The management of mine tailings presents a global challenge. Re-mining these tailings to recover remaining metals could play a crucial role in reducing the volume of stored tailings, as historical mining methods were less efficient than those used today. Consequently, mine wastes have the potential to become unconventional resources for critical minerals. To assess this potential, critical minerals and metals in the mine tailings were investigated through sampling, characterization, and 3D geostatistical modeling. The Bleïda copper mine tailings in Morocco were modeled, and residual copper resources were estimated using ordinary kriging (OK). Tailings were systematically sampled at a depth of 1.8 m using a triangular grid and tubing method. The metallic and mineralogical content of the samples was analyzed, and a numerical 3D model of the tailing’s facility was created using topographic drone surveys, geochemical data, and geostatistical modeling. The results from the 3D block model of the Bleïda tailings facility reveal that the volume of deposited tailings is 3.73 million cubic meters (mm³), equivalent to 4.85 million tonnes (Mt). Furthermore, based on the average copper grade (~0.3% by weight) in the studied part of the tailings pond, the copper resources are estimated at 2760 tonnes. Mineralogical characterization indicates that this metallic content is mainly associated with sulfide and carbonate minerals, which exhibit a low degree of liberation. This study aims to serve as a reference for assessing the reprocessing feasibility of tailings in both abandoned and active mines, thereby contributing to the sustainable management of mine tailings facilities. Geostatistical modeling has proven effective in producing tonnage estimates for tailings storage facilities and should be adopted by the industry to reduce the technical and financial uncertainties associated with re-mining.

This study presents a novel methodology for mapping Fault- and Thrust-based Structural Lineaments (FT-SL) in the rugged and inaccessible Oued-Laou watershed of the Rif Belt, Morocco. Combining optical (Landsat-8 OLI, Sentinel-2 MSI) and radar (Sentinel-1 SAR) remote sensing data, the research employs manual, semi-automatic, and automatic extraction methods enhanced by spatial filtering (Sobel, Laplacian, Kuan). A Knowledge-Based System (KBS) integrated with Multi-Criteria Decision Analysis (MCDA) evaluates the effectiveness of these methods, focusing on lineament statistics, orientation, density distribution, and correlation with existing geological maps. The results highlight Sentinel-1 SAR’s superior performance in detecting subsurface structures, while manual extraction yields the highest accuracy. This study also demonstrates the potential for generalizing this approach to other Alpine orogenic regions, such as the Alps, due to shared geological characteristics. The findings provide a robust framework for structural lineament mapping in mountainous terrains, addressing challenges of accessibility and data scarcity.

The current investigation concern Tiouit region in order to perceive potential mining zones. Remote sensing data were the main component to process. In order to spot geochemical anomalies associated with the target zones, multispectral ASTER data, gamma-ray spectrometry, and stream sediment data were used. Crosta and SAM applications granted to map a spatial distribution of propylitic, phyllic, clay, and ferruginous alteration zones. The normalized ratios let the delineation of potassic alteration zones, including the Tiouit gold mine characterized by an intense alteration in the Ikniwn granodiorite. Thus, the potassic alteration zones are consistently co-located with the tectonic structures materialized by strong fracturing densities. The stream sediment geochemical investigation revealed that the cited areas have an important potential of mineralization, with high concentrations of As (136 g/t), Cu (91 g/t), Pb (141 g/t), Zn (392 g/t), Ag (3 g/t), Mo (11 g/t), Mn (0.2 g/t), and Fe (10.63 %).

Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process. A key feature of this strategy is leveraging population search algorithms to fine-tune hyperparameters, thus boosting prediction accuracy. Notably, Bayesian optimization has been applied for the first time to select the most effective learning parameters for artificial neural network classifiers used for lithology identification. This technique utilizes the capability of Bayesian optimization to utilize past classification outcomes to enhance the lithology models performance based on physical parameters calculated from well log data. In a comparison of artificial neural network architectures, the Bayesian-optimized artificial neural network (BOANN) demonstrably achieved the superior classification accuracy in validation and significantly outperformed a non-optimized wide, bilayer, and tri-layer network configurations, indicating that incorporating Bayesian optimization can significantly advance lithofacies recognition, thus offering a more accurate and intelligent solution for identifying lithology.

Geophysical data inversion is a tool, which can be used to recover the subsurface distribution of physical properties from field data. Each type of geophysical data can be inverted using one or more inversion algorithms. In this paper, a set of geophysical magnetic and gravity data of the Hajjar area in Morocco, covering an extent of 3 . 2 × 1 . 6 km 2 , were used to make a 3D model of an orebody and to estimate the mineral reserve by potential-field geophysical data inversion and excess mass estimation. We thus promote the development and application of potential-field geophysical data inversion using the softwares Geosoft Oasis Montaj and Voxi Earth Modelling™ and the evaluation of its power compared to the excess mass estimation method. The process of inversion begins with data processing, then moves to analysis and interpretation, and ends with unconstrained Cartesian cut cell inversion. The results show a variation of - 0.22 mGal to 1.59 mGal for the gravity residual anomaly map, leading to have density variations from 2 . 45 g / cm 3 to 4 . 22 g / cm 3 , and a variation of - 232 nT to 1018 nT for the reduced magnetic anomaly map. Moreover, data inversion allowed us to create a 3D model of the orebody and of the adjacent geological formation, and to estimate the different parameters that characterize the orebody derived from the inversion results, which have been confirmed from survey data: (depth ≈ 160 m; maximum density ≈ 4 . 22 g / cm 3 ; minimum density ≈ 3 g / cm 3 ; mean density ≈ 3 . 61 g / cm 3 ; thickness of the overburden ≈ 120 m; dip ≈ 45 ∘ ; morphology ≈ lens; volume ≈ 4 . 8 × 10 6 m 3 ). It was therefore possible to evaluate the reserve, and to validate the reliability of the inversion by having a root mean square error between the exploited reserve and the calculated reserve of 13.5%, i.e. an insignificant difference between the real and calculated magnetic and gravity orebody responses, which support the validity of the results.

Geophysical data processing further constrained inversion is evolving progressively prevalent in geoscience domains for three-dimensional modelling and resources evaluation. The process is based on the magnetic and gravity data processing further constrained Cartesian cut cell inversion to discern the maximum of information about HAJJAR deposit in order to calculate its tonnage. This article exhibits data and inversion processing technique for tonnage calculation based on an important geophysical magnetic and gravity surveys of defined extent of HAJJAR region, which presents a great benefit to save time and have accurate and realistic results to a same case. Otherwise, the potential-field signatures of what are regarded to be geologically expressive features are sought within the magnetic and gravity data. The preliminary stage for tonnage calculation was residual anomaly processing and depth estimation of the orebody using spectral analysis method. However, progressing towards extracting the deposit signature, the used method leads to invest the gravity signature of the orebody in adequacy with the magnetic signature. Finally, the tonnage calculation was developed by constrained Cartesian cut cell inversion using Voxi Earth Modelling^TM. Obtained results were very important, given their qualitative and quantitative accuracy, which give an added value for the governmental geological and geophysical survey.