MULTI-SENSOR APPROACH FOR COBALT EXPLORATION IN ASTURIAS (SPAIN) USING MACHINE LEARNING ALGORITHMS

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Carvalho, M (Autor) Outra; Azzalini, A (Autor) Outra; Cardoso-Fernandes, J (Autor) FCUP; Santos, P (Autor) Outra; Alexandre Lima (Autor) FCUP; Ana Teodoro (Autor) FCUP

Ata de Conferência Internacional

Título: IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024

Páginas: 2122-2126

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Athens, GREECE, JUL 07-12, 2024

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 5 Citações

Outras Informações

ID Authenticus: P-016-WY0

DOI: 10.1109/igarss53475.2024.10640581

Abstract (EN): This study explores dimensionality reduction techniques, namely, PCA (Principal Component Analysis) and ICA (Independent Component Analysis), to condense Earth Observation (EO) data obtained from Landsat 9 and PRISMA satellites to detect alteration zones related to Cobalt (Co) mineralization in the Aramo mine, situated in Asturias, Spain, by employing Support Vector Machine (SVM) Machine Learning (ML) algorithm. The ICA-based models exhibit slightly better performance than PCA-based ones, particularly in delineating alteration zones in the Landsat 9 image, showing promising results in distinguishing alteration zones from host rocks, demonstrating the viability of these techniques applied to mineral exploration. However, the results show the need for refined field data collection methodologies to enhance prediction accuracy for more robust results, in the scope of the HORIZON Europe S34I project (https://s34i.eu/).

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5