An integrated singularity–energy analysis to reveal mineralization-related geochemical patterns

During the last two decades, local singularity analysis (LSA) has become a leading technique to enhance weak geochemical anomalies associated with non-linear ore-forming processes operating in complex Earth systems. Singularity maps of multiple ore-forming elements are preferably synthesized to reveal multi-element geochemical anomalies and to portray them as stronger mineralization-related geochemical signatures. In this regard, classifying anomalous components is crucial to identify different patterns of ore formation-related geochemical anomalies, a step forward to delimit target areas for metal exploration. This research puts forward an integrated singularity–energy (S–E) analysis dealing with the variety in the metal enrichment patterns obtained from singularity mapping of multiple ore-forming elements. As per the S–E methodology, the LSA is applied to the rasterized geochemical maps with the aim of reducing the adverse effects of overburden and enhancing weak geochemical anomalies. Relying on multi-sample energy statistics, a k-groups partitioning based on energy distance is then devised to classify singularity maps into $k$ patterns with contrasting probability distributions, thereby recognizing different patterns of multi-element geochemical anomalies. Eventually, prospectivity indices for the resulting S–E patterns are calculated to prioritize mineralization-related patterns and to automate the definition of exploration targets. The potential application of the S–E analysis is demonstrated by a stream sediment geochemical dataset (viz. Cu, Au, Pb, and Zn) pertaining to the Moalleman district, NE Iran. Moreover, additional geochemical patterns are recognized by concentration–area multifractal modeling of additive geochemical indices and by k-means clustering of singularity maps of ore-forming elements, serving as traditional references to constitute comparative analyses. Appraisal by success-rate curves indicates that metal enrichment patterns derived from S–E analysis, compared to those from traditional approaches, establish a more significant spatial conformity with hydrothermal- and epithermal-type mineralization events within the study area. The findings suggest that the proposed technique has robust properties to bring more efficient exploration knowledge and reliable evidence for prospecting buried and covered metal deposits within complex Earth systems.