情報地質 第28巻 第2号 090-091頁 2017年
Geoinformatics, vol.28, no.2, pp.090-091, 2017

Preliminary Geostatistical Modeling of Physical and Chemical Properties
in a Seafloor Hydrothermal Vent Area in the Mid-Okinawa TroughVitor Ribeiro de Sá*, Katsuaki Koike*, Tada-nori Goto* and Katsuhiko Suzuki***Graduate School of Engineering, Kyoto University, Katsura C1–2–215, Kyoto 615–8540, Japan.
E–mail: koike.katsuaki.5x@kyoto–u.ac.jp (K. Koike)** Research and Development Center for Submarine Resources, Japan Agency for Marine-Earth
Science and Technology (JAMSTEC), 2-15 Natsushima-cho, Yokosuka 237-0061, Japan.

Key words : Geostatistics, Hydrothermal deposits, Electrical resistivity, Geochemicalcomposition, Iheya North Knoll

1. Introduction
Hydrothermal fields account for important source ofeconomic mineral deposits called Volcanogenic Massive
Sulphide (VMS) types. These deposits correspond tomajor sources of Zn, Cu, Pb, Ag, and Au and significant
sources of Co, Sn, Se, Mn, Cd, In, Bi, Te, Ga, and Ge(Galley et al., 2007). Seafloor hydrothermal vent areas
have been important as a source of this type deposits.
The study area is a hydrothermal field on the Iheya
North Knoll located roughly 150 km NW of Okinawa
Island. The Okinawa Trough is a back-arc basing locatedin East China Sea between the Ryukyu Arc-Trenchsystem and the Eurasian continent (Tsuji et al., 2012;
Miyoshi et al., 2015). It is under extensional stress andits sea floor is covered with about 2 km-trick sediments(Letouzey and Kimura, 1986; Tsuji et al., 2012). Figure 1
depicts the drilling sites implemented by JAMSTEC
Strategic Innovation Promotion Program (SIP; redpoints) as well as the locations of an Integrated OceanDrilling Program (IODP) Expedition 311 (green points)
that collected samples with shallow drillings up to the 60
m depth. To compensate the lack of information, thereports of Integrated Ocean Drilling Program (IODP)
Expedition 311 (Takai et al., 2011) were used for thisstudy, providing more details. According to the reports,
the drilling C9015A is nearby active chimneys.
Having the data of electrical resistivity, this study
aims to describe how the spatial continuity changes withthe distance using variograms as tools to reach this goal.
Besides, geochemical data assessments of samples
collected at drillings C9015A and C9016A are analyzed
and discussed here. This paper is a preliminary outcomeof a project which purposes to develop a geological 3D
model applying geostatistical techniques to an
integrated data set, leading an optimizing mineral
exploration.

2. Methods
2.1. Electrical resistivity
In general, resistivity of rock is commonly controlled
by rock porosity and the salinity of the pore water. Inaddition, clay and sulfide are electrically polarized androcks containing them are highly conductive.
JAMSTEC-SIP provided six resistivity loggings,

indicating different layers with alike material content.

Fig. 1: Distribution of drills performed by JAMSTEC-SIP (redpoints) and IODP Exp. 311 (green points) in the study area.

2.2. Variography
Variogram is a useful tool to describe the process ofdetermining whether there is spatial correlation among
regionalized data measured in the field. Thisvariography includes: testing spatial correlation between
the field measurements of the same variable; computing
spatial covariance between the data, including
calculating semivariance for each pair of observations
and; presenting the calculated semivariances graphically91

as a semivariogram (Kresic, 2006).
In this step, the gauged electrical resistivity
beforehand is used as input to generate charts that
describe how their spatial continuity changes as thedistance is increased by the six omnidirectionalvariograms. The data were vertically calculated for eachdrilling site.

Acknowledgement: We sincerely thank Dr. Tatsuo
Nozaki and Dr. Yutaro Takaya for providing the chemical
analysis data obtained by D/V Chikyu.

2.3. Geochemical analysis
In general, geochemical analysis is aimed at thedetermination of elemental concentrations in a sample.
Likewise, our current data have been analyzed byinductively coupled plasma massive spectrometry(ICP-MS). These data are statistically interpreted, inorder to identify anomalies, which may be understood asstatistically grouping data and comparing them withgeology and sampling information.
Unlikely the current resistivity logging thatmeasurements reach more than the 100 m depth, thegeochemical data were limited within the shallow depths
at the location C9015A and C9016A, the 25 and 50 m
depth ranges below the sea floor (mbsf), respectively.

3. Results and Discussion
Based on the resistivity variograms related to the six
drillings (Fig. 2), all of them follow the characteristics ofa spherical model, which presents a linear behavior at
small separation distances near the origin but flattens
out at long distances. In general, the variograms show
behavior akin to each other. Aside from the drilling
C9015A, they may be described as variograms withsmall variance. As for C9015A, there is a large variance,
reflecting its resistivity logging that shows a discrepancyin its data, in particular between the 15 and 25 mbsf.
This study clarified an important trend: the further thedrilling is from the chimney the greater the range is,such as for C9011B, C9012A and C9013A, 43.65, 43.11
and 60.82 m, respectively.
The chemical analyses were carried out for a wide
range of elements, and the major ones (Zn, Cu, and Pb)
were statistically considered anomalous, regarding
measures of central tendency such as medians andhistograms. The latter and coefficient of variation (CV)can indicate whether a data set is heterogeneous. Thehistograms does not show the data fall into several
groups in any drilling, but measured CV using data from
C9016A show values higher than 2, i.e. they areheterogeneous. It may originate from a mixing of datafrom different facies with homogeneous values in thefacies. As for the correlation between the elements, Zn
showed strong correlation with Cu and Pb in bothdrillings.

4. Conclusion
The careful use of variogram can be a remarkable tool
to evaluate data set, such as electrical resistivity.
Although the wise choice of parameter at experimental
variogram, its model must be taken into account.
Otherwise, it can reflect an unreal environment.
Statistical analysis are important to identify
heterogeneity of data, averting a misleading
geostatistical approach. More researches are now
undergoing to better understanding the study area.

Fig. 2: Electrical resistivity variogram models of each drilling
performed by JAMSTEC-SIP in the study area.

References
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