第31回日本情報地質学会講演会 講演要旨集 11-12頁 2020年

Pluri-Gaussian simulation combined with principal component analysis fordelineating mineralized zones and ore solution flows
Vitor Ribeiro de Sá* and Katsuaki Koike**Graduate School of Engineering, Kyoto University, Katsura C1–2, Kyoto 615–8540, Japan.
E–mail: koike.katsuaki.5x@kyoto–u.ac.jp (K. Koike)Key words: Geostatistics, Stochastic simulation, Principal component analysis,
Pluri-Gaussian simulation, Geological model

1. Introduction
Sparsity and shortage of information is inherent tomost geostatistical projects. This pitfall makes modelers
puzzle over approaches to mitigate the setbacks andoptimize the available information. The combination ofdata-characterization and geostatistical methods can bea plausible way to minimize the problem.
This study aims to apply a combination of methods,
such as principal component analysis (PCA), and spatialmodeling techniques using conditional geostatisticalsimulations, turning bands (TBSIM) and pluri-Gaussian(PGSIM), to a real geochemical data set and lithologic
log data. The goal is to construct plausible 3D models ofgeochemical compositions and lithotypes in the study
area in order to identify the zones with more occurrenceof mineralization and propose a geological interpretationof fluid circulation in the study area.
The next sections introduce the study area and set out
the methodology and its perks to the current scenario.
Besides, the results are presented and discussed.
Although the methodology is applied to a specificenvironment of this study, such combination can be
widely used for a myriad of geological contexts.

2. Model Domain and Dataset
Following our preceding study (de Sá et al., 2020), a
200m700m250m domain, located around 1500m below
the sea level (mbsl) was selected (Fig. 1). Previous
geochemical studies have indicated that this area is richin Ba-Zn-Pb. The proposed methodology uses amulti-variate geochemical set (52 elements) and visualcore description information alongside X-ray diffraction(lithotypes) as input data for TBSIM and PGSIM
implementations, respectively. These data were sampledfrom six boreholes (black dots) spread along the E-W
direction, with variable lengths from 46m (borehole I) to180m (borehole III). The lithotypes classification isbroadly based on whether polymetallic sulfide minerals
and hydrothermal alteration are present.
Borehole I was drilled in a mound and is regarded as adischarge zone of the hydrothermal system. Borehole VI
presented little evidence of sulfide and altered material
in its sample core descriptions and geochemical analysis,
being more likely a recharge zone. In addition, a seismic
survey of the area identified the development of a fault
between boreholes V and VI (dashed line).

Figure 1: Dimension of study area and its spatial distribution ofboreholes. The black line shows the location of cross-section
for geostatistical results in Figure 2. The dashed line shows
the location of fault identified by a geophysical survey.

3. Methods
The proposed methodology integrates geochemicalmeasurements and lithotypes. Firstly, the entire
multivariate geochemical data is centered log-ratio (clr)transformed and eight moderate-highly correlated
elements are selected, such as Zn, Pb, Cu, Ag, Ba, Cd, Snand Mn. These elements are not only statisticallycorrelated but also are the most representative ones inmineralized zones in hydrothermal systems (Pirajno,2009). Next, this subset is used as input to apply PCA,
which main products are principal component values (PCvalues). TBSIM uses this variable to spatially locate
highly mineralized zones and set iso-surfaces to separate
the sulfide-rich zones from impoverished materials.
Finally, PGSIM is individually run in each zone. The
following subsections briefly describe the main
characteristics under PCA, TBSIM and PGSIM.
3.1. Principal component analysis (PCA)
PCA is a powerful tool to examine the interactions
between the various variables and find the most efficientlinear combination of them. Its efficiency stems fromits capability of the first two or three principal
components, PCs, to gather the greatest amount of totalvariance. PCA method reduces the dimensionality ofdata with many measured variables by transforming
these to a new, considerably smaller set of variables,PCs.
3.2. Turn bands simulation (TBSIM)
The principle of TBSIM is to produce a non-conditional

12

simulation at first. That is, yielding a map that reflects
the variogram, but the data is not honored. Afterwards,
in order to correct it, a map is obtained by interpolating
the experimental error between the measured data andnon-conditional simulated value at each data point(Chilès and Delfiner, 1999).
3.3. Pluri-Gaussian simulation (PGSIM)
This methodology aims to simulate categoricalvariables by combining multiple multi-Gaussianvariables using multiple thresholds. The gist of PGSIM
is to yield two continuous Gaussian fields using standard
multi-Gaussian techniques. Therefore, these fields aretruncated to produce categories, which the thresholding
relies on the value of both Gaussian fields (Mariethozand Caers, 2015).

4. Results and Discussion
Three cross-sections along E-W were selected to depict
a conceptual model for an expected lithotypes
distribution (Fig. 2a).
By applying PCA considering the eight geochemicalelements, the eigenvalues and total variance of eight
PC’s are computes. PC1 retains most of the informationof the input data and accounts for 73% of total variance
whereas PC2’s variance is 12.3% and the sum of thevariances from PC3 to PC8 is as small as 14.7%.
Therefore, only PC1 was selected for TBSIM to locate
high-metal-contentzonesandinterpretthesulfide/sulfate mineralization process.
The TBSIM result is shown as an E-W verticalcross-section (Fig. 2b) with iso-planes of the PC1 values
which reveal that PC values greater than PC1 = 4 arethickly distributed underneath the sulfide mound,suggesting stockwork formation, and a horizontal andstratiform mineralization seems to occur from themound toward the east until the inferred fault.
Considering the stratiform mineralization and effects ofhydrothermal activity from the western boundary to theinferred fault, this subarea was divided into three zones
following the iso-plane of PC1 = 4 for PGSIM. The top
zone is mainly unrelated to hydrothermal alteration andcomposed of primary and reworked sediments. The
middle zone is a major mineralization zone containing
the massive sulfide mound, stockwork, and horizontal
and stratiform subseafloor sulfide layer. The bottom zone
mainly consists of pervasively altered rock.
These results resize the model domain to optimize thePGSIM application. The easternmost limit wasrearranged to suit the distal edge of the polymetallic
sulfide body, shortening the E-W length from 700 to 500
m, while the vertical range remained unaltered.
The results of the PGSIM in each zone defined by
TBSIM are shown in the resized domain (Fig. 2c).
According to this model, the distribution of sulfide rocksuggests two fluid flows with high probability (redarrows). The ascent flows toward the sulfide mound andthe lateral flows from the stockwork zone toward its
adjacent permeable layers. The former flow may be
predominant because mineralization is concentrated on
the seafloor and in the shallow subseafloor. The latterflow induces large heat loss without forming a chimney
or mound and causes horizontal and stratiform
alteration and mineralization.

5. Conclusion
The combination of PCA and two geostatisticalsimulations, TBSIM and PGSIM are very efficient toclarify geologic structure and 3D distribution of metal
contents in the model domain. They can contribute to (i)construction of proper geologic and mineralization
models and (ii) identification of hydrothermal fluid-flow
systems and the accumulation mechanism of base metals
in seafloor hydrothermal fields.
Acknowledgement: We sincerely thank Dr. Tatsuo
Nozaki and Dr. Yutaro Takaya for providing the chemical
analysis data obtained by D/V Chikyu.

References
Chiles, J. P., Delfiner, P. (1999) Geostatistics: Modelingspatial uncertainty. Wiley.
de Sá, V. R., Koike, K., Goto, T.-N., Nozaki, T., Takaya, Y.,
Yamasaki, T. (2020) A combination of geostatisticalmethods and principal components analysis for
detectionofmineralizedzones
inseafloorhydrothermal systems. Natural Resources Research.
https://doi.org/10.1007/s11053-020-09705-4
Deutsch, C. V. (2002) Geostatistical reservoir
modeling. Oxford, Oxford University Press.
Mariethoz, G., Caers, J. (2015) Multi-point geostatistics:
Stochastic modeling with training images. Wiley.
Pirajno, F. (2009) Hydrothermal processes and mineralsystems. Springer.

Figure 2: Cross-sections along E-W showing (a) a conceptualmodel of study area, (b) E-W vertical cross-section along the line
in Figure 1, and (c) spatial modeling results of lithotype byPGSIM. The arrows in (c) show the interpreted hydrothermal
fluid flows.