Application of machine learning to proximal gamma-ray and magnetic susceptibility surveys in the Maritime Antarctic: assessing the influence of periglacial processes and landforms

• Main Authors: Mello, Danilo César de, Baldi, Clara Glória Oliveira, Moquedace, Cássio Marques, Oliveira, Isabelle de Angeli, Veloso, Gustavo Vieira, Gomes, Lucas Carvalho, Francelino, Márcio Rocha, Schaefer, Carlos Ernesto Gonçalves Reynaud, Fernandes-Filho, Elpídio Inácio, Júnior, Edgar Batista de Medeiros, Oliveira, Fabio Soares de, Souza, José João Lelis Leal de Souza, Ferreira, Tiago, Demattê, José A. M.
• Format: Text
• Language: English
• Published: 2024
• Subjects: Antarctic; Antarc*; Antarctica
• Online Access: https://doi.org/10.5194/gmd-2024-2; https://gmd.copernicus.org/preprints/gmd-2024-2/

Maritime Antarctica (M.A.) contains the most extensive and diverse lithological exposure compared to the entire continent. This lithological substrate reveals a rich history encompassing lithological, pedogeomorphological, and glaciological aspects of M.A., all influenced by periglacial processes. Although geophysical surveys can detect and provide valuable information to understand Antarctic lithologies and their history, such surveys are scarce on this continent and, in practice, almost non-existent. In this sense, we conducted a pioneering and comprehensive gamma-spectrometric (natural radioactivity) and magnetic susceptibility (κ) survey on various igneous rocks. The main objective was to create ternary gamma-ray and κ maps using machine learning algorithms, terrain attributes, and a nested-leave-one-out cross-validation method. Additionally, we investigated the relationship between the distribution of natural radioactivity and κ to gain insights into pedogeomorphological and periglacial processes and dynamics. For that, we used proximal gamma-spectrometric and κ data in different lithological substrates associated to terrain attributes. The geophysical variables were collected in the field from various lithological substrates, by use field portable equipment. The geophysical variables were collected in the field from various lithological substrates using portable equipment. These variables, combined with relief data and lithology, served as input data for modeling to predict and spatially map the content of radionuclides and κ by random forest algorithm (RF). In addition, we use nested-LOOCV as a form of external validation in a geophysical data with a small number of samples, and the error maps as evaluation of results. The RF algorithm successfully generated detailed maps of gamma-spectrometric and κ variables. The distribution of radionuclides and ferrimagnetic minerals was influenced by morphometric variables. Nested-LOOCV method evaluated ...