Overview/Background

The European Union faces significant challenges in securing critical raw materials (CRM) while balancing environmental protection, public acceptance, and technological innovation. This research examines how innovative "invisible mining" approaches, enabled by advances in robotics and miniaturisation, could help resolve conflicts between mineral extraction needs and environmental preservation goals, particularly in the context of the EU's Critical Raw Materials Act (CRMA). This paper addresses the growing tension between increased raw material demand for green technologies and the EU's stringent environmental protection mandates.

Methods

We analysed the intersection of technological innovation, policy frameworks, and social acceptance through a comprehensive review of EU-funded research projects in mining automation and robotics. We evaluated six major research initiatives from 2011-2026, examining their technological developments and potential applications. The analysis incorporates findings from case studies of mining operations in environmentally sensitive areas and assesses the viability of emerging business models in the mining sector. Special attention was given to projects developing autonomous robotic systems for underground operations and advanced sensing technologies for precise mineral extraction.

Results

The research identifies four key transformative elements for successful implementation of invisible mining: (1) technological advances in robotics and miniaturisation enabling precise, low-impact extraction through smaller diameter galleries and reduced waste rock production; (2) comprehensive and integrated resource recovery principles maximising resource efficiency while minimising environmental disturbance; (3) materials-as-a-service business models creating circular resource loops and transforming mining companies from mere extractors to long-term material stewards; and (4) development of new workforce competencies in advanced cognitive domains such as robotics, data science, and environmental management. The analysis reveals that more than 80% of CRM deposits in Europe are located near or within environmentally protected areas, highlighting the urgent need for innovative extraction approaches. Additionally, the study demonstrates how autonomous mining systems can operate in narrow drifts without human presence, eliminating the need for extensive ventilation and drainage systems.

Conclusions

The findings demonstrate that invisible mining, characterised by minimal surface disturbance and environmental impact, represents a viable solution to the EU's raw materials challenges. This approach, combined with new business models and advanced technologies, could significantly increase public acceptance of mining activities while meeting the EU's resource needs. Success requires a fundamental transformation of the mining sector, encompassing technological innovation, business model evolution, and workforce development. The research suggests that invisible mining could enable the coexistence of resource extraction and environmental protection, particularly in sensitive areas, while supporting the EU's transition to a green economy. The study emphasises that this transformation demands sustained investment in robotics research, development of circular economy practices, and reimagining of traditional mining business models to create a more sustainable and socially acceptable mining industry.

How to cite: Komac, M., Correia, V., and Falck, E.: Invisible Mining: A Novel Approach to Addressing EU Critical Raw Materials Challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18711, https://doi.org/10.5194/egusphere-egu25-18711, 2025.

The energy transition imposes to Europe the crucial challenge of securing a sustainable supply of critical raw materials (CRMs). The European Union's Critical Raw Materials Act represents a strategic response to this challenge, aiming to strengthen Europe's supply chain resilience and reduce dependence on foreign imports for materials essential to green technologies. Assessing Europe's domestic potential for CRMs is fundamental to achieving the Act's objectives of securing 10% of the EU's annual consumption through domestic extraction by 2030. This evaluation becomes particularly vital as demand for these materials is projected to surge with the widespread adoption of renewable energy technologies, electric vehicles, and energy storage systems.

In this context, European geological survey organizations (GSOs) play a key role, at national to EU levels. The EU-funded GSEU – Geological Service for Europe project, coordinated by EuroGeoSurveys, an international organization that brings together Europeans GSOs, aims at providing harmonized pan-European geoscientific data and expertise to support policy and decision making. The Raw Materials team, coordinated by BRGM, the French geological survey organization, has compiled a harmonized dataset of CRM deposits in Europe, controlled and updated by all national data providers, based on the 2023 CRM list of the European Commission. This dataset allows to assess and map the geological potential for CRM in Europe, globaly, per country and per commodity.

In addition to a harmonized and updated knowledge on the geological potential in Europe, mineral prospectivity mapping (MPM) plays a pivotal role by identifying areas with high potential for the discovery of new CRM deposits. Based on the harmonized dataset of CRM deposits in Europe produced by the GSEU Raw Materials team, the 1 to 1.5M lithostratigraphic and structural maps of Europe and a new data driven MPM method combining the DBA (Disc Based Association) data aggregation approach and Random Forest regression, we have produced pan-European prospectivity maps for a selection of CRM (Co, Cu, Li, Ni, Mg, Mn, Nb, Ni, Sb, Ta, V, W). These maps provide crucial information to both industry stakeholders and policymakers. They reduce exploration risks and costs by highlighting promising areas for detailed investigation, and they enable informed decisions about land use, environmental protection and resource management strategies.

In this presentation, we briefly describe the CRM deposits dataset compiled by the GSEU Raw Materials team, the maps and potential assessments for CRM in Europe, and the pan-European mineral prospectivity maps for selected critical commodities. We also briefly present the methodologies that were used to develop these products and discuss future developments of this work.

How to cite: Bertrand, G., Albert, C., and Vella, A.: Assessing the Critical Raw Materials potential in Europe to support the energy transition., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21220, https://doi.org/10.5194/egusphere-egu25-21220, 2025.

The energy transition presents a crucial challenge to Europe, with the necessity of securing a sustainable supply of Critical Raw Materials (CRM) as specified in the European Union's Critical Raw Materials Act. Reaching the goal set by the Act of securing 10% of the EU's annual consumption through domestic extraction by 2030 requires the assessment of Europe’s domestic potential for CRMs. The collection of available data regarding the known CRMs potential throughout Europe is needed to perform this assessment. This data collection allows to perform mineral potential mapping to highlight areas with potential for the discovery of new CRM deposits.

The EU-funded GSEU – Geological Service for Europe project, coordinated by EuroGeoSurveys, an international organization that brings together Europeans geological survey organizations, aims at providing harmonized pan-European geoscientific data and expertise to support policy and decision making. As part of this project, mineral prospectivity mapping methods are applied to outline areas with the highest likelihood to host potential mineralization. They allowed the production of pan-European prospectivity maps for a selection of CRM (Co, Cu, Li, Ni, Mg, Mn, Nb, Ni, Sb, Ta, V, W). Favorability maps highlight promising areas for mineral exploration, improving exploration benefit/costs ratio, reducing its environmental footprint and enabling informed decisions about land use, environmental protection, and resource management strategies. They provide crucial information to both industry stakeholders and policymakers.

These maps are produced using the “Disc-Based Association” (DBA) method in combination with a Random Forest supervised classification. This predominantly data-driven approach leverages spatial analysis and machine learning techniques to delineate prospective zones for mineral exploration, specifically targeting CRMs. The DBA method analyses neighboring associations of cartographic features over the studied area, producing a unique matrix presenting the multivariate features identified around each sample point. The Random Forest classification allows scoring of each sample points through a binary classification. The first class consist of sample points in the vicinity of known mineralization, accessed through the harmonized dataset of CRM deposits provided by the GSEU Raw Materials team, while the second class are all the other sample points. The classification process results in each point being given a score, displaying the favorability of an area for mineral exploration. The result of this classification allows the definition of favorable areas for mineral exploration throughout Europe.

In this presentation, we describe the methodology used to produce the favorability maps for CRMs in Europe using the data compiled by the GSEU Raw Materials team. We present some of the resulting favorability maps and discuss future developments and application of this methodology.

How to cite: Vella, A., Bertrand, G., Gumiaux, C., and Albert, C.: A continent-scale data-driven approach to map Critical Raw Materials potential in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21303, https://doi.org/10.5194/egusphere-egu25-21303, 2025.

The Kvartsevoe rare metal deposit in East Kazakhstan was discovered in 1967 and is being currently re-evaluated after decades of inactivity. The geology of the area consists mainly of Devonian to Carboniferous metasediments, folded during the latest consolidation phase of the Altai orogen (i.e. Late Carboniferous-Permian) and intruded by series of post-kinematic Permian granites. Metals and elements of economic interest, in particular Lithium, are found in a ~300 m wide and ~700m long pegmatite body, associated with medium-earth biotite granites of phase II of the Kalba complex (i.e. 286±1 Ma). The deposit is represented by a series of pegmatite veins located in one of the projections of the Alypkelsky granite massif, the sedimentary host rocks near the deposit are hornfels of variable metamorphism up to the point of transformation into tourmaline-graphite-quartz-mica hornfels. Numerous quartz veins are found in the close vicinity of the Kvartsevoe deposit. Field observations suggest that the latter veins are genetically linked to the pegmatites. They cross-cut Permian granites and Paleozoic metasediments, show regular trends and typically extend 10s to 100s of metres. We conducted an integrated geochemical-structural study of the veins. Our preliminary results suggest vein emplacement under strike-slip stress regime with NW-SE orientation for the axis of minimum principal stress. The study seems, in addition, to confirm the genetic link between the veins and the pegmatites. Therefore, our findings suggest that the pegmatites were also emplaced under the same stress field. This latter result may be used in the future to predict the orientations of the pegmatites hosting economic metals in the subsurface.

How to cite: Pascal, C., Mizernaya, M., Oitseva, T., Salmenbayev, E., Tursungaliev, D., and Kuzmina, O.: Preliminary paleostress study of the Kvartsevoe rare metals deposit, East Kazakhstan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12773, https://doi.org/10.5194/egusphere-egu25-12773, 2025.

How to cite: Xu, Z., Lin, J., Ye, Q., and Guo, Z.: UTLD: An Underground Thermal and LiDAR Dataset for Depth Estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18653, https://doi.org/10.5194/egusphere-egu25-18653, 2025.

Rare Earth Elements (REEs) have become critical for global technological advancements and, consequently, economic growth. Ensuring supply requires significant future exploration, potentially including the use of space-borne hyperspectral data for direct mapping of REEs. While space-borne detection of REEs has been demonstrated (e.g., Asadzadeh et al., 2024), this approach has limited application. Low concentrations of these valuable resources in most carbonatite host rocks and small sizes of ore zones represent a  major hurdle and complicate reliable detection and mapping efforts. 

We propose a comprehensive approach to remotely characterise carbonatites, which are known to host REEs, with the aim of improving our overall understanding of these unusual rocks and better identifying potentially fertile systems. Carbonatites are typically classified into three types: calcio-carbonatites, magnesio-carbonatites, and ferro-carbonatites. However, recent studies, such as Mitchell & Gittins (2022), suggest additional variants that don't fit these categories, indicating the current classification system may require further refinement. Regardless of classification complexities, the composite mineralogical phases of carbonatites are spectrally active and exhibit distinctive absorption features in hyperspectral data. Furthermore, the presence of alteration halos and the structural controls commonly associated with carbonatite structures make these sites well-suited for optical remote sensing studies by both hyperspectral and multispectral datasets. This paves the way for the development of a global carbonatite atlas based on remote sensing data.

We demonstrate the feasibility of the approach using two REE-bearing carbonatite complexes in Namibia—Lofdal and Marinkas-Quellen. We selected EnMAP provided by the German Aerospace Center (DLR) hyperspectral data as they are the most accurate to this date (Chakraborty et,al., 2024). We employed different processing techniques such as minimum wavelength mapping and spectral abundance analysis to map the carbonatite lithologies in each of the two sites individually. We then streamlined the workflow to identify common parameters and trained a decision tree to map the broader carbonatite footprints across both sites. In parallel, Sentinel-2 multispectral data was used to map geological structures (e.g., dykes, faults, and bedding) aiming to understand controls on carbonatite emplacement. A fusion-based resolution enhancement algorithm was also applied to integrate EnMAP with Sentinel-2 data, providing a more spatially detailed understanding of the targets. 

We aim to expand this study to include a wider range of carbonatite complexes, with the goal of creating a global carbonatite atlas. By covering diverse geological settings and ages, this atlas will capture the full spectrum of mineralogical variation and structural features, enhancing our understanding of carbonatite bodies. This atlas not only will promote the applications of remote sensing techniques in carbonatite studies but also provide a valuable basis for future exploration of REEs in carbonatite settings. 

1. Asadzadeh, S., Koellner, N., & Chabrillat, S. (2024). Detecting rare earth elements using EnMAP hyperspectral satellite data: a case study from Mountain Pass, California. Scientific Reports

2. Mitchell, R. H., & Gittins, J. (2022). Carbonatites and carbothermalites: A revised classification. Lithos

3. Chakraborty, R., Rachdi, I., Thiele, S., Booysen, R., Kirsch, M., Lorenz, S., ... & Sebari, I. (2024). A Spectral and Spatial Comparison of Satellite-Based Hyperspectral Data for Geological Mapping. Remote Sensing

How to cite: Chakraborty, R., Booysen, R., Asadzadeh, S., Thiele, S., and Gloaguen, R.: Towards a Global Carbonatite Atlas: A Satellite Remote Sensing Approach to Mapping and Characterization , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4279, https://doi.org/10.5194/egusphere-egu25-4279, 2025.

Smart mining integrates advanced geological, geophysical, and digital technologies—such as artificial intelligence (AI), the Internet of Things (IoT), robotics, and real-time monitoring—into traditional mining operations. This paradigm shift enhances efficiency, safety, and sustainability by enabling precise resource extraction, optimized resource management, and reduced environmental impact. As the mining industry faces challenges like resource depletion and environmental constraints, the adoption of smart mining methods becomes crucial for sustainable operations.

Central to smart mining is a high-accuracy, high-resolution, and time-lapse geological model (HHT geological model), which provides critical data for applications such as adaptive mining path planning, resource management, hazard assessment, and operational monitoring. Current geological models, while effective in some automated mining processes, lack dynamic coupling with mining equipment and disaster simulation tools, limiting their real-time applicability.

To address these limitations, we propose an integrated workflow to construct the HHT geological model: (1) Geophysical Exploration and Interpretation: Using multi-modal geophysical techniques (e.g., well logs, seismic surveys, transient electromagnetics), we invert geological properties (e.g., seismic impedance, wave speed, resistivity) and interpret structural features such as horizons, faults, voids, rock facies, and mineral boundaries. (2) Model Generation: Employing Triangulated Irregular Network (TIN) methods to create a detailed 3D geological framework. (3) Dynamic Updates via Continuous Monitoring: Utilizing data from seismic while mining (SWM), 4D seismic, and joint microseismic-electromagnetic monitoring to update the geological model as mining progresses.

The Key Innovations of our proposed workflow have three aspects: (1) we integrate geological, petrological, seismic, and electromagnetic data, combined with mining-induced seismic events, machinery running parameters, and video/image recognition technologies to enable high-resolution imaging and detection of coal seam thickness, fault zones, goaf areas, and subsidence columns, providing a comprehensive understanding of geological structures. (2) We apply Seismic While Mining (SWM) technology, which acquires continuous seismic data during mining operations, processed through reverse-time migration, cross-correlation, denoising, and source wavelet extraction, to dynamically image geological changes. A six-component seismometer further enhances constraints via virtual sonic well logging. (3) We apply the Real-time TIN regeneration method which incorporates the discrepancies between SWM-derived images and the prior model, ensuring accurate updates during mining.

We tested the platform in an underground coal mine near Erdos, Inner Mongolia, China, the SWM method successfully identified faults along a tunnel, later confirmed by mining reports. These results demonstrate the effectiveness of the integrated HHT geological model in revealing hidden geological features.

In conclusion, the HHT geological modeling is fundamental for realizing true smart mining. Merging multi-source geophysical data establishes a reliable seismic baseline, while the SWM system provides critical real-time monitoring of roof deformation, stress distribution, water infiltration, and rock bursts. The integration of these methods is essential to achieving a "transparent geological model" and advancing towards sustainable and intelligent mining practices.

How to cite: Zhou, T.: Towards Smart Mining: An Integrated Process for High-Accuracy, High-Resolution, and Time-Lapse Geological Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15713, https://doi.org/10.5194/egusphere-egu25-15713, 2025.

Muography is a rapidly developing geophysical method, that utilises high energy cosmic muon particles to explore the inner structure of large objects, such as volcanoes, pyramids or mountains. Cosmic muons originate from upper atmosphere and have a known, steady, angle dependent flux on the surface. Muons are absorbed as they pass through matter, depending on the density of the material along their trajectories. By comparing the expected and the measured muon flux and using geoinformatic models of the observed area it is possibble to calculate the density distribution inside these structures. Our group at the HUN-REN Wigner RCP focuses on muographic imaging including research, hardware development and geophysical applications. There are several ongoing muographic projects inside European mines. Our measurements were able to confirm known density anomalies in these areas. The method can be applied to a wide variety of problems, such as determining the shape and density of geological formations or ore bodies, the location of caves or fractured zones located up to a few hundred meters underground. The presentation describes the priciples of muography and demonstrates it’s usability with examples from multiple projects.

How to cite: Rábóczki, B., Surányi, G., Hamar, G., and Balázs, L.: Muography: A novel method of density measurement for mining and surveying, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-983, https://doi.org/10.5194/egusphere-egu25-983, 2025.

Multivariate data analysis in natural resources exploration can be beneficial for each variable investigated as the correlation between the variables increases the prediction accuracy and reduces the error variance. Geostatistical modeling of mineral deposits often encounters challenges in accurately representing spatial dependencies, particularly in complex geological formations and irregular sampling grids. While traditional Euclidean distances are commonly used, they may not adequately capture spatial relationships in such scenarios. Non-Euclidean distances, such as Manhattan and Chebyshev metrics, as well as geodesic distance (like a sphere manifold), offer alternative solutions that may better accommodate spatial fields with complex sampling grids. Such distances however may result in non-positive definite (thus not invertible) covariance matrices. This is further complicated when dealing with multivariate random fields as the resulting covariance-cross-covariance matrix may not be positive-definitive even in the Euclidean distance.

This study builds on prior research to evaluate spatial dependencies for Aluminum (Al) and Zinc (Zn) concentrations in geochemical datasets under both Euclidean and non-Euclidean distance metrics. The data values have undergone Gaussian Anamorphosis with the previously introduced CDKC method. The recently introduced Harmonic Covariance Estimation (HCE) model is applied to generate covariance structures for co-kriging predictions, as well as multivariate simulations. Such simulations can assist in exploring the uncertainty of estimation (for example the 90% confidence interval) after the back-transform. The ability of HCE to maintain positive-definite cross-covariance matrices is a critical focus, particularly in multivariate simulations.

In addition, this work investigates a separate dataset from a mine in Ireland, which includes Lead (Pb) and Zinc (Zn) concentrations. Here, the anisotropic form of the HCE model introduced and then applied in Euclidean space to account for directional dependencies. The performance of anisotropic HCE is then compared to kriging predictions using non-anisotropic HCE with non-Euclidean distances (Chebyshev, Manhattan, Spherical Manifold). This analysis aims to determine whether correcting for anisotropy or adopting non-Euclidean metrics yields better performance in this particular dataset, although more studies are required to reach a conclusion on the matter.

The investigation results indicate that the HCE model results in invertible, positive-definite matrices that can be used for simulations and predictions with non-Euclidean distances, offering insights into optimizing spatial modeling for irregular datasets and complex deposit structures.

The research project is implemented in the framework of H.F.R.I call “Basic research Financing (Horizontal support of all Sciences)” under the National Recovery and Resilience Plan “Greece 2.0” funded by the European Union – NextGenerationEU (H.F.R.I. Project Number: 16537)

How to cite: Pavlides, A., Koltsidopoulou, M. D., Chrysanthi, M., and Varouchakis, E.: Advancing Multivariate Simulations using Non-Euclidean Metrics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16370, https://doi.org/10.5194/egusphere-egu25-16370, 2025.

Abstract

Management of phosphate mine waste rock piles (PWRPs) is a critical challenge in the mining industry, particularly in regions like Morocco, which holds the world’s largest phosphate reserves. To this end, there is a need for an approach that focuses on real-time monitoring of waste rock heterogeneity, enabling more efficient resource recovery and environmental management. This study proposes a novel, multi-scale approach that integrates hyperspectral imaging, field spectroscopy, and explainable machine learning (XML) to characterize and map the mineralogical diversity of PWRPs at the Benguerir mine.  A total of 103 samples were collected from waste rock piles across an area of approximately 60 km², representing the full spectrum of mineralogical variability. Handheld X-ray fluorescence (XRF) analysis was conducted on the all the samples and revealed the dominance of SiO₂ (29.51 wt% ± 12.42), CaO (30.16 wt% ± 10.17), and P₂O₅ (7.23 wt% ± 4.21). These XRF analyses indicated the presence of silicate, carbonate, and phosphate-bearing materials. These findings were complemented by both PRISMA hyperspectral imaging, which captured spectral data across the visible to shortwave infrared (VSWIR) range. precise calibration and validation of the remote sensing outputs were conducted using field spectroscopy using the ASD FieldSpec 4 spectroradiometer.

To address the complexity of the spectral data, we developed an explainable machine learning framework based on SHapley Additive exPlanations (SHAP) and Convolutional Neural Networks (CNN). This framework not only improved classification accuracy (achieving 0.92 overall accuracy) but also provided interpretable insights into the spectral features driving mineral identification. Our results showed that the used model successfully differentiated four main waste rock categories: carbonate-rich, phosphate-rich, clay-dominated, and siliceous materials. The resulting maps offer a practical tool for real-time waste management and resource recovery. For instance, carbonate-rich materials, characterized by high CaO content, can be identified or used for construction applications, while phosphate-rich zones, with elevated P₂O₅ levels, can be flagged for potential recovery and further processing. This targeted approach ensures that waste materials are repurposed efficiently, aligning with circular economy principles. The study highlights the potential for automated, spectroscopy-based monitoring systems to support sustainable mining practices. Overall, this study demonstrates the power of combining cutting-edge remote sensing technologies with explainable machine learning to address the challenges of phosphate waste rock characterization. The methodology provides a scalable, cost-effective solution for mining operations worldwide, with significant implications for environmental sustainability, resource efficiency, and circular economy initiatives.

Keywords: Phosphate mine waste, Hyperspectral imaging, Field spectroscopy, Explainable machine learning (XML), Sustainable mining.

How to cite: El Mansour, A., Laamrani, A., Elghali, A., Hakkou, R., and Benzaazoua, M.: A Cutting-Edge Framework for Sustainable Phosphate Waste Characterization Using Hyperspectral Imaging and Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16097, https://doi.org/10.5194/egusphere-egu25-16097, 2025.

Over recent years, the use of hyperspectral infrared imaging has significantly increased in the mining sector, offering numerous applications from geological exploration and mining to sorting and the rehabilitation. However, this technology remains underutilized in the phosphate mining industry, particularly in countries like Morocco, where phosphates represent over 70% of the world's reserves. In this study, the objective is to investigate the use of hyperspectral infrared imagery as a tool to identify and characterize sedimentary phosphate facies for automated facies core logging applications as well as to identify the spectral signature of Carbonate-Fluorapatite (CFA), the primary phosphate mineral phase in sedimentary phosphates, in order to estimate its abundance.To achieve this, six samples have been carefully selected from the Benguerir phosphate sequence to represent the commonly encountered indurated facies. The samples were scanned using a core scanner equipped with three hyperspectral sensors: a Visible Near-Infrared (VNIR) camera, a Short-Wavelength Infrared (SWIR) camera, and a Medium-Wavelength Infrared (MWIR) camera. The covered wavelength interval ranges from 0.4 µm to 5.3 µm, with spatial resolutions varying from 0.117 mm/pixel to 0.228 mm/pixel. Eight facies were identified in the studied samples and characterized through petrography and XRF geochemical analysis of the whole rock. Subsequently, a spectral library was established for each of these facies. Moreover, a sample area rich in CFA was selected and characterized by automated SEM using Tescan Integrated Mineral Analyzer (TIMA). The results indicate that all the facies exhibit distinguishable signatures in the various VNIR, SWIR, and MWIR intervals. However, the SWIR and MWIR intervals proves to be the most effective sensors for distinguishing these facies. The results indicate also that the Spectral Angle Mapper (SAM) is the most efficient method, achieving an overall accuracy of 98,75% in distinguishing the studied facies in the MWIR wavelength range. Additionally, several statistical methods were also tested to estimate the abundance of CFA using the spectral signature derived from the comparison between the SEM mineral maps and corresponding hyperspectral images. Band rationing (B(3.4µm)/B(4.7µm)) * (B(3.4µm)/B(3.9µm)) has demonstrated effective in identifying and estimating the abundance of CFA demonstrating the potential of hyperspectral imaging as a rapid and cost-effective method for the characterization of phosphates in terms of their apatite content.

How to cite: Lkhaoua, H., Raji, O., Elghali, A., El bamiki, R., El alaoui el fels, A., and Benzaazoua, M.: Classification of Phosphate Sedimentary Facies and Estimation of Carbonate-Fluorapatite Abundance Using Hyperspectral Infrared Imaging, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2011, https://doi.org/10.5194/egusphere-egu25-2011, 2025.

ROBOMINERS (Bio-Inspired, Modular and Reconfigurable Robot Miners, Grant Agreement No. 820971, http://www.robominers.eu) was a European project funded by the European Commission's Horizon 2020 Framework Programme. The project aimed to test and demonstrate new mining and sensing technologies on a small robot-miner prototype (~1-2T) designed to target unconventional and uneconomical mineral deposits (technology readiness level 4 to 5).

As part of the ROBOMINERS sensors payload development, a set of mineralogical and geophysical sensors were designed to provide the necessary data to achieve “selective mining”, the ability to reduce mining waste production and to increase productivity of small mining machines. The robot should have the ability to react and adapt in real time to geological changes as it progresses through a mineralized body. The perception payload technologies demonstrated in the project are based on reflectance/fluorescence spectroscopy, laser-induced breakdown spectroscopy and Electrical Resistivity Tomography.

The field trials of the sensors have been carried out in the entrance of abandoned mine (baryte and lead mine, Ave-et-Auffe, Belgium), as well as in an open pit mine (bituminous shales mine in Kunda, Estonia) and in an underground lead mine (Mezica, Slovenia). These tests allowed to demonstrate the effectiveness of these sensors to provide realtime to sub-realtime mineralogical and geophysical data to a robotic drilling platform, paving the way for more autonomy in robotized mining machines.

How to cite: Burlet, C. and Stasi, G.: Automated mineral sensing for robotic miners: the ROBOMINERS perception payload, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17455, https://doi.org/10.5194/egusphere-egu25-17455, 2025.

Securing mine tailings represents a major environmental challenge. Metal mines frequently produce solid wastes containing iron (Fe) and sulfur (S), often associated with the toxic metalloid arsenic (As). Phytostabilisation often appears as a suitable option to decrease the dispersion of particles by erosion, at a moderate cost. However, site managers need a more comprehensive view of all the consequences linked to this remediation technique, notably the side effects on the other pathways controlling As and metals mobility out of the tailings. The present research aims to develop a tool for predicting the mobility and plant toxicity of As in and outside the assisted phytostabilised tailings dump, based on developing an innovative reactive transport model (RTM) explicitly integrating bacterially-catalysed reactions related to As, Fe and S metabolisms. This objective is addressed through an interdisciplinary approach combining geochemistry, numerical modelling, plant physiology, microbiology and omics approaches coupled with a good knowledge of the former mining sites operational management. To be sure to validate and calibrate the RTM with a robust dataset, experiments at different spatial and time scales have been conducted, notably a metric scale column experiment. This pilot experiment reproduces the different compartments of the dump: phytostabilised surface, underlying unsaturated zone, then saturated zone, with a controlled outlet discharge. A stainless-steel column was filled with 1200 kg of fine tailings from an old tin (Sn) mine. The tailings are watered at a regime close to that of the rainfall on the site, and average temperature and surface lighting (day/night) are controlled. Porewater is sampled monthly, and solids are analysed every 6 months by core sampling. The assisted phytostabilisation was started after 6 months of monitoring of the bare tailings: the surface layer was amended with limestone and compost and seeded with Festuca rubra. The tailings porewater contained, before assisted phytostabilisation, about 50 µg/L of As. This experiment demonstrates that redox reactions catalysed by microbial activities play a key role in As mobility. The following redox sequence has been indeed monitored in the water saturated level: denitrification, ferric iron reduction and reduction of AsV into AsIII, these last two reactions inducing mobilisation of As and Fe. Change in pore water chemistry is supported by the growth of an active microflora, notably AsIII-oxidising, AsV-reducing and FeIII-reducing micro-organisms, despite the low initial tailings content in microorganisms. These results were confirmed by batch experiments carried out parallel with the pilot study: slurries of tailings in water, spiked or not with low concentration of acetate, were incubated in anaerobic conditions. Results highlight that microbial activities are not limited by the amount (0.02% total organic carbon) and nature of organic matter initially present in the tailings. Experimental data allow to establish the first basis of a conceptual model of the network of stoichiometric metabolic reactions representing the redox sequence occurring in the tailings, that will support the development of a numerical model describing explicitly microbially-redox reactions as thermo-kinetically controlled reactions as well as an explicit growth of microbial population, calibrated with metagenomic and metaproteomic data. 

How to cite: Battaglia-Brunet, F., Thouin, H., Moreau, U., Milesi, V., Joulian, C., Tris, H., Charron, M., De Lary de Latour, L., Devau, N., Le Guédard, M., Pible, O., and Le Forestier, L.: Biogeochemical processes driving the fate of arsenic in phytostabilised mine tailings: elaboration of a conceptual model based on multi-scale experiments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9451, https://doi.org/10.5194/egusphere-egu25-9451, 2025.

The former uranium mine, Le Cellier, located in South of France, offers an opportunity to investigate the unsaturated flow and solute transport through a tailings pile resulting from heap leaching under real-world conditions (Ouedraogo et al., 2022; L’Hermite et al., 2024). Numerical simulations of one of the tailings pile were conducted to model the dynamics of the water flow. In order to tackle quality issues and to validate the hydrogeological model, we plan to make an artificial tracing test experiment. We developed a solute transport model for this pile to help the design of this experiment that will be carried out in the next future.

Conceptual one-dimensional (1D) systems representing the pile were simulated using the HYDRUS code for flow and conservative transport. The first results show that the model generates breakthrough curves exhibiting the same dynamics, irrespective of the top concentration of the injected dissolved solute. High values of hydraulic conductivity and longitudinal dispersivity accelerates solute transport, resulting in higher concentration peaks. Dual-porosity models yield significantly shorter residence times compared to single-porosity models, particularly during dry periods. The impact of climatic conditions before and during the tracer injection as well as the injection method have been also evaluated with this model.   

These findings suggest that artificial tracer experiments in the studied pile should be conducted under wet conditions and give useful information for the field implementation of the test. This simulation approach provides valuable insights for designing effective and realistic tracer test experiments. Our study shows that this type of field and modeling approach of tracer testing can help in mine water management strategies.

How to cite: Puelles-Ramírez, W., Jost, A., L'Hermite, P., Descostes, M., Reilé, B., and Plagnes, V.: Assesment for Water Flow and Solute Transport in Tailings Piles: a numerical modeling to design an artificial tracer test, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19998, https://doi.org/10.5194/egusphere-egu25-19998, 2025.

Mine drainage from abandoned mines is a major source of Arsenic (As); a ubiquitous, toxic and carcinogenic metalloid affecting over 200 million people worldwide. Recently, we observed extensive (up to 90%) co-removal of As in a vertical flow pond (VFP) passive treatment system that was designed to remove zinc from mine water drainage by precipitating ZnS following microbial sulphate reduction. However, the mechanism of As removal in the passive treatment system was unclear, even as microbial sulphate reduction is an emerging and cost-effective innovation for treating As contamination yet has received limited attention. Hence, the aim of this research was to investigate the main mechanism of As removal in the passive treatment system.

To understand the complex biogeochemical interactions of As with redox sensitive elements (Fe, S) and dissolved organic carbon (DOC), we conducted monthly field sampling over one year at the passive treatment system at the Force Crag abandoned mine site, Cumbria, UK. Aqueous sample and porewater of three depth profiles including overlying water in the VFP were collected and analysed for total element concentration, speciation (As, Fe) and DOC. Elemental composition was determined with ICP-MS. Speciation of As and Fe in aqueous phase were determined using solid phase extraction cartridges and phenanthroline method respectively, where DOC was determined with TOC Analyser.

The concentration of As (total, dissolved and colloidal) were consistently positively correlated with total, dissolved and colloidal Fe at the influent and four effluents, with concomitant decrease of both elements at the four effluents indicating potential influence of Fe on As mobility. Highest concentration of dissolved As and Fe were recorded in the porewater, which increased with depths, possibly due to vertical transportation and accumulation through the VFP, although highest level of DOC and sulphate in porewater may have caused competitive adsorption with As, resulting to weak retention of As on the binding sites. As(III) and Fe(II) were predominant in all aqueous samples, including the porewater, suggesting, to our surprise, the absence of redox transformations of As and Fe in the  VFP. Decreased As concentrations at the four effluents coincided with decreased redox potentials (anaerobic), decreased sulphate and increased DOC, indicating that organic substrates were available as electron donor and may have fuelled microbial sulphate reduction, and subsequently generating sulphide. Combined with geochemical modelling of mineral saturation indices, our results point to the precipitation of As sulphides and/or co-precipitation with Fe sulphides as the likely mechanism(s) through which As was scavenged in the treatment system. We suggest that this passive treatment system relying on microbial sulphate reduction could be further developed for treatment of As contamination in mine water effluents.

How to cite: Oroke, A., Jarvis, A., Rodriguez Freire, L., and Neumann, A.: Removal of Arsenic in a passive treatment system for mine drainage, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11360, https://doi.org/10.5194/egusphere-egu25-11360, 2025.