Using machine learning to estimate a key missing geochemical variable in mining exploration: Application of the Random Forest algorithm to multi-sensor core logging data

Scikit-learn: Machine Learning in Python

Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text

Computing, Computer Science, Artificial Intelligence +10

Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

Víctor Rodríguez‐Galiano, M. Sánchez-Castillo, Mario Chica‐Olmo et al. · Ore Geology Reviews · 2015 · 1.5K citations

Computing, Earth Sciences, Geological Engineering +12

Dealing with Zeros and Missing Values in Compositional Data Sets Using Nonparametric Imputation

Josep Antoni Martín Fernández · Mathematical Geology · 2003 · 601 citations

Public Health, Biostatistics, Statistics +7

Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines)

Emmanuel John M. Carranza, Alice G. Laborte · Computers & Geosciences · 2014 · 370 citations

Natural Sciences, Earth Sciences, Forestry +7

The Alteration Box Plot: A Simple Approach toUnderstanding the Relationship between Alteration Mineralogy andLithogeochemistry Associated with Volcanic-Hosted Massive Sulfide Deposits

Ross R. Large, JB Gemmell, Holger Paulick et al. · Economic Geology · 2001 · 307 citations

Natural Sciences, Earth Sciences, Geology +17