Mapping mineral prospectivity (MPM) is mostly beset with prediction uncertainties, which are generally categorized into (a) stochastic and (b) systemic types. The stochastic type is usually linked to the low quality as well as insufficiency/inefficiency of data used. In contrast, inaccurate selection of exploration criteria, exaggerated and arbitrary weighting of spatial evidence layers resulting from subjective judgment of analyst and applying an integration methodology, which is not able to consider the complexities of geological processes, are main sources of systemic type. This paper aims for reducing the second type of MPM uncertainty in delineating favorable exploration targets for Cu-Au mineralization in the Moalleman District, NE Iran. Thus, several efficient evidence layers were translated from geospatial criteria (e.g., geochemical, geological, structural and hydrothermal alterations) and were considered for integration purpose in the study area. Then, an improved data-driven simple additive weight (data-driven SAW) procedure was introduced for generating prospectivity model. In this procedure, prediction-area plots and frequency ratio method were applied for assigning objective weights to efficient evidence layers and their corresponding classes, respectively. Furthermore, a supervised algorithm for machine learning classification namely support vector machine (SVM) with radial basis function kernel was executed for comparison purposes. The results indicated that the two prospectivity models are succeeded in delineating favorable targets of mineralization; however, the SVM model is more reliable than data-driven SAW in predicting high-potential areas of mineralization.

绘制矿产远景图（MPM）主要受到预测不确定性的困扰，这些不确定性通常分为（a）随机和（b）系统类型。随机类型通常与所使用的数据质量低下以及功能不足/效率低下有关。相比之下，勘探标准选择不正确，分析人员的主观判断以及采用无法考虑地质过程复杂性的综合方法导致的空间证据层的夸张和任意加权，都是系统类型的主要来源。本文旨在减少第二种MPM不确定性，为伊朗东北部Moalleman区的铜金成矿确定有利的勘探目标。因此，从地理空间标准（例如地球化学，地质，构造和热液蚀变），并已考虑在研究区域进行整合。然后，引入了一种改进的数据驱动的简单累加权重（数据驱动的SAW）程序来生成预期模型。在此过程中，使用预测区域图和频率比率方法分别将目标权重分配给有效的证据层及其对应的类别。此外，为了进行比较，还执行了一种监督的机器学习分类算法，即具有径向基函数核的支持向量机（SVM）。结果表明，两种前景模型成功地确定了有利的矿化目标。但是，SVM模型在预测高潜在矿化区域方面比数据驱动的SAW更为可靠。结构和水热变化），并已考虑在研究区域进行整合。然后，引入了一种改进的数据驱动的简单累加权重（数据驱动的SAW）程序来生成预期模型。在此过程中，使用预测区域图和频率比率方法分别将目标权重分配给有效的证据层及其对应的类别。此外，为了进行比较，还执行了一种监督的机器学习分类算法，即具有径向基函数核的支持向量机（SVM）。结果表明，两种前景模型成功地确定了有利的矿化目标。但是，SVM模型在预测高潜在矿化区域方面比数据驱动的SAW更为可靠。结构和水热变化），并已考虑在研究区域进行整合。然后，引入了一种改进的数据驱动的简单累加权重（数据驱动的SAW）程序来生成预期模型。在此过程中，使用预测区域图和频率比率方法分别将目标权重分配给有效的证据层及其对应的类别。此外，为了进行比较，还执行了一种监督的机器学习分类算法，即具有径向基函数核的支持向量机（SVM）。结果表明，两种前景模型成功地确定了有利的矿化目标。但是，SVM模型在预测高潜在矿化区域方面比数据驱动的SAW更为可靠。