Mineral Prospectivity Mapping (MPM) is a multifaceted process relying heavily on experts' judgments. Notwithstanding the importance of human interpretations and cognitive knowledge in the success of exploration projects, human input, granted, is material to cognitive heuristics, subjectivity, and mental traits of geologists involved in the various stages of MPM. Knowledge-driven MPM used in greenfield areas – where few or no mineral deposits are known – use experts' opinions for assigning weights to exploration targeting criteria. This issue introduces a systemic uncertainty that eventually propagates to the generated targets. This study, therefore, intends to propose a methodology for modulating the effects of this type of uncertainty in knowledge-driven MPM. Aiming to attain this objective, a procedure combining Monte Carlo simulation with fuzzy logic was articulated and applied to a suite of mineral systems-derived targeting criteria derived by geochemical and geological data in a case study. The proposed procedure returns three components, namely (a) the modulated prospectivity model, (b) uncertainty, and (c) confidence. In this method, plots of uncertainty and confidence versus prospectivity values are used for target generation; low-risk targets are those marked by low uncertainty, high confidence, and high prospectivity values. In this study, low-risk targets occupy merely 0.5% of the study area, showcasing the applied framework's efficacy for reducing the search space in greenfield exploration.
