System. Their formulation, however, was \n\n\n\nlimited to anti-plane (SH) wave scattering. This approach has also been \n\n\n\nused to examine the site response of homogeneous and non-homogeneous \n\n\n\ntopographic structures subjected to in-plane compression (P) and shear \n\n\n\n(SV) waves (A. Amorosi et al 2018), and (Yoshida, 2018). \n\n\n\n3.5 Discrete Wavenumber Method \n\n\n\nThe discrete wavenumber method proposed by (C. Lacave et al. 2008) is \n\n\n\nused to measure the 2-D response of alluvial basins. The use of a double \n\n\n\nFourier transforms to transform the direct problem from the space and \n\n\n\ntime domain to the horizontal wavenumber and frequency domain is the \n\n\n\nframework of this approach. To solve the problem numerically, a \n\n\n\ndiscretization in both space and time, and thus in wavenumber and \n\n\n\nfrequency, is used. Meanwhile, (J. Riepl et al 2000) investigated the \n\n\n\nmethod accounts for one irregular interface that separates the underlying \n\n\n\nhard rock from the sedimentary basin fill. Table 2 below summarizes the \n\n\n\nnumerical method used in 2-D ground response analysis. \n\n\n\nTable 2: Summary from Prior Studied \n\n\n\nMethod Software Reference \n\n\n\nFinite Element \nMethod \n\n\n\nQUAD4M \n(A. Pagliaroli et al. 2018) \n\n\n\n( S. Amoroso et al. 2018) \n\n\n\nQuake/W \n\n\n\n(M. E. Hasal and R. Iyisan \n2014) \n\n\n\n(M. Bararpour et al 2016) \n\n\n\nPlaxis (P. P. Capilleri et al. 2018) \n\n\n\nSpectral \nElement \nMethod \n\n\n\nSPECFEM2D \n(C. Du and G. Wang 2015) \n\n\n\n(A. Cipta et al. 2018) \n\n\n\nDiscrete \nWavenumber \nMethod \n\n\n\nAki-Larner \nMethod \n\n\n\n(C. Lacave et al. 2008) \n\n\n\n(J. Riepl et al 2000) \n\n\n\n4. CONCLUSION \n\n\n\nThe review on the literature on the 2-D ground response studies shows \n\n\n\nthat most studies focus on site effects in ground response analysis. \n\n\n\nMoreover, to perform the numerical 2-D ground response studies, finite \n\n\n\ndifference and finite element method were the popular approaches among \n\n\n\nresearchers. Studies on the following areas are still inadequate and \n\n\n\ndeserve attention of future research for more understanding of the 2-D \n\n\n\nground response studies: \n\n\n\n\u2022 Development seismic microzonation \n\n\n\n\u2022 Seismic wave propagation in soil \n\n\n\n\u2022 Study on edge effects \n\n\n\n\u2022 Study on seismic response. \n\n\n\nREFERENCES \n\n\n\nAngelo, A., Daniela, B., Gaetano, F. 2018. Evaluation of Seismic Site Effects \n\n\n\nBy Means Of 1D, 2D And 3D Finite Element Analyses. A Case Study. \n\n\n\nPaper presented at the Conference: 16th European Conference on \n\n\n\nEarthquake Engineering at: Thessaloniki \n\n\n\nAthanasius C., Phil Cummins, D., Masyhur I., Sri H. 2018. Basin Resonance \n\n\n\nand Seismic Hazard in Jakarta, Indonesia. Geosciences. \n\n\n\nDOI:10.3390/geosciences8040128 \n\n\n\nAtilla, A. 2004. Recent Advances in Earthquake Geotechnical Engineering \n\n\n\nand Microzonation. Springer \n\n\n\nBilal, O., Recep, I., Emre, M.H., Hadi, K., Hiroaki, Y. 2021. Assessment of the \n\n\n\nDesign Spectrum with Aggravation Factors  by 2D Nonlinear \n\n\n\nNumerical Analyses: A Case Study in Gemlik Basin, Turkey. Bulletin of \n\n\n\nEarthquake Engineering doi: 10.21203/rs.3.rs-415483/v1 \n\n\n\nCamilo, P., Youssef, M.A., Hashash. 2009. Damping Formulation for \n\n\n\nNonlinear 1D Site Response Analyses. Soil Dynamics and Earthquake \n\n\n\nEngineering \n\n\n\nCarolina, V., John, D., Andreas H. Nie. 2019 Guidance on \n\n\n\n Conducting 2D Linear Viscoelastic Site Response Analysis Using \n\n\n\na Finite Element Code, Journal of Earthquake Engineering, DOI: \n\n\n\n10.1080/13632469.2019.1568931 \n\n\n\nChunyang, D., Gang, W. 2015. Seismic Response Analysis of Heterogeneous \n\n\n\nSoils Using 2D Spectral Element Method. The Twenty-Eight KKHTCNN \n\n\n\nSymposium on Civil Engineering November 16-18, 2015, Bangkok, \n\n\n\nThailand \n\n\n\nCorinne, L., Pierre-Yves, B., Marc, K., Martin, G.K. 2008. 2D Equivalent \n\n\n\nLinear Site Effect Simulation: Example Applications to Two Deep Valley. \n\n\n\nBulletin of Earthquake Engineering DOI: 10.1007/s10518-007-9054-1 \n\n\n\nDimitri, K., Jean-Pierre, V. 1998. The Spectral Element Method: An Efficient \n\n\n\nTool To Simulate The Seismic Response Of 2D and 3D Geological \n\n\n\n Structures. Bulletin of the Seismological Society of America \n\n\n\nFrancisco, A., Francisco, L., Juan, C., Ayes, Z., Carlos, O., Vargas, M., \n\n\n\nAlejandro, V. 2015. Site Response Analysis (SRA): A Practical \n\n\n\nComparison among Different Dimensional Approaches. Fundamentals \n\n\n\nto Applications in Geotechnics, DOI: 10.3233/978-1-61499-603-3-\n\n\n\n1041 \n\n\n\nGovindaraju, L., Ramana, G.V., Hanumantharao, C. Sitharam, T.G. 2004 Site-\n\n\n\nSpecific Ground Response Analysis. Current Science. \n\n\n\nGuoxing, C., Dandan, J., Jiao, Z., Jian, S., Xiaojun, L. 2015. Nonlinear Analysis \n\n\n\non Seismic Site Response of Fuzhou Basin, China. Bulletin of the \n\n\n\nSeismological Society of America. Vol. 105, No. 2A, pp. 928\u2013949, April \n\n\n\n2015, DOI: 10.1785/0120140085 \n\n\n\nJishnu, R.B, Naik, S.P., Patra, N.R., Malik, J.N. 2013 Ground Response \n\n\n\nAnalysis of  Kanpur Soil along Indo-Gangetic Plains. Soil Dynamics and \n\n\n\nEarthquake Engineering. \n\n\n\nKramer, S.L. 1996. Geotechnical Earthquake Engineering. Prentice Hall, \n\n\n\nNew Jersey \n\n\n\nMar, T., Albert, M., Sara, F., Peter, F. 2006. 1D and 2D Seismic Wave \n\n\n\nPropagation Modeling In The Cerdanya Valley (Catalonia, Spain). Third \n\n\n\nInternational Symposium on the Effects of Surface Geology on Seismic \n\n\n\nMotion Grenoble, France, 30 August - 1 September 2006 Paper Number: \n\n\n\n67. \n\n\n\nMazlina, M., Liew, M.S., Adnan, A., Harahap, I.S.H., Hamid, N.H. 2021. \n\n\n\nGround Response Analysis for Stiff and Soft Soil Under Different \n\n\n\nEarthquake Events: A Comparison. In: Mohammed B.S., Shafiq N., \n\n\n\nRahman M. Kutty S., Mohamad H., Balogun AL. (eds) ICCOEE2020. \n\n\n\nICCOEE 2021. Lecture Notes in Civil  Engineering, vol 132. Springer, \n\n\n\nSingapore. https://doi.org/10.1007/978-981-33- 6311-3_101 \n\n\n\nMiksat, J., Wen, K.L., Wenzel, F., Sokolov, V., Chen, C.T. 2010. Numerical \n\n\n\nModelling Of Ground Motion in the Taipei Basin: Basin and Source \n\n\n\nEffects. Geophys. J. Int. doi: 10.1111/j.1365-246X.2010.04818.x \n\n\n\nMir Mohammad Hossein, S.M., Asadolahi Pajouh, M., Mir Mohammad \n\n\n\nHosseini, F. 2010. The Limitations Of Equivalent Linear Site Response \n\n\n\nAnalysis Considering Soil Nonlinearity Properties, Fifth International \n\n\n\nConferences On Recent Advances In  Geotechnical Earthquake \n\n\n\nEngineering And Soil Dynamics. \n\n\n\nMohammad, B., Asskar, J., Hamid R.T. 2016. The Effect of 2D Slope and \n\n\n\nValley on Seismic Site Response. Arabian Journal of Geosciences 2016. \n\n\n\nMohammad, B., Asskar, J., Hamid, R.T. Avakoli. 2016. The Effect of 2D Slope \n\n\n\nand Valley on Seismic Site Response. Arabian Journal of Geosciences. \n\n\n\nMohsen, K., Behrouz, G., Abdollah, S.B., Ali, K. 2007. Amplification pattern \n\n\n\nof 2D semi-sine-shaped valleys subjected to vertically propagating \n\n\n\nincident waves. Communications in Numerical Methods in \n\n\n\n Engineering \n\n\n\nMohsen, K., Mohammad, K.J., Abdollah, S.B., Arash, R., Behrouz, G. 2006. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(1) (2021) 35-40 \n\n\n\n\n\n\n\n \nCite the Article: Norazah Arjuna, Azlan Adnan, Nabilah Abu Bakar, Nabila Huda Aizon, Noor Sheena Herayani Harith (2021). 2-Dimensional Ground Response Analysis: \n\n\n\nA Review. Malaysian Journal of Geosciences, 5(1): 35-40. \n \n\n\n\n\n\n\n\nTime-domain two-dimensional site response analysis of non-\n\n\n\nhomogeneous topographic structures by a hybrid BE/FE method. Soil \n\n\n\nDynamics and Earthquake Engineering. DOI: \n\n\n\n10.1016/j.soildyn.2005.12.008 \n\n\n\nMurat, E.H., Recep, I. 2014. A numerical study on comparison of 1D and \n\n\n\n 2D Seismic Responses of a Basin in Turkey. American Journal of \n\n\n\nCivil  Engineering 2014; 2(5):123-133 DOI: \n\n\n\n10.11648/j.ajce.20140205.11 \n\n\n\nMurat, E.H., Recep, I. 2014. A Numerical Study on Comparison of 1D and \n\n\n\n2D Seismic Responses of A Basin In Turkey. American Journal of Civil \n\n\n\nEngineering. DOI: 10.11648/j.ajce.20140205.11 \n\n\n\nNautiyal, P.R., Dhiraj B., Dubey, M., Ramanand, W. 2019. Ground Response \n\n\n\nAnalysis: Comparison of 1D, 2D And 3D Approach, Proceedings of the \n\n\n\nIndian Geotechnical Conference IGC 2019, 19-21 Dec SVNIT Surat \n\n\n\nNozomu, Y. 2014. Geological and Earthquake Engineering Book Series. \n\n\n\nSpringer. \n\n\n\nPagliaroli, A., Aprile, V., Chamlagain, D., Lanzo, G., Poovarodom, N. 2018. \n\n\n\nAssessment of Site Effects in The Kathmandu Valley, Nepal, During The \n\n\n\n2015 Mw 7.8 Gorkha Earthquake Sequence Using 1D And 2D Numerical \n\n\n\nModelling.  Engineering Geology  \n\n\n\nPehlivan, M., Rathje, E.M., Gilbert, R.B. 2012 Influence of 1D and 2D Spatial \n\n\n\nVariability on Site Response Analysis, 15 WCEE Lisboa. \n\n\n\nPeyman, A., Kami, M., Domniki, A. 2021. A Systematic Analysis of Basin \n\n\n\nEffects On Surface Ground Motion. Soil Dynamics and Earthquake \n\n\n\nEngineering, 141, February 2021, 106490 \n\n\n\nPiera, P.C., Maria, R.M., Ernesto, M., Todaro, M. 2018. Two-Dimensional Site \n\n\n\nSeismic Response Analysis for a Strategi Building in Catania. Annals of \n\n\n\nGeophsics. DOI: 10.4401/ag-7704 \n\n\n\nRecep, I., Hadi, K. 2013. A Numerical Study on the Basin Edge Effect on Soil \n\n\n\nAmplification. Bulletin of Earthquake Engineering, DOI: \n\n\n\n10.1007/s10518-013-9451-6 \n\n\n\nReda, H., Rahali, Y., Ganghoffer, J.F., Lakiss, H. 2016. Wave Propagation \n\n\n\nAnalysis In  2D Nonlinear Hexagonal Periodic Networks Based \n\n\n\nOn Second Order Gradient  Nonlinear Constitutive Models. \n\n\n\nInternational Journal of Non\u2013Linear Mechanics  \n\n\n\nReddy, M.V.R.K., Mohanty, S., Shaik, R. 2021. Comparative Study of 1D, 2D \n\n\n\nand 3D Ground Response Analysis of Pond Ash from Odisha Under \n\n\n\nDifferent Earthquake Motions. In: Latha Gali M., Raghuveer Rao P. (eds) \n\n\n\nGeohazards. Lecture Notes in Civil Engineering, vol 86. Springer, \n\n\n\nSingapore. DOI: https://doi.org/10.1007/978-981-15-6233-4_37 \n\n\n\nRiepl, J., Zahradni, K.J., Plicka, V., Bard, P.Y. 2000. About the Efficiency of \n\n\n\nNumerical 1-D and 2-D Modelling of Site Effects in Basin Structures. \n\n\n\nPure and Applied Geophysics. Doi:10.1007/s000240050002 \n\n\n\nSara, A., Iolanda, G., Marco, T., Giuseppe, D.G., Giuliano, M. 2018. 2D Site \n\n\n\nResponse Analysis Of A Cultural Heritage: The Case Study Of The Site Of \n\n\n\nSanta Maria Di Collemaggio Basilica (L\u2019aquila, Italy). Bulletin of \n\n\n\n Earthquake Engineering, DOI: 10.1007/s10518-018-0356-2 \n\n\n\nSemblat, J.F. 2011. Modeling Seismic Wave Propagation and Amplification \n\n\n\nin 1D/2D/3D Linear and Nonlinear Unbounded Media. International \n\n\n\nJournal of Geomechanics, International Jal of Geomechanics (ASCE). \n\n\n\n doi:10.1061/(ASCE)GM.1943-5622.0000023 \n\n\n\nShukla, D., Solanki, C.H. 2021. Equivalent 1D Ground Response Analysis \n\n\n\n(GRA) of Black Cotton Soil for Three Different Sites Near Indore City. In: \n\n\n\nLatha Gali M., Raghuveer Rao P. (eds) Geohazards. Lecture Notes in Civil \n\n\n\nEngineering, vol 86. Springer, Singapore. DOI: \n\n\n\nhttps://doi.org/10.1007/978-981-15-6233-4_46 \n\n\n\nSteven, L. Kramer 1996 Geotechnical Earthquake Engineering Book. \n\n\n\nPrentice-Hall International Series. \n\n\n\nTheodoulidis, N., Cultrera, G., Cornou, C., Bard, P.-Y., Boxberger, T., DiGiulio, \n\n\n\nG., Imtiaz, A., Kementzetzidou, D., Makra, K., The Argostoli NERA. 2018. \n\n\n\nBasin Effects on Ground Motion: The Case of a High- Resolution \n\n\n\nExperiment In Cephalonia (Greece). Bulletin of Earthquake \n\n\n\nEngineering, DOI: /10.1007/s10518-017-0225-4 \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 07-12 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2020.07.12 \n\n\n\n \nCite the Article: Ehsan Momeni, Mahmoud Reza Sahebi, Ali Mohammadzadeh (2020). Classification Of High-Resolution Satellite Images Using Fuzzy Logics Into \n\n\n\nDecision Tree. Malaysian Journal of Geosciences, 4(1): 07-12. \n \n\n\n\n\n\n\n\n\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \nRESEARCH ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2020.07.12 \n\n\n\n\n\n\n\n\n\n\n\nCLASSIFICATION OF HIGH-RESOLUTION SATELLITE IMAGES USING FUZZY \nLOGICS INTO DECISION TREE  \n\n\n\nEhsan Momenia, Mahmoud Reza Sahebib, Ali Mohammadzadehb \n\n\n\na Department of Earth Sciences, The University of Memphis, 445 State, Memphis, TN 38111, USA  \nb Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, No. 1346, ValiAsr Street, Mirdamad cross, Tehran, Iran \n*Corresponding Author Email: hsn.momeni@gmail.com \n\n\n\n \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 05 December 2019 \nAccepted 12 January 2020 \nAvailable online 05 February 2020 \n\n\n\n In this paper, DTFL an image classifier based on Decision Tree and Fuzzy Logics is proposed. At the \nbeginning of classification using DTFL, each pixel is located at the highest level of a decision tree where it \nbelongs to the combination of all classes. DTFL transfers a pixel to a lower level of the decision tree where \nthe pixel belongs to a combination of fewer classes. Decision-making about transfers is based on fuzzy logic \nwith seven different membership functions including triangular-shaped, trapezoidal-shaped, \u03c0-shaped, \nbell-shaped, Gaussian, differential S-shaped and multiplicative S-shaped. Eventually, pixels will reach the \nlowest level of the decision tree where it belongs to only one class. For accuracy assessment, DTFL was used \nto classify a GeoEye-1 image. The overall accuracy of 96.14% and a kappa coefficient of 96.06% were \nreached by DTFL. In comparison, the overall accuracy of 89.91% and a kappa coefficient of 89.77% were \nreached by a Maximum Likelihood Classifier, MLC. In the case of applying a threshold in MLC to reach the \nsame accuracy as DTFL, 8.73% of pixels take the non-classified label while using DTFL all the pixels get a \nproper label. The results indicate that the proposed classifier extracts more information from images.  \n\n\n\nKEYWORDS \n\n\n\nsupervised classification, GeoEye-1, fuzzy membership function, decision tree, Iran. \n\n\n\n1. INTRODUCTION \n\n\n\nDuring the last decades, a wide range of pattern recognition techniques \nhas been invented to extract information from remotely sensed data such \nas satellite images. Meanwhile, the flexibility and performance of image \nclassifiers make them one of the main recognition techniques for \nextracting information from satellite images. Image classification is \ndefined as a decision-making process in which a label of a class (or more \nclasses) is assigned to each pixel of an image with the maximum \nconfidence of assignment (Gomez et al., 2016; Momeni, 2011). Traditional \nimage classification methods carry out a hard classification output based \non the fundamental law of \u201cone pixel-one class\u201d. The fundamental law \nclears that a pixel is either a full member of a class or not. Consequently, \ntraditional image classification methods are not considered as the best \nmethod for classifying mixed or imprecise pixels in an image (Momeni, \n2011). On the other hand, advanced image classification methods, such as \ngenetic algorithms and expert systems, have many issues with image \nclassification, such as no general standards for defining the architecture, \ntraining requirements and time-consuming training (Gomez et al., 2016; \nGhosh et al., 2014). \n \nIn recent decades, fuzzy logic has been implemented in various fields of \nstudy such as control systems, image processing and classification of \nremotely sensed data (Momeni, 2011; Zimmermann, 2011). A fuzzy \nclassifier estimates the contribution of each class in each pixel. In other \nwords, a fuzzy classifier assumes that each pixel in the image is a \ndecomposable unit and, accordingly, works on a new fundamental law of \n\n\n\n\u201cone pixel-several class\u201d to extract more information about pixels \n(Momeni, 2011; Ghosh et al., 2010; Dutta, 2009). A group researchers,  \nprovided a survey on different image classification methods and \nconcluded fuzzy logic as one of the most reliable image classification \nmethods (Akgun et al., 2004; Weng and Lu, 2007). In addition, some other \nscholars have investigated applications of fuzzy image classification and \nclaimed that fuzzy classifiers are among the most powerful tools for the \nclassification of satellite images (Bai et al., 2014; Xu et al., 2019; Joseph and \nChockalingam, 2017). Pixel-based classification is still among the most \npractical classification approaches due to simplicity, low computational \ncost and high reliability (Gonzalez et al., 2016). However, pixel-based \nclassification is not accurate to classify mixed pixels with overlapped \nspectral characteristics. Even though applying thresholds can increase the \naccuracy and reliability in the labeling of pixels, but thresholds omit some \nof the mixed pixels from the classification process. In that case, omitted \npixels get a non-classified label (Momeni, 2011; Joseph and Chockalingam, \n2017). \n \nThis paper is aimed to introduce a novel satellite images classifier, called \nDTFL, based on a Decision-Tree and Fuzzy Logic. DTFL is able to classify \nall the pixels in an image and avoids non-classified labeling. It also takes \nthe advantages of fuzzy concepts to increases the accuracy of classification \nin comparison with traditional classifiers such as Maximum Likelihood \n(ML). In order to assess the performance of the proposed classifier, DTFL \nwas used in the classification of a high-resolution GeoEye-1 satellite image \nof the Azadi  Complex, Iran, and the results were compared with the results \nof image classification using the ML classifier. Using an advanced image \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 07-12 \n\n\n\n\n\n\n\n \nCite the Article: Ehsan Momeni, Mahmoud Reza Sahebi, Ali Mohammadzadeh (2020). Classification Of High-Resolution Satellite Images Using Fuzzy Logics Into \n\n\n\nDecision Tree. Malaysian Journal of Geosciences, 4(1): 07-12. \n \n\n\n\n\n\n\n\nclassifier such as DTFL helps the city and regional planners to understand \ntheir surroundings more precisely to offer a more reliable plan for the \nfuture of the region. In particular, regional farm managers can estimate the \namount of different agricultural products more precisely and will make \nkey strategic plans based on more accurate knowledge. The rest of this \npaper is organized as the following: Section Two describes the proposed \nDTFL classifier. The principal of the decision tree approach and different \nfuzzy membership functions, as well as different reasoning rules,  are \ndiscussed in the same section. A brief description of datasets and results \nof using DTFL for classification is discussed in Section Three. Section Four \nis dedicated to discussions and lastly, the concluding remarks are given in \nSection Five. \n\n\n\n2.   METHODOLOGY \n\n\n\nDTFL is a sub-pixel supervised classification method based on the tree \nmethod and fuzzy logic. At the beginning of the image classification using \nDTFL, each pixel is located at the highest level of a decision tree. Each level \nrepresents the number of confusions in the classification. Each pixel \nbelongs to the combination of all the initial classes at the highest level of \nthe decision tree. DTFL classifies each pixel in a smaller combination of \nclasses by defining a hypothesis. The hypothesis is based on the transfer \nof each pixel from a higher level to a lower level and needs validation. \nDecision making about the transfer of each pixel to a lower level is based \non fuzzy logic. Using an iterative process, each pixel is transferred from the \nhigher level to the lower one (Figure 1). The iterative process continues \nuntil either of the two following conditions is satisfied: \n\n\n\nA) A state having a single class (called a leaf) is reached  \n\n\n\nB) No new state is reached after the iteration  \n\n\n\nIn the first case, the pixel is classified as a pure pixel and get a single label \nwhile in the latter case, the pixel is classified as a mixed pixel and get a \nlabel according to its level on the tree (avoids non-classified label).  \n\n\n\n2.1 Decision Tree \n\n\n\nDecision Tree (DT) classification is a hierarchical process where at each \nlevel, a test is applied to one or more attribute values and makes one or \nmore outcomes. The outcome(s) may be either a leaf resembling a class \n(or more classes) or a decision node (or more decision nodes) describing \na further test on the attribute values which creates a branch or sub-tree of \nthe tree (Al-Obeidat et al., 2015). The classification process is completed \nby passing the pixel down the tree until either a leaf is reached or no new \nstate is reached after the iteration. In the latter case, the pixel is classified \nas a mixed pixel and get a proper label according to its level. The structure \nof a decision tree classifier is shown in Figure 1 (Momeni, 2011). \n\n\n\n\n\n\n\nFigure 1: Structure of a decision tree classifier (Momeni, 2011) \n\n\n\n2.1.1 Schematic presentation of a decision tree \n\n\n\nConsider a multispectral image of an area whose pixels have to be \nclassified. Let\u2019s assume CL as the set of all k possible classes. Each pixel in \nthe image has to be classified into one or more (for mixture pixels) of the \nk possible classes in CL. Figure 2 shows the outline of interpreting a pixel \nin a four-classes problem (k=4) where CL= {C1, C2, C3, C4}. \n\n\n\n\n\n\n\nFigure 2: Outline of interpreting a pixel in a decision tree classification \n\n\n\nwith four initial classes (Momeni, 2011) \n\n\n\nEach node in Fig represents a state of uncertainty for the pixel during the \n\n\n\nclassification. For instance, the node {C1, C2} indicates the state: \u201cThe pixel \nmay belong unconditionally to the C1 or unconditionally to the C2 or to a \ncombination of C1 and C2\u201d. The connector lines in Figure 2 represent \nhypotheses through which state transfers can occur. For instance, the \nconnector line between {C1, C2, C3} and {C1,C2} indicates the hypothesis \nthat: \u201cThe pixel belongs to one or more classes in the set of {C1, C2} with \nthe assumption that it belongs to one or more of classes in the set of {C1, \nC2, C3}\u201d. The inference mechanism chooses appropriate hypotheses to test. \nEach hypothesis is either true or false. According to the results of those \ntests, the inference mechanism infers the new state of the pixel. \n\n\n\n2.1.2 Inference mechanism \n\n\n\nThe interpretation of a pixel is an iterative process of inference. Each \niteration includes three steps: \n \n\n\n\nStep1: selecting appropriate hypothesis for the test; \nStep2: testing the validity of the selected hypothesis; \nStep3: analyzing the passed hypothesis in order to infer the next state. \n\n\n\n \nThe iterative process continues until either of the following conditions is \nsatisfied: \n \n\n\n\nA) A state with a single class (a leaf) is reached (e.g., states {C1} or \n\n\n\n{C2}). In such cases, a solid decision is reached and the pixel gets a \n\n\n\nsingle label of the class; or \n\n\n\nB) No new state is reached after iteration. In such cases, a solid \n\n\n\ndecision could not be reached either due to lack of knowledge or \n\n\n\ndue to the case of a mixed pixel (e.g., pixel covering more than one \n\n\n\nland cover classes). In such cases, the pixel is considered as a \n\n\n\nmixed pixel and get a proper label of classes. \n\n\n\nThe decision in the latter case assigns a set of possible classes to the pixel \nand avoids the non-classified label.  For example, assigning {C1, C2} to a \npixel means the pixel is a mixture of class {C1} and {C2} and other classes \n(such as {C3} or {C4}) have no contributions in the pixel. In comparison, \ntraditional classifiers such as MLC, omit mixed pixels and simply assign a \nnon-classified label to them.  \n\n\n\n2.1.3 Selecting appropriate hypotheses \n\n\n\nAs Figure 2 shows, classification schema is separated into different levels \nbased on the number of classes each level contains. In other words, the \nlevel of a state indicates the degree of uncertainty. For instance, four \npossible states are available at the level-3, where each of them is a unique \ncombination of three land cover classes. Consequently, at any given state \nsuch as S, the set of possible hypotheses is in the form of DS \u2192 , where \n\n\n\nD\u2282 S. The sublevel of a hypothesis DS \u2192  is defined as d where d is the \n\n\n\nlevel of D (e.g., the number of classes in D). The process of inferring always \nstarts at the maximum uncertainty and the first sublevel. The process then \ncontinues iteratively. At the end of each iteration, the new states and \nsublevels are deduced for the next iteration. \n\n\n\n2.1.4 Testing chosen hypotheses \n\n\n\nFor testing the chosen hypotheses, appropriate rules must be selected and \ntheir validity should be verified. The basic representation of rules is as the \nfollowing (Equ. 1): \n\n\n\nBASE RULE:    IF    [CL1, \u2026, CLn]  THEN  hypotheses H0 is true          (Equ. 1) \n\n\n\nWhere CLi is logical constraints on one or more attributes of pixels.  \nIn a real classification problem usually more than one rule is defined. In \nthat case, if R1,\u2026, Rm is a set of rules which leads to a hypothesis Hk , then \nHk is satisfactory if all of R1,\u2026 , Rm is satisfied. To analyze a rule, each of its \ncomponents, e.g. CLi, is validated. If all the components are true then the \nrule is valid. \n\n\n\n2.1.5 Analysis of the passed hypotheses to choose the next state \n\n\n\nSuppose the state T of level-t and the hypothesis sublevel-d in an iteration. \nThen two possibilities for the rule are: \n \nA) The rule is not passed: \n\n\n\nwhich indicates that all the hypotheses of sublevel-d are rejected. \nTherefore, the decision is transferred to the next sublevel (e.g. d=d+1). If d \nis equal to t, then the pixel belongs to one or more of the classes in T. \nOtherwise, the next iteration continues with d and the same state T. \n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 07-12 \n\n\n\n\n\n\n\n \nCite the Article: Ehsan Momeni, Mahmoud Reza Sahebi, Ali Mohammadzadeh (2020). Classification Of High-Resolution Satellite Images Using Fuzzy Logics Into \n\n\n\nDecision Tree. Malaysian Journal of Geosciences, 4(1): 07-12. \n \n\n\n\n\n\n\n\nB) The rule is passed: \n\n\n\nSuppose the hypothesis was T \u27f6 D. Then, the new state is T = D. If the level \nof this new state is one, the pixel is classified into a class in new-S and the \nanalysis of the pixel is terminated. Otherwise, the new sublevel is new-d=1. \nThe progress to the next iteration with new-T and new-d is continued. \n\n\n\n2.2 Fuzzy Logic \n\n\n\nThere are three steps in a fuzzy method for the classification of remote \nsensing images. In the first step, the contribution of each class in each pixel \nis estimated using a fuzzification process with a proper membership \nfunction (MF). In this step, each pixel is converted to a matrix of \nmembership degrees. In the second step, proper reasoning rules are \napplied to the fuzzy inputs (e.g. to the matrix of membership degrees) to \nachieve a fuzzy classification of pixels. In this step, normalization of data \nmay be required. Lastly, in the third step, a defuzzification process is \napplied to create a hard classification output (Ghosh et al., 2014; Ghosh et \nal., 2010; Dutta, 2009; Xu et al., 2019; Al-Obeidat et al., 2015; Melgani et al., \n2000; Zhao, 2008).  \n\n\n\n2.2.1 Fuzzification  \n\n\n\nThe fuzzy domain includes several fuzzy sets representing the bands. Each \nfuzzy set (band) contains fuzzy subsets that indicate the land cover classes. \nEach fuzzy subset (e.g. land cover c), in a given fuzzy set (e.g. band b) is \ndefined by a membership function, )(, bcb xf . where \n\n\n\nbx is the value of the \n\n\n\nspectral pixel of X  in the band b and land cover c. \n\n\n\n \nIn the proposed classifier, DTFL, seven different fuzzy membership \nfunctions were used for fuzzification including triangular shape, \ntrapezoidal shape, \u03c0 shape, bell shape, Gaussian, differential S shape and \nmultiplicative S shape. The mathematical expression of each membership \nfunction is as the following (Momeni, 2011; Zhao, 2008; Matlab, 2017): \n \nA) Triangular shape (Equ. 2) \n\n\n\n\n\n\n\n\uf0fe\n\uf0fd\n\uf0fc\n\n\n\n\uf0ee\n\uf0ed\n\uf0ec\n\n\n\n\uf0fe\n\uf0fd\n\uf0fc\n\n\n\n\uf0ee\n\uf0ed\n\uf0ec\n\n\n\n\u2212\n\n\n\n\u2212\n\n\n\n\u2212\n\n\n\n\u2212\n= 0,,minmax),,,(\n\n\n\nbx\n\n\n\nxc\n\n\n\nab\n\n\n\nax\ncbaxTriangular\n\n\n\n                               (Equ.2) \n\n\n\n \nB) Trapezoidal shape (Equ. 3) \n\n\n\n\n\n\n\n\uf0fe\n\uf0fd\n\uf0fc\n\n\n\n\uf0ee\n\uf0ed\n\uf0ec\n\n\n\n\uf0fe\n\uf0fd\n\uf0fc\n\n\n\n\uf0ee\n\uf0ed\n\uf0ec\n\n\n\n\u2212\n\n\n\n\u2212\n\n\n\n\u2212\n\n\n\n\u2212\n= 0,,1,minmax),,,,(\n\n\n\ncd\n\n\n\nxd\n\n\n\nab\n\n\n\nax\ndcbaxlTrapezoida\n\n\n\n                        (Equ.3) \n\n\n\n \nC) \u03c0 shape (Equ. 4) \n\n\n\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\n\n\n\uf0fe\n\n\n\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\n\n\n\uf0fd\n\n\n\n\uf0fc\n\n\n\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\n\n\n\uf0ee\n\n\n\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\uf0ef\n\n\n\n\uf0ed\n\n\n\n\uf0ec\n\n\n\n\uf0a3\n\n\n\n\uf0a3\uf0a3\n+\n\n\n\n\uf0f7\n\uf0f8\n\n\n\n\uf0f6\n\uf0e7\n\uf0e8\n\n\n\n\uf0e6\n\n\n\n\u2212\n\n\n\n\u2212\n\n\n\n+\n\uf0a3\uf0a3\uf0f7\n\n\n\n\uf0f8\n\n\n\n\uf0f6\n\uf0e7\n\uf0e8\n\n\n\n\uf0e6\n\n\n\n\u2212\n\n\n\n\u2212\n\u2212\n\n\n\n\uf0a3\uf0a3\n\n\n\n\uf0a3\uf0a3\n+\n\n\n\n\uf0f7\n\uf0f8\n\n\n\n\uf0f6\n\uf0e7\n\uf0e8\n\n\n\n\uf0e6\n\n\n\n\u2212\n\n\n\n\u2212\n\u2212\n\n\n\n+\n\uf0a3\uf0a3\uf0f7\n\n\n\n\uf0f8\n\n\n\n\uf0f6\n\uf0e7\n\uf0e8\n\n\n\n\uf0e6\n\n\n\n\u2212\n\n\n\n\u2212\n\n\n\n\uf0a3\n\n\n\n=\n\n\n\nxd\n\n\n\ndx\ndc\n\n\n\ncd\n\n\n\ndx\n\n\n\ndc\nxc\n\n\n\ncd\n\n\n\ncx\n\n\n\ncxb\n\n\n\nbx\nba\n\n\n\nab\n\n\n\nbx\n\n\n\nba\nxa\n\n\n\nab\n\n\n\nbx\n\n\n\nax\n\n\n\ndcbaxPi\n\n\n\n2\n\n\n\n0\n\n\n\n2\n\n\n\n2\n21\n\n\n\n1\n\n\n\n2\n21\n\n\n\n2\n2\n\n\n\n0\n\n\n\n),,,,(\n\n\n\n2\n\n\n\n2\n\n\n\n2\n\n\n\n2\n\n\n\n                                (Equ.4) \n\n\n\n \nD) Bell shape (Equ.5) \n\n\n\nb\n\n\n\na\n\n\n\ncx\ncbaxBell\n\n\n\n2\n\n\n\n1\n\n\n\n1\n),,,(\n\n\n\n\u2212\n+\n\n\n\n=\n\n\n\n                                                                  (Equ. 5) \n\n\n\n \nE) Gaussian (Equ.6) \n\n\n\n\n\n\n\n2\n\n\n\n2\n\n\n\n2\n\n\n\n)(\n\n\n\n)..( s\n\n\n\nmx\n\n\n\nesmxGauss\n\n\n\n\u2212\u2212\n\n\n\n=                                                           (Equ.6) \n\n\n\n \nF) Differential S shape (Equ. 7) \n\n\n\n\n\n\n\n)()(2211\n21211 1\n\n\n\n1\n\n\n\n1\n\n\n\n1\n),,,,(\n\n\n\ncxacxa\nee\n\n\n\ncacaxDsigm\n\u2212\u2212\u2212\u2212\n\n\n\n+\n\u2212\n\n\n\n+\n=\n\n\n\n                        (Equ. 7) \n\n\n\n \nG) Multiplicative S shape (Equ. 8) \n\n\n\n\n\n\n\n   \n)()(2211\n\n\n\n21211 1\n\n\n\n1\n\n\n\n1\n\n\n\n1\n),,,,(\n\n\n\ncxacxa\nee\n\n\n\ncacaxPsigm\n\u2212\u2212\u2212\u2212\n\n\n\n+\n\uf0b4\n\n\n\n+\n=\n\n\n\n                       (Equ.8) \n\n\n\n2.2.2 Reasoning rule \n\n\n\nAccording to literature, a low fuzzy membership value MIN rule (so-called \nintersection rule) is a simple, fast and effective reasoning rule for many \nfuzzy problems (Ghosh et al., 2014; Ghosh et al., 2010; Melgani et al., \n2000). In addition to the MIN rule, a high fuzzy membership value MAX \nrule (so-called union rule) is also considered as an effective reasoning rule \n(Ghosh et al., 2014; Ghosh et al., 2010; Melgani et al., 2000). However, \nintersection and union rules are generally not powerful enough for \nsophisticated classification processes where there are highly overlapped \nclasses. In the current work, 12 different reasoning rules were used \nincluding minimum, maximum, multiplicative, collective, collective second \npower, mathematical mean, geometric mean, harmonic mean, minimum/ \nmaximum, mathematical mean\u00d7 minimum/ maximum, geometric mean \u00d7 \nminimum/ maximum and harmonic mean \u00d7minimum/ maximum. The \nmathematical expression of each reasoning rule is shown in the following \n(Momeni, 2011): \n \nA) Minimum (Equ. 9):  )}(),...,({)( ,,1\n\n\n\n' xfxfMinxf cBcc =                  (Equ. 9) \n\n\n\n\n\n\n\nB) Maximum (Equ. 10): )}(),...,({)( ,,1\n\n\n\n' xfxfMaxxf cBcc =                 (Equ. 10) \n\n\n\n\n\n\n\nC) Multiplicative (Equ. 11):  \uf0d5 =\n=\n\n\n\nB\n\n\n\nb cbc xfxf\n1 ,\n\n\n\n' )()(                              (Equ. 11) \n\n\n\n\n\n\n\nD) Collective (Equ. 12):  \uf0e5 =\n=\n\n\n\nB\n\n\n\nb cbc xfxf\n1 ,\n\n\n\n' )()(                                  (Equ. 12) \n\n\n\n\n\n\n\nE) Collective second power (Equ. 13): \uf0e5 =\n=\n\n\n\nB\n\n\n\nb cbc xfxf\n1\n\n\n\n2\n\n\n\n,\n\n\n\n' )()(      (Equ. 13) \n\n\n\n\n\n\n\nF) Mathematical mean (Equ. 14):  \n\uf0e5 =\n\n\n\n=\nB\n\n\n\nb cbc xf\nB\n\n\n\nxf\n1 ,\n\n\n\n' )(\n1\n\n\n\n)(\n            (Equ. 14) \n\n\n\n\n\n\n\nG) Geometric mean (Equ. 15):  B\nB\n\n\n\nb cbc xfxf\n1\n\n\n\n1 ,\n\n\n\n' ])([)( \uf0d5 =\n=             (Equ. 15) \n\n\n\n\n\n\n\nH) Harmonic mean (Equ. 16):  \n\n\n\n\uf0e5 =\n\n\n\n=\nB\n\n\n\nb\ncb\n\n\n\nc\n\n\n\nxf\n\n\n\nB\nxf\n\n\n\n1\n,\n\n\n\n'\n\n\n\n)(\n\n\n\n1\n)(\n\n\n\n                        (Equ. 16) \n\n\n\nI) Minimum / Maximum (Equ. 17): \n\n\n\n\n\n\n\n\n\n\n\n)}(),...,({\n\n\n\n)}(),...,({\n)(\n\n\n\n,,1\n\n\n\n,,1'\n\n\n\nxfxfMax\n\n\n\nxfxfMin\nxf\n\n\n\ncBc\n\n\n\ncBc\n\n\n\nc =\n                                             (Equ. 17) \n\n\n\n\n\n\n\nJ) Mathematical mean \u00d7 Minimum / Maximum (Equ. 18):  \n\n\n\n\n\n\n\n)}(),...,({\n\n\n\n)}(),...,({\n)(\n\n\n\n1\n)(\n\n\n\n,,1\n\n\n\n,,1\n\n\n\n1 ,\n\n\n\n'\n\n\n\nxfxfMax\n\n\n\nxfxfMin\nxf\n\n\n\nB\nxf\n\n\n\ncBc\n\n\n\ncBcB\n\n\n\nb cbc \uf0b4= \uf0e5 =\n\n\n\n                      (Equ. 18) \n\n\n\n\n\n\n\nK) Geometric mean \u00d7 Minimum / Maximum (Equ. 19):  \n\n\n\n\n\n\n\n)}(),...,({\n\n\n\n)}(),...,({\n])([)(\n\n\n\n,,1\n\n\n\n,,1\n1\n\n\n\n1 ,\n\n\n\n'\n\n\n\nxfxfMax\n\n\n\nxfxfMin\nxfxf\n\n\n\ncBc\n\n\n\ncBcB\nB\n\n\n\nb cbc \uf0b4= \uf0d5 =\n\n\n\n                     (Equ. 19) \n\n\n\n\n\n\n\nL) And Harmonic mean \u00d7 Minimum / Maximum (Equ. 20): \n\n\n\n)}(),...,({\n\n\n\n)}(),...,({\n\n\n\n)(\n\n\n\n1\n)(\n\n\n\n,,1\n\n\n\n,,1\n\n\n\n1\n,\n\n\n\n'\n\n\n\nxfxfMax\n\n\n\nxfxfMin\n\n\n\nxf\n\n\n\nB\nxf\n\n\n\ncBc\n\n\n\ncBc\n\n\n\nB\n\n\n\nb\ncb\n\n\n\nc \uf0b4=\n\n\n\n\uf0e5 =\n\n\n\n                       (Equ. 20) \n\n\n\n2.2.3 Defuzzification  \n\n\n\nIn the proposed classifier, DTFL, the last step is a hard classification using \nMAX or MIN operations as the simplest defuzzifiers. Other defuzzification \nmethods, such as centroid of the area or mean of maximum, are used in \nother problems such as control systems (Momeni, 2011; Ghosh et al., \n2014; Ghosh et al., 2010). However, in the proposed approach, DTFL, each \npixel is assigned to the class c with the highest membership value using \nthe MAX operator. The mathematical expression of the MAX operator is as:  \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 07-12 \n\n\n\n\n\n\n\n \nCite the Article: Ehsan Momeni, Mahmoud Reza Sahebi, Ali Mohammadzadeh (2020). Classification Of High-Resolution Satellite Images Using Fuzzy Logics Into \n\n\n\nDecision Tree. Malaysian Journal of Geosciences, 4(1): 07-12. \n \n\n\n\n\n\n\n\nMAX:        )()(,,...,2,1 xFxFcjandcj jc \uf0b3\uf0b9\uf0ce\uf022        (Equ. 21) \n\n\n\nwhere )(xFj\n\n\n\n is the membership value for the jth class (Momeni, 2011).  \n\n\n\n \nFigure 3 illustrates the flowchart of classification using DTFL. In that \nflowchart, two parts of DTFL, the decision tree and the fuzzy logic, are \nshown simultaneously. \n\n\n\n\n\n\n\nFigure 3: Flowchart of DTFL classifier \n\n\n\n3. EXPERIMENTAL RESULTS \n\n\n\nIn order to evaluate the accuracy and performance of the proposed \nmethod, a GeoEye-1 satellite image of Azadi Stadium, Iran, (Figure 4) was \nclassified using DTFL. In the study area nine different dominant classes \nwere determined as \u201cblue-seats\u201d and \u201cwhite-seats\u201d from the first floor of \nthe stadium, \u201clawn\u201d and \u201csoil\u201d and \u201cpedestrian-gravel\u201d from the playing \nfields, \u201cconcrete-seats\u201d from second floor of the stadium, \u201casphalt-road\u201d \nand \u201ccement-wall\u201d from the surrounding of the stadium, and \u201cwater\u201d from \nnorthern lake of the stadium. The GeoEye-1 image was taken on Jan 07, \n\n\n\n2009 with GSD of 16 inches (0.41 m) in panchromatic mode and GSD of 65 \ninches (1.65 m) in a multispectral mode with 11 bits per pixel as  \nradiometric resolution (Momeni et al., 2018; Bagheri et al., 2011). Even \nthough GeoEye-1 is able to acquire images with a resolution of 41 cm, but \nunder the company\u2019s current operating license from the National \nOceanographic and Atmospheric Agency (NOAA) images must be \nresampled down to half a meter for commercial customers (Momeni et al., \n2018). This resampling will mix up some pixels and classes and decreases \nthe accuracy of classification. \n \nA set of training data were used to estimate the statistical parameters of \neach class for each reasoning rule. In this study, training data were \ncollected based on 1:2000 topographic maps and ground surveying. \nFinally, the satellite image of the study area was classified using DTFL with \nseven different fuzzy membership functions and 12 different decision \nrules. Membership functions included triangular shape, trapezoidal shape, \n\u03c0 shape, bell shape, Gaussian, differential S shape and multiplicative S \nshape. Decision rules included Minimum, Maximum, Multiplicative, \nCollective, Collective second power, mathematical mean, geometric mean, \nharmonic mean, minimum/ maximum, mathematical mean \u00d7 minimum/ \nmaximum, geometric mean \u00d7 minimum/ maximum and harmonic mean \u00d7 \nminimum/ maximum.  \n \nTable 1 and Table 2 summarize the Kappa coefficients and overall \naccuracies of image classification using DTFL. \n\n\n\n\n\n\n\nFigure 4: GeoEye-1 satellite image of Azadi Stadium (Tehran, Iran)\n\n\n\n\n\n\n\n\n\n\n\nTable 1: Kappa coefficients of classification using DTFL with different membership functions \n\n\n\n \nMembership function \n\n\n\nTriangular shape Trapezoidal shape \u03c0 shape Bell shape Gaussian \nDifferential \n\n\n\nS shape \nMultiplicative \n\n\n\nS shape \nMinimum 96.07 94.66 95.55 94.17 95.61 95.55 95.55 \n\n\n\nCollective 95.57 93.74 94.82 93.85 94.93 94.82 94.82 \nMultiplicative 94.65 92.24 93.61 91.65 93.94 93.61 93.61 \nMaximum 95.25 94.03 94.90 94.23 94.75 94.90 94.90 \n\n\n\nMinimum /Maximum 95.95 94.06 93.25 63.23 95.36 93.25 93.25 \nCollective second power 95.31 93.53 94.65 93.65 94.70 94.65 94.65 \nMathematical mean 95.00 93.08 94.32 93.33 94.30 94.32 94.32 \n\n\n\nGeometric mean 95.82 94.38 95.59 95.13 95.33 95.59 95.59 \nHarmonic mean 96.09 94.76 95.87 95.25 95.62 95.87 95.87 \nMathematical mean \u00d7 Minimum / \nMaximum \n\n\n\n95.54 93.53 94.56 92.72 95.08 94.56 94.56 \n\n\n\nGeometric mean \u00d7 Minimum / \nMaximum \n\n\n\n95.42 92.85 94.14 91.77 94.75 94.14 94.14 \n\n\n\nHarmonic mean \u00d7 Minimum / \nMaximum \n\n\n\n95.26 92.39 93.59 90.99 94.41 93.59 93.59 \n\n\n\nTable 2: Overall accuracies of classification using DTFL with different membership functions \n\n\n\n \nMembership function \n\n\n\nTriangular \nshape \n\n\n\nTrapezoidal \nshape \n\n\n\n\u03c0 shape Bell shape Gaussian \nDifferential S \n\n\n\nshape \nMultiplicative S \n\n\n\nshape \n\n\n\nMinimum 96.12 94.73 92.33 94.25 95.67 95.60 95.60 \n\n\n\nCollective 95.63 93.83 92.18 93.93 94.99 94.89 94.89 \n\n\n\nMultiplicative 94.72 92.34 90.05 91.76 94.02 93.69 93.69 \n\n\n\nMaximum 95.32 94.11 92.76 94.30 94.82 94.97 94.97 \n\n\n\nMinimum /Maximum 96.00 94.14 91.69 63.70 95.42 93.34 93.34 \n\n\n\nCollective second power 95.38 93.61 92.11 93.74 94.77 94.72 94.72 \n\n\n\nMathematical mean 95.07 93.17 91.68 93.42 94.37 94.39 94.39 \n\n\n\nGeometric mean 95.87 94.46 92.87 95.19 95.39 95.65 95.65 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 07-12 \n\n\n\n\n\n\n\n \nCite the Article: Ehsan Momeni, Mahmoud Reza Sahebi, Ali Mohammadzadeh (2020). Classification Of High-Resolution Satellite Images Using Fuzzy Logics Into \n\n\n\nDecision Tree. Malaysian Journal of Geosciences, 4(1): 07-12. \n \n\n\n\n\n\n\n\n4.   DISCUSSION \n\n\n\nTo prove the performance of image classification using DTFL, the results \nwere compared with the results of the Maximum Likelihood classification. \nThe best result of DTFL (using triangulation shape membership function \nand harmonic mean as reasoning rule) is compared with the result of the \nMaximum Likelihood (ML) in Table 3. \n \n\n\n\nTable 3: Summary of the image classification by ML and DFTL \n\n\n\nKappa coefficient (%) Overall accuracy (%) Classifier  \n\n\n\n89.77 89.91 ML \n\n\n\n96.06 96.14 DTFL \n\n\n\n \nFrom Table 1 to Table 3, it is concluded that the results of classification by \nDTFL in the best mode (using a triangular-shaped membership function  \n \n\n\n\n \nand harmonic-mean as reasoning rule) is better than the results of ML \nclassifier. DTFL improves the accuracy of the classification for more than \n6% in comparison with ML. In practice, it is common to use a threshold in \nthe ML classifier to increase the accuracy and precision of the classification \n(Momeni, 2011). In this case, low-reliability pixels are excluded from the \nclassification process and get a non-classified label. Therefore, the \naccuracy of the rest of the pixels in the classification will be improved. By \ndefining a threshold in the ML method to achieve the same accuracy of the \nDTFL in this case study, 8.73% of the pixels took the non-classified label. \nTherefore, using ML 8.73% of pixels are excluded in the future decision \nmakings as there is no idea about their mixed contents. However, as it \nalready mentioned in Section Two, DTFL is able to classify all the pixels \nand avoids non-classification labels. Therefore, using DTFL, we have more \nknowledge about mixed pixels and their contents. The confusion matrix \nresulted from DTFL (using triangulation shape membership function and \nharmonic mean as reasoning rule) is shown in Table 4.\n\n\n\n\n\n\n\nTable 4: Confusion matrix resulted by DTFL classifier \n\n\n\nTrue Class \n\n\n\nResulted class \nC1 C2 C3 C4 C5 C6 C7 C7 C9 Sum \n\n\n\nMixed of C1&C2&C4&C5&C7&C8&C9 0 0 0 0 0 1 0 0 0 1 \n\n\n\nMixed of C2&C4&C6&C8 0 0 0 13 0 0 0 0 0 13 \n\n\n\nMixed of C2&C4&C6 0 0 0 1 0 0 0 0 0 1 \n\n\n\nMixed of C2&C3&C9 0 0 3 0 0 0 0 0 0 3 \n\n\n\nMixed of C2&C3 0 0 2 0 0 0 0 0 0 2 \n\n\n\nMixed of C2&C5 0 5 0 0 18 0 0 0 0 23 \n\n\n\nMixed of C1&C7 0 0 0 0 0 0 4 0 0 4 \n\n\n\nMixed of C4&C7 0 0 0 0 0 0 5 0 0 5 \n\n\n\nMixed of C1&C8 0 0 0 2 0 0 0 0 0 2 \n\n\n\nMixed of C2&C8 0 0 0 0 0 0 0 5 0 5 \n\n\n\nMixed of C&C8 0 0 0 1 0 0 0 0 0 1 \n\n\n\nMixed of C3&C9 0 0 37 0 0 0 0 0 35 72 \n\n\n\nC1 178 0 0 1 0 0 3 0 0 182 \n\n\n\nC2 2 287 0 0 40 0 0 10 0 339 \n\n\n\nC3 0 0 4982 0 0 0 0 0 189 5171 \n\n\n\nC4 0 0 0 555 0 0 1 0 0 556 \n\n\n\nC5 1 6 0 0 266 0 0 0 0 273 \n\n\n\nC6 0 0 0 0 0 485 0 0 0 485 \n\n\n\nC7 0 0 0 0 0 0 289 0 0 289 \n\n\n\nC8 0 0 0 3 0 0 0 315 0 318 \n\n\n\nC9 0 0 138 0 0 0 0 0 5741 5879 \n\n\n\nSum 181 298 5162 576 324 486 302 330 5965 \n13624 \n\n\n\n13624 \n\n\n\nNo. Ground-Truth pixels 181 298 5162 576 324 486 302 330 5965 13624 \n\n\n\nNo. Non-classified pixels 0 0 0 0 0 0 0 0 0 0 \n\n\n\nWhere C1 to C9 are \u201cblue-seats\u201d, \u201cwhite-seats\u201d, \u201clawn\u201d, \u201csoil\u201d, \u201cpedestrian-gravel\u201d, \u201cconcrete-seats\u201d, \u201casphalt-road\u201d, \u201ccement-wall\u201d and \u201cWater\u201d, respectively. \nAlso, the confusion matrix resulted from an ML classifier is shown in Table 5. \n\n\n\n\n\n\n\nTable 5: Confusion matrix resulted in ML classifier \n\n\n\nTrue Class \n\n\n\nResulted class \nC1 C2 C3 C4 C5 C6 C7 C7 C9 Sum \n\n\n\nC1 158 0 0 0 0 0 1 4 0 163 \n\n\n\nC2 0 231 0 0 2 0 0 0 0 233 \n\n\n\nC3 0 0 4454 0 0 0 0 0 267 4721 \n\n\n\nC4 0 0 0 556 0 0 0 0 0 556 \n\n\n\nC5 3 59 0 0 322 0 0 2 0 386 \n\n\n\nC6 0 0 0 0 0 486 0 0 0 486 \n\n\n\nC7 3 0 0 3 0 0 298 0 0 304 \n\n\n\nC8 3 1 0 9 0 0 0 309 0 322 \n\n\n\nHarmonic mean 96.14 94.83 92.92 95.31 95.68 95.93 95.93 \n\n\n\nMathematical mean \u00d7 Minimum / \nMaximum \n\n\n\n95.60 93.61 90.91 92.81 95.15 94.63 94.63 \n\n\n\nGeometric mean \u00d7 Minimum / \nMaximum \n\n\n\n95.48 92.95 90.08 91.87 94.82 94.22 94.22 \n\n\n\nHarmonic mean \u00d7 Minimum / \nMaximum \n\n\n\n95.32 92.49 88.39 91.10 94.48 93.67 93.67 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(1) (2020) 07-12 \n\n\n\n\n\n\n\n \nCite the Article: Ehsan Momeni, Mahmoud Reza Sahebi, Ali Mohammadzadeh (2020). Classification Of High-Resolution Satellite Images Using Fuzzy Logics Into \n\n\n\nDecision Tree. Malaysian Journal of Geosciences, 4(1): 07-12. \n \n\n\n\n\n\n\n\nC9 0 0 191 0 0 0 0 0 5073 5264 \n\n\n\nSum 167 291 4645 568 324 486 299 315 5340 \n12435 \n\n\n\n12435 \n\n\n\nNo. Ground-Truth pixels 181 298 5162 576 324 486 302 330 5965 13624 \n\n\n\nNo. Non-classified pixels 14 7 517 8 0 0 3 15 625 \n1189 \n\n\n\n\u22488.73% \n\n\n\nWhere C1 to C9 are \u201cblue-seats\u201d, \u201cwhite-seats\u201d, \u201clawn\u201d, \u201csoil\u201d, \u201cpedestrian-gravel\u201d, \u201cconcrete-seats\u201d, \u201casphalt-road\u201d, \u201ccement-wall\u201d and \u201cWater\u201d, respectively. \n \n \nAs Table 4 and Table 5 show, due to the threshold in ML to obtain the same \naccuracy as DTFL, 1189 pixels have been excluded from the classification \nprocess and there is no idea about their contents. However, using DTFL \nonly 117 pixels were detected as mixed pixels while we know the \ncontribution of possible classes in those pixels. Thus, we will have valuable \ninformation about the contribution of each class in those pixels. That \ninformation helps regional planners and agricultural managers to more \nrealistic future planning. \n\n\n\n5. CONCLUSION \n\n\n\nIn this paper, we proposed a novel classifier, DTFL, based on fuzzy logic \nand decision tree. At the beginning of classification, each pixel is located at \nthe highest level of a decision tree. At that level, each pixel belongs to the \ncombination of all initial classes. DTFL classifies each pixel in a fewer \nnumber of classes using some hypotheses. As DTFL transfers a pixel from \nhigher levels of the tree to the lower levels, the uncertainty about the \ncontents of the pixel decreases. Decision making about transfers and \nhypotheses are based on fuzzy logic. In the classification of a GeoEye-1 \nsatellite image, DTFL reached the overall accuracy of 96.14% and the \nKappa coefficient of 96.06% using triangular membership function and \nharmonic mean reasoning rule. DTFL reached a higher accuracy in \ncomparison with the overall accuracy of 89.91% and the kappa coefficient \nof 89.77% resulted in a Maximum Likelihood classification. Defining a \nthreshold in the Maximum Likelihood method to obtain the same accuracy \nas the DTFL leaves 8.73% of pixels without any label (as non-classified \npixels). However, DTFL was able to assign a proper label to all pixels.  \n \nThe results of this study approved the performance of the proposed \nclassifier, DTFL. However, in order to improve the classifier, including \nother types of membership functions, such as L-function, in the fuzzy logic \nis suggested. In addition, applying DTFL to study less-distinctive \nagricultural products using multi-spectral satellite images may better \nreveal the strength of DTFL. Including advanced artificial intelligence \nmethods, such as a genetic algorithm, to the training section of the \nproposed method may improve the overall accuracy as well as the Kappa \ncoefficient of the classification. Also, as future studies, researchers may \nmodify and improve DTFL for unsupervised classification of satellite \nimages. \n\n\n\nREFERENCES \n\n\n\nAkgun, A., Eronat, A., Turk, N., 2004. Comparing different satellite image \n\n\n\nclassification methods: an application in ayvalik district, western \n\n\n\nTurkey. XXth ISPRS Congress, Retrieved from \n\n\n\nhttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.643.3422\n\n\n\n&rep=rep1&type=pdf \n\n\n\nAl-Obeidat, F., Al-Taani, A., Belacel, N., 2015. A fuzzy decision tree for \n\n\n\nprocessing satellite images and Landsat data. Procedia Computer \n\n\n\nScience. Procedia Computer Science, 52, 1192-1197. doi: \n\n\n\nhttps://doi.org/10.1016/j.procs.2015.05.157 \n\n\n\nBagheri, S.M.P., Momeni, E., Fard, F.S.N., 2011. Updating urban maps using \n\n\n\nGeoEye stereo pair satellite images. Shahrnegar, 55(12), 103-112. \n\n\n\nBai, Y., Feng, M., Jiang, H., 2014. Assessing the consistency of five global \n\n\n\nland cover data sets in China. Remote Sensing, 6(9), 8739-8759. \n\n\n\nDOI:10.3390/rs6098739 \n\n\n\nDutta, A., 2009. Fuzzy c-mean classification of multispectral data \n\n\n\nincorporating spatial contextual information by using Markov  \n\n\n\n\n\n\n\n\n\n\n\n     random field (Master thesis). International Institute for Geo \n\n\n\ninformation Science and Earth Observation. Retrieved from:  \n\n\n\nGhosh, A., Shankar, U., Bruzzone, L., 2010. Neuro-fuzzy-combiner: effective \n\n\n\nmultiple classifier systems. International Journal of Knowledge \n\n\n\nEngineering and Soft Data Paradigms, 2(2), 107-129. DOI: \n\n\n\n10.1504/IJKESDP.2010.034678 \n\n\n\nGhosh, S., Biswas, S., Sarkar, D., 2014. A novel neuro-fuzzy classification \n\n\n\ntechnique for data mining. Egyptian Informatics Journal, 15, 129\u2013147, \n\n\n\nhttps://doi.org/10.1016/j.eij.2014.08.001 \n\n\n\nG\u00f3mez, C., White, J., Wulder, M., 2016. Optical remotely sensed time series \n\n\n\ndata for land cover classification: A review. ISPRS Journal of \n\n\n\nPhotogrammetry and Remote Sensing, 55\u201372. \n\n\n\nhttps://doi.org/10.1016/j.isprsjprs.2016.03.008 \n\n\n\nGonz\u00e1lez, M., Minuesa, C., del Puerto, I., 20161. Maximum likelihood \n\n\n\nestimation and expectation\u2013maximization algorithm for controlled \n\n\n\nbranching processes. Computational Statistics and Data Analysis, 93, \n\n\n\n209\u2013227. https://doi.org/10.1016/j.csda.2015.01.015goo.gl/nLkxkh \n\n\n\nJoseph, G., Chockalingam, J., 2017. Fundamentals of Remote Sensing. \n\n\n\nUniversities Press, ISBN: 9789386235466.  \n\n\n\nMatlab. 2017. Fuzzy logic toolbox, MatLab \n\n\n\nMelgani, F., Hashemy, B., Taha, S., 2000. An explicit fuzzy supervised \n\n\n\nclassification method for multispectral remote sensing images. IEEE \n\n\n\ntransactions on geoscience and remote sensing, 38(1), 287-295. DOI: \n\n\n\n10.1109/36.823921 \n\n\n\nMomeni, E., 2011. Satellite image classification by tree method and fuzzy \n\n\n\nalgorithm (Master\u2019s thesis). Retrieved from: \n\n\n\nhttps://ganj.irandoc.ac.ir/articles/524840 \n\n\n\nMomeni, E., Fard, F.S.N., Haghi, H., 2018. Accuracy Assessment of GeoEye-\n\n\n\n1 Satellite Images for Updating Large-Scale Maps in Iran. Journal of \n\n\n\nGeography & Natural Disasters, 8 (1). DOI: 10.4172/2167-\n\n\n\n0587.1000219 \n\n\n\nWeng, Q., Lu, D., 2007. A survey of image classification methods and \n\n\n\ntechniques for improving classification performance. International \n\n\n\nJournal of Remote Sensing, 28(5), 823\u2013870. DOI: \n\n\n\n10.1080/01431160600746456 \n\n\n\nXu, J., Feng, G., Zhao, T., Sun, X., Zhu, M., 2019. Remote sensing image \n\n\n\nclassification based on semi-supervised adaptive interval type-2 fuzzy \n\n\n\nc-means algorithm. Computers & Geosciences, 131, Pp. 132-143. \n\n\n\nhttps://doi.org/10.1016/j.cageo.2019.06.005 \n\n\n\nZhao, X., 2008. A. Stein, Integration of multi-source information via a fuzzy \n\n\n\nclassification method for wetland grass mapping. The International \n\n\n\nArchives of the Photogrammetry, Remote Sensing, and Spatial \n\n\n\nInformation Sciences, XXXVII(Part B7), 1463-1470, Retrieved from \n\n\n\nhttps://www.isprs.org/proceedings/XXXVII/congress/7_pdf/9_ThS-\n\n\n\n17/11.pdf \n\n\n\nZimmermann, H., 2011. Fuzzy set theory and its applications. Springer. \n\n\n\nRetrieved from \n\n\n\nhttps://pdfs.semanticscholar.org/a31d/fa5eb97fced9494dfa1d88578\n\n\n\nda6827bf78d.pdf. \n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 07-11 \n\n\n\nCite The Article:K. Khanchoul, Z.A. Boukhrissa (2019). Assessing Suspended Sediment Yield In The Saf Saf Gauged Catchment, No rtheastern Algeria .  \nMalaysian Journal of Geosciences, 3(2): 07-11. \n\n\n\n\n\n\n\n  ARTICLE DETAILS \n\n\n\n Article History: \n\n\n\nReceived 04 January 2019 \nAccepted 07 February 2019 \nAvailable Online 26 February 2019\n\n\n\nABSTRACT\n\n\n\nMany studies on sediment transport have been carried out on Algerian rivers but few studies have been undertaken \n\n\n\nin catchments of the Northeast of Algeria. The scarcity or discontinuity on sediment transport measurements \n\n\n\nreduces knowledge about soil loss. In some cases, researchers find often difficulties to apply the most suitable \n\n\n\nmethods to estimate sediment load. The present work represents an assessment of suspended sediment yield from \n\n\n\nthe Saf Saf catchment (322 km2) over 39 years. Long-term annual suspended sediment loads are estimated using \n\n\n\nnon-linear power model, developed on mean discharge class technique as a sediment rating curve. There is a \n\n\n\nchallenge to estimate suspended sediment load in the Saf Saf catchment, which is distinguished by rapid discharge \n\n\n\nvariation. A second aim is to examine monthly and annual variations in discharge, suspended concentration, rainfall \n\n\n\nand load in this river and to find causes for these variations. The results show that the mean annual sediment yield \n\n\n\nis equal to 477 T km-2 yr-1 during the study period. Moreover, the long term variability analysis of sediment load \n\n\n\nseems to be very high from year to year depending on climatic conditions. The analysis of annual sediment load \n\n\n\nshows a decreasing trend along 39 years, mainly from 1997. Most sediment loads are transported during the winter \n\n\n\nseason, which represents 78% of the total sediment load. The understanding of sediment transport relationships \n\n\n\ngained from this study should provide a good starting point for researchers and policymakers to begin addressing \n\n\n\nsediment issues within the catchment. \n\n\n\nKEYWORDS \n\n\n\nNortheastern Algeria, Catchment, Sediment rating curve, Sediment load prediction. \n\n\n\n1. INTRODUCTION \n\n\n\nSoil erosion that occurs within fluvial basins has a significant impact on \nlandscape degradation, reservoir siltation and on decreasing agricultural \nproductivity. Due to this high erosion level, many authors have been \ninterested since the last decades to assess sediment load and study the \nspatial and temporal erosion variability on different river basins in \nAlgeria. \n\n\n\nRecent published studies of suspended sediment transport in \nnortheastern Algeria are extremely limited and those that have been \ncarried out have focused on determining some overall transport rate on \nan annual basis [1]. Sediment yield from a catchment is an integrated \nresult of all water erosion and transport processes occurring in the entire \ncontributing area [2]. For the different gauged systems, suspended \nsediment yield was computed from rating curves established from \ndifferent period term measurement series [3-8]. \n\n\n\nMediterranean climate is identified by seasonal contrast, where the soil \nerodibility can be affected by the dry and warm summer climate, and \nwhere the soil erosion can be affected by the concentration of precipitation \nevents, particularly in the fall [9-11]. This event may lead to serious \ndegradation of the soil with further negative damage on natural resources \n[12, 13]. Both climate changes and also human interventions are \nresponsible for the dramatic reductions in runoff and sediment load [14-\n16]. \n\n\n\nTo predict reliable quantity and rate of sediment transport from land \nsurface into streams, rivers, to identify erosion problem areas within a  \n\n\n\ncatchment and to propose the best management practices to reduce \nerosion impact, models should be used [10,17,18]. Uncertainty of the \nsediment concentration prediction has been extensively discussed when \nconstructing a model and, depending on the catchment characteristics, \ngenerally good concentration predictions are obtained with errors smaller \nthan 15% [19, 20]. \n\n\n\nThis study aims to contribute to a better understanding of suspended \nsediment transport from a headwater catchment under humid conditions \nby using a temporal scale of rainfall, water discharge and suspended \nsediment data, located in northeast of Algeria. More specifically, the \nobjectives are to: (i) investigate the applicability of a regression between \nmeasured suspended sediment concentration (SSC) and water discharge \n(Q) at different time scales (1976-2015); and (ii) identify the short-\ntemporal variability of the sediment load responses. \n\n\n\n2. STUDY AREA \n\n\n\nThe Saf Saf catchment is located on the ridge of the Tell mountains, \nnortheast of Algeria and has an area of 322 km2 at the gauging station of \nKhemakhem (Figure1). Several geologic formations of the Mio-Pliocene \norogeny are characterized by limestone, sandstone and weak rocks. The \nstudy catchment has 44% of its area covered by weathered or un-\nconsolidated geologic formations that generate very erodible soils such as \nmarly limestone of Senonian, Triasic gypseous-sandy clay, clayey \nconglomerate of Miocene and the lower Numidian clay; usually affected by \nfolds and faults. The areas with gypsum clay and clay are highly dissected \nby gullies [1].  \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI : http://doi.org/10.26480/mjg.02.2019.07.11 \n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN : MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nASSESSING SUSPENDED SEDIMENT YIELD IN THE SAF SAF GAUGED CATCHMENT, \nNORTHEASTERN ALGERIA \n\n\n\nK. Khanchoul*, Z.A. Boukhrissa\n\n\n\nDepartment of Geology, Soils and Sustainable Development Laboratory, Badji Mokhtar University-Annaba, Algeria. \n*Corresponding Author e-mail: kamel.khanchoul@univ-annaba.dz \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 07-11 \n\n\n\nCite The Article:K. Khanchoul, Z.A. Boukhrissa (2019). Assessing Suspended Sediment Yield In The Saf Saf Gauged Catchment, No rtheastern Algeria .  \nMalaysian Journal of Geosciences, 3(2): 07-11. \n\n\n\nMost spectacular landslides occur in soils developed on gypsic clay, \nprovoked mainly by bank erosion at the stream base of Bou Adjeb and \nKhemakhem rivers. Rotational slides are associated with soils on clayey \nmarl and are visible along the Bou Adjeb at slopes exceeding 20%. \n\n\n\nFigure 1: Location map of the Saf Saf catchment. \n\n\n\nIn the Saf Saf catchment more than 50% of the basin area is occupied by \ncultures (wheat and barley). Sparse forest and shrubs (Oleo-lentiscus and \nErica europa) cover 30% of the basin and are found mainly on poorly \ndeveloped soils on sandstone and conglomerate. Overgrazing is observed \nin pasture and open shrubland, where plants are exposed to \nintensive grazing for extended period of time [21]. \n\n\n\nThe Saf Saf catchment belongs to a temperate and humid climate of the \nMediterranean type, with a slightly fresh winter and a hot dry summer. \nBased on recorded daily rainfall of the 39-year period, the Saf Saf basin is \ncharacterized by irregular annual precipitation, with a mean annual \nrainfall of 661 mm and a mean annual temperature of 21\u00b0C. There are \nrainfall events greater than 30 mm/day during an average of 4 days/year \nfrom November to February.  \n\n\n\n3. METHODS \n\n\n\n3.1 Suspended sediment measurements and analysis \n\n\n\nSurveys of suspended sediment concentration and water discharge are \nbeing carried out in the Saf Saf River at the gauging station of Khemakhem, \ncontrolled by the National Agency of Hydraulics (ANRH) in Constantine \nand Algiers. \n\n\n\nThe water sampling is performed at surface water using one-liter plastic \nbottles, and the analysis of the samples are done in the laboratory. With \nevery flow measurement, water samples are taken close to the bed of the \nriver in order to determine suspended sediment concentration. The \nsediment concentration of each sample is calculated by filtration method, \nevaporating the sample, and then weighing the remaining sediment [22]. \nThe filter and the mud contained in the bottle is weighed after drying in a \nspecial oven for 30 minutes at a temperature of 110\u00b0C. It is assumed that \nthe addition of dissolved solids to the suspended sediment through \nevaporation is negligible. The samples are taken every day in dry weather \n(no flood) and every quarter-hour or half-hour during flood periods, \nwhich makes 650 daily datasets in 39-year period (1976\u20132014) [23]. \nWater flows are directly given by the rating curve H = f (Q) and the heights \nof water measured by a limnimetric ladder and float water level recorder.  \n\n\n\n3.2 Sediment rating curve development \n\n\n\nReliable estimates of daily suspended sediment load are difficult to \nachieve in the absence of detailed time series of suspended sediment \nconcentration and flow. Thus, for storm events with no water samples, \n\n\n\ninstantaneous flow and sediment transport data are used to develop \nsediment transport ratings and to reconstruct the missing suspended \nsediment concentration records [24]. Generally, the mathematical \nfunctions that best-fit sediment rating curves are not linear and among the \nones mostly used are the power type. \n\n\n\nThe hydrometric record for the Khemakhem gauging station consists of \nintermittent daily measurements of suspended sediment concentration. \nObtaining an accurate estimation of the monthly and annual sediment \nloads from these data require considerable attention to statistical details \n[5]. The sediment rating curve technique has been widely used by \nresearchers for a large variety of purposes. the sediment rating curve is \ndefined as the statistical relationship between suspended sediment \nconcentration (SSC) and stream discharge (Q) [25]. Several researchers \nhave used SRCs to estimate suspended sediment concentration and/or \nsediment load for subsequent flux computations in the absence of actual \nSSC [6, 25-28].  \n\n\n\nSince the most straightforward rating curve approach typically uses an \nempirically calibrated power-law relation between water discharge and \nsediment concentration, the sediment concentrations are calculated with \na sediment rating curve by making use of log transformation of the \nrelationship between daily mean concentration and daily mean water \ndischarge such as [29, 30]: \n\n\n\n  C = aQb    (1) \n\n\n\nwhere C is suspended sediment concentration (g/l), Q is water discharge \n(m3/s), and a and b are regression coefficients. \n\n\n\nThe used regression has provided a fairly low coefficient of correlation (r \n= 0.50) and the C-Q data show a scatter distribution mainly at low and \nmedium values (Figure 2). The developed regression on all separate \nmeasurements has underestimated the true sediment load by 61% when \ndeveloped on all 650 datasets. Due to the underestimation of sediment \nload, a further has to be used to provide a better prediction of sediment \nload. Therefore, an attempt has been followed by using water discharge \nclasses' technique [4, 5]. \n\n\n\nThe chosen technique of rating relationships is started by sorting and \nregrouping the data into distinct classes of water discharge. The definition \nof the width of each class interval depends on the data base in question. \nFor the low discharge values, the class interval can be narrow and may \ncontain more values in a discharge class [1]. This class interval will become \nprogressively wider as the data base becomes small at high water \ndischarges. The mean sediment concentration in every class is computed \nand entered in a logarithmic plot to determine the regression equation and \nto check the goodness of fit of the developed regression. \n\n\n\nFigure 2: Scatter plot of water discharge (Q) versus sediment \nconcentration (C) values. \n\n\n\nOptimization of the SRC method is then validated by comparing the \npredicted against observed values on scatter plots. Consequently, the use \nof log-transformed variables usually introduces a bias to the \nretransformed equations [3, 4]. When the least squares logarithmic \nregression procedure results in underestimation because of the bias given \nto the values below the fitted line by the regression a non parametric \ncorrection for the transformation bias may be used. Miller has proposed \nthe following bias correction factor (CF) [31]: \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 07-11 \n\n\n\nCite The Article:K. Khanchoul, Z.A. Boukhrissa (2019). Assessing Suspended Sediment Yield In The Saf Saf Gauged Catchment, No rtheastern Algeria .  \nMalaysian Journal of Geosciences, 3(2): 07-11. \n\n\n\n  CF = exp (0.5\u03c3\u00b2),  where \uf0732 = 1/(N-1)*\n)\u00b2'ClnC(ln\n\n\n\nn\n\n\n\n1i\n\n\n\nii\uf0e5\n=\n\n\n\n\u2212\n  (2)                  \n\n\n\n\u03c32, Ci and C'i are the variance (in base-10 logarithms), the measured and \nestimated concentration, respectively. The corrected equation is written \nas: \n\n\n\nC = CF x aQb    (3) \n\n\n\nThe computation of the daily sediment load (SL) in tonnes is given by the \nsum of the product of the three variables Q (m3/s), C (g/l) and T (duration \nof time in seconds). \n\n\n\nTo test the validation of the sediment rating curve technique, it is possible \nto use the Nash\u2013Sutcliffe model efficiency factor, which is to assess the \npredictive power of hydrological models such as the sediment transport. \nThe best model is selected based on the EF value approaching one. The \nmodel efficiency factor is estimated using the equation: \n\n\n\nEF = 1 - \n\n\n\n\uf0e5\n\n\n\n\uf0e5\n\n\n\n=\n\n\n\n=\n\n\n\n\u2212\n\n\n\n\u2212\nn\n\n\n\ni\n\n\n\nn\n\n\n\ni\n\n\n\nCCi\n\n\n\niCCi\n\n\n\n1\n\n\n\n1\n\n\n\n)\u00b2(\n\n\n\n)\u00b2'(\n\n\n\n (4) \n\n\n\nwhere n is the total number of observations, Ci the ith observed value, C\nthe mean of observed values, and C'i the ith predicted value. \n\n\n\n4. RESULTS AND DISCUSSION\n\n\n\n4.1 Sediment load estimations \n\n\n\nIn this research, the relative sensitivity values are evaluated and found in \nthe parameter estimation process. The sediment rating curve developed \nby the C-Q relationship for available data at Saf Saf catchment is presented \nin Figure 3. The latter figure is presenting the relationship developed from \nthe total set of the mean water discharge values and the mean sediment \nconcentrations. The best-fit power function line through the data \nunderestimates the suspended sediment concentration at low water \ndischarges and slightly overestimates sediment load at high discharges.  \n\n\n\nIn the discharge(Q) and sediment concentration (C) relationship, the SRC \nhas yielded an R of 0.93 as shown in Figure 3a. The best fit power function \nshows more variation between the observed and estimated discharge. \nAfter using the allocated dataset (42) for testing, the observed and \nestimated R is 0.90 as seen in Figure 3b. \n\n\n\nFigure 3: Scatter plot of : (a) relationship between water discharge and \nsediment concentration, (b) observed and estimated sediment \nconcentration. \n\n\n\nThe used discharge class technique has shown that the sediment rating \ncurve procedure, even with infrequent sampling, can give satisfactory \nresults. The application of the datasets of mean discharges and mean \nsediment concentrations has revealed interesting preliminary results \n(Table 1). The rating relationship is applied to the daily flow data and the \nresulting estimates of annual sediment load (SL) are compared with the \nloads computed from the observed (measured) record of sediment \nconcentration in Table 1. The error of estimation is a measure of the \naccuracy of predictions in percentage. The percentage is calculated as \nfollows: \n\n\n\n% error = [(predicted SL value) - (measured SL value)/(measured SL \nvalue)] \uf0b4100   (5) \n\n\n\nThe errors mentioned in Table 1 demonstrate that the annual sediment \nload estimated without using a correction for bias has underestimated the \nSL by 15.45%, but, when introducing the correction (CF), the \nunderestimation has decreased to 7.46%. In general, it seems from Table \n1 that the application of the discharge class technique has given lower \nerrors in annual sediment load estimates. \n\n\n\nTable 1: Rating curve estimates of measured and calculated sediment \nloads from the continuous record. \n\n\n\nSediment load (SL) SL  \n(\uf0b4106 \n\n\n\ntonnes) \n\n\n\nError \n(%) \n\n\n\nCorrected \nSL  \n\n\n\n(\uf0b4106 \ntonnes) \n\n\n\nError \n(%) \n\n\n\nContinuous \nconcentration record \n\n\n\n11.22 \n\n\n\nSingle rating : one \nregression line \n\n\n\n9.48 -15.45 10.38 -7.46 \n\n\n\nIn addition, the calculation of the EF has also provided information about \nthe predictive capabilities of the model. The EF value is equal to 0.54 for \nthe single regression model (corrected concentration dataset). Threshold \nvalues to indicate a model of sufficient quality have been suggested \nbetween 0.5 < NSE < 0.65. Moriasi and others suggest that a EF of greater \nthan 0.50 is satisfactory in catchment models [32]. \n\n\n\n4.2 Suspended sediment load Variation \n\n\n\nIn the following discussion, we are going to explain the variations in \nsuspended sediment load in the Saf Saf catchment. The total suspended \nsediment load during 39 years is 5989.57\u00d7103 tonnes, corresponding to a \nmean annual sediment yield of 476.95 T/km2/year. The annual suspended \nsediment load vary significantly from one year to another (Figure 4). For \nexample, during the two years 1984 and 1985, streamflow and sediment \n\n\n\nload are much higher than average, represented by Q  = 1.483 m3/s and \n\n\n\nSL = 153.58x103 tonnes, due to exceptional rainstorms in January (154\n\n\n\nmm), February (234 mm), December (318 mm) 1984 and January (114\nmm), March (134 mm) 1985; during which 3032.35\u00d7103 tonnes of \nsuspended sediment are transported in the Saf Saf River. These two years \nhave contributed in sediment transport with 50.63% of the total sediment\nload. \n\n\n\nFigure 4: Variation of annual water discharge and sediment load in the \nstudy catchment. \n\n\n\n\nhttps://en.wikipedia.org/wiki/Hydrology\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 07-11 \n\n\n\nCite The Article:K. Khanchoul, Z.A. Boukhrissa (2019). Assessing Suspended Sediment Yield In The Saf Saf Gauged Catchment, No rtheastern Algeria .  \nMalaysian Journal of Geosciences, 3(2): 07-11. \n\n\n\nSurprisingly, during the years 2011 and 2013, which are wetter than \naverage, only 1% of the 39-year sediment load are carried out of the Saf \nSaf basin, mainly due to a low sediment supply into the river or a few \nsediment sampling during these years. Overall, this observation can be \ngeneralized on the period after the year 1996, where there is a dramatic \ndecrease in sediment supply and/or sediment sampling, giving a priori \nless information on sediment long-term trends. \n\n\n\nThe study of seasonal distribution of sediment load indicates that \n4652.03\u00d7103 tonnes are transported during the winter season from \nDecember to February, which is 77.67% of the total sediment load (Figure \n5). The spring season, represented by March, April and May, is the second \nhighest sediment load occurred during this season a load reaching \n1168.94\u00d7103 tonnes, i.e. 19.52% of the total sediment load. The major \ncontrast between the winter and other seasons depends on the intensity \nof rainstorms [33]. Early in the wet winter season, rainstorms are able to \nsupply great volumes of suspended sediment (Figure 5), because this \ndelivery is certainly due to the fact that the highest sedimentation rates \nare located in agricultural and arable lands on steep slopes. This is \nprobably due to the influence of tillage practices on soil loss because they \nincrease soil erosion rate and sediment losses. \n\n\n\nDuring great floods or when floodplain and hillslope vegetation (with \nsparse grassland and shrubs) do not stabilize this sediment, large \nquantities may be flushed from the system. Such cycles of rapid bank \nerosion or gullying and subsequent healing and deposition are common in \nthis basin. Despite higher discharges in January and February, sediment \nloads fail to rise to the loads observed in December, indicating less \nsediment supply due to the following explanation: when frequent \nprecipitation occurs, easily erodible soil is detached and transported \nduring the first few storms (in November and December), leaving  less  \nerodible  soil  for  subsequent   storms.  This can result in decreasing \nerosion rates from rain event to rain event [34]. Also, it is seen from Figure \n5a that the storms in December transport the highest monthly sediment \nload of 2112.92\u00d7103 tonnes and mean sediment concentration equal to \n7.67g/l at high rainfall amounts (Figure 5b).  \n\n\n\nFigure 5: Variation of: (a) monthly suspended sediment loads and \nmonthly mean discharges; (b) monthly mean suspended sediment \nconcentrations, monthly mean discharges and monthly mean rainfalls. \n\n\n\nNevertheless, during storms, the surface runoff is sufficient to wash \nsediment from slopes and the flow is sufficient to provoke bank erosion \nand landslides along the Bou Adjeb and Khemakhem rivers and their \ntributaries (Figure 6). During December to February, high sediment \nconcentrations contribute to the high sediment loads (Figure 5b). Later in \nthe spring season concentration is lower but the discharges are fairly high \n(Figure 5b) resulting in substantial load (Figure 5a). \n\n\n\nFigure 6: Photographs showing bank erosion and landsliding in the Bou \nAdjeb and Khemakhem rivers.  \n\n\n\nThe suspended sediment load of the autumn months is higher in \nNovember with 132\u00d7103 tonnes (Figure 5a). This value is mainly \nrepresented by the rise of rainstorms, runoff and sediment concentration \nin November 1976, 1982, 1986, 1990 and 2012 with daily values in \nsediment loads, sediment concentrations and water discharges that vary \nas : 3159-19516 tonnes, 1.30-8.58 g/l, 6.1-27.32 m3/s respectively. The \nsummer months from June to August are often dry and the evapo-\ntranspiration is high. The basin has the lowest sediment supply in this \nseason where the loads do not exceed 2.45\u00d7103tonnes. \n\n\n\n5. CONCLUSION \n\n\n\nBy studying the suspended sediment dynamics at the outlet of the Saf Saf \ncatchment, the relationship of water discharge and sediment \nconcentration is determined to assess sediment load over 39-year period. \nThe remarkable observed scatter of the suspended sediment \nconcentrations underlines the importance of including suspended \nsediment rating curves that is developed by least square regression on \nlogged means in discharge classes, and which has produced satisfactory \nestimations of sediment load. In fact, the high sensitivity of SSC to local \nsources in the Saf Saf basin is responsible for the complexity of the Q-C \nrelationship, which cannot be adequately characterized using a simple \nrating curve. \n\n\n\nSediment loads have shown significant decreasing trends from the 1997 \nto 2015. The prevailing climatic conditions may have significant control \nover the water flux and suspended sediment load patterns in the Saf Saf \nbasin. However, the other possibility is that data sources are either \nmissing or are not available, which makes the estimation of sediment load \nless reliable. The mean annual suspended sediment yield during different \nrainstorm events of the 39 years is equal to 477 T/ km2/year, a rather high \nvalue for the environment caused by steep slopes. The study basin is \nshowing a substantial high annual variability of the sediment loads and \nmean concentrations. \n\n\n\nSuspended sediment load during the winter season accounts for 78% of \nthe total suspended sediment load during the study period and is caused \nby the impact of heavy rainstorms during a season with fairly low \nvegetation cover and tillage activity in the agricultural fields, and by the \nfact that the vulnerable lithological formations that are easily supplied to \nstreams due to steep slopes. \n\n\n\nIn the winter, the existence of mass wasting such as landslides and bank \nerosion are mainly triggered by natural events like flooding and change in \nsoil moisture. These erosive processes may lead to produce high sediment \nload. During the spring, runoff and sediment loads fail to rise to the loads \nobserved in the winter, because of more plant cover and land use that can \nexplain a large part of the hydrological and erosive variability in this basin. \nThe mean monthly concentration and rainfall are highest in December \nafter the long summer-fall dry period, the mean monthly load is also high. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 3(2) (2019) 07-11 \n\n\n\nCite The Article:K. Khanchoul, Z.A. Boukhrissa (2019). Assessing Suspended Sediment Yield In The Saf Saf Gauged Catchment, No rtheastern Algeria .  \nMalaysian Journal of Geosciences, 3(2): 07-11. \n\n\n\nThe present study provides a great opportunity to continue the research \nand further develop and refine the methodology. Sediment concentration \nis a critical parameter that can provide an opportunity to evaluate the \nseriousness of potential environmental impact and serve as an accurate \ndescription of erosion and transport processes.  \n\n\n\nREFERENCES \n\n\n\n[1] Khanchoul, K., Jansson, M.B., Lange, Y. 2007. Comparison of suspended \nsediment yield in two catchments, northeast Algeria. Zeitschrift f\u00fcr \nGeomorphologie, 51, (1), 63-94. \n\n\n\n[2] Selmi, K., Khanchoul, K. 2016. Sediment load estimation in the Mellegue \ncatchment, Algeria. Journal of Water and Land Development, 31 (X\u2013XII), \n129-137. \n\n\n\n[3] Ferguson, R.I. 1986. River loads underestimated by rating curves. \nWater Resources Research, 22, 74-76. \n\n\n\n[4] Jansson, M.B. 1997. Comparison of sediment rating curves developed \non load and on Concentration. Nordic Hydrology, 28 (3), 189-200. \n\n\n\n[5] Khanchoul, K., Altschul, R., Assassi, F. 2009. Estimating suspended \nsediment yield, sedimentation controls and impacts in the Mellah \nCatchment of Northern Algeria. Arabian Journal of Geosciences, 2 (3), 257-\n271. \n\n\n\n[6] Khanchoul, K., Boukhrissa, Z.A., Acidi, A., Altschul, R. 2012. Estimation \nof suspended sediment transport in the Kebir drainage basin, Algeria. \nQuaternary International, 262, 25-31. \n\n\n\n[7] Achite, M., Ouillon, S. 2016. Recent changes in climate, hydrology and \nsediment load in the Wadi Abd, Algeria (1970\u20132010). Hydrology and Earth \nSystem Sciences, 20 (4), 1355-1372. \n\n\n\n[8] Megnounif, A., Terfous, A., Ouillon, S. 2013. A graphical method to study \nsuspended sediment dynamics during flood events in the Wadi Sebdou, \nNW Algeria (1973\u20132004). Journal of Hydrology, 497, 24-36. \n\n\n\n[9] Le Bissonnais,Y., Blavet, D., Noni, G., Laurent, J., Asseline, J., Chenu, C. \n2007. Erodibility of mediterranean vineyard soils: relevant aggregate \nstability methods and significant soil variables. European Journal of Soil \nScience, 58, 188-195. \n\n\n\n[10] Briak, H., Moussadek, R., Aboumaria, K., Mrabet, R. 2016. Assessing \nsediment yield in Kalay a gauged watershed (Northern Morocco) using GIS \nand SWAT model. International Soil and Water Conservation Research, 4, \n177-185. \n\n\n\n[11] Ramos, M.C., Mart\u00ednez\u2010Casasnovas, J.A. 2009. Impacts of annual \nprecipitation extremes on soil and nutrient losses in vineyards of NE \nSpain. Hydrological Processes, 23 (2), 224-235. \n\n\n\n[12] Smith, H.G, Sheridan, G.J.,  Lane, P.N.J.,   Nyman, P., Haydon, S. 2011.\nWildfire effects on water quality in forest catchments: A review with \nimplications for water supply.  Journal of Hydrology, 396 (1-2), 170-192. \n\n\n\n[13] Stoof, C.R., Ferreira, A.J.D., Mol W., van den Berg, J., Kort, A.D., Drooger, \nS., Erik, C.S., Mansholt ,A.U., Ferreira, C.S.S., Ritsema, C.J. 2015. Soil surface \nchanges increase runoff and erosion risk after a low\u2013moderate severity \nfire. Geoderma, 239-240, 58-67. \n\n\n\n[14] Gao, P., Mu, X.M., Wang, F., Li, R. 2011. Changes in streamflow and \nsediment discharge and the response to human activities in the middle \nreaches of the Yellow River. Hydrology and Earth System Sciences, 15, 1-\n10. \n\n\n\n[15] Raclot, D., Inoubli, N., Moussa, R., Habaieb, H., Le Bissonnais, Y. 2015. \nSoil cracking effects on hydrological and erosive processes in \nMediterranean cultivated vertisols. In Proceedings of the 1st International \nConference Afro-Mediterranean Soils: Constraints and Potentialities for \nDurable Management, Marrakech, Morocco. \n\n\n\n[16] Zuo, D., Xua, Z, Yao, W., Jin, S., Xiao, P., Ran, D. 2016. Assessing the \neffects of changes in land use and climate on runoff and sediment yields \nfrom a watershed in the Loess Plateau of China. Science of The Total \nEnvironment, 544, 238-250. \n\n\n\n[17] Yesuf, H.M, Assen, M., Alamirew, T., Melesse, A.M. 2015. Modeling of \nsediment yield in Maybar gauged watershed using SWAT, northeast \nEthiopia. Catena, 127, 191-205. \n\n\n\n[18] Buyukyildiz, M., Kumcu, S.Y. 2017. An estimation of the suspended \nsediment load using adaptive network based fuzzy inference system, \nsupport vector machine and artificial neural network models. Water \nResources Management, 31,1343-1359. \n\n\n\n[19] Horowitz, A. J. 2008. Determining annual suspended sediment and\nsediment-associated trace element and nutrient fluxes. Science of Total \nEnvironment, 400, 315-343. \n\n\n\n[20] Slaets, J.I.F., Piepho, H.P., Schmitter, P., Hilger, T., Cadisch, G. 2017. \nQuantifying uncertainty on sediment loads using bootstrap confidence \nintervals. Hydrology and Earth System Sciences, 21, 571-588. \n\n\n\n[21] Khanchoul, K., Saaidia, B., Altschul, R. 2018. Variation in sediment \nconcentration and water discharge during storm events in two \ncatchments, northeast of Algeria. Earth Sciences Malaysia, 2 (2), 01-09. \n\n\n\n[22] Boukhrissa, Z.A., Khanchoul, K., Le Bousonnais, Y., Tourki, M. 2013. \nPrediction of sediment load by sediment rating curve and neural network \n(ANN) in El Kebir catchment, Algeria. Journal of Earth System Science, 122, \n1303-1312. \n\n\n\n[23] Madani Cherif, H., Khanchoul, K., Bouanani, A., Terfous, A. 2017. \nPrediction of sediment yield at storm period in Northwest Algeria. Arabian \nJournal of Geoscience, 10 (9), 1-17. \n\n\n\n[24] Fiandino, M. 2004. Apports de mati\u00e8res en suspension par les fleuves \nc\u00f4tiers \u00e0 l\u2019Etang de Berre (Bouches-du-Rh\u00f4ne, France). Etudes de \ng\u00e9ographie physique, suppl. no XXXI. Travaux du BVRE, du Mont-Laz\u00e8re. \nUMR6012 \u00abEspace\u00bb - \u00e9quipe G.V.E, Nice. \n\n\n\n[25] Khaleghi, M.R., Varvani, J. 2018. Sediment Rating Curve Parameters \nRelationship with Watershed Characteristics in the Semiarid River \nWatersheds. Arabian Journal for Science and Engineering, 43, 3725-3737. \n\n\n\n[26] Horowitz, A.J. 2003. An evaluation of sediment rating curve for \nestimating suspended sediment concentration for subsequent flux \ncalculations. Hydrological Processes, 17, 3387\u20133409. \n\n\n\n[27] Kao, S.J., Lee, T.Y., Milliman, J.D. 2005. Calculating highly fluctuated \nsuspended sediment fluxes from mountainous rivers in Taiwan. TAO, 16 \n(3), 653-675. \n\n\n\n[28] Amamra, A., Khanchoul, K., Eslamian, S., Hadj Zobir, S. \n2018.  Suspended sediment estimation using regression and artificial \nneural network models: Kebir watershed, northeast of Algeria, North \nAfrica. International Journal of Hydrology Science and Technology, 8 (4), \n352-371. \n\n\n\n[29] Walling, D.E. 1978. Suspended sediment and solute response \ncharacteristics of the river Exe, Devon, England. In: Davidson-Arnott R, \nNickling W (eds) Research in fluvial systems. Geoabstracts, Norwich, UK, \npp 169-197. \n\n\n\n[30] Asselman, N.E.M. 2000. Fitting and interpretation of sediment rating \ncurves. Journal of Hydrology, 234, 228-248. \n\n\n\n[31] Miller, D.M. 1984. Reducing transformation bias in curve fitting. The \nAmerican Statistician, 38 (2), 124-126. \n\n\n\n[32] Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Binger, R.L., Harmel, R.D., \nVeith, T.L. 2007. Model evaluation guidelines for systematic quantification \nof accuracy in watershed simulations. Transactions of the American \nSociety of Agricultural and Biological Engineers, 50 (3), 885-900. \n\n\n\n[33] Khanchoul, K., Jansson, M.B. 2008. Sediment rating curves developed \non stage and seasonal means in discharge classes for the Mellah wadi, \nAlgeria. Geografiska Annaler, 90A (3), 227-236. \n\n\n\n[34] Holz, D.J., Williard, K.W.J., Edwards, P.J., Schoonover, J.E. 2015. Soil \nErosion in Humid Regions: A Review. Journal of Contemporary Water \nResearch and Education, 154, 48-59. \n\n\n\n\nhttps://onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Ramos%2C+M+C\n\n\nhttps://onlinelibrary.wiley.com/action/doSearch?ContribAuthorStored=Mart%C3%ADnez-Casasnovas%2C+J+A\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0022169410006748#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0022169410006748#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0022169410006748#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0022169410006748#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0022169410006748#!\n\n\nhttps://www.sciencedirect.com/science/journal/00221694/396/1\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0016706114003528#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0016706114003528#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0016706114003528#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0016706114003528#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0016706114003528#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0016706114003528#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0016706114003528#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0016706114003528#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0016706114003528#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0016706114003528#!\n\n\nhttps://www.sciencedirect.com/science/article/pii/S0016706114003528#!\n\n\nhttps://www.sciencedirect.com/science/journal/00489697\n\n\nhttps://www.sciencedirect.com/science/journal/00489697\n\n\nhttps://www.sciencedirect.com/science/journal/03418162/127/supp/C\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 41-50 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2021.41.50 \n\n\n\n \nCite the Article: \u00d6zcan \u00c7ak\u0131r (2021). Transverse Isotropic Crust Structure Beneath the Northwest and Central North Anatolia Revealed by Seismic Surface Waves \n\n\n\nPropagation. Malaysian Journal of Geosciences, 5(2): 41-50. \n \n\n\n\n\n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nREVIEW ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2021.41.50 \n\n\n\n\n\n\n\n\n\n\n\nTRANSVERSE ISOTROPIC CRUST STRUCTURE BENEATH THE NORTHWEST AND \nCENTRAL NORTH ANATOLIA REVEALED BY SEISMIC SURFACE WAVES \nPROPAGATION  \n \n\u00d6zcan \u00c7ak\u0131r \n \n\n\n\nAssociate Professor, S\u00fcleyman Demirel University, Department of Geophysics, 32260 Isparta, Turkey. \n*Corresponding Author E-mail: ozcancakir@sdu.edu.tr \n \nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 20 July 2021 \nAccepted 24 August 2021 \nAvailable online 10 September 2021 \n\n\n\n \nThe Anatolian crust, which is abnormally hot, is widely deformed by subduction related volcanism. Suture \nzones, transform faults, thrusts and folds and metamorphic core complexes add to the geological complexity. \nVolcanic provinces such as Western, Central and Eastern Anatolia and Galatea are recognized as distinct \nfeatures in the region. The middle-to-lower crust depths appear to be intruded by horizontal sills and the \nupper crust by vertical dykes. Both horizontal sills and vertical dykes leave anisotropic signs detected as \nVertical Transverse Isotropy (VTI) that is explored by Love and Rayleigh surface wave inversions, i.e., Love-\nRayleigh wave discrepancy which arises because the dykes and sills act differently against the Love and \nRayleigh surface waves. The current study gives emphasis to the Northwest and Central North Anatolia \nutilizing both single-station and two-station tomography techniques to recover the two-dimensional group \nand phase speed charts from which one-dimensional dispersion inversions are implemented. The one-\ndimensional inversions are joined to construct the three-dimensional crust of the studied region. The shear-\nwave anisotropy is used to locate the anisotropy in the crust. The vertical dykes in the upper crust fit into \nnegative VTI around -10% while the horizontal sills in the middle-to-lower crust yield positive VTI around \n12%. The vertical magma flows within the vertical dykes and the horizontal magma flows within the \nhorizontal sills contribute constructively to the anisotropy created by the special shape orientations of sills \nand dykes. The earthquakes hypocenter distribution and high and low speeds alongside the VTI provide \nsignificant clues to differentiate between diverse geological districts.  \n\n\n\nKEYWORDS \n\n\n\nAnatolia, Crust, Surface Wave, Tomography, Transverse Isotropy. \n\n\n\n1. INTRODUCTION \n\n\n\nThe crust and upper mantle system beneath the Anatolian plate and the \n\n\n\nsurrounding area is complex with dipping slabs (subducted, torn and \n\n\n\ndetached), pervasive felsic to mafic volcanism, metamorphic core \n\n\n\ncomplexes, fold and thrust belts, faults, and suture zones (\u015eengo r and \n\n\n\nY\u0131lmaz, 1981). The tectonic processes caused deformational strain in the \n\n\n\ncrust producing anisotropic structures having the Crystallographic or \n\n\n\nLattice Preferred Orientation (CPO or LPO), which is essentially \n\n\n\nindependent of the seismic wavelength. The CPO of elastically anisotropic \n\n\n\nolivine mineral is commonly employed to study the flow pattern within the \n\n\n\nearth (Confal et al., 2018). The diffusion creep in the upper mantle and the \n\n\n\nplastic flow (dislocation slip) in the middle-to-lower crust are believed to \n\n\n\nbe mechanisms leading to the CPO in the wave propagating medium \n\n\n\n(Miyazaki et al., 2013; Mainprice and Nicolas, 1989). \n\n\n\nStatistical allocation of mineral CPOs in the mineral aggregate, grain nature \n\n\n\nand allocation, grain edge allocation and disorientation within grain \n\n\n\naccumulate, which are the circumstances defining the rock micro-fabric, \n\n\n\ndictate the seismic velocity and its directional dependence when dynamic \n\n\n\nrecrystallization texture occurs (Wenk et al., 1997). Different CPO may \n\n\n\ndevelop from one mineral to another. For instance, plagioclase and alkali \n\n\n\nfeldspar and mica and clay minerals show high P and S-wave anisotropy \n\n\n\ngreater than 50% as single-crystal, but plagioclase and alkali feldspar \n\n\n\nminerals are known to show weak CPOs while mica and clay minerals \n\n\n\ndevelop strong CPOs in rocks making them main candidates for the seismic \n\n\n\nanisotropy in the crust (Almqvist and Mainprice, 2017). \n\n\n\nAnother type of anisotropy is due to the Shape Preferred Orientation \n\n\n\n(SPO). Crustal heterogeneities such as fracture and crack systems, sills and \n\n\n\ndykes, oriented melt pockets, sheet-like melts, stack of thin layers \n\n\n\nrepeating themselves with a periodicity having length scale shorter than \n\n\n\nthe seismic wavelength result the SPO anisotropy (Kendall, 2000). The \n\n\n\nAnatolian crust is extensively intruded by subduction-related magmatism \n\n\n\ndue to the north dipping Afro-Arabian slab in the eastern Mediterranean. \n\n\n\nThis magmatic activity has produced vertical dyke formations in the upper \n\n\n\ncrust and horizontal sill formations in the middle-to-lower crust leading to \n\n\n\nthe SPOs (\u00c7ak\u0131r, 2018, 2019). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 41-50 \n\n\n\n\n\n\n\n \nCite the Article: \u00d6zcan \u00c7ak\u0131r (2021). Transverse Isotropic Crust Structure Beneath the Northwest and Central North Anatolia Revealed by Seismic Surface Waves \n\n\n\nPropagation. Malaysian Journal of Geosciences, 5(2): 41-50. \n \n\n\n\n\n\n\n\nIn general terms, sills fed by dykes join to create magma chambers and \n\n\n\nplutons, which is the central device to augment the continental crust \n\n\n\n(Chanceaux and Menand, 2014).  Such intrusions also increase the crustal \n\n\n\nheat above the geothermal gradient. Menand has provided a review about \n\n\n\nigneous bodies where it is reported that sills and dykes generated by \n\n\n\nconsecutive and distinct pulses of magma intrusions form some large \n\n\n\nplutons inside the Earth (Menand, 2011). The three main mechanisms \n\n\n\nphysically control the sill formation. The first mechanism is related to the \n\n\n\nstiffness difference where dykes halt and sills accumulate at the boundary \n\n\n\nseparating the upper strong stratum from the lower weaker stratum. The \n\n\n\nsecond one is linked to the heterogeneous flow characteristics in the crust \n\n\n\nwhere sills arise within the weakest flexible zones.  \n\n\n\nThe third one is due to the rotation of principal stress components (\ud835\udf0e1 >\n\n\n\n\ud835\udf0e2 > \ud835\udf0e3) where sills arise when the least principal stress component (\ud835\udf0e3) \n\n\n\nconverts to upright supporting the sub-horizontal intrusions. Moreover, \n\n\n\nwithin the solidifying magma there exists fractional crystallization since \n\n\n\neach mineral in the magma source has its own crystallization temperature \n\n\n\nwhich removes early solidifying minerals from the liquid magma changing \n\n\n\nthe liquid composition leading to a wide range of rock formations \n\n\n\n(Meschede and Warr, 2019). In relation to the latter case, there exists \n\n\n\nviscous flow in the magma melt where these minerals solidified with \n\n\n\nuneven crystal augmentation rates and dissimilar sizes (e.g., pyroxene, \n\n\n\nolivine, feldspar, mica, and amphibole) freely rotate to result in the SPOs. \n\n\n\nSince the crystal structure and the crystal shape are directly related, the \n\n\n\nSPO in the latter case necessitates the development of CPO (Mainprice and \n\n\n\nNicolas, 1989). \n\n\n\nThe aim of the current study is to show that the shear-wave speed \n\n\n\nanisotropy evaluated from the seismic surface waves are effective to study \n\n\n\nthe magmatic intrusions in the Anatolian crust. The data analysis \n\n\n\ntechnique adapted herein is also utilized in other studies in the literature \n\n\n\n(\u00c7ak\u0131r 2018, 2019; Lee et al., 2021; Alkan and \u00c7\u0131nar, 2021). We utilize Love-\n\n\n\nRayleigh surface wave dispersion data (fundamental mode) where single-\n\n\n\nstation (or source-to-station) and two-station (or inter-station or source-\n\n\n\nto-station-pair) group speeds and two-station phase speeds are measured \n\n\n\nfor the analysis. The group and phase speed curves are solved in \n\n\n\ncooperation to establish the one-dimensional (1-D) shear-wave velocity \n\n\n\nand depth profiles. The Love-Rayleigh discrepancy is obvious in the \n\n\n\ndetected surface waves and is engaged to analyze the anisotropic \n\n\n\nstructures under the interested area. The Vertical Transverse Isotropy \n\n\n\n(VTI), which is the simplest form of anisotropy, is effectively employed to \n\n\n\nmodel the current detected anisotropy largely instigated by igneous rocks, \n\n\n\ni.e., negative VTI in the upper crust and positive VTI in the middle-to-lower \n\n\n\ncrust (Fu and Li, 2015). Since the principles relevant to the wave \n\n\n\npropagating medium with considered anisotropy are broadly elucidated \n\n\n\nelsewhere, the respective details are omitted here (\u00c7ak\u0131r, 2019).  \n\n\n\nThe Anatolian plate is unusually hot as exposed by the prevalent \n\n\n\ngeothermal activity and high heat flow detections (~110 mW/m2 \u2013 I lk\u0131\u015f\u0131k, \n\n\n\n1995) (Pasvanog lu and Gu ltekin, 2012; Pasvanog lu and \u00c7elik, 2019). The \n\n\n\nseismic speeds in the crust and uppermost mantle are abnormally slower \n\n\n\ncompared to a usual earth assemblage \u2013 e.g., IASP91 (Kennett and Engdahl, \n\n\n\n1991; Delph et al., 2015; \u00c7ak\u0131r, 2018, 2019). \u00c7ak\u0131r and Erduran have \n\n\n\nutilized teleseismic P and S receiver functions to study the 1-D shear-wave \n\n\n\nvelocity structure of the crust and upper mantle beneath station ANTO \n\n\n\n(near central Anatolia) where they have detected a shallow Lithosphere\u2013\n\n\n\nAsthenosphere Boundary \u2013 LAB at ~70-km depth and a Low Velocity Zone \n\n\n\n\u2013 LVZ atop the 410-km discontinuity (\u00c7ak\u0131r and Erduran, 2011). A group \n\n\n\nresearcher has analyzed teleseismic S receiver functions to determine the \n\n\n\nlithosphere thickness underneath the Anatolian plate (Kind et al., 2015). \n\n\n\nThey have predicted shallow LAB in the depth range 80-100 km and a \n\n\n\nvelocity reversal (i.e., LVZ) above the 410-km discontinuity, which was \n\n\n\ninterpreted as a zone of partial melt. A group researcher has reported that \n\n\n\nthe shear wave (Sn) attenuation relatively strong in the asthenosphere \n\n\n\nunderlying the shallow lithosphere probably resulted from partially \n\n\n\nmolten asthenosphere (Go k et al., 2003). The above results are interpreted \n\n\n\nas resulting from the detachment of the Afro-Arabian slab alongside \n\n\n\nuprising buoyant asthenosphere (Keskin, 2003; Biryol et al., 2011; Portner \n\n\n\net al., 2018).  \n\n\n\nThere exist few geophysical studies to delineate the seismic crust \n\n\n\nassemblage underneath the Northwest and Central North Anatolia (Yolsal-\n\n\n\n\u00c7evikbilen et al., 2012; Licciardi et al., 2018). On the other hand, the \n\n\n\ntectonic growth of Anatolia was subject to many seismological studies \n\n\n\n(Bak\u0131rc\u0131 et al., 2012; Warren et al., 2013). Few studies exist employing the \n\n\n\nLove and Rayleigh surface waves to inspect the VTI crust formation below \n\n\n\nAnatolia (\u00c7ubuk-Sabuncu et al., 2017; \u00c7ak\u0131r, 2018, 2019). The current \n\n\n\nstudy affords important evidence about the depth distribution of crustal \n\n\n\nmagmatic rocks (i.e., sills and dykes) beneath the Northwest and Central \n\n\n\nNorth Anatolia, which is not reported elsewhere. The horizontal sills are \n\n\n\nemplaced in the middle-to-lower crust and the vertical dykes in the upper \n\n\n\ncrust. The findings related to the shear-wave speeds may help us \n\n\n\ndetermine if these sill bodies are already cooled (i.e., high speed zones) or \n\n\n\nstill cooling (i.e., low speed zones). \n\n\n\nThere are several accelerometer and broadband stations in the study area, \n\n\n\nwhich have been continuously recording earthquakes since year 2010 \n\n\n\n(accelerometer stations) and year 2006 (broadband stations). We take the \n\n\n\nadvantages of these recordings until year 2020 to invert the surface waves \n\n\n\nfor the determination of underground structure (shear-wave speed and \n\n\n\nanisotropy). Both group and phase speeds of fundamental mode surface \n\n\n\nwaves are concurrently employed in the inversion. The Love and Rayleigh \n\n\n\ndispersion curves are attained for the propagation pathways and then \n\n\n\nthese dispersion curves are inverted employing a tomography procedure \n\n\n\nto accomplish the two-dimensional (2-D) dispersion curve charts. For each \n\n\n\ngrid point on the 2-D dispersion curve chart we invert the corresponding \n\n\n\ndispersion data to obtain the 1-D shear-wave speed-depth profile and then \n\n\n\ncombine these solutions to assemble the 3-D crust speeds under the \n\n\n\nstudied area. We eventually propose crustal VTI models to elucidate the \n\n\n\napparent Love-Rayleigh wave discrepancy.  \n\n\n\n2. GEOLOGICAL SETTING \n\n\n\n\n\n\n\nFigure 1: Basic geologic map of the Northwest and Central North Anatolia \n\n\n\nis demonstrated (adapted from MTA, 2002; also see Emre et al., 2013; \n\n\n\nAkbas et al., 2017). GVP stands for Galatean Volcanic Province, NAF for \n\n\n\nNorth Anatolian Fault and \u00c7B for \u00c7ank\u0131r\u0131 Basin. The small inset shows the \n\n\n\nPalaeotectonic assemblages in Turkey where BZS stands for Bitlis-Zagros \n\n\n\nSuture, GC for Greater Caucasus, IAES for Izmir-Ankara-Erzincan Suture, \n\n\n\nIPS for Intra-Pontide Suture, ITS for Inner-Tauride Suture, KB for Kura \n\n\n\nBasin, MS for Marmara Sea, RB for Rioni Basin, RSM for Rhodope-Strandja \n\n\n\nMassif, SAS for Sevan-Akera Suture, TB for Thrace Basin, WP for Western \n\n\n\nPontides (modified from Okay 2008). The rectangle shows the studied \n\n\n\narea. \n\n\n\nThe studied region composed of Istanbul Zone, Central and Western \n\n\n\nPontides, Anatolide-Tauride Platfrom and K\u0131r\u015fehir Massif is cut by the \n\n\n\nNorth Anatolian Fault \u2013 NAF and is sutured by the Ankara-Erzincan Suture \n\n\n\n\u2013 IAES (Figure 1). The NAF and the associated sub-branches extend in \n\n\n\napproximately E-W direction producing the main seismicity in the studied \n\n\n\nregion as shown by the epicenters of earthquakes in Figure 2 (red color \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 41-50 \n\n\n\n\n\n\n\n \nCite the Article: \u00d6zcan \u00c7ak\u0131r (2021). Transverse Isotropic Crust Structure Beneath the Northwest and Central North Anatolia Revealed by Seismic Surface Waves \n\n\n\nPropagation. Malaysian Journal of Geosciences, 5(2): 41-50. \n \n\n\n\n\n\n\n\nstars \u2013 upper panel). The earthquake catalog adapted from the database of \n\n\n\nDisaster and Emergency Management Presidency (AFAD) include \n\n\n\nearthquakes with magnitude range 3.5 \u2264 \ud835\udc40\ud835\udc64 \u2264 6.5, focal depth \u2264 40 km and \n\n\n\n29-year time span from 1990 to 2019. Some profiles are selected to \n\n\n\nexamine the current findings in 2-D cross sections. These black color \n\n\n\ncircles in Figure 2 (upper panel) highlight the epicenters happening closer \n\n\n\nto profiles A-A' and B-B'. Figure 2 also illustrates these important surface \n\n\n\ngeological units. In Northern Anatolia, ancient ocean basins in the Tethyan \n\n\n\nconvergence system between the Pontide and Anatolide\u2013Tauride \n\n\n\ncontinents were subducted during the Palaeogene (Pourteau et al., 2016).  \n\n\n\n\n\n\n\nFigure 2: The seismicity in the studied area is shown by the red stars \n\n\n\n(upper panel). The seismicity represented by black circles occurs along \n\n\n\nprofiles A-A' and B-B' designated in Figure 7. The principal tectonic units \n\n\n\nare marked using the following abbreviations \u2013 Afyon Zone: AZ; Anatolide-\n\n\n\nTauride Platform: ATP; Central Pontides: CP; \u00c7ank\u0131r\u0131 Basin: \u00c7B; Galatean \n\n\n\nVolcanic Province: GVP; Inner-Tauride Suture Zone: ITSZ; Intra-Pontide \n\n\n\nSuture Zone: IPSZ; Istanbul Zone: IZ; Izmir-Ankara-Erzincan Suture Zone: \n\n\n\nIAESZ; K\u0131r\u015fehir Massif: KM; Marmara Sea: MS; Menderes-Tauride Platform: \n\n\n\nMTP; North Anatolian Fault Zone: NAFZ;  Sakarya Continent: SC; Sinop-\n\n\n\nBoyabat Basin: SBB; Tav\u015fanl\u0131 Zone: TZ; Tuzgo lu  Fault Zone: TFZ; Uludag  \n\n\n\nMassif: UM; Western Pontides: WP; Zonguldak-Bart\u0131n Basin: ZBB. The \n\n\n\nlower panel shows the earthquakes hypocenters as a function of latitudes \n\n\n\nand longitudes. The arrows (purple color in upper panel and yellow color \n\n\n\nin lower panel) indicate those earthquakes grouped around a certain \n\n\n\nlocality. \n\n\n\nTo the west the subduction zone between the Menderes-Tauride Platform \n\n\n\n(MTP) and the Sakarya Continent (SC) is characterized by the Izmir-\n\n\n\nAnkara Suture Zone \u2013 IASZ (Okay and Tu ysu z 1999). The Intra-Pontide \n\n\n\nSuture Zone \u2013 IPSZ represents a plunge zone between the Istanbul Zone \u2013 \n\n\n\nIZ and the SC (Okay and Tu ysu z, 1999). To the east the subduction zone \n\n\n\nbetween the Central Pontides (CP) and the Anatolide\u2013Tauride Platform \n\n\n\n(ATP) is characterized by the Ankara-Erzincan Suture Zone \u2013 AESZ while \n\n\n\nto the southeast the Inner-Tauride Suture Zone \u2013 ITSZ marks the thrust \n\n\n\nzone between the K\u0131r\u015fehir Massif (KM) and the ATP (Okay and Tu ysu z \n\n\n\n1999). The \u00c7ank\u0131r\u0131 basin \u2013 \u00c7B overlapping the Izmir-Ankara-Erzincan \n\n\n\nSuture Zone \u2013 IAESZ consists of pre-Middle Miocene sedimentary fill (>3-\n\n\n\nkm thick) and is underlain by Upper Cretaceous ophiolites and granitoids \n\n\n\nof the KM (Kaymakci et al., 2009).  \n\n\n\nThe Galatean Volcanic Province \u2013 GVP (Early\u2013Middle Miocene) taking \n\n\n\nplace between the IASZ and the IPSZ-NAFZ (Figure 2) comprises of dacites, \n\n\n\nandesites and pyroclastites and rhyolites and small amount of basaltic \n\n\n\ntrachyandesites and trachybasalts where small magma surges or dykes are \n\n\n\nevident (Varol et al., 2014). There exist basin structures characterizing the \n\n\n\nsouthern Black Sea (back-arc basin) margin where the Tethys Ocean \n\n\n\nsubducted below the Pontides, i.e., to the northeast the Sinop-Boyabat \n\n\n\nBasin \u2013 SBB and to the northwest the Zonguldak-Bart\u0131n Basin \u2013 ZBB \n\n\n\n(Hippolyte et al., 2016). Some researchers have reported that the Tav\u015fanl\u0131 \n\n\n\nZone \u2013 TZ reveals the high-pressure low-temperature (HP\u2013LT) \n\n\n\nmetamorphism (e.g., blueschist facies) to the south of the IASZ where a \n\n\n\nremnant subduction edge evolving from early obduction phases to \n\n\n\ncontinental plunge occurs and the subduction accretionary composite is \n\n\n\nsqueezed between the ATP and the obducted ophiolites (non-\n\n\n\nmetamorphic) (Plunder et al., 2013).  \n\n\n\nThe Afyon Zone \u2013 AZ is another metamorphic zone (greenschist facies) \n\n\n\nbetween the TZ to the north and the Anatolides to the south comprising \n\n\n\nmeta-sedimentary and meta-volcanic rocks composed of dacite, rhyolite \n\n\n\nand trachyandesite (O zdamar et al., 2013). The Uludag  Massif \u2013 UM is an \n\n\n\nexhumed mountain range consisting of high-grade metamorphic \n\n\n\n(amphibolite-facies gneiss, marble, and amphibolite) and intrusive early \n\n\n\nEocene granitic rocks (Topuz and Okay, 2017). The Tuzgo lu  Fault Zone \u2013 \n\n\n\nTFZ creates significant seismicity in central Anatolia along mostly dip slip \n\n\n\nand partly right-lateral strike slip motions (Y\u0131ld\u0131r\u0131m, 2014).  The lower \n\n\n\npanel in Fig. 2 displays the regional earthquake hypocenters in 3-D where \n\n\n\nsome earthquakes with focal depth as deep as 40 km show four distinct \n\n\n\ngroups (pointed by yellow arrows in lower panel and by purple arrows in \n\n\n\nupper panel) clustering in a narrow window of latitudes and longitudes, \n\n\n\nwhich might be due to earthquakes caused by active magmatic intrusions \n\n\n\nin the region. \n\n\n\n3. MATERIALS AND METHODS \n\n\n\nModern seismic waveform data and processing techniques extensively \n\n\n\ntested and verified by the researchers are currently utilized (Tang and \n\n\n\nZheng, 2013; Wu et al., 2016; Dixit et al., 2017). Minor-to-strong sized \n\n\n\nshallow earthquakes in the Anatolian plate and the surrounding area \n\n\n\nprovide unique opportunity to study the seismic surface waves for the \n\n\n\ncrust properties using both single-station and two-station techniques.  \n\n\n\n3.1 Waveform data from seismic stations \n\n\n\nThe Anatolian plate is densely covered by accelerometer stations. There \n\n\n\ncurrently exist 678 of these stations with 50 km or less distance between \n\n\n\nneighboring stations deployed by the Turkish Disaster and Emergency \n\n\n\nManagement Presidency (AFAD, 2019). The studied area having the \n\n\n\nlongitude range 28.2oE\u201338.2oE and the latitude range 38.4oN\u201342.2oN \n\n\n\npresently has 164 such accelerometer stations from which we attain three-\n\n\n\ncomponent accelerograms. The recording instruments are Geosig-\n\n\n\nGMPLUS-AC-73 (47 stations), Sara-ACEBOX-SA10 (26 stations) and \n\n\n\nGuralp-CMG5TD-CMG-5T (91 stations). Some accelerometer stations have \n\n\n\nrecording history going back to year 2010 while some stations have one \n\n\n\nyear or less recording history contributing only one or two recordings. The \n\n\n\naccelerograms multiplied by the instrument constant are delivered in units \n\n\n\nof cm/s2 by AFAD and the transfer function is not processed. The two \n\n\n\ninstruments [i.e., CMG5TD-CMG-5T/Guralp (GURALP, 2013) and GMPLUS-\n\n\n\nAC-73/Geosig (GEOSIG, 2012; also, personal message)] have flat response \n\n\n\nwith zero-degree phase shift and zero dB amplitude gain from dc to at least \n\n\n\n10 Hz. The instrument (i.e., ACEBOX-SA10/Sara) has two different transfer \n\n\n\nfunctions for operation dates before and after 07 April 2017 for which we \n\n\n\nemploy the related pole-zero files distributed by the instrument supplier \n\n\n\n(SARA, 2017; also, personal contact). \n\n\n\nThe Anatolian plate has also hundreds of three-component broadband \n\n\n\nseismic stations operated by Kandilli Observatory and Earthquake \n\n\n\nResearch Institute (KOERI). We employ 54 of these stations taking place in \n\n\n\nthe studied area. There are also other waveform data resources that we \n\n\n\nutilize; i.e., 39 broad-band stations installed and operated by the project \n\n\n\n\u201cNorth Anatolian Fault (NAF) Passive Seismic Experiment\u201d in the time \n\n\n\nperiod from January 2006 to May 2008, 62 broad-band stations installed \n\n\n\nand operated in a temporary array \u201cDense Array for Northern Anatolia \u2013 \n\n\n\nDANA\u201d with a station spacing of ~7 km in the time period from May 2012 \n\n\n\nto October 2013 and 72 broad-band stations installed and operated by the \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 41-50 \n\n\n\n\n\n\n\n \nCite the Article: \u00d6zcan \u00c7ak\u0131r (2021). Transverse Isotropic Crust Structure Beneath the Northwest and Central North Anatolia Revealed by Seismic Surface Waves \n\n\n\nPropagation. Malaysian Journal of Geosciences, 5(2): 41-50. \n \n\n\n\n\n\n\n\nproject \u201cContinental Dynamics: Central Anatolian Tectonics \u2013 CDCAT\u201d in \n\n\n\nthe time period from May 2013 to May 2015 where 13 stations among the \n\n\n\nCDCAT stations having locations relevant to this work are employed herein \n\n\n\n(O zacar et al., 2006; Kahraman et al., 2015; Abgarmi et al., 2017). The pole-\n\n\n\nzero files corresponding to each broadband station are used to remove the \n\n\n\neffect of seismometer response. \n\n\n\nFor the single-station surface wave analysis the regional earthquakes \n\n\n\noccurred within the borders of the interested area were scanned from year \n\n\n\n2005 to year 2019 where the event search resulted 709 earthquakes with \n\n\n\nmagnitude \ud835\udc40\ud835\udc64 \u2a7e 3.5. The waveforms were visually selected among which \n\n\n\n365 events satisfied our selection criteria. Generally, the handpicked \n\n\n\nearthquakes have the magnitude band 3.5 \u2a7d \ud835\udc40\ud835\udc64 \u2a7d 5.7 and the focal depth \n\n\n\ninterval 0.4 \u2a7d \u210e \u2a7d 46.11 km. The source constraints (i.e., epicenter \n\n\n\nlocation, date, origin time, magnitude, depth) of the earthquakes that we \n\n\n\nutilize are adopted from the AFAD collection, which is also validated with \n\n\n\nthe EMSC (European-Mediterranean Seismological Centre) and USGS \n\n\n\n(United States Geological Survey) catalogs. The E-W and N-S \n\n\n\naccelerograms are rotated into the Transverse and Radial accelerograms \n\n\n\nusing the theoretical back-azimuth and are converted into the particle \n\n\n\nvelocity by means of the traditional integration. The Love surface wave \n\n\n\ndata is obtained from the transverse component and the Rayleigh surface \n\n\n\nwave data from the vertical component. The observed waveforms are \n\n\n\nband-pass filtered in the period band 5-20 s. \n\n\n\nFor the two-station surface wave analysis those earthquakes with \n\n\n\nmagnitudes \ud835\udc40\ud835\udc64 \u2a7e 4.1 occurred in the same time interval (years 2005-to-\n\n\n\n2019) outside the interested area were also scanned where this event \n\n\n\nsearch resulted 942 earthquakes. The surface wave energy is attenuated \n\n\n\nwith distance and this attenuation increases with decreasing wavelength. \n\n\n\nThe epicentral distance range of the two-station events is selected to be \n\n\n\n<3000 km so that at least 10-s (if not 5-s) period surface waves are existent \n\n\n\non the recordings surviving through the attenuation along the propagation \n\n\n\ndistance. The waveforms were again visually examined after which 667 \n\n\n\nevents yielding effective two-station pathways fulfilled our selection \n\n\n\nmeasures. The handpicked earthquakes have the focal depth interval 0.9 \u2a7d\n\n\n\n\u210e \u2a7d 49.9 km and the magnitude band 4.1 \u2a7d \ud835\udc40\ud835\udc64 \u2a7d 7.3. The resultant \n\n\n\nevents are employed to attain the two-station phase and group speeds. \n\n\n\n3.2 Multiple Filter Technique \n\n\n\nTo determine the single-station and two-station group speeds of the \n\n\n\nfundamental mode we apply the Multiple Filter Technique by means of the \n\n\n\nnarrow-band Gaussian filter exp[\u2212\ud835\udefc (\ud835\udc53 \u2212 \ud835\udc53\ud835\udc50)2 \ud835\udc53\ud835\udc50\n2\u2044 ] where \ud835\udc53\ud835\udc50 denotes the \n\n\n\ncenter frequency of the filter (MFT \u2013 Herrmann, 2002). The filter \n\n\n\nbandwidth is controlled by \ud835\udefc that exchanges resolution going from time \n\n\n\ndomain to frequency domain, i.e., smaller \ud835\udefc results broader filter in the \n\n\n\nfrequency domain while the time domain signal becomes shorter in \n\n\n\nduration.  After some trial-and-error applications we set \ud835\udefc = 12.5. Smaller \n\n\n\n\ud835\udefc (e.g., \ud835\udefc \u2a7d 6.25) wears down the observational features of the group \n\n\n\nspeed curves while larger \ud835\udefc (e.g., \ud835\udefc \u2a7e 25) often results in fragmented and \n\n\n\nnoisy dispersion curves. In fact, the ideal \ud835\udefc filter parameter is a function of \n\n\n\ndistance and for the distances shorter than 1000 km, recommends using \n\n\n\n\ud835\udefc = 25 (Herrmann, 2002).  We presently apply the MFT within the \n\n\n\ndistance range 50-800 km. The seismic scattering, higher modes and noise \n\n\n\ninterfere with the fundamental mode energy. We implement the Phase-\n\n\n\nMatched Filter to alleviate the effect of such distortions (e.g., see \u00c7ak\u0131r, \n\n\n\n2018, 2019; PMF \u2013 Herrin and Goforth, 1977). \n\n\n\nThe definition given in Eq. (1) utilizing the cross-correlation is employed \n\n\n\nfor the two-station investigation of surface waves. The source phase is \n\n\n\nexcluded by taking the two stations on the same great circle pathway with \n\n\n\nthe source. Some abnormality from the great circle pathway is tolerable \n\n\n\nsince the great circle condition is not regularly fulfilled.  The two angles \n\n\n\n(i.e., \ud835\udf031 \u2a7d 1 and \ud835\udf032 \u2a7d 5 ) is used to regulate the abnormality of the great \n\n\n\ncircle pathway. Here the angle \ud835\udf031 measures the azimuthal change of the \n\n\n\nsource to the two receivers and the angle \ud835\udf032 corresponds to the azimuthal \n\n\n\nchange between the source to the first receiver and the first receiver to the \n\n\n\nsecond receiver. The two-station phase speed computed from the cross-\n\n\n\ncorrelogram in frequency domain is quantified as follows. \n\n\n\n\ud835\udc50(\ud835\udf14) =\n\ud835\udf14\ud835\udc5f\n\n\n\n\ud835\udc4e\ud835\udc5f\ud835\udc50\ud835\udc61\ud835\udc4e\ud835\udc5b{\ud835\udc3c\ud835\udc5a\ud835\udc4e\ud835\udc54 [\ud835\udf13(\ud835\udf14)] \ud835\udc45\ud835\udc52\ud835\udc4e\ud835\udc59\u2044 [\ud835\udf13(\ud835\udf14)]} + 2n\ud835\udf0b\n                                      (1) \n\n\n\nIn Eq. (1), the cross-correlogram is given by the complex function \ud835\udf13(\ud835\udf14), \n\n\n\nthe inter-station distance by \ud835\udc5f and the angular frequency by \ud835\udf14. The \n\n\n\nquantity 2n\ud835\udf0b in the denominator specifies the ambiguity in the \n\n\n\ndetermination of the phase speed \ud835\udc50(\ud835\udf14). We work with the MFT given in \n\n\n\nHerrmann where a different control (IG) is made available to \n\n\n\nsynchronously estimate the phase and group speeds from the inter-station \n\n\n\ncross-correlograms (Herrmann, 2002). \n\n\n\n3.3 2-D Tomographic Speed Charts \n\n\n\nWe perform the 2-D speed tomography for both group and phase speeds. \n\n\n\nThe single-station group speeds are obtained for each station-source \n\n\n\npathway. The two-station group speeds are attained for each station-\n\n\n\nstation pathway where the first station is treated as a source and the \n\n\n\nsecond station as a receiver. Rawlinson and Sambridge have provided a \n\n\n\ntomography code that we utilize to implement the 2-D speed tomography \n\n\n\n(Rawlinson and Sambridge, 2003). Supposing a constant radius and \n\n\n\nvariable latitude and longitude a spherical coordinate system with 2-D \n\n\n\nspherical shell is applied to compute the travel times from point sources to \n\n\n\nreceivers by solving the Eikonal equation along with a fast-marching \n\n\n\nmethod (FMM). The travel time model on the spherical shell is defined by \n\n\n\nbi-cubic B-spline interpolation on a grid of nodes. The association between \n\n\n\nthe travel time and speed is non-linear for which a linearized inversion \n\n\n\nscheme is utilized. To prevent the non-uniqueness in the inversion, \n\n\n\nsmoothing and damping controls are used. The following objective \n\n\n\nfunction \ud835\udf19(\ud835\udc5a) is iteratively solved to minimize the differences between \n\n\n\nthe detected and computed travel times (Tarantola, 1987). \n\n\n\n\ud835\udf19(\ud835\udc5a) = [\ud835\udc54(\ud835\udc5a) \u2212 \ud835\udc51]\ud835\udc47\ud835\udc36\ud835\udc51\n\u22121[\ud835\udc54(\ud835\udc5a) \u2212 \ud835\udc51] + \ud835\udf0e[\ud835\udc5a \u2212 \ud835\udc5a\ud835\udc5c]\ud835\udc47\ud835\udc36\ud835\udc5a\n\n\n\n\u22121[\ud835\udc5a \u2212 \ud835\udc5a\ud835\udc5c]\n\n\n\n+ \ud835\udefe\ud835\udc5a\ud835\udc47\ud835\udc37\ud835\udc47\ud835\udc37\ud835\udc5a                                                                   (2) \n\n\n\nwhere \ud835\udc51 and \ud835\udc54(\ud835\udc5a) stand for the detected and computed travel times, \n\n\n\nrespectively. The model and data covariance matrices are described by \ud835\udc36\ud835\udc5a\n\u22121 \n\n\n\nand \ud835\udc36\ud835\udc51\n\u22121, respectively. The damping (\ud835\udf0e = 0.75) and smoothing (\ud835\udefe = 1.50) \n\n\n\nfactors are effective to deter the inverted model (\ud835\udc5a) from proceeding too \n\n\n\nfar from the initial model (\ud835\udc5a\ud835\udc5c) while making the consequent model \n\n\n\nsmooth where the model smoothness matrix is given by \ud835\udc37. Eq. (2) is solved \n\n\n\ntwice, i.e., first time for the group speeds and second time for the phase \n\n\n\nspeeds. The speed grid is defined by a 2-D mesh size given by 0.1o x 0.1o in \n\n\n\nlatitude and longitude. This corresponds to 39 mesh points along the \n\n\n\nlatitude range 38.4o\u201342.2o and 101 mesh points along the longitude range \n\n\n\n28.2o\u201338.2o, i.e., 39 \u00d7 101 = 3939 nodes designate the speed grid. \n\n\n\n4. INVERSION RESULTS \n\n\n\nThe period dependent inversion results are presented in two sections, i.e., \n\n\n\ngroup speed charts and phase speed charts from which group and phase \n\n\n\nspeed curves at each grid point are established for both Love and Rayleigh \n\n\n\nsurface waves. These dispersion curves are then converted into 1-D shear-\n\n\n\nwave speed-depth profiles, which are collectively interpreted for the 3-D \n\n\n\nseismic image of the crust assemblage beneath the concerned area.  \n\n\n\n4.1 2-D Group Speed Charts \n\n\n\nThere are 164 accelerometer and 176 broadband recording stations, and \n\n\n\n365 earthquakes (sources) utilized in the single-station analysis. The \n\n\n\nrecording stations are also treated as sources in conjunction with the two-\n\n\n\nstation analysis. Therefore, the number of sources in the single-station \n\n\n\nanalysis is (164+176+365) = 705. The tomography source code requires \n\n\n\ntwo input files from the user. The first file includes the travel time data \n\n\n\nconsisting of three columns where the first column states if there is data \n\n\n\nfor the station-source pathway, the second column is the travel time and \n\n\n\nthe third column is the standard error for the corresponding data. The \n\n\n\nsecond file includes the information regarding the locations of the \n\n\n\nreceivers and sources. For each surface wave period from 5 to 20 s with 1 \n\n\n\ns interval a separate travel times file is arranged and is used in the \n\n\n\ntomographic inversion. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 41-50 \n\n\n\n\n\n\n\n \nCite the Article: \u00d6zcan \u00c7ak\u0131r (2021). Transverse Isotropic Crust Structure Beneath the Northwest and Central North Anatolia Revealed by Seismic Surface Waves \n\n\n\nPropagation. Malaysian Journal of Geosciences, 5(2): 41-50. \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 3: Group-speed checkerboard tests for the Love (right) and \n\n\n\nRayleigh (left) waves are shown. The cell size of perturbation is 0.50o x \n\n\n\n0.50o. The stars are source locations, and the triangles are recording \n\n\n\nseismic positions. The ray-paths are drawn using the thinnest line \n\n\n\navailable in the drawing program.  \n\n\n\nThe checkerboard tests outlined in Figure 3 are run to recognize the \n\n\n\nproficiency of the single-station group speeds data of Love (right panel) \n\n\n\nand Rayleigh (left panel) surface waves covering the studied area. \n\n\n\nEmploying the smoothing and damping factors (i.e., \ud835\udefe = 1.50 and \ud835\udf0e =\n\n\n\n0.75) this test simulates the surface wave propagation adopting the \n\n\n\nreceiver-source distribution obtained from the actual data. The \n\n\n\ncheckerboard model has perturbations (\u00b10.8 km/s) overlaid on the \n\n\n\nbackground medium with a constant speed (i.e., 2.8 km/s). The ray-paths \n\n\n\nrepresenting the surface waves at 10-s period are superimposed on the \n\n\n\nsolution model along with stars (sources) and triangles (stations). Note \n\n\n\nthat some locations show the stars overlaid by the triangles at which \n\n\n\nlocations we have two-station group speed contributions as well. Both \n\n\n\nRayleigh and Love checkerboard tests reveal that the ray-path coverage is \n\n\n\nenough to reasonably restore these earth assemblies with the cell \n\n\n\ndimension of 0.50o x 0.50o. However, there exists some resolution loss \n\n\n\ntowards the edges of the studied region. There is still some smearing of the \n\n\n\ncheckerboard patterns particularly to the east-southeast and these high \n\n\n\nand low speed perturbations particularly in the border region are not \n\n\n\ndetermined well. In the following, we work with the well-determined \n\n\n\nregion away from the edges. \n\n\n\n\n\n\n\nFigure 4: 2-D group-speed tomography of the detected Love (b) and \n\n\n\nRayleigh (a) waves (10-s period). The triangles are recording positions and \n\n\n\nthe stars are source locations. The frequency histograms for the Love (d) \n\n\n\nand Rayleigh (c) waves show the travel time misfit of the solution model. \n\n\n\nBoth single-station and two-station recording geometries are used. \n\n\n\nThe real group travel times data acquired from the Love and Rayleigh \n\n\n\nsurface waves with 10-s period are inverted in the next step. Figure 4 \n\n\n\ndisplays the conforming 2-D tomographic group speed charts achieved in \n\n\n\n10 iterations. For the appropriate speed assemblage (or the background \n\n\n\nmedium) we assign the constant group speed at 3.0 km/s for the Love \n\n\n\nsurface waves (Figure 4b) and 2.8 km/s for the Rayleigh surface waves \n\n\n\n(Figure 4a). The white color regions in Figures 4a and 4b correspond to \n\n\n\nthese regions not resolved by the existing surface waves. The orange and \n\n\n\nred color regions identify group speeds slower than the assumed \n\n\n\nbackground speed while these blue color tones identify the faster regions. \n\n\n\nThese regions matching the Galatean Volcanic Province (GVP) and the \n\n\n\nfracture zones of the North Anatolian Fault (NAF) are mostly characterized \n\n\n\nby the slower surface wave group speeds (see Figure 2). These other \n\n\n\nregions associated with the \u00c7ank\u0131r\u0131 Basin (\u00c7B), Marmara Sea (MS), Sinop-\n\n\n\nBoyabat Basin (SBB) and Zonguldak-Bart\u0131n Basin (ZBB) commonly \n\n\n\ntransmit slower surface waves. Away from the above regions the surface \n\n\n\nwaves are generally faster, i.e., K\u0131r\u015fehir Massif (KM) and perhaps part of \n\n\n\nIzmir-Ankara-Erzincan Suture Zone (IAESZ). \n\n\n\nThe concluding model (10-s period) resolves most of the detected group \n\n\n\ntravel times, but there exist some poorly resolved travel times as revealed \n\n\n\nby the frequency histograms shown in Figures 4c and 4d. The Rayleigh \n\n\n\nsurface wave rays traversing the studied area amount to 5090 (Figure 4c) \n\n\n\nand the sum of rays for the Love surface waves is 4961 (Figure 4d). Figure \n\n\n\n4d illustrates that 62% of the Love group travel times are resolved with \n\n\n\nresiduals in the interval \u22122.5 \u2a7d \ud835\udc47\ud835\udc5f\ud835\udc52\ud835\udc60 \u2a7d 2.5 s. The upper 17% has the \n\n\n\nresiduals in the interval \u22127.5 \u2a7d \ud835\udc47\ud835\udc5f\ud835\udc52\ud835\udc60 < \u22122.5 s and the lower 14% has the \n\n\n\nresiduals in the interval 2.5 < \ud835\udc47\ud835\udc5f\ud835\udc52\ud835\udc60 \u2a7d 7.5 s. The remaining 7% are resolved \n\n\n\nwith larger residuals (s). For the 10-s Rayleigh group travel times \n\n\n\nanalogous residuals are valid as displayed in Figure 4c. It is preferable that \n\n\n\nthe detected data is largely resolved by the concluding model so that the \n\n\n\nmentioned residuals stay smaller, but the ambient noise factor that we \n\n\n\nattempt to pacify in the data assessment still causes some disturbance. \n\n\n\nThe effect of the source phase on the two-station group speed estimation \n\n\n\nis alleviated through the cross-correlation procedure, but this influence \n\n\n\npersists on the single-station estimation. Therefore, errors in the \n\n\n\ndetermination of source parameters (i.e., location and source time) \n\n\n\neffective through the source radiation patterns may lead to erroneous \n\n\n\ngroup speed estimations (\u00c7ak\u0131r and Erduran, 2001). To restrict the \n\n\n\nearthquake misplacement and source time errors we have utilized three \n\n\n\nsource catalogs (i.e., AFAD, EMSC and USGS). Additionally, complex wave \n\n\n\npropagation effects such as refraction, multi-pathing and scattering \n\n\n\nprobably generate off-azimuth arrivals from various geological \n\n\n\nassemblages with diverse borders (e.g., K\u0131r\u015fehir Massif, Izmir-Ankara-\n\n\n\nErzincan Suture, Istanbul Zone). The above noise conditions have caused \n\n\n\nsome poorly resolved group travel times with larger residuals, but the \n\n\n\npercentage of such errors in the data set is small, i.e., less than 7%. For the \n\n\n\nother surface wave periods in the range 5-20 s we conducted equivalent \n\n\n\ngroup speed tomographic inversions like these in Figure 4. \n\n\n\n4.2 2-D Phase Speed Charts \n\n\n\nIt is mentioned above that there are 667 earthquakes producing two-\n\n\n\nstation phase speeds at (164+176) = 340 recording stations. In a similar \n\n\n\nmanner to the group speed analysis described in the above subsection, we \n\n\n\nperform the phase speed tomographic inversions. The checkerboard tests \n\n\n\nsketched in Figure 5 [using the same smoothing and damping factors \n\n\n\n(i.e., \ud835\udefe = 1.50 and \ud835\udf0e = 0.75) and accepting the receiver-source spreading \n\n\n\ntaken from the real data] are used to check the tomographic ability of the \n\n\n\ntwo-station phase speeds data of Love (right panel) and Rayleigh (left \n\n\n\npanel) surface waves. Note that there exist stations (green triangles) not \n\n\n\ninvolved in any of the present (10-s period) two-station pathways and that \n\n\n\nthe first station close to the earthquake is marked by a red star overlaid by \n\n\n\na green triangle.  \n\n\n\nThe earthquake locations mostly outside the figure frame are not shown in \n\n\n\nFigure 5. The checkerboard model is represented by perturbations (\u00b10.8 \n\n\n\nkm/s) superimposed on the propagating medium with a constant speed \n\n\n\n(i.e., 2.8 km/s). The earth assemblages with the cell dimension of 0.65o x \n\n\n\n0.65o are satisfactorily resolved by both Rayleigh and Love surface wave \n\n\n\nphase speeds as displayed by the checkerboard tests using the real ray-\n\n\n\npath coverage. On the other hand, there is some smearing on the \n\n\n\ncheckerboard patterns particularly to the west and towards the region \n\n\n\nlimits the resolution gets weaker that we circumvent. The group speed \n\n\n\ntomography has better ray-path coverage than that of the phase speed \n\n\n\ntomography (i.e., compare Figures 3 and 5). \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 41-50 \n\n\n\n\n\n\n\n \nCite the Article: \u00d6zcan \u00c7ak\u0131r (2021). Transverse Isotropic Crust Structure Beneath the Northwest and Central North Anatolia Revealed by Seismic Surface Waves \n\n\n\nPropagation. Malaysian Journal of Geosciences, 5(2): 41-50. \n \n\n\n\n\n\n\n\n\n\n\n\nFigure 5: Phase-speed checkerboard tests for the Love (right) and \n\n\n\nRayleigh (left) waves are shown. The cell size of perturbation is 0.65o x \n\n\n\n0.65o. The stars are source locations, and the triangles are recording \n\n\n\npositions. \n\n\n\nThe real phase travel times data derived from the Love and Rayleigh \n\n\n\nsurface waves at 10-s period are inverted in 10 iterations as shown by the \n\n\n\n2-D tomographic phase speed charts in Figure 6. The constant phase speed \n\n\n\nat 3.4 km/s (Figure 6b) is assigned for the Love surface waves to represent \n\n\n\nthe appropriate speed assemblage (or the background medium). The \n\n\n\nbackground phase speed for the Rayleigh surface waves is accepted as 3.2 \n\n\n\nkm/s (Figure 6a). There exist some regions not resolved by the present \n\n\n\nsurface waves, i.e., these white color regions in Figures 6a and 6b. The color \n\n\n\npalette used in the group speed tomography above is similarly used for the \n\n\n\nphase speed tomography. When compared to the group speed tomography \n\n\n\n(Figure 4), the phase speed tomography (Figure 6) shows somewhat \n\n\n\nsmoother 2-D speed variations.  \n\n\n\n\n\n\n\nFigure 6: 2-D phase-speed tomography of the detected Love (b) and \n\n\n\nRayleigh (a) waves (10-s period). The triangles are recording positions and \n\n\n\nthe stars are source locations. The frequency histograms for the Love (d) \n\n\n\nand Rayleigh (c) waves show the travel time misfit of the solution model. \n\n\n\nBecause two-station recording geometry is used, recording positions and \n\n\n\nsource locations may coincide. \n\n\n\nThe GVP and the fracture zones of the NAF are characterized by the slower \n\n\n\nsurface waves, which is particularly evident on the Rayleigh surface wave \n\n\n\nresults (Figure 6). The lower speed basin assemblages (i.e., \u00c7B and ZBB) \n\n\n\nexcept the SBB are not apparent on the 2-D phase speed tomographic \n\n\n\ncharts as clearly as visualized on the group speed charts (Figure 4). The \n\n\n\nhigher speed KM is clearly visible on the Love surface waves, but the IAESZ \n\n\n\nis not possible to follow on the speed charts. In this respect, it is possible \n\n\n\nto state that the group speed charts serve more detailed structure \n\n\n\ninformation than that provided by the phase speed charts although the \n\n\n\nlatter may also be due to the differences in the ray-path coverages (again \n\n\n\ncompare Figures 3 and 5).  \n\n\n\nThe frequency histograms in Figures 6c and 6d illustrate how the \n\n\n\nconcluding model at 10-s period elucidates the detected phase travel \n\n\n\ntimes. The number of rays traversing the considered area is 1757 in the \n\n\n\ncase of Rayleigh surface waves (Figure 6c) and 1628 for the Love surface \n\n\n\nwaves (Figure 6d). Figure 6d identifies that 85% of the Love phase travel \n\n\n\ntimes are resolved with residuals in the band \u22122.5 \u2a7d \ud835\udc47\ud835\udc5f\ud835\udc52\ud835\udc60 \u2a7d 2.5 s. The \n\n\n\nupper band (\u22127.5 \u2a7d \ud835\udc47\ud835\udc5f\ud835\udc52\ud835\udc60 < \u22122.5) includes 4% of the residuals and the \n\n\n\nlower band (2.5 < \ud835\udc47\ud835\udc5f\ud835\udc52\ud835\udc60 \u2a7d 7.5) involves 9% of the residuals. The rest counts \n\n\n\n2% of the residuals (s). Figure 6c shows that similar residuals are effective \n\n\n\nfor the 10-s Rayleigh phase travel times. The detected phase travel times \n\n\n\nare typically disturbed by ambient noise that we try to avoid during the \n\n\n\ndata analysis. In addition, some two-station ray-paths show slightly off-\n\n\n\nazimuth arrivals where the associated cross-correlogram is influenced by \n\n\n\nthe residue source phase causing somewhat erroneous phase travel times. \n\n\n\n4.3 Cross Section \n\n\n\nWe now move to creating the period-dependent local group and phase \n\n\n\nspeed curves on a grid with size 0.1o x 0.1o. The corresponding \n\n\n\ngeographical locations are marked in Figure 7 by the black dots with white \n\n\n\ndots on the center where the group and phase speed curves are \n\n\n\nconcomitantly recovered. There also exist some locations marked by the \n\n\n\nblack dots where the group speed curves are only available. The local \n\n\n\nRayleigh group and phase speed curves are jointly inverted to determine \n\n\n\nthe 1-D vertically polarized S-wave speed model (Vsv) of the crust and the \n\n\n\nsame process is repeated on the local Love group and phase speed curves \n\n\n\nto determine the 1-D horizontally polarized S-wave speed model (Vsh). The \n\n\n\ninversion process is the damped least-squares applied to every local \n\n\n\ndispersion curve on the grid where the crust is considered as a layered \n\n\n\nhalf-space assemblage with 40-km depth. The following parameter is \n\n\n\nutilized to enumerate the strength of the Vertical Transverse Isotropy \u2013 \n\n\n\nVTI. \n\n\n\n\ud835\udf02 =\n(\ud835\udc49\ud835\udc60\u210e \u2212 \ud835\udc49\ud835\udc60\ud835\udc63)\n\n\n\n\ud835\udc49\ud835\udc60\n\n\n\n \u2026 \ud835\udc49\ud835\udc60\n2 =\n\n\n\n(2\ud835\udc49\ud835\udc60\ud835\udc63\n2 + \ud835\udc49\ud835\udc60\u210e\n\n\n\n2 )\n\n\n\n3\n                                                               (3) \n\n\n\nwhere the Voigt isotropic average S-wave speed is given by Vs. The VTI (\ud835\udf02) \n\n\n\nis positive if Vsh > Vsv and is negative if Vsv > Vsh. The details regarding the 1-\n\n\n\nD dispersion inversion strategy, the VTI system and the matters related to \n\n\n\nthe inversion power and structural simplification of Love-Rayleigh surface \n\n\n\nwave data are important. On these issues, we refer to the published work \n\n\n\nin the literature. For instance, the references therein have made the \n\n\n\nrespective discussions that we also follow here (\u00c7ak\u0131r, 2018, 2019). Figure \n\n\n\n7 also presents eight profiles of which the first one (i.e., A-A') is picked to \n\n\n\nillustrate how the inverted S-wave speed and anisotropy change in 2-D \n\n\n\n(depth and distance) underneath the studied area. \n\n\n\n\n\n\n\nFigure 7: The eight profiles (A-A', B-B', C-C', D-D', E-E', F-F', G-G', H-H') \n\n\n\nchosen to draw some conclusions are shown. Solid circles with white dots \n\n\n\nindicate the locations of attained dispersion curves (both group and phase \n\n\n\nspeeds) and solid circles specify the locations of attained group speed \n\n\n\ncurves. \n\n\n\n4.3.1 Cross Section A-A' \n\n\n\nWe show the cross section (A-A') in Figure 8 where the crust configuration \n\n\n\nin the NW-SE direction is demonstrated. A rich color scale is applied to \n\n\n\ndepict the anomalies in greater detail and abbreviations for Sill (S) and \n\n\n\nDyke (D) assemblages are used to avoid clutter in the presentation. The \n\n\n\ndetected (fundamental mode) Love and Rayleigh dispersion curves (group \n\n\n\nand phase speeds) are inverted to determine the SH-wave (Vsh) and SV-\n\n\n\nwave speeds (Vsv), respectively. The Voigt isotropic average Vs speeds \n\n\n\ncomputed from Eq. (3) are shown in the upper panel implying that the \n\n\n\ncrust assemblage below the studied area is complex in all the depth range. \n\n\n\nAbove the half-space with the Vs > 4.0 km/s we have inferred five depth \n\n\n\nsegments (white lines indicating irregular layer interfaces) operating on \n\n\n\nthe color-coded speed-depth deviations underneath profile A-A'. The \n\n\n\nspeed values approximating the Vs in each layer are listed on the right \n\n\n\nwhere all the layers show considerable sideways changes. Among these \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 41-50 \n\n\n\n\n\n\n\n \nCite the Article: \u00d6zcan \u00c7ak\u0131r (2021). Transverse Isotropic Crust Structure Beneath the Northwest and Central North Anatolia Revealed by Seismic Surface Waves \n\n\n\nPropagation. Malaysian Journal of Geosciences, 5(2): 41-50. \n \n\n\n\n\n\n\n\nlayers the fourth and fifth ones from the top have the highest lateral \n\n\n\nchanges with high and low speed zones. The inverted Vsh and Vsv speeds in \n\n\n\nthe crust assemblage beneath profile A-A' are highly different from each \n\n\n\nother at some depth ranges pointing to the Love-Rayleigh wave \n\n\n\ndiscrepancy, which can be explained by the Vertical Transverse Isotropy \u2013 \n\n\n\nVTI. If the speed assemblage in the earth is isotropic, then the Love and \n\n\n\nRayleigh wave dispersions show consistency (i.e., Vsh ~Vsv). \n\n\n\n\n\n\n\nFigure 8: The inverted average S-wave speeds along with the \n\n\n\nsuperimposed earthquake hypocenters (upper panel) and the respective \n\n\n\nVTI (lower panel) beneath profile A-A' are illustrated. The vertical arrows \n\n\n\nin the lower panel are used to indicate likely upward magma movements \n\n\n\nwith which the deep negative VTIs are associated. \n\n\n\nThe lower panel in Figure 8 presents the cross section demonstrating the \n\n\n\nVTI beneath profile A-A'. The VTI (\ud835\udf02) computed from Eq. (3) indicates \n\n\n\nnegative (Vsv > Vsh) and positive (Vsh > Vsv) anomalies where some \n\n\n\nabbreviations are used, i.e., D stands for the vertical dyke assemblages \n\n\n\n(negative anomalies) and S for the horizontal sill assemblages (positive \n\n\n\nanomalies). The upper crust is typically occupied by the negative \n\n\n\nanomalies (\ud835\udf02~ -10%) and the middle-to-lower crust includes the positive \n\n\n\nanomalies (\ud835\udf02~ 10%). The Love surface waves with periods shorter than \n\n\n\n~8-s are slowed down by the vertical dyke assemblages in the upper crust \n\n\n\n(i.e., Vsv > Vsh). Below the dyke units the Rayleigh surface waves with \n\n\n\nperiods longer than ~12-s are slowed down by the horizontal sill \n\n\n\nassemblages made of thin layers with high and low speeds in the middle-\n\n\n\nto-lower crust (i.e., Vsh > Vsv). Note that the negative VTI (i.e., Vsv > Vsh) \n\n\n\ntypically overlies the positive VTI (i.e., Vsh > Vsv). Also note that in the upper \n\n\n\npanel, we label some depths as high-speed zone (HSZ) and low-speed zone \n\n\n\n(LSZ). If the magmatic intrusion is still cooling, then it may correspond to \n\n\n\nweakened medium where the seismic waves are decelerated (Lees, 2007). \n\n\n\nIn other words, the HSZ corresponds to already cooled rocks having high \n\n\n\nstrength and the LSZ correlates with still cooling rocks having low \n\n\n\nstrength. \n\n\n\nIn Figure 8 (upper panel), we also show the names of the geological \n\n\n\ndistricts (superimposed on the Vs cross section) touching profile A-A' (NW-\n\n\n\nSE). The profile commences with the Istanbul Zone (IZ) in the NW and \n\n\n\ncontinues towards the SE with the North Anatolian Fault (NAF), Intra-\n\n\n\nPontide Suture Zone (IPSZ), Galatean Volcanic Province (GVP), Izmir-\n\n\n\nAnkara-Erzincan Suture Zone (IAESZ), \u00c7ank\u0131r\u0131 Basin (\u00c7B) and Inner-\n\n\n\nTauride Suture Zone (ITSZ). The Istanbul Zone (IZ) a micro-continent split \n\n\n\nfrom the ancient Laurasia to the north is underlain by a high-speed zone \n\n\n\n(HSZ ~3.8 km/s) in the lower crust. We interpret this HSZ with negative \n\n\n\nVTI (relatively weak around -5%) resulting from the subduction-related \n\n\n\nmagmatism (already cooled) associated with the vanishing of the Intra-\n\n\n\nPontide Ocean between the SC and the IZ in the Early Cretaceous \n\n\n\n(Akbayram et al., 2013). The NAF along which the volcanism occurs \n\n\n\nupward through the tension fractures is characterized by a few \n\n\n\nearthquakes shallower than 10-km depth around 370-km distance \n\n\n\n(Adiyaman et al., 2001). The yellow color line depicting the SE-inclined \n\n\n\nfocal depths reaching 20-km depth represents the IPSZ along which the S-\n\n\n\nwave speed is ~3.5 km/s in the middle crust and then reverts to a low-\n\n\n\nspeed zone (LSZ ~3.3 km/s) in the middle-to-lower crust.  Part of the IPSZ-\n\n\n\nlower-crust around 410-km distance is perturbed by HSZ with the S-wave \n\n\n\nspeed ~3.8 km/s raised from ~3.6 km/s. This high-speed material with \n\n\n\nnegative VTI around -4% is interpreted as solidified magma emplacement \n\n\n\nsurging up from the sub-Moho depths.  \n\n\n\nAgain in Figure 8, the middle-to-lower crust within the IPSZ is \n\n\n\ncharacterized as positive VTI ~10% (i.e., horizontal sill assemblages) and \n\n\n\nthe IPSZ-upper-crust reveals negative VTI (around -10%) indicative of the \n\n\n\nvertical dyke assemblages. The GVP geographically closely packed with the \n\n\n\nIPSZ, and the NAF is post-collisional associated with the trans-tensional \n\n\n\ntectonics along the NAF (Wilson et al., 1997). The assemblage under the \n\n\n\nIAESZ depicted by the pink line bears similarities to the one under the IPSZ \n\n\n\nalthough the respective LSZ in the middle-to-lower crust is deeper and is \n\n\n\nmore pronounced with slower S-waves (i.e., ~3.2 km/s). Within the \n\n\n\n\u00c7ank\u0131r\u0131 Basin \u2013 \u00c7B the S-waves slow down to ~2.5 km/s from ~2.8 km/s \n\n\n\nevident in the upper crust where there are also some other sedimentary \n\n\n\nfills (SF) characterized by the S-wave speed ~2.6-2.7 km/s. The K\u0131r\u015fehir \n\n\n\nMassif \u2013 KM like the Menderes Massif \u2013 MM perturbed by the intrusions \n\n\n\nfrom the sub-crustal depths shows high speeds in the middle-to-lower \n\n\n\ncrust (\u00c7ak\u0131r, 2018). The Anatolide-Tauride Platform (ATP) to the southeast \n\n\n\nof the ITSZ displays comparatively higher S-wave speeds (~3.6 km/s) and \n\n\n\nthe positive VTI (around 9%) in the middle-to-lower crust and the negative \n\n\n\nVTI (around -10%) in the upper crust. The magmatic rocks with intra-plate \n\n\n\nto subduction- and collision-influenced affinity alongside the \n\n\n\nasthenospheric upsurge are frequent beneath the Anatolian plate and the \n\n\n\ninversion results (i.e., speed-anisotropy-depth distribution) herein \n\n\n\nsupport this observation (Dilek and Altunkaynak, 2007; Altunkaynak and \n\n\n\nDilek, 2013; Nikogosian et al., 2018). \n\n\n\n5. DISCUSSIONS AND CONCLUSION \n\n\n\nThe studied region is bordered by the IA(E)SZ to the south and the \n\n\n\nsouthern Black Sea cost to the north and the NAFZ and IPSZ run through \n\n\n\nthe region in ~E-W direction (Figure 2). We have utilized the \n\n\n\nLove/Rayleigh dispersion curves (group and phase speeds \u2013 fundamental \n\n\n\nmode) to study the crust assemblage down to 40-km depth. The detected \n\n\n\nLove/Rayleigh dispersion data demonstrate discrepancy, i.e., isotropic \n\n\n\nmodel is not enough to describe the wave propagating medium.  \n\n\n\nAnisotropic modeling (i.e., Vertical Transverse Isotropy \u2013 VTI) is found \n\n\n\nappropriate to model the detected dispersion data. The detected \n\n\n\ndispersion curves analogous to pathways between the source and receiver \n\n\n\nare converted to individual dispersion curves on a 0.1o x 0.1o sized grid \n\n\n\nmaking use of 2-D tomographic inversions. The checkerboard tests are \n\n\n\nperformed to examine the effectiveness of the pathway\u2019s coverage of the \n\n\n\nexisting surface wave data. Figure 3 demonstrates that the Love/Rayleigh \n\n\n\ngroup speed data can determine the lateral crust assemblages with size \n\n\n\n0.50o x 0.50o or ~55 km resolve length. However, we see that there is still \n\n\n\nsome confusion with the checkerboard patterns particularly to the E-SE \n\n\n\nwhere the inverted anomalies slightly mix with each other alongside the \n\n\n\nedges and corners. The Love/Rayleigh phase speed data have weaker \n\n\n\npathways coverage and therefore the resultant resolution is coarser with \n\n\n\nlarger size 0.65o x 0.65o or ~72 km resolve length (Figure 5). The inversion \n\n\n\nresults revealed on the cross section (Figure 8) confirm the resolve length \n\n\n\n~55 km chiefly governed by the group speed data.  \n\n\n\nSmall-scale collections made of dykes and sills implanted in these \n\n\n\nanomalous assemblages act together to yield the detected VTI within the \n\n\n\nearth. One should note that the resolve length above emphasizes the \n\n\n\nresolve of the embedding assembly, but not the resolve of the small-scale \n\n\n\n(embedded) collection. The inversions reveal that under the Northwest \n\n\n\nand Central North Anatolia the small-scale collections in the middle-to-\n\n\n\nlower crust (i.e., horizontal sill assemblages) unusually slow down the \n\n\n\nRayleigh surface waves (i.e., Vsh > Vsv) while another type of small-scale \n\n\n\ncollections in the upper crust (i.e., vertical dyke assemblages) abnormally \n\n\n\ndecelerates the Love surface waves (i.e., Vsv > Vsh). The mineral alignment \n\n\n\nin the magma flow within these small-scale collections acts to increment \n\n\n\nthe detected VTI. \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 41-50 \n\n\n\n\n\n\n\n \nCite the Article: \u00d6zcan \u00c7ak\u0131r (2021). Transverse Isotropic Crust Structure Beneath the Northwest and Central North Anatolia Revealed by Seismic Surface Waves \n\n\n\nPropagation. Malaysian Journal of Geosciences, 5(2): 41-50. \n \n\n\n\n\n\n\n\nThe suture zones populated by accretionary complexes and ophiolites may \n\n\n\nact like fault zones. They behave seismogenic creating earthquakes in the \n\n\n\nupper crust and flexible producing shear zones in the lower crust and may \n\n\n\nextend into the deeper lithospheric mantle. The current findings imply that \n\n\n\nthe partially melted magma from the sub-crustal depths move upward to \n\n\n\nfill the gap between previous lithospheric plates for which the conforming \n\n\n\nmagma intrusions develop as horizontal sill assemblages in the middle-to-\n\n\n\nlower crust creating the positive VTI. This magma emplacement is perhaps \n\n\n\nstill cooling since the matching shear-wave speed is relatively low (i.e., Vs \n\n\n\n~3.2 km/s). There are inner-suture-zone earthquakes with focal depths as \n\n\n\ndeep as ~30-km, which is deeper than the fragile upper crust. These \n\n\n\nearthquakes principally in the lower crust may result from the upward \n\n\n\nmagma movements within the suture zones. The partially melted magma \n\n\n\nis likely due to the uprising buoyant asthenosphere resulting from tearing, \n\n\n\ndetachment, and erosion of the northerly Afro-Arabian slab beneath the \n\n\n\nAnatolian plate (Portner et al., 2018; Keskin, 2003; Biryol et al., 2011).  \n\n\n\nThe general crust assemblage interpreted in terms of the Vs and \ud835\udf02 (Eq. 3) \n\n\n\nand earthquakes hypocenter distribution is shown to have six stratums \n\n\n\nincluding the half-space. The stratum boundaries demonstrate irregular \n\n\n\nfluctuations due to the perturbations by the vertical magma intrusions \n\n\n\noriginating from the sub-Moho depths.  The intrusions come forward as \n\n\n\nlow speed (likely still cooling) and high speed (likely already cooled) \n\n\n\nzones. Depending on the physical circumstances the intrusions develop as \n\n\n\neither vertical dykes or horizontal sills. The Shape Preferred Orientation \u2013 \n\n\n\nSPO due to the dykes and sills and the Crystallographic Preferred \n\n\n\nOrientation \u2013 CPO due to the vertical and horizontal magmatic flows within \n\n\n\nthe dyke/sill system yield the positive or negative Vertical Transverse \n\n\n\nIsotropy \u2013 VTI detected by the current surface wave data. The negative VTI \n\n\n\nresults from Vsv > Vsh and relates to the vertical dyke assemblages. On the \n\n\n\nother hand, Vsh > Vsv yields the positive VTI and is associated with the \n\n\n\nhorizontal sill assemblages. \n\n\n\nThe current findings are highlighted in the following list. \n\n\n\na) The suture zones [i.e., Izmir-Ankara-(Erzincan) Suture Zone \u2013 \n\n\n\nIA(E)SZ and Intra-Pontide Suture Zone \u2013 IPSZ] are typically \n\n\n\nidentified as having Low Speed Zone \u2013 LSZ along with horizontal sill \n\n\n\nemplacement in the middle-to-lower crust in which there \n\n\n\nsometimes exist earthquakes as deep as 25-km focal depth perhaps \n\n\n\nin relation to contemporary magma flow with deeper origin, which \n\n\n\nis perhaps still in the process of cooling. \n\n\n\nb) The other suture zone (i.e., Inner-Tauride Suture Zone \u2013 ITSZ) does \n\n\n\nnot show signs as strong as shown by IA(E)SZ and IPSZ that can be \n\n\n\nused to find its location on the cross sections. The ITSZ mostly \n\n\n\nextends outside the frame that the current surface wave data covers \n\n\n\nand more data covering the Central Anatolian Volcanic Province as \n\n\n\nwell as southern Anatolia should help better delineate the ITSZ. \n\n\n\nc) Beneath the North Anatolian Fault Zone \u2013 NAFZ the uppermost crust \n\n\n\nis mostly characterized by low S-wave speed in the range 2.4-2.6 \n\n\n\nkm/s due to the deformation zones filled with sedimentary deposits \n\n\n\nand the lower crust is categorized by high S-wave speed ~3.8 km/s \n\n\n\ndue to the trans-tensional intrusions from the sub-Moho depths. \n\n\n\nd) The \u00c7ank\u0131r\u0131 Basin \u2013 \u00c7B covering part of the K\u0131r\u015fehir Massif \u2013 KM to \n\n\n\nthe north is categorized by the S-waves slowing down to ~2.5 km/s \n\n\n\nfrom ~2.8 km/s in the uppermost crust.  \n\n\n\ne) The Galatean Volcanic Province \u2013 GVP taking place between the IASZ \n\n\n\nand IPSZ does not demonstrate any specific feature on the cross \n\n\n\nsections in terms of the Vs and \ud835\udf02 that can be used to distinguish it \n\n\n\nfrom the nearby districts.  \n\n\n\nf) The Afyon Zone \u2013 AZ is categorized by intrusions from the sub-\n\n\n\ncrustal depths causing HSZ in the lower crust, horizontal sill \n\n\n\nassemblage in the mainly middle crust and vertical dyke assemblage \n\n\n\nin the upper crust. \n\n\n\ng) Beneath the Tav\u015fanl\u0131 Zone \u2013 TZ the crust assemblage does not \n\n\n\ndemonstrate significant VTI. \n\n\n\nh) Beneath the Uludag  Massif \u2013 UM the crust assemblage is highly \n\n\n\nanisotropic. There exists vertical dyke in the lower crust as thick as \n\n\n\n25-km in which the S-wave speed is ~3.8 km/s conforming to a \n\n\n\npropagating medium perhaps already cooled and the respective \n\n\n\nnegative VTI is around -10%. This dyke is topped by horizontal sill \n\n\n\nin the middle crust with the respective positive VTI around 10%. \n\n\n\ni) There exist some LSZs in the upper crust. The neighboring hot \n\n\n\nspring waters (GEOTERM, 2019) imply that these LSZs are still in \n\n\n\nthe process of cooling. \n\n\n\nj) The crust assemblage beneath the KM is relatively fast and show \n\n\n\npositive VTI mostly in the middle crust. The negative VTI in the \n\n\n\nlower crust is existent but relatively weak. These are also true for \n\n\n\nthe Central Pontides \u2013 CP and Anatolide-Tauride Platform \u2013 ATP. \n\n\n\nk) There exist earthquake accumulations in narrow regions which are \n\n\n\nlikely activated by the neighboring upward magmatic intrusions \n\n\n\ncausing local stress build-up near the surface. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nWe are grateful to the anonymous reviewers for critically reviewing the \n\n\n\nmanuscript. We express our sincere gratitude to AFAD (Disaster and \n\n\n\nEmergency Management Presidency) for providing the accelerograms and \n\n\n\nto KOERI (Kandilli Observatory and Earthquake Research Institute) for \n\n\n\nproviding the seismograms. We gratefully acknowledge the use of Generic \n\n\n\nMapping Tool in figures. The accelerometer pole-zero files are kindly \n\n\n\ndelivered by the instrument distributors. \n\n\n\nCONFLICT OF INTEREST  \n\n\n\nThe author states that there is no conflict of interest. \n\n\n\nREFERENCES \n\n\n\nAbgarmi, B., Delph, J.R. O zacar, A.A., Beck, S.L., Zandt, G., Sandvol, E., \n\n\n\nTurkelli, N., Biryol, C.B., 2017. Structure of the crust and African slab \n\n\n\nbeneath the central Anatolian plateau from receiver functions:  new \n\n\n\ninsights on isostatic compensation and slab dynamics. Geosphere, 13(6), \n\n\n\nPp. 1774\u20131787. \n\n\n\nAdiyaman, O ., Chorowicz, J., Arnaud, O.N., Gu ndogdu, M.N., Gourgaud, A., \n\n\n\n2001. Late Cenozoic tectonics and volcanism along the North Anatolian \n\n\n\nFault: new structural and geochemical data. Tectonophysics, 338(2), \n\n\n\n2001, Pp. 135\u2013165. \n\n\n\nAFAD, 2019. Strong ground motion database of Turkey (TR-NSMN. \n\n\n\nDisaster and Emergency Management Presidency, Ankara, Turkey. \n\n\n\nAkba\u015f, B., Akdeniz, N., Aksay, A., Altun, I ., Balc\u0131, V., Bilginer, E., Bilgi\u00e7, T., \n\n\n\nDuru, M., Ercan, T., Gedik, I ., Gu nay, Y., Gu ven, I .H., Hakyemez, H. Y., Konak, \n\n\n\nN., Papak, I ., Pehlivan, \u015e., Sevin, M., \u015eenel, M., Tarhan, N.,Turhan, N., \n\n\n\nTu rkecan, A., Ulu, U ., Ug uz, M.F., Yurtsever, A., others, 2017. Geological \n\n\n\nmap of Turkey. MTA (General Directorate of Mineral Research and \n\n\n\nExploration of Turkey) Publications. \n\n\n\nAkbayram, K., Okay, A., Satir, M., 2013. Early Cretaceous closure of the \n\n\n\nIntra-Pontide Ocean in western Pontides (northwestern Turkey. Journal \n\n\n\nof Geodynamics, 65(3), Pp. 38\u201355. \n\n\n\nAlkan, H., \u00c7\u0131nar, H., 2021. The lithospheric structure underneath the \n\n\n\nCircum Black Sea: Teleseismic receiver functions and Rayleigh wave \n\n\n\nphase velocity analysis, Journal of Asian Earth Sciences, 206, Pp. 104652. \n\n\n\nAlmqvist, B.S.G., D. Mainprice, D., 2017. Seismic properties and anisotropy \n\n\n\nof the continental crust: Predictions based on mineral texture and rock \n\n\n\nmicrostructure. Reviews of Geophysics, 55(2), Pp. 367\u2013433. \n\n\n\nAltunkaynak, \u015e., Dilek, Y., 2013. Eocene mafic volcanism in northern \n\n\n\nAnatolia: its causes and mantle sources in the absence of active \n\n\n\nsubduction. International Geology Review, 55(13), Pp. 1641\u20131659. \n\n\n\nBak\u0131rc\u0131, T., Yoshizawa, K., \u00d6zer, M.F., 2012. Three-dimensional S-wave \n\n\n\nstructure of the upper mantle beneath Turkey from surface wave \n\n\n\ntomography. Geophysical Journal International, 190(2), Pp. 1058\u20131076. \n\n\n\nBiryol, B.C., Beck, S.L., Zandt, G., O zacar, A. A., 2011. Segmented African \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 41-50 \n\n\n\n\n\n\n\n \nCite the Article: \u00d6zcan \u00c7ak\u0131r (2021). Transverse Isotropic Crust Structure Beneath the Northwest and Central North Anatolia Revealed by Seismic Surface Waves \n\n\n\nPropagation. Malaysian Journal of Geosciences, 5(2): 41-50. \n \n\n\n\n\n\n\n\nlithosphere beneath the Anatolian region inferred from teleseismic P-\n\n\n\nwave tomography. Geophysical Journal International, 2011, 184(3), Pp. \n\n\n\n1037\u20131057. \n\n\n\n\u00c7ak\u0131r, O ., 2018. Seismic crust structure beneath the Aegean region in \n\n\n\nsouthwest Turkey from radial anisotropic inversion of Rayleigh and Love \n\n\n\nsurface waves. Acta Geophysica, 66(6), Pp. 1303\u20131340. \n\n\n\n\u00c7ak\u0131r, O ., 2019. Love and Rayleigh waves inverted for vertical transverse \n\n\n\nisotropic crust structure beneath the Biga Peninsula and the \n\n\n\nsurrounding area in NW Turkey. Geophysical Journal International, \n\n\n\n216(3), Pp. 2081\u20132105. \n\n\n\n\u00c7ak\u0131r, O ., Erduran, M., 2001. Effect of earth structure and source time \n\n\n\nfunction on inversion of single station regional surface waves for rupture \n\n\n\nmechanism and focal depth. Journal of the Balkan Geophysical Society, \n\n\n\n4(4), Pp. 69\u201390. \n\n\n\n\u00c7ak\u0131r, O ., Erduran, M., 2011. On the P and S Receiver Functions Used for \n\n\n\nInverting the One-Dimensional Upper Mantle Shear-Wave Velocities. \n\n\n\nSurveys in Geophysics, 32(1), Pp. 71\u201398. \n\n\n\nChanceaux, L., Menand, T., 2014. Solidification effects on sill formation: An \n\n\n\nexperimental approach. Earth and Planetary Science Letters, 403(19), \n\n\n\nPp. 79\u201388. \n\n\n\nConfal, J.M., Faccenda, M., Eken, T., Taymaz, T., 2018. Numerical simulation \n\n\n\nof 3-D mantle flow evolution in subduction zone environments in \n\n\n\nrelation to seismic anisotropy beneath the eastern Mediterranean \n\n\n\nregion. Earth and Planetary Science Letters, 497(17), Pp. 50\u201361. \n\n\n\n\u00c7ubuk-Sabuncu, Y., Taymaz, T., Fichtner, A., 2017. 3-D crustal velocity \n\n\n\nstructure of western Turkey: Constraints from full-waveform \n\n\n\ntomography. Physics of the Earth and Planetary Interiors, 270(9), Pp. \n\n\n\n90\u2013112. \n\n\n\nDelph, J.R., Biryol, C.B., Beck, S.L., Zandt, G., Ward, K.M., 2015. Shear wave \n\n\n\nvelocity structure of the Anatolian Plate: anomalously slow crust in \n\n\n\nsouthwestern Turkey. Geophysical Journal International, 202(1), Pp. \n\n\n\n261\u2013276. \n\n\n\nDilek, Y., Altunkaynak, \u015e., 2007. Cenozoic Crustal Evolution and Mantle \n\n\n\nDynamics of Post-Collisional Magmatism in Western Anatolia. \n\n\n\nInternational Geology Review, 49(5), Pp. 431\u2013453. \n\n\n\nDixit, M., Singh, A.P., Mishra, O.P., 2017. Rayleigh wave group velocity \n\n\n\ntomography of Gujarat region, Western India and its implications to \n\n\n\nmantle dynamics. Journal of Seismology, 21(4), Pp. 809\u2013823. \n\n\n\nEmre, \u00d6., Duman, T.Y., \u00d6zalp, S., Elmac\u0131, H., Olgun, \u015e., \u015earo\u011flu F, 2013. Active \n\n\n\nfaults map of Turkey (scale:1/1.125.000). MTA (General Directorate of \n\n\n\nMineral Research and Exploration of Turkey) Publications, Ankara.  \n\n\n\nFu, Y.V., Li, A., 2015. Crustal shear wave velocity and radial anisotropy \n\n\n\nbeneath the Rio Grande rift from ambient noise tomography. Journal of \n\n\n\nGeophysical Research, 120(2), Pp. 1005\u20131019. \n\n\n\nGEOSIG, 2012. Geosig, AC-73 triaxial force balance accelerometer. \n\n\n\nhttp://www.seismicsystems.net/images/pdfs/GS_AC-\n\n\n\n73_Leaflet_V12.pdf. Accessed 15 July 2019 \n\n\n\nGEOTERM, 2019. Geothermal areas in Turkey. Ministry of Energy and \n\n\n\nNatural Resources, Ankara, Turkey. https://www.enerji.gov.tr/tr-\n\n\n\nTR/Sayfalar/Jeotermal. Accessed 15 August 2019 \n\n\n\nG\u00f6k, R., Sandvol, E., T\u00f6rkelli, N., Seber, D., Barazangi, M., 2003. Sn \n\n\n\nattenuation in the Anatolian and Iranian plateau and surrounding \n\n\n\nregions. Geophysical Research Letter, 30(24), Pp. 8042. \n\n\n\nGURALP, 2013. Guralp systems, CMG-5TD digital accelerometer, \n\n\n\noperator's guide.  https://www.guralp.com/documents/MAN-050-\n\n\n\n0005.pdf.  Accessed 15 July 2019 \n\n\n\nHerrin, E., Goforth, T., 1977. Phase-matched filters: application to the study \n\n\n\nof Rayleigh waves. Bulletin of the Seismological Society of America, \n\n\n\n67(5), Pp. 1259\u20131275. \n\n\n\nHerrmann, R.B., 2002. Computer Programs in Seismology, version 3.30, St. \n\n\n\nLouis University, Missouri. \n\n\n\nHippolyte, J.-C., N. Espurt, N., Kaymakci, N., Sangu, E., M\u00fcller, C., 2016. \n\n\n\nCross-sectional anatomy and geodynamic evolution of the Central \n\n\n\nPontide orogenic belt (northern Turkey. International Journal of Earth \n\n\n\nSciences, 105(1), Pp. 81-106. \n\n\n\nI lk\u0131\u015f\u0131k, O.M., 1995. Regional heat flow in western Anatolia using silica \n\n\n\ntemperature estimates from thermal springs. Tectonophysics, 244(1\u20133), \n\n\n\nPp. 175\u2013184. \n\n\n\nKahraman, M., Cornwell, D.G., Thompson, D.A., Rost, S., Houseman, G.A., \n\n\n\nT\u00fcrkelli, N., Teoman, U., Altuncu-Poyraz, S., Utkucu, M., G\u00fclen, L., 2015. \n\n\n\nCrustal-scale shear zones and heterogeneous structure beneath the \n\n\n\nNorth Anatolian Fault Zone, Turkey, revealed by a high-density \n\n\n\nseismometer array. Earth and Planetary Science Letters, 430(22), Pp. \n\n\n\n129\u2013139. \n\n\n\nKaymakci, N., \u00d6z\u00e7elik, Y., White, S.H., van Dijk, P.M., 2009. Tectono-\n\n\n\nstratigraphy of the \u00c7ankiri Basin: Late Cretaceous to early Miocene \n\n\n\nevolution of the Neotethyan suture zone in Turkey, in Collision and \n\n\n\nCollapse at the Africa-Arabia-Eurasia Subduction Zone, edited by D. J. J. \n\n\n\nHinsbergen et al. Geological Society, London, Special Publication, \n\n\n\n311(1), Pp. 7\u2013106. \n\n\n\nKendall, J.-M., 2000. Seismic anisotropy in the boundary layers of the \n\n\n\nmantle. In: Karato, S., Stixrude, L., Liebermann, R.C., Masters, T.G. Forte, \n\n\n\nA.M. (eds) Earth's Deep Interior: Mineral Physics and Tomography from \n\n\n\nthe Atomic to the Global Scale, Geophysical Monograph Series, 117, \n\n\n\nAmerican Geophysical Union, Pp. 149-175. \n\n\n\nKennett, B.L.N., Engdahl, E.R., 1991. Travel times for global earthquake \n\n\n\nlocation and phase association. Geophysical Journal International, \n\n\n\n105(2), Pp. 429-465. \n\n\n\nKeskin, M., 2003. Magma generation by slab steepening and breakoff \n\n\n\nbeneath a subduction-accretion complex: An alternative model for \n\n\n\ncollision-related volcanism in Eastern Anatolia, Turkey. Geophysical \n\n\n\nResearch Letters, 30(24), Pp. 8046. \n\n\n\nKind, R., Eken, T., Tilmann, F., Sodoudi, F., Taymaz, T., Bulut, F., Yuan, X., \n\n\n\nCan, B., Schneider, F., 2015. Thickness of the lithosphere beneath Turkey \n\n\n\nand surroundings from S-receiver functions. Solid Earth, 6(3), Pp. 971\u2013\n\n\n\n984. \n\n\n\nLee, S.-J., Kim, S., Rhie, J., Kang, T.-S., Kim, Y., 2021. Upper crustal shear \n\n\n\nwave velocity and radial anisotropy beneath Jeju Island volcanoes from \n\n\n\nambient noise tomography, Geophysical Journal International, 225, Pp. \n\n\n\n1332\u20131348. \n\n\n\nLicciardi, A., Eken, T., Taymaz, T., Agostinetti, N.P., Yolsal-\u00c7evikbilen, S., \n\n\n\n2018. Seismic anisotropy in central north Anatolian Fault Zone and its \n\n\n\nimplications on crustal deformation. Physics of the Earth and Planetary \n\n\n\nInteriors, 277(4), Pp. 99\u2013112. \n\n\n\nMainprice, D., Nicolas, A., 1989. Development of shape and lattice \n\n\n\npreferred orientations: application to the seismic anisotropy of the \n\n\n\nlower crust. Journal of Structural Geology, 11(1\u20132), Pp. 175\u2013189. \n\n\n\nMenand, T., 2011. Physical controls and depth of emplacement of igneous \n\n\n\nbodies: A review. Tectonophysics, 500(1\u20134), Pp. 11\u201319. \n\n\n\nMeschede, M., Warr, L.N., 2019. The Geology of Germany: Rocks and the \n\n\n\nGeological Record. Regional Geology Reviews, Pp. 9\u201318. \n\n\n\nMiyazaki, T., Sueyoshi, K., Hiraga, T., 2013. Olivine crystals align during \n\n\n\ndiffusion creep of Earth\u2019s upper mantle. Nature, 502(7471), Pp. 321\u2013\n\n\n\n326. \n\n\n\nMTA, 2002. Geological map of Turkey (scale:1/500000). MTA (General \n\n\n\nDirectorate of Mineral Research and Exploration of Turkey) \n\n\n\nPublications, Ankara.  \n\n\n\nNikogosian, I.K., Bracco Gartner, A.J.J., van Bergen, M.J., Mason, P.R.D., van \n\n\n\nHinsbergen, D.J.J., 2018. Mantle sources of recent Anatolian intraplate \n\n\n\nmagmatism: A regional plume or local tectonic origin? Tectonics, 37(12), \n\n\n\nPp. 4535\u20134566. \n\n\n\n\nhttp://www.seismicsystems.net/images/pdfs/GS_AC-73_Leaflet_V12.pdf\n\n\nhttp://www.seismicsystems.net/images/pdfs/GS_AC-73_Leaflet_V12.pdf\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 5(2) (2021) 41-50 \n\n\n\n\n\n\n\n \nCite the Article: \u00d6zcan \u00c7ak\u0131r (2021). Transverse Isotropic Crust Structure Beneath the Northwest and Central North Anatolia Revealed by Seismic Surface Waves \n\n\n\nPropagation. Malaysian Journal of Geosciences, 5(2): 41-50. \n \n\n\n\n\n\n\n\nOkay, A.I., 2008. Geology of Turkey: A synopsis. Anschnitt, 21(1), Pp. 19\u2013\n\n\n\n42.https://web.itu.edu.tr/~okay/makalelerim/91_geology_of_turkey_a\n\n\n\nnschnitt_2008.pdf \n\n\n\nOkay, A.I., T\u00fcys\u00fcz, O., 1999. Tethyan sutures of northern Turkey, In: \n\n\n\nDurand, B., Jolivet, L., Horv\u00e1th, D., S\u00e9rranne, M. (eds) The Mediterranean \n\n\n\nbasins: tertiary extension within the Alpine Orogen. Geological Society, \n\n\n\nLondon, Special Publications, 156(1), Pp. 475\u2013515. \n\n\n\n\u00d6zacar, A.A., Biryol, C.B., Zandt, G., Beck, S.L., 2006. North Anatolian Fault \n\n\n\nPassive Seismic Experiment: Receiver Functions along a Major Strike-\n\n\n\nSlip Fault, AGU Fall Meeting Abstracts. \n\n\n\n\u00d6zdamar, \u015e., Billor, M.Z., Sunal, G., Esenli, F., Roden, M.F., 2013. First U\u2013Pb \n\n\n\nSHRIMP zircon and 40Ar/39Ar ages of metarhyolites from the Afyon\u2013\n\n\n\nBolkardag Zone, SW Turkey: Implications for the rifting and closure of \n\n\n\nthe Neo-Tethys. Gondwana Research, 24(1), Pp. 377\u2013391. \n\n\n\nPasvano\u011flu, S., \u00c7elik, M., 2019. Hydrogeochemical characteristics and \n\n\n\nconceptual model of \u00c7aml\u0131dere low temperature geothermal prospect, \n\n\n\nnorthern Central Anatolia. Geothermics, 79(3), Pp. 82\u2013104. \n\n\n\nPasvano\u011flu, S., G\u00fcltekin, F., 2012. Hydrogeochemical study of the Terme \n\n\n\nand Karakurt thermal and mineralized waters from Kir\u015fehir Area, \n\n\n\ncentral Turkey. Environmental Earth Sciences, 66(1), Pp. 169\u2013182. \n\n\n\nPlunder, A., Agard, P., Chopin, C., Okay, A.I., 2013. Geodynamics of the \n\n\n\nTav\u015fanl\u0131 zone, western Turkey: Insights into subduction/obduction \n\n\n\nprocesses. Tectonophysics, 608(25), Pp. 884\u2013903. \n\n\n\nPortner, D.E., Delph, J.R., Biryol, C.B., Beck, S.L., Zandt, G., \u00d6zacar, A., \n\n\n\nSandvol, E., T\u00fcrkelli, N., 2018. Subduction termination through \n\n\n\nprogressive slab deformation across Eastern Mediterranean subduction \n\n\n\nzones from updated P-wave tomography beneath Anatolia. Geosphere, \n\n\n\n14(3), Pp. 1\u201319. \n\n\n\nPourteau, A., Oberha nsli, R., Candan, O., Barrier, E., Vrielynck, B., 2016. \n\n\n\nNeotethyan closure history of western Anatolia: a geodynamic \n\n\n\ndiscussion. International Journal of Earth Sciences, 105(1), Pp. 203\u2013224. \n\n\n\nRawlinson, N., Sambridge, M., 2003. Seismic traveltime tomography of the \n\n\n\ncrust and lithosphere. Advances in Geophysics, 46(1), Pp. 81\u2013198. \n\n\n\nSARA, 2017. Sara electronics instruments, SA10 force balance \n\n\n\naccelerometer. http://www.k-\n\n\n\ndoit.com/?tpf=common/save_as&file_path=/attachment/201805&file_\n\n\n\nname=1525844823937739.pdf&orig_name=SA10_DATASHEET_ENG.p\n\n\n\ndf. Accessed 15 July 2019 \n\n\n\n\n\n\n\n\u015eeng\u00f6r, A.M.C., Y\u0131lmaz, Y., 1981. Tethyan evolution of Turkey: a plate \n\n\n\ntectonic approach. Tectonophysics, 75(3\u20134), Pp. 181\u2013241. \n\n\n\nTang, Q., Zheng, C., 2013. Crust and upper mantle structure and its tectonic \n\n\n\nimplications in the South China Sea and adjacent regions. Journal of \n\n\n\nAsian Earth Sciences, 62(1), Pp. 510\u2013525. \n\n\n\nTarantola, A., 1987. Inverse Problem Theory, pp. 187\u2013255, Elsevier \n\n\n\nScience Company Inc.  \n\n\n\nTopuz, G., Okay, A.I., 2017. Late Eocene\u2013Early Oligocene two-mica granites \n\n\n\nin NW Turkey (the Uluda\u011f Massif): Water-fluxed melting products of a \n\n\n\nmafic metagraywacke. Lithos, Pp. 268\u2013271(1), 334\u2013350. \n\n\n\nVarol, E., Temel, A., Y\u00fcr\u00fcr, T., Gourgaud, A., Bellon, H., 2014. Petrogenesis \n\n\n\nof the Neogene bimodal magmatism of the Galatean Volcanic Province, \n\n\n\nCentral Anatolia, Turkey. Journal of Volcanology and Geothermal \n\n\n\nResearch, 280(11), Pp. 14\u201329. \n\n\n\nWarren, L.M., Beck, S., Biryol, C., Zandt, G., \u00d6zacar, A., Yang, Y., 2013. Crustal \n\n\n\nvelocity structure of Central and Eastern Turkey from ambient noise \n\n\n\ntomography. Geophysical Journal International, 194(3), Pp. 1941\u20131954. \n\n\n\nWenk, H.-R., Canova, G., Br\u00e9chet, Y., Flandin, L., 1997. A deformation-based \n\n\n\nmodel for recrystallization of anisotropic materials. Acta Materialia, \n\n\n\n45(8), Pp. 3283\u20133296. \n\n\n\nWessel, P., Smith, W.H.F., Scharroo, R., Luis, J.F., Wobbe, F., 2013. Generic \n\n\n\nMapping Tools: Improved version released. EOS, Transactions, American \n\n\n\nGeophysical Union, 94(45), Pp. 409\u2013410. \n\n\n\nWilson, M., Tankut, A., Gule\u00e7, N., 1997. Tertiary volcanism of the Galatia \n\n\n\nprovince, north-west Central Anatolia, Turkey. Lithos, 42(1\u20132), Pp. 105\u2013\n\n\n\n121. \n\n\n\nWu, T., Zhang, S., Li, M., Qin, W., Zhang, C., 2016. Two crustal flowing \n\n\n\nchannels and volcanic magma migration underneath the SE margin of \n\n\n\nthe Tibetan Plateau as revealed by surface wave tomography. Journal of \n\n\n\nAsian Earth Sciences, 132(18), Pp. 25\u201339. \n\n\n\nY\u0131ld\u0131r\u0131m, C., 2014. Relative tectonic activity assessment of the Tuz G\u00f6l\u00fc \n\n\n\nFault Zone; Central Anatolia, Turkey. Tectonophysics, 630(17), Pp. 183\u2013\n\n\n\n192. \n\n\n\nYolsal-\u00c7evikbilen, S., Biryol, C.B., Beck, S., Zandt, G., Taymaz, T., Ad\u0131yaman, \n\n\n\nH.E., \u00d6zacar, A. A., 2012. 3-D crustal structure along the North Anatolian \n\n\n\nFault Zone in north-central Anatolia revealed by local earthquake \n\n\n\ntomography. Geophysical Journal International, 188(3), Pp. 819\u2013849. \n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 11-16 \n\n\n\nCite the article: Adegbuyi, O., Ogunyele, A. C., Akinyemi, O. M. (2018). Petrology and Geochemistry of Basement Gneissic Rocks around \nOka-Akoko, Southwestern Nigeria. Malaysian Journal of Geosciences, 2(2): 11-16. \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nISSN: 2521-0920 (Print)\nISSN: 2521-0602 (online)\nCODEN: MJGAAN \n\n\n\nDOI:     https://doi.org/10.26480/mjg.02.2018.11.16 \n\n\n\nPETROLOGY AND GEOCHEMISTRY OF BASEMENT GNEISSIC ROCKS AROUND \n\n\n\nOKA-AKOKO, SOUTHWESTERN NIGERIA \n\n\n\nAdegbuyi, O., *Ogunyele, A. C., Akinyemi, O. M. \n\n\n\nDepartment of Earth Sciences, Adekunle Ajasin University, P.M.B. 001, Akungba-Akoko, Ondo State, Nigeria. \n*Corresponding Author\u2019s Email: abimbola.ogunyele@aaua.edu.ng\n\n\n\nThis   is   an   open   access   article   distributed   under   the   Creative   Commons   Attribution   License,   which   permits   unrestricted   use,   distribution, \n\n\n\nand reproduction in any medium, provided the original work is properly cited \n\n\n\n ARTICLE DETAILS \n\n\n\nArticle History: \n\n\n\nReceived 10 June 2018 \nAccepted 16 July 2018 \nAvailable online 1 August 2018 \n\n\n\n ABSTRACT \n\n\n\nThe gneissic rocks of Oka-Akoko area forms part of the Migmatite-Gneiss-Quartzite Complex of the \nSouthwestern Nigerian Basement Complex. The petrologic units in the study area include grey gneiss, granite \ngneiss, Older granite, charnockite and minor felsic and basic rocks. Twelve gneissic rock samples comprising six \ngranite gneiss and six grey gneiss from the area were collected for petrographic and geochemical analyses. \nPetrographic analysis revealed that the granite gneiss is more enriched in quartz and alkali feldspar than the grey \ngneiss. The grey gneiss is richer in plagioclase, hornblende and opaques compared to the granite gneiss. \nInterpretation of petrographic and geochemical analyses results revealed that Oka-Akoko granite gneiss and \ngrey gneiss were derived from igneous protoliths of granitic and granodioritic compositions respectively. The \ngrey gneiss is ferroan, alkalic to alkali-calc and metaluminous suggesting that its igneous protolith(s) is a M-type \ngranitoid derived from melting of rocks from upper mantle or lower crustal region under conditions of limited \navailability of H2O and low oxygen fugacity while the granite gneiss is magnesian, alkali-calc and slightly \nperaluminous suggesting that its igneous protolith(s) is an I-type granitoid derived from the partial melting of \ncrustal igneous rocks. \n\n\n\nKEYWORDS \n\n\n\nBasement Complex, gneissic rocks, igneous protoliths, Oka-Akoko, peraluminous. \n\n\n\n1. INTRODUCTION \n\n\n\nOka-Akoko is located in Akoko Southwest Local Government Area of \n\n\n\nOndo State, Southwestern Nigeria. The area lies within longitudes 05o46\u2019 \n\n\n\n\u2013 05o50\u2019E and latitudes 07o23\u2019 \u2013 07o28\u2019N. It is bounded in the north by \n\n\n\nIkare-Akoko, in the west by Akungba-Akoko, in the east by Epinmi-Akoko, \n\n\n\nand in the south by Ikun- and Afo-Akoko. The area forms part of the \n\n\n\nPrecambrian Basement Complex of Southwestern Nigeria (Figure 1).  \n\n\n\nThe Basement Complex of Nigeria comprises mainly of the Migmatite-\n\n\n\nGneiss-Quartzite Complex rocks of Archaean to Paleoproterozoic age \n\n\n\n(ca.>2.0 Ga), Upper Proterozoic Schist Belts and Older Granitoids of \n\n\n\nPan-African age (500-750Ma) which intrude the former two units [1-7]. \n\n\n\nOka-Akoko area comprises mainly of gneisses in association with \n\n\n\nporphyritic Older granite, charnockite, pegmatite, aplite, vein quartz and \n\n\n\namphibolitic inclusions (Figure 2). The gneisses are of two types: granite \n\n\n\ngneiss and grey gneiss. [1,8] referred to grey gneiss in the area as early \n\n\n\nand quartzo-feldspathic gneiss respectively, and explained that it is \n\n\n\ngranodioritic to tonalitic\u2013quartz-dioritic in composition. The grey gneiss \n\n\n\nis the second most abundant rock type in the area forming enclaves \n\n\n\nwithin the granite gneiss (Figure 2). It is dark grey to dark green in colour \n\n\n\nand medium-coarse grained with well-developed thin mineralogical \n\n\n\nbands (Figure 3). The light-coloured bands are quartzo-feldspathic while \n\n\n\nthe dark coloured bands are rich in ferromagnesian minerals. The grey \n\n\n\ngneiss contains intrusions of pegmatite and quartzo-feldspathic veins and \n\n\n\nis regarded as the oldest rock in the area [1].  \n\n\n\nThe granite gneiss is light grey in colour, medium-coarse grained and \n\n\n\ncharacterised by weak foliation defined by the alignment of streaks of \n\n\n\nlight and dark coloured minerals (Figure 4). The granite gneiss contains \n\n\n\nxenoliths of the grey gneiss and amphibolite. This suggests that the \n\n\n\ngranite gneiss post-dates the grey gneiss in the study area. \n\n\n\nFigure 1: Outline geological map of Nigeria showing Oka-\n\n\n\nAkoko (study area) (modified after [4]) \n\n\n\nThese gneisses (grey and granitic varieties) are widespread in the area \n\n\n\nconstituting about 90% of the rock types found in the area and have been \n\n\n\nintruded by the Pan-African granitoids (granite, charnockite, pegmatite \n\n\n\nand aplite). They occur as massive rugged hills and rolling plains \n\n\n\nassuming batholithic dimensions and forming impressive outcrops which \n\n\n\ntower few hundred metres above the surrounding lowlands and showing \n\n\n\ndifferent types of geological structures such as folds, faults, foliation, joints \n\n\n\netc (Figures 3 and 4) [9]. These structures suggest that the area has been \n\n\n\nsubjected to at least two phases of deformation. \n\n\n\n\nmailto:abimbola.ogunyele@aaua.edu.ng\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 11-16 \n\n\n\nCite the article: Adegbuyi, O., Ogunyele, A. C., Akinyemi, O. M. (2018). Petrology and Geochemistry of Basement Gneissic Rocks around \nOka-Akoko, Southwestern Nigeria. Malaysian Journal of Geosciences, 2(2): 11-16. \n\n\n\nFigure 2: Geological map of Oka-Akoko \n\n\n\nMetamorphism in the area is of amphibolite to granulite facies grade \n\n\n\n[8]. Geological studies of the area and adjacent areas such as Ikare, \n\n\n\nArigidi and Idoani have been undertaken by several researchers \n\n\n\n[1,8,10-12]. \n\n\n\nThis paper is aimed at determining the mineralogy, geochemical \n\n\n\ncompositions and petrogenesis of the basement gneisses around \n\n\n\nOka-Akoko. These characteristics of the gneisses are yet to be fully \n\n\n\nknown. \n\n\n\n2. METHODOLOGY\n\n\n\nThe study area was geologically mapped and twelve (12) fresh \n\n\n\nrepresentative gneissic rock samples comprising six granite gneiss \n\n\n\nand six grey gneiss were collected for petrographic and geochemical \n\n\n\nstudies. Thin sections of the samples were prepared and studied \n\n\n\nunder a petrographic microscope. The minerals present in the thin \n\n\n\nsections were identified and counted by the microscope and \n\n\n\nphotomicrographs were captured. X-Ray Fluorescence (XRF) \n\n\n\nspectrometer was used to determine the major elements present in \n\n\n\nthe gneissic rocks.  \n\n\n\nFigure 3: Folded grey gneiss intruded by pegmatites in Oka-Akoko \n\n\n\nFigure 4: Folded and jointed granite gneiss containing inclusion of grey \n\n\n\ngneiss in Oka-Akoko \n\n\n\nDetailed processes of the methods of study are contained in [13]. \n\n\n\nThe mineralogical and geochemical results were plotted on \n\n\n\ndiscrimination diagrams for the purpose of petrological classification, \n\n\n\ndetermination of chemical affinities and petrogenesis. \n\n\n\n3. RESULTS AND DISCUSSION\n\n\n\nPetrographic analysis results (Table 1) revealed that Oka-Akoko \n\n\n\ngranite gneiss samples contain quartz (31.2\u201337.7 wt. %), plagioclase \n\n\n\n(17.4\u201323.9 wt. %), opaque minerals (2.5\u20136.1 wt. %), biotite (12.3\u2013\n\n\n\n23.0 wt. %), microcline (16.6\u201325.2 wt. %), orthoclase (1.2\u20136.7 wt. %) \n\n\n\nand hornblende (2.1\u20137.3 wt. %). In the grey gneiss samples, the \n\n\n\nfollowing result was obtained: quartz (27.0\u201328.8 wt. %), plagioclase \n\n\n\n(24.6\u201326.7 wt. %), opaque minerals (7.3\u20138.7 wt. %), biotite (11.5\u2013\n\n\n\n16.1 wt. %), microcline (8.2\u201311.4 wt. %), orthoclase (4.4\u20136.9 wt. %) \n\n\n\nand hornblende (9.4\u201313.0 wt. %). QAP diagram revealed that the \n\n\n\ngranite gneiss is granitic while the grey gneiss is granodioritic in \n\n\n\ncomposition (Figure 5) [14]. \n\n\n\nTable 1: Modal compositions of the granite gneiss and grey gneiss around Oka-Akoko (values in wt. %) \n\n\n\nMinerals \n\n\n\nGranite Gneiss Grey Gneiss \n\n\n\nGGN1 GGN2 GGN3 GGN4 GGN5 GGN6 Av. GGN gGN1 gGN2 gGN3 gGN4 gGN5 gGN6 Av. gGN \n\n\n\nQuartz 34.0 36.7 31.2 33.3 34.6 31.9 33.62 28.0 27.1 27.9 27.0 28.8 27.0 27.63 \n\n\n\nPlagioclase 17.4 23.1 21.2 22.9 19.9 18.7 20.53 24.0 26.1 24.6 26.6 26.7 26.1 25.68 \n\n\n\nMicrocline 19.1 24.7 16.6 25.2 19.0 21.1 20.95 11.4 8.0 11.4 11.2 10.1 11.3 10.57 \n\n\n\nOrthoclase 2.0 1.2 5.2 1.4 2.1 6.7 3.10 5.1 4.9 6.1 4.4 6.9 5.5 5.48 \n\n\n\nHornblende 2.1 2.1 7.1 3.8 7.3 2.4 4.13 13.0 12.1 9.4 9.8 10.1 9.5 10.65 \n\n\n\nBiotite 20.3 10.6 14.1 13.0 15.1 15.1 14.70 11.1 14.1 14.5 11.5 12.1 16.1 13.23 \n\n\n\nOpaques 5.4 3.7 4.9 2.5 3.1 6.1 4.28 8.7 7.1 8.7 7.3 7.1 7.1 7.67 \n\n\n\nTotal 100.2 99.7 100.3 102.1 101.1 102.0 101.31 101.3 99.4 102.6 97.8 101.8 102.6 100.91 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 11-16 \n\n\n\nCite the article: Adegbuyi, O., Ogunyele, A. C., Akinyemi, O. M. (2018). Petrology and Geochemistry of Basement Gneissic Rocks around \nOka-Akoko, Southwestern Nigeria. Malaysian Journal of Geosciences, 2(2): 11-16. \n\n\n\nFigure 5: QAP diagram for Oka-Akoko granite gneiss and grey gneiss [14] \n\n\n\nGeochemical analysis result (Table 2) shows that the granite gneiss of \nOka-Akoko contain predominantly SiO2 (65.34\u201369.78 wt. %), Al2O3 \n(15.68\u201317.46 wt. %), K2O (4.33\u20134.67 wt. %), Na2O (3.62\u20134.42 wt. %), \nFeO (1.33\u20132.03 wt. %) and Fe2O3 (1.01\u20131.74 wt. %) while the grey \ngneiss contains mainly SiO2 (59.49\u201362.76 wt. %), Al2O3 (15.67\u201318.66 \nwt. %), Na2O (4.31\u20135.31 wt. %), K2O (3.12\u20135.65 wt. %), Fe2O3 (2.67\u20135.53 \nwt. %), FeO (2.11\u20133.67 wt. %) and CaO (2.63\u20134.53 wt. %).  \n\n\n\nThe granite gneiss of Oka-Akoko is more silicic than the grey gneiss. \nHence, based on silica content, the granite gneiss is silicic or acidic \nwhile the grey gneiss is intermediate in composition.  This further \nsupports the granitic nature of the granite gneiss a n d  \ng r a n o d i o r i t i c  composition of the grey gneiss as revealed by the \nQAP diagram (Figure 5). Also, the granite gneiss contains more K2O than \nthe grey gneiss and this is reflected in the higher amount of K-\nfeldspars (microcline and orthoclase) present in the former than the \nlatter. However, the grey gneiss contains more TiO2, Al2O3, FeOtotal, \nMgO, CaO, and Na2O than the granite gneiss and this account for the \nhigher amount of opaques (iron minerals and others), hornblende, and \nplagioclase in the grey gneiss than the granite gneiss. \n\n\n\nTable 2: Major element compositions of granite gneiss and grey gneiss around Oka-Akoko (values in wt. %) \n\n\n\n Major   \n\n\n\n Oxides \n\n\n\nGranite Gneiss Grey Gneiss \n\n\n\nGGN1 GGN2 GGN3 GGN4 GGN5 GGN6 gGN1 gGN2 gGN3 gGN4 gGN5 gGN6 \n\n\n\nSiO2 66.72 65.34 69.78 66.73 66.40 68.10 62.66 60.76 59.49 62.60 59.50 62.76 \n\n\n\nTiO2 0.44 0.41 0.4 0.43 0.46 0.40 0.41 0.93 1.04 0.81 0.50 0.90 \n\n\n\nAl2O3 16.52 17.46 15.68 17.43 16.68 16.73 15.73 17.82 18.66 15.67 17.50 17.70 \n\n\n\nFe2O3 1.63 1.74 1.01 1.64 1.33 1.80 2.67 2.53 2.87 4.10 5.53 3.32 \n\n\n\nFeO 2.03 1.98 1.33 1.33 2.07 1.52 3.67 4.33 3.97 2.34 3.23 2.11 \n\n\n\nMnO 0.05 0.07 0.25 0.31 0.10 0.09 0.11 0.11 0.12 0.11 0.13 0.12 \n\n\n\nMgO 1.00 1.22 0.68 0.81 1.21 0.98 0.66 1.67 1.29 1.70 1.31 1.10 \n\n\n\nCaO 2.62 2.74 1.58 2.22 2.35 1.38 2.63 4.12 4.43 2.84 4.53 2.63 \n\n\n\nNa2O 4.40 3.78 4.42 4.40 4.42 3.62 5.31 4.31 4.33 4.40 4.31 5.31 \n\n\n\nK2O 4.33 4.57 4.35 4.33 4.67 4.66 4.65 3.12 3.74 5.65 4.43 3.34 \n\n\n\nP2O5 0.17 0.18 0.16 0.15 0.16 0.14 0.12 0.12 0.11 0.12 0.11 0.11 \n\n\n\nLOI 0.11 0.33 0.23 0.19 0.13 0.43 0.71 0.91 0.10 0.12 0.20 0.48 \n\n\n\nTOTAL 100.02 99.82 99.88 99.79 99.98 99.85 99.33 99.81 99.99 100.34 101.28 99.98 \n\n\n\nOka-Akoko granite gneiss has lower silica content than the Arigidi, Ilesha, \n\n\n\nJebba and Idofin-Osi-Eruku granite gneisses but higher silica content \n\n\n\nthan the NE Obudu granitic gneiss (Table 3) [11,15-18]. The Al2O3 and \n\n\n\ntotal alkali (Na2O + K2O) contents of Oka-Akoko granite gneiss is higher \n\n\n\nthan that of Arigidi, Ilesha, Jebba, Idofin-Osi-Eruku and NE Obudu \n\n\n\ngranitic gneisses indicating its higher peraluminous and alkaline nature. \n\n\n\nThe major element composition of Oka-Akoko granite gneiss is very \n\n\n\nsimilar to that of Idofin-Osi-Eruku and Arigidi-Akoko granite \n\n\n\ngneisses; however, the latter is less alkaline and aluminous and contains \n\n\n\nmore cafemic oxides (CaO, FeOtotal, and MgO). The Kabala grey gneiss \n\n\n\nand Idofin-Osi-Eruku early or grey gneiss are more siliceous and sodic \n\n\n\nthan the Oka-Akoko grey gneiss but the latter has higher TiO2, Al2O3, \n\n\n\nFe2O3, FeO, MnO, MgO, K2O and CaO than the Kabala and Idofin-Osi-\n\n\n\nEruku grey gneisses [19]. \n\n\n\nDiscrimination diagrams of Garrel & McKenzie and Tarney indicate that \n\n\n\nall Oka-Akoko granite gneiss and grey gneiss samples show preference \n\n\n\nfor igneous field, hence, they are of igneous origin (Figures 6 and 7) \n\n\n\n[20,21]. K2O versus Na2O discrimination diagram of Middleton also \n\n\n\nconfirms that the protoliths of both the granite gneiss and grey gneiss are \n\n\n\nmagmatic rocks because they plot outside the eugeosynclinal field \n\n\n\n(Figure 8) which represents sedimentary protoliths field [22]. \n\n\n\nFigure 6: Na2O/Al2O3 versus K2O/Al2O3 discrimination diagram for Oka-\n\n\n\nAkoko granite gneiss and grey gneiss [20] \n\n\n\n\n\n\n\n\nCite the article: Adegbuyi, O., Ogunyele, A. C., Akinyemi, O. M. (2018). Petrology and Geochemistry of Basement Gneissic Rocks around \nOka-Akoko, Southwestern Nigeria. Malaysian Journal of Geosciences, 2(2): 11-16. \n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 11-16 \n\n\n\nTable 3: Comparism of average major element composition of the granite gneiss and grey gneiss of Oka-Akoko \nwith similar rocks from other parts of Nigeria \n\n\n\nFigure 7: TiO2 versus SiO2 discrimination diagram for Oka-Akoko \n\n\n\ngranite gneiss and grey gneiss [21] \n\n\n\nFigure 8: K2O versus Na2O discrimination diagram for Oka-Akoko \n\n\n\ngranite gneiss and grey gneiss [22] \n\n\n\n1 = Average major element composition of Oka-Akoko \ngranite gneiss (present work). \n\n\n\n2 = Average major element composition of Arigidi-Akoko \ngranite gneiss, Southwestern Nigeria [11] \n\n\n\n3 = Average chemical composition of granitic gneiss of \nIlesha schist belt, Southwestern Nigeria [15]. \n\n\n\n4 = Average major element composition of granitic \ngneiss of Jebba area, Southwestern Nigeria [16]. \n\n\n\n5 = Average major element composition of granitic \ngneiss of NE Obudu, Southeastern Nigeria [18]. \n\n\n\n6 = Average major element composition of granite \ngneiss of Idofin-Osi-Eruku area, Southwestern Nigeria \n[17]. \n\n\n\n7 = Average major element composition of Oka-Akoko \ngrey gneiss (present work). \n\n\n\n8 = Average chemical composition of Kabala migmatitic \ngrey gneisses, Northwestern Nigeria [19]. \n\n\n\n9 = A v e r a g e  chemical composition of early gneiss of \nIdofin-Osi-Eruku area, Southwestern Nigeria [17]. \n\n\n\nOn the K2O versus SiO2 plot, both the granite gneiss and grey gneiss \nplot dominantly in the shoshonitic field due to their enrichment in K2O \n(Figure 9) [23]. The discrimination plot of Irvine and Baragar shows \nthat the granite gneiss is sub-alkaline while the grey gneiss is sub-\nalkaline to alkaline (Figure 10) [24]. The grey gneiss is ferroan (that is, \nFeOtotal enriched) while the granite gneiss is magnesian (MgO \nenriched) as revealed by the FeOtotal/(FeOtotal + MgO) versus SiO2 \ndiagram (Figure 11) [25]. Ferroan rocks are usually associated with \nconditions of limited availability of H2O and low oxygen fugacity [25]. \n\n\n\nFigure 9: K2O versus SiO2 plot for Oka-Akoko granite gneiss  \n\n\n\nand grey gneiss [23]\n\n\n\nMajor \n\n\n\nOxides \n1 2 3 4 5 6 7 8 9 \n\n\n\nSiO2 67.18 70.92 75.79 76.11 60.82 68.86 61.30 66.34 70.41 \n\n\n\nTiO2 0.42 0.45 0.03 0.29 0.67 0.40 0.88 0.59 0.33 \n\n\n\nAl2O3 16.75 13.09 11.80 11.83 15.08 15.10 17.26 15.91 14.82 \n\n\n\nFe2O3 1.53 2.32 1.53 2.82 5.39 3.45 3.50 2.26 3.17 \n\n\n\nFeO 1.71 3.76 - - - - 3.28 2.04 - \n\n\n\nMnO 0.15 0.32 0.02 0.02 0.06 0.06 0.12 0.08 0.06 \n\n\n\nMgO 1.00 1.11 0.44 0.11 1.35 1.04 1.17 1.29 0.17 \n\n\n\nCaO 2.15 3.20 0.44 0.42 3.09 2.49 3.53 3.28 3.32 \n\n\n\nNa2O 4.17 1.81 1.60 3.53 3.37 3.41 4.67 5.22 4.81 \n\n\n\nK2O 4.49 1.45 6.58 4.43 4.55 4.27 4.16 1.61 1.62 \n\n\n\nP2O5 0.16 - 0.06 0.03 0.42 0.11 0.12 0.15 0.13 \n\n\n\nLOI 0.24 - 1.14 0.35 0.60 - 0.42 0.61 - \n\n\n\nTOTAL 99.95 98.43 99.43 99.94 99.40 99.19 100.39 99.47 99.38 \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 11-16 \n\n\n\nCite the article: Adegbuyi, O., Ogunyele, A. C., Akinyemi, O. M. (2018). Petrology and Geochemistry of Basement Gneissic Rocks around \nOka-Akoko, Southwestern Nigeria. Malaysian Journal of Geosciences, 2(2): 11-16. \n\n\n\nFigure 10: K2O + Na2O against SiO2 plot for Oka-Akoko granite \ngneiss and grey gneiss [24] \n\n\n\nFigure 11: FeOtotal/( FeOtotal + MgO) vs. SiO2 diagram for Oka-Akoko [25]  \n\n\n\nFurthermore, Oka-Akoko granite gneiss is alkali-calcic while the \n\n\n\ngrey gneiss is alkalic to alkali-calcic as revealed by the Na2O + K2O \n\n\n\n- CaO against SiO2 diagram (Figure 12) [25]. The granite gneiss \n\n\n\nplots in the peraluminous field while the grey gneiss plots in the \n\n\n\nmetaluminous field on the Al2O3/(Na2O + K2O) against Al2O3/( Na2O + \n\n\n\nK2O + CaO) molecular diagram of Maniar and Piccoli (Figure 13) [26]. \n\n\n\nThe metaluminous nature of the grey gneiss coupled with its ferroan \n\n\n\nand alkalic to alkali- calc characteristics suggest that its igneous \n\n\n\nprotolith(s) is a M-type granitoid derived from melting of rocks of \n\n\n\nupper mantle or deeper crustal region under conditions of limited \n\n\n\navailability of H2O and low oxygen fugacity [25,27,28]. The \n\n\n\nprotolith(s) of the granite gneiss is inferred to be I-type granitoid \n\n\n\nderived from the partial melting of crustal igneous rocks as revealed by \n\n\n\nits slight peraluminous nature (Figure 14). \n\n\n\nFigure 12: K2O + Na2O - CaO versus SiO2 plot for Oka-Akoko \n\n\n\ngranite gneiss and grey gneiss [25] \n\n\n\nFigure 13: Al2O3/(Na2O + K2O) versus Al2O3/(CaO + Na2O + K2O) \n\n\n\nmolecular plot for Oka-Akoko granite gneiss and grey gneiss [26] \n\n\n\nFigure 14: Al2O3/(CaO + Na2O + K2O) versus SiO2 plot for Oka- \n\n\n\nAkoko granite gneiss [29] \n\n\n\nThe results obtained from this study correlates with the works of [1,8] on \nthe basement gneissic rocks of Ikare area, Southwestern Nigeria. The \nOka-Akoko grey gneiss is similar in composition to the Kabala gneiss \nwhich is granodioritic [19]. The granite gneiss of Oka-Akoko is similar to \nIdofin-Osi-Eruku and Jebba granite gneisses which are also \nperaluminous, potassic, calcic to alkalic and silica-rich and inferred to be \nproducts of partial melting of crustal rocks [16,17]. \n\n\n\n4. CONCLUSION\n\n\n\nThis study shows that the granite gneiss and grey gneiss of Oka-Akoko \nare orthogneisses of granitic and granodioritic compositions respectively. \nThe grey gneiss is ferroan, alkalic to alkali-calc and metaluminous \nsuggesting that its igneous protolith(s) is a M-type granitoid derived from \nmelting of rocks from upper mantle or lower crustal region under \nconditions of limited availability of H2O and low oxygen fugacity while \nthe granite gneiss is magnesian, alkali-calc and slightly peraluminous \nsuggesting that its igneous protolith(s) is an I-type granitoid derived from \npartial melting of crustal igneous rocks. \n\n\n\n\n\n\n\n\nCite the article: Adegbuyi, O., Ogunyele, A. C., Akinyemi, O. M. (2018). Petrology and Geochemistry of Basement Gneissic Rocks around \nOka-Akoko, Southwestern Nigeria. Malaysian Journal of Geosciences, 2(2): 11-16. \n\n\n\nMalaysian Journal of Geosciences (MJG) 2(2) (2018) 11-16 \n\n\n\nREFERENCES \n\n\n\n[1] Rahaman, M.A. (1976). Review of the Basement Geology of \nSouthwestern Nigeria. In: Kogbe, C. A. (ed.) Geology of Nigeria. \nElizabethan Publ. Co. Lagos, 41-48. \n\n\n\n[2] Ajibade, A.C. (1982). Origin and emplacements of the Older granites of \nNigeria: some evidence from the Zungeru region. Journal of Mining and \nGeology, 19 (1), 221-230. \n\n\n\n[3] Turner, D.C. (1983). Upper Proterozoic Schist Belts in the Nigerian \nSector of the Pan-African Province of West Africa. Precambrian Research, \n21, 55-79. \n\n\n\n[4] Woakes, M., Rahaman, M.A., Ajibade, A.C. (1987): Some Metallogenetic \nFeatures of the Nigerian Basement. Journal of African Earth Sciences, \n6(5), 655-664. \n\n\n\n[5] Ekwueme, B.N. (1990). Rb-Sr ages and petrologic features of \nPrecambrian rocks from Oban massif, southeastern Nigeria. Precambrian \nResearch, 47, 271-286. \n\n\n\n[6] Annor, A.E. (1995). U-Pb Zircon age for Kabba-Okene granodiorite \ngneiss: Implication for Nigeria\u2019s Basement Chronology. Africa Rev., 2, \n101-105. \n\n\n\n[7] Obaje, N.G. (2009). Geology and Mineral Resources of Nigeria. Lecture \nNotes in Earth Sciences 120. Springer-Verlag Berlin Heidelberg. 221 p. \n\n\n\n[8] Rahaman, M.A., Ocan, O. (1988). The Nature of Granulite Facies \nMetamorphism in Ikare Area, Southwestern Nigeria. In: Oluyide, P. O., \nMbonu, W. C., Ogezi, A. E. O., Egbuniwe, I. G., Ajibade, A. C. and Umeji, A. C. \n(eds.) Precambrian Geology of Nigeria. Geological Survey of Nigeria, \nKaduna. pp. 157-163. \n\n\n\n[9] Ajayi, I.R., Adegbuyi, O., Afolabi, O.M., Oniya, E.O. (2006). Terrestrial \nGamma Dose Rates in Akoko, Southwestern Nigeria. Science Research \nAnnals, 2 (1), 53-57. \n\n\n\n[10] Erinfolami, T.G. (2009). Petrographic and Geochemical Studies of the \nBasement Complex rocks in Idoani district, Ondo State, Nigeria. \nUnpublished B.Sc. Dissertation, Dept. of Earth Sciences, AAUA. 94 p. \n\n\n\n[11] Ademeso, O.A., Adeyeye, O. (2011). The Petrography and Major \nElement Geochemistry of the Granite Gneiss of Arigidi area, S/W, Nigeria. \nNature and Science, 9 (5), 7-12. \n\n\n\n[12] Adegbuyi, O., Ogunyele, A.C., Odindu, M., Erinfolami, T.G. \n(2017). Geochemical Characteristics and Petrogenesis of Basement \nRocks in Idoani Area, Ondo State, Southwestern Nigeria. \nInternational Journal of Advanced Geosciences, 5 (2), 102-108. \n\n\n\n[13] Akinyemi, O.M. (2014). Petrographic and Geochemical studies \nof Basement Gneissic Rocks in Oka-Akoko area of Ondo State, \nSouthwestern Nigeria. Unpublished B.Sc. Dissertation, Dept. of Earth \nSciences, AAU, Akungba-Akoko. 78 p. \n\n\n\n[14] Streckeisen, A.L. (1976). To each Plutonic Rock its proper \nName. Earth Science Reviews, 12. \n\n\n\n[15] Oyinloye, A.O. (2004). Petrochemistry, Pb isotope systematics \nand geotectonic settings of the granite gneiss in Ilesha schist belt, \nSouthwestern Nigeria. Global Journal of Science, 2 (1), 1-13. \n\n\n\n[16] Okonkwo, C.T., Winchester, J.A. (2004). Geochemistry of granitic \nrocks in Jebba area, Southwestern Nigeria. Journal of Mining and Geology, \n40 (2), 95-100. \n\n\n\n[17] Odewumi, S.C., Olarewaju, V.O. (2013). Petrogenesis and Geotectonic \nSettings of the Granitic Rocks of Idofin-Osi-Eruku Area, Southwestern \nNigeria using Trace Element and Rare Earth Element Geochemistry. \nJournal of Geology & Geosciences, 2 (1), 1-8. \n\n\n\n[18] Bassey, E.E. (2009). Petrochemistry and petrogenesis of granite \ngneiss of northeast Obudu, Bamenda massif, southeastern Nigeria. \nJournal of Mining and Geology, 45 (2), 59-71. \n\n\n\n[19] Kroner, A., Ekwueme, B.N., Pidgeon, R.T. (2001). The Oldest Rocks in \nWest Africa: SHRIMP Zircon Age for Early Archean Migmatitic \nOrthogneiss at Kaduna, Northern Nigeria. The Journal of Geology, 109, \n399-406. \n\n\n\n[20] Garrel, R.M., Mackenzie, F.T. (1971). Evolution of Sedimentary Rocks. \nW.W. Norton and Co. Int. New York, 394 p. \n\n\n\n[21] Tarney, J. (1977). Petrology, Mineralogy and Geochemistry of the \nFarkland Plateau Basement Rocks. Site 330. Deep Sea Drilling Project, \nInitial Report, 36, 893-921. \n\n\n\n[22] Middleton, E.V. (1960). Chemical Composition of Sandstone. \nGeological Society of America Bulletin, 71, 1011-1026. \n\n\n\n[23] Richwood, P.C. (1989). Boundary lines within petrologic diagrams \nwhich use oxides of major and minor elements. Lithos, 22, 247-263. \n\n\n\n[24] Irvine, T.N., Baragar, W.R.A. (1971). A guide to the chemical \nclassifications of the common volcanic rocks. Canadian Journal of Earth \nSciences, 8, 523-548. \n\n\n\n[25] Frost, B.R., Barnes, C.G., Collins, W.J., Arculus, R.J., Ellis, D.J., Frost, C.D. \n(2001). A geochemical classification for granitic rocks. Journal of \nPetrology, 42, 2033-2048. \n\n\n\n[26] Maniar, P.D., Piccoli, P.M. (1989). Tectonic discrimination of \ngranitoids. Geological Society of America Bulletin, 101, 635-643. \n\n\n\n[27] Taylor, S.R., McLennan, S.M. (1981). The composition and evolution \nof the continental crust: rare earth elements evidence from sedimentary \nrocks. Philosophical Transactions of the Royal Society of London A, 30, \n381-399. \n\n\n\n[28] Tarney, J., Windley, B.F. (1977). Chemistry, thermal gradients and \nevolution of the lower continental crust. Journal of Geological Society of \nLondon, 134, 153-172. \n\n\n\n[29] Dombrowski, A., Henjes-Kunst, F., Hohndorf, A., Kroener, A., Okrusch, \nM., Richter, P. (1995). Orthogneisses in the Sperssart Crystalline Complex, \nnorth-west Bavaria: Silurian granitoid magmatism at an active \ncontinental margin. Geologische Rundschau, 84, 399-411. \n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal Geosciences (MJG) 1(1) (2017) 13-26\n\n\n\nContents List available at RAZI Publishing\n\n\n\nMalaysian Journal of Geosciences\nJournal Homepage: http://www.razipublishing.com/journals/malaysian-journal-of-geosciences-mjg/\n\n\n\nActive Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features \nOf Bukit Tinggi Region\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu \nBakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad  \n1Department Of Geology, University of Malaya, 50603 Kuala Lumpur.Minerals and Geoscience Department Malaysia, Headquarters Bangunan Tabung \nHaji, Jalan Tun Razak, 50658 Kuala Lumpur.3School of science and technology, Universiti Malaysia Sabah, 88999 Kota Kinabalu, Sabah.4School of \nEnvironmental and Natural Resource Sciences, UniversitiKebangsaan Malaysia, Bangi. Selangor.5Faculty of Engineering, Technology & Built Environment, \nUCSI University, 56000 Kuala Lumpur.6Malaysian remote Sensing Agency, JalanTun Ismail, 50480 Kuala Lumpur.7Malaysian Metereology Departmet. \nJalan Sultan, 46667 Petaling Jaya, Selangor.\n\n\n\nARTICLE DETAILS ABSTRACT\nArticle history:\n\n\n\nReceived 22 January 2017  \nAccepted 03 February 2017  \nAvailable online 05 February 2017\n\n\n\nKeywords:\n\n\n\n Active faults, earthquakes, paleoearth-\nquakes, Bukit Tinggi, tectonic landform.\n\n\n\nIn this paper, we summarize the results of recent geomorphic investigations of active faults in Peninsular Malaysia \nwith emphasize on Bukit Tinggi region using IFSAR and field verification.  The evidences for active faulting, and \ntheir characteristics are discussed.  Several fault segments within the Bukit Tinggi fault zone are  deemed active. \nThe Bukit Tingg fault zone is considered to be active and is a potential source of future earthquakes. Outside \nBukit Tinggi area, the Benus and Karak faults are also deemed active. These fault zones show the following \nactive neotectonic geomorphic features: 1) displays geomorphic features indicative of recent fault activity; 2) \nshow evidence for displacement in young (Late Quaternary) deposits or surfaces; and/or 3) is associated with \na pattern of microearthquakes suggestive of an active faults. They were ancient faults that were reactivated in \nthe Quaternary period and continued into the present. The magnitude of paleoearthquake estimated from the \nactivity and stream offsets suggest a  minimum of 6 magnitude on the Richter scale have affected the region due \nto movements along these faults. Over the past decades, Peninsular Malaysia has experienced mild earthquakes. \nVirtually all earthquakes recorded in Peninsular Malaysia are under magnitude 5.0. However, the regognition of \nactive faults exhibiting active tectonic landforms suggestes that these faults have produced damaging earthquakes \nbefore and have potential to trigger similar tremors in the future.\n\n\n\nIntroduction\nDue to a lack of large, damaging earthquakes during historical time, \nPeninsular Malaysia  has not been considered to be a seismically active \ncountry.  However, it is still subjected to seismic hazards and not free from \nearthquake damage. However, there is no reliable, long-term earthquake \nrecord and an absence of historical fault surface ruptures. Therefore,  it \nis necessary to examine the geologic and geomorphic record, in order to \nquantify the activity on suspected active faults, and thereby determine \ntheir contribution to the seismic hazards of the country. In this paper, we \nsummarize the results of recent geomorphic investigations of active faults \nin Peninsular Malaysia with emphasize on Bukit Tinggi region using IFSAR \nwith field verification. \n\n\n\nThe evidences for active faulting, and the characteristics of these faults are \ndiscussed.  Several fault segments within the Bukit Tinggi fault zone are  \ndeemed active. The Bukit Tingg fault zone is considered to be active and is \na potential source of future earthquakes. The fault zone show the following \nactive neotectonic geomorphic features: 1) displays geomorphic features \nindicative of recent fault activity; 2) show evidence for displacement in \nyoung (Late Quaternary) deposits or surfaces; and/or 3) is associated with \na pattern of microearthquakes suggestive of an active faults.\nTectonic setting\n\n\n\nPeninsualr Malaysia (Figure 1) is situated on SUNDALAND, the southern \nprotrusion of the Eurasian Plate.  Sundaland is a region that comprised \nof the Malay Peninsula and Maritime Southeast Asia islands of Sumatra, \nJava, Borneo and surrounding smaller islands. It is situated at the core \nof Sundaland that has been considered a tectonically stable region since \nthe Cenozoic. Generally, it is considered as tectonically stable with low \nseismicity profile. It experienced dam induced seismicity of low magnitude \n(less than 4.5 on the Richter scale) at Kenyir Dam in 1985\nSeimic hazard\n-Tremors from far-field earthquakes:\nThe peninsula is bounded by two of the most seismically active plate\nboundaries. To the west the inter-plate boundary between the Indo-\nAustralian and Eurasian Plates defined by the Sunda Subduction Trench and \nthe inter-plate boundary between Eurasian and Philippine Plates to the east \nPhillipines Subduction Trench. It is  situated close to the most seismically\nactive plate boundaries between the Indian-Australian Plate and Eurasian\nPlate in the west and between Philippine Plate and Eurasian Plate in the\neast. It occassionally experienced tremors  due to earthquakes from these\nfar-field sources. \n-Tsunami:\n\n\n\nIn also did not escape the 2004 megathrust tsumani. The tsunami raised the \nalarm that the penisula is not free from seismic hazard.  \n\n\n\n-Local earthquakes:\nSince 2007 the peninsula is experincing ocassional earthquakes of local\norigin for example the 2007-09 Bukit Tinggi earthquakes. These events\nstirred awareness among the public and authorities on the potential seismic \nhazard and risk faced by the peninsula. \n\n\n\nThus, empirical evidence suggests that Malaysia is not totally free from \nseismic risks. Recent seismicity in Peninsular Malaysia  has been confined \nto low levels with no clear association with existing mapped faults. The \nidentification of  active faults is the subject of this study. \nYoung active neotectonic defromation\nThe identification of neotectonic deformation and active faults in Peninsular \nMalaysia has been hampered by: \n\n\n\n1) the comparative lack of fault studies; \n2) extensive weathering of bedrock and extremely active erosion acting\ntogether to prevent the preservation of all but the most resistant geomorphic \nfeatures; \n3) large areas of thick forest vegetation, and \n4) the probable low slip rates of the intraplate faults in Peninsular Malaysia. \n\n\n\nThese factors result in a lack of recognizable, long-lived surface faulting \ngeomorphic features. Despite these shortcomings, Quaternary deformation \ninvestigations in Peninsular Malaysia have been successful and indicate \nthat it is ongoing and that these activity may pose a seismic hazard (e.g., \nJMG, 2008 and 2012). Lacking a well-defined Quaternary framework for \nPeninsular Malaysia, recent studies (e.g., JMG, 2008 and 2012) concentrated \non the relationship of earhquake epicentres to geomorphic lineament  \nexpression of faulting, and the comparison with the features observed along \nother active faults in similar tectonic settings worldwide. \nIn Peninsular Malaysia Raj (1979) reported the presence of a Quaternary \nfault near Bentong Pahang. Tjia (2010) and Mustaffa Kamal Shuib (2011 & \n2012) summarized some evidences for Quaternary deformational activities \nonshore and offshore of Peninsular Malaysia. The evidences of active \ntectonic deformations include an Early Quaternary pillow-basalt flow near \nKuantan on the eastern shore of the Peninsula is traversed by long fractures \norientated parallel to faults in the pre-Tertiary basement. \n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\nISSN: 2521-0920  (Print) \nISSN:  2521-0602 (Online)\n\n\n\nhttps://doi.org/10.26480/mjg.01.2017.13.26\n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any \nmedium, provided the original work is properly cited. \n\n\n\n\n http://www.razipublishing.com/journals/galeri-warisan-sains-gws/ \n\n\nhttp://doi.org/10.26480/mjg.01.2017.13.26\n\n\nhttps://doi.org/10.26480/mjg.01.2017.13.26\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n14\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\nThe fractures in the basalt are essentially vertical and are evident \nmanifestations of reactivation of the older faults. In Southeast Johor at \nthe edge of the Penyu basin, crustal uplift of 0.5 - 0.8 m during the past \n5000 years is suggested by an abrasion platform that is that much higher \ncompared to the eustatic Holocene sea-level curve of the Peninsula which \nwas established from almost a hundred radiometrically determined bio-\nshoreline indicators. In the northwest on the shores of Langkawi, a 2500-\nyear old abrasion platform is cut by a long fault zone whose associated \nsecondary structures suggest sinistral displacement.\nActive major faults\nGenerally, major fault in the Malay Peninsula (Figure 1) appeared to be \ninactive. However, a series of large earthquakes in recent years had changed \nthe tectonic scenario in the Southeast Asian region, including the Peninsular \nMalaysia. In spite of its crustal stability, the 2004-Sumatera earthquake had \ncaused horizontal shifts of GPS monuments in the Peninsula in the order of \nup to 7 mm. There are also indications of co-seismic uplifts. In Langkawi, \nstacked doublets of (recently live specimens) barnacle-oyster bands suggest \nuplift in the order of half a meter by the same event. The Malay Basin region \noffshore the Peninsula is on stable crust, and yet seismic shows major, deep-\nreaching faults to approach the seabed to within 150 m, indicating activity \non these structures to have persisted into the Pleistocene. \n\n\n\nA series of seismic activities with local epicentres (Figure 1)  occurred in \nthe peninsular since 1978. From 1978 to 2006 Peninsular experirnced \nlocal earthquakes up to 4.6 Mw (IRIS Earthquake Database). From 2007 to \n2010 it experience earthqukes up to 4.2 Mw (MMD). The series of seismic \nactivities is believed as preliminary indications of the reactivation of major \nfaults in Peninsular Malaysia. Therefore, many believed that the reactivation \nof the faults system in Peninsular Malaysia was associated with the great \nSumatra-Andaman Earthquake (26 December 2004), Nias Earthquake (28 \nMarch 2005) and Bengkulu Earthquake (12 September 2007). Subsequently, \nlocal earthquakes that had occurred in Bukit Tinggi (between 30 November \n2007 to 25 May 2008), Jerantut (17 March 2009), Manjong (29 April 2009) \nand Kuala Pilah (29-30 November 2009) were associated with these events. \nRecently, the Southern Sumatra Earthquake that occurred on 30 September \n2009 had reactivated the Bukit Tinggi Fault system, and caused a series of \n7 weak local earthquakes around the Bukit Tinggi area (8 October and 4 \nDecember 2009). Another weak tremor of 2.6 magnitude was recorded on \n10 March 2010 at 11.10pm local time, at Tasik Kenyir area at latitude 5.1\u00b0N \nand longitude 102.8\u00b0E, 43km southwest of K.Terengganu, 22km west of \nKuala Berang.  \n\n\n\nA plot of the earthquake epicentres  on the regional fault maps of Peninsular \nMalaysia is shown in figure 1. The epicentres apparently seem to be diffusely \ndistributed throughout the Peninsular, typical of intraplate seismicity.  \nHowever in a closer look they seem to be close to and aligned along major \nfaults. These epicentres were aligned along the NNW bukit Tinggi fault zone, \nN-S Kenus and Karak faults (Bukit Tinggi epicentres), the NNW Lepar fault \n(Jerantur epicentre) , Bokbak fault zone (Baling epicentre), Mersing fault \nzone (mersing epicentre), Terengganu fault (Kenyir epicentre). These faults \nshow prominent lineaments but does not show any surface rupture related \nto the present earthquakes. They are believed to have been reactivated to \ngive rise to the local earthquakes\nIdentification of active faults in Bukit Tinggi area\n\n\n\n1) Earthquake epicentres and fault relationship\nIn the Bukit Tinggi region (Figure 2 and 3), the epicentres are diffusely\ndistributed arround Bukit Tinggi. Their distribution in relation to geology\nis shown in figure 4. They seem to align along NW-SE, NNE-SSW and N-S \ntrends. They  coincide with the major faults which are clearly seens as\nnegative lineaments on SRTM and IFSAR (Figures 2 -4) images. They were\nfound to coincide with the following lineaments: a) the Bukit Tinggi fault\nZone lineamentgs (NW-SE), b) WNW trend within the Bukit Tinggi fault\nzone, c) E-W, d) NE and e) N-S trends,\n\n\n\nThey are bounded by the Bukit Tinggi and Hulu Kelang fault zones. Several \nof the epicenters are aligned parallel to the orientation of the Bukit Tinggi \nfault zone, which is northwest, and other epicenters follow the northerly \nKarak fault trend and some northeasterly trend. On SRTM, these fault zones \nappear as through-going structures.  The Bukit Tinggi fault zone can be \ntraced for a distance of more than 100km between Kuala Kubu Bahru in \nthe northwest to Kuala Kelawang in the southeast. Bukit Tinggi fault zone \ntogether with the Hulu Kelang-Kongkoi fault zone (Shu, 1989) it forms a \nfault zone about 7km wide. The fault strikes 310O to 325O. To the southeast, \nthe Bukit Tinggi fault zone is deviated right-laterally by eastern strands of \nthe Karak fault zone and merged with the Kongkoi fault zone near Kuala \n\n\n\nLumpur. The Bukit Tinggi fault zone shows left lateral displacements of two \nkilometres. Many hotsprings are located along these fault zones and their \nextension (figure 2). This suggests that the faults are deep seated. The focal \nmechanisms of the largest 3.5mb event along the Bukit Tinggi fault is mostly \nstrike slip with some dip slip, while that of three events are strike slip fault \ntype (Siti Norbaizura 2011).\n\n\n\nThe distribution of the epicentres within the viccinity of Bukit Tinng New \nVillage on IFSAR is shown in Figure 5.The relationship the earthquake \nepicentres with the extracted lineaments is shown in Figure 6. Figure 7 \nshows the distance of epicenters from lineaments. The analysis noted that \nthe earthquake epicenters are found diffuse in the vicinity along:\na) the Bukit Tinggi fault Zone trend (NW)\nb) WNW trend along the Bukit Tinggi fault zone.\nc) E-W   and d) NE trends,\nsuggesting ongoing reacvtivation of these faults.This is consistent with the\nfindings of JMG (2012) which suggest that the relationship of the recent \nearthquake epicentres with known faults as \u201cestablished\n2) Geomorphic features of active faulting\nTo determine the active segments of the faults it is necessary to analyze the \ngeomorphology of the area. In this study, we applied digital enhanced IFSAR \nimages (figure 5) for data analysis. The digital enhanced IFSAR images study \nand interpretation is to assist in delineating small-scale neotectonic features \nand to define the orientation and direction of the investigated active fault \nsegments. Practically, automatic and visual interpretation was used. This\nis regarded as the prime and most effective approach for identification of \nneotectonic or active fault geomorphic features. \nThe following geomorphological features were picked up in the IFSAR\nanalysis and verified in the field:\n\n\n\na) Primary neotectonic features/ landforms\n\n\n\nSeveral morphotectonic pieces of evidence can be recognized from the \nIFSAR data. Among them, the most outstanding features are steep-sided \nbasins, triangular facets and steep scarps.\n\n\n\ni) Steep-sided Quaternary alluvial basins.\nIFSAR analysis reveals several pieces of morphotectonic features such\nas steep-sided Quaternary alluvial basins with steep and faceted scarps.\nFigure 8, 9 and 10 shows the steep-sided Quaternary alluvial basins as\nmapped based on IFSAR. In Bukit Tinggi the basin trend NW along the\nBukit Tinng fault zone. At Janda Baik area there are several narrow linear\nsub-parallel NE trending alluvial basins along the NE lineaments (Figure\n8). When superimposed on lineament and fault maps (Figure 8 - 10), it is\nnoted that the steep-sided basins are bounded by steep lineaments. Thus\nit is interpreted as fault scarps features. Within the basin the surface is cut\nby sub-parallel lineaments that define a terraced topography (figure 10). \nThese suggest that the alluvial basin is fault-controlled and have undergone \ninternal displacements suggesting the basin infillings were subjected to \ninternal deformation.\nFigure 11 shows a panoramic view of the alluvial plain bounded by steep\nscarps. Outcrops along the scarps reveal the presence of fine to coarse \ngrain alluvial deposits (figure 12). These deposits contain large boulders at \ntheir base and exhibit warped bedding (figure 12A). These implied that the \nalluvium have undergone neotectonic deformations.\n\n\n\nb) Secondary neotectonic features The drainage network derived \nfrom IFSAR DEM is shown in figure 13. It shows that the main drainage \npattern is flowing from NW to SE. The distributaries flowed from N-S and \nfrom W to E.  From the drainage pattern it is noted that there are several\nplaces where the streams form dog-leg pattern. From the pattern it was\nnoted that the dog-legs are due to stream off-sets, beheadments and shifting \nstreams (figure 13 & 14).\n\n\n\na) Stream off-sets (figure 13 & 14).) can be located at several \nplaces along the river at several localities. At these localities the stream\nhas been shifted for about 500m to form the dog-leg pattern. The offset\nline is generally extended on both sides along negative lineaments of about \n5 km long bounded triangular facets with hour glass geometry typical of\nactive faulting. One or two earthquake epicentres may aligned along the\nlineaments. These suggest young fault controlled on the stream offsets.\n\n\n\nb) Shifting streams (figure 13& 14). At one locality the major stream \nform dog-legged pattern due to stream offsets of about 600m. Along the\noffset region a small quaternary basin developed bonded by steep scarps. In \naddition to that it was found several parallel curvilinear abandoned\n\n\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n15\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\n streams (marked 1,2,and 3) arranged in an en-echelon manner marked \nthe different stages of the offset movements as shown in figure 13  occur \nin the basin. These abandoned stream terminated along the shift / stream \noffset line which coincide with an earthquake epicenter location. This \nfurther support the presence of young fault movements along the offset \nline shifting the streams. During the progressing movements the old \nstream alignment was abandoned and the river took on a new course \ncreating the shifting abandoned streams.\n\n\n\nc) Beheaded streams. Figure 13 & 14 shows several examples \nof beheaded streams. These beheaded streams are defined as several \ndistributary streams that were offset along an offset line and shifted into a \nsingle major stream. These are attributed to young fault movements along \nthe offset line.\nIdentification of active faults in Bentong- Karak area\nAs shown earlier, several of the epicenters are aligned parallel to the \norientation of the Bukit Tinggi fault zone, which is northwest, and other \nepicenters follow the northerly Benus and Karak fault zones trend. The \nBentong and Karak fault zones (figure 15) occurring in the study area are \nclearly shown on the Shuttle Radar Topography Mission (SRTM) image  \nas a 5km wide northerly trending negative lineaments. It is bounded on \nthe western side by a prominent N-S lineament passing through Taman \nBenus Jaya and to the east by another prominent N-S lineament passing \nthrough Karak. The two bounding faults bounds several less prominent \nN-S trending lineaments. The Karak Fault Zone trends 3500-3550-. In the \nnorthern part it pass and seem to terminate in Benmore tong-Karak area. It \nextension to the south passes through  Kajang-Sg. Long area. The zone has \na length of more than 55km with a maximum width to the south of about \n28km. The fault zone is a Right lateral (Tjia, 1972) fault. It is Intercept by \nBukit Tinggi, Hulu Kelang and Kuala Lumpur fault zones.\n\n\n\nNo local earthquake epicentres are located in the area but in Bukit Tinggi \narea there are epicentres that are aligned N-S along the trend of  the major \nfaults in this area. Young  active movements along these faults are shown \nby stream offsets as shown in figures 16 and 17. There are 2 fault traces \nthat show features of young movements with continuous length of 4.5 km \nand 10.7 km .Their outstanding features are very sharp bend (dog-leg) of \nthe stream and distinct offsets and clear scarp facets. These  offset streams \noccurring in this fault trace performs a sinistral (left-lateral)l offset of \nseveral hunded meter stream offsets\nDISCUSSION\nFrom this geomorphic study, we recognize both old and young faults. \nRemote-sensing information clearly indicates that the old faults are the \nmost prominent fault traces, as observed from field, and satellite images. \nThese faults trends in the NW-SE, NE-SW and N-S directions. The  NW-\nSE Bukit Tinggi Fault and the N-S Benus and Karak Faults mostly follows \nthe predated regional geological structures which developed prior to the \nCenozoic times. Others interesting lineament features, which are regarded \nas old faults, are also observed in the east-west and northeast-southwest \ndirections. \n\n\n\nWithin the Bukit Tinggi fault zone, IFSAR  revealed the presence of  \nQuaternary basins, and young geomorphic or active tectonic landforms \nsuch as stream offsets, beheaded streams, steep scarps and faceted spurs. \nThey formed short lineaments that coincided with earrthquake epicentres. \nThe are believed to be the surface menisfestation of active faults that \noccurred as short segments within the Bukit Tinggi fault zone. Most of \nthese active fault segments trend NW_SE parallel to the main fault zone. \nOthers trend WNW-ESE,and  NNE-SSW. From the sense of offsets they \nexhibit both dip \u2013 slip and strike\u2013slip movements with sinistral motions \nlikely due to the secondary effect of the active movements along the Bukit \nTinggi fault zone.\n\n\n\nAt Bentong and Karak area, the Benus and Karak faults also shows \nevidences of active faulting in the form of stream offsets. It is of particular \ninterest that these recently reactivated  faults produced active geomorphic \nfeatures which were likely related to paleoearthquakes suggesteing that \nthey are active faults. Based on the presence of active geomorphic features, \nthe magnitude of the paleoearhquake that occurred must be not less than \n6 on tghe Richter Scale.\n\n\n\nFurther study is in progress to determine the ages of these paleoearthquake \noccurence to determine their magnitude, slip rates and reccurrence \nintervals for seismic hazard and risk analysis.\n\n\n\nCONCLUSIONS\nIn this research, It is concluded that there are several likely active faults in \nPeninsular Malaysia based on earthquake epicentres distribution. Present \ngeomorphic study from satellite images  and earthquake evidences \nclearly depicts that within the NW trending Bukit Tinggi fault zone, there \nare severa strands of both oblique and parallel active fault segments \nsuggesting that the main Bukit Tinggi fault is an active fault. Offsets \nstreams also suggest active faulting along the Benus and Karak faults.\n\n\n\nThey were ancient faults that were reactivated in the Quaternary period \nand continued into the present. The magnitude of paleoearthquake \nestimated from the activity and stream offsets suggest a  minimum of 6 \nmagnitude on the Richter scale have affected the region due to movements \nalong these faults.\nOver the past decades, Peninsular Malaysia has experienced mild \nearthquakes. Virtually all earthquakes recorded in Peninsular Malaysia \nare under magnitude 5.0. However, the regognition of active faults \nexhibiting active tectonic landforms suggestes that these faults have \nproduced damaging earthquakes before and have potential to trigger \nsimilar tremors in the future.\n\n\n\nReferences\n1. Ben-Avraham, Z., Emery, K.O., 1973. Structural framework of \nSunda Shelf. American Association of Petroleum Geologists Bulletin 57, \n2323\u20132366.\n2. Che Noorliza Lat, 1997a. Seismicity of Kenyir, Terengganu. \nAbstract. Warta Geologi. 23(3), 184-185. \n3. Che Noorliza Lat, 1997b. Dam-induced seismicity of Kenyir \nTerengganu. Abstract. Warta Geologi. 23(2), 68. \n4. Che Noorliza Lat, 2002.  Reservoir Induced Seismicity (RIS): \nA Case Study from Kenyir Terengganu, Geophysical Contributions in the \nEnvironmental Studies and Conservation Seminar Proceedings (on CD), \nGeological Society Malaysia. \n5. Gobbett, D.J. and Hutchison, C.S., 1973. Geology of the Malay \nPeninsula (West Malaysia and Singapore), Wiley-Interscience, New York \n438 pp .\n6. Mustaffa Kamal Shuib, 2008. A preliminary interpretation of \nthe recent Bukit Tinggi earthquakes using SRTM DEM. Warta Geologi, \n34(1), 5\u20137\n7. Mustaffa Kamal Shuib (2009). The recent Bukit Tinggi \nearthquakes and its relationship to major structures . Geological Society \nof Malaysia, Bulletin 55, July 2009, pp. 1 \u2013 6\n8. Mustaffa Kamal Shuib. 2011. Evidences for recent seismicities \nand dating of active faulting in NW Peninsular Malaysia, NATIONAL \nGEOSCIENCE CONFERENCE 2011, 11 June 2011 to 12 June 2011, \nGeological Society of Malaysia.\n9. Mustaffa Kamal Shuib. 2012. Paleoearthquakes and active \nfaulting activities in Peninsular Malaysia. Seminar Teknikal Kebangsaan \nGempabumi dan tsunami, Abstrak. Petaling Jaya 13-14 Disember 2012, \nJabatan Metereologi Malaysia, www.met.gov.my/images/pdf/national.../\ndr_mustaffa_abstract.pdf\n10. Raj, J.K. (1979): A Quaternary fault in Peninsular Malaysia. \nNewsletter Geol. Soc. Malaysia, v.5, 3-5. \n11. Siti Norbaizura Bt Mat Said (2011) Focal mechanism \ndeterminations of local earhquakes in Peninsular Malaysia. EARTHQUAKE \nTECHNICAL SEMINAR. DECEMBER 20.TH\u2013 21ST, 2011. Metrological \ndepartment of Malaysia.\n12. Tjia, H.D., 1996. Sea-level changes in the tectonically stable \nMalay\u2013Thai peninsula. Quaternary International 31, 95\u2013101. \n13. Tjia, H.D., 2010. Growing evidences of active deformation in \nthe Malay Basin region. Bulletin of the Geological Society of Malaysia 56 \n(2010) 35 \u2013 40, doi: 10.7186/bgsm2010005 http://geology.um.edu.my/\ngsmpublic/BGSM/bgsm56/bgsm2010005.pdf\n\n\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n16\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n17\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n18\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n19\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n20\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n21\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n22\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n23\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n24\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n25\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\n\n\n\n\n\nMustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul, Ismail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che \nAbas, Roziah Che Musa, Zahid Ahmad / Malaysian Journal of Geosciences 1(1) (2017) 12\u201324\n\n\n\n26\n\n\n\nCite this article as: Active Faults In Peninsular Malaysia With Emphasis On Active Geomorphic Features Of Bukit Tinggi Region Mustaffa Kamal Shuib, Mohammad Abdul Manap, Felix Tongkul,  \nIsmail Bin Abd Rahim, Tajul Anuar Jamaludin, Noraini Surip5, Rabieahtul Abu Bakar,  Mohd Rozaidi Che Abas, Roziah Che Musa, Zahid Ahmad/ Mal. J. Geo 1(1) (2017) 12-24\n\n\n\n\n\n\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 01-07 \n\n\n\n\n\n\n\n\n\n\n\nQuick Response Code Access this article online \n\n\n\n\n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.01.2022.01.07 \n\n\n\n \nCite The Article: Matthew Coffie Wilson, Joseph Larbi, Isabella Ivy Kangah, Enock Anison (2022). Petro-Mechanical Studies of The Stratigraphic Units of The Tarkwaian \n\n\n\nSupergroup in Tarkwa \u2013 Implications For The Structural and Mechanical Competence of Rocks. Malaysian Journal of Geosciences, 6(1): 01-07. \n\n\n\n \nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n \n \nREVIEW ARTICLE \n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) \n  \n\n\n\nDOI: http://doi.org/10.26480/mjg.01.2022.01.07 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPETRO-MECHANICAL STUDIES OF THE STRATIGRAPHIC UNITS OF THE \nTARKWAIAN SUPERGROUP IN TARKWA \u2013 IMPLICATIONS FOR THE STRUCTURAL \nAND MECHANICAL COMPETENCE OF ROCKS  \n \nMatthew Coffie Wilsona,* Joseph Larbib, Isabella Ivy Kangahb, Enock Anisonb \n \na Kwame Nkrumah University of Science and Technology, Department of Geological Engineering, University Post Office, Private Mail Bag, \nKumasi \u2013 Ghana. \nb National Service Personnel, Kwame Nkrumah University of Science and Technology, Department of Geological Engineering, University Post \nOffice, Private Mail Bag, Kumasi \u2013 Ghana. \n*Corresponding Author Email: regimatt2003@yahoo.co.uk; mcwilson.coe@knust.edu.gh \n \nThis is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and \nreproduction in any medium, provided the original work is properly cited. \n \n\n\n\nARTICLE DETAILS  ABSTRACT  \n\n\n\nArticle History: \n \nReceived 07 November 2021 \nAccepted 10 November 2021 \nAvailable online 21 December 2021 \n\n\n\n\n\n\n\nMain purpose of this paper is to determine the mineralogical composition and mechanical properties of the \nTarkwaian Supergroup and compare them. This project will help determine whether the rock units of the \nTarkwaian Supergroup are competent based on their mineral contents and strength of the rocks to be \nquarried and used as aggregates for construction and also determine the structural features that were formed \nin the various rock units due to the deformation that took place. Values obtained from the Unconfined \nCompressive Strength (UCS) test were (21.50-77.10) N/mm2 with the Kawere conglomerate having the \nlowest, and Tarkwa Phyllite having the highest strength. The Huni sandstone, Banket Quartzite and Kawere \nconglomerate were classified as weak rocks, but the Tarkwa Phyllite was classified as a medium rock, \naccording to Attewell and Farmer\u2019s (1976) classification. The Huni sandstone, Banket Quartzite and Kawere \nconglomerate are made up of grains of varying degrees of roundness and maturity. The Huni sandstone, \nBanket Quartzite, and Tarkwa Phyllite exhibit foliation. Micro-folding is also observed in the Kawere \nconglomerate. Petrographic studies of the rocks classify them as metasedimentary rocks weathering. Based \non the weak strengths of the various rock units, they are not suitable to be used as aggregates for construction \nbut can be used for other purposes such as construction sand and dimension stones. \n\n\n\nKEYWORDS \n\n\n\nmechanical strength, thin section, foliation, deformation, petrology \n\n\n\n1. INTRODUCTION \n\n\n\nThe western unit of Ghana is made of the Birimian and Tarkwaian systems, \nand associated syn- and post-Birimian granitoid intrusions. The Birimian \nis made of Proterozoic volcanics and meta-sediments. The Tarkwaian \nsystem consist of middle Proterozoic molasses type (shallow) water \nsediments. The Tarkwaian Supergroup was formed as a result of the \ndeformation of the Birimian Supergroup (Block et al., 2016). The \ndeformation resulted in series of folding of the Birimian rocks. The crest \nof the folds was eroded into the synclinal zones. There was constriction in \nthe synclinal zones and dilation in the anticlinal zones, which generated \ncracks along the limbs of the synclines. The fractured zones, served as \npathways for magma eruption in the synclinal zones to form the volcanic \nbelts and the anticlinal zones formed the basin in the Tarkwaian \nSupergroup (Eisenlohr and Hirdes, 1992). \n\n\n\nThe Huni sandstone is the topmost and youngest rock unit (Figure 1) of \nthe Tarkwaian super group (Junner et al., 1942). Huni sandstones are grey \nin color. The Huni sandstones show slight foliations combined with high \ninduration and dominate with ripple marks features (Brako et al., 2020). \nThe second stratigraphic unit of the Tarkwaian super group is the Tarkwa \nphyllite (Figure 1). The phyllite range in thickness between 120 and 400m \n(Junner et al., 1942). The Banket series is the third stratigraphic unit of the \n\n\n\nTarkwaian super group (Figure 1). The Banket series is made of reefs that \nhold gold mineralization (Junner et al.,1942). Kawere group is the basal \nand oldest unit of the Tarkwaian super group (Figure 1) consisting of \nfeldsparthic, carbonate-spotted quartzites, grit, breccias and \nconglomerates (Kesse, 1985). Petrography is the detailed study of rocks; \nthe description in the field, macroscopic and microscopic analysis of \nminerals under a petrographic microscope. A petrographic microscope is \na specific type of microscope used to look at geological materials, including \nthin sections, smear slides, and mineral mounts (Johnson and Liu, 2020).  \n\n\n\nSeveral studies have been done on the Tarkwaian Supergroup of which \ninclude that (Zorlu et al., 2007; Brako et al., 2020). The works of some \nresearchers only prepared thin sections perpendicular to the obvious \nbedding planes of the Ankara Sandstones of South Africa (Zorlu et al., \n2007). That is, the thin sections of the Ankara Sandstones were not \nprepared in both horizontal and vertical directions. Also, study was \nperformed to compare two (2) rock units of the same geologic province \nbut in different localities (Brako et al., 2020). No relation was made to \npetrographic analysis of all the four stratigraphic units of the Tarkwaian \nSupergroup. Also, no mechanical analysis or general prediction model has \nbeen proposed on the Stratigraphic units of the Tarkwaian Supergroup. In \nour study, different stratigraphic units but the same formation of the \nTarkwaian Supergroup will be compared. Also, the work of Brako et al., \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 01-07 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Joseph Larbi, Isabella Ivy Kangah, Enock Anison (2022). Petro-Mechanical Studies of The Stratigraphic Units of The Tarkwaian \n\n\n\nSupergroup in Tarkwa \u2013 Implications For The Structural and Mechanical Competence of Rocks. Malaysian Journal of Geosciences, 6(1): 01-07. \n\n\n\n\n\n\n\n2020, did not consider the mechanical properties of all the rocks of the \nstratigraphic units of the Tarkwaian Supergroup. \n\n\n\n \nFigure 1: Geological map of the stratigraphic units of the Tarkwaian \n\n\n\nSupergroup (Junner et al., 1942). \n\n\n\n2. GEOLOGIC SETTING \n\n\n\nAccording to the Man Shield of the West African Craton (WAC) comprises \na western domain consisting essentially of Archean rocks of Liberian age \n(3.0-2.5 Ga) and an eastern domain consisting of Birimian rocks of Early \nProterozoic age (2.4-2.0 Ga) which have been folded, metamorphosed, and \nintruded by granitoids during the Eburnean event at about 2.0-1.8 Ga \n(Bonhomme, 1962). This orogeny is characterized by isoclinal folding, and \nintrusion of pre-, syn-, and post-tectonic granites (Eisenlohr and Hirdes, \n1992). The evolution of the orogenic system can be divided into an early \naccretionary phase that transitioned to the regional and diachronous \nEburnean collisional orogeny around 2.16-2.15 Ga, and which continued \nuntil 2.05-1.98 Ga (Feybesse et al., 2006a; Baratoux et al., 2011; Perrouty \net al., 2012; Block et al., 2016a). \n\n\n\nThe majority of Ghana is underlain by metamorphosed Paleoproterozoic \n(2300-1900 Ma) rocks of the volcano sedimentary Birimian Supergroup \nand the overlying clastic sedimentary Tarkwaian Supergroup that make \nup the Man Shield of the WAC (Oberth\u00fcr et al., 1998; Griffis et al., 2002; \nGriffis et al., 2002). All the Birimian and Tarkwaian units in Ghana have \nbeen metamorphosed to lower greenschist facies (Griffis et al., 2002). \nHowever, the preserved greenschist facies metamorphism could be a \nretrograde assemblage that was preceded by peak amphibolite facies \nmetamorphism (John et al., 1999; Yao and Robb, 2000). \n\n\n\nThe Paleoproterozoic activity is divided into two main phases, namely, the \nEoeburnean (ca. 2266 and 2150 Ma) and the Eburnean (ca. 2130 and 1980 \nMa) phases (Baratoux et al., 2011; 2015; Block et al., 2015; de Kock et al., \n2012; Feybesse et al., 2006a; Hein, 2010; Perrouty et al., 2012; Vidal et al., \n1996). The Eoeburnean phase is dominated by mafic and felsic volcanism, \ngranitic emplacement and folding as a result of a collisional event and \ncrustal thickening while the Eburnean phase is dominated by plutonic \nactivities (Baratoux et al., 2015; Lambert-Smith et al., 2016). Ghana falls in \nthe eastern part of the Man Shield where the geological terrains is \nidentified as a Paleoproterozoic (Birimian/Tarkwaian) terrain which \nhosts the known gold deposits occupying the west and northern parts of \nthe country (Hirdes et al., 1992). \n\n\n\n3. SAMPLING AND METHODOLOGY \n\n\n\nRocks of the four stratigraphic units were collected from four different \nlocations within the Tarkwa Municipality. A rock type was sampled from \neach unit; quartzite from the Banket, phyllite from the Tarkwa phyllite, \nsandstone from the Huni-sandstone and conglomerate from the Kawere \nConglomerate. The Banket series was sampled at the University of Mines \nBasic School (N 50 18' 1.36188'', W 10 59' 46.12812''), the Tarkwa Phyllite \nfrom the University of Mines and Technology water falls (N 50 18' 5.796'', \nW 20 0' 10.10412''), the Huni Sandstone from Tamso, and the Kawere \nconglomerate from Bonsa. Samples were collected using a geological \n\n\n\nhammer. Also, attitudes of rock units were taken using a Brunton \ngeological compass before sampling. \n\n\n\n3.1 Petrological Description \n\n\n\n3.1.1 Macroscopic Description of Samples \n\n\n\nSamples were described and the minerals identified macroscopically with \nthe aid of a hand lens.  \n\n\n\n3.1.2 Preparation of Thin Sections \n\n\n\nFor each stratigraphic unit, a total of four thin sections were prepared. \nThin sections were prepared to a thickness of 30\ud835\udfb5m. Below is the \nprocedure for how thin sections were prepared. \n\n\n\n(i) Samples to be analysed were selected;  \n\n\n\n(ii) Samples were washed with water to clear them of all sand particles; \n\n\n\n(iii) Samples were cut into small specimen blocks using the cutting, \ntrimming and slabbing machine;  \n\n\n\n(iv) Lower side of specimen block were grinded using appropriate \nabrasives (emery papers used were P60, P120, P240, P400, P600 and \nP1200);  \n\n\n\n(v) Bonding of specimen to glass slide using a compound of epoxy and a \nhardener. The epoxy and hardener were mixed in the ratio of 15:2 in ml;\n  \n\n\n\n(vi) Levelling and grinding of the rock specimen bonded to the glass slide \nusing the cutting and polishing machine. \n\n\n\n(vii) Polishing of specimen using the polishing machine. \n\n\n\n \nFigure 2: Small sample blocks that were cut to be used for thin sections \n\n\n\nat the laboratory. \n\n\n\n3.2 Laboratory Testing of Rock Samples using Unconfined \nCompressive Strength Testing  \n\n\n\nThis test was aimed at obtaining the maximum axial compressive stress \nthe rock sample can bear under zero confining stresses. The Huni \nsandstone, Tarkwa Phyllite, and Kawere conglomerate were tested using \nthe UCS test. The test was carried out in the Civil Engineering Laboratory \n(KNUST). The samples used were cylindrical rock cores obtained from the \nrocks under study. The cylindrical cores were obtained using the rock \ncoring machine. The core length to diameter ratio of each sample was \nensured to be 2.5-3.0 inches in accordance with the International Society \nof Rock Mechanics. The cylindrical surfaces of each core were prepared in \norder to be flat and smooth using the saw machine. The rock cores were \nplaced between the two platens (one at the top and the other at the \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 01-07 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Joseph Larbi, Isabella Ivy Kangah, Enock Anison (2022). Petro-Mechanical Studies of The Stratigraphic Units of The Tarkwaian \n\n\n\nSupergroup in Tarkwa \u2013 Implications For The Structural and Mechanical Competence of Rocks. Malaysian Journal of Geosciences, 6(1): 01-07. \n\n\n\n\n\n\n\nbottom). A load was continuously applied at a rate of 1.0Mpa/s. The stress \nand deformation was continuously recorded till failure occurred in the \nform of a crack. Different formulae were used to calculate for the different \nstrengths such as: axial strain, diametric strain, compressive strain, \nuniaxial compressive strength, modulus of elasticity, Poisson\u2019s ratio, etc. \n\n\n\n3.2.1 Schmidt Hammer Test \n\n\n\nOwing to the inability to perform the UCS test on the Banket quartzite \nsamples, the compressive strength of the rock was determined using the \nSchmidt or Rebound Hammer test. The Rebound Hammer measures the \nelastic properties or strength of rocks or concretes, mainly their surface \nhardness and penetration resistance. The test was carried out at the \nGeological Engineering laboratory, KNUST. \n\n\n\n3.2.2 Procedure \n\n\n\nFor testing, smooth, clean and dry surface sample was selected. The point \n\n\n\nof impact was set at least 20 mm away from any edge or shape \ndiscontinuity. For taking a measurement, the rebound hammer was held \nat right angles to the surface of the concrete member. The test was thus \nconducted horizontally on vertical surfaces or vertically upwards or \ndownwards on horizontal surfaces. The Rebound hammer test was \nconducted around all the points of observation on all accessible faces of \nthe structural element. Concrete surfaces were thoroughly cleaned before \ntaking any measurement. Around each point of observation, six readings \nof rebound indices were taken and average of these readings after deleting \noutliers as per IS: 8900-1978 became the rebound index for the point of \nobservation. The rebound value was read off along the graduated scale on \nthe rebound hammer (IS-13311 (Part 2):1992). \n\n\n\n4. RESULTS \n\n\n\n4.1 Petrological Results \n\n\n\n4.1.1 Megascopic Description \n\n\n\nTable 1: Megascopic description of the stratigraphic units of the Tarkwaian Supergroup \nSAMPLE ID MINERAL \n\n\n\nCOMPOSITION \nDESCRIPTION COLOUR ROCK TYPE OTHER NOTES \n\n\n\nKVI,  \nKV2,  \nKH1,  \nKH2 \n\n\n\nQuartz, feldspar, \nmuscovite \n\n\n\nSedimentary rock with \nrounded pebbles in it. \nPresence of exfoliation. \nThe rock is fractured. \n\n\n\nDark grey Kawere \nconglomerate \n\n\n\nPebble size: longest length= 5.52cm \nshortest length= 2.552cm. \nQuartz pebbles imbedded in fine \nmatrix. The average size of the \nKawere pebbles confirms that they \nare conglomerates. The long and \nshort length of the oval pebble nature \ndepicts the loading on top of the \npebbles as well \n\n\n\nBH1,  \nBH2,  \nBV1,  \nBV2 \n\n\n\nQuartz, feldspar, clay, \nmuscovite \n\n\n\nFoliation, sharp edged \nwhen it breaks this \nmeans the rock is brittle, \nslightly weathered at the \ntop at a length of 1.5cm \nwith color changing to \nreddish brown, fresh at \nthe rest of the base. \n\n\n\nReddish this \nbrown to grey \n\n\n\nBanket series \n(Quartzite) \n\n\n\nFormed by weathering of Igneous \nrocks. Diagenesis of this rock forms a \nsandstone. The sandstone undergoes \nfurther heating and compaction to \nform the quartzite.  \n\n\n\nTPV1, \nTPV2,  \nTPH1,  \nTPH2 \n\n\n\nNo mineral was observed \nbecause the rock was \nvery fine. \n\n\n\nPlaty, presence of \nfoliation, thinly bedded \nwith average thickness of \nl0.52cm, Presence of \ncurved edges \n\n\n\nGrey, brown, and \nreddish brown \nwhen it weathers. \nThe weathering \nwas observed at \nthe bedding planes \nforming a foliation \nplane. \n\n\n\nTarkwa \nphyllite \n\n\n\nRock unit is slightly  \nJointed. The joints increase surface \narea for weathering to occur.  \n\n\n\nHSV1, \nHSV2, \nHSH1, \nHSH2, \n\n\n\nQuartz,  \nBiotite \nMuscovite \n \n \n\n\n\nMedium to coarse  \ngrained.  \nModerately to highly \nweathered.  \n\n\n\nGrey  Huni- \nsandstone \n\n\n\nRounded quartz grains dominating \nrock unit.  \n\n\n\n(a)\n\n\n\n\n\n\n\n(b)\n\n\n\n \n(c)\n\n\n\n\n\n\n\n(d)\n\n\n\n \nFigure 3: Megascopic observation of: (A) Huni sandstone sampled at \n\n\n\nTamso-Tarkwa behind GOIL filling station; (B) Tarkwa phyllite at \nUniversity of Mines and Technology Waterfalls, Tarkwa; (C) Banket \nquartzite sampled at University Basic School, Tarkwa in Ghana; (D) \n\n\n\nKawere conglomerate at Bonsa, Western Region in Ghana \n\n\n\n4.1.2 Microscopic Description \n\n\n\nA splitting force (F1) is exhibited from Figure 4A within the minerals \npresent at the middle. Grey lines are visible within the minerals at the \nmiddle of the photomicrograph. These exhibit ripple marks which can be \nseen under the reflective microscope. This gives an indication that the \nHuni-sandstone was deposited by water. A glassy mineral is observed to \nbe overgrowing on the porphyroblastic mineral (Figure 4B). Some part of \nthe porphyroblastic mineral is observed to be splitting from the large \nmineral. There is also a micro-fault like structure before the splitting of the \nlarge mineral (Figure 4B). The staurolite mineral observed is a \nmetamorphic mineral which shows that the rock has undergone \nmetamorphism. The presence of quartz grain undergoing deformation to \nform a dark brown mineral (Figures 4 C and D). The quartz minerals are \nfractured and dissolves at the boundaries. The entire rock is also fractured, \nand the fractures are filled with biotite minerals and other dark minerals. \n\n\n\n \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 01-07 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Joseph Larbi, Isabella Ivy Kangah, Enock Anison (2022). Petro-Mechanical Studies of The Stratigraphic Units of The Tarkwaian \n\n\n\nSupergroup in Tarkwa \u2013 Implications For The Structural and Mechanical Competence of Rocks. Malaysian Journal of Geosciences, 6(1): 01-07. \n\n\n\n\n\n\n\n\n\n\n\n \nFigure 4: Photomicrographs of Huni sandstone displaying some \n\n\n\ngeological structures and phenocrysts of quartz (Qz), plagioclase (Pl), \nalkali(Kfs), biotite (Bt), staurolite (St), and some opaque minerals (Opq) \nprepared in both: horizontal direction (A) and (B); and vertical direction \n\n\n\n(C) and (D) \n\n\n\nThe foliation plane is filled with micaceous minerals (Biotite and Iron \nChlorite) that serve as planes of weakness. The Phyllite is foliated with \nminerals aligned in the horizontal direction (Figure 5). The Tarkwa \nPhyllite has undergone some form of deformation. It is observed that S1 \nwas the first foliation plane formed, followed by S2 as an inclined bright \ncolored line across the slide. S1 is a foliation plane dominated by biotite \nand is parallel to the bedding plane of the rock. S2 cross cuts S1 at the top \nright (Figure 5A). Another foliation line (S3) is also seen to cross cut the \nbiotite dominated foliation (Figure 5B). Therefore, S3 is the youngest \nfoliation. The various foliation planes indicate that certain forces acted on \nthe rock during metamorphism. Figure 5 shows massive growths of dark \nminerals being formed as a result of metamorphism. The packing density \nof the minerals are seen to be tightly packed with fine small grains. In a \nhigher magnification (x40 and x60), the minerals were observed to be \nfused into each other, hence their individual boundaries were dissolved. \n\n\n\n\n\n\n\nFigure 5: Photomicrographs of phyllite prepared in vertical direction \nshowing foliations (S1, S2, S3) and deformation (D1) geological \n\n\n\nstructures observed at cross polarization light (XPL) \n\n\n\n\n\n\n\nFigure 6: Photomicrographs of Banket quartzite displaying some \ngeological structures and phenocrysts of quartz (Qz), plagioclase (Pl) \n\n\n\nalkali feldspar (Kfs), biotite (Bt), muscovite (Ms) and some opaque \nminerals (Opq) prepared in the vertical direction \n\n\n\nFrom Figure 6, the Banket quartzite is observed to be deformed by \nshearing. It has a \ud835\udf0e-type of shearing deformation. The \ud835\udf0e-type deformation \nrepresents an inhomogeneous flow in the edge of a lens shaped aggregate. \nThere are lenses throughout the rock. The shear forces are parallel to the \nbedding plane or plane of deposition. This is probably why the lenses are \nobserved in the vertical direction. The foliation plane that forms the lenses \nhave a domain filled with brown and dark minerals, and microlithons of \nfractured quartz. Outgrowths of dark minerals are seen within the \nmicrolithons but could not be identified by the use of the reflective \nmicroscope. Figure 6B shows a \u0444-type deformation at the top domain of \nthe thin section. The \u0444-type deformation implies that a shear and \nstretching force acted on the Banket quartzite and is observed in the \nvertical direction. Some form of fracturing is also observed throughout the \nrock in both the vertical and horizontal directions. \n\n\n\n\n\n\n\n\n\n\n\nFigure 7: Photomicrographs of Kawere conglomerate displaying some \ngeological structures and phenocrysts of quartz (Qz), plagioclase (Pl), \n\n\n\nalkali feldspar (Kfs), biotite (Bt), muscovite (Ms) and some opaque \nminerals (Opq) prepared in the vertical direction (A, C, D) and horizontal \n\n\n\ndirection (B). S1 = first foliation; D1 = first deformation; D2 = second \ndeformation \n\n\n\nFigure 7 shows the presence of a quartz stringer (D2 intrusion). The quartz \nstringer (D2) cut through the main rock and the D1 (main intrusion). The \nintrusion D1 first intruded the rock and served as a cementing material. \nFigure 7A also shows the formation of an \u2018M shape\u2019 fold microstructure as \na continuation of D2. This is evidence of compressional forces that acted on \nthe rock and also shows a ductile flow. A contact between the intrusion \nand the rock pebbles are observed in Figure 7D. Figure 7C, reveals \nmigmatitic folding which is formed as a result of intense heat and pressure \nduring metamorphism. This type of folding usual occurs along shear zones. \nThe fold (Figure 7C) cuts through the sets of quartz stringers. The Quartz \nstringers are also observed in Figure 7B and a foliation plane S1 that cuts \nthe quartz veins. \n\n\n\n4.2 Mechanical Results \n\n\n\n \nFigure 8: Uniaxial Compressive Strength results for Huni-sandstone \n\n\n\n \nFigure 9: Uniaxial Compressive Strength results for Huni-sandstone \n\n\n\n(EJI011) \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 01-07 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Joseph Larbi, Isabella Ivy Kangah, Enock Anison (2022). Petro-Mechanical Studies of The Stratigraphic Units of The Tarkwaian \n\n\n\nSupergroup in Tarkwa \u2013 Implications For The Structural and Mechanical Competence of Rocks. Malaysian Journal of Geosciences, 6(1): 01-07. \n\n\n\n\n\n\n\n \nFigure 10: Uniaxial Compressive Strength results for Tarkwa Phyllite \n\n\n\n \nFigure 11: Uniaxial Compressive Strength results for Tarkwa Phyllite \n\n\n\n(EJI022) \n\n\n\n \nFigure 12: Uniaxial Compressive Strength results for Kawere \n\n\n\nConglomerate \n\n\n\nFigure 13 shows common failure modes observed in intact rocks of the \nTarkwaian Supergroup after UCS testing. Sample A (Kawere \nconglomerate) exhibited axial splitting, samples B and C (Huni sandstone) \nexhibited shear failure and sample D (Tarkwa phyllite) exhibited multiple \nshear failure. Figures 8, 9, 10, 11 and 12 show the stress \u2013 strain plots for \nthe various intact rocks that were cored for the uniaxial compressive \nstrength test. Stress-strain plot represents the intact samples increasing \nforces. The dip represents the yield strength which is an indication that \nthe material has deformed and it represents permanent deformation. The \npart of the graph after the dip is known as the ultimate strength (Attewell \nand Farmer, 1976). This represents the stress the intact cores can \nwithstand while being stretched or pulled during ductile deformation. The \nend of the curve represents the fracture point which indicates when the \nrock deforms completely. The graphs of figures 8 and 12 exhibited a \ncombination of brittle and ductile behavior, whereas figures 9 and 10 only \nexhibited brittle behaviour. For the Banket quartzite, no deformational \nbehavior was observed because a Schmidt hammer was used to determine \nits UCS. \n\n\n\nTable 2: Data of the UCS results of the Huni sandstone, Tarkwa \nphyllite, Kawere conglomerate and Schmidt-Hammer Test of the \n\n\n\nquartzite of the Banket series \nD \n(mm) \n\n\n\nLength (mm) Area \n(mm2) \n\n\n\n Rock Type \n\n\n\n45 \nS. No \nEJI001 \nEJI011 \nEJI002 \nEJI022 \nEJI004 \n\n\n\n90 \nUCS (MPa) \n(N/mm2) \n36.50 \n20.65 \n62.30 \n77.10 \n21.50 \n\n\n\n1590.43 \nE \n(kN/mm2) \n0.42 \n0.11 \n0.06 \n0.32 \n0.19 \n\n\n\n \nMax \n(kN) \n47.01 \n32.77 \n99.11 \n122.67 \n34.26 \n\n\n\nHuni \nsandstone \nHuni \nsandstone \nTarkwa \nphyllite \nTarkwa \nphyllite \nKawere \nconglomerate \n\n\n\n \nS. No \nBS \n\n\n\n(SCHMIDT-HAMMER TEST) \n(H)REB \n26 \n31 \n30 \n369 \n20 \n36 \nMean \n\n\n\nUCS \n(N/mm2) \n14.00 \n36.00 \n26.00 \n36.00 \n10.00 \n36.00 \n26.33 \n\n\n\nMPa \n14.00 \n36.00 \n26.00 \n36.00 \n10.00 \n36.00 \n26.33 \n\n\n\nRock Type \nquartzite \n \n\n\n\n\n\n\n\n \nFigure 13: Intact rock samples after UCS test. A = Kawere conglomerate, \n\n\n\nB and C = Huni sandstone and D = Tarkwa phyllite. \n\n\n\n5. DISCUSSIONS AND CONCLUSION \n\n\n\nFrom the study, rock samples from the four stratigraphic units of the \nTarkwaian Supergroup were tested for their Unconfined Compressive \nStrength (UCS) and Schmidt Hammer tests (Table 2). Results from \nmechanical strength tests showed that, the Kawere conglomerate have the \nlowest strength whereas the Tarkwa phyllite have the highest strength. \nThe strength of the various rock formations is discussed below starting \nwith the rock unit that has the least strength.  \n\n\n\nKawere conglomerate having the lowest strength among the Tarkwaian \nsuper group had a UCS of 21.50N/mm2. The Kawere conglomerate consists \nof large, rounded pebbles that are cemented together by a siliceous \nmaterial. The Kawere conglomerate had a weak bond between the pebbles \nand the cementing material because of the shape of the pebbles. The \nKawere conglomerate failed by splitting.  \n\n\n\nThe Banket rock unit was tested for its UCS using the Schmidt hammer test \nwhich gave an average UCS OF 26.33N/mm2. Various factors include the \ntype of cementing material binding the mineral grains of the rock. The \nmineral grains of the Banket series are cemented together by a brown clay \nmineral as revealed by thin section analysis of the rock. Also, the Banket \nrock was marked with foliations that serve to reduce the strength of the \nrock. No failure mode was observed for the Banket rock because its UCS \nwas determined using the Schmidt hammer test.  \n\n\n\nHuni sandstone being the rock unit stronger than the Banket rock had a \nUCS of 20.65N/mm2. Petrographic analysis of the Huni sandstone revealed \nthat it contained less clay minerals as compared with the Banket rock. \nAlso, minerals of the Huni sandstone were dominated by quartz and \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 01-07 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Joseph Larbi, Isabella Ivy Kangah, Enock Anison (2022). Petro-Mechanical Studies of The Stratigraphic Units of The Tarkwaian \n\n\n\nSupergroup in Tarkwa \u2013 Implications For The Structural and Mechanical Competence of Rocks. Malaysian Journal of Geosciences, 6(1): 01-07. \n\n\n\n\n\n\n\nfeldspars. Quartz is one mineral that has a high hardness, and it is resistant \nto deformation and weathering. The Huni sandstone exhibited plane \nfailure when it was tested for its UCS.  \n\n\n\nTarkwa phyllite which is the second rock unit of the Tarkwaian Super \ngroup had the highest UCS, being 69.70N/mm2. The thin section analysis \nof the Tarkwa phyllite showed that the rock unit is made up of very fine-\ngrained minerals that are closely arranged into a tighter state of packing. \nThe Tarkwa phyllite is also made up of bedding planes that lie parallel to \nthe direction of mineral orientation. Rocks of this formation had fewer \npores as compared to the other rock formations. The Tarkwa phyllite \nexhibited failure by multiple shears during the testing process. \n\n\n\nThe various petrographic features tend to influence the unconfined \ncompressive strength (UCS) of various rock formations. The presence of \nclay minerals, fractures and micro pores tend to reduce the rock\u2019s strength \nwhereas the presence of silicate minerals such as quartz tend to increase \nthe rock\u2019s strength and its resistance to deformation. However, no one \npetrographic property confirms the strength of a particular rock unit. \nMost, if not all petrographic properties must be present to influence the \nstrength of a particular rock unit, whether to increase the strength or \nreduce it. \n\n\n\nFrom a study rocks can be classified based on their strengths. From Table \n2, EJI001 and EJI011 (Huni sandstones) are sandstone samples which are \nconsidered slightly to moderately weathered samples having uniaxial \ncompressive strengths of 36.50 MPa and 20.65 MPa respectively. Sample \nEJI001 and EJI011 (Huni sandstones) therefore can be classified as weak \nrocks. The grains of the sandstones were observed to be rounded. The \npacking density of the rounded grains suggest that the bond between \nindividual grains are not very strong. This explains why the unconfined \ncompressive strength of the intact rock Huni-sandstone samples failed \nafter applying a force of 47.01KN max.  \n\n\n\nAttewell and Farmer classify strengths (40-80) MPa as medium. The \nTarkwa Phyllite samples (EJI002 &EJI022), thus fall within the medium \nstrength classification of rocks. The crystals of the foliated metamorphic \nrock were observed to be elongated (Figure 4). It was observed opaque \nminerals as well as other clay minerals such as iron-rich chlorite indicate \nthat some portions of the rocks are weathered. Also, the presence of small \namounts of Orthoclase (an alkali Feldspar) shows that the rocks can \nundergo further weathering. \n\n\n\nThe rate of chemical weathering was as a result of warm surface \nconditions. The higher the temperature of crystallization, the less stable \nthese minerals are at the low temperatures found near the earth\u2019s surface. \nFrom the quartz, muscovite and K-feldspar (orthoclase) are felsic minerals \nwhiles biotite and Na-plagioclase are intermediate minerals. All the \nminerals in the Tarkwa rocks falls within felsic and intermediate minerals. \nThe presence of foliation and metamorphic minerals such as staurolite and \ntourmaline in the rocks prove that the rocks have undergone \nmetamorphism.  \n\n\n\nThe rocks have high amount of silica. Strength testing indicated that Huni \nsandstone, Banket Quartzite and Kawere conglomerate had UCS values \nfrom 20 \u2013 40N/mm2. Attewell and Farmer (1976), classifies these rocks of \nsuch strengths as weak rocks. The Tarkwa phyllite had UCS higher than 60 \nN/mm2. Attewell and Farmer classify such rocks as medium in strength. \nFrom the petrographic analysis, the Tarkwaian rocks are of felsic and \nintermediate minerals with a small percentage of secondary minerals like \nTourmaline and staurolite. The secondary minerals prove that the rocks \nundergo alteration during metamorphism.  \n\n\n\nThe presence of foliation in some of these rocks also prove that the rocks \nhave undergone some form of metamorphism. There are opaque minerals \nas well as other clay minerals such as iron-rich chlorite which indicates \nthat some portions of the rocks are weathered. Also, the presence of small \namounts of Orthoclase (an alkali Feldspar) shows that the rocks can \nundergo further weathering. Also, a shear zone of intense deformation was \nobserved with some opaque minerals along the shear zone, under the \npetrographic microscope.  \n\n\n\nPetrographic analysis of these rock units confirms the presence of high \namount of SiO2 in the various rock units and feldspars which weather \nunder tropical climatic conditions in the Tarkwa area. The minerals that \nmake up these rocks then make them susceptible to chemical weathering. \nThe low unconfined compressive strengths of these rocks as well as their \ncompositional texture and particle size distribution make them non-\nsuitable aggregates for construction. However, the Huni-sandstone \nweathers to form sand which can be used for other purposes. \n\n\n\nACKNOWLEDGEMENT \n\n\n\nI hereby acknowledge the tireless effort and effective contribution of my \n\n\n\nco-authors towards the production of this article. \n\n\n\nREFERENCES \n\n\n\nAbouchami, W., Boher, M., Michard, A. and Albarede, F., 1990. A major 2.1 \nGa old event of mafic magnetism in West Africa: An early stage of crustal \naccretion. Journal of Geophysics Resource, 95, Pp. 17605-17629. \n\n\n\nASTM. 2014. Standard Test Methods for Compressive Strength and Elastic \nModuli of Intact Rock Core Specimens under Varying States of Stress and \nTemperatures, ASTM International - West Conshohocken, PA, D7012-\n14. \n\n\n\nASTM. 2016. Standard Test Method for Determination of the Point Load \nStrength Index of Rock and Application to Rock Strength Classifications. \nASTM International, West Conshohocken, PA, D5731-16. \n\n\n\nAttewell, P.B., Farmer, I.W., 1976. Principles of engineering geology. \nChapman and Hall, London. \n\n\n\nBaratoux, L., Meteika, V., Naba, S., Jessell, M.W., Gr\u00e9goire, M., Ganne, J., \n2011. Juvenile Paleoproterozoic crust evolution during the Eburnean \norogeny (2.2-2.0 Ga), western Burkina Faso. Precambr. Res., 191, Pp. 18-\n45. \n\n\n\nBlock, S., Ganne, J., Baratoux, L., Zeh, L., Parra-Avila, A., Jessell, M., Ailleres, \nL. and Siebenaller, L., 2015. Petrological and geochronological \nconstraints on lower rocks crust exhumation during Paleoproterozoic \n(Eburnean) orogeny, NW Ghana, West African Craton. Journal of \nMetamorphic Geology, 33, Pp. 463-494. \n\n\n\nBlock, S., Baratoux Lenka, Zeh A., Laurent, O., Bruguier, O., Jessell, M., \nAilleres, L., Sagna, R., Parra-Avila L. A., Bosch, D., 2016. Paleoproterozoic \njuvenile crust formation and stabilisation in the south-eastern West \nAfrican Craton (Ghana); new insights from U-Pb-Hf zircon data and \ngeochemistry. Precambrian Research, 287, Pp. 1-30. ISSN 0301-9268. \n\n\n\nBonhomme, M., 1962. Contribution \u00e0 l\u2019\u00e9tude g\u00e9ochronologique de la \nplateforme de l\u2019Ouest africain. In G\u00e9ologie et Min\u00e9ralogie, 5, pp.62. Gap: \nImpriemerie Louis-Jean. \n\n\n\nBowen, N.L., 1928. The evolution of the igneous rocks. Dover Publications. \n\n\n\nBrako, B.A., Foli, G., Amedjoe, C., Gawu, S.K.Y., 2020. Petrographic Study of \nSome Paleoproterozoic Sedimentary Rocks in The Chagupana and \nTarkwa Areas of Ghana. International journal of Geography and Geology, \nPp. 38-52. DOI: 10.18488/journal.10.2020.91.38.54. \n\n\n\nde Kock, G.S., Th\u00e9veniaut, H., Botha, P.M.W., Gyapong, W., 2012. Timing the \nstructural events in the Paleoproterozoic Bole-Nangodi belt terrane and \nand adjacent Maluwe Basin, West African Craton, in central west Ghana. \nJ. Afr. Earth Sci., 65, Pp. 1-24. \n\n\n\nEisenlohr, B.N., and Hirdes, W., 1992. The structural development of early \nProterozoic Birimian and Tarkwaian rocks in southwest Ghana, West \nAfrica: Journal of African Earth Science., 14 (3), Pp. 313-325. \n\n\n\nFeybesse, J.L., Billa, M., Guerrot, C., Duguey, E., Lescuyer, J.L., Mil\u00e9si, J.P., \nBouchot, V., 2006a. The Paleoproterozoic Ghanaian province: \ngeodynamic model and ore controls, including regional stress modeling. \nPrecambr. Res., 149, Pp. 149-196. \n\n\n\nGriffis, R.J., Barning K, Agezo, F.L., Akosah, F.K., 2002. Gold deposits of \nGhana. Minerals Commission Report. \n\n\n\nHein, K.A.A., 2010. Succession of structural events in the Goren greenstone \nbelt (Burkina Faso): Iimplications for West African tectonics. Journal of \nAfrican Earth Sciences, 56, Pp. 83\u201394. \n\n\n\nHirdes, W., Davis, D.W. and Eisenlohr, B.N., 1992. Reassessment of \nProterozoic granitoid ages in Ghana on the basis of U/Pb zircon and \nmonazite dating. Precambrian Research, 56, Pp. 89-96. \n\n\n\nISRM. 1979. Suggested Methods for Determining the Uniaxial Compressive \nStrength and Deformability of Rock Materials. International Journal of \nRock Mechanics and Mining Sciences and Geomechanics Abstracts, 16, \nPp. 2. \n\n\n\n\nhttps://www.astm.org/Standards/D7012.htm\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 6(1) (2022) 01-07 \n\n\n\n\n\n\n\n \nCite The Article: Matthew Coffie Wilson, Joseph Larbi, Isabella Ivy Kangah, Enock Anison (2022). Petro-Mechanical Studies of The Stratigraphic Units of The Tarkwaian \n\n\n\nSupergroup in Tarkwa \u2013 Implications For The Structural and Mechanical Competence of Rocks. Malaysian Journal of Geosciences, 6(1): 01-07. \n\n\n\n\n\n\n\nISRM. 1983. Suggested methods for determining the strength of rock \nmaterials in triaxial compression: Revised edition. International Journal \nof Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 20 \n(6), Pp. 285-290. doi.org/10.1016/0148-9062(83)90598-3. \n\n\n\nISRM. 1985. Suggested method for determining point load strength. \nInternational Journal of Rock Mechanics and Mining Sciences and Geo-\nMechanical Abstract, 22 (2), Pp. 51-60. \n\n\n\nISRM Conference, 2006. Research gate Publication \u2013 ISBN 0 415 41001 0. \n\n\n\nJessell, M.W., and Li\u00e9geois, J.P., 2015. 100 years of research on the West \nAfrican Craton. Journal of African earth sciences, 112, Pp. 377-381. doi: \n10.1016/j.jafrearsci.2015.10.008. \n\n\n\nJohn, T., Kleimd, R., Hirdes, W. and Loh, G., 1999. The Metamorphic \nEvolution of the Palaeoproterozoic (Birimian) Volcanic Ashanti belt \n(Ghana, West Africa). Precambrian Research, 98, Pp. 11 \u2013 30. \n\n\n\nJohnson, E., and Liu, C.J., 2020. Introduction to Petrology, Pp. 75. \n\n\n\nJunner, N.R., Hirst, T., and Service, H., 1942. The Tarkwa Goldfield. Gold \nCoast Geological Survey, Memoir No., 6, Pp. 48 \u2013 55.  \n\n\n\nKesse, G.O., 1985. The Rock and Mineral Resources of Ghana. A. A. Balkema, \nRotterdam. \n\n\n\nLambert-Smith, J.S., Lawrence, D.M., Mueller, W., Treloar, P.J., 2016. \nPalaeotectonic setting of the southeastern K\u00e9dougou-K\u00e9ni\u00e9ba Inlier, \nWest Africa: new insights from igneous trace element geochemistry and \nU-Pb zircon ages. Precambr. Res., 274, Pp. 110-135. \n\n\n\nLaubscher, D.H., 1990. A geomechanics classification system for rating of \nrock mass in mine design. Journal South African Inst. Of Mining and \nMetallurgy, 90 (10), Pp. 257 \u2013 273. \n\n\n\nLeube, A., Hirdes, W., Mauer, R., Kesse, G.O., 1990. The Early Proterozoic \nBirimian Supergroup of Ghana and some aspects of its associated gold \nmineralization. Precambr. Res., 46, Pp. 139-165. \n\n\n\nOberth\u00fcr, T., Vetter, U., Davis, D.W., Amanor, J.A., 1998. Age constraints on \ngold mineralization and Paleoproterozoic crustal evolution in the \nAshanti belt of southern Ghana. Precambr. Res., 89, Pp. 129-143. \n\n\n\nPerrouty, S., Aill\u00e9res, L., Jessel, M.W., Baratoux, L., Bourassa, Y., Crawford, \nB., 2012. Revised Eburnean geodynamic evolution of the gold rich \nsouthern Ashanti belt, Ghana, with new field and geophysical evidence \nof pre-Tarkwaian deformations. Precambr. Res., 204-205, Pp. 12-39. \n\n\n\nVidal, M., Delor, C., Pouclet, A., Sim\u00e9on, Y., Alric, G., 1996. Evolution \ng\u00e9odynamique de 780 l\u2019Afrique de l\u2019Ouest entre 2,2 Ga et 2 Ga: le style \n\"arch\u00e9en\" des ceintures vertes et des 781 ensembles s\u00e9dimentaires \nbirimiens du nord-est de la C\u00f4te d\u2019Ivoire. Bulletin de la Soci\u00e9t\u00e9 782 \ng\u00e9ologique de France, 167 (3), Pp. 307-319. \n\n\n\nYao, Y. and Robb, L.J., 2000. Gold mineralization in Paleoproterozoic \ngranitoids at Obuasi, Ashanti region, Ghana: Ore geology, geochemistry \nand fluid characteristics. South African Journal of Geology, 103, Pp. 255-\n278. \n\n\n\nZorlu, K., Gokceoglu, C., Ocakoglu, F., Nefeslioglu, H.A., Acikalin, S., 2007. \nPrediction of Uniaxial Compressive Strength of Sandstones using \nPetrography-based models, Pp. 1-18. \n\n\n\n \n\n\n\n\nhttps://www.sciencedirect.com/science/article/pii/0148906283905983?via%3Dihub\n\n\n\n"
"\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 79-85\n\n\n\nQuick Response Code Access this article online \n\n\n\nWebsite: \n\n\n\nwww.myjgeosc.com \n\n\n\nDOI: \n\n\n\n10.26480/mjg.02.2020.79.85 \n\n\n\nCite the Article: Omigie J.I., Alaminiokuma G.I.(2020). Petrophysical Evaluation Of Reservoirs For Hydrocarbon Reserve Estimation In Eastern Central Swamp Depobelt, \nNiger Delta. Malaysian Journal of Geosciences, 4(2): 79-85.\n\n\n\nISSN: 2521-0920 (Print) \nISSN: 2521-0602 (Online) \nCODEN: MJGAAN \n\n\n\nRESEARCH ARTICLE \n\n\n\nMalaysian Journal of Geosciences (MJG) \n\n\n\nDOI: http://doi.org/10.26480/mjg.02.2020.79.85\n\n\n\nPETROPHYSICAL EVALUATION OF RESERVOIRS FOR HYDROCARBON RESERVE \n\n\n\nESTIMATION IN EASTERN CENTRAL SWAMP DEPOBELT, NIGER DELTA \n\n\n\nOmigie J.I., Alaminiokuma G.I.* \n\n\n\nDepartment of Earth Sciences, Federal University of Petroleum Resources Effurun P.M.B. 1221, Effurun, Nigeria. \n\n\n\n*Corresponding Author\u2019s e-mail: alaminiokuma.godswill@fupre.edu.ng \n\n\n\nThis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction \nin any medium, provided the original work is properly cited. \n\n\n\nARTICLE DETAILS ABSTRACT\n\n\n\nArticle History: \n\n\n\nReceived 25 March 2020 \nAccepted 27 April 2020 \nAvailable online 18 May 2020\n\n\n\nPetrophysical properties were evaluated in five wells in eastern Central Swamp Depobelt, Niger Delta using \nwell logs. Analyses by Kingdom Suite software reveal that reservoirs\u2019 thicknesses ranged between 24.5ft in \nSNG in Afam 16 to 200.5ft in SNB in Obeakpu 005. Volume of shale varies within and across all the wells with \nvalues <30% of the total thicknesses. Relative permeability to water (Krw) ranges from 0.00 to >1.00 across \nthe wells. Reservoirs SNE and SNF in Afam 16 have average Krw of 0.00 implying 100% water-free hydrocarbon \nproduction. SNC reservoir in Afam 15 and Afam 16 has average Krw >1 implying 100% water production. The \nrelative permeability to oil (Kro) is very high in reservoirs with high hydrocarbon saturation. SNH in Korokoro \n006 has average hydrocarbon saturation of 85.70% and Kro of 0.89. SNB in Obeakpu 005 has average absolute \npermeability of 62,086.9mD. Reservoirs SNB, SNC and SND contain no producible hydrocarbon in Afam 15 but \ncontain producible hydrocarbon in Afam 16, Korokoro 003 and Obeakpu 005 wells. Reservoirs SNE, SNF, SNG \nand SNH in Afam 15, Afam 16, Korokoro 003 and Korokoro 006 contain producible hydrocarbon with the \nexception of SNF in Korokoro 003. Afam 15 and Afam 16 are mainly gas-producing with estimated gas-in-place \nranging from 72,630.27cu.ft/acre in SNB in Afam 15 to 1,534,667.86cu.ft/acre in SNH in Afam 16 while \nKorokoro 003, Korokoro 006 and Obeakpu 005 are mainly oil-producing with estimated oil-in-place ranging \nfrom 47,590.26bbl/acre in SNB in Korokoro 003 and 387,754.83bbl/acre in SNB in Obeakpu 005.  \n\n\n\nKEYWORDS \n\n\n\nPetrophysical Properties, Reservoirs, Well Logs, Central Swamp Depobelt, Niger Delta.\n\n\n\n1. INTRODUCTION \n\n\n\nThe evaluation of petrophysical properties of reservoirs is an important \nphase in converting information from raw log data into estimated \nquantities of oil, gas, and water in a formation. These estimated quantities \nare used to evaluate reservoirs and determine whether a well completion \nis necessary. Some of the essential petrophysical parameters needed to \nevaluate reservoirs include: porosity, permeability, lithology and \nreservoir thicknesses, formation resistivity factor, water saturation, \nhydrocarbon saturation, movable oil saturation, index of oil movability, \nbulk volume water and bulk volume oil (Schlumberger, 1989). This study \nis aimed at effectively and optimally delineating reservoirs necessary to \naccurately interpret hydrocarbon distribution in the sedimentary fill of \neastern Central Swamp Depobelt, Niger Delta with the objectives of \nestimating reservoir thicknesses, volume of shale, effective porosity, water \nand hydrocarbon saturations, relative and absolute permeabilities and \ngas-in-place and oil-in-place of the different reservoirs in wells by \npetrophysical analyses. \n\n\n\n Several researches have been conducted to evaluate reservoirs and \nestimate the hydrocarbon potential of different parts of the Niger Delta. \nKalu et. al., 2020 re-evaluated Emerald Field in the Niger Delta Basin, \nNigeria using analyses of well logs, seismic facies, petrophysical and \nseismic attributes to produce 3D structural model of the Field as well as \nidentify and estimate the petrophysical properties of the reservoirs in the \n\n\n\nField. Results show that 3 hydrocarbon-bearing reservoirs: Emy A, B and \nC were penetrated by the 4 wells studied. The results also reveal that \nreservoir porosity ranges from 10 to 29%, hydrocarbon saturation ranges \nfrom 0.75 to 0.84, water saturation ranges from0.16 to 0.25, volume of \nshale ranges from 0.24 to 0.33 and net-to-gross ranges from 0.72 to 0.93. \nFive seismic facies were identified within the study area. Results also \nsuggest that the environment of deposition at different locations within \nthe Field are distributary channel fills, overbank and floodplain deposits, \nwhich depicts paralic zone. Two prospects (Emerald prospects A and B) \nand one lead were identified within the study area. The risk evaluation and \nestimated volume of hydrocarbon-in-place ranked Emerald prospect B as \nthe highest.  \n\n\n\nA group of researchers identified reservoirs as well as hydrocarbon \npresence and determined petrophysical parameters using well and \nseismic data in TIM Field, south-western offshore Niger Delta (Nwaezeapu \net al., 2019). Three horizons corresponding to selected sands tops (sands \nD, E and F) were mapped. Results of the analysis show an average porosity \nvalue of 0.23, water saturation value of 0.32 and an average net-to-gross \nvalue of 0.62. The total hydrocarbon proven reserves for mapped horizons \n(sand D, E, and F) were evaluated to be 39.04MMBO of oil, and 166.13BCF \nof gas for sand E. More so, Saadu and Nwankwo, 2018 evaluated the \npetrophysical parameters and hydrocarbon volume from seismic and well \nlogs data within the Central Swamp Depobelt in the Niger Delta. Two \nreservoirs, GA and GB were delineated from the logs. Two horizons and \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 79-85 \n\n\n\nCite the Article: Omigie J.I., Alaminiokuma G.I.(2020). Petrophysical Evaluation Of Reservoirs For Hydrocarbon Reserve Estimation In Eastern Central \nSwamp Depobelt, Niger Delta. Malaysian Journal of Geosciences, 4(2): 79-85. \n\n\n\nseveral faults were mapped from the seismic sections. The faults mapped \nare mainly synthetic and antithetic, characteristic of the Niger Delta. The \naverage porosity and permeability estimated for reservoir GA are 20% and \n1338mD respectively. Similarly, the estimated average porosity and \npermeability for reservoir GB are 21% and 1392mD respectively. The \nresults show that the oil-bearing zones in the reservoirs are porous and \nhighly permeable. There was no gas present in either of the reservoirs. The \nestimated initial hydrocarbon-in-place for reservoirs GA and GB are 57.84 \nMMSTB and 48.43 MMSTB respectively.  \n\n\n\nAlaminiokuma and Ofuyah, 2017 delineated porosity and permeability \nregimes in hydrocarbon-bearing reservoirs of Nembe Creek Field, Niger \nDelta by digitizing and correlating gamma ray, resistivity, and density logs \nfrom three wells: Nembe 01, Nembe 02 and Nembe 03 respectively. \nResults obtained from the analyses of these composite logs reveal eight \npotential hydrocarbon-bearing reservoirs. These reservoir sands were \nobserved to have very good to excellent average porosities ranging from \n29 to 45%. Permeability values were excellent within these reservoirs and \nrange from 2200 to 5789mD. Hydrocarbon saturation was observed to be \nhigh in all the reservoir sands, ranging from 64 to 81% with corresponding \nwater saturation from 19 to 36%. The regimes observed indicate that \nporosity and permeability increase with depth.  \n\n\n\nAnother group of researchers evaluated petrophysical parameters, \nidentified reservoirs and hydrocarbon presence in Greater Ughelli \nDepobelt of Niger Delta using 3D seismic and well data (Emina et al., \n2016). Four hydrocarbon-bearing reservoirs within the depth range of \n6743 to 9045ft, having volume of shale (Vsh) ranging from 15.32 to 29.06% \nwere interpreted. The total porosity of the reservoirs ranges from 24.63 to \n34.01%, while the effective porosity ranges from 17.26 to 31.71%, \nindicating that the reservoirs have very good porosities. The ratio of the \nnet-to-gross thickness of the reservoirs ranges from 0.720 to 0.980 while \nthe water saturation values range from 19.87 to 29.07%. From the water \nsaturation deductions, the hydrocarbon saturation ranges from 70.93 to \n78.86% of gas in the given reservoir.  \n\n\n\nAccordingly, this study which focuses on appraising hydrocarbon \n\n\n\nreservoirs based on petrophysical parameters is beneficial in delineating \n\n\n\nzones of overpressure, reservoirs geometries, estimating hydrocarbon \n\n\n\nreserves and in discriminating gas, oil and water bearing zones in the \n\n\n\nNiger Delta. \n\n\n\n2. LOCATION AND BRIEF GEOLOGY OF THE STUDY AREA \n\n\n\n2.1 Location of the Study Area \n\n\n\nFigure 1: Map of the Niger Delta showing Depobelts and location of the \n\n\n\nstudy area - eastern Central Swamp Depobelt (Doust and Omatsola \n\n\n\n1990). \n\n\n\nThe study area is located within OML 11 in the eastern part of the Central \n\n\n\nSwamp Depobelt of the Tertiary Niger Delta (Figure 1). The Central \n\n\n\nSwamp Depobelt is one of the most prolific oil provinces in the whole of \n\n\n\nthe Niger Delta as revealed by the sheer number of ields, sizes, reserves \n\n\n\nand production levels (Ozumba, 2018).  \n\n\n\n2.2 Brief Geology of the Study Area \n\n\n\nIn the conventional sections, collapsed crests grading into the K-type and \n\n\n\nfaulted rollover structures characterize the study area. At depth, this trend \n\n\n\nis also characterized by mainly deep footwall closures with some hanging \n\n\n\nwall components in a limited sense (Ozumba, 2018). Sedimentation within \n\n\n\nthe Central Swamp Depobelt was mainly wave-dominated and the activity \n\n\n\nof the Opuama Canyon has been initiated on the north-western flank of the \n\n\n\nDelta (Petters, 1984). The oldest sediments in the Central Swamp \n\n\n\nDepobelt are dated around the Latest Oligocene. Sedimentation in this \n\n\n\narea was mainly starved at this time as only shales of the Akata Formation \n\n\n\nwere deposited (Corredor et al., 2005). Deposition of the paralic facies \n\n\n\n(Agbada Formation) probably took place from the earliest Miocene times \n\n\n\nonwards.  \n\n\n\n3. METHODOLOGY \n\n\n\n3.1 Wells Location and Field Data \n\n\n\nThe petrophysical parameters are evaluated by digitizing, correlating and \n\n\n\ncomputing Gamma Ray, Spontaneous Potential, Resistivity, Neutron, Sonic \n\n\n\nand Density logs from Wells: Afam 15 & 16, Korokoro 003 & 006 and \n\n\n\nObeakpu 005 using the Seismic Micro Technology (SMT, Kingdom Suite) \n\n\n\nsoftware. Figure 2 is the base map showing the well locations in the \n\n\n\nstudy area. \n\n\n\nFigure 2: Base map showing well locations in the study area \n\n\n\n3.2 Delineation of Lithology and Hydrocarbon-bearing Zones \n\n\n\nGamma Ray (GR) and Spontaneous Potential (SP) logs were digitized to \n\n\n\ndelineate the lithology penetrated by the Wells. A combination of GR log \n\n\n\nand induction resistivity logs were analyzed to delineate the hydrocarbon-\n\n\n\nbearing sands (Timur, 1968). Zones of possible oil accumulations are \n\n\n\nindicated by high resistivity values whereas water zones have low \n\n\n\nresistivity values. Figure 3 shows the lateral correlation of the reservoir \n\n\n\nsands across the Wells.  \n\n\n\n3.3 Determination of Gross/Net Thickness of the Reservoirs \n\n\n\nThe gross reservoir thickness, H is determined by picking the tops and \n\n\n\nbases of the reservoir sands across the Wells (Figure 3). Shale thickness, \nhshale within the reservoir sands is obtained by defining shale and sand \nbaselines respectively on the Gamma ray log. \n\n\n\n3.4 Computation of Petrophysical Parameters in the Study Area \n\n\n\nThe computations of the petrophysical parameters using established \n\n\n\nempirical relations are done using equations (1) to (16) in Tables 1a and \n\n\n\n1b below: \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 79-85 \n\n\n\nCite the Article: Omigie J.I., Alaminiokuma G.I.(2020). Petrophysical Evaluation Of Reservoirs For Hydrocarbon Reserve Estimation In Eastern Central \nSwamp Depobelt, Niger Delta. Malaysian Journal of Geosciences, 4(2): 79-85. \n\n\n\nTable 1a: Equations for computing the petrophysical parameters in the study area \n\n\n\nParameters  PETROPHYSICAL PARAMETERS \n\n\n\nNet \n\n\n\nReservoir \n\n\n\nSand \n\n\n\nThickness \n\n\n\n(ft) \n\n\n\nGamma Ray \n\n\n\nIndex, IGR \n\n\n\nVolume of \n\n\n\nShale \n\n\n\nTotal \n\n\n\nPorosity, \n\n\n\n\u03d5T\n\n\n\nEffective \n\n\n\nPorosity, \u03d5E \n\n\n\nFormation \n\n\n\nResistivity \n\n\n\nFactor, F  \n\n\n\nWater \n\n\n\nSaturation, \n\n\n\nSw \n\n\n\nIrreducible \n\n\n\nWater \n\n\n\nSaturation, \n\n\n\nSwirr \n\n\n\nFlush Zone \n\n\n\nWater \n\n\n\nSaturation, \n\n\n\nSxo \n\n\n\nEquations (1) (2) (3) (4) (5) (6) (7) (8) (9) \n\n\n\nh  H \n\n\n\nhshale \n\n\n\nH = Gross \n\n\n\nReservoir \n\n\n\nSand \n\n\n\nThickness \n\n\n\n( )\n( )minmax\n\n\n\nminlog\n\n\n\nGRGR\n\n\n\nGRGR\nIGR\n\n\n\n\u2212\n\n\n\n\u2212\n=\n\n\n\n\n\n\n\n(Dresser, \n\n\n\n1979) \n\n\n\n( )17.23083.0 \u2212\uf02a= GRsh IV\n\n\n\n(Larionov, 1969) fma\n\n\n\nbma\nT\n\n\n\n\uf072\uf072\n\n\n\n\uf072\uf072\n\uf066\n\n\n\n\u2212\n\n\n\n\u2212\n=\n\n\n\n\n\n\n\n(Dresser, \n\n\n\n1979) \n\n\n\n\u03c1ma = 2.65 \n\n\n\ngcm-3 for \n\n\n\nsandstone\n\n\n\ns; \u03c1b \n\n\n\n=Bulk \n\n\n\ndensity; \n\n\n\n\u03c1f=1.0 \n\n\n\ngcm-3 for \n\n\n\nwater and \n\n\n\n0.87 gcm-3 \n\n\n\nfor oil.  \n\n\n\n\uf05b \uf05dshshTeff V\uf02a\u2212= \uf066\uf066\uf066\n\n\n\n(Dresser, 1979) \n\n\n\n\u03d5T  = total \n\n\n\nporosity; \u03d5sh = \n\n\n\nlog reading in a \n\n\n\nshale zone, Vsh \n\n\n\n= volume of \n\n\n\nshale  \n\n\n\nm\n\n\n\na\nF\n\n\n\n\uf066\n=\n\n\n\n(Archie, \n\n\n\n1942) \n\n\n\na = \n\n\n\nTortuosity \n\n\n\ntaken as \n\n\n\n0.62; m = \n\n\n\ncementation \n\n\n\nexponent \n\n\n\ntaken as 2 for\n\n\n\nsands. \n\n\n\nn\n\n\n\nm\n\n\n\nt\n\n\n\nw\nw\n\n\n\nR\n\n\n\naR\nS\n\n\n\n1\n\n\n\n\uf0f7\uf0f7\n\uf0f8\n\n\n\n\uf0f6\n\uf0e7\uf0e7\n\uf0e8\n\n\n\n\uf0e6\n=\n\n\n\n\uf066\n\n\n\n(Archie, \n\n\n\n1942) \n\n\n\nRw = \n\n\n\nResistivity \n\n\n\nof the \n\n\n\ninterstitial \n\n\n\nwater; Rt \n\n\n\n= True \n\n\n\nresistivity \n\n\n\nof the \n\n\n\nFormation\n\n\n\n;      n = \n\n\n\nsaturation \n\n\n\nexponent \n\n\n\ntaken as 2 \n\n\n\n2000\n\n\n\nF\nS\n\n\n\nirrw =\n2\n\n\n\n1\n\n\n\n\uf0fa\n\uf0fa\n\uf0fb\n\n\n\n\uf0f9\n\n\n\n\uf0ea\n\uf0ea\n\uf0eb\n\n\n\n\uf0e9\n=\n\n\n\nR\nR\n\n\n\nS\nxo\n\n\n\nmf\n\n\n\nxo\n\n\n\nF\n\n\n\n(Archie, \n\n\n\n1942) \n\n\n\nTable 1b: Equations for computing the petrophysical parameters in the study area (continued) \n\n\n\nParametes  PETROPHYSICAL PROPERTIES \n\n\n\nPermeability \n\n\n\n(K) \n\n\n\nRelative \n\n\n\nPermeability \n\n\n\nto Oil (Kro) \n\n\n\nRelative \n\n\n\nPermeability to \n\n\n\nWater (Krw) \n\n\n\nHydrocarbon \n\n\n\nSaturation \n\n\n\n(Sh) \n\n\n\nResidual \n\n\n\nHydrocarbon \n\n\n\nSaturation \n\n\n\n(Shr) \n\n\n\nOil-in-Place  \n\n\n\n(OIP) \n\n\n\n(bbl/acre)  \n\n\n\nGas-in-Place  \n\n\n\n(GIP) \n\n\n\n(cu.ft/acre) \n\n\n\nEquations (10) (11) (12) (13) (14) (15) (16) \n\n\n\n2\n3250\n\n\n\n\uf0fa\n\uf0fa\n\uf0fb\n\n\n\n\uf0f9\n\n\n\n\uf0ea\n\uf0ea\n\uf0eb\n\n\n\n\uf0e9 \uf02a\n=\n\n\n\nirrwS\nK\n\n\n\n\uf066\n\n\n\n(Wyllie and \n\n\n\nRose, 1950)  \n\n\n\n( )\n( )S\n\n\n\nS\nK\n\n\n\nwirr\n\n\n\nw\nro\n\n\n\n\u2212\n\n\n\n\u2212\n=\n\n\n\n1\n\n\n\n1\n2\n\n\n\n1.2\n\n\n\n( )\n( ) \uf0fa\uf0fb\n\n\n\n\uf0f9\n\uf0ea\n\uf0eb\n\n\n\n\uf0e9\n\n\n\n\u2212\n\n\n\n\u2212\n=\n\n\n\nS\n\n\n\nSS\nK\n\n\n\nwirr\n\n\n\nwirrw\nrw\n\n\n\n1\n\n\n\n3\n\n\n\n( )%100 wh SS \u2212=\nR\nR\n\n\n\nS\nxo\n\n\n\nmf\n\n\n\nhr\n\n\n\nF\n\u2212= 1\n\n\n\n\ud835\udc42\ud835\udc3c\ud835\udc43\n\n\n\n= 77580\u2205\ud835\udc34\u210e(1\n\n\n\n\u2212 \ud835\udc46\ud835\udc64) \n\n\n\n\u03a6 = Porosity \n\n\n\n(%) \n\n\n\nA = Area \n\n\n\ndrained in \n\n\n\nacres \n\n\n\nH = thickness \n\n\n\n(ft) \n\n\n\nSw = Water \n\n\n\nSaturation (%) \n\n\n\n\ud835\udc3a\ud835\udc3c\ud835\udc43\n\n\n\n= 43560\u2205\ud835\udc34\u210e(1\n\n\n\n\u2212 \ud835\udc46\ud835\udc64) \n\n\n\n\u03a6 = Porosity \n\n\n\n(%) \n\n\n\nA = Area \n\n\n\ndrained in \n\n\n\nacres \n\n\n\nH = thickness \n\n\n\n(ft) \n\n\n\nSw = Water \n\n\n\nSaturation (%) \n\n\n\nFigure 3: Lateral correlation of the reservoir sands across the wells \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 79-85 \n\n\n\nCite the Article: Omigie J.I., Alaminiokuma G.I.(2020). Petrophysical Evaluation Of Reservoirs For Hydrocarbon Reserve Estimation In Eastern Central \nSwamp Depobelt, Niger Delta. Malaysian Journal of Geosciences, 4(2): 79-85. \n\n\n\n4. RESULTS \n\n\n\n4.1 Hydrocarbon Reservoir Sands \n\n\n\nSeven prospective reservoir sands: SNB, SNC, SND, SNE, SNF, SNG and SNH \n\n\n\nwere identified in the wells. These reservoir sands are continuous in the \n\n\n\nwells except in AFAM 15 where SNF, SNG and SNH and in OBEAKPU 005 \n\n\n\nwhere SND, SNE, SNF, SNG and SNH are not penetrated by these wells.  \n\n\n\n4.2 Average Petrophysical Parameters for the Reservoir Sands in \n\n\n\nthe Wells \n\n\n\nTables 2a and 2b and Figures 4 to 10 respectively show the computations \n\n\n\nand plots of the average petrophysical parameters obtained for the \n\n\n\nreservoir sands in the wells.\n\n\n\nTable 2a: The average petrophysical parameters of the reservoirs in the study area \n\n\n\nWell SAND RESERVOIR \n\n\n\nSAND \n\n\n\nTHICNKESS \n\n\n\n(ft) \n\n\n\nVsh(%) \u03d5e(%) F Rt(\u2126-m) Ro(\u2126-m) Sw(%) Sh(%) Swirr(%) \n\n\n\nAfam-15 \n\n\n\nSNB 117 4.84 28.84 10.21 1.73 1.12 93.02 6.98 7.08 \n\n\n\nSNC 85 8.51 23.23 18.88 4.32 1.99 88.71 11.29 9.24 \n\n\n\nSND 93.5 4.06 24.83 14.75 1.62 1.51 94.13 5.87 8.39 \n\n\n\nSNE 64 7.50 23.63 16.20 91.37 1.62 21.80 78.20 8.83 \n\n\n\nAfam-16 \n\n\n\nSNB 122 12.53 28.20 11.90 6.55 0.87 67.20 32.80 7.29 \n\n\n\nSNC 71 15.48 24.97 24.11 70.83 1.70 37.67 62.33 9.35 \n\n\n\nSND 90 5.61 26.22 18.73 49.55 1.28 45.95 54.05 8.25 \n\n\n\nSNE 58 7.64 27.21 12.58 119.80 0.84 12.89 87.11 7.71 \n\n\n\nSNF 123.5 10.93 23.66 15.53 100.90 1.00 12.70 87.30 8.70 \n\n\n\nSNG 24.5 21.63 19.26 30.77 15.86 1.89 37.76 62.24 11.61 \n\n\n\nSNH 168 6.37 23.92 18.74 145.03 1.13 10.20 89.80 8.96 \n\n\n\nKorokoro-\n\n\n\n003 \n\n\n\nSNB 156.5 3.39 36.01 6.29 4.02 0.45 56.71 43.29 5.60 \n\n\n\nSNC 54 9.18 33.73 7.62 34.08 0.53 16.77 83.23 6.11 \n\n\n\nSND 159 7.79 31.35 8.42 5.11 0.56 48.88 51.12 6.46 \n\n\n\nSNE 77.5 5.79 30.53 8.80 62.73 0.58 16.26 83.74 6.62 \n\n\n\nSNF 111 7.27 21.67 17.79 2.76 1.12 63.59 36.41 9.38 \n\n\n\nSNG 52.5 21.43 21.94 18.48 53.20 1.15 20.05 79.90 9.45 \n\n\n\nSNH 98.5 14.29 24.01 15.39 92.81 0.94 20.27 79.73 8.61 \n\n\n\nKorokoro-\n\n\n\n006 \n\n\n\nSNE 73 11.07 29.73 9.41 9.64 0.57 29.50 70.50 6.83 \n\n\n\nSNF 106 12.90 34.08 7.16 2.48 0.42 41.16 58.84 5.96 \n\n\n\nSNG 55.5 26.94 26.60 30.90 16.94 1.08 28.45 71.55 9.50 \n\n\n\nSNH 102 8.39 23.46 17.41 99.55 0.99 14.30 85.70 8.99 \n\n\n\nObeaku-\n\n\n\n005 \n\n\n\nSNB 200.5 14.59 30.68 19.15 58.02 0.59 26.33 73.67 8.21 \n\n\n\nSNC 50 24.14 25.67 16.65 33.07 0.50 30.95 69.05 8.64 \n\n\n\nVsh-Volume of Shale; \u03d5e-Effective Porosity Corrected for Shale; F-Formation Factor; Rt-True Resistivity; Ro-Formation Resistivity at 100% \n\n\n\nWater Saturation; Sw-Water Saturation, Sh-Hydrocarbon Saturation; Swirr-Irreducible Water Saturation \n\n\n\nTable 2b: The average petrophysical parameters of the reservoirs in the study area(contd.) \n\n\n\nWell SAND K(mD) Kro Krw Sxo(%) Shr(%) RESERVE ESTIMATE \n\n\n\nGIP(cu.ft/acre) \n\n\n\nAfam-15 \n\n\n\nSNB 9530.79 0.04 0.92 98.09 1.91 72,630.27 \n\n\n\nSNC 2536.49 0.11 1.54 95.89 4.11 140,436.68 \n\n\n\nSND 3883.68 0.02 0.95 98.55 1.45 - \n\n\n\nSNE 2909.43 0.74 0.02 70.78 29.22 533,192.34 \n\n\n\nGIP(cu.ft/acre) \n\n\n\nAfam-16 \n\n\n\nSNB 405.00 0.40 0.44 90.34 9.66 293,792.15 \n\n\n\nSNC 5669.52 0.65 1.19 74.40 25.60 573,878.19 \n\n\n\nSND 4205.76 0.48 0.57 79.68 20.32 402,910.14 \n\n\n\nSNE 7472.17 0.89 0.00 63.40 36.60 597,490.35 \n\n\n\nSNF 2363.25 0.91 0.00 64.70 35.30 1,087,560.78 \n\n\n\nSNG 1013.79 0.53 0.11 80.21 19.79 121,405.03 \n\n\n\nSNH 2532.33 0.99 0.03 61.02 38.98 1,534,667.86 \n\n\n\n(OIP) (bbl/acre) \n\n\n\nKorokoro-003 \n\n\n\nSNB 47050.98 0.23 0.20 88.13 11.87 47,590.26 \n\n\n\nSNC 90663.50 0.78 0.01 68.19 31.81 118,602.08 \n\n\n\nSND 16655.69 0.32 0.13 85.41 14.59 86,972.45 \n\n\n\nSNE 12973.64 0.81 0.01 67.10 32.90 149,342.64 \n\n\n\n\nmailto:Rw@Tf(\u2126-m)\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 79-85 \n\n\n\nCite the Article: Omigie J.I., Alaminiokuma G.I.(2020). Petrophysical Evaluation Of Reservoirs For Hydrocarbon Reserve Estimation In Eastern Central \nSwamp Depobelt, Niger Delta. Malaysian Journal of Geosciences, 4(2): 79-85. \n\n\n\n\n\n\n\nSNF 941.00 0.15 0.22 91.27 8.73 - \n\n\n\nSNG 1497.66 0.78 0.02 70.21 29.79 75,310.12 \n\n\n\nSNH 2588.92 0.77 0.02 69.22 30.78 141,232.15 \n\n\n\n(OIP) (bbl/acre) \n\n\n\nKorokoro-006 \n\n\n\nSNE 11161.31 0.57 0.03 77.36 22.64 121,281.49 \n\n\n\nSNF 34366.80 0.37 0.05 83.69 16.31 162,635.01 \n\n\n\nSNG 14437.86 0.63 0.06 75.18 24.82 84,097.09 \n\n\n\nSNH 2343.64 0.89 0.01 65.38 34.62 230,133.87 \n\n\n\n(OIP) (bbl/acre) \n\n\n\nObeaku-005 \nSNB 62086.92 0.74 0.20 68.92 31.08 387,754.83 \n\n\n\nSNC 10645.45 0.64 0.12 72.98 27.02 72,953.98 \n\n\n\nK-Permeability; Kro-Relative Permeability to Oil; Krw-Relative Permeability to Water; Sxo-Flush Zone Water Saturation, Shr-Residual \n\n\n\nHydrocarbon Saturation, mD-Milli Darcy, OIP-Oil-in-place, GIP- Gas-in-place. \n\n\n\nFigure 4: Reservoir sand thicknesses    Figure 5: Volume of shale in the reservoir sands \n\n\n\nFigure 6: Porosity of the reservoir sands    Figure 7: Water and hydrocarbon saturations \n\n\n\nFigure 8: Relative permeabilitis to oil and water      Figure 9: Absolute permeability of the reservoir sands \n\n\n\nFigure 10: Reserve estimate in the reservoir sands \n\n\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 79-85 \n\n\n\nCite the Article: Omigie J.I., Alaminiokuma G.I.(2020). Petrophysical Evaluation Of Reservoirs For Hydrocarbon Reserve Estimation In Eastern Central \nSwamp Depobelt, Niger Delta. Malaysian Journal of Geosciences, 4(2): 79-85. \n\n\n\n5. DISCUSSION\n\n\n\n5.1 Afam 15 \n\n\n\nFour reservoir sands SNB, SNC, SND and SNE are delineated in this well \n\n\n\nwith SNB having the highest thickness of 117 ft and SNE the lowest \n\n\n\nthickness of 64ft.  SNB, SNC, SND and SNE contain average volume of shale \n\n\n\nranging from 4.06 to 8.51%. Effective porosity in these reservoirs are \n\n\n\nalmost entirely uniform ranging from 23.23 to 28.84%. Water saturation \n\n\n\nis observed to be high in SNB, SNC and SND ranging from 88.71 to 94.13% \n\n\n\nwith SNE having the lowest value of 21.80%. Conversely, hydrocarbon \n\n\n\nsaturation was highest in SNE with a value of 78.20% and low in SNB, SNC \n\n\n\nand SND ranging from 5.87 to 11.29% indicating that low hydrocarbon \n\n\n\nsaturation implies high water saturation and vice versa. Reservoirs SNB, \n\n\n\nSNC and SND contain essentially water while SNE contains some amount \n\n\n\nof hydrocarbon with water (Wyllie and Rose, 1950). The relative \n\n\n\npermeability to water, Krw is highest in SNC with a value of 1.54 and high \n\n\n\nin SNB and SND with values of 0.92 and 0.95 respectively. SNE has the \n\n\n\nlowest value of 0.02. This is not the case with Kro for which SNE has the \n\n\n\nhighest value of 0.74 while SNB and SND have the lowest values of 0.02 \n\n\n\nand 0.04 respectively. SNC has a low Kro value of 0.11. Very low values of \n\n\n\nrelative permeability to oil implies that little or no hydrocarbon will be \n\n\n\nproduced in such reservoirs. \n\n\n\nMore water than hydrocarbon will be produced from reservoirs SNB, SNC \n\n\n\nand SND because of their very high relative permeability to water while \n\n\n\nmore hydrocarbon than water will be produced in reservoir sand SNE due \n\n\n\nto its low Krw. SNB has the highest absolute permeability of 9530.79mD \n\n\n\nwhile SNC, SND and SNE have permeabilities of 2536.49mD, 3883.68mD \n\n\n\nand 2909.43mD respectively. This implies that hydrocarbon will flow \n\n\n\neasily in SNB than in SNC, SND and SNE. The estimated Gas-in-place are \n\n\n\n72,630.27cu.ft/acre in SNB, 140,436.68cu.ft/acre in SNC and \n\n\n\n533,192.34cu.ft/acre in SNE. This is mainly a gas-producing well. \n\n\n\n5.2 Afam 16 \n\n\n\nSeven reservoir sands SNB, SNC, SND, SNE, SNF, SNG and SNH are \n\n\n\ndelineated in this well with SNH having the highest thickness of 168ft and \n\n\n\nSNG the lowest thickness of 24.5ft. The volume of shale is highest in the \n\n\n\ndeep section with a value of 21.63% in SNG and lowest in the middle \n\n\n\nsection with values ranging from 5.61 to 10.93% in SND, SNE and SNF. \n\n\n\nAverage effective porosity in these reservoirs are almost entirely uniform \n\n\n\nin the shallow section ranging from 23.66 to 28.20% except in SNG in the \n\n\n\ndeep section with a low value of 19.26%. Water saturation is observed to \n\n\n\nbe high in SNB with a value of 67.20% and low in SNE, SNF and SNH with \n\n\n\nvalues ranging between 10.20 and 12.89%. Conversely, hydrocarbon \n\n\n\nsaturation is high in SNE, SNF and SNH with values of 87.11, 87.30 and \n\n\n\n89.0% respectively and low in SNB with a value of 32.80%. Reservoir SNB \n\n\n\ncontains essentially water, SNC, SND and SNG have hydrocarbon \n\n\n\nsaturation >50% and so contain significant hydrocarbon with some water \n\n\n\nwhile the SNE, SNF and SNH contain mainly hydrocarbon.  The relative \n\n\n\npermeability to water, Krw is highest in SNC with a value of 1.19. SNH has \n\n\n\na low value of 0.03 while SNE and SNF have 0.00 Krw values indicating \n\n\n\nzones from which water-free production is expected (Asquith and Gibson, \n\n\n\n1982).  \n\n\n\nOn the other hand, Kro is high in SNE, SNF and SNH with values of 0.89, 0.91 \n\n\n\nand 0.99 respectively while SNB, SNC, SND and SNG have low values of \n\n\n\n0.40, 0.65, 0.48 and 0.53 respectively. More water than hydrocarbon will \n\n\n\nbe produced from reservoirs SNB, SNC, SND and SNG because of their high \n\n\n\nKrw while more hydrocarbon than water will be produced in reservoir SNE, \n\n\n\nSNF and SNH due to its high Kro. SNE has the highest absolute permeability \n\n\n\nof 7472.17mD while SNB has the lowest absolute permeability of \n\n\n\n405.00mD. Absolute permeability is fairly good in SNC, SND, SNF, SNG and \n\n\n\nSNH. This implies that hydrocarbon will flow easily in SNE than in SNB and \n\n\n\nothers. SNF and SNH have the highest estimated GIP of 1,087,560.78 and \n\n\n\n1,534,667.86cu.ft/acre respectively while SNG has the lowest GIP of \n\n\n\n121,405.03cu.ft/acre. SNB, SNC, SND and SNE have GIP values ranging \n\n\n\nfrom 293,792.15 to 597,490.35cu.ft/acre. This is mainly a gas-producing \n\n\n\nwell. \n\n\n\n5.3 Korokoro 003 \n\n\n\nSeven reservoir sands SNB, SNC, SND, SNE, SNF, SNG and SNH are \n\n\n\ndelineated in this well with SNB and SND having the highest thicknesses \n\n\n\nof 156.5ft and 159ft respectively while SNC and SNG have the lowest \n\n\n\nthicknesses of 54.0ft and 52.5ft respectively. The volume of shale is highest \n\n\n\nin the deep section with a value of 21.43% in SNG and lowest in the shallow \n\n\n\nto middle sections with values ranging from 3.39 to 9.18% in SNB, SNC, \n\n\n\nSND, SNE and SNF. Average effective porosity is uniform in the shallow to \n\n\n\nmiddle sections ranging from 30.53 to 36.01% in SNB, SNC, SND and SNE \n\n\n\nand also uniform in the deep section ranging from 20.04 to 21.67% in SNF, \n\n\n\nSNG and SNH. This shows that porosity decreases with depth. Water \n\n\n\nsaturation is observed to be high in SNB, SND, and SNF ranging from 48.88 \n\n\n\nto 53.59% and low in SNC, SNE, SNG and SNH with values ranging between \n\n\n\n16.26 and 20.27%. Hydrocarbon saturation is high in SNC, SNE, SNG and \n\n\n\nSNH with values of 83.23, 83.74, 79.90 and 79.73% respectively and <50% \n\n\n\nin SNB and SNF but a little >50% in SND. Reservoirs SNC, SNE, SNG and \n\n\n\nSNH contain essentially hydrocarbon, SNB contains significant amount of \n\n\n\nwater while SND contain some amount of water with hydrocarbon.  \n\n\n\nThe relative permeability to water, Krw is extremely low in SNC, SNE, SNG \n\n\n\nand SNH with values ranging from 0.01 to 0.02. SND has a low value of 0.13 \n\n\n\nwhile SNB and SNF have Krw values of 0.20 and 0.22 respectively. SNC, SNE, \n\n\n\nSNG and SNH have high Kro values ranging from 0.77 to 0.81 while SNB, \n\n\n\nSND and SNF have lower values of 0.23, 0.32 and 0.15 respectively. More \n\n\n\nhydrocarbon than water will be produced from reservoirs SNC, SNE, SNG \n\n\n\nand SNH because of the very high Kro while more water than hydrocarbon \n\n\n\nwill be produced in reservoirs SNB, SND and SNF due to low Kro. SNC has \n\n\n\nthe highest absolute permeability of 90663.50mD while SNF has the \n\n\n\nlowest absolute permeability of 941.00mD. The absolute permeability is \n\n\n\nfairly good in SNB, SND and SNE. This implies that hydrocarbon will flow \n\n\n\neasily in SNC than in SNF and others. SNC, SNE and SNH have the highest \n\n\n\nestimated Oil-in-place, OIP of 118,602.08, 149,342.64 and \n\n\n\n141,232.15bbl/acre respectively while SNB has the lowest OIP of \n\n\n\n47,590.26bbl/acre. SND and SNG have OIP values of 86,972.45 and \n\n\n\n75,310.12bbl/acre respectively. This is also mainly an oil-producing well. \n\n\n\n5.4 Korokoro 006 \n\n\n\nFour reservoir sands SNE, SNF, SNG and SNH are delineated in this well \n\n\n\nwith SNF and SNH having the highest thicknesses of 106ft and 102ft \n\n\n\nrespectively and SNG the lowest thickness of 55.5ft. SNB, SNC, and SND are \n\n\n\nnot continuous in this well may be due to a period of break in deposition \n\n\n\nof sediments. These reservoir sands contain average volume of shale \n\n\n\nranging from 8.39% in SNH to 26.94% in SNG. Effective porosity in these \n\n\n\nreservoirs are almost entirely uniform ranging from 23.46% in SNH to \n\n\n\n34.08% in SNF. Water saturation is observed to be <50% in all the \n\n\n\nreservoirs. SNF has a value of 41.16%, SNE 29.50% and SNG 28.45%. SNH \n\n\n\nhas the lowest value of 14.30%. Conversely, hydrocarbon saturation is \n\n\n\nobserved to be >50% in all the reservoirs with SNH having the highest \n\n\n\nvalue of 85.70 %, SNE 70.50 %, SNF 58.84% and SNG 71.55%.  \n\n\n\nAll the reservoirs essentially contain significant amount of hydrocarbon \n\n\n\nthan water. The relative permeability to water, Krw is extremely low in all \n\n\n\nfour reservoirs ranging from 0.01 in SNH to 0.06 in SNG. Relative \n\n\n\npermeability to oil, Kro is high SNE, SNG and SNH with values ranging from \n\n\n\n0.57 to 0.89. SNF has a low Kro value of 0.37. More hydrocarbon than water \n\n\n\nwill be produced from reservoirs SNE, SNG and SNH because of their very \n\n\n\nhigh relative permeability to oil. SNF has the highest absolute permeability \n\n\n\nvalue of 34366.80mD while SNH has K values of 2343.64mD. SNE and SNG \n\n\n\nhave fairly good K values of 11161.31mD and 14437.86mD respectively. \n\n\n\nThis is an indication that hydrocarbon will flow easily in SNF than in SNH \n\n\n\nand others. The estimated oil-in-place, OIP are 121,281.49bbl/acre in SNE, \n\n\n\n162,635.01bbl/acre in SNF, 84,097.09bbl/acre in SNG and \n\n\n\n230,133.87bbl/acre in SNH. This is mainly an oil-producing well. \n\n\n\n5.5 Obeakpu 005 \n\n\n\nTwo reservoir sands SNB and SNC are delineated in this well with SNB \n\n\n\nhaving the highest thickness of 200.5ft and SNC the lowest thickness of \n\n\n\n\nmailto:Rw@Tf(\u2126-m)\n\n\n\n\n\n\nMalaysian Journal of Geosciences (MJG) 4(2) (2020) 79-85 \n\n\n\nCite the Article: Omigie J.I., Alaminiokuma G.I.(2020). Petrophysical Evaluation Of Reservoirs For Hydrocarbon Reserve Estimation In Eastern Central \nSwamp Depobelt, Niger Delta. Malaysian Journal of Geosciences, 4(2): 79-85.\n\n\n\n50ft. SNB contains average volume of shale value of 14.59% while SNC \n\n\n\ncontain 24.14%. The average effective porosity in these reservoirs are \n\n\n\n30.68% and 25.67% respectively. Water saturation, Sw is observed to be \n\n\n\n26.33% in SNB and 30.95% in SNC while the hydrocarbon saturation, Sh is \n\n\n\n73.67% in SNB and 69.05% in SNC. Both reservoirs contain essentially \n\n\n\nhydrocarbon with some water. The relative permeability to water, Krw in \n\n\n\nSNB is 0.20 and 0.12 in SNC while the relative permeability to oil, Kro is \n\n\n\n0.74 in SNB and 0.64 in SNC. More hydrocarbon than water is expected to \n\n\n\nbe produced from both reservoirs because of their very high relative \n\n\n\npermeability to oil. SNB has the highest absolute permeability value of \n\n\n\n62086.92mD while SNC has K value of 10645.45mD. This implies that \n\n\n\nhydrocarbon will flow easily in SNB than in SNC. The estimated volume of \n\n\n\noil-in-place in the reservoir sands are 387,754.83bbl/acre in SNB and \n\n\n\n72,953.89bbl/acre in SNC. \n\n\n\n6. CONCLUSION \n\n\n\nFive wells (AFAM 15, AFAM 16, KOROKORO 003, KOROKORO 006 and \n\n\n\nOBEAKPU 005) were investigated and seven prospective reservoirs (SNB, \n\n\n\nSNC, SND, SNE, SNF, SNG and SNH) consisting of sand and shale in \n\n\n\nalternating sequence were delineated. All the identified reservoir sands \n\n\n\nare continuous except those not penetrated in shallow wells such as Afam \n\n\n\n15 and Obeakpu 005. Reservoir thickness, volume of shale, effective \n\n\n\nporosity, relative and absolute permeabilities, hydrocarbon and water \n\n\n\nsaturations and gas-in-place and oil-in-place were evaluated in order to \n\n\n\ndetermine the reservoirs\u2019 potential of this field.  \n\n\n\nMost of the reservoir sands in all the wells are observed to have significant \n\n\n\nthicknesses to accumulate hydrocarbon in exploitable quantities. \n\n\n\nThicknesses ranging from 24.5ft in SNG reservoir in AFAM 16 up to 200.5ft \n\n\n\nin SNB reservoir in OBEAKPU 005 are measured. The volume of shale \n\n\n\nvaries within and across all the wells with values <30% of the total \n\n\n\nthicknesses. This implies that the there are more sands than shales in the \n\n\n\nreservoirs. Shaly-sand zones have higher effective porosity for intervals \n\n\n\nwith low volume of shale while porosity within sandy-shale zones with \n\n\n\nhigh volume of shale are considerably reduced. Thus, the effect of \n\n\n\nshaliness in the sands is responsible for the reduction in the porosity \n\n\n\nbecause the zones with lowest porosity have the highest volume of shale.  \n\n\n\nRelative permeability to water (Krw) in reservoirs ranges from 0.00 to \n\n\n\n>1.00 across the wells. Sand SNE and SNF in Afam 16 have average Krw of \n\n\n\n0.00 which implies that 100% water-free hydrocarbon production is \n\n\n\nexpected. SNC in Afam 15 and Afam 16 has average Krw >1 which implies \n\n\n\nthat 100% water production is expected from this reservoir. The relative \n\n\n\npermeability to oil (Kro) is very high in reservoirs with high hydrocarbon \n\n\n\nsaturation. SNH in Korokoro 006 has an average hydrocarbon saturation \n\n\n\nof 85.70% and Kro of 0.89. Absolute permeability is generally high across \n\n\n\nall the wells. SNB in Obeakpu 005 has average permeability value of \n\n\n\n62,086.9mD. Reservoir sands SNB, SNC and SND contain no producible \n\n\n\nhydrocarbon in Afam 15 but contain producible hydrocarbon in Afam 16, \n\n\n\nKorokoro 003 and Obeakpu 005 wells. Reservoir sands SNE, SNF, SNG and \n\n\n\nSNH in Afam 15, Afam 16, Korokoro 003 and Korokoro 006 contain \n\n\n\nproducible hydrocarbon with the exception of SNF in Korokoro 003.  \n\n\n\n7. RECOMMENDATION \n\n\n\nFurther research should be conducted with petrographic analysis of the \n\n\n\nreservoir sands using more wells, core and seismic data for detailed \n\n\n\nevaluation of type and distribution of shale since these affect reservoir \n\n\n\nqualities and reserve estimation. Future development of all the wells \n\n\n\nshould be focused on deeper reservoirs with the exception of Obeakpu 005 \n\n\n\nwhich has producible hydrocarbon reservoirs within shallow depth \n\n\n\ninterval. \n\n\n\nREFERENCES \n\n\n\nAlaminiokuma, G.I. and Ofuyah, W.N., 2017. Porosity-Permeability \n\n\n\nRegimes in Reservoirs for Hydrocarbon Prospectivity in Nembe Creek \n\n\n\nField, Niger Delta. Journal of Applied Geology and Geophysics. 5(2), 62-\n\n\n\n70. \n\n\n\nArchie, G.E., 1942. The Electrical Resistivity as an Aid in Determining Some \n\n\n\nReservoir Characteristics. Journal Petroleum Technology, 5(1), 54-62. \n\n\n\nAsquith, G.B. and Gibson, C.R., 1982, Basic Well Log Analysis for Geologists. \n\n\n\nTulsa, AAPG, 66, 216. \n\n\n\nCorredor, F., Shaw, J.H., Billeti, F., 2005. Structural Styles in Deep Water \n\n\n\nFold and Thrust Belts of the Niger Delta. AAPG Bulletin 89, 753-780. \n\n\n\nDoust, H., and Omatsola, E., 1990. Niger Delta, in, Edwards, J.D., and \n\n\n\nSantogrossi, P.A., eds., Divergent/Passive Margin Basins, AAPG Memoir \n\n\n\n48: Tulsa, American Association of Petroleum Geologists, 239-248. \n\n\n\nDresser Atlas, 1979. Log Interpretation Charts: Houston, Texas, Dresser \n\n\n\nIndustries, Inc., 07 p.  \n\n\n\nEmina R., Obiadi I.I., Obiadi C.M., 2016. Evaluation and Prospect \n\n\n\nIdentification in the Olive Field, Niger Delta Basin, Nigeria. J Pet Environ \n\n\n\nBiotechnol. 7, 284. doi:10.4172/2157-7463.1000 \n\n\n\nKalu, C.G., Obiadi, I.I., Amaechi, P.O. and Ndeze, C.K., 2020. Petrophysical \n\n\n\nanalysis and reservoir characterization of emerald field, Niger Delta \n\n\n\nBasin, Nigeria. Asian J. Earth Sci., 13, 21-36. \n\n\n\nLarionov, V.V., 1969. Borehole Radiometry: Moscow, U.S.S.R., Nedra. \n\n\n\nNwaezeapu, V.C., Ezenwaka, K.C. and Ede, T.A., 2019. Egyptian Journal of \n\n\n\nPetroleum, 28(3), 273-280 \n\n\n\nOzumba, B.M., 2018. Stratigraphic Framework of the Western Central \n\n\n\nSwamp of the Niger Delta, Nigeria. Sci Environm. 1: 105 \n\n\n\nPetters, S.W., 1984. An Ancient Submarine Canyon in the Oligocene - \n\n\n\nMiocene of the Western Niger Delta. Sedimentology 31, 805-810.  \n\n\n\nSaadu, Y.K. and Nwankwo, C.N., 2018. Petrophysical Evaluation and \n\n\n\nVolumetric Estimation Within Central Swamp Depobelt, Niger Delta, \n\n\n\nusing 3-D Seismic and Well Logs. Egyptian Journal of Petroleum,  27(4), \n\n\n\n531-539. \n\n\n\nSchlumberger Well Service, Houston, 1989. Log Interpretation \n\n\n\nPrinciples/Application Houston, Schlumberger Education Services, 24 p. \n\n\n\nTimur, A., 1968. An Investigation of Permeability, Porosity, And Residual \n\n\n\nWater Saturation Relationships for Sandstone Reservoirs. The Log \n\n\n\nAnalyst, 9, 8-17.   \n\n\n\nWyllie, M.R.J. and Rose, W.D., 1950. Some Theoretical Considerations \n\n\n\nRelated to the Quantitative Evaluations of the Physical Characteristics of \n\n\n\nReservoir Rock from Electric Log Data: Journal of Petroleum \n\n\n\nTechnology, 189, 105-110 \n\n\n\n\nhttps://www.sciencedirect.com/science/article/pii/S1110062117301058#!\n\n\nhttps://www.sciencedirect.com/science/journal/11100621\n\n\nhttps://www.sciencedirect.com/science/journal/11100621/27/4\n\n\n\n"