Abstract
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.