Utilizing Random Forest algorithm for identifying mafic and ultramafic rocks in the Gameleira Suite, Archean-Paleoproterozoic basement of the Brasília Belt, Brazil

Abstract

Geological mapping techniques have continuously advanced, particularly with the increasing use of remote data acquisition methods, such as aerogeophysical data analysis and the use of machine learning techniques, both has become essential for mapping and understanding geological environments. Mafic-ultramafic bodies, which may be linked to metallic ore deposits, can be mapped using these innovative techniques. In Brazil, the Tocantins Province is reknown for its primary Cu–Co–Ni–Cr ore deposits, which are found in mafic-ultramafic rocks. The Tocantins Province contains several mafic-ultramafic bodies that have received less attention than the well-known deposits in the Brasília Belt. The Gameleira Suite is a geological unit located in the basement of the North Brasília Belt. Our study aims to use machine learning to identify new potential mafic-ultramafic occurrences in the Gameleira Suite. To achieve this, aerogeophysical surveys data, including magnetometry and radiometry, were used in conjunction with remote sensing. The data collected from aerogeophysics surveys were compiled into a database. The Random Forest algorithm was used to analyze this database, with 1.96% (from a total of 84.535 samples) used for training and generating the predictive map. Three approaches were used to verify and evaluate the data: analyzing magnetic lineaments to assess the influence of structural tectonic factors on body positioning, collecting field samples, and utilizing Magnetic Vector Inversion (MVI) to analyze the magnetic properties of the projected bodies at depth. This was done because the Gameleira Suite bodies display magnetic remanence. Our findings suggest that the utilization of these methods, in conjunction with the verification techniques employed, aids in the mapping and identification of mafic-ultramafic rocks. Machine learning can improve cartography by identifying new occurrences or indicating areas that need more detailed mapping. The use of additional geological knowledge and information not included in the model is crucial because the predictive map does not inherently represent geological truth. It is necessary to interpret the results from a geological perspective.