第32回日本情報地質学会講演会 講演要旨集 027-028頁 2021年

Machine Learning Application to Delineating Metal-rich Veins
Vitor Ribeiro de Sá* and Katsuaki Koike**Graduate School of Engineering, Kyoto University, Katsura C1–2, Kyoto 615–8540, Japan.
E–mail: ribeirodesa.vitor.7c@kyoto-u.ac.jp; koike.katsuaki.5x@kyoto–u.ac.jp

Key words: Hierarchical clustering, Epithermal deposit, Au–Ag mineralization, Geologic model

1. Introduction
Geological/geostatistical domain definition plays apivotal role in mineral resource modeling. Machinelearning (ML) techniques, such as cluster analysis, hasbeen gained notoriety in earth science for their versality
to furnish tools to handle sparse information in wideareas. ML and field studies can act in concert to improvethe understanding of geological phenomenon.
This study aims to apply hierarchical clustering
analysis (HCA) to a real geochemical data set from alow-sulfidation gold deposit. Such type of deposits
account for significant occurrences of Au and Ag, besides
base metals (i.e. Cu–Pb–Zn) (Pirajno, 2009). HCA canyield categories that assemble data with similar
characteristics, chemical-wise. As it turns out, veins andveinlets, where higher Au–Ag concentrations are hosted
in this deposit, can be identified. Highlighting thesefeatures aids effective field survey and provide elements
to carry out geostatistical models in order to delineate
target areas for future exploration in the study area.
The next sections introduce the study area and set out
the methodology and its perks to the current study case.
Besides, the results are presented and discussed.
Although the methodology is applied to a specificenvironment of this study, HCA can be widely used for a
myriad of geological contexts.

of log (Au), the main occurrences of Au–Ag mineralized
bodies are situated in the middle study area.

2. Study Area and Dataset
The Sirawai deposit, Mindanao, Philippines was
selected as a case study area. The target zone is set in a
200 m  210 m hilly field, elevation 280–330 meters
above sea level (masl), where 56 drill sites (black dots)
collected samples to analyze Au, Ag, Cu, Pb and Zn
grades (Fig. 1a). This field is marked for several
subparallel sulfide-quartz veins of 300–500 m length,
which their strike 20–30° NW and dip 60–70° SW withthe 1.0–5.1 m width and the average 2.5 m can beobserved. Such structures host the greatest Au and Ag
grades in the area.
The proposed methodology uses Au and Ag grade
measurements, given in g/t, in varied depths. Fig. 1bshows the location of 56 drill sites and the length of thedrill holes, 46 vertical holes (5 – 50 m depth) and ten
inclined holes (70 m length) at dip 60°. The samples are
separated by 1 m interval. Both precious metals arecorrelated in this deposit (linear correlation coefficient ρ= 0.69). Au grades varies from nearly zero to 183.3 g/t(average 0.46). Ag presents wider variation, from 0.01 to1141 g/t (mean 12.75 g/t). According to the highest values

Figure 1. Dimension of study area and its spatial distribution ofdrill sites. (a) Detailed topography of the study area with 56
drill sites (black dots), and (b) 3D representation of the drillsites and holes lengths and log (Au) concentrations.

3. Methods
3.1. General structure of methodology
The proposed methodology assembles Au–Ag available
data into relatively homogeneous groups or clusters. Themembers of a cluster are at once alike and at same time
unlike members of other groups. To attain this goal,
firstly, Au–Ag data are log-transformed, and theircorrelation is statistically verified. In sequence, HCA isapplied to assign samples that are Rich, Intermediate
and Poor in Au and Ag grades. The following subsection

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briefly describe the main HCA characteristics and thecluster number selection criterion.
3.2. Hierarchical Clustering Analysis (HCA)
HCA is an unsupervised machine learning techniquewhich joins the most similar observations, thensuccessively connects the next most similar observations
to these. The similarities of all pairs of observations arecalculated in a square matrix. Those pairs that share
more similarities are merged, and the matrix isrecomputed. Such procedures are run by averaging thesimilarities that the combined observations have withother observations. This process iterates until thesimilarity matrix is reduced to 22 (Davis, 2012). The“elbow” method is used to select the optimal number ofclusters. Such method consists in graphically observing
the magnitude of inertia considering number of clusters.
Inertia is the sum of squared distances of samples totheir closest cluster centers.

4. Results and Discussion
The point after which the inertia start decreasing in a
linear fashion is deemed the elbow point (Fig. 2a). Thus,
for the given data, we conclude that the optimal numberof clusters is three: Poor (purple), Intermediate (yellow)
and Rich (green) (Fig. 2b).
Poor cluster is impoverished in both precious metals
and represents roughly 31% of the samples. Gold grades
range from 0.001 to 0.2 g/t (mean 0.02 g/t) while silver
values vary from 0.01 to 5.0 g/t (average 1.85 g/t).
Cluster considered as Intermediate occurrence of these
metals has minimum grades approximately 0.1 g/t andits maximum grade is 0.8 g/t, mean 0.11. Intermediate
grades of Ag ranges from 0.9 to 74 g/t, mean 8.07 g/t.
These samples are the most representative among thedata, approximately 57%. The most enriched samples inAu and Ag are encompassed by cluster Rich andaccounts for nearly 11% of the total data. Au minimum
grade is 0.15 g/t and reaches 183 g/t, averaging 3.54 g/t.
Ag ranges from 5.9 to 1141 g/t, mean 67.65 g/t.
Cluster Rich is more conspicuous in shallow parts over
the area. However, in the central region of the study
area its occurrence takes place in deeper parts, whichsuggest the nested geological structures (i.e. veins andveinlets). Impoverished samples (cluster Poor) arerelegated to deeper portions and more predominant inperipheral zones of the study area. These extreme
clusters have no direct contact in the entire area. Cluster
Intermediate plays as a buffer zone between Rich andPoor clusters.
We interpret the cluster Rich as possible evidence ofthe sulfide quartz veins in central area and deeper zones.
When this cluster takes place in shallow zones andadjacent regions of the study area, it can be soil enrichedby weathering. Cluster Intermediate is regarded ashydrothermal alteration halos when it is situated near
Au–Ag rich veins whereas regions or zones slightly
affected by hydrothermal activity, otherwise. On theother hand, Cluster Poor stands for the regions or zones
where hydrothermal did not take place or its influence
was weak.

5. Conclusion
The HCA application is demonstrated to be effective

for classifying the samples of precious metals (gold andsilver) grades. In addition, this methodology aids to gain
insight into vein continuities in subsurface. HCA cancontribute to (a) yield categorical data and (b) support
the construction of geological models using geostatisticalmethods, e.g. sequential indicator and pluri-Gaussian
simulations as de Sá et al. (2021).
Acknowledgement: We sincerely thank Dr. Hiroshi
Takahashi of Itochu Mineral Resources Development
Corp. for providing the geological and metal analysis
data of the Sirawai mine.

References
Davis, J. C. (2012) Statistics and data analysis in geology. Wiley,New York, 646 p.
de Sá, V. R., Koike, K., Goto, T.-N. et al. (2021) 3D geostatisticalmodeling of metal contents and lithofacies for mineralization
mechanism determination of a seafloor hydrothermal depositin the middle Okinawa Trough, Izena Hole. Ore Geology
Reviews, htps://doi.org/10.1016/j.oregeorev.2021.104194
Pirajno, F. (2009). Hydrothermal processes and mineral systems,
Springer, Berlin, 1250 p.

Figure 2. (a) Plot of inertia gains of hierarchical clusteringresults, (b) Biplot of log(Au) and log(Ag) showing cluster
features, and (c) 3D representation of the cluster spatial
distribution.