Mineral exploration combined with prospectivity mapping has become the standard process for utilising mineral exploration data. Nowadays, most techniques integrate multiple layers of information and use machine learning for both data-driven and knowledge-driven approaches. This study introduces a novel and generalised methodology for comparing different layers of information by using superpixels instead of pixels to identify similarities. This methodology provides an enhanced statistical representation of regions, facilitating and enabling effective comparisons. Three different statistical distance measures were considered: Kullback–Leibler divergence, Wasserstein distance and total variation distance. We apply the proposed process to data from the Antofagasta region of northern Chile, a well-known area for metallogenic belts, that contain notable copper reserves. Each metric was used and compared, resulting in different similarity maps highlighting interesting mineral exploration areas. The study results lead to the conclusion that the proposed methodology can be applied at different scales and helps in the identification of areas with similar characteristics.

矿产勘探与前景测绘相结合已成为利用矿产勘探数据的标准流程。如今，大多数技术都集成了多层信息，并将机器学习用于数据驱动和知识驱动的方法。这项研究引入了一种新颖且通用的方法，通过使用超像素而不是像素来比较不同层的信息来识别相似性。该方法提供了增强的区域统计表示，促进并实现有效比较。考虑了三种不同的统计距离度量：Kullback-Leibler 散度、Wasserstein 距离和总变异距离。我们将所提出的过程应用于智利北部安托法加斯塔地区的数据，该地区是著名的成矿带地区，含有大量的铜储量。每个指标都被使用和比较，产生不同的相似性地图，突出显示有趣的矿产勘探区域。研究结果得出的结论是，所提出的方法可以应用于不同的规模，并有助于识别具有相似特征的区域。