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

Title
MULTI-SENSOR APPROACH FOR COBALT EXPLORATION IN ASTURIAS (SPAIN) USING MACHINE LEARNING ALGORITHMS
Type
Article in International Conference Proceedings Book
Year
2024
Authors
Carvalho, M
(Author)
Other
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Azzalini, A
(Author)
Other
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Santos, P
(Author)
Other
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Ana Teodoro
(Author)
FCUP
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Conference proceedings International
Pages: 2122-2126
IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Athens, GREECE, JUL 07-12, 2024
Other information
Authenticus ID: P-016-WY0
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/).
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 5
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