A Methodology for Similarity Area Searching Using Statistical Distance Measures: Application to Geological Exploration

IF 4.8 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Felipe Navarro, Gonzalo Díaz, Marcia Ojeda, Felipe Garrido, Diana Comte, Alejandro Ehrenfeld, Álvaro F. Egaña, Gisella Palma, Mohammad Maleki, Juan Francisco Sanchez-Perez
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引用次数: 0

Abstract

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.

Abstract Image

利用统计距离度量进行相似性区域搜索的方法:应用于地质勘探
矿产勘探与远景测绘相结合已成为利用矿产勘探数据的标准流程。如今,大多数技术都整合了多层信息,并在数据驱动和知识驱动方法中使用机器学习。本研究介绍了一种新颖的通用方法,通过使用超像素而不是像素来识别相似性,从而比较不同的信息层。这种方法提供了一种增强的区域统计表示法,促进并实现了有效的比较。我们考虑了三种不同的统计距离测量方法:库尔巴克-莱伯勒发散、瓦瑟斯坦距离和总变异距离。我们将提议的流程应用于智利北部安托法加斯塔地区的数据,该地区是著名的金属矿带地区,铜储量显著。我们使用并比较了每种度量方法,得出了不同的相似性地图,突出了有趣的矿产勘探区域。研究结果得出的结论是,提议的方法可适用于不同规模,并有助于确定具有相似特征的地区。
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来源期刊
Natural Resources Research
Natural Resources Research Environmental Science-General Environmental Science
CiteScore
11.90
自引率
11.10%
发文量
151
期刊介绍: This journal publishes quantitative studies of natural (mainly but not limited to mineral) resources exploration, evaluation and exploitation, including environmental and risk-related aspects. Typical articles use geoscientific data or analyses to assess, test, or compare resource-related aspects. NRR covers a wide variety of resources including minerals, coal, hydrocarbon, geothermal, water, and vegetation. Case studies are welcome.
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