Identification of Multi-Element Geochemical Anomalies for Cu–Polymetallic Deposits Through Staged Factor Analysis, Improved Fractal Density and Expected Value Function

The success of exploration geochemistry requires identification of multi-element geochemical signatures. It has been revealed that Zhongdian island arc zone is an important area with supersized or large Cu–polymetallic deposits. In this regard, we proposed a hybrid method to identify multi-element geochemical anomaly for Cu–polymetallic deposits based on staged factor analysis, improved fractal density and expected value function. In this study, two factors including Cu–Zn and Mo–Pb associations were recognized after four stages of factor analysis of stream sediment geochemical data from Zhongdian area, Yunnan, China. Then, an improved fractal density model was developed to reveal the geochemical distribution patterns of these factors by introducing the parameter P c (a form of singularity index) based on "general fractal topography". The results demonstrate that the proposed model enhanced the anomaly-background structure of Cu geochemical distribution pattern, and effectively characterized the anisotropic scale-invariance of local singularities. Furthermore, the assessment of Cu anomaly uncertainty showed there were about 33% of the Cu occurrences in the low probability areas, indicating these areas based on the Cu anomaly remain uncertain for Cu–polymetallic deposits. Thus, to generate a stronger anomalous signature for Cu–polymetallic potential mapping, the expected value function was utilized for the integration of the two different geochemical signatures, and the fuzzy algebraic sum operator was used also for comparison purpose. The contrasting results demonstrated that the proposed method can increase the intensity of favorable anomalies, reduce the uncertainty and optimize the prospecting areas.