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.
- Introduction
Depth estimation is a crucial task in photogrammetry and computer vision. The underground scenes, characterized by low-light conditions, high dusty, and narrow structures, pose challenges in depth estimation using existing visual-based datasets. We provide an Underground Thermal image and Lidar Dataset (UTLD) for depth estimation over underground scenes. It contains stereo thermal images and the corresponding point clouds achieved by stereo laser scanners over three different underground mines. We tested some monocular depth estimation methods on the UTLD dataset to highlight the challenges and opportunities. Figures 1-2 show the acquisition scenes and platforms, respectively.
Figure. 1. UTLD dataset real collection environment
Figure. 2. Data Collection Platform
- Method Testing
We selected four existing monocular depth estimation methods, each implemented using their official source codes. Figure 3 compares the depth maps of different mathods on the dataset. The methods predict large objects well but struggle with distant targets and fine-grained details. Nevertheless, they capture the geometric structures. Besides, we presents the evaluation metrics for these methods on the UTLD dataset, where the PixelFormer method achieves the best performances (not included in the text).
Figure. 3. Depth maps of different methods on the UTLD dataset.
- Conclusion & Prospects
This study introduces the UTLD dataset and validates the feasibility of monocular depth estimation methods in underground mines. In future work, we will improve the image quality under high dust underground scenes. Besides, semantic segmentation will be involved to promote the practical adoption of vision systems in smart applications of underground mines.
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.
The growing demand for Rare Earth Elements and Critical Minerals (REE-CM) has heightened interest in extracting these elements from secondary resources, such as coal waste. Similar to traditional mining, resource mapping and prospecting to identify high concentration “hot zones” is key to prioritizing extraction efforts. Mapping REE-CM in unconventional sources is challenging due to low and variable concentrations and complex material characteristics. This study introduces an AI-aided, drone based multi-physics approach to rapidly characterize REE-CM hot zones in coal mine tailings. Our methodology integrates geophysical, radiological, hyperspectral and other technologies deployed on drones, complemented by other ground and laboratory analytical techniques. AI algorithms are key for integrating and interpreting complex multi-physics datasets to identify REE hot zones and optimize sensor selection and deployment. Field demonstrations at coal refuse and ash sites in Pennsylvania were carried out to validate the practical feasibility of this approach. The results revealed promising links between drone-acquired multi-physical signals and REE concentrations, and REE predictions with AI were validated with ground truth. Our study validated the feasibility of using drone-based multi-physics surveys to map REE concentrations in coal wastes to enhance their economic viability for recovery and guide extraction prioritization.
How to cite: Wu, Y., Chou, C., Chung, J., Dafflon, B., Panaro, J., Quiter, B., Rofors, E., Tibaut, R., Wang, J., Whittaker, M., and Wu, J.: Rare Earth Elements – Multiphysics AI-aided Autonomous Prospecting (REE -MAP), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2614, https://doi.org/10.5194/egusphere-egu25-2614, 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.
The Algerian government issued many artisanal gold mining authorizations to formalize gold-bearing quartz vein mining within the Hoggar shield. However, as observed all-over the world, miners have no technical knowledge and not use basics prospecting tools during mining; generally, they don’t incorporate geology and mineralogy knowledge in mining practices. The objective of this study is to provide artisanal miners with recommendations to enhance the sustainability of their mining projects by employing rational and optimized exploitation methods. For this study, nine artisanal mine sites were selected within the Taskret gold field, at approximately 150 Km from eastern Tamanrasset. The ore deposits consist mainly of gold-bearing quartz veins hosted by metamorphic rocks. Firstly, we will conduct a comprehensive study of the mineralogy of run of mine (ROM) samples, including both ore and rocks, using X-ray diffraction (XRD). Thin and thick sections will be meticulously prepared from the rock fragments and ores, enabling us to determine mineralogy and textures through optical microscopy, scanning electron microscopy (SEM-EDX), and electron microprobe techniques. Gold grade determination will be performed using fire assay, while chemical characterization of other elements will be carried out through ICP-MS analyses. This holistic approach will provide us with vital insights into the geological and mineralogical characteristics of ROM materials, allowing us to make recommendations for enhancing the sustainability of artisanal mining practices in the Taskret gold field. Indeed, before any mining operations and ore treatment the good understanding of the ore mineralogy is very important to optimize the gold recovery and to minimize environmental impact of the activity. This project will significantly contribute to a rational and sustainable artisanal mining in Algeria, especially in the Hoggar, by giving scientific recommendation based on mineralogy of gold bearing minerals.
How to cite: Issaad, M. and Bakelli, A.: Towards sustainable activity in artisanal gold mining in Hoggar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-700, https://doi.org/10.5194/egusphere-egu25-700, 2025.
Based on the geodynamic environment, basic geological characteristics of minerals and so on, gold deposits in China are divided into 11 categories, of which tectonic fracture altered rock, mid-intrudes and contact zone, micro-fine disseminated and continental volcanic types are the main prospecting kinds. The metallogenic age of gold deposits in China is dominated by the Mesozoic and Cenozoic. According to the geotectonic units, geological evolution, geological conditions, spatial distribution, gold deposits types, metallogenic factors etc., 42 gold concentration areas are initially determined, and have a concentrated distribution feature. On the basis of the gold exploration density, gold concentration areas are divided into high, medium and low level areas. High ones are mainly distributed in the central and eastern regions. 93.04% of the gold exploration drillings are within 500 meters, but there are some problems such as less and shallower of drilling verification etc.. The paper discusses the resource potentials of gold deposits, and proposes the future prospecting directions and suggestions. The deep and periphery of old mines in the central and eastern regions and western area especially in Xinjiang and Qinghai will be the future key prospecting one, and have huge potential gold reserves. If the exploration depth is extended to 2,000 meters shallow, the gold resources will double.
How to cite: Wang, B., Li, J., and Li, J.: Characteristics and Key Exploration Directions of Gold Deposits in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7554, https://doi.org/10.5194/egusphere-egu25-7554, 2025.
Ferromanganese nodules, rich in cobalt (Co), nickel (Ni), copper (Cu), manganese (Mn), and rare earth elements (REEs), are important marine mineral resources with the utmost capacity for commercial employment in the future. Recently, the discovery of high abundant Co-rich nodules in the Western Pacific has attracted significant attention. The prediction of nodule abundance is a vital geological problem to be solved in marine mineral resource exploration. Based on the multisource geological data of the study area in the western Pacific Ocean acquired through acoustic, optic and geological sampling, a stochastic probabilistic prediction for nodule abundance was developed via Gaussian process regression (GPR). The results revealed that the predicted abundance of nodules ranged from 0 to 71.2 kg/m2, with an average abundance of 26.3 kg/m2. The high-abundance (~30.0 kg/m2) nodules are mainly distributed in the deep-sea basins around several seamounts, and they may be spatially coupled with the Co-rich crust distributed over seamounts in the targeted study area. Compared to traditional machine learning approaches, such as stepwise linear regression, regression trees and support vector machine, intelligent prediction of nodule abundance by GPR is achieved with improved accuracy. Moreover, with the predicted abundance, the prediction error is obtained simultaneously by GPR. The deep-sea basins between the Magallan and Marcus-Wake seamounts are considered potential areas for further exploration of Co-rich ferromanganese nodules in the western Pacific Ocean.
How to cite: Yang, Y., Song, H., Hong, S., Li, X., Ren, J., Liu, Y., Yu, M., and He, G.: Prediction of the abundance of ferromanganese nodules using Gaussian Process Regression based on multisource geological data in the western Pacific deep-sea basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7895, https://doi.org/10.5194/egusphere-egu25-7895, 2025.
Rare earth elements are important resources and they can be widely used in smart phones, electric vehicles, and smart home appliances, etc. Recently, countries around the world pay attentions to their own rare earth resources and set policies to cope with the country's future development. Therefore, rare earth resources have obviously become valuable strategic materials. Rare earth minerals are mainly occurred in placer depositions in Taiwan. The literature on rare earth resources in Taiwan is quite limited; there are only a few studies on the characteristics of heavy sand deposits, and only a few about the distribution of heavy minerals in southwestern Taiwan. Therefore, this study utilizes the drainage basin of Zengwun River (the upstream, midstream and downstream of the river) as the study site for rare earth resources in southwestern Taiwan. Using the sediments in the river and offshore as study samples, the systematic investigation on the properties of rare earth minerals in river sands and sea sands will be deeply studied. This study aims to investigate the relevant characteristics of rare earth resources (microstructure, types of rare earth minerals, and concentrations of rare earth elements, etc.) and to provide the comprehensive results pertaining to the potential placer rare earth ore in the drainage basin of Zengwun River of southwestern Taiwan. The XRD data indicated that the samples contained major minerals of quartz, feldspar, muscovite/illite, kaolinite, and chlorite; some minor minerals of rutile, calcite, and monazite (rare earth mineral). The SEM results showed that these monazites can be divided into detrital and aggregated monazites. The aggregated monazite presented two different occurrences. Type I aggregated monazite displayed a skeletal morphology associated with quartz and feldspar inclusions. Type II aggregated monazite was symbiotic with allanites or TiO2 polymorphs. The REE concentration in this study area will be evaluated and compared with the UCC average and active REE mining countries.
How to cite: Chen, Y.-H., Huang, C.-C., and Yeh, H.-L.: Study on the mineralogical and geochemical characteristics within sediments in southwestern Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7634, https://doi.org/10.5194/egusphere-egu25-7634, 2025.
Mineralization in the Limerick Basin, located in southwest Ireland, uniquely associates with volcanic rocks, unlike other mineralized zones in the Irish midlands, where mineral systems align with large-scale normal faults. To better visualize the subsurface structures influencing Limerick’s mineralization, we conducted 2D gravity inversion incorporating geological and petrophysical constraints.
Conventional methods of deterministic inversion involve adding a model norm term to the data misfit term in the objective function to regularize an ill-posed problem and obtain stable solutions. While previous studies on constrained deterministic inversion have modified the model norm to include prior information or constraints in geophysical inversion, the complex nature of geological priors makes encoding this information mathematically and computationally challenging. To tackle this problem, we implemented a deep generative model, specifically a conditional variational autoencoder (cVAE)-based inversion framework, to incorporate structural constraints derived from drill hole and petrophysical data.
Initially, we tested this framework on a synthetic case by training the cVAE on thousands of 2D density models comprising geological features analogous to the field geology and populated with density values consistent with the drill core measurements acquired from the study area. Artificial drill holes were created to fix the depths of geological units at the drill hole contact points across all training models, ensuring that the predicted models adhered to prior constraints. Following training, we tested the network on some test data, which showed that the predicted models successfully captured the structural and petrophysical property constraints. The geometries of the geological features were also well recovered.
We applied this method to gravity data from a NW-SE trending profile crossing the western part of Limerick Syncline. Thousands of density models were generated using drill hole data, incorporating measured rock densities for training. Since the profile’s central and deeper sections lacked sufficient geological data for direct validation of the results, we implemented a hypothesis-testing approach. In each hypothesis, geological features were added to the training density models based on prior geological knowledge of the study area. If simulated data from an inverted model failed to match field data, more geological features were added to the training models in the next hypothesis, and the workflow was repeated to achieve a low data misfit.
The inversion provided three key insights into the study area’s geology. First, it identified potential volcanic intrusions in the southern Limerick Syncline, possibly extending from depths below the basement. Second, it estimated the syncline’s geometry in areas with limited geological constraints. Third, it revealed a sharp vertical displacement in stratigraphy, indicating a potential south-dipping fault in the northwest portion of the syncline. This fault may have influenced mineralizing fluid migration, playing a critical role in mineral deposit localization.
How to cite: Chakraborti, P., Sun, J., and Melo, A.: An improved characterization of the subsurface in the Limerick Basin (Ireland) using deep generative model-based 2D gravity inversion constrained with drill hole and petrophysics data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-655, https://doi.org/10.5194/egusphere-egu25-655, 2025.
Muography is a most novel tool for geophysical density mapping. This
developing interdisciplinary research area is based on the detection of
muons originating from cosmic rays, allowing density-based non-destructive
investigations of the interior of objects up to the size of a mountain.
The cornerstone of the technology is that muons lose energy depending on
the density of the rock and the distance it travels through it. Thus, the
number and direction of the incoming muons can be used to determine
density anomalies (e.g. cavity, cave, ore) during data processing.
Our Group in HUN-REN WignerRCP Budapest is internationally renowned in
the development of high-performance muon-detectors, data processing
procedures, and exploring new applications for muography.
Recently we have developed a muographic-survey planning software, thus
for the various scenarios the optimal detector configuration and
orientations could be calculated.
Reliability of this novel technology and any new equipments shall be
proven in well-known sites. The Jánossy Underground Laboratory (JURLab) in
Budapest is a simple-geometry multi-level underground infrastructure,
excellent for detector verifications and quantifying limits of underground
muographic surveys.
We will present recent measurement series from JURLab campaigns;
validation of predicted yields with real data, and its implementation for
the tomographic inversion. Case studies and pilots from mining
applications will be shown.
How to cite: Stefán, B. A., Balázs, L., Hamar, G., Surányi, G., and Varga, D.: Design of MWPC-based muography measurements for geophysical research, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-940, https://doi.org/10.5194/egusphere-egu25-940, 2025.
Securing raw material supply for high-tech products and reducing their ecological-economic footprint has become a pressing challenge for our society due to increasing demands while natural resources deplete. One solution is seen in closing material loops by recycling. But to ensure successful re-entry of secondary resources into the production chain essentially relies on the accurate identification of materials in mixed waste streams in order to allow for precise sorting into as pure as possible material types. A particular relevant, but at the same time particularly challenging, task relates to the identification of metal alloys. A wide variety has been engineered to provide highly specific functionalities of individual metals such as, for example, steel in the automotive industry. Innovation over many years resulted in cars containing multiple high-performance steel alloys. At their end-of-life, car recycling routines can sort out concentrates of steel, but mixing the different alloys prevents the recycling material from meeting the quality criteria needed for new car production, and hence, cause downcycling. Although several sensor-based sorting solutions are available to map qualitative material differences for many waste streams, a precise and quantitative solution is needed to differentiate between steel alloy types. LIBS provides a promising solution as it allows for elemental analysis along with concentration information in a fast and contact-free manner compatible with conveyor-belt operations.
With this contribution, we highlight the challenges of steel alloy detection using LIBS and point out solutions for analytical workflows and practical applications. This involves especially the detailed investigation of measurement parameters, establishment of calibration models for most relevant elements and discuss potential influences from disturbances such as from surface coating. The results suggest a successful discrimination of automotive-relevant steel alloys. The workflow hence, provides the basis for improved alloy-specific sorting products. Providing such analytical tools and corresponding workflows will help for increasing the quality of recycling and reducing the risk of increasingly complex recycling mixtures after multiple cycles. In this context, accurate quantitative LIBS results provide one cornerstone to future innovations on material recycling by products that at least partially re-enter high-performance product cycles.
How to cite: Fuchs, M. C., Patil, R., Singh, A., Regulan, G., Madriz Diaz, Y. C., Ziegenrücker, R., and Gloaguen, R.: Evaluating LIBS analysis for improved steel alloy identification in end-of-life vehicle recycling , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6028, https://doi.org/10.5194/egusphere-egu25-6028, 2025.
Sedimentary phosphate rocks are crucial for global food security, contributing to over 90% of the fertilizer industry's needs. However, their exploration and mining face significant challenges due to substantial horizontal and vertical variations in phosphorus concentrations within the strata. Traditional characterization methods are time-consuming and costly, requiring complex sample preparation, which often limits the spatial resolution of measurements across the ore body. On the other hand, infrared hyperspectral core scanning has emerged as a proven technique for rapid characterization of mineral assemblages along drill cores, which by leveraging advanced machine learning algorithms, offers a powerful tool for predicting geochemical variations. In this context, our study aims to assess the ability of hyperspectral infrared imagery to rapidly quantify the distribution of P₂O₅ in phosphate drill cores using a non-destructive methodology. For this, a ~65-meter drill core from the phosphatic series of Ben Guerir (Morocco) was analyzed. P₂O₅ measurements were acquired using a Thermo Fisher XL5 portable XRF (pXRF), and hyperspectral images were collected using a SPECIM SisuROCK core-scanner with SWIR (1000–2500 nm) and MWIR (2700–5200 nm) cameras. To predict P₂O₅ concentrations from infrared spectra recorded in hyperspectral imagery, we explored a direct method, using high-performing machine learning algorithms trained on a ~5-meter drill core dataset. When applied to the whole drill core dataset, the machine learning algorithms—Random Forest Regressor, KernelRidge Regressor, Gradient Boosting, Support Vector Regressor, and K-Nearest Neighbors— reported good predictive performance with strong correlations of 78%, 78.2%, 67.1%, 74.9%, and 68.7% in the SWIR region and 81.2%, 83%, 80.2%, 83.24%, and 82% in the MWIR region, respectively. Direct estimation of P₂O₅ using the Support Victor Regression model on MWIR imagery thus represents a more effective approach, offering significant potential for P₂O₅ chemical mapping and improved phosphorus resource estimation with a low mean absolute error of 3.29. Further improvements could be achieved by employing a larger training dataset and deep learning algorithms.
How to cite: Mazigh, M., Raji, O., and Benzaazoua, M.: Assessing SWIR and MWIR Hyperspectral Imaging for Rapid Estimation of P2O5 Distribution in Sedimentary Phosphate Drill Cores, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19825, https://doi.org/10.5194/egusphere-egu25-19825, 2025.
PERSEPHONE is a Horizon Europe project (Grant Agreement No.101138451) dedicated to autonomous exploration and extraction of deep mineral deposits. The project has been created in support to the increasing demand for raw materials, which compel European mining companies to extract ore at greater depths. In this framework, current mining operations and traditional technologies face significant challenges in maintaining profitability while aligning with the European Green Deal's environmental objectives and ensuring worker safety.
To address these challenges and enable sustainable development, PERSEPHONE focuses on developing innovative technologies for resource-efficient extraction and near-mine exploration of critical raw materials. PERSEPHONE’s vision includes reducing the scale of mining equipment to optimize operations in challenging environments, integrating autonomous systems for risk-aware navigation, and digitalization of the extraction process. A key innovation is the creation of digital twins, supported by the validation of key enabling technologies at Technology Readiness Level 5 (TRL 5). Additionally, the project introduces groundbreaking approaches to online near-mine exploration, core analysis, and advanced data analytics for mine expansion and decision support.
In the first half of the project, several of its key technologies have been tested in the laboratory and controlled underground environments at mine sites. More specifically, the initial tests of autonomy stack for high accuracy navigation have been carried out. Additionally, a multispectral camera has been integrated with a developed autonomy package that combines 3D LiDAR and RGB-D camera. This payload has been mounted on the Unitree robotic platform for the initial combined data collection.
Ultimately, PERSEPHONE aims to digitalize and automate the mining value chain, advancing towards sustainable, efficient, and safe exploration and extraction practices. The project contributes significantly to achieving the EU’s critical raw material goals while supporting the transition to a greener economy.
How to cite: Patel, A., Koval, A., Satpute, S. G., Nikolakopoulos, G., Burlet, C., and Stasi, G.: The PERSEPHONE project: Autonomous Exploration and Extraction of Deep Mineral Deposits, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19608, https://doi.org/10.5194/egusphere-egu25-19608, 2025.
In Greenland, mining has been undertaken in remote areas for more than 150 years and long before legislation for environmental protection was implemented. The Black Angel mine by the Affalikassaa fjord in Central West Greenland served as a marble quarry, known as Maarmorilik, operating in the 1930s when a metal-sulphide ore body was discovered in the ‘Black Angel’ mountain on the other side of the fjord. This discovery led to establishment of the Black Angel lead-zinc mine, operating from 1973-1990 with a total of 13.5 million tons of ore produced from an ore body located 600 meters up a vertical mountain side and only accessible via cable cart spanning 1500 meters across the fjord. Mining was done by the “Room-and-Pillar” method, where ore was crushed inside the mine before being transported via cable cart to the processing facility. Large amounts of pyrite and sphalerite bearing waste rock were dumped directly out of mine tunnel openings at approx. 600 meters altitude onto the steep mountain slopes below as well as and onto the “Tributary Glacier” towards the Greenland Ice Sheet. Since deposition, the waste rock has been exposed to the elements with significant leaching of heavy metals and dispersion of fine particles into the terrestrial and marine environments. Environmental monitoring since mine-closure in 1990 has documented a widespread pollution of Pb in the area. However, the knowledge on the geochemical composition of the different waste rock dumps, their relative contributions to both historical, current and future releases of heavy metals to the environment as well as future risk due to permafrost thaw and surface erosion is limited by a lack of widespread geochemical characterization of deposited waste rock.
In the current study we present the first large scale in-situ pollution monitoring at the legacy Black Angel mine, using portable X-Ray Fluorescence spectrometry (pXRF). pXRF has been shown to provide fast, accurate and cost-effective results for many heavy metals in sediment and soil, enabling effective in-situ identification of pollution hot-spots. Results from this study show significant variation in heavy metal content between different waste rock dump sites. The highest concentrations of Pb, Zn and Cd are found in the North Face Dump and 17xCut established early in the mine's history, and lower, yet still significant concentrations are found in the much younger Tributary Glacier dump. We find that the waste rock from the Tributary glacier has been reworked and transported downstream where we measure increased concentrations of heavy metals. This highlights the large environmental risks associated with depositing mine waste on dynamic landforms. Current surface and bank erosion of fine particles from waste rock dumps will likely continue in the future as a warmer climate may increase erosion potential in response to large precipitation events as well as changes in snow cover. The remaining environmental risk is generally dominated by the mine closure strategy of leaving waste rock exposed to the environment, with only limited impacts from future warming and thawing permafrost.
How to cite: Frigaard Rasmussen, C., Søndergaard, J., Tommerup Vad, K., and Juncher Jørgensen, C.: Pollution monitoring at the Black Angel legacy mine in West Greenland using in-situ portable X-Ray Fluorescence (pXRF) measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1258, https://doi.org/10.5194/egusphere-egu25-1258, 2025.
The environmental and social issues related to abandoned mines are prevalent worldwide. Each country has developed various chemical, physical, and biological mine reclamation technologies to address these challenges, and the results have primarily been published in papers or patents. Therefore, analyzing relevant papers and patents to understand the trends in the mine reclamation industry is essential. This study conducts a quantitative analysis of papers and patents related to mine reclamation technologies to identify the latest technological trends, address limitations, and propose future R&D development directions. Using Data Envelopment Analysis (DEA), this study evaluates the efficiency of diffusing papers and patents produced by national R&D investments in related industries, academia, research institutions, and government agencies. The input variables included the number of papers and patents, whereas the output variables comprised the number of citations for papers and patents and the number of triadic patent families. Using a comparative analysis of efficiency across countries, this study derives insights into the knowledge dissemination effects of research outcomes at the national level. To enhance knowledge dissemination and its impact in each country, research centered on solving current issues, improving data reliability, promoting multidisciplinary studies, and strengthening international cooperation is necessary. This study is significant as it provides future research directions for mine reclamation technologies and facilitates the application and commercialization of the developed technologies.
How to cite: Bae, S. W. and Bae, J.: Analysis of Knowledge Spillover Effects Using Data Envelopment Analysis on Papers and Patents Related to Mine Reclamation Technology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7522, https://doi.org/10.5194/egusphere-egu25-7522, 2025.
The use of alkali-activated materials presents a sustainable approach to replacing conventional construction resources while promoting waste valorization, in line with the goals of the blue economy for environmentally responsible development. This study explores the innovative use of mine waste (MW) from an abandoned lead-zinc (Pb-Zn) mining site in Northern Tunisia as a cost-effective, high-adsorption additive in the production of metakaolin-based geopolymers. Metakaolin (sourced from Vicente Pereira, Ovar, Portugal) was partially substituted with MW in varying proportions (0%, 5%, 10%, 20%, and 30%). The geopolymer formulations maintained constant molar ratios of SiO₂/Al₂O₃ and Na₂O/Al₂O₃ at 1 to minimize the use of sodium silicate and sodium hydroxide, leading to the development of environmentally friendly geopolymers with a reduced carbon footprint.
The study assessed how the incorporation of MW influences the geopolymers' microstructure, mechanical strength, and ability to adsorb methylene blue dye. Chemical analysis of MW revealed elevated concentrations of hazardous elements, up to 2.23 wt.% Pb and 8.2 wt.% Zn, highlighting the importance of stabilizing these elements to prevent environmental contamination. Scanning Electron Microscopy (SEM) indicated varying degrees of geopolymerization across different formulations, predominantly featuring amorphous phases. After 28 days of curing, samples with 5 wt.% and 10 wt.% MW exhibited the highest compressive strengths of 25 MPa and 30 MPa, respectively.
The adsorption capacity of the developed geopolymers was evaluated using Methylene Blue (MB) dye, with experiments focusing on the effects of adsorbent dosage and contact time. Adsorption kinetics closely followed the pseudo-second-order model, while the Langmuir isotherm model best described the adsorption behavior. Notably, samples with 30 wt.% and 0 wt.% MW achieved the highest adsorption capacities, demonstrating the beneficial role of MW in enhancing the properties of alkali-activated metakaolin geopolymers and its potential to partially substitute metakaolin.
How to cite: Nouairi, J., Andrejkovičová, S., Karoui, O., Pinho, T., Rebelo, R., Gonçalves, G., Camerlenghi, A., Ghribi, M., and Rocha, F.: Stabilizing hazardous mine waste in alkali-activated geopolymers for pollution mitigation at abandoned mining sites, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17917, https://doi.org/10.5194/egusphere-egu25-17917, 2025.
Post-mining sites are prone to complex processes related to the ceased mining and disturbance of the rock mass around the excavations. Therefore, such sites require continuous monitoring to minimize threats associated with, e.g. occurrence of often unexpected discontinuous deformations such as sinkholes. This study focuses on the development and analysis of a database of sinkholes in the former “Babina” brown coal mine in Western Poland. The mine site is located in the SW part of the complex glaciotectonic area of the “Muskau Arch”. It was subjected to long-term open-pit and shallow underground mining. The primary objective of the study is to create a comprehensive database of sinkholes, based on analysis of differential digital elevation model and derivatives of digital elevation model such as slope and hillshade maps. The structure of the database includes dependent variables such as geographical location and dimensions of sinkholes, as well as parameters representing potential causative factors including: geological, mining, geophysical and topographical characteristics (exploratory variables). It will be used to analyse and model the relationship of sinkhole occurrence with potential causative factors of their occurrence in the project no. 2021/43/B/ST10/02157.
The geodatabase was developed using ArcGIS software from ESRI, encompassing information on more than 230 identified sinkholes. Each sinkhole in the database is comprehensively described by a range of attributes. The exploratory variables include total depth of mining, distance to the first underground level, distance to shafts and adits, location of brown coal outcrops locally named gizers, proximity to coal seams (geological mining factors). Among the topographical factors the following attributes have been stored: slope of the terrain, distances to former open pits, anthropogenic lakes and waste heaps, land cover types. The geophysical data include results of gravimetric observations (anomalies in the gravitational field). Whereas, the hydrogeological data include results of underground water modelling.
The construction of the database was done by using advanced spatial data processing tools such as Map Algebra Statistics and Surface Functions, as well as extract value to feature tools. These functions were used to calculate and to extract raster values associated with location of sinkholes in addition distance tools where used to determine parameters derived from vector data that include for example database of underground working.
The dataset was subjected to a comprehensive statistical analysis, which included developing descriptive statistics encompassing histograms of the values of dependent variables (sinkhole parameters) and independent variables (factors potentially influencing the formation of deformations). An exploratory data analysis was also conducted to determine correlations between variables.
The results of the study have allowed analysing weighted spatial distribution of sinkholes in the post-ming area. The weights included parameters of sinkholes. Further research is aimed at developing predictive models with a machine learning approach. The models will be used to identify areas prone to future sinkhole formation.
The results of the study confirm the complexity of post-mining impacts and the necessity for further detailed analysis of the changes taking place in the study area.
The research has been financed from the OPUS National Science Centre projects grants no. 2019/33/B/ST10/02975 and no. 2021/43/B/ST10/02157.
How to cite: Walerysiak, N. and Blachowski, J.: Geodatabase of Sinkholes in the the Post-Mining Area of the Brown Coal Mine “Babina” (W Poland), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18583, https://doi.org/10.5194/egusphere-egu25-18583, 2025.
The ever-growing demand for critical resources, including metals and metalloids elements, has resulted in a dramatic increase of tailings worldwide. Tailings may contain significant concentration of potentially harmful elements like lead or arsenic. During rainfall events, runoff and associated erosion may result in contaminant dispersion to river systems, which may be particularly deleterious for ecosystems and significantly affect human health. While the massive impact of tailing dam failures has been studied in the literature, much less attention has been paid to estimating the diffuse dispersion from tailings under the effects of rainfall and runoff. Recent works however suggested that it may be a significant risk for populations, considering the significant number of tailings scattered all over the World (Macklin et al., 2023).
In the current study, more than 2000 tailings were inventoried over France. This study proposes to build a methodology to assess both the catchments structural and functional connectivity linking tailings to river channels, in a selected set of catchments located in contrasted environments (i.e. catchments located in lowland, upland and mountainous areas), in France. The proposed methodology makes use of national-scale databases, including rainfall, discharge, and suspended sediment concentration time series as well as catchments characteristics (e.g. morphology and land use). The aim of this study is to encourage discussions on the topic of catchment-scale assessment of contaminant dispersion from mining wastes under the effects of rainfall and runoff. It should ultimately help decision-makers to prioritize tailings for management plan design.
Macklin M.G., Thomas C.J., Mudbhatkal A., Brewer P.A., Hudson-Edwards K.A., Lewin J., Scussolini P., Eilander D., Lechner A., Owen J., Bird G., Kemp D., Mangalaa K.R. (2023). Impacts of metal mining on river systems: a global assessment. Science, 381:1345-1350.
How to cite: Grangeon, T., Vandromme, R., Florian, M., Grangeon, S., Ferfoglia, M., Lafortune, S., Terrier, M., and Cerdan, O.: Catchment-scale evaluation of potential particulate contaminant dispersion from post-mining sites under the effects of water erosion, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15620, https://doi.org/10.5194/egusphere-egu25-15620, 2025.
Potash tailings piles from the mining of potassium salts present considerable environmental challenges concerning surface and groundwater. Uncovered piles are primarily composed of saline residues such as sodium chloride, magnesium sulfate and magnesium chloride. To mitigate the interaction between saline residues and rainwater, some piles have been covered by different soil types in some regions of the world, including Germany, to act as a physical barrier to prevent water-salt contact. In this way, the amount of infiltrated water is reduced, thereby reducing the amount of salts that can be leached and transported to the underlying water bodies. The extent to which the soil cover prevents the contact of infiltrated rainwater will depend on the hydraulic parameters of each soil type, how many soil layers make up the overall soil cover, how the soil layers are distributed, and on the hydrological situation of each region. While climatic factors such as precipitation are fundamental controlling factors, the type and distribution of vegetation play a crucial role in the efficiency of the pile cover. The objective of this research is therefore to quantify the effect of vegetation on infiltration and evapotranspiration in a vegetated soil cover over a hypothetical potash tailings pile by numerical simulation. For this purpose, different types of vegetation are analyzed, represented by their hydrological parameters leaf area index, depth and root density. The seasonal variations of the vegetation represented by temporally changing parameter values are also taken into consideration. Different depths of the cultivation layer for vegetation, the stabilization layer, the drainage layer and the sealing layer are regarded. The numerical simulation is carried out with the Advanced Terrestrial Simulator (ATS), a software which allows surface-subsurface coupling through continuity conditions of pressure in both zones. The software solves the diffusion wave equation for surface flow and Richard’s equation for the subsurface flow. Additionally, ATS implements the Priestley-Taylor model for potential evapotranspiration. Together with vegetation parameters, this enables the calculation of actual evapotranspiration and, subsequently, the water balance of the soil cover. Results from 2D simulations demonstrate the ability of the model to represent the relevant coupled processes outlined above. The simulated infiltration patterns provide valuable insights for optimizing cover design and vegetation selection, contributing to the development of more effective solutions for groundwater protection in potash tailings piles areas.
How to cite: Silva Monsalves, F. E. and Graf, T.: Numerical Simulation of the Effect of Vegetation on Infiltration in Soil Covers of Potash Tailings Piles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15851, https://doi.org/10.5194/egusphere-egu25-15851, 2025.
