Support vector machine: A tool for mapping mineral prospectivity

In this contribution, we describe an application of support vector machine (SVM), a supervised learning algorithm, to mineral prospectivity mapping. The free R package e1071 is used to construct a SVM with sigmoid kernel function to map prospectivity for Au deposits in western Meguma Terrain of Nova Scotia (Canada). The SVM classification accuracies of ‘deposit’ are 100%, and the SVM classification accuracies of the ‘non-deposit’ are greater than 85%. The SVM classifications of mineral prospectivity have 5–9% lower total errors, 13–14% higher false-positive errors and 25–30% lower false-negative errors compared to those of the WofE prediction. The prospective target areas predicted by both SVM and WofE reflect, nonetheless, controls of Au deposit occurrence in the study area by NE–SW trending anticlines and contact zones between Goldenville and Halifax Formations. The results of the study indicate the usefulness of SVM as a tool for predictive mapping of mineral prospectivity.