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.

矿产远景图 (MPM) 是一个多方面的过程，严重依赖于专家的判断。尽管人类的解释和认知知识在勘探项目的成功中很重要，但人类的投入对于参与 MPM 各个阶段的地质学家的认知启发式、主观性和心理特征来说是重要的。知识驱动的 MPM 用于绿地区域（已知很少或没有矿床）使用专家的意见为勘探目标标准分配权重。这个问题引入了系统的不确定性，最终会传播到生成的目标。因此，本研究旨在提出一种方法，用于在知识驱动的 MPM 中调节此类不确定性的影响。为了实现这一目标，在一个案例研究中，将蒙特卡罗模拟与模糊逻辑相结合的程序阐明并应用于一套由地球化学和地质数据得出的矿物系统衍生的目标标准。建议的程序返回三个组成部分，即（a）调制的前景模型，（b）不确定性和（c）置信度。在这种方法中，不确定性和置信度与前景值的关系图用于目标生成；低风险目标是那些具有低不确定性、高置信度和高前景值的目标。在这项研究中，低风险目标仅占研究区域的 0.5%，展示了应用框架在减少绿地勘探中的搜索空间方面的功效。