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Mineral prospectivity mapping using machine learning techniques for gold exploration in the Larder Lake area, Ontario, Canada
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SYSNO ASEP 0583897 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Mineral prospectivity mapping using machine learning techniques for gold exploration in the Larder Lake area, Ontario, Canada Author(s) Liu, H. (CN)
Harris, J. (CA)
Sherlock, R. (CA)
Behnia, P. (CA)
Grunsky, E. (CA)
Naghizadeh, M. (CA)
Rubingh, K. (CA)
Tuba, G. (CA)
Roots, E. A. (CA)
Hill, Graham J. (GFU-E) ORCIDArticle number 107279 Source Title Journal of Geochemical Exploration. - : Elsevier - ISSN 0375-6742
Roč. 253, October (2023)Number of pages 16 s. Publication form Print - P Language eng - English Country NL - Netherlands Keywords mineral prospectivity mapping (MPM) ; machine learning ; partial least-squares-discriminant analysis (PLS-DA) ; Random Forest (RF) ; Larder Lake area OECD category Geology Method of publishing Limited access Institutional support GFU-E - RVO:67985530 UT WOS 001180785400001 EID SCOPUS 85169820071 DOI 10.1016/j.gexplo.2023.107279 Annotation A mineral prospectivity map (MPM) focusing on gold mineralization in the Larder Lake region of Northern Ontario, Canada, has been produced in this study. We have used the Random Forest (RF) algorithm to use 32 predictor maps integrating geophysical, geochemical, and geological datasets from various sources that represent vectors to gold mineralization. It is evident from the efficiency of classification curves that MPMs generated are robust. The unsupervised algorithms, K -means and principal component analysis (PCA) were used to investigate and visualize the clustering nature of large geochemical and geophysical datasets. We used RQ-mode PCA to compute variable and object loadings simultaneously, which allows the displays of observations and the variables at the same scale. PCA biplots of the Larder Lake geochemical data show that Au is strongly correlated with W, S, Pb and K, but inversely correlated with Fe, Mn, Co, Mg, Ca, and Ni. The known gold mineralization locations were well classified by RF with the accuracy of 95.63 %. Furthermore, partial least squares -discriminant analysis (PLS-DA) model combines 3D geophysical clusters and geochemical compositions, which indicates the Au -rich areas are characterized with low to mid resistivity - low susceptibility properties. We conclude that the Larder Lake -Cadillac deformation zone (LLCDZ) is relatively more fertile than the Lincoln-Nipissing shear zone (LNSZ) with respect to gold mineralization due to deeper penetrating faults. The intersection of the LLCDZ and network of high -angle NE -trending cross faults acts as key conduits for gold endowments in the Larder Lake area. This study innovatively combined multivariate geological, geochemical, and geophysical datasets via machine learning algorithms, which improves identification of geochemical anomalies and interpretation of spatial features associated with gold mineralization. Workplace Geophysical Institute Contact Hana Krejzlíková, kniha@ig.cas.cz, Tel.: 267 103 028 Year of Publishing 2024 Electronic address https://www.sciencedirect.com/science/article/abs/pii/S0375674223001267
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