Identification of pegmatite bodies, at a province scale, using machine learning algorithms: preliminary results

Ana Claudia M. Teodoro; Douglas Santos; Joana Cardoso-Fernandes; Alexandre Lima; Marco Brönner

doi:10.1117/12.2599600

12 September 2021 Identification of pegmatite bodies, at a province scale, using machine learning algorithms: preliminary results

Ana Claudia M. Teodoro, Douglas Santos, Joana Cardoso-Fernandes, Alexandre Lima, Marco Brönner

Author Affiliations +

Proceedings Volume 11863, Earth Resources and Environmental Remote Sensing/GIS Applications XII; 1186308 (2021) https://doi.org/10.1117/12.2599600
Event: SPIE Remote Sensing, 2021, Online Only

Abstract

Several raw materials for “green” energy production, such as high purity quartz, lithium, rare earth elements, beryllium, tantalum, and caesium, can be sourced from a rock type known as pegmatite. The GREENPEG project (https://www.greenpeg.eu/), started in May 2020, is developing and testing new and advanced exploration technologies and algorithms to be integrated and upscaled into flexible, ready-to-use economically efficient and sustainable methods for finding buried pegmatites and their “green” technology raw materials. One of the tasks of this project aims to apply different image processing techniques to different satellite images (Landsat, ASTER, and Sentinel-2) in order to automatically identify pegmatite bodies. In this work, we will present the preliminary results, regarding the application of machine learning algorithms (ML), more specifically, random forests (RF) and support vector machines (SVM) to one of the study areas of the project in Tysfjord, northern Norway, to identify pegmatite bodies. To be able to determine the classes that would make up the study area, geological data of the region, such as lithological maps, aeromagnetic data, and high-resolution aerial photographs, were used to define the four classes (1. pegmatites, 2. water bodies, 3. vegetation, 4. granite). All training locations were randomly selected, with 25% of the samples split into testing, and the remaining 75% split for training. The SVM algorithm presented more promising results in relation to overfitting and final image classification than RF. Testing the algorithms with several variables of parameters was able to make the process more efficient. Acknowledgments This study is funded by European Union’s Horizon 2020 innovation programme under grant agreement No 869274, project GREENPEG: New Exploration Tools for European Pegmatite Green-Tech Resources.

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Ana Claudia M. Teodoro, Douglas Santos, Joana Cardoso-Fernandes, Alexandre Lima, and Marco Brönner "Identification of pegmatite bodies, at a province scale, using machine learning algorithms: preliminary results", Proc. SPIE 11863, Earth Resources and Environmental Remote Sensing/GIS Applications XII, 1186308 (12 September 2021); https://doi.org/10.1117/12.2599600

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