Título: Automated Mapping of the Boundary Between Continental and Oceanic Crusts (COB/COT) Using Convolutional Neural Networks
Autores: Thiago Theiry de Oliveira, Adrião Duarte Dória Neto, Allan de Medeiros Martins, David Lopes de Castro, Alanny Christiny Costa de Melo, Daniel Teixeira dos Santos, Diogenes Custodio de Oliveira & Odilon Keller Filho
Resumo: This preliminary study investigates the feasibility of developing a methodology based on Machine Learning techniques, specifically deep neural networks (Deep Learning), for the automated mapping of the boundary between continental and oceanic crusts (COB/COT, Continent-Ocean Boundary/Continent-Ocean Transition). The mapping was performed using multi-channel seismic data, applying criteria to manually identify of both crust types by domain experts. These mappings were then integrated into Machine Learning routines with the objective of automating the identification of this crustal boundary, thereby providing greater accuracy and efficiency in the geological interpretation process.
Palavras-chave: Artificial intelligence; Machine learning; Geophysics; Neural networks.
Páginas: 8
Código DOI: 10.21528/CBIC2025-1191679
Artigo em PDF: CBIC_2025_paper1191679.pdf
Arquivo BibTeX:
CBIC_2025_1191679.bib