Mineral prospectivity mapping (MPM) is crucial for efficient mineral exploration, where prospective zones are identified in a cost-effective manner. This study focuses on generating prospectivity maps for hydrothermal polymetallic mineralization in the Feizabad area, in northeastern Iran, using unsupervised anomaly detection methods, i.e., isolation forest (IForest) and deep isolation forest (DIF) algorithms. As mineralization events are rare and complex, traditional approaches continue to encounter difficulties, despite advances in MPM. In this respect, unsupervised anomaly detection algorithms, which do not rely on ground truth samples, offer a suitable solution. Here, we compile geospatial datasets on the Feizabad area, which is known for its polymetallic mineralization showings. Fourteen evidence layers were created, based on the geology and mineralization characteristics of the area. Both the IForest and DIF algorithms were employed to identify areas with high mineralization potential. The DIF, which uses neural networks to handle non-linear relationships in high-dimensional data, outperformed the traditional decision tree-based IForest algorithm. The results, evaluated through a success rate curve, demonstrated that the DIF provided more accurate prospectivity maps, effectively capturing complex, non-linear relationships. This highlights the DIF algorithm’s suitability for MPM, offering significant advantages over the IForest algorithm. The present study concludes that the DIF algorithm, and similar unsupervised anomaly detection algorithms, are highly effective for MPM, making them valuable tools for both brownfield and greenfield exploration.

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.

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 (IForest) and Extended Isolation Forest (EIF) to enhance MPM and exploration targeting. In accordance with the conceptual model of porphyry copper deposits, key evidence layers were generated, including fault density, multi-element geochemical signatures, proximity to various alteration types (phyllic, argillic, propylitic, and iron oxide), as well as proximity to intrusive rocks. These layers were integrated using IForest 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 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 IForest, as it provides a clear and distinct tuning pattern, whereas EIF lacks such clarity, complicating the optimization process.