Innovative Hyperspectral Data Fusion for Enhanced Mineral Prospectivity Mapping

Roberto De La Rosa, Michael Steffen, Ina Storch, Andreas Knobloch, Joana Cardoso-Fernandes, Morgana Carvalho, Mercedes Suárez Barrios, Juan Morales Sánchez-Migallón, Petri Nygren, Vaughan Williams, Ana Cláudia Teodoro

2025

Abstract

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.

Paper Citation

in Harvard Style

De La Rosa R., Steffen M., Storch I., Knobloch A., Cardoso-Fernandes J., Carvalho M., Barrios M., Sánchez-Migallón J., Nygren P., Williams V. and Teodoro A. (2025). Innovative Hyperspectral Data Fusion for Enhanced Mineral Prospectivity Mapping. In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: S34I; ISBN 978-989-758-741-2, SciTePress, pages 317-328. DOI: 10.5220/0013497900003935

in Bibtex Style

@conference{s34i25,
author={Roberto De La Rosa and Michael Steffen and Ina Storch and Andreas Knobloch and Joana Cardoso-Fernandes and Morgana Carvalho and Mercedes Barrios and Juan Sánchez-Migallón and Petri Nygren and Vaughan Williams and Ana Teodoro},
title={Innovative Hyperspectral Data Fusion for Enhanced Mineral Prospectivity Mapping},
booktitle={Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: S34I},
year={2025},
pages={317-328},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013497900003935},
isbn={978-989-758-741-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: S34I
TI - Innovative Hyperspectral Data Fusion for Enhanced Mineral Prospectivity Mapping
SN - 978-989-758-741-2
AU - De La Rosa R.
AU - Steffen M.
AU - Storch I.
AU - Knobloch A.
AU - Cardoso-Fernandes J.
AU - Carvalho M.
AU - Barrios M.
AU - Sánchez-Migallón J.
AU - Nygren P.
AU - Williams V.
AU - Teodoro A.
PY - 2025
SP - 317
EP - 328
DO - 10.5220/0013497900003935
PB - SciTePress