A Spatial Data-Driven Approach for Mineral Prospectivity Mapping

Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposits. However, detailed knowledge-based geological exploration and interpretations generally involve significant costs, time, human resources. In this study, an ensemble machine learning approach was tested using geoscience datasets to map Cu-Au Pb-Zn in the Cobar Basin, NSW, Australia. The input (magnetic, gravity, faults, electromagnetic, magnetotelluric data layers) were chosen by considering their association with mineralization patterns. Three algorithms, namely random forest (RF), support vector (SVM), maximum-likelihood (MaxL) classification, applied data. results of three algorithms ensembled produce maps over Basin improved classification accuracy. findings demonstrate good agreement known occurrence points existing developed weights-of-evidence (WofE) method. ability capture training accurately simplicity proposed make it advantageous complex methods, serve as preliminary evaluation technique. methodology can be modified different facilitating investigations other regions providing guidance more detailed, high-resolution investigations.