A Multi-Constraint Framework for Geochemical Anomaly Detection Based on Compositional Data Analysis and Spatial Statistics: Implications for Copper Mineralization in Eastern Tianshan

Geochemical anomaly detection plays a critical role in mineral exploration, yet conven-tional methods are often limited by compositional effects, sensitivity to outliers, and in-sufficient consideration of spatial relationships. To address these issues, this study pro-poses an integrated analytical framework that combines compositional data analysis and spatial statistics for robust geochemical anomaly identification. The framework incor-porates isometric log-ratio (ILR) transformation to eliminate the closure effect, robust principal component analysis (RPCA) to extract stable geochemical patterns, local indi-cators of spatial association (LISA) 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 sensi-tivity to weak anomalies, and better integration of compositional and spatial constraints. These advantages highlight its effectiveness for geochemical anomaly detection and mineral prospectivity mapping in complex geological settings.