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TitleA multivariate statistical approach identifying the areas underlain by potential porphyry-style Cu mineralization, south-central British Columbia, Canada
AuthorChen, S; Plouffe, A; Hattori, K
SourceJournal of Geochemical Exploration vol. 202, 2019 p. 13-26, https://doi.org/10.1016/j.gexplo.2019.03.016
Year2019
Alt SeriesNatural Resources Canada, Contribution Series 20180288
PublisherElsevier BV
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf (Adobe® Reader®); html
ProvinceBritish Columbia
NTS92P/01; 92P/02; 92P/03; 92P/06; 92P/07; 92P/08; 92P/09; 92P/10; 92P/11; 92P/14; 92P/15; 92P/16; 93A/01; 93A/02; 93A/03
AreaBonaparte Lake
Lat/Long WENS-121.4833 -120.3333 52.0333 51.2000
Subjectseconomic geology; mathematical and computational geology; geochemistry; surficial geology/geomorphology; mineral deposits; porphyry deposits; porphyry copper; polymetallic ores; mineral potential; mineral exploration; exploration methods; drift prospecting; statistical methods; mineralization; ore mineral genesis; mineral enrichment; mineral occurrences; provenance; till geochemistry; size fractionation; clays; glacial deposits; tills; mineral associations; bedrock geology; lithology; igneous rocks; volcanic rocks; mafic volcanic rocks; intrusive rocks; intrusions; batholiths; copper geochemistry; gold geochemistry; lead geochemistry; molybdenum geochemistry; glacial history; ice flow; Canadian Cordillera; Intermontane Belt; Quesnellia Terrane; Chilcotin Group; Nicola Group; Thuya Batholith; Cache Creek Terrane; Slide Mountain Terrane; Ancestral North America; Kootenay Terrane; Raft Batholith; Takomkane Batholith; principal component analysis; medoids; geochemical signatures; algorithms; unsupervised random forest; ice-flow directions; Phanerozoic; Cenozoic; Quaternary; Tertiary; Mesozoic; Cretaceous; Jurassic; Triassic; Paleozoic; Permian; Carboniferous; Precambrian; Proterozoic
Illustrationslocation maps; geoscientific sketch maps; tables; pie charts; plots
ProgramPorphyry systems, Targeted Geoscience Initiative (TGI-5)
Released2019 04 01
AbstractThe Bonaparte Lake area in southern part of the Quesnellia terrane of the Canadian Cordillera is proximal (< 50 km) to several large porphyry Cu-Au and Cu-Mo deposits. This area has potential for finding more porphyry type Cu deposits, but is overlain by a thick and nearly continuous cover of glacial sediments, which adds difficulty in finding mineral deposits in underlying bedrocks. This study uses geochemical data following an aqua regia leach on the clay fraction (< 2 micrometers) of basal till (n=726) to assess the mineral potential in the study area. Principal component analysis (PCA) captures four PCs containing large fractions of Cu variance: PC2 (31.2%), PC6 (17%), PC3 (16.8%) and PC16 (12.6%). Copper is associated with Au-Mo-Ag-As-Hg-Pb-Sb-Zn-Ni-Co-Mn in PC2; Au-Pb-Mn-W in PC6, Cr-V-Ti in PC3 and Pb-Se-Hg in PC16. The elemental assemblages in PC2, PC6 and PC16 may reflect the geochemical signature of underlying porphyry-style polymetallic mineralization, whereas the element association in PC3 may be originated from mafic volcanic rocks of the Chilcotin Group. Partitioning around medoids based on the dissimilarity matrix produced by unsupervised random forest using elements associated with Cu yields two groups, Group A and Group B, with minor overlap. Group A likely represents till samples with a composition influenced by a mineralized provenance because the contents of elements (Cu, Au, As, Mo, Pb and Zn) are much higher than samples in Group B. The Group A samples are mostly located in the areas underlain by the Nicola Group volcanic rocks and the northern boundary of the Thuya Batholith. Group A till samples are dominantly located close to or down-ice from known mineral occurrences, suggesting that more porphyry-Cu mineralization and other multi-elemental enrichments occur in these areas, such as Au, Mo, Pb and Zn. This study demonstrates the usefulness of multivariate analysis and unsupervised machine learning in detecting potential Cu mineralization in bedrocks based on the composition of basal till. The statistical methods used in this study may be applicable to large scale geochemical data of sediments to evaluate multi-element assemblages associated with a variety of mineralization in bedrocks.
Summary(Plain Language Summary, not published)
The paper presents a statistical method to look at the geochemical composition of glacial deposits as a mean of evaluating the mineral potential of a region. The statistical analysis of the data looks at elemental concentrations and association to define the mineral potential of a region. The tested region located in south-central British Columbia, possesses a high potential for copper mineralization. It is a step towards artificial intelligence to investigate large geochemical databases.
GEOSCAN ID313088