Mineral potential mapping (MPM) can recognise irregular patterns of mineralization-related indicator features and proxies. It serves as an anomaly detection technique, given that mineralization itself is a rare geological event. In this regard, unsupervised anomaly detection (UAD) algorithms could be effective in identifying high potential zones of mineralization accounting for irregular pattern recognition. The main advantage of these algorithms lies in their ability toexploit geo-datasets without requiring any form of annotation. In this study, eight evidence layers were first created based on the conceptual model of mineral deposits to build a model of Fe prospectivity in the Esfordi region of Yazd province, located in east-central Iran. Then, three unsupervised anomaly detection algorithms, namely deep autoencoder (DAE), one-class support vector machine (OC-SVM), and isolation forest (IForest) were employed to assess Fe prospectivity in the area. The prediction-area (P-A) plot was subsequently used to evaluate the efficacy of the three prospectivity models. Finding indicate that the deep autoencoder outperforms the other adopted machine learning methods in identifying high potential areas of Fe mineralization. Considering the significance of hyperparameters in the implementation of these algorithms, we also investigate the application of the P-A plot to identify optimal hyperparameter values, thereby enhancing the performance of the Fe prospectivity model. The results demonstrate that in IForest and DAE, and to some extent OC-SVM, experts can adjust hyperparameters without relying on labelled data, achieving a commendable level of detection performance. This innovative approach and workflow are broadly applicable to regional-scale mineral exploration across diverse metallogenic provinces globally.
