Collection
Special Issue: Machine Learning-based Mapping for Mineral Exploration
Mineral prospectivity mapping as a computer-based approach to delineate targeted areas for a specific type of mineral deposit has changed from being knowledge driven to data driven to today’s big data analytics. There are increasing applications of machine learning algorithms in mapping mineral prospectivity and identifying geochemical anomalies association with mineralization. The proposed special issue will document case studies and current researchers demonstrating progress of machine learning-based mineral prospectivity mapping.
Main topics:
• Mineral prospectivity mapping by machine (deep) learning
• Geochemical anomaly mapping by machine (deep) learning
• Big data analytics for geochemical anomaly or mineral prospectivity mapping
• Numerical simulation for mineral exploration
Editors
-
Renguang Zuo
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074, China
-
Emmanuel John M. Carranza
Department of Geology, University of the Free State, Bloemfontein, South Africa
Articles (7 in this collection)
-
-
Bayesian Decomposition Modelling: An Interpretable Nonlinear Approach for Mineral Prospectivity Mapping
Authors (first, second and last of 7)
-
-
Supervised Mineral Prospectivity Mapping via Class-Balanced Focal Loss Function on Imbalanced Geoscience Datasets
Authors (first, second and last of 9)
-
-
-
