Uncertainties in GIS-Based Mineral Prospectivity Mapping: Key Types, Potential Impacts and Possible Solutions

Abstract

GIS-based mineral prospectivity mapping (MPM) is a computer-aided methodology for delineating and better constraining target areas deemed prospective for mineral deposits of a particular type. The underlying algorithms are well-established and well-understood, but on the whole, MPM that is a multi-faceted and multi-criteria approach, is faced with a high degree of uncertainty. We distinguish three principal types of uncertainties: (1) data-related (e.g., the sometimes erroneous, inadequate, incomplete, unevenly distributed or poorly resolved nature of the input data); (2) model-related (e.g., the diversity and inherent natural variability of mineral deposits, our lack of complete knowledge of the targeted mineral deposit type, and our imperfect ability to interpret geoscience datasets); and (3) judgment-related (e.g., the influence of cognitive heuristics and biases). In this contribution, we review and characterize the key uncertainties listed above and provide possible solutions as to how they may be recognized and mitigated in the context of MPM. This review also clearly illustrates the need for future studies designed to carefully monitor each step of the MPM process and aims at reducing uncertainty by, for example, (1) using carefully vetted, high-quality input data, (2) developing targeting models based on the best possible understanding of the underlying mineral deposit models and backed by machine learning-based simulations of likely ore-forming processes, and (3) adopting advanced methods such as deep learning algorithms for effective integrating of predictor maps.