Geochemical Anomaly Detection via Supervised Learning: Insights from Interpretable Techniques for a Case Study in Pangxidong Area, South China

Machine learning (ML) algorithms are widely applied across various fields due to their ability to extract high-level features from large training datasets. However, their use in geochemical prospecting and mineral exploration remains limited because mineralization—a rare geological event—often results in insufficient training samples for supervised ML. Generating adequate training data is thus essential for applying supervised ML in this domain. In this study, we augmented training samples by utilizing adjacent samples centered around known mineral deposits and then employed random forest (RF) modeling to identify multivariate geochemical anomalies associated with mineralization. To evaluate the robustness of data augmentation and gain insights into the geochemical survey data, we applied interpretable ML techniques—feature importance and partial dependence plots (PDPs)—to clarify the data processing within mineral prospectivity mapping. The proposed methodology was tested in the Pangxidong Area, South China. The identified geochemical anomalies show strong spatial correlation with known mineral deposits, while feature importance rankings and PDPs validate the effectiveness of the proposed methodology. This practice enhances the applicability of supervised ML in geochemical prospecting and mineral exploration as well as the application of interpretable techniques for understanding data processing of multi-geoinformation.