Stability-optimized extended isolation forest: A robust framework for geochemical anomaly detection

Rapid and reliable identification of multivariate geochemical anomalies is critical for delineating prospective mineralized zones and reducing uncertainty in mineral exploration targeting. Extended isolation forest (EIF) is a powerful unsupervised ensemble learning algorithm that efficiently isolates anomalies from high-dimensional geochemical datasets using randomly oriented hyperplane partitions. Previous studies have demonstrated the effectiveness of EIF in multivariate geochemical anomaly detection and mineral potential modeling. However, its performance can be significantly affected by stochastic variability arising from random partitioning and random subsampling during isolation tree construction, which may result in unstable anomaly patterns and inconsistent exploration targets in complex geological environments. To mitigate this limitation, we developed a robust unsupervised framework for the identification of multivariate geochemical anomalies associated with gold mineralization in the Southwestern Yilgarn Craton, Australia. The proposed framework integrates robust factor analysis (RFA), a Jaccard-based stability index and EIF to enhance the reliability and reproducibility of anomaly detection. RFA was first applied to compositional soil geochemical data to identify the most significant pathfinder elements associated with gold mineralization, which were subsequently used as input variables for the EIF model. The model was then optimized using a Jaccard-based stability criterion to ensure consistent anomaly detection across repeated independent runs. Model performance was assessed using area under the receiver operating characteristic curve (AUC). The obtained AUC value of 0.82 indicates strong predictive capacity, confirming that the generated anomaly map effectively delineates mineralization-related geochemical patterns and provides a reliable proxy for mineral prospectivity mapping. Overall, the proposed framework offers a robust and reproducible unsupervised approach for multivariate geochemical anomaly detection with strong applicability in both greenfield and brownfield mineral exploration settings.