Preprints
https://doi.org/10.5194/gmd-2024-2
https://doi.org/10.5194/gmd-2024-2
Submitted as: model evaluation paper
 | 
15 Apr 2024
Submitted as: model evaluation paper |  | 15 Apr 2024
Status: this preprint is currently under review for the journal GMD.

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

Danilo César de Mello, Clara Glória Oliveira Baldi, Cássio Marques Moquedace, Isabelle de Angeli Oliveira, Gustavo Vieira Veloso, Lucas Carvalho Gomes, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes-Filho, Edgar Batista de Medeiros Júnior, Fabio Soares de Oliveira, José João Lelis Leal de Souza Souza, Tiago Ferreira, and José A. M. Demattê

Abstract. 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 algorithm performance accurately with limited samples, generating robust mean maps. The highest thorium levels were observed in elevated, flat, and west beach areas, where detrital materials from periglacial erosion came through fluvioglacial channels. Lithology and pedogeomorphological processes-controlled thorium contents. Steeper areas formed a ring with the highest uranium contents, influenced by lithology and geomorphological-periglacial processes (rock cryoclasty, periglacial erosion, and heterogeneous deposition). Felsic rocks and areas less affected by periglacial erosion had the highest potassium levels, while regions with sulfurization-affected pyritized-andesites near fluvioglacial channels showed the lowest potassium contents. Lithology and pedogeochemical processes governed potassium levels. The κ values showed no distinct distribution pattern. Pyritized-andesite areas had the highest levels due to sulfurization and associated pyrrhotite, promoting iron release. Conversely, Cryosol areas, experiencing freezing and thawing activity, had the lowest κ values due to limited ferrimagnetic mineral formation. Lithology and pedological-periglacial processes in Cryosols played a significant role in controlling κ values. In regions characterized by diverse terrain attributes and abundant active and intense periglacial processes, the spatial distribution of geophysical variables does not reliably reflect the actual lithological composition of the substrate. The complex interplay of various periglacial processes in the area, along with the morphometric features of the landscape, leads to the redistribution, mixing, and homogenization of surface materials, contributing to the inaccuracies in the predicted-spatialized geophysical variables.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Danilo César de Mello, Clara Glória Oliveira Baldi, Cássio Marques Moquedace, Isabelle de Angeli Oliveira, Gustavo Vieira Veloso, Lucas Carvalho Gomes, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes-Filho, Edgar Batista de Medeiros Júnior, Fabio Soares de Oliveira, José João Lelis Leal de Souza Souza, Tiago Ferreira, and José A. M. Demattê

Status: open (until 10 Jun 2024)

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Danilo César de Mello, Clara Glória Oliveira Baldi, Cássio Marques Moquedace, Isabelle de Angeli Oliveira, Gustavo Vieira Veloso, Lucas Carvalho Gomes, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes-Filho, Edgar Batista de Medeiros Júnior, Fabio Soares de Oliveira, José João Lelis Leal de Souza Souza, Tiago Ferreira, and José A. M. Demattê
Danilo César de Mello, Clara Glória Oliveira Baldi, Cássio Marques Moquedace, Isabelle de Angeli Oliveira, Gustavo Vieira Veloso, Lucas Carvalho Gomes, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes-Filho, Edgar Batista de Medeiros Júnior, Fabio Soares de Oliveira, José João Lelis Leal de Souza Souza, Tiago Ferreira, and José A. M. Demattê

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Short summary
The study explores Maritime Antarctica's geology, shaped by periglacial forces, using pioneering gamma-spectrometric and magnetic surveys on igneous rocks due to limited Antarctic surveys. Machine learning predicts radionuclide and magnetic content based on terrain features, linking their distribution to landscape processes, morphometrics, lithology, and pedogeomorphology. Inaccuracies arise due to complex periglacial processes and landscape complexities.