Exploration
Ahmed Mahmoud Abdelhameed; Maher Abdelateef El Amawy; Ayman Mahrous; Mohamed El-Khouly; Adel Fathy
Abstract
Hyperspectral imaging (HSI), combined with advanced machine learning algorithms (MLAs), has unlocked novel research opportunities and revolutionized geological mapping by enabling precise lithological classification. Accurately detailed geological mapping is one of the most essential requirements for ...
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Hyperspectral imaging (HSI), combined with advanced machine learning algorithms (MLAs), has unlocked novel research opportunities and revolutionized geological mapping by enabling precise lithological classification. Accurately detailed geological mapping is one of the most essential requirements for targeting mineralization. However, achieving comprehensive lithological mapping remains a challenge, hindering systematic mineral exploration. This work explores the use of PRISMA hyperspectral data and the Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms to objectively map the Precambrian rock assemblages at the El Ineigi area in the Central Eastern Desert (CED) of Egypt. For this purpose, PRISMA data in HDF5 format were first pre-processed and subsequently transformed through principal component analysis (PCA). The processed spectral data were then combined with extensive fieldwork and previously existing geological maps and classified using SVM and ANN to achieve enhanced discrimination of the exposed rock units in the study area. Our results conclusively demonstrate the exceptional capability of PRISMA data for detailed lithological mapping. The SVM and ANN classification achieved remarkably high overall accuracy, successfully generating a robust geological map that clearly discriminates between various Neoproterozoic basement rock units in the El Ineigi area. Through the integration of diagnostic spectral signatures with field verification, we confidently identified all major mappable units, including metavolcanics, metagabbro-diorite complexes, younger granites, and Wadi deposits. The proposed integrated approach demonstrates superior performance compared to traditional mapping techniques, offering enhanced discrimination precision and operational efficiency. These findings strongly support the combined use of PRISMA hyperspectral data and MLAs for lithological mapping applications.
Exploration
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Solio Marino Arango-Retamozo; Joe Alexis Gonzalez-Vasquez
Abstract
The geochemical and spatial characterization of legacy mine tailings is essential for identifying reprocessing opportunities and informing environmental management. However, the high compositional complexity of polymetallic tailings requires robust multivariate approaches. This study evaluates and compares ...
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The geochemical and spatial characterization of legacy mine tailings is essential for identifying reprocessing opportunities and informing environmental management. However, the high compositional complexity of polymetallic tailings requires robust multivariate approaches. This study evaluates and compares the performance of four unsupervised clustering algorithms Euclidean K-Means, Riemannian K-Means, Gaussian Mixture Model (GMM), and Agglomerative Clustering applied to 927 samples from the Quiulacocha tailings deposit in Peru, using six major elements (Zn, Pb, Cu, Fe, Ag, Au) and spatial coordinates. All methods consistently identified three main geochemical domains. Cluster 1 was enriched in Cu and Au, Cluster 2 in Pb and Fe, and Cluster 3 in Zn, Ag, and Fe. Covariance-based methods (Riemannian K-Means and Agglomerative Clustering) outperformed others in internal validation (Silhouette scores up to 0.58) and consistency (Adjusted Rand Index = 1.00), offering more interpretable and geologically coherent partitions. CLR transformation reduced clustering performance, highlighting the importance of preserving raw geochemical variance for spatial segmentation. These findings demonstrate the effectiveness of multivariate clustering for unraveling compositional heterogeneity in tailings and delineating domains of potential economic value. The approach provides a quantitative framework for supporting reprocessing decisions, reducing risk, and guiding future research on mine waste valorization.
Exploration
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Solio Marino Arango-Retamozo; Joe Alexis Gonzalez-Vasquez; Kevin Daniel Rondo-Jalca
Abstract
The classification of mineral resources significantly impacts mine planning, economic feasibility, and regulatory compliance. Despite its importance, such classification frequently depends on the subjective judgment of the Qualified Person (QP), owing to the absence of internationally standardized technical ...
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The classification of mineral resources significantly impacts mine planning, economic feasibility, and regulatory compliance. Despite its importance, such classification frequently depends on the subjective judgment of the Qualified Person (QP), owing to the absence of internationally standardized technical criteria for delineating resource categories. To mitigate this limitation, an innovative methodology integrating clustering based on Riemannian geometry with machine learning techniques was developed for mineral resource classification. A database of 5,654 composited samples from 185 diamond drill holes in a copper deposit in central Peru was utilized to classify 318,443 blocks. Copper grades were estimated through Ordinary Kriging (RMSE = 0.102; MAE = 0.069), generating geostatistical variables kriging variance, average distance to samples, and number of samples that served as input features for the classification. Clustering was performed using both classical KMeans and Riemannian KMeans, followed by spatial smoothing via XGBoost and Random Forest algorithms. Absolute coordinates were incorporated to address spatial discontinuities in classification outputs. The combination of the Riemannian model with Random Forest produced the highest classification performance, with a Silhouette index of 0.26 and a Davies-Bouldin index of 0.72. The resulting metal content was estimated at 4.24 Mt of copper at 0.44% grade (measured), 6.49 Mt at 0.34% Cu (indicated), and 7.68 Mt at 0.32% Cu (inferred), demonstrating close alignment with QP estimates while exhibiting improved spatial coherence. In summary, the Riemannian-based approach outperformed classical KMeans and conventional classification methods, providing a more robust, objective, and globally consistent alternative.
Exploration
Mohammed A.Amir; Hamzah S. Amir; Mokhtar Farkash
Abstract
Permeability estimation is an essential phase in assessing the hydrocarbon potential within porous media and designing reservoir management methods. Recently, machine learning (ML) methodologies have gained prominence in the prediction of permeability. The initial stage in constructing highly reliable ...
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Permeability estimation is an essential phase in assessing the hydrocarbon potential within porous media and designing reservoir management methods. Recently, machine learning (ML) methodologies have gained prominence in the prediction of permeability. The initial stage in constructing highly reliable ML models is to identify the optimum combinations of input logs, as permeability is a highly sensitive parameter; this step is essential and can influence model accuracy. While feature engineering methods provide valuable insights in selecting suitable input logs, the effectiveness of these logs or their combinations remains underexplored, particularly in the context of high-heterogeneity reservoirs. The current study intends to save time by evaluating the effectiveness of twelve distinct models, each constructed using a Multi-Layer Perceptron (MLP), based on various combinations of input logs using data from the Nubian reservoir, Sirt Basin, Libya. The methodology involved several steps, including preprocessing, splitting, optimization, and validation. The findings demonstrate that single-input logs, mainly the Gamma-ray (GR), bulk density (RHOB), and sonic logs (DT), exhibited higher correlation coefficients compared to the multiple log combinations. The GR model attained the best R² of 0.994, indicating its sensitivity in capturing non-linear relationships. On the other hand, multi-log models achieved variable accuracy, resulting in increased learning complexity. The study highlights the efficiency of selecting the optimal combination of input logs, providing practical guidance for ML-based permeability prediction in heterogeneous reservoirs.
Exploration
Mohammadreza Agharezaei; Ardeshir Hezarkhani
Abstract
Geochemical exploration as an advantageous exploration method mostly deals with anomaly separation and related endeavors. Many experts have suggested various types of anomaly identification methods. The intention of this research is introduction of a new method for separating geochemical anomalies based ...
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Geochemical exploration as an advantageous exploration method mostly deals with anomaly separation and related endeavors. Many experts have suggested various types of anomaly identification methods. The intention of this research is introduction of a new method for separating geochemical anomalies based on the Fibonacci sequence for the first time. The Fibonacci features of datasets were clarified and the method was introduced and applied on a dataset of bore-hole samples of Hired gold deposit located in southern Khorasan province, Iran. The main result of this study is the successfully establishing of a Fibonacci-based procedure that leads to separate geochemical anomalies. The determined thresholds by this method were compared with U-statistics and Concentration-Volume fractal modeling. Evaluation of the results revealed high consistence between the outcomes of the methods. The U-Statistics threshold for background was 105 ppb for gold and the Fibonacci Transformation method’s threshold was 109 ppb. This new method specified 170 ppb for gold moderate anomaly and the C-V fractal determined 169 ppb which are almost the same. The performance of the new method was assessed by calculating misclassification errors. The average total misclassification error was 0.023 which is acceptable and quite reasonable since the methods are fundamentally different. As the other main results of this study, it is confirmed that one of the Fibonacci features which is defined as Fibonacci index (FI) fluctuates among element pairs in the same way that geochemical correlation does. The FI could be considered as a genetic-related factor in geochemical studies and ore evolution researches.
Exploration
Hamed Norouzi; Aliakbar Daya
Abstract
Estimating mineral reserves in exploration or extraction projects is a critical and challenging process. It must be conducted precisely, regardless of the mining scale and mineral type. With the growing significance of mineral resources in economic and industrial development, the importance of adopting ...
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Estimating mineral reserves in exploration or extraction projects is a critical and challenging process. It must be conducted precisely, regardless of the mining scale and mineral type. With the growing significance of mineral resources in economic and industrial development, the importance of adopting advanced technologies in mineral assessment has also surged. Modern spatial grade modeling techniques can play a pivotal role in decision-making processes. This study aims to compare the performance and capabilities of two popular machine learning methods, including Gaussian Process Regression (GPR) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) in spatial grade modeling of copper at the Chehel Kureh Copper deposit. The dataset comprises 42 drill holes with an average copper grade of 0.18%. Each core sample data point includes seven variables: three spatial coordinates (X, Y, and Depth), lead grade, zinc grade, lithology and copper grade, which serves as the target variable. The Gaussian Process Regression (GPR) and Multilayer Perceptron (MLP-ANN) neural network were employed for copper grade estimation. To make a better assessment, the hyperparameters of both models were optimized using the Bayesian Optimization algorithm. The results showed that the Gaussian Process Regression outperformed MLP-ANN, achieving an RMSE of 0.04 and a coefficient of determination (R²) of 0.89 compared to an RMSE of 0.05 and a coefficient of determination (R²) of for MLP-ANN, suggest the superiority of the Gaussian Process Regression method in estimating copper grade spatial variability.
Exploration
Poorandokht Soltani; Amin Roshandel Kahoo; Hamid Hassanpour
Abstract
Seismic methods are among the primary and most effective techniques for hydrocarbon exploration, as they enable comprehensive imaging and interpretation of the Earth's subsurface. However, accurate interpretation of seismic data requires detailed analysis of geological structures, often involving complex ...
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Seismic methods are among the primary and most effective techniques for hydrocarbon exploration, as they enable comprehensive imaging and interpretation of the Earth's subsurface. However, accurate interpretation of seismic data requires detailed analysis of geological structures, often involving complex and subjective decision-making processes. Constructing an initial geological model that aligns with seismic observations is a critical first step, but it is inherently non-unique and heavily influenced by the interpreter’s experience and preferences. Among various subsurface structures, salt domes are of particular interest due to their unique physical characteristics and their critical role in hydrocarbon entrapment, drilling risk management, and subsurface storage applications. Their distinct seismic textures, compared to surrounding sediments, make them identifiable using seismic texture attributes. Nevertheless, the manual delineation of salt dome geobody is a time-consuming and potentially error-prone task, especially given the volume, redundancy, and complexity of the seismic attributes used. To overcome these challenges, we propose a novel unsupervised framework for automatically identifying salt dome geobody in 2D seismic sections. The method begins by extracting a diverse set of seismic texture attributes, including both conventional attributes and novel texture descriptors derived from advanced image analysis techniques. Following attribute extraction, a attribute selection phase using techniques such as Laplacian Score is employed to eliminate redundant, irrelevant, or highly correlated attributes, thereby enhancing model efficiency and interpretability. The reduced set of relevant attributes is then used as input for clustering algorithms based on metaheuristic optimization techniques. These algorithms aim to partition the seismic data into meaningful clusters that correspond to geological attributes, particularly salt domes. Validation against multiple expert interpretations demonstrates the robustness and high accuracy of the proposed method. Results emphasize the capability of unsupervised clustering approaches especially those guided by metaheuristic strategies—in reducing interpretation uncertainty and improving segmentation quality.
Exploration
Omid Robatjazi; Alireza Arab-Amiri; Keyvan Khayer
Abstract
Accurate delineation of subsurface controlling structures within complex geological settings is critical for reliable targeting of hematite mineralization, yet remains challenging. Interpretations relying on a single geophysical dataset typically suffer from limited structural resolution and interpretation ...
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Accurate delineation of subsurface controlling structures within complex geological settings is critical for reliable targeting of hematite mineralization, yet remains challenging. Interpretations relying on a single geophysical dataset typically suffer from limited structural resolution and interpretation ambiguity. This study integrates magnetic and geoelectrical datasets to investigate subsurface structures controlling hematite mineralization in the Aqda area, Yazd Province, Iran. Magnetic data were processed using reduction to the IGRF and several enhancement filters, including vertical and horizontal derivatives, analytic signal, and the Centre for Exploration Targeting (CET) technique. The results revealed five major magnetic anomalies trending northeast–southwest and northwest–southeast, interpreted as fault‑controlled intrusive bodies. Two dominant structural trends identified by CET, NE–SW and NW–SE correspond to the magnetic lineaments and delineate zones of high mineral potential. To validate these structures, seven IP‑RS profiles were acquired and inverted using the smooth‑model approach in RES2DINV software. The integrated resistivity and chargeability sections confirmed the position of the inferred faults and highlighted zones of elevated chargeability consistent with hematite mineralization. The combination of both datasets improved the structural resolution and significantly reduced interpretation ambiguity. This integrated approach demonstrates that the magnetic and geoelectrical methods complement each other and provide an effective tool for delineating mineralized zones in complex geological environments.
Exploration
Hasan Feizi Anhar; Ali Imamalipour; Peyman Afzal
Abstract
Geochemical zoning is a key concept in exploration geochemistry. It provides an effective means of predicting the erosion level of mineralization, distinguishing supra-ore from sub-ore halos, and identifying concealed ore bodies. While classical geochemical zoning methods have been widely applied for ...
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Geochemical zoning is a key concept in exploration geochemistry. It provides an effective means of predicting the erosion level of mineralization, distinguishing supra-ore from sub-ore halos, and identifying concealed ore bodies. While classical geochemical zoning methods have been widely applied for decades, this study introduces an enhanced three-dimensional geochemical zoning model specifically tailored for the Sungun porphyry deposit, based on geochemical data obtained from 264 drill cores comprising a total of 33,368 rock samples. The model is constructed using ratios of factors derived from Staged Factor Analysis (SFA) of ore-related major (Cu and Mo) and minor (Cd, Mn, Pb, Zn, and Ag) elements, and further refined through fractal modeling for classification. Fractal modeling method (C-V) clearly shows four distinct populations and three breakpoints, which to supergene (0.9–1.4%), hypogene (0.6–0.9%), and oxidized zones (0.1–0.6%). The application of the method to the Sungun porphyry system reveals a strong spatial correlation between the zoning index, copper grade distribution, and alteration patterns. SFA effectively separates supra-ore and sub-ore elements, while fractal modeling improves the robustness of zoning classification. Integration of the developed 3D zoning index with copper grade models reveals a clear structural relationship among alteration, geochemical ratios, and copper distribution. The proposed approach enhances the resolution of porphyry deposit zoning, offering improved targeting accuracy and reduced risk in deep drilling exploration.
Exploration
Alireza Sadoughi; Ali Aalianvari; Hamidreza Shahbazian
Abstract
The study investigated how time-dependent viscosity affects the penetration length of cement-based grouts prepared with saline and fresh water. An idealized horizontal fracture, represented by two smooth, parallel, and frictionless plates, was assumed. The grout viscosity, varying over time, was analyzed ...
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The study investigated how time-dependent viscosity affects the penetration length of cement-based grouts prepared with saline and fresh water. An idealized horizontal fracture, represented by two smooth, parallel, and frictionless plates, was assumed. The grout viscosity, varying over time, was analyzed to determine the maximum penetration length under a constant injection period. A fracture model was developed and meshed in Gambit, and the two-phase fluid behavior with time-dependent viscosity was simulated in ANSYS Fluent. One saline water function and two fresh water functions were examined. The saline grout was tested at 1475 and 1625 kg/m³, while the fresh water grouts were analyzed at 1475 kg/m³. The resulting penetration lengths were 1.384 m and 1.789 m for the fresh water grouts, and 0.789 m and 0.427 m for the saline grouts, respectively. The outcomes reveal that saline water grout penetrates less effectively than fresh water grout. Furthermore, the effect of density was found to be minor compared to viscosity variations, though differences between saline and fresh water systems were clearly evident. This study introduces a stable grout formulation without additives, contrasting with previous research that relied on additives and adjustments to the water-to-cement ratio, which led to grout instability over time. Utilizing CFD simulations, this research models a two-phase water-cement mixture with varying densities and viscosities, treated as a non-Newtonian fluid. Furthermore, the viscosity of the grout over time under hydraulic pressure is examined, providing valuable insights into grout behavior under subsurface conditions.
Exploration
Marco Antonio Cotrina-Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Jorge Chira-Fernandez; Cesar De la cruz-Poma; Solio Marino Arango-Retamozo
Abstract
The sustained increase in mining waste, particularly in the form of tailings, poses a significant environmental and economic challenge, especially in contexts where these deposits retain residual metal content. This study assessed the gold potential of Tailings Deposit I at La Cienega (Peru) by integrating ...
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The sustained increase in mining waste, particularly in the form of tailings, poses a significant environmental and economic challenge, especially in contexts where these deposits retain residual metal content. This study assessed the gold potential of Tailings Deposit I at La Cienega (Peru) by integrating geostatistical estimation and machine learning models optimized through metaheuristic algorithms. The methodology involved geochemical characterization, three-dimensional estimation using Ordinary Kriging (OK) as a geostatistical method, and prediction of gold grades through three models: XGBoost optimized with Particle Swarm Optimization (XGB+PSO), Support Vector Regression with Genetic Algorithm (SVR+GA), and Random Forest optimized using Ant Colony Optimization (RF+ACO). Estimates were validated using Leave-One-Out cross-validation and performance metrics including RMSE, MAE, Bias, and correlation coefficient (R). The RF+ACO model achieved an RMSE of 0.32 ppm, MAE of 0.24 ppm, Bias of 0.006, and an R value of 0.56. Average predicted grades ranged from 1.14 to 1.33 ppm, with estimated gold contents between 981.00 and 1,147.12 ounces, while OK yielded 1,028.77 ounces at an average grade of 1.19 ppm. These findings suggest that properly optimized machine learning models can provide reasonable estimates of metal content in tailings, particularly in settings characterized by high spatial heterogeneity and limited geological continuity.
Exploration
Marco Antonio Cotrina-Teatino; Jairo Jhonatan Marquina-Araujo
Abstract
Integrating entropy-based uncertainty analysis with machine learning offers a novel approach to improving lithological classification in mineral exploration. This study applies supervised algorithms to predict lithology from spatial and geochemical data collected at a gold deposit in northern Peru. The ...
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Integrating entropy-based uncertainty analysis with machine learning offers a novel approach to improving lithological classification in mineral exploration. This study applies supervised algorithms to predict lithology from spatial and geochemical data collected at a gold deposit in northern Peru. The dataset includes 2,129 composited samples from 140 drillholes, containing spatial coordinates (East, North, Elevation) and gold content (Au). Six classifiers were tested: Random Forest, XGBoost, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, and Multilayer Perceptron. Stratified five-fold cross-validation was applied to a 70/30 train-test split. The best performance was achieved by ANN-MLP (94.5% accuracy) and XGBoost (93.9%), with F1-scores above 94%. In zones of low uncertainty, models reached up to 100% precision, while accuracy dropped to 71.9% in highly uncertain regions. Entropy-based uncertainty mapping highlighted areas of geological ambiguity, such as lithological boundaries or sparsely sampled zones. The Friedman test confirmed statistically significant differences among classifiers (p < 0.001). These findings demonstrate that combining machine learning with spatial uncertainty quantification enhances both predictive reliability and geological interpretability, offering a practical tool for guiding exploration and reducing risk in complex mineral systems.
Exploration
Abbas Bahroudi; Salman Farahani
Abstract
The increasing depletion of near-surface ore deposits and the growing complexity of subsurface geological environments have intensified the need for data-driven, three-dimensional frameworks in mineral exploration. This study introduces an integrated 3D ore prospectivity modeling approach that combines ...
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The increasing depletion of near-surface ore deposits and the growing complexity of subsurface geological environments have intensified the need for data-driven, three-dimensional frameworks in mineral exploration. This study introduces an integrated 3D ore prospectivity modeling approach that combines a Deep Autoencoder (DAE) with Monte Carlo Dropout (MCD)-based uncertainty quantification to generate both high-resolution prospectivity predictions and robust estimates of model confidence. A multi-source geoscientific dataset—comprising geology, geochemistry, geophysics, and borehole information—from the Siahcheshmeh intrusion-related gold system in northwestern Iran was voxelized into a unified 3D grid. The multi-scale convolutional DAE architecture effectively learned latent spatial patterns associated with alteration zones, structural intersections, and geophysical anomalies, while 50 stochastic forward passes via MCD enabled the decomposition of aleatoric and epistemic uncertainties. The proposed DAE–UQ model achieved an accuracy of 96.8% and an ROC-AUC of 0.96, outperforming conventional autoencoders, CNNs, and Random Forest models by 4–5%. High-prospectivity regions (>0.72) accounted for only 24% of the model volume yet captured 68% of mineralized borehole intercepts. Uncertainty analysis revealed elevated uncertainty at the margins of data-sparse zones, and excluding high-uncertainty voxels increased prediction accuracy to 98.6%. The spatial correspondence between high-prospectivity voxels, Au–Cu anomalies, silicification halos, and transpressive fault systems validates the geological reliability of the model outputs. Overall, the DAE–UQ framework offers a scalable, uncertainty-aware solution for 3D mineral prospectivity analysis in structurally complex metallogenic terrains. Its strong generalizability and robustness highlight its potential for application to other deposit types and emerging multi-source geoscience datasets.
Exploration
Reza Moezzi nasab; Alireza Arab Amiri; Abolghasem Kamkar-Rouhani; Meysam Davoodabadi Farahani
Abstract
Mineral prospectivity modeling in structurally complex and vertically heterogeneous geological systems requires analytical frameworks capable of capturing nonlinear feature interactions and depth-dependent variability. This study evaluates the predictive performance of a deep self-attention neural network ...
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Mineral prospectivity modeling in structurally complex and vertically heterogeneous geological systems requires analytical frameworks capable of capturing nonlinear feature interactions and depth-dependent variability. This study evaluates the predictive performance of a deep self-attention neural network within a fully 3D mineral prospectivity modeling framework applied to the Chah-Mousa copper deposit, Iran. The modeling domain was discretized into twenty-one independent elevation levels to assess depth-consistent predictive behavior. Model performance was evaluated using ROC–AUC analysis, confusion-matrix-derived metrics, and success-rate curve assessment. The deep self-attention model achieved a mean ROC–AUC of approximately 0.83, indicating strong discriminative capability between mineralized and non-mineralized domains. Averaged across elevation slices, classification performance remained stable (Accuracy ≈ 0.83, Precision ≈ 0.69, Recall ≈ 0.75, F1-score ≈ 0.72), demonstrating vertical generalization and resistance to shallow overfitting. Success-rate analysis revealed that more than 50% of known mineralized occurrences are concentrated within the top 10% of predicted prospectivity areas, confirming strong ranking efficiency for exploration prioritization. The probabilistic outputs exhibit spatial coherence aligned with structural corridors and alteration zones, indicating that the attention mechanism effectively captures nonlinear geological relationships. The results demonstrate that deep self-attention architectures provide statistically robust, depth-consistent, and operationally meaningful predictions for 3D mineral exploration targeting in structurally controlled copper systems.
Exploration
Abdelhamid Bajadi; Driss El Azzab; Anas Driouch; Mohammed ouchchen; Mohammed Jalal TAZI
Abstract
The Bou Azzer–El Graara inlier, located in Morocco’s central Anti-Atlas, is well known for its significant cobalt mineralization, genetically associated with a Pan-African serpentinized ultrabasic ophiolitic massif. In this context, a structural study was conducted in the Aït Ahmane ...
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The Bou Azzer–El Graara inlier, located in Morocco’s central Anti-Atlas, is well known for its significant cobalt mineralization, genetically associated with a Pan-African serpentinized ultrabasic ophiolitic massif. In this context, a structural study was conducted in the Aït Ahmane area, situated at the eastern end of the Bou Azzer mining district, with the aim of analyzing structural lineaments, which constitute a fundamental tool in geological mapping and mineral exploration. The methodological approach is based on the interpretation of multispectral remote sensing data to map surface lineaments and compare them with structures observed underground. The processing applied to the Landsat 8 OLI imagery includes radiometric and atmospheric corrections, followed by principal component analysis (PCA), which enhances the discrimination of linear structures and allows the production of reliable lineament maps. In parallel, underground geological mapping was carried out in the F53 vein deposit, at two lower exploitation levels, to characterize mineralized structures at depth. The integration of surface and subsurface datasets highlights two main structural families. The first, trending N–S to NE–SW, is associated with cobalt-bearing structures hosted within diorites. The second, oriented NW–SE to WNW–ESE, corresponds to cobalt-mineralized tectono-lithological contacts between serpentinites, basic rocks, and diorites. The correlation between surface-mapped lineaments and deep-seated structures is significant, emphasizing the structural continuity between the surface and subsurface domains.
Exploration
Naresh Kumar Katariya; Bhanwar Singh Choudhary
Abstract
Slope stability and bench safety in iron ore open-pit mines in western India are comprehensively analysed in this research. To evaluate current mining conditions and identify areas at risk, the study integrates comprehensive field observations, laboratory testing, and advanced slope stability modelling ...
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Slope stability and bench safety in iron ore open-pit mines in western India are comprehensively analysed in this research. To evaluate current mining conditions and identify areas at risk, the study integrates comprehensive field observations, laboratory testing, and advanced slope stability modelling using Slide 6.0 software. Factors of safety (FOS) of various mining sections varied from 0.475 to 1.495, as per limit equilibrium analysis with Slide 6.0. This signifies the presence of possibly unstable slopes that require certain stabilisation measures to ensure operational safety. The research considers how significant environmental factors, like temperature, wind speed, rainfall, and soil moisture, influence slope stability in addition to the geotechnical analysis. Rainfall and soil moisture were found to have a high and statistically significant positive correlation (Pearson correlation = 0.706, p = 0.005), implying that an increase in rainfall results in increased soil moisture content, which in turn affects the behaviour of slopes. Also, a moderate degree of negative relationship between temperature and wind speed was revealed (partial correlation = -0.593, p = 0.042), meaning that smaller wind speeds are characteristically associated with increased temperatures. These findings highlight the importance of continuous monitoring of the environment in open-pit mine operations and the importance of considering environmental factors when assessing slope stability. The information collected in this study provides a solid foundation for developing valuable recommendations intended to enhance safety, better control slopes, and promote the long-term development of mining activities in the region.
Exploration
Marco Antonio Cotrina-Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Solio Marino Arango-Retamozo; Joe Alexis Gonzalez-Vasquez
Abstract
Traditional geostatistical methods such as kriging exhibit limitations by assuming linear and symmetric dependencies, which can lead to smoothed estimates and the loss of local variability. To address these issues, this study applies Archimedean copulas (Clayton, Gumbel, and Frank) for the estimation ...
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Traditional geostatistical methods such as kriging exhibit limitations by assuming linear and symmetric dependencies, which can lead to smoothed estimates and the loss of local variability. To address these issues, this study applies Archimedean copulas (Clayton, Gumbel, and Frank) for the estimation of copper ore grades in a deposit located in Peru. A total of 5,654 composites, each 15 meters in length, were obtained from 185 diamond drill holes. The data were transformed to a uniform scale to allow for copula fitting. Dependence structures were modeled by lag distance, with the dependence parameter fitted using fifth-degree polynomials, and three-dimensional conditional estimation was implemented. Results indicate that ordinary kriging yielded RMSE = 0.161, MAE = 0.104, R2 = 0.692, and a correlation of 0.861. The Clayton copula slightly improved these metrics (RMSE = 0.154, MAE = 0.101, R2 = 0.717, R = 0.871), while the Gumbel copula captured higher local variability (RMSE = 0.161, MAE = 0.116, R2 = 0.692, R = 0.855). The Frank copula achieved the best performance with RMSE = 0.137, MAE = 0.090, R2 = 0.778, and R = 0.905. In conclusion, Archimedean copulas significantly enhance geostatistical estimation by better capturing spatial dependence, offering a robust alternative to classical geostatistical methods.
Exploration
Ukpata Austin Odo; Jude S Ejepu; Bernd Striewski
Abstract
The mining sector must address the growing challenges of resource management, safety issues, and environmental impact concerns. All stages of the mining life cycle need essential geospatial technologies to address the mentioned challenges. This article examines how Geographic information systems (GIS), ...
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The mining sector must address the growing challenges of resource management, safety issues, and environmental impact concerns. All stages of the mining life cycle need essential geospatial technologies to address the mentioned challenges. This article examines how Geographic information systems (GIS), remote sensing (RS), LiDAR, drone mapping, and positioning systems find applications in mineral exploration, mine planning, operational monitoring, and post-mining rehabilitation. Artificial intelligence (AI) and machine learning (ML) systems enhance the functional potential of these technologies through predictive modeling capabilities, which work in conjunction with real-time analytic functions. The research shows that these technologies enable better decision-making, performance optimisation, and environmental risk reduction. Modern mining relies entirely on these technologies because they support accurate resource assessment, optimise design operations, and help enforce safety standards and environmental codes. Adopting such technologies requires resolving implementation costs, addressing data integration issues, and acquiring the necessary technical expertise. The future development of mining technology should focus on enhancing the integration of geospatial information platforms, creating sustainable solutions for medium-sized mining operations at affordable prices, and developing predictive evaluation systems utilizing AI algorithms. The mining industry accomplishes safer operation methods through efficient technologies, enhancing sustainability.
Exploration
Joshua Chisambi; Leornard Kalindekafe; Kettie Magwaza; Ruth Mumba; Martin Kameza
Abstract
The Nathenje region in central Malawi hosts significant gold mineralization within high-grade metamorphic rocks of the Mozambique Belt, yet remains underexplored despite extensive artisanal mining activity. The structural controls on primary bedrock gold mineralization within these high-grade metamorphic ...
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The Nathenje region in central Malawi hosts significant gold mineralization within high-grade metamorphic rocks of the Mozambique Belt, yet remains underexplored despite extensive artisanal mining activity. The structural controls on primary bedrock gold mineralization within these high-grade metamorphic rocks remain poorly understood, limiting systematic exploration and resource development. We conducted integrated field mapping, structural analysis, petrographic examination, and geochemical sampling to characterize gold mineralization controls in the Nathenje prospect, central Malawi. Detailed structural measurements combined with stereographic analysis reveal three deformation phases, with gold mineralization predominantly associated with D₂ transpressional structures. Fire assay results demonstrate significant gold concentrations (0.15–5.0 g/t Au) in arsenopyrite-bearing quartz veins, with the highest grades systematically occurring at structural complexity zones. Petrographic analysis reveals native gold particles (5–50 μm) intimately associated with arsenopyrite along grain boundaries and within microfractures, indicating coupled precipitation processes. Critically, we identify a hierarchical structural control system operating from regional NE-SW trending shear zones to microscale sulphide boundaries, with fold hinges, dilutional jogs, and amphibolite-gneiss contacts yielding consistently higher gold grades (>3 g/t Au) than other structural settings. Our results establish the first comprehensive structural model for gold mineralization in central Malawi's metamorphic terrain and provide specific targeting criteria applicable to similar high-grade metamorphic environments throughout the East African Orogen.
Exploration
Shirin Jahanmirir; Ali Aalianvari; Hossein Ebrahimpour-Komleh
Abstract
This paper introduces the Human Mental Search (HMS) algorithm as a novel and superior optimization technique for predicting groundwater seepage in tunnel environments. Traditional methods for predicting such seepage often struggle with the complexities of subterranean water flow, particularly in heterogeneous ...
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This paper introduces the Human Mental Search (HMS) algorithm as a novel and superior optimization technique for predicting groundwater seepage in tunnel environments. Traditional methods for predicting such seepage often struggle with the complexities of subterranean water flow, particularly in heterogeneous geological conditions, and while machine learning approaches have offered improvements, they often require significant computational resources. The HMS algorithm, inspired by human cognitive processes, employs memory recall, adaptive clustering, and strategic selection to efficiently refine solutions. Our results demonstrate that HMS significantly outperforms established algorithms in predicting groundwater seepage, achieving an R² value of 0.9988, a Mean Squared Error (MSE) of 0.0002, and a Root Mean Squared Error (RMSE) of 0.0137. In comparison, the Whale Optimization Algorithm (WOA) achieved an R² of 0.9951 with much higher MSE and RMSE, and other methods, like Genetic Programming and ANN-PSO, show higher error values. The HMS algorithm also showed a Variance Accounted for (VAF) of 99.88% and a Mean Absolute Error (MAE) of 0.0041, further validating its high predictive accuracy. This study highlights the HMS algorithm’s superior performance and computational efficiency for optimizing groundwater seepage predictions, positioning it as a powerful tool for geotechnical engineering applications, including real-time monitoring.
Exploration
Shaghayegh Esmaeilzadeh; Ali Moradzadeh; omid Asghari; Reza Mohebian
Abstract
Seismic inversion is a critical technique for estimating the spatial distribution of petro-elastic properties in the subsurface, based on the seismic reflection data. This work introduces an iterative geostatistical seismic inversion method, designed to address challenges in complex geological settings ...
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Seismic inversion is a critical technique for estimating the spatial distribution of petro-elastic properties in the subsurface, based on the seismic reflection data. This work introduces an iterative geostatistical seismic inversion method, designed to address challenges in complex geological settings by incorporating self-updating local variogram models. Unlike the conventional approaches that rely on a single global variogram or fixed local variograms, the proposed method dynamically updates the spatial continuity models at each iteration using automatic variogram modeling and clustering of variogram parameters. The optimal number of clusters is determined using three cluster validity indices: Silhouette Index (SI), Davies-Bouldin Index (DB), and Calinski-Harabasz Index (CH). The method’s effectiveness was evaluated using a three-dimensional non-stationary synthetic dataset, demonstrating robust convergence when employing the SI and CH indices, with both achieving a high global correlation coefficient of 0.9 between the predicted and true seismic data. Among these, the CH index provided the best balance between the computational efficiency and inversion accuracy. The results highlight the method’s ability to effectively capture local spatial variability, while maintaining a reasonable computational cost, making it a promising approach for seismic inversion in complex sub-surface environments.
Exploration
Seyedeh Golaleh Hosseini; Kourosh Shahriar; Mohammadamin Karbala
Abstract
Mine drainage remains a critical challenge in ensuring the safety and sustainability of mining operations, as it is often complicated by complex subsurface flow behaviors and mechanical stress interactions. This study proposes an integrated three-phase framework for analyzing and optimizing drainage ...
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Mine drainage remains a critical challenge in ensuring the safety and sustainability of mining operations, as it is often complicated by complex subsurface flow behaviors and mechanical stress interactions. This study proposes an integrated three-phase framework for analyzing and optimizing drainage systems at the Angouran lead–zinc mine. In the first phase, the hydro-mechanical behavior of the rock mass was simulated using UDEC software, demonstrating that increased normal stress reduces fracture aperture and permeability. The simulated pore pressure (4.5×10⁵ Pa) closely matched the field measurements (4.4×10⁵ Pa), with only a 2.2% deviation. In the second phase, a multi-criteria decision-making approach using the Analytic Hierarchy Process (AHP) and input from 32 domain experts identified Q4 (very high quality) and Q2 (medium quality) indicators as the most influential criteria. In the third phase, three machine learning models—linear regression, polynomial regression, and artificial neural networks (ANNs)—were trained on piezometric data to predict water discharge. The ANN model outperformed the other models, achieving an R² of 0.94 and RMSE of 0.18, effectively capturing the nonlinear dynamics of groundwater flow within the mine. The findings highlight that the integration of numerical modeling, expert-based decision analysis, and AI-driven prediction provides a robust and innovative approach for designing and managing mine dewatering systems, with potential applicability to other complex hydrogeological environments.
Exploration
Balbir Nagal; Ajay Krishna Prabhakar; Mahesh Pal
Abstract
This study delineates groundwater potential (GWP) zones across Haryana, India, for the year 2023 using geospatial techniques integrated with the analytical hierarchy process (AHP). Multiple thematic layers, including slope, land use/land cover (LULC), soil, geology, drainage density (DD), lineament density ...
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This study delineates groundwater potential (GWP) zones across Haryana, India, for the year 2023 using geospatial techniques integrated with the analytical hierarchy process (AHP). Multiple thematic layers, including slope, land use/land cover (LULC), soil, geology, drainage density (DD), lineament density (LD), elevation, rainfall, and topographic wetness index (TWI), were generated using datasets from SRTM, Sentinel-2, food and agriculture organization (FAO), and the India meteorological department (IMD) and weighted through the AHP. These layers were integrated using weighted overlay analysis (WOA) to generate the final GWP map. The GWP map was validated against field groundwater level (GWL) data from 646 wells recorded in 2018 by the central ground water board (CGWB), resulting in an accuracy of 77.55 percent. This confirmed the reliability of the geographic information system (GIS) and AHP technique. The study reveals that moderate GWP zones dominate (43.71%) the region, followed by high (33.24%) and very high (11.96%) zones, whereas low and very low GWP zones cover 7.59% and 3.51% of the area, respectively. The findings indicate that Haryana’s groundwater distribution is largely stable, with minor variation observed between 2018 and 2023. This shows stable aquifer behaviour and relatively unchanged recharge and extraction patterns over the five-year period. The outcomes of this study are valuable for strategic groundwater management, especially in arid and semiarid regions of Haryana state.
Exploration
Satyajeet Parida; Abhishek Kumar Tripathi; Tarek Salem Abdennaji; Yewuhalashet Fissha
Abstract
Coal quality is predominantly determined by its Gross Calorific Value (GCV), which directly influences its economic valuation. Traditional empirical formulas for GCV estimation, though effective, become inefficient and laborious when handling large datasets. To address this, machine learning (ML) techniques ...
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Coal quality is predominantly determined by its Gross Calorific Value (GCV), which directly influences its economic valuation. Traditional empirical formulas for GCV estimation, though effective, become inefficient and laborious when handling large datasets. To address this, machine learning (ML) techniques offer a robust alternative for accurate and rapid predictions. This study employs seven coal quality parameters. Total Moisture (TM), Ash (ASH), Volatile Matter (VM), Hydrogen (H), Carbon (C), Nitrogen (N), and Sulphur (S), as independent variables to develop predictive models for GCV. Four conventional regression techniques, namely Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT), along with two robust regression models Random Sample Consensus (RANSAC) and Huber Regressor (HR) are explored. The dataset comprises coal samples from five Asia-Pacific countries: China, Indonesia, Korea, the Philippines, and Thailand. Comparative performance analysis reveals that the robust regression models significantly outperform the conventional ML techniques. The RANSAC and Huber Regressor models achieve superior prediction accuracy with R² values of 0.9941 and 0.9952, respectively. These findings highlight the potential of robust regression approaches for reliable GCV estimation, facilitating efficient coal quality assessment in large-scale applications.
Exploration
Hamid Geranian; Mohammad Amir Alimi
Abstract
This study employs Sentinel-2 satellite images along with the random forest algorithm to create a regional geological map. For this purpose, the independent variables consist of the images for 10 Sentinel-2 bands of the Khosuf-I region, while the class labels consist of a geological map of Khosuf-I divided ...
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This study employs Sentinel-2 satellite images along with the random forest algorithm to create a regional geological map. For this purpose, the independent variables consist of the images for 10 Sentinel-2 bands of the Khosuf-I region, while the class labels consist of a geological map of Khosuf-I divided into three and fifteen rock units. The classification accuracy of the resulting model is 90.97 and 84.85% for the three-class training and testing data, and 94.76 and 63.92% for the fifteen-class training and testing data, respectively. These models are then applied to the Sentinel-2 satellite images' data of the Birjand-IV region to prepare two preliminary geological maps. The Birjand-IV region's three-class geology map reveals that igneous rocks are present in the northern and southern regions, while sedimentary rocks occupy the middle section and metamorphic rocks are found within the region's igneous masses. Similarly, the fifteen-class geology map of Birjand-IV indicates that andesite, dacite, intermediate tuff rock units, and metamorphic rocks characterize the northern region. Conversely, the southern part of the region is mainly composed of ophiolite, flysch sediments, basaltic and ultra-basic volcanic rocks, and limestone and shale interlayers. Field studies in three areas confirm the accuracy of the preliminary geology maps.
