Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: State-of-the-art and outlook

Mineral exploration targeting is a highly complex decision-making task. Two key risk factors, the quality of exploration data and robustness of the underlying conceptual targeting model, have a strong impact on the effectiveness of this decision-making. Geographic information systems (GIS) can be used not only for compiling, integrating, interrogating and interpreting diverse exploration data, but also for targeting by employing powerful mathematical algorithms, an approach that is commonly referred to as mineral potential modelling or mineral prospectivity mapping (MPM). Here, we pose and examine key aspects around the question of how can we get better at mineral exploration targeting using GIS? We do this by (1) reviewing the fundamental aspects of MPM, (2) identifying significant deficiencies of MPM, and (3) discussing possible solutions to alleviating or eliminating these deficiencies. In particular, we discuss how these deficiencies can be overcome by adopting an intelligence amplification system, such as the recently proposed exploration information system (EIS) for translating critical ore forming processes into spatially predictive criteria (i.e., predictor maps and spatial proxies) and improving decision-making in mineral exploration targeting.

Automated summary

Mineral exploration targeting is a complex decision-making task influenced by key risk factors such as the quality of exploration data and robustness of targeting models. Geographic information systems (GIS) can be used for compiling, integrating, interrogating, and interpreting exploration data, as well as for targeting using mathematical algorithms known as mineral potential modelling or prospectivity mapping (MPM). Overcoming deficiencies in MPM and adopting systems like exploration information systems (EIS) can improve decision-making in mineral exploration targeting.