# Multichannel Analysis of Surface Waves (MASW) to Estimate the Shear Wave Velocity for Engineering Characterization of Soils at Hawassa Town, Southern Ethiopia

Authors: Alemayehu Ayele; Kifle Woldearegay; Matebie Meten
Journal: International Journal of Geophysics
 (2022)

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## Abstract

Site characterization is a prerequisite for the successful and economic design of engineering structures and earthworks by providing geological information for any proposed project. Until now, no detail study has been carried out on the site characterization and classification using shear wave velocity (Vs) up to the top 30 m depth in Hawassa town. For this study, multichannel analysis of surface waves (MASW) was used to determine the variation of Vs for a proper civil engineering design in the town. In addition, vertical electrical sounding (VES) and standard penetration test (SPT) were employed to characterize the near-surface materials. The Vs30 map was prepared for Hawassa town using the estimated Vs30 values which ranges from 248.9 m/s to 371.3 m/s while the SPT-N values were ranges from 5bpf to 50bpf. The correlation of Vs and SPT-N values has been done by considering both corrected and uncorrected SPT-N values. The VES result showed that groundwater is found at a shallow depth. The correlation of Vs and SPT-N value was validated using regression model. The 1D Vs profile and 2D cross-section showed low Vs at a shallow depth. The near-surface soils of the town are classified based on the Vs30 as site class C (stiff soil and soft rock) and D class (stiff soils) according to the NEHRP (Natural Earthquake hazards Reduction Program) and as subsoil classes B and C according to the Eurocode 8. The geotechnical tests also showed that the soils in the study area are silty sand, sand and silty sand with some gravel. The low Vs values observed at a shallow depth should be given much attention during foundation design for the stability of civil engineering structures.


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## Body

## 1. Introduction

Population growth, rapid urbanization, and the demand of new land for infrastructures expansion of many Ethiopian cities including Hawassa town requires providing the necessary information about an existing soil condition, elastic moduli, bedrock depth, ground stiffness, site class and groundwater table for the safe design, construction, and its long service. Insufficient or inadequate soil characterization leads to unsatisfactory design which may subsequently result in serious structural damage or failure. Therefore, a detail geophysical and geotechnical site characterization plays an important role to identify a suitable site for any proposed structures and thus to prevent the engineering structures and human life from any future unexpected catastrophic consequences [1, 2].Civil engineering structures in Hawassa town are constructed with no detailed information of the subsoil characterization which acts as the basic foundation material that support and stabilize the any engineering structures [3, 4]. Structural failure is usually linked to the use of substandard materials and poor investigation of the subsoil materials. The main reason for structural failure emanates from inadequate understanding on geophysical and geotechnical properties of a soil. Moreover, the nature/type of soils, geologic sequence, and structures and water bearing units also affect the stability of civil engineering structures. These properties provide reliable information to effectively characterize the heterogeneous subsoil beneath an engineering site so as to construct successful structure [5, 6]. Buildings, roads, bridges, and high ways are infrastructures that contribute significantly to the sustainable development of the town. Therefore, it is necessary to have an adequate plan for a proper construction of the superstructure for a long time service and minimize an economic loss [7]. A detail understanding on the type, nature and characteristics of the soil, geological structures, depth of a groundwater table, and competent bedrock is most valuable to geotechnical and earthquake engineers for a proper site selection, design of an engineering structures, and identification of the possible adverse and unfavorable conditions in the study area in order to adopt proper remedial measures [8, 9].Several studies have used integrated geophysical and geotechnical investigations to characterize its suitability of the near-surface materials for road, building constructions, and foundation [10–13]. On this regards, multichannel analysis of surface waves (MASW) are widely used to measure the shear wave velocity (Vs) of near-surface materials as these methods are reliable, noninvasive, and low-cost procedures in engineering applications to identify potentially unsuitable geological conditions, determine small-strain stiffness of soil and rock, foundation instability analysis, and know site classes [14–16]. Rayleigh waves that propagate from the ground surface to subsurface are used to estimate Vs from the dispersion curve and thus the average shear wave velocity (Vs30) is estimated from the measured Vs to determine the site class for site response analysis, earthquake resistant design and safe building provision [17, 18]. It is also an important geotechnical parameter to obtain the near-surface dynamic properties of a soil which in turn is essential for the engineering design of a small to large projects such as roads, buildings, and highways. The vertical electrical sounding (VES) is proven to be very efficient and applicable technique in groundwater investigation, landfill site, and engineering site characterization and foundation stability analysis [19, 20]. This is particularly applied to engineering site investigation as it measures the resistivity variation within the near-surface as a function of factors like grain size distribution, soil structure and stratification, and degree of water saturation. Therefore, this helps to decide on the suitability of the site for the effective design of structural foundations and sustainable building constructions.Due to its simplicity, the standard penetration test (SPT) is the most widely used dynamic in-situ test in geotechnical site investigation for describing the dynamic properties of a soil in foundation stability analysis [21]. Many researchers have used SPT-N values to identify the stiffness of a soil, depth of groundwater table, soil profile/type, site class, and relative density as these are very important to assure the suitability of a site for a building foundation [22–24]. Different researchers have been correlated Vs and SPT-N values to check the degree of relationship between these parameters [25–28]. In the current study, the detail soil properties and type, site classes, stiffness, elastic moduli, and correlation of Vs and SPT-N values, competent bedrock depth, layer boundaries, and depth of water table has been studied with in a view of engineering site characterization to ensure the suitability of a site for any civil engineering structure [29, 30]. The main aim of this study was investigating the near surface geological materials in Hawassa town using MASW, VES, and SPT survey and thus determines whether the study area is suitable for a safeguard construction and indestructible infrastructure development in the future. This can be done by identifying the soil type and stiffness, estimating Vs30, and providing site class for the selecting a proper foundation.


## 2. Location and Geological Setting of the Study Area

The town of Hawassa is situated in the southern Main Ethiopian Rift (MER) at the center of Hawassa caldera, on the eastern and southeastern sides of Lake Hawassa (Figure1). The study area is geographically bounded between longitude 7°1′ to 7°5′N and latitude 38°28′ to 38°29′E. The MER is an active plate boundary between the Nubian and Somalian plates which controls the seismic activity in the East African region [31]. The seismicity in and around Hawassa town includes earthquakes of minor to intermediate magnitudes (M <6) that have caused structural damage to buildings and infrastructures in the town [32–35]. In addition, this town is located in the shade of two silicic volcanoes that have emerged from the floor of the middle Pleistocene Corbetti Caldera [36]. According to [36] the lithological units in Hawassa town and its environs include fluvial sediments, alluvial sediments, polygenetic sediments, Wondo Koshe pumice fall and flow deposits, scoria cones, tuff cones, Hawassa basalts, and Hawassa rhyolitic ignimbrites (Figure 2).Figure 1
Location map of Hawassa town in Southern Ethiopia.Figure 2
Geological map of Hawassa area (modified after [36]).


## 3. Materials and Methods

### 3.1. Geotechnical and Borehole Data

SPT approach was applied to determine soil stiffness, thickness, and depth of the near-surface materials [37, 38]. For this purpose, forty one SPT borehole data were collected from private organizations for the site characterization, correlation of Vs and SPT-N values, and seismic site classification [39, 40] (Figure 3). All the collected SPT-N data was corrected using the correction factor as stated in Table 1 for hammer efficiency, borehole diameter, rod length, and sampler liner [41, 42] according to equation (1). Then, the obtained N60 was used for the correlation with Vs. Laboratory tests were conducted, including grain size analysis, moisture content, and Atterberg limits, on selected soil samples to determine the geotechnical properties and to identify the soil type/properties and grain sizes of each soil according to the specification of [43] of soils in the study area.
(1)N60=EH∗CB∗CS∗CR∗SPT−N0.6.Figure 3
Data acquisition using Geode seismograph instrument in the field.Table 1
Correction factors used in the field to determine SPT-N values in this study [41].
FactorsTermsEquipment variableCorrection factorHammer efficiencyEHDonut hammer0.5Borehole diameterCB65–155 m1Sampling methodCSStandard sampler1Rod lengthCR3–4 m0.754–6 m0.856–10 m0.95>10 m1.00where N60 is the corrected SPT-N value in the field, EH is the hammer efficiency, CB is the borehole diameter, CS is the sampling method, CR is the rod length and SPT-N is the measured value in the field. Forty borehole data was obtained from the south design construction and construction supervision enterprise (Figure4) [44]. This information was used to prepare the borehole distribution and groundwater level maps in the study area using ArcGIS.Figure 4
Location of boreholes, SPT-N, MASW, and VES data points in Hawassa town.


### 3.2. Geophysical Data Acquisition

#### 3.2.1. MASW Data Acquisition

Nineteen MASW data points at 5 m spacing were used for data acquisition on the study area (Figure3). MASW data sites were selected based on the accessibility of a site to spread the geophones. To measure the Vs, MASW measurements were carried out across the investigated sites (Figure 4) using Geode seismograph instrument. This system consists of 24 geophone channels (4.5HZ). Surface waves were generated by striking a metal plate which is tightly coupled to the ground surface by a 10 kg sledge hammer. The number of impacts (i.e. stacks) at each shooting point was 5 to increase the signal-to-noise ratio and thus improve the data quality. MASW data acquisition parameters were employed in the present study is listed in Table 2.Table 2
MASW data acquisition parameters.
Energy/sourceSledge hammerGeophone24 geophones of 4.5 Hz frequencyStack5Geophone distance5 mLength of spread115 mShot positionEvery 5 m interval (starting from -2.5 m to 117.5 m)Recoding time2000 msSampling0.125 msFilterOpen


#### 3.2.2. VES Data Acquisition

In the current study area, two VES surveys were conducted for this investigation using ABEM Terrameter SAS 300C Resistivity Meter (Figure4). Four electrodes (two current electrode and two potential electrodes) were used in this study to make a contact to the ground. The Schlumberger array/configuration was used for VES data collection by 150 m current electrode spacing (AB/2) according to the accessibility of a site for measurement. On the other hand, the potential electrode (MN) spacing starts from 0.5 m to 45 m were used to investigate the near-surface earth materials. Each VES points contain 16 apparent resistivity measurements. For the two VES surveys, the measured apparent resistivity values were plotted against the AB/2 on a log-log scale to control the consistency of values in the study area.


### 3.3. Data Processing

#### 3.3.1. MASW

Based on the geological conditions of the study area, five representative sites of MASW data points were selected for the detail analysis. The shot gathers acquired during the MASW were analyzed using SeisImager/SW software to obtain the dispersion curves, 1D and 2D Vs cross-section of the investigated area. The cross correlation of common midpoint (CMP) gather was applied to generate the dispersion curve at the frequency domain. The Vs were calculated from the phase velocity versus frequency curve using the phase velocity dispersion curve. The linear slope of each component on the swept frequency record was used to compute the phase velocity. The spatial variation of Vs with depth was observed in the 1D Vs profile whereas the spatial variation of Vs with depth versus distance was seen in the 2D Vs cross section in the investigated area.


#### 3.3.2. VES

In order to estimate the layer parameters (layer thickness and true resistivity) in the study area, the IP2WIN [45] inversion software was used. The main steps followed in this study to determine the layer thickness and true resistivity were inputting the field data, fitting the measured, and calculated curves and invert these curves. More number of iterations was used to get the best fit for the measured and calculated resistivity curves. The final model was used to interpret and relate with the geological conditions of the study area.


## 3.1. Geotechnical and Borehole Data

SPT approach was applied to determine soil stiffness, thickness, and depth of the near-surface materials [37, 38]. For this purpose, forty one SPT borehole data were collected from private organizations for the site characterization, correlation of Vs and SPT-N values, and seismic site classification [39, 40] (Figure 3). All the collected SPT-N data was corrected using the correction factor as stated in Table 1 for hammer efficiency, borehole diameter, rod length, and sampler liner [41, 42] according to equation (1). Then, the obtained N60 was used for the correlation with Vs. Laboratory tests were conducted, including grain size analysis, moisture content, and Atterberg limits, on selected soil samples to determine the geotechnical properties and to identify the soil type/properties and grain sizes of each soil according to the specification of [43] of soils in the study area.
(1)N60=EH∗CB∗CS∗CR∗SPT−N0.6.Figure 3
Data acquisition using Geode seismograph instrument in the field.Table 1
Correction factors used in the field to determine SPT-N values in this study [41].
FactorsTermsEquipment variableCorrection factorHammer efficiencyEHDonut hammer0.5Borehole diameterCB65–155 m1Sampling methodCSStandard sampler1Rod lengthCR3–4 m0.754–6 m0.856–10 m0.95>10 m1.00where N60 is the corrected SPT-N value in the field, EH is the hammer efficiency, CB is the borehole diameter, CS is the sampling method, CR is the rod length and SPT-N is the measured value in the field. Forty borehole data was obtained from the south design construction and construction supervision enterprise (Figure4) [44]. This information was used to prepare the borehole distribution and groundwater level maps in the study area using ArcGIS.Figure 4
Location of boreholes, SPT-N, MASW, and VES data points in Hawassa town.


## 3.2. Geophysical Data Acquisition

### 3.2.1. MASW Data Acquisition

Nineteen MASW data points at 5 m spacing were used for data acquisition on the study area (Figure3). MASW data sites were selected based on the accessibility of a site to spread the geophones. To measure the Vs, MASW measurements were carried out across the investigated sites (Figure 4) using Geode seismograph instrument. This system consists of 24 geophone channels (4.5HZ). Surface waves were generated by striking a metal plate which is tightly coupled to the ground surface by a 10 kg sledge hammer. The number of impacts (i.e. stacks) at each shooting point was 5 to increase the signal-to-noise ratio and thus improve the data quality. MASW data acquisition parameters were employed in the present study is listed in Table 2.Table 2
MASW data acquisition parameters.
Energy/sourceSledge hammerGeophone24 geophones of 4.5 Hz frequencyStack5Geophone distance5 mLength of spread115 mShot positionEvery 5 m interval (starting from -2.5 m to 117.5 m)Recoding time2000 msSampling0.125 msFilterOpen


### 3.2.2. VES Data Acquisition

In the current study area, two VES surveys were conducted for this investigation using ABEM Terrameter SAS 300C Resistivity Meter (Figure4). Four electrodes (two current electrode and two potential electrodes) were used in this study to make a contact to the ground. The Schlumberger array/configuration was used for VES data collection by 150 m current electrode spacing (AB/2) according to the accessibility of a site for measurement. On the other hand, the potential electrode (MN) spacing starts from 0.5 m to 45 m were used to investigate the near-surface earth materials. Each VES points contain 16 apparent resistivity measurements. For the two VES surveys, the measured apparent resistivity values were plotted against the AB/2 on a log-log scale to control the consistency of values in the study area.


## 3.2.1. MASW Data Acquisition

Nineteen MASW data points at 5 m spacing were used for data acquisition on the study area (Figure3). MASW data sites were selected based on the accessibility of a site to spread the geophones. To measure the Vs, MASW measurements were carried out across the investigated sites (Figure 4) using Geode seismograph instrument. This system consists of 24 geophone channels (4.5HZ). Surface waves were generated by striking a metal plate which is tightly coupled to the ground surface by a 10 kg sledge hammer. The number of impacts (i.e. stacks) at each shooting point was 5 to increase the signal-to-noise ratio and thus improve the data quality. MASW data acquisition parameters were employed in the present study is listed in Table 2.Table 2
MASW data acquisition parameters.
Energy/sourceSledge hammerGeophone24 geophones of 4.5 Hz frequencyStack5Geophone distance5 mLength of spread115 mShot positionEvery 5 m interval (starting from -2.5 m to 117.5 m)Recoding time2000 msSampling0.125 msFilterOpen


## 3.2.2. VES Data Acquisition

In the current study area, two VES surveys were conducted for this investigation using ABEM Terrameter SAS 300C Resistivity Meter (Figure4). Four electrodes (two current electrode and two potential electrodes) were used in this study to make a contact to the ground. The Schlumberger array/configuration was used for VES data collection by 150 m current electrode spacing (AB/2) according to the accessibility of a site for measurement. On the other hand, the potential electrode (MN) spacing starts from 0.5 m to 45 m were used to investigate the near-surface earth materials. Each VES points contain 16 apparent resistivity measurements. For the two VES surveys, the measured apparent resistivity values were plotted against the AB/2 on a log-log scale to control the consistency of values in the study area.


## 3.3. Data Processing

### 3.3.1. MASW

Based on the geological conditions of the study area, five representative sites of MASW data points were selected for the detail analysis. The shot gathers acquired during the MASW were analyzed using SeisImager/SW software to obtain the dispersion curves, 1D and 2D Vs cross-section of the investigated area. The cross correlation of common midpoint (CMP) gather was applied to generate the dispersion curve at the frequency domain. The Vs were calculated from the phase velocity versus frequency curve using the phase velocity dispersion curve. The linear slope of each component on the swept frequency record was used to compute the phase velocity. The spatial variation of Vs with depth was observed in the 1D Vs profile whereas the spatial variation of Vs with depth versus distance was seen in the 2D Vs cross section in the investigated area.


### 3.3.2. VES

In order to estimate the layer parameters (layer thickness and true resistivity) in the study area, the IP2WIN [45] inversion software was used. The main steps followed in this study to determine the layer thickness and true resistivity were inputting the field data, fitting the measured, and calculated curves and invert these curves. More number of iterations was used to get the best fit for the measured and calculated resistivity curves. The final model was used to interpret and relate with the geological conditions of the study area.


## 3.3.1. MASW

Based on the geological conditions of the study area, five representative sites of MASW data points were selected for the detail analysis. The shot gathers acquired during the MASW were analyzed using SeisImager/SW software to obtain the dispersion curves, 1D and 2D Vs cross-section of the investigated area. The cross correlation of common midpoint (CMP) gather was applied to generate the dispersion curve at the frequency domain. The Vs were calculated from the phase velocity versus frequency curve using the phase velocity dispersion curve. The linear slope of each component on the swept frequency record was used to compute the phase velocity. The spatial variation of Vs with depth was observed in the 1D Vs profile whereas the spatial variation of Vs with depth versus distance was seen in the 2D Vs cross section in the investigated area.


## 3.3.2. VES

In order to estimate the layer parameters (layer thickness and true resistivity) in the study area, the IP2WIN [45] inversion software was used. The main steps followed in this study to determine the layer thickness and true resistivity were inputting the field data, fitting the measured, and calculated curves and invert these curves. More number of iterations was used to get the best fit for the measured and calculated resistivity curves. The final model was used to interpret and relate with the geological conditions of the study area.


## 4. Vs30, Shear Modulus (G30max) and Modulus of Elasticity (E30) Calculations

Vs profiles, which are obtained by inverting Rayleigh wave dispersion curves, are used to determine Vs30 [46]. Vs30 values were calculated in this study using [47, 48]
(2)Vs30=30∑i=0Ndi/Vi,where di and vi denote the thickness (in meters) and shear wave velocity of the ith formation or layer, respectively, from a total of N layers in the top 30 m. The empirical link between P-wave velocities (Vp) and Vs was used to estimate Vp of the porous/saturated materials in the investigated area. The Vp of saturated materials is 1500 m/s by [49] but these values range between 1200 m/s and 1800 m/s according to [50, 51]. To estimate Vp, the measured Vs was used according to [52].
(3)Vp=1.11∗Vs+1290,where Vp is the compressional wave velocity which can be estimated from the measured Vs and Vs is the shear wave velocity measured from the field using MASW. The Gmax and E30 estimated from the measured Vs was used to characterize the investigated site [53]. For this purpose, G30max and E30 which are given in equation (4) [54] and 5 [55], respectively, were used to characterize the soil condition of the sites in the study area.
(4)G30max=ρVs302,where G30max is the average shear modulus (Pa) in the top of 30 depth and Vs30 is the average shear wave velocity (m/s) up to a depth of 30 m andρ is the average density of the soil (in kg/m3).
(5)E30=2Vs302ρ+σ,where E30 is the average modulus of elasticity (Pa) at a depth of 30 m, Vs30 is the average shear wave velocity (m/s) in the top of 30 m depth, andσ is Poisson ratio and a standard value of 0.25 was used [56].


## 5. Correlation of Vs and SPT-N

The correlation between Vs and SPT-N value was used for an engineering site characterization [57]. Table 3 shows the statistical links between Vs and SPT-N values proposed by the several researchers for this purpose. They also looked at empirical power-law relationships between Vs and SPT-N values like Vs=ASPT−NB, where A controls the amplitude of Vs and the SPT-N value curve, B controls the relationship curvature, Vs is the shear wave velocity, and SPT-N represents the standard penetration test values as shown in Table 3. The Vs values were predicted from the SPT-N values using a regression relationship for sandy soil in this study, and it was compared to other available empirical equation around the world.Table 3
Existing empirical correlations between Vs and SPT-N values employed for this investigation depending on the soil type from the other experts.
S.noAuthorsVs (m/sec)Sandy soils1Shibata [58]Vs=32SPT−N0.52Ohta et al. [59]Vs=87.2SPT−N0.363Imai [60]Vs=80.6SPT−N0.3314Imai and Tonouchi [61]Vs=97SPT−N0.3145Seed et al. [62]Vs=56.4SPT−N0.56Sykora and Stokoe [63]Vs=100.5SPT−N0.297Lee [64]Vs=57.4SPT−N0.498Hasanҫebi and Ulusay [65]Vs=90.82SPT−N0.3199Hanumantharao and Ramana [66]Vs=79SPT−N0.43410Dikmen [67]Vs=73SPT−N0.3311Rahman [68]Vs=82SPT−N0.382912This studyVs=115SPT−N0.28The validation of the regression equation for the predicted Vs and measured Vs values is required to ensure the model produced for the study area is reliable. Researchers employed the normal consistency ratio (Cd) and comparison between predicted and measured Vs to validate the predicted Vs [69, 70]. The predicted Vs were validated using a Cd and comparison of measured Vs in this investigation. Cd is also calculated using equation (6) [67] as the ratio of the difference between the Vspre and Vsmeas divided by SPT-N values
(6)Cd=Vsmeas−VspredSPT−Nvalues,where Vsmeas is the measured shear wave velocity, Vspred is the calculated Vs using the newly proposed regression equation for the study area, and SPT-N values are the measured SPT-N values.


## 6. Results and Discussion

### 6.1. VES

The resistivity models created by inverting the VES data revealed differences in resistivity (ρ) with depth in the investigated area. In Figure 5(a), the VES1 showed HA type curve (ρ1>ρ2<ρ3<ρ4) with an acceptable RMS error of 1.83 percent. There are four geo-electrical layers. As shown in Table 4, the first layer has a high ρ layer (250 ohm-m at a depth of 0.847 m), the second layer has a very low ρ layer (11.7 ohm-m at a depth of 2.85 m), which is considered a shallow aquifer, the third layer has a low ρ layer (40.2 ohm-m at a depth of 195 m), and the fourth layer has a very high ρ layer (303ohm-m). As illustrated in Figure 5(b), the inverted VES2 revealed HKK type curve ρ1>ρ2<ρ3>ρ4<ρ5>ρ6. In total, there are six geo-electrical strata in which the first layer exhibits a high ρ zone (53.5 ohm-m at 0.75 m), whereas the second layer depicts a low ρ layer (19.3 ohm-m at 3.18 m), which is probably interpreted as a shallow aquifer. The third layer has a very high ρ (126 ohm-m at a depth of 6 m), the fourth layer has a very low ρ (10.9 ohm-m at a depth of 12.3 meters), the fifth layer has an ρ increase (27.4 ohm-m at a depth of 21.4 m), and the sixth layer has shown a decrease in ρ (23.5 ohm-m). The study has shown similar trends with that of [71]. The spatial analysis of borehole data in Hawassa town showed that the groundwater level ranges from 4.88 m to 55.9 m. As shown in Figure 5(c), the groundwater level in the northwestern and southwestern parts of the study area is shallower than that of the southeastern part (see the Supplementary Table S1 and Figure S1). The groundwater level in Hawassa town is generally shallow and increases in its depth from the western to the eastern parts of the town. The VES and borehole data points were taken from different sites in the study area as shown in Figure 4. However, the VES data showed that groundwater table probably found at a shallower depth than from the borehole data.Figure 5
(a) VES1 invertedρ graph, (b) VES2 inverted ρ graph, and (c) groundwater level map from the borehole in Hawassa town.
(a)(b)(c)Table 4
Thickness (h) and resistivity (ρ) of VES 1 and VES 2 at Hawassa town.
VES 1VES 2LayerDepth (m)Resistivity (Ω m)LayerDepth (m)Resistivity (Ω m)h10.847250h10.7553.9h22.8511.7h23.1819.3h319540.2h36126303h412.310.9h521.427.423.5


### 6.2. 1D Vs Profiles and 2D Vs Cross-Section

The findings from five sites (MASW 1, MASW 2, MASW 3, MASW 4, and MASW 5) were provided in this study. The RMS is less than 7% and thus it was acceptable. The Vs models in Figure6 showed that the typical sites have Vs values range from 168 m/s to 542 m/s whereas Vp values which were estimated from the measured Vs values ranging from 1477 m/s to 1892 m/s (Supplementary Table S2 and Figure S2). Vp was estimated from Vs values. Vs and Vp values exhibited significant depth variability for three sites (MASW 1, MASW 2, and MASW 5): (a) a decrease in value was observed in the upper layer (0 to 5 m) which indicated lithological variation; (b) an increase in value was observed in the depth range of 5 m to 25 m; and (c) a decrease and nearly constant values were observed at depths greater than 25 m. However, because of lithological alterations, the Vs and Vp values of MASW 3 decreased from 0 to 25 m before start it starts to drop or remain constant from 25 m onwards. The top layer (depth 0 to 5 m) of MASW 4 showed a drop in Vs and Vp values, indicating lithological variation, although the value remained constant at depths greater than 5 m. The dispersion curve in (Figure 7(a)) was constructed at a frequency of 5.4 Hz to 19 Hz and a phase velocity from 200 m/s to 450 m/s. Due to weak overtone image, the dispersion curve at a frequency below 5.4 Hz was not shown for MASW 1 (Figure 7(a)) but the dispersion curves at a frequency below 5.2 Hz were not shown for MASW 2, MASW 3, MASW 4, and MASW 5 (Figures 7(b)–7(e)). The dispersion curves were showed at a frequency interval of 5.2 Hz to 19 Hz for selected sites in the study area but the phase velocity for the selected sites were range from 200 m/s to 450 m/s. The phase velocity and frequency curves revealed a smooth curve that begins high on the left and then drops low on the right for MASW 1, MASW 2, MASW 3, and MASW 5 (Figures 7(a)–7(c) and 7(e)) but phase velocity and frequency curves showed almost straight line for MASW 4.Figure 6
Vs and Vp plots for typical sites in the study area.Figure 7
Dispersion curves from (a) MASW 1, (b) MASW 2, (c) MASW 3, (d) MASW 4, and (e) MASW 5.
(a)(b)(c)(d)(e)The Vs values vary from 191 m/s to 542 m/s for 1D Vs profile for MASW 1 (Figure8(a)). The data revealed that a rise from 200 m/s at the surface to 542 m/s at a depth of 0 and 20 m, respectively, and then it drops from 540 m/s to 501 m/s at a depth of 20.8 m and 30 m, respectively, due to its lithological variation. As it was observed in Figure 8(b), the 2D Vs cross-section of MASW 1 showed Vs variation at a lateral distance of 120 m. In addition, it showed a low Vs values between 0 to 5 m, a very high Vs contrast between 12 m and 25 m which is most probably caused by the response of stiff materials. There was also a slight decrease in Vs values at a depth of 30 m. The 1D Vs values for MASW 2 (Figure 9(a)) increases from 232 m/s to 473 m/s at a depth of 3 m and 24 m, respectively, but the Vs values decreases from 470 m/s to 420 m/s at a depth of 24.5 m and 30 m, respectively. The 2D Vs cross-section for MASW 2 (Figure 9(b)) showed variations in Vs values across a distance of 115 m in which Vs values is being increased from 260 m/s to 382 m/s at a depth of 0 and 5 m, respectively, reducing at a depth of 10 m and increased in Vs at a depth of 13 m. Vs increased from 228 m/s at the surface to 406 m/s at a depth of 24.2 m, then dropped from 400 m/s to 200 m/s at a depth of 30 m as can be seen from 1D Vs profile from MASW 3 (Figure 10(a)). The 2D Vs cross-section of MASW 3 (Figure 10(b)) showed a change in Vs over a distance of 120 m along the survey line. The Vs increasing from 206 m/s to 294 m/s at a depth of 0 and 10 m, respectively, over a distance of 0 to 45 m but low Vs zone showed over a distance of 45 m to 120 m at a depth of 15 m.Figure 8
(a) MASW 1 1D profile and (b) 2D Vs cross-section.
(a)(b)Figure 9
(a) MASW 2 1D profile and (b) 2D Vs cross-section.
(a)(b)Figure 10
(a) MASW 3 1D Vs profile and (b) MASW 2D vs cross-section.
(a)(b)At MASW 4, Vs ranges from 223 m/s to 300 m/s (Figure11(a)). As compared to the other sites, MASW 4 showed a small increment in Vs with depth in which Vs reached a maximum of 300 m/s. The 2D Vs cross-section of MASW 4 showed a spatial variation over a distance of 110 m (Figure 11(b)) with a general increase in Vs with depth in which a low Vs ranging from 206 m/s to 272 m/s near to the surface to a depth of 7 m and a higher Vs which is greater than 292 m/s showed at a depths of 30 m. Moreover, for MASW 5 Vs values range from 300 m/s to 450 m/s at the surface to a depth of 30 m (Figure 12(a)). The Vs was found to decrease from 300 m/s at the surface to 450 m/s at a depth of 24 m but showed slight decrease in the Vs at a depth of 27.8 m. The 2D Vs cross-section has shown for MASW 5 the variation of Vs values across a distance of 110 m (Figure 12(b)). The Vs increased from 272 m/s (at the surface) to 471 m/s at a depth of 30 m. Finally, 2D Vs cross-section revealed Vs values of 426 m/s for depths ranging from 16 m to 30 m over a distance of 0 to 110 m.Figure 11
(a) MASW 4 1D Vs profile and (b) MASW 4 2D Vs cross-section.
(a)(b)Figure 12
(a) MASW 5 1D Vs profile and (b) MASW 5 2D Vs cross-section.
(a)(b)


### 6.3. Vs30, G30max, and E30 Map

The Vs30 data from several sites were collected and used to create a Vs30 map for Hawassa town. The Vs30 values for Hawassa town range from 248.9 m/s to 371.3 m/s as can be seen from Figure13(a). A very low Vs30 values, which range from 248.9 m/s m/s to 279.5 m/s, were found in the northern and to some extent in central parts of the study area while the low Vs30 values, which range from 279.6 m/s to 310.1 m/s, were found in the eastern, some parts of the western, and central areas. A high Vs30 values, ranging from 310.2 m/s to 340.7 m/s were found in the western, southern, and some central parts of the study area. In the southern as well as some sites in the western and eastern parts of the study area, very high Vs30 values, ranging from 340.5 m/s to 371.3 m/s, were found. Hence, the study area (Table 5) falls under C and D site classes according to [72] but classes B and C according to [73]. When Vs30 values were correlated to [72] soil classes, the majority of the soils in the study area were classified as stiff soils with a minor portion of very stiff soil or soft rock but the study area is mostly covered with medium dense sand and very dense sand according to [73].Figure 13
(a) Vs30 map of Hawassa town, (b) Gmax30 map of Hawassa town, (c) E30 map of Hawassa town, and (d) Lithological correlation between SPTN Values (BH1, BH4, and HU) with Vs for MASW 1.
(a)(b)(c)(d)Table 5
Vs30 values of the respective site classes in Hawassa town.
Site codesVs30 (m/s)Site classes of Hawassa townAccording to [72]According to [73]MASW 1362CBMASW 2350DCMASW 3301DCMASW 4269DCMASW 5371CBMASW 6301DCMASW 7346DCMASW 8266DCMASW 9261DCMASW 10268DCMASW 11271DCMASW 12276DCMASW 13356DCMASW 14357DCMASW 15270DCMASW 16348DCMASW 17267DCMASW 18265DCMASW 19249DCThe G30max and E30 maps were generated to characterize Hawassa town. The near-subsurface materials up to the depth 30 m were mapped using G30max values which vary from 0.991 MPa to 2.83 MPa in this investigation. The G30max map (Figure13(b)) revealed the presence of (a) high stiff materials in the southern, southeastern, and southwestern corridors and (b) very low stiff materials in the northern, western, and central parts of the study area. Due to its lower stiffness, the northern part of the study area requires more attention in terms of its engineering design attention than the southern portion of the study area, which has a higher stiffness. The E30 map was prepared to characterize the degree of deformation in earth materials for settlement studies in the study area. The E30 map showed that the E30 values range from 2.68 MPa to 7.63 MPa. This map (Figure 13(c)) revealed that the northern and central parts of the study area have a lower modulus of elasticity, indicating a higher deformation whereas the southern part has got a higher modulus of elasticity, implying a smaller deformation. This means that in the northern portion of the study area, settlement analysis is required more than that of the southern part. The lithological correlation between SPT-N borehole, Vs and borehole log (BH1 and BH4: HU) were done for MASW 1 to understand the vertical stratification of the study area (Figure 13(d)). It was showed that a decrease in Vs and SPT-N values at a depth of 0 to 6 m and increases at a depth of 6 m to 18 m due to the presence of very dense soil and soft rocks (weathered and highly fractured basalt).


### 6.4. Geotechnical Soil Characterization

Grain size analysis for representative soil samples from the study area was conducted using sieve analysis. The grain size distribution ranges from 0% to 5% for gravel, 25% to 89% for sand and 11% to 80% for nonplastic silt in the study area (Figure14(a)). Hence, the Atterberg limits were not determined or not possible (NP) to determine for the representative soils in the study area as it is dominated by nonplastic cohessionless soils (Table 6). The soils in the study area soil were dominantly silty sand and sand according to the Unified Soil Classification System (USCS). The result of the current study is similar with that of [71].Figure 14
Grain size distribution curves of soil in the study area.Table 6
Summarized geotechnical results of soils in the study area.
Borehole and pit codeDepth (m)Atterberg limit%USCSLLPLPIGravelSandFineBH-1Adr3NPNPNP—2575Silty sand soilSMBH-1HU3NPNPNP—955Poorly graded sand soilSPBH-2Mr3NPNPNP—2080Silty sand soilSMBH-2R3NPNPNP52570Silty sand with some gravel soilSMBH-2Ru3NPNPNP—3862Silty sand soilSMBH-2Sg3NPNPNP—3367Silty sand soilBH-2Wt3NPNPNP—4555Silty sand soilBH-1Yw3NPNPNP—5050Silty sand soilBH-2Ab3NPNPNP—4060Silty sand soilBH-1Bh3NPNPNP—4456Silty sand soilBH-2Tsf3NPNPNP—4159Silty sand soilTP-2AIB3NPNPNP—3961Silty sand soilBH-1SPC3NPNPNP—2575Silty sand soilBH-1ZB3NPNPNP—3466Silty sand soilPIT12NPNPNP—3268Silty sand soil


### 6.5. Correlation of Vs between SPT-N and N60

For the regression correlation of Vs and SPT-N values using the power law, data pairs of Vs, and SPT-N were used. The relationship between Vs and SPT-N values was investigated using a linear regression approach. Attempts were made in this work to derive Vs from SPT-N and N60 data for Hawassa town (Supplementary TableS3). The geology of the study area is characterized by sandy soil to according to SPT-N values, well inventory, and MASW data. As a result, for the study area with a similar soil types, different empirical equations were compared to the newly developed Vs using SPT-N and N60 regression equations. Figure 15(a) depicts the linear regression equation derived in this study as well as other equations developed for similar soil characteristics (i.e. sandy soil). The SPT-N value has a considerable effect on Vs estimate as evidenced by the high Pearson correlation coefficient (R2). The proposed correlation between Vs and SPT-N values for Hawassa town is given in equation (7) as follows.
(7)Vs=115SPT−N0.28r=0.84andR2=0.7102forsandysoil.Figure 15
(a) correlations between Vs, SPT-N and N60 of the current and previous studies, (b) Cd graphs of sandy soil, and (c) comparison between predicted and measured Vs for sandy soil.
(a)(b)(c)The correlation model was made using the power law (equation (8)) and the Vs and N60 proposed equations for the town of Hawassa (Figure 10(a)).
(8)Vs=122.98N600.2807r=0.714andR2=0.5102forsandysoil.As stated in equations (7) and (8), strong relation from R2 were found for Vs and SPT-N values rather than the Vs and N60 values in this investigation. In general, an equation with a high R2 value can predict more accurately than the one with a lower value. Hence, the results showed that the predicting equation based on SPT-N values (equation (7)) has a more predictive capacity to forecast Vs than N60 in this study (equation (8)). Furthermore, the suggested Hawassa town linear regression model was tested using Cd and a comparison of Vspred and Vsmeas. Figure 15(b) shows a comparison of Cd and SPT-N levels for sandy soils in the study area. It should be noticed that Cd’s average value was close to zero (Supplementary Table S4 and Figure S3). This means that the predicted Vs value is quite close to the measured one. As a result, the Cd ratio validation test for the newly proposed regression equation developed for the study area estimates Vs accurately and confidently. In addition, comparisons between Vsmeas and Vspred were done. Figure 15(c) depicts the Vsmeas and Vspred for the validation of the newly proposed regression equation for Hawassa town. All of the data points fall between the 1 : 0.5 and 1 : 1.3 slope lines. However, majority of the data fall inside the 1 : 1 line slope indicating that the predictive model is the best fit for study the area (Supplementary Table S5 and Figure S4).


## 6.1. VES

The resistivity models created by inverting the VES data revealed differences in resistivity (ρ) with depth in the investigated area. In Figure 5(a), the VES1 showed HA type curve (ρ1>ρ2<ρ3<ρ4) with an acceptable RMS error of 1.83 percent. There are four geo-electrical layers. As shown in Table 4, the first layer has a high ρ layer (250 ohm-m at a depth of 0.847 m), the second layer has a very low ρ layer (11.7 ohm-m at a depth of 2.85 m), which is considered a shallow aquifer, the third layer has a low ρ layer (40.2 ohm-m at a depth of 195 m), and the fourth layer has a very high ρ layer (303ohm-m). As illustrated in Figure 5(b), the inverted VES2 revealed HKK type curve ρ1>ρ2<ρ3>ρ4<ρ5>ρ6. In total, there are six geo-electrical strata in which the first layer exhibits a high ρ zone (53.5 ohm-m at 0.75 m), whereas the second layer depicts a low ρ layer (19.3 ohm-m at 3.18 m), which is probably interpreted as a shallow aquifer. The third layer has a very high ρ (126 ohm-m at a depth of 6 m), the fourth layer has a very low ρ (10.9 ohm-m at a depth of 12.3 meters), the fifth layer has an ρ increase (27.4 ohm-m at a depth of 21.4 m), and the sixth layer has shown a decrease in ρ (23.5 ohm-m). The study has shown similar trends with that of [71]. The spatial analysis of borehole data in Hawassa town showed that the groundwater level ranges from 4.88 m to 55.9 m. As shown in Figure 5(c), the groundwater level in the northwestern and southwestern parts of the study area is shallower than that of the southeastern part (see the Supplementary Table S1 and Figure S1). The groundwater level in Hawassa town is generally shallow and increases in its depth from the western to the eastern parts of the town. The VES and borehole data points were taken from different sites in the study area as shown in Figure 4. However, the VES data showed that groundwater table probably found at a shallower depth than from the borehole data.Figure 5
(a) VES1 invertedρ graph, (b) VES2 inverted ρ graph, and (c) groundwater level map from the borehole in Hawassa town.
(a)(b)(c)Table 4
Thickness (h) and resistivity (ρ) of VES 1 and VES 2 at Hawassa town.
VES 1VES 2LayerDepth (m)Resistivity (Ω m)LayerDepth (m)Resistivity (Ω m)h10.847250h10.7553.9h22.8511.7h23.1819.3h319540.2h36126303h412.310.9h521.427.423.5


## 6.2. 1D Vs Profiles and 2D Vs Cross-Section

The findings from five sites (MASW 1, MASW 2, MASW 3, MASW 4, and MASW 5) were provided in this study. The RMS is less than 7% and thus it was acceptable. The Vs models in Figure6 showed that the typical sites have Vs values range from 168 m/s to 542 m/s whereas Vp values which were estimated from the measured Vs values ranging from 1477 m/s to 1892 m/s (Supplementary Table S2 and Figure S2). Vp was estimated from Vs values. Vs and Vp values exhibited significant depth variability for three sites (MASW 1, MASW 2, and MASW 5): (a) a decrease in value was observed in the upper layer (0 to 5 m) which indicated lithological variation; (b) an increase in value was observed in the depth range of 5 m to 25 m; and (c) a decrease and nearly constant values were observed at depths greater than 25 m. However, because of lithological alterations, the Vs and Vp values of MASW 3 decreased from 0 to 25 m before start it starts to drop or remain constant from 25 m onwards. The top layer (depth 0 to 5 m) of MASW 4 showed a drop in Vs and Vp values, indicating lithological variation, although the value remained constant at depths greater than 5 m. The dispersion curve in (Figure 7(a)) was constructed at a frequency of 5.4 Hz to 19 Hz and a phase velocity from 200 m/s to 450 m/s. Due to weak overtone image, the dispersion curve at a frequency below 5.4 Hz was not shown for MASW 1 (Figure 7(a)) but the dispersion curves at a frequency below 5.2 Hz were not shown for MASW 2, MASW 3, MASW 4, and MASW 5 (Figures 7(b)–7(e)). The dispersion curves were showed at a frequency interval of 5.2 Hz to 19 Hz for selected sites in the study area but the phase velocity for the selected sites were range from 200 m/s to 450 m/s. The phase velocity and frequency curves revealed a smooth curve that begins high on the left and then drops low on the right for MASW 1, MASW 2, MASW 3, and MASW 5 (Figures 7(a)–7(c) and 7(e)) but phase velocity and frequency curves showed almost straight line for MASW 4.Figure 6
Vs and Vp plots for typical sites in the study area.Figure 7
Dispersion curves from (a) MASW 1, (b) MASW 2, (c) MASW 3, (d) MASW 4, and (e) MASW 5.
(a)(b)(c)(d)(e)The Vs values vary from 191 m/s to 542 m/s for 1D Vs profile for MASW 1 (Figure8(a)). The data revealed that a rise from 200 m/s at the surface to 542 m/s at a depth of 0 and 20 m, respectively, and then it drops from 540 m/s to 501 m/s at a depth of 20.8 m and 30 m, respectively, due to its lithological variation. As it was observed in Figure 8(b), the 2D Vs cross-section of MASW 1 showed Vs variation at a lateral distance of 120 m. In addition, it showed a low Vs values between 0 to 5 m, a very high Vs contrast between 12 m and 25 m which is most probably caused by the response of stiff materials. There was also a slight decrease in Vs values at a depth of 30 m. The 1D Vs values for MASW 2 (Figure 9(a)) increases from 232 m/s to 473 m/s at a depth of 3 m and 24 m, respectively, but the Vs values decreases from 470 m/s to 420 m/s at a depth of 24.5 m and 30 m, respectively. The 2D Vs cross-section for MASW 2 (Figure 9(b)) showed variations in Vs values across a distance of 115 m in which Vs values is being increased from 260 m/s to 382 m/s at a depth of 0 and 5 m, respectively, reducing at a depth of 10 m and increased in Vs at a depth of 13 m. Vs increased from 228 m/s at the surface to 406 m/s at a depth of 24.2 m, then dropped from 400 m/s to 200 m/s at a depth of 30 m as can be seen from 1D Vs profile from MASW 3 (Figure 10(a)). The 2D Vs cross-section of MASW 3 (Figure 10(b)) showed a change in Vs over a distance of 120 m along the survey line. The Vs increasing from 206 m/s to 294 m/s at a depth of 0 and 10 m, respectively, over a distance of 0 to 45 m but low Vs zone showed over a distance of 45 m to 120 m at a depth of 15 m.Figure 8
(a) MASW 1 1D profile and (b) 2D Vs cross-section.
(a)(b)Figure 9
(a) MASW 2 1D profile and (b) 2D Vs cross-section.
(a)(b)Figure 10
(a) MASW 3 1D Vs profile and (b) MASW 2D vs cross-section.
(a)(b)At MASW 4, Vs ranges from 223 m/s to 300 m/s (Figure11(a)). As compared to the other sites, MASW 4 showed a small increment in Vs with depth in which Vs reached a maximum of 300 m/s. The 2D Vs cross-section of MASW 4 showed a spatial variation over a distance of 110 m (Figure 11(b)) with a general increase in Vs with depth in which a low Vs ranging from 206 m/s to 272 m/s near to the surface to a depth of 7 m and a higher Vs which is greater than 292 m/s showed at a depths of 30 m. Moreover, for MASW 5 Vs values range from 300 m/s to 450 m/s at the surface to a depth of 30 m (Figure 12(a)). The Vs was found to decrease from 300 m/s at the surface to 450 m/s at a depth of 24 m but showed slight decrease in the Vs at a depth of 27.8 m. The 2D Vs cross-section has shown for MASW 5 the variation of Vs values across a distance of 110 m (Figure 12(b)). The Vs increased from 272 m/s (at the surface) to 471 m/s at a depth of 30 m. Finally, 2D Vs cross-section revealed Vs values of 426 m/s for depths ranging from 16 m to 30 m over a distance of 0 to 110 m.Figure 11
(a) MASW 4 1D Vs profile and (b) MASW 4 2D Vs cross-section.
(a)(b)Figure 12
(a) MASW 5 1D Vs profile and (b) MASW 5 2D Vs cross-section.
(a)(b)


## 6.3. Vs30, G30max, and E30 Map

The Vs30 data from several sites were collected and used to create a Vs30 map for Hawassa town. The Vs30 values for Hawassa town range from 248.9 m/s to 371.3 m/s as can be seen from Figure13(a). A very low Vs30 values, which range from 248.9 m/s m/s to 279.5 m/s, were found in the northern and to some extent in central parts of the study area while the low Vs30 values, which range from 279.6 m/s to 310.1 m/s, were found in the eastern, some parts of the western, and central areas. A high Vs30 values, ranging from 310.2 m/s to 340.7 m/s were found in the western, southern, and some central parts of the study area. In the southern as well as some sites in the western and eastern parts of the study area, very high Vs30 values, ranging from 340.5 m/s to 371.3 m/s, were found. Hence, the study area (Table 5) falls under C and D site classes according to [72] but classes B and C according to [73]. When Vs30 values were correlated to [72] soil classes, the majority of the soils in the study area were classified as stiff soils with a minor portion of very stiff soil or soft rock but the study area is mostly covered with medium dense sand and very dense sand according to [73].Figure 13
(a) Vs30 map of Hawassa town, (b) Gmax30 map of Hawassa town, (c) E30 map of Hawassa town, and (d) Lithological correlation between SPTN Values (BH1, BH4, and HU) with Vs for MASW 1.
(a)(b)(c)(d)Table 5
Vs30 values of the respective site classes in Hawassa town.
Site codesVs30 (m/s)Site classes of Hawassa townAccording to [72]According to [73]MASW 1362CBMASW 2350DCMASW 3301DCMASW 4269DCMASW 5371CBMASW 6301DCMASW 7346DCMASW 8266DCMASW 9261DCMASW 10268DCMASW 11271DCMASW 12276DCMASW 13356DCMASW 14357DCMASW 15270DCMASW 16348DCMASW 17267DCMASW 18265DCMASW 19249DCThe G30max and E30 maps were generated to characterize Hawassa town. The near-subsurface materials up to the depth 30 m were mapped using G30max values which vary from 0.991 MPa to 2.83 MPa in this investigation. The G30max map (Figure13(b)) revealed the presence of (a) high stiff materials in the southern, southeastern, and southwestern corridors and (b) very low stiff materials in the northern, western, and central parts of the study area. Due to its lower stiffness, the northern part of the study area requires more attention in terms of its engineering design attention than the southern portion of the study area, which has a higher stiffness. The E30 map was prepared to characterize the degree of deformation in earth materials for settlement studies in the study area. The E30 map showed that the E30 values range from 2.68 MPa to 7.63 MPa. This map (Figure 13(c)) revealed that the northern and central parts of the study area have a lower modulus of elasticity, indicating a higher deformation whereas the southern part has got a higher modulus of elasticity, implying a smaller deformation. This means that in the northern portion of the study area, settlement analysis is required more than that of the southern part. The lithological correlation between SPT-N borehole, Vs and borehole log (BH1 and BH4: HU) were done for MASW 1 to understand the vertical stratification of the study area (Figure 13(d)). It was showed that a decrease in Vs and SPT-N values at a depth of 0 to 6 m and increases at a depth of 6 m to 18 m due to the presence of very dense soil and soft rocks (weathered and highly fractured basalt).


## 6.4. Geotechnical Soil Characterization

Grain size analysis for representative soil samples from the study area was conducted using sieve analysis. The grain size distribution ranges from 0% to 5% for gravel, 25% to 89% for sand and 11% to 80% for nonplastic silt in the study area (Figure14(a)). Hence, the Atterberg limits were not determined or not possible (NP) to determine for the representative soils in the study area as it is dominated by nonplastic cohessionless soils (Table 6). The soils in the study area soil were dominantly silty sand and sand according to the Unified Soil Classification System (USCS). The result of the current study is similar with that of [71].Figure 14
Grain size distribution curves of soil in the study area.Table 6
Summarized geotechnical results of soils in the study area.
Borehole and pit codeDepth (m)Atterberg limit%USCSLLPLPIGravelSandFineBH-1Adr3NPNPNP—2575Silty sand soilSMBH-1HU3NPNPNP—955Poorly graded sand soilSPBH-2Mr3NPNPNP—2080Silty sand soilSMBH-2R3NPNPNP52570Silty sand with some gravel soilSMBH-2Ru3NPNPNP—3862Silty sand soilSMBH-2Sg3NPNPNP—3367Silty sand soilBH-2Wt3NPNPNP—4555Silty sand soilBH-1Yw3NPNPNP—5050Silty sand soilBH-2Ab3NPNPNP—4060Silty sand soilBH-1Bh3NPNPNP—4456Silty sand soilBH-2Tsf3NPNPNP—4159Silty sand soilTP-2AIB3NPNPNP—3961Silty sand soilBH-1SPC3NPNPNP—2575Silty sand soilBH-1ZB3NPNPNP—3466Silty sand soilPIT12NPNPNP—3268Silty sand soil


## 6.5. Correlation of Vs between SPT-N and N60

For the regression correlation of Vs and SPT-N values using the power law, data pairs of Vs, and SPT-N were used. The relationship between Vs and SPT-N values was investigated using a linear regression approach. Attempts were made in this work to derive Vs from SPT-N and N60 data for Hawassa town (Supplementary TableS3). The geology of the study area is characterized by sandy soil to according to SPT-N values, well inventory, and MASW data. As a result, for the study area with a similar soil types, different empirical equations were compared to the newly developed Vs using SPT-N and N60 regression equations. Figure 15(a) depicts the linear regression equation derived in this study as well as other equations developed for similar soil characteristics (i.e. sandy soil). The SPT-N value has a considerable effect on Vs estimate as evidenced by the high Pearson correlation coefficient (R2). The proposed correlation between Vs and SPT-N values for Hawassa town is given in equation (7) as follows.
(7)Vs=115SPT−N0.28r=0.84andR2=0.7102forsandysoil.Figure 15
(a) correlations between Vs, SPT-N and N60 of the current and previous studies, (b) Cd graphs of sandy soil, and (c) comparison between predicted and measured Vs for sandy soil.
(a)(b)(c)The correlation model was made using the power law (equation (8)) and the Vs and N60 proposed equations for the town of Hawassa (Figure 10(a)).
(8)Vs=122.98N600.2807r=0.714andR2=0.5102forsandysoil.As stated in equations (7) and (8), strong relation from R2 were found for Vs and SPT-N values rather than the Vs and N60 values in this investigation. In general, an equation with a high R2 value can predict more accurately than the one with a lower value. Hence, the results showed that the predicting equation based on SPT-N values (equation (7)) has a more predictive capacity to forecast Vs than N60 in this study (equation (8)). Furthermore, the suggested Hawassa town linear regression model was tested using Cd and a comparison of Vspred and Vsmeas. Figure 15(b) shows a comparison of Cd and SPT-N levels for sandy soils in the study area. It should be noticed that Cd’s average value was close to zero (Supplementary Table S4 and Figure S3). This means that the predicted Vs value is quite close to the measured one. As a result, the Cd ratio validation test for the newly proposed regression equation developed for the study area estimates Vs accurately and confidently. In addition, comparisons between Vsmeas and Vspred were done. Figure 15(c) depicts the Vsmeas and Vspred for the validation of the newly proposed regression equation for Hawassa town. All of the data points fall between the 1 : 0.5 and 1 : 1.3 slope lines. However, majority of the data fall inside the 1 : 1 line slope indicating that the predictive model is the best fit for study the area (Supplementary Table S5 and Figure S4).


## 7. Conclusions

The VES and borehole data indicated that a groundwater table is found at shallow depth in the study area. The Vs values for Hawassa town from a 1D profile range from 191 m/s to 542 m/s whereas the Vs30 values ranges from 248.9 m/s to 371.3 m/s. The 2D Vs cross-section has shown the low velocity layer at a shallow depth that need good engineering design. SPT-N values in the study area range from 5bpf to 50bpf. The SPT-N values showed sandy soil with low to medium stiffness at a shallow depth. According to the Natural Earthquake Hazard Reduction Program (NEHRP) classification methodology, the near-surface materials in Hawassa town are classified as site classes C and D but according to the Eurocode (EC8) protocols, they are classified as site classes B and C. Therefore, the seismic design for site class D in the study area is required to minimize the earthquake hazards. The near-surface materials in Hawassa town have Gmax30 values ranging from 0.991 MPa to 2.83 MPa, and E30m values ranging from 2.68 MPa to 7.63 MPa. The produced Vs30, Gmax30, and E30 maps showed low values in the northern part and high values in the southern part of the study area. Therefore, the northern part needs more attention with respect to engineering design than the southern part of the study area. The results of Cd,R2, and comparison of Vspred and Vsmeas showed that the newly proposed regression equation can be effectively used to determine Vs for engineering site characterization in areas with similar geological ad geotechnical conditions in Ethiopia and elsewhere in the world if there is limited budget and time. The grain size analysis showed that the soils in the study area are silty sand and sand soil. For full subsurface characterization in the study area, 3D Vs modeling and microtremor surveys are needed. The findings of this study can be used as first-hand information for future seismic hazard evaluation and site response analysis in Hawassa town and its environs.



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*Source: 7588306-2022-12-19.xml*