Machine Learning Methods for Quantifying Uncertainty in Prospectivity Mapping of Magmatic-Hydrothermal Gold Deposits: A Case Study from Juruena Mineral Province, Northern Mato Grosso, Brazil

Machine Learning Methods for Quantifying Uncertainty in Prospectivity Mapping of Magmatic-Hydrothermal Gold Deposits: A Case Study from Juruena Mineral Province, Northern Mato Grosso, Brazil

Mineral prospectivity mapping (MPM), like other geoscience fields, is subject to a variety of uncertainties. When data about unfavorable sites to find deposits (i.e., drill intersections to barren rocks) is lacking in MPM using machine learning (ML) methods, the synthetic generation of negative data...

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Bibliographic Details
Main Authors: Victor Silva dos Santos, Erwan Gloaguen, Vinicius Hector Abud Louro, Martin Blouin
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Minerals
Subjects:
mineral prospectivity mapping
machine learning
quantification of uncertainty
data integration
Juruena Mineral Province
Online Access:https://www.mdpi.com/2075-163X/12/8/941

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