Predictive Geodata Analytics in Mineral Exploration: Integrating Geophysical and Remote Sensing Data

Although one of the essential cornerstones for economic development is mineral exploration, it is usually constrained by geological complexities, high costs and subsurface characterization uncertainties. The emergence of predictive geospatial modeling in recent years act as an innovative approach in reducing exploration risk and enhancing mineral discovery procedure. In view of this, mineral exploration geoscientists can map out mineralization zones with improved efficiency and confidence by combing geophysics and remote sensing data within an advanced spatial modeling framework. This presentation gives an overview of the basic concepts and geospatial predictive modeling workflows applied in mineral exploration, succeeded by an exploration of the principles and objectives of predictive modeling. The role of geophysical and remote sensing datasets—such as magnetic, radiometric, gravity, and multispectral imagery—in identifying mineralization patterns is highlighted. Furthermore, the presentation highlights the classification of mineral prospectivity modeling into knowleddge-driven and data-driven approaches. Whereas knowledge-driven approahes employ expert undertanding on mineralisation, data-driven approaches utilises spatial data relationships to generate prospectivity maps. Finally, case studies from Ghana's Birimian greenstone belt are presented to demonstrate the practical application of the predictive modeling approaches, highlighting how geophysical and remote sensing datasets are integrated to delineate prospective zones within a certain geological contexts.