MultiMiner: New Earth Observation data processing algorithms for mineral exploration and mine site monitoring

Earth Observation (EO), as a tool to improve efficiency of mineral exploration and mine site monitoring, requires easily accessible robust highly automated data processing algorithms. The Horizon Europe funded Research and Innovation Action project “Multi-source and Multi-scale Earth observation and Novel Machine Learning Methods for Mineral Exploration and Mine Site Monitoring” (MultiMiner, 2023–2036) develops innovative machine learning solutions to support the critical raw material (CRM) independency of EU. We develop and utilize self-supervised or weakly supervised machine learning solutions which require a low number of in situ reference data. This presentation showcases the recent advancements of the MultiMiner project and highlights of application of the novel machine learning algorithms in selected case studies for mineral exploration and mine site monitoring.
In the MultiMiner project, robust, transferable, scalable and automated tools are developed for mineral exploration. These tools are based on multi-source EO data at multiple data scales and platforms and are implemented into a stand-alone software. The tools include a Mineral Mapping Algorithm (MMA) to perform an automatic spectral feature extraction from deposit-type related reference spectra from a customized reference mineral spectral library. Additionally, workflows to perform automated machine learning interpretation of the multiscale EO data mapping results are developed to produce value added mineral maps of alteration zone or proxy minerals. Finally, a Mineral Prospectivity Wizard GUI is developed, facilitating multi-scale mineral mapping and automatic data interpretation in a guided step-by-step process to analyse EO data even usable for non-remote sensing experts.  The developed algorithms are expected to improve accuracy and time-efficiency of direct mineral identification of CRMs and other raw materials.
To reduce disruptions to mining operations and monitor environmental aspects of operating and closed mine sites, MultiMiner creates timely mine site monitoring methods. A novel Generic Mine Site Monitoring (GMSM) algorithm, capable of combining multi-source EO data at various temporal, spatial and spectral resolutions, and requiring only a limited amount of in situ data, is developed. The GMSM algorithm leverages EO foundation models for different modalities, and includes support of temporal information as well. The GMSM algorithm can automatically monitor impacts of mining on the environment, such as water quality and acid mine drainage mapping, or combined monitoring of atmospheric and surface dust. Furthermore, success of rehabilitation activities, including monitoring the revegetation status and Tailings Storage Facility (TSF) dismantling are researched. EO-based solutions for improving mining safety and mitigating operational risks are proposed in terms of ground moisture monitoring and open pit and TSF dam stability monitoring.
To unlock the potential of EO data, including Copernicus Sentinel-1 and Sentinel-2, EnMAP, drone-borne hyperspectral, radiometric and multiband SAR as well as in situ collected spectral data, we present case studies to demonstrate and validate the use of the MultiMiner machine learning -based algorithms at five test sites in Europe. The acquired field data are harmonized following project-specific guidelines and subsequently, the metadata of the thus acquired field data are safeguarded in a project database. In the presentation, we give a brief overview of the guidelines and the database.