A step towards the automatization of predictive geological mapping

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyA step towards the automatization of predictive geological mappingAuthors: Rafael Pires de LimaMarcos FerreiraIago CostaRafael Pires de Lima Geological Survey of BrazilSearch for more papers by this author, Marcos Ferreira Geological Survey of BrazilSearch for more papers by this author, and Iago Costa Geological Survey of BrazilSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3750705.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Standardized geological mapping is paramount for understanding available resources, developing a long-term plan of land use and management, and can lead to new ore discoveries. These are some of the reasons that explain why geological mapping is important for multiple stakeholders, including the public and private sectors. Traditional geological mapping includes several field campaigns necessary to acquire samples that are analyzed during the mapping process. Ideally, the sampling of such campaigns would follow a regularly spaced grid. However, time, budget, and access constraints make it difficult to have regularly sampled regions, and geologic mappers frequently make use of other regional data to aid their interpretation. Superficial geology has a high correlation with geophysical methods, such as airborne-acquired magnetic and gamma-ray spectrometer surveys, as well as orbital remote sensing data. These data are readily and largely available for many regions around the globe and the current rise in the application of machine-learning techniques can help in the acceleration of their interpretation and analysis. Furthermore, the automatization of such analysis has the potential to guide geoscientists to areas that need a thorough investigation, leading to more reliable mapping. We document the steps that are being developed for the automatization of predictive geological mapping using airborne geophysical and orbital remote sensing data, as well as the current limitations and opportunities for enhancement. Our objective is to facilitate widespread access to machine learning techniques for predictive mapping for a larger community of geological mappers. The code we discuss here can be accessed at https://github.com/marcosbr/predictive-geology-maps and so far has been used to generate over 40 predictive geologic maps at 1:100,000 scale. Keywords: geological map, predictive mapping, automatization, machine learningPermalink: https://doi.org/10.1190/image2022-3750705.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Rafael Pires de Lima, Marcos Ferreira, and Iago Costa, (2022), "A step towards the automatization of predictive geological mapping," SEG Technical Program Expanded Abstracts : 1437-1441. https://doi.org/10.1190/image2022-3750705.1 Plain-Language Summary Keywordsgeological mappredictive mappingautomatizationmachine learningPDF DownloadLoading ...