Determination of geospatial criteria and prediction of potential areas for porphyry copper deposits in Kazakhstan
Shah Z. Bayramov E. Neafie J. Seltmann R. Rahnama Z.
2025Frontiers Media SA
Frontiers in Earth Science
2025#13
Determining spatial parameters related to mineral deposits is crucial for analysing mineral prospectivity. These spatial parameters, called Geospatial Criteria (GC), can serve as input data for modelling mineral prospectivity. Porphyry Copper (Cu) deposits are the major source of the world’s Cu supply, and reliance on these deposits has significantly increased over the past decade. Kazakhstan produces 2%–4% of global Cu production, with most sources are porphyry Cu deposits. This study applies data-driven and knowledge-based overlay techniques to define region-scale GC for porphyry Cu deposits in Kazakhstan to be used for mineral prospectivity modelling. The spatial attributes of highly prospective porphyry Cu deposits in Kazakhstan are systematically integrated into a Geographic Information System (GIS) to identify and evaluate the GC. Regional spatial datasets, including tectonics, lithological, geochronological, gravity, and magnetic data related to porphyry Cu deposits in Kazakhstan are comprehensively compiled and integrated within the GIS environment. An overlay analysis and Venn diagram analysis are performed using anomalous geospatial data. This results in a prospective area map based on GC highlighting targeted zones for porphyry Cu deposits in Kazakhstan. The study presents a framework for mineral prospectivity mapping that is scalable and can be applied to similar geological settings worldwide. It enhances exploration targeting, reduces costs, and supports sustainable resource discovery. Copyright
exploration , geographic information system , geospatial criteria , Kazakhstan , porphyry Cu deposits
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School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan
Institute of Smart Systems and Artificial Intelligence (ISSAI), Nazarbayev University, Astana, Kazakhstan
Institute of Geodesy and Geoinformation Science, Technical University of Berlin, Berlin, Germany
School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
Centre for Russian and Central Eurasian Mineral Studies, Natural History Museum, London, United Kingdom
Anglo Rem CA Exploration Mining Ltd., Astana, Kazakhstan
School of Mining and Geosciences
Institute of Smart Systems and Artificial Intelligence (ISSAI)
Institute of Geodesy and Geoinformation Science
School of Sciences and Humanities
Centre for Russian and Central Eurasian Mineral Studies
Anglo Rem CA Exploration Mining Ltd.
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