A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence

A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence

Novel mineral prospectivity modelling presented here applies knowledge-driven feature extraction to a data-driven machine learning approach for tungsten mineralisation. The method emphasises the importance of appropriate model evaluation and develops a new Confidence Metric to generate spatially ref...

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Bibliographic Details
Main Authors: Christopher M. Yeomans, Robin K. Shail, Stephen Grebby, Vesa Nykänen, Maarit Middleton, Paul A.J. Lusty
Format: Article
Language:English
Published: Elsevier 2020-11-01
Series:Geoscience Frontiers
Subjects:
Machine learning
Mineral prospectivity modelling
Mineral exploration
Random Forest™
Tungsten
SW England
Online Access:http://www.sciencedirect.com/science/article/pii/S1674987120301353

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http://www.sciencedirect.com/science/article/pii/S1674987120301353

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