Predicting rock type and detecting hydrothermal alteration using machine learning and petrophysical properties of the Canadian Malartic ore and host rocks, Pontiac Subprovince, Québec, Canada

Charles L. Bérubé, Gema R. Olivo, Michel C. Chouteau, Stéphane Perrouty, Pejman Shamsipour, Randolph J. Enkin, William A. Morris, Leonardo Feltrin and Raphaël Thiémonge

Article (2018)

An external link is available for this item

Department:	Department of Civil, Geological and Mining Engineering
PolyPublie URL:	https://publications.polymtl.ca/39577/
Journal Title:	Ore Geology Reviews (vol. 96)
Publisher:	Elsevier
DOI:	10.1016/j.oregeorev.2018.04.011
Official URL:	https://doi.org/10.1016/j.oregeorev.2018.04.011
Date Deposited:	18 Apr 2023 15:02
Last Modified:	25 Sep 2024 16:24

Cite in APA 7:

Bérubé, C. L., Olivo, G. R., Chouteau, M. C., Perrouty, S., Shamsipour, P., Enkin, R. J., Morris, W. A., Feltrin, L., & Thiémonge, R. (2018). Predicting rock type and detecting hydrothermal alteration using machine learning and petrophysical properties of the Canadian Malartic ore and host rocks, Pontiac Subprovince, Québec, Canada. Ore Geology Reviews, 96, 130-145. https://doi.org/10.1016/j.oregeorev.2018.04.011

APA 7 (HTML)ASCII CitationAtomBibTeXDOIsDublin CoreEndNote (.ENW)MARC (ISO 2709/UTF-8) (.MRC)MARC (UTF-8) (.MRK8)MARC XMLMPEG-21 DIDLMultiline CSVOAI-ORE Resource Map (Atom Format)OPENAIREOpenURL ContextObjectOpenURL ContextObject in SpanRDF+N-TriplesRDF+N3RDF+XMLReferReference Manager (.RIS)Simple Metadata

Statistics

Dimensions

Repository Staff Only

View Item