With the increasing use of machine learning for big data analytics, several methods have been implemented for the purpose of exploration targeting using mineral potential mapping in a GIS environment. Random forests (RF) have been successfully applied to data-driven mineral potential mapping using relatively small numbers of input maps that have typically been pre-classified by a geologist familiar with the mineral system being targeted. However, it is useful to understand how well RF perform for mineral potential mapping when a large number of multi-class categorical or non-thresholded numeric input maps are used in the classification or when weighted or ranked training data are used. Four different implementations of RF are presented to examine how the results vary depending on the degree of intervention from an expert in the modeling process. A case study has been devised using data from the eastern Lachlan Orogen in New South Wales (Australia) for the purposes of targeting porphyry Cu–Au mineralization related to the Macquarie Arc. The results demonstrate that the use of a large number of multi-class categorical or non-thresholded numeric predictive input maps results in a poor mineral potential map outcome. An expert review to determine reclassifications or thresholds that produce geologically meaningful maps as proxies for the mineral system being targeted results in more effective RF-based mineral potential maps being produced. Weighting or ranking the deposits used as training data produces more narrowly defined prospective areas that may assist with targeting tier-one economic deposits. Comparison of the RF results to a standard weights of evidence analysis highlighted some significant differences in which predictive maps should be considered important for modeling, and in the extent of prospective area delineated from each output mineral potential map.
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The author would like to thank the Geological Survey of New South Wales for financial and in-kind support for the WofE mineral potential mapping presented in this paper. Thanks also go to the many geologists in industry, government, and academia who have contributed to the data collection and mineral system ideas used in this study. Two anonymous reviewers and the editor are also thanked for their comments, which helped to improve the quality of the manuscript.
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Ford, A. Practical Implementation of Random Forest-Based Mineral Potential Mapping for Porphyry Cu–Au Mineralization in the Eastern Lachlan Orogen, NSW, Australia. Nat Resour Res 29, 267–283 (2020). https://doi.org/10.1007/s11053-019-09598-y
- Lachlan Orogen
- Machine learning
- Mineral potential mapping
- Porphyry copper–gold
- Random forests
- Weights of evidence