Geochemical anomaly detection plays a critical role in mineral exploration, yet conventional methods are often limited by compositional effects, sensitivity to outliers, and insufficient consideration of spatial relationships. To address these issues, this study proposes an integrated analytical framework that combines compositional data analysis and spatial statistics for robust geochemical anomaly identification. The framework incorporates isometric log-ratio (ILR) transformation to eliminate the closure effect, robust principal component analysis (RPCA) to extract stable geochemical patterns, local indicators of spatial association (LISAs) to characterize spatial clustering, and compositional balance analysis (CoBA) to enhance anomaly signals. The method is applied to the Barkol Lake area in the Eastern Tianshan, a key metallogenic belt within the Central Asian Orogenic Belt. The results reveal significant geochemical anomalies characterized by Cu-associated element assemblages (e.g., Cu–Ni–Cr), which are spatially correlated with major fault zones and volcanic–intrusive complexes. The identified anomalies show strong consistency with known mineral occurrences and delineate several prospective targets for copper polymetallic mineralization. Compared with conventional approaches, the proposed framework demonstrates improved robustness to outliers, enhanced sensitivity to weak anomalies, and better integration of compositional and spatial constraints.
Liao et al. (Tue,) studied this question.