The operation of large-scale ore-forming processes triggers the development of neighboring mineral deposits of the same or related types in a metallogenic province. While these deposits often bear striking similarities, variations in local geological settings cause differences in many deposit features. Therefore, in a metallogenic province, geochemical, geophysical, and geological signatures of local areas mineralized with a certain deposit type can show considerable inherent differences. The application of deposit-type locations as training sites, thus, introduces a type of stochastic uncertainty into data-driven mineral prospectivity mapping (MPM), impairing the predictive capability of this activity. This study delves into this type of uncertainty and applies an ensemble technique combining bootstrapping and naïve Bayes classifiers to measure this uncertainty and lessen its impact on the MPM-generated exploration targets. Two components, one representing the quantified uncertainty and the other a modulated predictive model, are retained by the proposed framework. This framework was applied to a suite of mineral-systems derived targeting criteria of skarn-type Cu mineralization in the Alborz–Azerbaijan magmatic belt of northern Iran. The predictive results derived by the proposed technique outperformed those derived using a single classifier, showcasing its efficacy. In addition, a novel approach is described and applied to demarcating exploration targets marked by low uncertainty.

大型成矿过程的运行触发了成矿省内相同或相关类型的邻近矿床的开发。虽然这些矿床通常具有惊人的相似性，但当地地质环境的变化导致许多矿床特征的差异。因此，在一个成矿省，具有某种矿床类型的局部矿化区域的地球化学、地球物理和地质特征可以表现出相当大的内在差异。因此，将矿床类型位置用作培训地点，将一种随机不确定性引入到数据驱动的矿产远景图 (MPM) 中，削弱了该活动的预测能力。本研究深入研究了这种不确定性，并应用了一种结合自举法和朴素贝叶斯分类器的集成技术来衡量这种不确定性，并减少其对 MPM 生成的勘探目标的影响。提议的框架保留了两个组件，一个代表量化的不确定性，另一个代表调制的预测模型。该框架被应用于伊朗北部 Alborz-Azerbaijan 岩浆带中一套基于矽卡岩型铜矿化目标标准的矿物系统。所提出的技术得出的预测结果优于使用单个分类器得出的结果，展示了其有效性。此外，还描述了一种新方法并将其应用于划定具有低不确定性的勘探目标。