A balanced mineral prospectivity model of Canadian magmatic Ni (± Cu ± Co ± PGE) sulphide mineral systems using conditional variational autoencoders

With the increasing demand for raw materials, innovative exploration techniques are needed to discover large mineral deposits that are accessible from the surface. In recent years, various supervised machine learning techniques have proven effective for mineral prospectivity modelling (MPM). However...

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Bibliographic Details

Published in:	Ore geology reviews Vol. 175; p. 106329
Main Authors:	Nagasingha, Lahiru M.A., Bérubé, Charles L., Lawley, Christopher J.M.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.12.2024
Subjects:	Conditional variational autoencoders Critical minerals Data augmentation Generative modelling Mineral prospectivity modelling Mineral prospectivity modelling Data augmentation Conditional variational autoencoders Critical minerals Generative modelling
ISSN:	0169-1368
Online Access:	Get full text
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