Machine Learning-Based Mapping for Mineral Exploration

We briefly review the state-of-the-art machine learning (ML) algorithms for mineral exploration, which mainly include random forest (RF), convolutional neural network (CNN), and graph (GCN). In recent years, RF, a representative shallow algorithm, CNN, deep approach, have been proved to be powerful tools ML-based mapping exploration. future, GCN deserves more attention exploration because of its ability capture spatial anisotropy mineralization applicability within irregular study areas. Finally, we summarize original contributions six papers comprising this special issue.