A Framework for Data-Driven Mineral Prospectivity Mapping with Interpretable Machine Learning and Modulated Predictive Modeling
Nini Mou(China Geological Survey), Xiang Sun(China University of Geosciences (Beijing)), Emmanuel John M. Carranza(University of the Free State), Gongwen Wang(China University of Geosciences (Beijing))
Cited by 41
Related Papers
Multi- and hyperspectral geologic remote sensing: A review
|International Journal of Applied Earth Observation and Geoinformation|2011|1.1k
Multi - and hyperspectral geologic remote sensing : a review
|ResearchOnline at James Cook University (James Cook University)|2011|812
Arsenic geochemistry and health
|Environment International|2004|582
Support vector machine: A tool for mapping mineral prospectivity
|Computers & Geosciences|2010|510
Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN)
|Remote Sensing of Environment|2007|438