Unsupervised Anomaly Detection for Mineral Prospectivity Mapping Using Isolation Forest and Extended Isolation Forest Algorithms

Unsupervised anomaly detection algorithms have gained significant attention in the field of mineral prospectivity mapping (MPM) due to their ability to reveal hidden mineralization zones by effectively modeling complex, nonlinear relationships between exploration data and mineral deposits. This study utilizes two tree-based anomaly detection algorithms, namely, isolation forest (IF) and extended isolation forest (EIF), to enhance MPM and exploration targeting. According to the conceptual model of porphyry copper deposits, several evidence layers were generated, including fault density, multi-element geochemical signatures, proximity to various alteration types (phyllic, argillic, propylitic, and iron oxide), and proximity to intrusive rocks. These layers were integrated using IF and EIF algorithms, and their results were subsequently compared with a geological map of the study area. The comparison revealed a high degree of overlap between the identified anomalous zones and geological features, such as andesitic rocks, tuffs, rhyolites, pyroclastics, and intrusions. Additionally, quantitative assessments through prediction-area plots validated the efficacy of both models in generating prospective targets. The results highlight the significant influence of hyperparameter tuning on the accuracy of prospectivity models. Furthermore, the study demonstrates that hyperparameter tuning is more intuitive and straightforward in IF, as it provides a clear and distinct tuning pattern, whereas EIF lacks such clarity, complicating the optimization process.