Abstract

Mineral prospectivity mapping (MPM) is a new tool in mineral exploration with the objective of gathering and integrating multi-source evidence to provide decision-making criteria for exploration targeting. Ideally, the MPM approach accounts for the uncertainties associated with different predictive modeling techniques. The cell-based association (CBA) is a computational workflow that addresses the prediction-related bias of the MPM protocols. In this contribution, the CBA technique was employed to prioritize grid cells according to the importance of mineralized geological features and to identify geological settings favorable for ore deposition. The Kuh-Lakht epithermal gold deposit in Central Iran was adopted as a case study to examine the relevance of the proposed workflow and define target areas for brownfield exploration. To assess the significance of the proposed technique in dealing with evidence layers rather than training locations (i.e., mineralized/non-mineralized sites), two geochemical evidence layers, single-element and multi-element signatures, were used to train the CBA technique. An appraisal analysis through prediction-area plots and success-rate curves indicated that multi-element geochemical signatures are more relevant for delineating mineralized geological structures and vectoring for concealed gold mineralization. Notably, the input of single-element geochemical data into the CBA workflow enabled the prediction of additional exploration targets, whereas multi-element geochemical data failed to achieve the same results. The results indicated that the CBA technique can efficiently cope with the uncertainty of insufficient data input and permit the delineation of exploration targets. Additionally, our results suggest that the incorporation of a ranking system of critical geological factors that facilitate the gold mineralization into conventional MPM protocols can improve the robustness of mineral exploration targeting.

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