Prospectivity maps for polymetallic skarn and orogenic gold mineralization in the Rio Piranhas-Seridó Domain, Borborema Province (NE Brazil): An aid to exploration targeting
Year: 2026
Type: article
Abstract: In this study, predictive mineral prospectivity modelling (MPM) was applied to the Bodó, Serra da Umburana, Currais Novos–Santa Luzia, and Caicó–São Fernando–Jucurutu areas of the Seridó Mineral Province, Borborema Province, Northeast Brazil. The MPM was designed to target tungsten (W), molybdenum (Mo), gold (Au), and copper (Cu) mineralization associated with structurally controlled polymetallic skarn and orogenic gold deposits. The predictive models were developed through the integration of geological, geochemical, and geophysical data using the Multiclass Index Overlay (MIO) methodology. Thematic maps with different prospectivity ranges were generated and validated through cumulative curves relating predictive indices to the spatial distribution of the known mineral occurrences. The results demonstrated high model efficiency, with 62% to 88% of the occurrences concentrated in areas of high to very high prospectivity, representing 5% to 25% of the total area of each map. The high prospectivity areas include both known mineralized trends as well as areas yet to be investigated. As such, our MPM results not only confirm the potential of the known mining districts but also present new research and exploration frontiers. Geochemical signatures extracted through factor analysis (FA) revealed robust multielement patterns that support the skarn-type mineralization models. Mineralogical and lithogeochemical data from samples of mineralized zones and/or panned concentrates from prospective targets reinforce the polymetallic nature (W-Mo-Au-Cu) typical of skarn deposits in the study areas. Based on the results obtained from the validation of the prospectivity models, it is concluded that the integrated, mineral systems-based approach is effective in guiding mineral exploration in geologically complex terrain. The predictive maps constitute strategic tools that help reduce uncertainty associated with exploration targeting and attract investment to the region, which is timely given the growing global demand for critical metals.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7124163919
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