Felipe Navarro, Gonzalo Díaz, Marcia Ojeda, Felipe Garrido, Diana Comte, Alejandro Ehrenfeld, Álvaro F. Egaña, Gisella Palma, Mohammad Maleki, Juan Francisco Sanchez-Perez
{"title":"A Methodology for Similarity Area Searching Using Statistical Distance Measures: Application to Geological Exploration","authors":"Felipe Navarro, Gonzalo Díaz, Marcia Ojeda, Felipe Garrido, Diana Comte, Alejandro Ehrenfeld, Álvaro F. Egaña, Gisella Palma, Mohammad Maleki, Juan Francisco Sanchez-Perez","doi":"10.1007/s11053-024-10385-7","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Natural Resources Research","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s11053-024-10385-7","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
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.
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.