In this paper, we explore unsupervised cluster analysis to aid mineral prospectivity mapping (MPM) in two aspects: (1) to cluster geochemical data for MPM based on detailed analysis of evidence maps and (2) to explore coherence of spatial signatures at/around mineralized locations as well as outliers of geochemical data. To do so, a systematic procedure is proposed based on the Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA). Through this procedure, the detailed analysis of evidence maps in Hezuo–Meiwu district, Gansu Province, China, which portray five selected geochemical elements, showed that clusters with and without mineralized locations provide insight to weighing of each evidence. Finally, through the integration of evidence maps, the favorability score map yielded high AUC (> 0.80) for delineating various mineralized locations in the study area, which proves the efficacy of unsupervised cluster analysis as an aid to MPM. Moreover, the coherence of spatial signatures of known mineralized locations, which comprise a training dataset, is vital to data-driven MPM. Groupings of mineralized locations based on the ISODATA and visual inspection supported by PCs from principal component analysis imply that different deposit types may share the same or similar spatial signature and outliers in geochemical data may be potential training samples used for data-driven MPM. Mineralized locations of the same deposit type may show significant dissimilarity. However, this provides insights into selecting mineralized/non-mineralized locations for creation of training datasets. Interestingly, in the study area, major mineralized locations in zones divided by regional fault are clustered separately into two groups. This result not only proves that cluster analysis is effective for exploring the coherence of spatial signatures at/around mineralized locations, but it also justified our previous study, whereby we performed MPM by zones using machine learning algorithms.

在本文中，我们将从两个方面探讨无监督的聚类分析，以帮助进行矿物前瞻图谱（MPM）：( 1）基于证据图谱的详细分析对MPM的地球化学数据进行聚类；（2）探索/周围空间特征的连贯性矿化位置以及地球化学数据的异常值。为此，提出了一种基于迭代自组织数据分析技术算法（ISODATA）的系统程序。通过该程序，对甘肃省合左市-梅屋地区的证据图进行了详细分析，描绘了五个选定的地球化学元素，结果表明，有矿化位置和无矿化位置的星团都可以为权衡每个证据提供依据。最后，通过证据图的整合，可喜度评分图产生了较高的AUC（> 0。80）划定研究区域内的各种矿化位置，这证明了无监督聚类分析作为MPM的辅助手段的功效。此外，包含训练数据集的已知矿化位置的空间特征的一致性对于数据驱动的MPM至关重要。基于ISODATA的矿化位置分组以及PC从主成分分析支持的目测检查表明，不同的矿床类型可能共享相同或相似的空间特征，而地球化学数据中的异常值可能是用于数据驱动MPM的潜在训练样本。同一矿床类型的矿化位置可能显示出明显的差异。但是，这提供了选择矿化/非矿化位置以创建训练数据集的见识。有趣的是，在研究区域，被区域断层划分的区域中的主要矿化位置分别分为两组。该结果不仅证明聚类分析对于探索矿化位置处/周围的空间特征的一致性是有效的，而且还证明了我们先前的研究的正确性，即我们使用机器学习算法按区域执行了MPM。