Mineral exploration has traditionally been an expensive and time-consuming venture, with early-stage work often dependent on labour intensive programmes to locate potential targets. These efforts require significant investment yet offer no guarantee of success. More recently, advances in both data‑driven and knowledge‑based prospectivity analysis have reshaped how we assess geological potential, improving exploration efficiency, reducing risk, and helping focus exploration efforts in areas of higher geological prospectivity, where discoveries are most likely to be made.

This presentation outlines a scalable prospectivity workflow and demonstrates how regional‑scale models can be progressively refined to support local, deposit‑scale and near‑mine exploration targeting approaches. Using examples from previously developed models together with a review of complementary datasets from Kazakhstan, the talk highlights how mineral systems based prospectivity assessments can be translated into a practical and effective exploration tool across a variety of spatial scales. The talk highlights how both contemporary and historic public geological datasets available across Kazakhstan can be integrated into desktop-based exploration and prospectivity studies for various deposit styles. Through this integration, key components of the mineral system can be constrained, from deep crustal structures identified through gravity and magnetic data, to prospective lithologies, minerals of interest, and surface structural trends using geological mapping, through to geochemical anomalism delineated from mineral occurrence records and geochemical datasets. This approach supports the prioritisation of prospective areas and the development of well supported exploration targets.