Mineral prospectivity mapping using machine learning techniques for gold exploration in the Larder Lake area, Ontario, Canada

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985530%3A_____%2F23%3A00583897" target="_blank" >RIV/67985530:_____/23:00583897 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0375674223001267" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0375674223001267</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.gexplo.2023.107279" target="_blank" >10.1016/j.gexplo.2023.107279</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Mineral prospectivity mapping using machine learning techniques for gold exploration in the Larder Lake area, Ontario, Canada

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Mineral prospectivity mapping using machine learning techniques for gold exploration in the Larder Lake area, Ontario, Canada

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10505 - Geology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Geochemical Exploration

ISSN

0375-6742

e-ISSN

1879-1689

Svazek periodika

253

Číslo periodika v rámci svazku

October

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

107279

Kód UT WoS článku

001180785400001

EID výsledku v databázi Scopus

2-s2.0-85169820071