Unsupervised geochemical anomaly detection in under-explored areas using multi-point geostatistics and autoencoder: A 1:50000 stream sediment case study in the southeastern Hubei, China

The identification of geochemical weak anomalies in data-gap or minimally explored areas-where labeled mineral occurrence data are scarce or completely absent-poses a significant challenge to conventional supervised methods that rely on known positive samples for training. This study proposes a fully unsupervised framework that combines optimized Multiple-Point Statistics (MPS) with a Fusion Convolutional Autoencoder (FCAE), applied to geochemical data from 1:50,000-scale stream sediments in the Gaoqiao-Jiugong (Edongnan) area in southeastern Hubei, China. Under the principle of multifractal scale invariance, the MPS training image (TI) construction strategy was optimized to capture high-order spatial structural patterns, generating geologically consistent spatial distributions while preserving multi-scale heterogeneity and mitigating the over-smoothing effects inherent in conventional interpolation methods. These MPS outputs serve as high-quality input representations for subsequent unsupervised FCAE, extracting composite weak anomalies through reconstruction error analysis without any pre-labeled mineral occurrence data. Application results demonstrate that the MPS-generated maps exhibit superior structural conformity with known geological features compared with IDW and Kriging methods. Quantitative validation based on the maximum percentile ranking within a 500 m buffer around 18 known mineral occurrences (2 deposits, 14 occurrences, 2 low-potential showings) shows that MPS-FCAE achieves a mean percentile of 0.60% in high-potential occurrences, outperforming the IDW-FCAE counterpart (0.79%), with prominent improvements in deposit-scale anomaly detection (0.77% vs. 1.58%) and a maximum 45-fold enhancement for weak Au and Hg anomalies. Crucially, MPS-FCAE correctly downgraded two low-potential showings to percentiles above 6%, while IDW-FCAE retained them within the top 3% due to the bullseye effect. The composite anomalies derived from FCAE are associated with known Au deposits and reveal previously unidentified exploration targets in data-gap regions, while yielding fewer false-positive anomalies than conventional manual anomaly delineation methods. These findings demonstrate that the proposed unsupervised MPS-FCAE framework effectively addresses the issues of inaccurate spatial structural characterization and insensitive detection of weak anomalies in medium-scale (1:50,000) geochemical surveys, offering a label-independent, transferable methodology for mineral exploration in sparsely sampled terrains.