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

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

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

Conference proceedings International

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

Pages: 2122-2126

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Athens, GREECE, JUL 07-12, 2024

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 4 Citations

Other information

Authenticus ID: 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/).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5