The Xiaohongshilazi Pb-Zn-(Ag) ore district, located in the Jizhong–Yanbian Metallogenic Belt in NE China, presents challenges in identifying geochemical anomalies and prospecting minerals due to thick covers, complex geological evolution, and mining activities. To overcome these obstacles, we adopted an innovative approach that combines multifractal theory with concentration-area (C-A) modelling and local singularity analysis (LSA) to characterize the geochemical signature. Initially, factor analysis of log-transformed data was used to identify ore-forming factors, which were then modelled using C-A and LSA. Subsequently, modelling results were compared to conventional maps generated using inverse distance weighted (IDW) interpolation. Additionally, the C-A model was employed to establish thresholds for singularity indices generated by LSA for target mapping. The results revealed a close relationship between Factor 1 (F1) and mineralization. The C-A model of F1 failed to detect weak anomalies compared to the IDW-interpolated map, while LSA not only identified weak anomalies but also enhanced the detection of reliable anomalies. Three prospecting targets (PT-01, PT-02, and PT-03) were identified. Validation of PT-01 using new drillholes and existing mineralization confirms the effectiveness of targets and the accuracy of the model. Our approach provides a robust alternative for prospecting ore districts encountering similar challenges.

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