Innovative Hyperspectral Data Fusion for Enhanced Mineral Prospectivity Mapping

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2025

Autores

La Rosa, R (Autor) Outra; Steffen, M (Autor) Outra; Storch, I (Autor) Outra; Knobloch, A (Autor) Outra; Cardoso Fernandes, J (Autor) FCUP; Carvalho, M (Autor) Outra; Barrios, MS (Autor) Outra; Sánchez Migallón, JM (Autor) Outra; Nygren, P (Autor) Outra; Williams, V (Autor) Outra; Teodoro, AC (Autor) Outra

Ata de Conferência Internacional

Título: Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management

Páginas: 317-328

11th International Conference on Geographical Information Systems Theory, Applications and Management, GISTAM 2025

Porto, 1 April 2025 through 3 April 2025

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-018-H9P

DOI: 10.5220/0013497900003935

Abstract (EN): To meet the European Union¿s growing demand for critical raw materials in the transition to green energy, this study presents a novel, cost-effective, and non-invasive methodology for mineral prospectivity mapping. By integrating hyperspectral data from satellite, airborne, and ground-based sources with deep learning techniques, we enhance mineral exploration efficiency. We employ Bayesian Neural Networks (BNNs) to predict mineral prospective areas while providing uncertainty estimates, improving decision-making. To address the challenge of obtaining reliable negative labels for supervised learning, Self-Organizing Maps (SOMs) are used for unsupervised clustering, identifying barren areas through co-registration with known mineral occurrences. We illustrate this approach in the Aramo Unit in Spain, a geologically complex region with Cu-Co-Ni mineralized veins. Our workflow integrates local geology, mineralogy, geochemistry, and structural data with hyperspectral data from PRISMA, airborne Specim AisaFenix, LiDAR and ground-based spectroradiometry. By leveraging learning techniques and high-resolution remote sensing, we accelerate exploration, reduce costs, and minimize environmental impact. This methodology supports the EU¿s S34I project by delivering high-value, unbiased datasets and promoting sustainable, cutting-edge mineral exploration technologies. © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11