Original Research Paper
Rock Mechanics
P GANESAN; Ritesh D Lokhande; Siddhartha Roy; Hemant Agrawal
Abstract
Subsidence associated with underground coal mining is a significant geotechnical concern in many coal-producing regions. The extraction of coal over large areas from underground often leads to the collapse of overlying strata into the goaf, subsequently causing surface subsidence. The extent of this ...
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Subsidence associated with underground coal mining is a significant geotechnical concern in many coal-producing regions. The extraction of coal over large areas from underground often leads to the collapse of overlying strata into the goaf, subsequently causing surface subsidence. The extent of this subsidence varies widely across mines, depending on several factors, including mine geometry, geological discontinuities, physico-mechanical properties of the overlying strata, extraction method, seam thickness, and depth of working. Among these, the angle of draw (AoD) plays a critical role in delineating the subsidence-affected zone, particularly in underground coal mining. Accurate prediction of AoD is essential for safe mine planning and the mitigation of subsidence-related hazards. In the present study, a comprehensive field investigation was conducted to collect mine operational parameters from various underground coal mines. Using this dataset, Genetic Programming (GP) was employed to model the relationship between AoD and key mining and geological parameters. The developed GP model demonstrated a strong correlation between predicted and measured AoD values, with a coefficient of determination (R) = 0.7921, highlighting the model’s predictive capability. Additionally, a sensitivity analysis (SA) was performed to identify the most influential input parameters affecting AoD. The analysis indicated that, while all five input variables significantly impact AoD, the compressive strength of overlying strata exhibited the highest influence (sensitivity score = 0.98). The findings of this study provide a data-driven approach to predict the angle of draw in underground coal mines, offering valuable insights for improved mine design, extraction strategies, and surface infrastructure protection.
Original Research Paper
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.
Original Research Paper
Environment
Tingze Li; Yu Wang; Genyuan Tan
Abstract
Effective gas drainage in coal mines necessitates the precise optimization of borehole parameters to reduce gas pressure and prevent gas outbursts. However, current drilling designs predominantly rely on field experience rather than site-specific quantitative analysis of geological conditions, leading ...
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Effective gas drainage in coal mines necessitates the precise optimization of borehole parameters to reduce gas pressure and prevent gas outbursts. However, current drilling designs predominantly rely on field experience rather than site-specific quantitative analysis of geological conditions, leading to limitations in adaptability. This study establishes a COMSOL-based multiphysics coupling model that integrates stress-permeability interactions, gas adsorption-desorption kinetics, and fracture-induced permeability evolution to evaluate the gas drainage performance of cross-measure boreholes in floor strata. Simulation results indicate that directional borehole spacing is the most influential factor: reducing the spacing from 25 m to 20 m significantly increases gas drainage efficiency by 31.4%, while extending the drainage duration from 90 days to 270 days expands the influence radius by more than 35%. In contrast, variations in borehole diameter (75-115 mm) and negative pressure (10-90 kPa) exert a negligible impact on gas pressure (with a variation of less than 5%), reflecting limited sensitivity. The optimal borehole location is determined to be at the lower boundary of the mining-induced fracture zone. A gradient layer analysis further confirms that the perforation depth should match the range of the plastic deformation zone (15-25 m). The proposed parametric optimization strategy provides a quantitative framework for directional drilling design, enabling the matching of borehole layout with the scale of fracture development. These findings contribute to enhancing the accuracy of gas control and the engineering adaptability of gas drainage systems under complex geological conditions.
Original Research Paper
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.
Original Research Paper
Environment
Snežana Brajević; Aleksandar Simić; Vera Karličić; Nikola Milanović; Monika Stojanova; Blažo Lalević; Željko Dželetović
Abstract
Permanent mining generates substantial amounts of flotation tailings with highly unfavourable physical and chemical properties, often devoid of vegetation. Their stabilization relies on phytoremediation, particularly through the establishment of grass cover. Successful revegetation requires sufficient ...
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Permanent mining generates substantial amounts of flotation tailings with highly unfavourable physical and chemical properties, often devoid of vegetation. Their stabilization relies on phytoremediation, particularly through the establishment of grass cover. Successful revegetation requires sufficient nutrient availability and the activity of soil microorganisms that transform nutrients into plant-accessible forms. However, the interactions between plants, nutrients, and microflora during this process remain poorly understood. This study aimed to investigate the temporal dynamics and interrelationships within the plant–nutrient–microorganism system during the revegetation of flotation waste using four grass species—tall fescue, red fescue, meadow fescue, and perennial ryegrass. Plants were grown under controlled conditions on flotation tailings with different fertilizer treatments: organic (NPK 4:4:4) and mineral (NPK 20:20:20) fertilizers at varying concentrations (1% and 2% O; 0.25% and 0.5% M) and irrigation levels (50% and 75% of field water capacity). Microbial diversity (culturable bacteria, ammonifiers, fungi, and actinomycetes) was used as an indicator of remediation efficiency. Organic fertilization had the most pronounced effect, improving plant height, biomass yield, and microbial activity, particularly in tall fescue. Bacteria and ammonifiers responded positively to mineral fertilization under higher irrigation in red fescue and to organo-mineral treatment under lower irrigation in perennial ryegrass. The highest abundance of actinomycetes occurred under reduced irrigation in red fescue and perennial ryegrass. Overall, perennial ryegrass demonstrated the strongest correlation between cultivation conditions, microbial activity, and phytoremediation potential, highlighting its suitability for the ecological rehabilitation of flotation tailings.
Original Research Paper
Mineral Processing
Ayman M. Ibrahim; Han Wang; Dianwen Liu; Peilun Shen; Hassan A. Osman; Muhannad Alhaj; Mohamed A. Ibrahim; Misbah Khalil
Abstract
In this study, Pb²⁺ and STG were used as effective co-activation reagents before sodium sulfide treatment to assess their effect on smithsonite floatability. The flotation test results showed that sodium sulfide nonahydrate (SSN) and sodium diethyl dithiocarbamate (DDTC) concentrations significantly ...
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In this study, Pb²⁺ and STG were used as effective co-activation reagents before sodium sulfide treatment to assess their effect on smithsonite floatability. The flotation test results showed that sodium sulfide nonahydrate (SSN) and sodium diethyl dithiocarbamate (DDTC) concentrations significantly impacted floatability, achieving maximum recoveries of 89.32% and 93.19%, respectively. XPS analysis confirmed that Na₂S treatment introduced sulfur species onto the smithsonite surface, enhancing collector attachment and facilitating the formation of PbS and ZnS. FTIR analysis further substantiated that co-activation enhances DDTC adsorption via C=S vibrations, thereby increasing the number of active sites available for interaction with the collector relative to direct sulfidation. FESEM-EDS and AFM analysis at pH 9 confirmed that co-activation resulted in the formation of denser, cloud-like layers of PbS and ZnS on the surface. These layers improved flotation efficiency, increased hydrophobicity, and strengthened DDTC interaction, thus promoting flotation. Additionally, ToF-SIMS and EPMA analyses indicated higher Pb⁺ and S⁻ intensities in the co-activation system, confirming enhanced surface reactivity and substantiating the increased activity and diversity of sulfidation products. This study offers an effective approach to enhancing smithsonite flotation recovery by optimizing surface chemistry and collector attachment.
Original Research Paper
Exploitation
Shambhavi sinha; Anup Tripathi; Akhil Avchar; Mritunjay kumar
Abstract
Accurate assessment of rock mass quality in marble quarries remains challenging because conventional empirical classification systems are largely strength-dominated and insufficiently sensitive to discontinuity-controlled block instability. This study proposes a quarry-specific empirical framework, termed ...
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Accurate assessment of rock mass quality in marble quarries remains challenging because conventional empirical classification systems are largely strength-dominated and insufficiently sensitive to discontinuity-controlled block instability. This study proposes a quarry-specific empirical framework, termed the Marble Rock System (MRS), designed to explicitly capture structural, hydro-mechanical, and alteration-driven controls governing bench-scale stability in dimension stone marble quarries. The primary objective was to develop and validate an empirically grounded classification system using machine learning as an independent diagnostic tool rather than as a black-box predictor.A comprehensive geomechanical database comprising 85 quarry-scale records was developed from three active marble quarries in southern Rajasthan, India. Six physically interpretable parameters intact strength, weathering or serpentinization, joint frequency, joint surface condition, groundwater influence, and block stability were incorporated into the MRS framework. Supervised machine learning models, including artificial neural networks, support vector machines, and linear regression, were trained to predict independently derived factors of safety for validation. Model performance was evaluated using coefficient of determination, root mean square error, cross-validation, and classification metrics.Results show that MRS-based models achieved consistently higher predictive accuracy, improved class separability, and more stable generalization than models trained using conventional Rock Mass Rating inputs. Sensitivity analysis revealed that block stability and joint characteristics dominate stability prediction, while intact strength plays a secondary role. These findings confirm that marble quarry slope behaviour is primarily discontinuity-controlled. The proposed MRS provides a physically interpretable, empirically validated framework for quarry-scale stability assessment and offers a robust alternative to conventional classification systems for operational decision-making.
Original Research Paper
Rock Mechanics
Hossein Azad; Hamid Chakeri; Hadi Shakeri
Abstract
Mechanized tunnelling in soft soils often results in ground settlement both around the tunnel and at the surface, which can potentially damage urban infrastructure and surrounding buildings. Several geological and operational factors influence the extent of ground settlement. This paper investigates ...
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Mechanized tunnelling in soft soils often results in ground settlement both around the tunnel and at the surface, which can potentially damage urban infrastructure and surrounding buildings. Several geological and operational factors influence the extent of ground settlement. This paper investigates the actual ground settlement caused by over 10 kilometers of tunnelling along Tabriz Metro Line 2, with a particular focus on the materials and positions of the tunnelling machine. The results show that 55-60% of the total settlements occur behind the shield of the tunnelling machine, which is consistent with Thewes’ (2009) diagram. The surrounding soil was categorized, and using data from settlement pins, the actual Volume Loss (VL) was analyzed across three geological sections consisting of sandy, clayey, and mixed materials. The findings reveal that volume loss in sandy materials is greater than in clayey and mixed soils, at approximately 1.02%. Additionally, the volume loss in mixed soils was calculated to be 0.82%, while in clay soils, it was 0.53%. To assess the impact of different materials on surface settlement, numerical modeling was carried out using Plaxis 3D software. The numerical results, considering volume losses of 1.05% for sandy materials, 0.8% for mixed materials, and 0.5% for clay materials, closely matched the actual settlement data.
Original Research Paper
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.
Original Research Paper
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.
Original Research Paper
Exploitation
Sina Ghavami; Ebrahim Ghasemi; Mohammad Hossein Kadkhodaei; Ali Farhadian
Abstract
Consumption of cutting and polishing tools is a critical economical parameter during quarrying and processing of granitic building stones, which is highly affected by stone abrasivity. So, estimation of abrasivity for these stones is a very important issue. There are several methods to determine the ...
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Consumption of cutting and polishing tools is a critical economical parameter during quarrying and processing of granitic building stones, which is highly affected by stone abrasivity. So, estimation of abrasivity for these stones is a very important issue. There are several methods to determine the stone abrasivity. One of the most commonly used methods is Cerchar abrasivity index (CAI). This study mainly focuses on investigating the relationship between CAI with petrographic and physico-mechanical properties of granitic building stones. For this purpose, 14 different types of commercial granitic building stones, collected from different regions of Iran, were subjected to laboratory investigations and the effect of the petrographic and physico-mechanical properties of these stones on CAI was examined using simple and multiple regression analysis. Meaningful and reasonable relationships were observed. According to the obtained results, equivalent quartz content (EQC) of granitic building stones was found to be the most effective parameter on CAI. Using linear and nonlinear regression analysis, two empirical correlations for CAI prediction based on EQC were developed. The results showed that both linear and nonlinear correlations have high performance with determination coefficients (R2) of 0.876 and 0.882, respectively. These correlations can determine the CAI with acceptable error, with root mean square error (RMSE) and mean absolute error (MAE) values of 0.135 and 0.105, respectively. Furthermore, the relationship between the diamond segment wear (SW) and CAI was investigated for the studied stones. The results showed that SW is directly related to the CAI, and there is a strong linear correlation between these two parameters with R2 of 0.787. The proposed correlation can be applied for fast prediction of cutting tool wear for practical applications in building stone processing plants with circular sawing machine, which can lead to enhanced cutting efficiency and productivity.
Original Research Paper
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.
Original Research Paper
Exploitation
Mehrnaz Mohtasham
Abstract
In open-pit mining, haulage equipment accounts for a significant portion of total operating costs. Optimizing fleet performance is therefore crucial for reducing costs and improving productivity. Within this system, loading equipment plays a key role, as truck efficiency depends heavily on loader performance. ...
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In open-pit mining, haulage equipment accounts for a significant portion of total operating costs. Optimizing fleet performance is therefore crucial for reducing costs and improving productivity. Within this system, loading equipment plays a key role, as truck efficiency depends heavily on loader performance. The match factor, a metric that evaluates compatibility between loaders and trucks, is commonly used to enhance fleet efficiency. However, many existing approaches fail to account for practical mining conditions such as equipment downtime, accurate truck cycle times, and material fragmentation resulting from blasting. These omissions can lead to inaccurate fleet performance evaluations and higher operational costs. This study proposes an improved match factor method that incorporates these critical variables. It includes equipment downtime, truck cycle time estimates based on travel routes, and material fragmentation. The model applies to both homogeneous and heterogeneous fleet configurations and integrates the operational efficiency coefficient of each machine to reflect real conditions more accurately. The model was tested using data from the Sungun copper mine. The match factor values were calculated both with and without accounting for equipment downtime, and loader capacities were adjusted according to the size distribution of blasted material. Results showed that in heterogeneous fleet operations, the match factor increased from 0.74 to 0.85 when operational efficiency was included. Subsystem analyses also revealed match factor values below 1, indicating a need for additional trucks. Overall, the enhanced model enables more efficient equipment use, reduces loader idle time, and contributes to substantial operating-cost savings.
Original Research Paper
Exploitation
saeideh Qaedrahmat; Javad Gholamnejad; Ali dabagh
Abstract
The scheduling of short-term production in open-pit mining requires determining an optimal extraction sequence for blocks to fulfill multiple goals over a short-term monthly, weekly and daily planning horizon. These goals include meeting required limits on ore grade, production tonnage, waste removal, ...
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The scheduling of short-term production in open-pit mining requires determining an optimal extraction sequence for blocks to fulfill multiple goals over a short-term monthly, weekly and daily planning horizon. These goals include meeting required limits on ore grade, production tonnage, waste removal, and slope constraints. One of the key objectives of Short-Term Production Scheduling (STPS) is to ensure a stable and continuous supply of ore to the processing plant, while minimizing operating costs through measures such as reducing unnecessary equipment movements and variation in feed quality. However, one of the major obstacles to the operational feasibility of STPS is the limited working space available for equipment, as well as the excessive equipment movement between benches within each scheduling period. To tackle these challenges, this paper employs an Integer Goal Programming (IGP) with a new constraint that limits active benches per period, enhancing the practicality of production schedules. Unlike previous GP-based STPS models, it improves operational feasibility by ensuring extraction continuity and minimizing equipment movement. The model was tested on a copper deposit using GAMS software. The results show that by applying this new constraint, the average number of active benches per month was reduced from 14 to 10 )36% reduction) and the number of extraction periods per bench from 6 to 4 (33% reduction) without violating the existing constraints such as ore grade, tonnage, or slope. This approach improves equipment efficiency, reduces fuel consumption, reducing equipment relocation costs, promoting operational continuity of extraction and enhances operational feasibility in real conditions.
Original Research Paper
Rock Mechanics
Navid Afrasiabi; Mehdi Noroozi; Ahmad Ramezanzadeh
Abstract
In this research, the effect of geometric parameters of closely joints on rock cutting efficiency by TBM disc cutter is studied using PFC3D software. A validated numerical model of linear cutting machine test is developed and the efficiency of disc cutter is investigated on rock mass specimens with different ...
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In this research, the effect of geometric parameters of closely joints on rock cutting efficiency by TBM disc cutter is studied using PFC3D software. A validated numerical model of linear cutting machine test is developed and the efficiency of disc cutter is investigated on rock mass specimens with different joint configurations (possible combination of dip angles of 30, 60, 90 degrees with joint spacings of 3, 5, 10, 15, 20 cm). Numerical modeling results reveal that in general, the joint spacing has a greater effect on rock cutting efficiency than joint orientation. If the joint spacing is less than 10 cm, the role of the joint angle is reduced and the distances between the joints control the efficiency. When the joints are close together and have a spacing of less than 10 cm, particularly 3 to 5 cm, the best cutting efficiency can be achieved for a joint angle of 90 degrees. The cutting coefficient is decreased by increasing the joint spacing and the maximum CC occurs at a joint spacing of 5 cm. For joint spacing more than 10 cm, the joints with a 90 degrees dip angle have the greatest impact on the specific energy and reduce cutting efficiency. The best disc cutter efficiency and the minimum required normal force is achieved when joint spacing is more than 10 cm and the angle between the joints and advance direction of the disc cutter is 60 degrees. In the tunnel excavation process, with increasing joint spacing, the TBM machine thrust is more important than its torque. The findings of this research provide a basis for predicting TBM efficiency through joint characteristics.
Original Research Paper
Exploitation
Mohammad Reza Rezaei; Majid Noorian-Bidgoli
Abstract
Drilling and blasting are crucial operations in open-pit mining, aimed at optimizing rock fragmentation, minimizing negative effects like backbreak and flyrock, and reducing costs, while enhancing efficiency and minimizing environmental and infrastructure impacts. This study focuses on optimizing drilling ...
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Drilling and blasting are crucial operations in open-pit mining, aimed at optimizing rock fragmentation, minimizing negative effects like backbreak and flyrock, and reducing costs, while enhancing efficiency and minimizing environmental and infrastructure impacts. This study focuses on optimizing drilling and blasting patterns at the Miduk copper mine using Multi-Criteria Decision-Making (MCDM) methods. The primary objectives were to achieve optimal fragmentation, minimize specific charge and drilling costs, and reduce undesirable phenomena like backbreak and flyrock caused by blasting. A total of 52 blasting patterns implemented at the mine were evaluated using various MCDM techniques, including TOPSIS, ELECTRE, VIKOR, and COCOSO. By constructing decision matrices and ranking the alternatives in each method, the most suitable blasting pattern was identified. The Copeland method was further applied to integrate the results from the decision models and establish a consensus on the final ranking of blasting patterns based on the criteria. The study's innovation lies in the application of advanced MCDM techniques to optimize drilling and blasting patterns, as well as the integration of results to enhance the decision-making process's accuracy. The optimal blasting pattern (M_Patt_03) was found to feature a burden of 6.5 meters, a spacing of 8 meters, and a borehole diameter of 150 millimetres, offering the best balance of fragmentation, charge efficiency, and drilling costs, while minimizing backbreak and flyrock. This study demonstrates the effectiveness of MCDM methods in optimizing complex engineering challenges in surface mining, providing a comprehensive framework for evaluating multiple criteria simultaneously and enabling more informed and balanced decision-making.
Original Research Paper
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.
Original Research Paper
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.
Original Research Paper
Rock Mechanics
Shadman Mohammadi Bolbanabad; Masoud Monjezi; Vahab Sarfarazi
Abstract
The characteristics of fragment size distribution caused by blasting operations in open-pit mines have a direct impact on the economic performance and productivity of mining companies. In this study, dynamic impact loading tests were carried out using the Split Hopkinson Pressure Bar (SHPB) system under ...
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The characteristics of fragment size distribution caused by blasting operations in open-pit mines have a direct impact on the economic performance and productivity of mining companies. In this study, dynamic impact loading tests were carried out using the Split Hopkinson Pressure Bar (SHPB) system under a constant pressure of 12.5 MPa to investigate the influence of both the edge notch length and its position relative to the incident bar on the size distribution of fragmented iron ore. By analyzing the fragmentation distribution characteristics of specimens subjected to controlled laboratory impact loading, this study focuses on fundamental rock breakage mechanisms relevant to blasting operations in open-pit iron ore mines, where the fragmented material classified into three particle size categories: large, medium, and fine fragments. Based on this classification, the variation in the mass percentage of fragments with respect to notch length and its position relative to the incident bar was investigated. Ultimately, within the context of laboratory-scale fragmentation analysis, an effective range of notch lengths and positions relative to the incident bar was identified for achieving optimal fragmentation. The results revealed that a notch length between 0.2 and 0.4 and a notch position between L/2 and 2L/3 from the incident bar (where L equals sample length), produced the most favorable fragment size distribution. These findings can help link laboratory-scale fracture behavior to field-scale rock fragmentation considerations and contribute to a broader understanding of breakage processes in mining engineering.
Original Research Paper
Environment
Fatemeh Vesmoridi; Feridon Ghadimi
Abstract
A total of 400 stream sediment samples were analyzed for 13 elements, and stepwise factor analysis was employed to generate geochemical maps indicative of mineralization. This method was utilized to develop a Geochemical Mineralization Probabilistic Index (GMPI) through a novel approach that produces ...
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A total of 400 stream sediment samples were analyzed for 13 elements, and stepwise factor analysis was employed to generate geochemical maps indicative of mineralization. This method was utilized to develop a Geochemical Mineralization Probabilistic Index (GMPI) through a novel approach that produces geochemical evidence maps derived from stream sediment data. The study comprised a three-stage factor analysis of geochemical data collected from the Khomain Dehno region. The first factor included Zn, Pb, As, and Cd, accounting for 41.63% of the variance. The second factor comprised Mn, Mo, and Zr, explaining 21.86% of the variance, while the third factor consisted of Fe, Cu, and Ti, representing 7.79% of the variance. The cumulative variance explained by these three factors was 81%. Furthermore, a novel intelligent methodology, termed Relevant Vector Regression (RVR), enhanced with Cocoa Search (CS) and Harmony Search (HS) algorithms, is proposed for the prediction of the GMPI. The HS and CS algorithms were integrated with the RVR model to optimize its hyperparameters. In these models, Zn, Pb, As, and Cd served as input variables, while the GMPI was designated as the output variable. The performance of the predictive models was evaluated using Mean Squared Error (MSE) and the Coefficient of Determination (R²). The results indicated that the RVR model optimized with the HS algorithm exhibits superior performance, achieving an R² value of 0.99256 and an MSE of 0.0031455. These findings underscore the efficacy of the proposed approach for accurate GMPI estimation.
Original Research Paper
Environment
Mohammad Hadi Salehzadeh; Hadi Farhadian; Saeed Yousefi; Mohammad Dehju
Abstract
This study aims to assess the environmental impacts of coal mining in the Eastern Alborz region, focusing on coal mines from 2013 to 2021, using remote sensing techniques. Landsat 8 satellite images were digitized based on key environmental indices, including NDVI, NDWI, NDSI, and NDBI, and subsequent ...
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This study aims to assess the environmental impacts of coal mining in the Eastern Alborz region, focusing on coal mines from 2013 to 2021, using remote sensing techniques. Landsat 8 satellite images were digitized based on key environmental indices, including NDVI, NDWI, NDSI, and NDBI, and subsequent statistical analyses and evaluations were conducted for the study areas. To distinguish the effects of mining from those of climate change, the results were compared with a reference area located within a natural resource block (baseline area), and the outcomes were thoroughly analyzed. The findings indicate that the combined impacts of mining and climate change have caused significant environmental degradation in the region. In particular, vegetation cover has experienced a sharp decline in recent years, while soil erosion has increased at a slower rate. Projections of mining impacts on vegetation and soil were made by calculating the average NDVI and NDSI indices for 2030 and 2050 in the studied areas. These projections suggest that NDVI is expected to decrease by 0.25 by 2030 and by 0.72 by 2050, indicating further vegetation loss in the coming decades. In contrast, analysis of the NDWI index reveals no clear trend in soil moisture changes over the study period. Given the climatic conditions of the selected areas, it is essential to monitor, manage, and mitigate environmental risk factors to prevent the expansion of drought into northern forests, highlighting the need for appropriate intervention measures.
Original Research Paper
Environment
Jalil Hanifehnia; Akbar Esmaeilzadeh; Solat Atalou; Reza Mikaeil
Abstract
Blasting is a crucial technique in mining for rock fragmentation, but it can lead to environmental impacts like vibrations, flyrock, and backbreak. Accurately predicting and controlling these effects is essential for improving safety and minimizing damage to equipment and infrastructure. This research ...
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Blasting is a crucial technique in mining for rock fragmentation, but it can lead to environmental impacts like vibrations, flyrock, and backbreak. Accurately predicting and controlling these effects is essential for improving safety and minimizing damage to equipment and infrastructure. This research aims to predict flyrock distances (FR) at the Sungun Copper Mine through the application of artificial intelligence (AI) models in conjunction with statistical approaches. Initially, a linear multivariate regression (LMR) model was constructed to establish the correlation between blasting parameters and flyrock range. Subsequently, an artificial neural network based on a multilayer perceptron (ANN-MLP) was developed and further optimized using two advanced hybrid algorithms: the Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). These algorithms were employed to calibrate the neural network’s weights and biases using variables such as number of blast holes, hole spacing, burden, total charge, specific drilling, charge per hole, and specific charge. Results showed that the ANN‑MLP model outperformed the LMR model, with performance metrics of root mean square error (RMSE = 9.31 m), mean absolute error (MAE = 7.10 m), and coefficient of determination (R² = 0.81) during the test phase. However, optimization of the ANN model with ICA and ACO significantly improved prediction accuracy. Among the hybrid models, the ICA-ANN model performed best with RMSE = 5.66 m, MAE = 4.60 m, and R² = 0.89, showing a considerable improvement over the LMR and ANN-MLP models. Sensitivity analysis further highlighted total charge and number of holes as the most influential parameters affecting flyrock dispersion. Overall, the findings underscore the potential of hybrid AI frameworks in advancing predictive modeling for safer and more efficient blasting operations.
Original Research Paper
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.
Original Research Paper
Rock Mechanics
Ebrahim Ebrahimnezhad Sadigh; Kazem Badv
Abstract
Understanding the rheological behavior of soft marine and lacustrine sediments is crucial for the success of geotechnical and civil engineering projects. Coastal and offshore structures such as artificial islands, lake causeways, piers, and oil platforms directly interact with these sediments. Their ...
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Understanding the rheological behavior of soft marine and lacustrine sediments is crucial for the success of geotechnical and civil engineering projects. Coastal and offshore structures such as artificial islands, lake causeways, piers, and oil platforms directly interact with these sediments. Their safe and stable performance depends on accurate characterization of sediment behavior under complex loading conditions. This study investigates the rheological properties of soft sediments from Lake Urmia, Iran, through a combined experimental and numerical approach. Two key tests were performed: extrusion tests and unconfined compression tests. The extrusion tests were conducted on both undisturbed and remolded samples under various conditions, including different loading rates, moisture contents, and discharge orifice sizes. For the numerical simulation, the Bonded Particle Discrete Element Method (BPDEM) was employed, with the model's micro-parameters calibrated using experimental extrusion test data. The numerical results showed excellent agreement with experimental data: the force-displacement curve was replicated with less than 2% error. The calibrated model also successfully simulated the unconfined compression test, reproducing the stress-strain curve with less than 2% deviation from laboratory results. These findings demonstrate the accuracy of BPDEM in modeling soft sediment behavior. The results indicate that integrating laboratory methods with BPDEM modeling provides a powerful tool for analyzing soft sediments. This approach is particularly effective for Holocene and Late Pleistocene soft to ultra-soft sediments, offering reliable predictions of rheological and mechanical behavior in geotechnical applications.
Original Research Paper
Mine Economic and Management
mahdi sanei; Mohammadreza Ameri
Abstract
The risks associated with mining activities constitute a critical area of inquiry within Islamic jurisprudence, particularly because mineral resources serve as strategic assets that significantly influence global economic stability. A rigorous examination of these hazards through a jurisprudential framework ...
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The risks associated with mining activities constitute a critical area of inquiry within Islamic jurisprudence, particularly because mineral resources serve as strategic assets that significantly influence global economic stability. A rigorous examination of these hazards through a jurisprudential framework underscores the necessity of formulating effective, ethically grounded strategies for their mitigation to ensure the responsible and equitable exploitation of mineral reserves. In modern industrial contexts, mining operations are increasingly confronted with a wide spectrum of hazards, ranging from physical and chemical risks to environmental, social, and health-related challenges, each of which poses substantial threats to human welfare, ecological integrity, and the sustainability of natural resources. Employing an analytical–descriptive methodology, this study systematically investigates these hazards and contextualizes them within established jurisprudential principles. Through this alignment, the article proposes comprehensive strategies—including targeted education, heightened awareness, expert consultation, continuous evaluation, and robust monitoring mechanisms —to reduce or eliminate mining-related risks throughout the processes of policy formulation, legislative development, and operational implementation. Adherence to these jurisprudentially informed measures not only minimizes potential harm to individuals and the environment but also ensures the provision of appropriate remedies and compensation in cases involving negligence or procedural lapses. Consequently, the study emphasizes that employers, mine proprietors, technical supervisors, mining personnel, and governmental authorities each bear distinct and critical responsibilities in the collective effort to mitigate and ultimately eliminate mining hazards.
Review Paper
Rock Mechanics
Shadman Mohammadi Bolbanabad; vahab sarfarazi; Masoud Monjezi
Abstract
One of the critical steps in experimental research is the precise preparation of specimens. This study aims to develop and present a comprehensive methodology for preparing magnetite iron ore specimens containing non-persistent edge notches for dynamic testing, as well as iron ore and ice specimens for ...
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One of the critical steps in experimental research is the precise preparation of specimens. This study aims to develop and present a comprehensive methodology for preparing magnetite iron ore specimens containing non-persistent edge notches for dynamic testing, as well as iron ore and ice specimens for uniaxial and Brazilian tests. Core drilling was performed using diamond drills with diameters of 54 mm for uniaxial and Brazilian tests and 22 mm for dynamic Split Hopkinson Pressure Bar (SHPB) tests. Non-persistent edge notches with a thickness of 3 mm, controlled length, and inclination were created using a cutter, and their geometric quality was verified through meticulous inspection. For ice specimens, filling the notches with water in tubes matching the specimen diameter provided optimal uniformity and stability. Additionally, precise control of parameters such as core and drill parallelism, drilling speed, cooling water flow, and environmental conditions (dry, saturated, and frozen) preserved the structural integrity and quality of the specimens. The results demonstrated that systematically following these procedures, along with detailed documentation of geometric and environmental specimen features, enables the production of intact, standardized, and reproducible specimens, ensuring reliable and consistent examination of the mechanical response and fracture of magnetite iron ore under both dynamic and quasi-static conditions.
Original Research Paper
Environment
Marco Antonio Cotrina Teatino; Jairo Jhonatan Marquina-Araujo; Jose Nestor Mamani-Quispe; Juan Antonio Vega-Gonzalez; Moises Bartolome Guia-Pianto
Abstract
The Quiulacocha tailings deposit in central Peru, containing 70 Mt of historical mine waste, presents both environmental risks and opportunities for secondary metal recovery. This study applies data-driven machine learning techniques to estimate the remaining silver resources using 927 one-meter composites ...
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The Quiulacocha tailings deposit in central Peru, containing 70 Mt of historical mine waste, presents both environmental risks and opportunities for secondary metal recovery. This study applies data-driven machine learning techniques to estimate the remaining silver resources using 927 one-meter composites from 40 vertical drillholes. Three supervised learning models—Random Forest (RF), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost)—were trained using spatial coordinates (X, Y, Z) as the sole input features. Model validation was performed using leave-one-out cross-validation (LOOCV), and results were benchmarked against ordinary kriging (OK). Among the models, RF delivered the highest predictive performance (mean error = 0.53 g/t, RMSE = 7.21 g/t, R = 0.82), outperforming OK (R = 0.63, RMSE = 10.47 g/t). Block model predictions indicated higher silver content from machine learning models: 1,532.86 t (RF), 1,542.16 t (XGBoost), and 1,492.09 t (KNN), compared to 1,463.73 t from OK. Additionally, XGBoost maintained superior grade-tonnage relationships under elevated cutoff thresholds, highlighting its potential to delineate high-grade subdomains within the deposit. These findings confirm the value of machine learning in resource estimation under conditions of low spatial continuity, such as tailings, where material mixing and irregular deposition patterns limit correlation across space.
Original Research Paper
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.
Original Research Paper
Exploitation
Eslam Ghojoghi; Hamid Mansouri; Mohamad Ali Ebrahimi Farsangi; Esmat Rashedi
Abstract
Back break is an undesirable consequence of rock blasting, which causes explosive energy loss and reduces operation efficiency. Consequently, it is essential to forecast it to exercise control and avert the occurrence of operational cost losses. The objective of this scientific research is to utilize ...
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Back break is an undesirable consequence of rock blasting, which causes explosive energy loss and reduces operation efficiency. Consequently, it is essential to forecast it to exercise control and avert the occurrence of operational cost losses. The objective of this scientific research is to utilize Deep Neural Network, Extreme Gradient Boosting, and Lasso Regression in conjunction with Gravitational Search Algorithm to make predictions and minimization regarding the occurrence of blast-induced back break at Gol-e-Gohar 4 iron ore mine, Sirjan, Kerman, Iran. The constructed models comprise a set of nine input parameters, encompassing blasting design parameters, and rock geomechanical properties and produces back break as single output. The datasets used for training and evaluation consist of 266 blasting records extracted from Gol-e-Gohar 4 iron ore mine. The results obtained showed that the Deep Neural Network model with R2 of 0.81 and MSE of 0.70 has better performance over the Extreme Gradient Boosting and Lasso regression models to predict back break. Furthermore, the application of optimization algorithm resulted in optimized parameter values, which minimize back break.
Original Research Paper
Environment
Sadegh Abedi; Mohamad Reza Karimi; Alireza Alinezhad
Abstract
Achieving sustainable mining development is increasingly vital in addressing environmental challenges, meeting global decarbonization demands, and progressing toward a Net-Zero Emissions (NZE) future. This study proposes an integrated framework to advance sustainable mining in Iran, with a particular ...
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Achieving sustainable mining development is increasingly vital in addressing environmental challenges, meeting global decarbonization demands, and progressing toward a Net-Zero Emissions (NZE) future. This study proposes an integrated framework to advance sustainable mining in Iran, with a particular focus on the roles of emerging technologies and environmental regulations. The core research question investigates how combining fuzzy decision-making methods with intelligent modeling can guide the mining sector toward NZE goals. A multi-stage mixed-methods approach was employed. Initially, key variables were identified using the fuzzy Delphi method and expert judgment. The hesitant fuzzy analytic hierarchy process (HFAHP) was then applied to prioritize and weigh the main factors. Subsequently, fuzzy DEMATEL and interpretive structural modeling (ISM) were utilized to uncover causal relationships and hierarchical dependencies among variables. Finally, the adaptive neuro-fuzzy inference system (ANFIS) simulated potential pathways for achieving sustainable mining. Findings highlight four critical variables—carbon pricing policies, investment costs, global metal prices, and technological innovation—as the most influential drivers. Moreover, ANFIS results indicate that strengthening these factors significantly increases the likelihood of achieving the NZE scenario. Overall, the proposed model serves as a practical decision-support tool for policymakers and mining stakeholders, aiding in policy design, investment strategy develop.
Original Research Paper
Rock Mechanics
amir rezaei; vahab sarfarazi; mohammad fatehi marji; mohammad omidi manesh
Abstract
This study provides an in-depth examination of the failure characteristics of rock salt samples subjected to punch shear testing, emphasizing the analysis of fracture processes and the material’s mechanical response. Given the diverse industrial applications of rock salt, the need for more detailed ...
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This study provides an in-depth examination of the failure characteristics of rock salt samples subjected to punch shear testing, emphasizing the analysis of fracture processes and the material’s mechanical response. Given the diverse industrial applications of rock salt, the need for more detailed studies in this field is evident. The study employs an integrated approach combining practical experiments and numerical simulations using PFC2D software. The results reveal that the failure response of rock salt is governed by critical factors such as the loading rate and the material’s inherent mechanical properties. Laboratory observations indicate that fractures primarily initiate from structurally weak zones, with stress concentration at contact areas being the main cause of tensile-shear failures in the samples. The findings of this study can serve as a foundation for establishing novel quality evaluation criteria for rock salt, underscoring the need for continued research efforts to improve safety and performance in related engineering applications.
Review Paper
Mine Economic and Management
Mohamad Reza ameri; Mohammad Mehdi Rajaei; Abuzar Faraji
Abstract
AbstractThis study provides a systematic bibliometric and thematic review of research on risk assessment in the mining industry. The focus is on fuzzy inference systems (FIS), artificial intelligence (AI), and hybrid FIS–AI approaches. A dataset of 1,607 articles from Scopus was analyzed to identify ...
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AbstractThis study provides a systematic bibliometric and thematic review of research on risk assessment in the mining industry. The focus is on fuzzy inference systems (FIS), artificial intelligence (AI), and hybrid FIS–AI approaches. A dataset of 1,607 articles from Scopus was analyzed to identify publication trends, geographic distribution, citation patterns, and key themes. Using the PRISMA protocol, titles and abstracts were screened, and relevant studies were selected for detailed review The results indicate a steady growth in research output over the past decade, reflecting the increasing importance of intelligent systems in addressing uncertainty and complexity in mining operations. Developed countries tend to prioritize AI-driven methods such as machine learning, neural networks, and hybrid systems. In contrast, developing countries place greater reliance on fuzzy logic approaches, particularly in contexts where reliable data are limited. This methodological divergence underscores uneven technological development and highlights the existing knowledge gap across regions. Three main research pillars are identified: safety (39%), operational efficiency (45%), and environmental sustainability (16%). Methodologically, fuzzy approaches dominate (48%), followed by AI (34%) and hybrid methods (18%). These findings confirm the global relevance of AI and FIS in mining risk assessment and emphasize the need for collaboration to close existing gaps.
Original Research Paper
Rock Mechanics
Mostafa Rahimiyan; Mohammad Ataei; Reza Kakaie; Hossein Khosravi
Abstract
The identification of rock discontinuities is a critical factor in the field of mining and construction projects. Traditional methods for conducting this task is often difficult, time-consuming, poses risks to the human safety, and lead to incomplete evaluations. With introduction of unmanned aerial ...
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The identification of rock discontinuities is a critical factor in the field of mining and construction projects. Traditional methods for conducting this task is often difficult, time-consuming, poses risks to the human safety, and lead to incomplete evaluations. With introduction of unmanned aerial vehicles (UAV) has changed this process and has allowed to cover all the area in a short time without endangering employees. The aim of this paper is to employ deep learning using python programming language to develop and train a neural network based on the UNET++ architecture in order to identify rock surface discontinuities automatically by means of UAV-captured imagery. It is also addresses challenges associated with supervised learning, particularly overfitting, by implementing data augmentation techniques and reducing model parameters by approximately 6%. Consequently, the pixel-wise precision criterion improved significantly from 53.27% to 75.6%. Especially, this work stands out from other studies by focusing specifically on UAV imagery for geological assessments, employing a dual strategy to overcome overfitting, and demonstrating effective performance despite the limited training data. The result showed that the model is capable to identify rock discontinuities accurately and is a suitable method for the mining and construction industries.
Review Paper
Exploitation
Hassan Bakhshandeh Amnieh; Ebrahim Arefmand; Abbas Majdi
Abstract
The Power deck blasting technique is widely used in open-pit and underground mines to optimize explosive energy for effective rock fragmentation while minimizing adverse effects. This study examines the influence of primer location and air column length on ground vibration and limestone rock mass damage ...
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The Power deck blasting technique is widely used in open-pit and underground mines to optimize explosive energy for effective rock fragmentation while minimizing adverse effects. This study examines the influence of primer location and air column length on ground vibration and limestone rock mass damage through field tests and numerical simulations at the Nardaghi limestone mine. A two-hole blast using the Power deck method was performed, with vibrations recorded by a three-axis seismograph. The maximum particle velocity reached 70.22 mm/s at 11 meters from the blast. Field inspections indicated that damage was limited to areas around the blast hole openings. Numerical results show that placing the primer at the bottom of the explosive column reduces ground vibration by 37% compared to middle or top positions, whereas the top primer location causes greater surface damage. Damage at the blast hole bottom was comparable across all primer locations. Fixing the primer at the bottom, the effect of air column length (0.4 to 2 meters) on vibration and damage was studied. Increasing the air column length up to 1.4 meters increased vibration, but longer lengths led to significant vibration reduction. Maximum rock mass damage occurred at an air column length of 0.6 meters, indicating optimal energy transfer. The results highlight the critical effects of primer position and air column length on blasting outcomes. The best primer placement is at the bottom of the explosive column, and the optimal air column length is 0.6 meters to balance vibration control and fragmentation efficiency.
Original Research Paper
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.
Original Research Paper
Mineral Processing
Faraz Soltani; Hadi Naghavi; Hossna Darabi; Arsalan Parvaneh; Mobin Chagh Siah
Abstract
The main objective of the present study is to evaluate the feasibility of using gravity separation methods, including heavy bromoform liquid, spiral, and shaking table, for the primary concentration of gold from low-grade Siah Jangal ore (Sistan and Baluchistan Province, Iran). Characterization studies ...
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The main objective of the present study is to evaluate the feasibility of using gravity separation methods, including heavy bromoform liquid, spiral, and shaking table, for the primary concentration of gold from low-grade Siah Jangal ore (Sistan and Baluchistan Province, Iran). Characterization studies indicated that gold is mainly present as inclusions or within the lattice of pyrite and siderite minerals. For this reason, the potential of gravity separation methods using bromoform heavy liquid with a density of 2.89 g/cm³ was initially investigated in three size fractions: +1180, -1180+500, and -500 µm, where the maximum grade of 2.78 g/t with a recovery of 76.7% was obtained. The results showed that in coarser size ranges, both the grade and recovery of gold decreased. In spiral tests, the highest grade and recovery of gold were 2.33 g/t and 62.58%, respectively. The results of the shaking table experiments showed that, given a concentrate-to-feed weight ratio of 12%, a grade of 2.54 g/t could be achieved with a recovery of 73.81%, which, by eliminating a significant amount of tailings (about 88% of the feed), significantly reduces the operating expenses of subsequent processes (including flotation, oxidation, and leaching). It can be concluded that gravity methods, especially the shaking table, can serve as low-risk, low-cost, and environmentally friendly approaches for concentrating low-grade gold ores.
Original Research Paper
Environment
Ramin Mohammadi pour; Hossein Ali Akhlaghi Amiri; Hamed Janani
Abstract
This study evaluates the flocculation performance of six starch-based flocculants—native starch, starch-grafted polyacrylamide (St-g-PAM), anionic starch, cationic starch, and two dual-modified derivatives, anionic starch-grafted polyacrylamide (A-St-g-PAM) and cationic starch-grafted polyacrylamide ...
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This study evaluates the flocculation performance of six starch-based flocculants—native starch, starch-grafted polyacrylamide (St-g-PAM), anionic starch, cationic starch, and two dual-modified derivatives, anionic starch-grafted polyacrylamide (A-St-g-PAM) and cationic starch-grafted polyacrylamide (C-St-g-PAM)—on real iron ore tailings from four industrial sources representing different mining regions of Iran: North-East, West, Central Plateau, and South. The flocculants, previously developed via a straightforward one-step synthesis method, were assessed in terms of settling velocity, supernatant clarity, and zeta potential of flocs under controlled conditions (solid contents: 0.5–4 wt%; dosage: 80 ppm). Experimental results revealed that dual-modified flocculants consistently outperformed other variants: A-St-g-PAM and C-St-g-PAM achieved the highest settling rates (up to 0.82 cm/s at 2 wt.% solids) and produced supernatant turbidity values below 15 NTU, compared to >80 NTU for native starch. Zeta potential measurements confirmed enhanced particle destabilization, with floc surface charges approaching −20 mV after treatment. Given their facile synthesis route, high efficiency, and biodegradability, these dual-functional flocculants emerge as promising candidates for large-scale industrial dewatering. The findings highlight their potential as environmentally friendly substitutes for conventional synthetic flocculants, particularly in water-scarce mining regions where efficient water recovery and sustainable tailings management are urgent priorities.
Original Research Paper
Rock Mechanics
Milad Manafi; Hamed Molladovoodi; Hamid Chakeri
Abstract
Tunneling in urban areas is associated with various challenges that must be carefully evaluated during pre-construction studies. Among these challenges, tunnel excavation through fault zones is particularly critical and has been widely investigated. Previous studies have primarily focused on the displacement ...
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Tunneling in urban areas is associated with various challenges that must be carefully evaluated during pre-construction studies. Among these challenges, tunnel excavation through fault zones is particularly critical and has been widely investigated. Previous studies have primarily focused on the displacement of tunnel linings under different fault movement conditions. In the present study, the effects of three key parameters, ground movement magnitude, grout layer thickness, and fault plane angle, on the induced bending moments and normal forces were examined. The numerical results indicate that ground movement magnitude has the most significant influence on induced stresses, whereas grout layer thickness and fault plane angle exhibit comparable effects. The analyses further show that a 100% increase in ground movement leads to a 60.67% rise in the induced normal force. Increasing the grout layer thickness reduces the induced forces by 32.9%, while a larger fault plane angle decreases the normal force by 34.52%. The modeling outcomes also reveal that grout layer thickness is the most influential factor effecting the induced bending moments. These findings provide valuable insights for evaluating the structural capacity and potential failure of tunnel lining crossing fault zones.
Original Research Paper
Mineral Processing
Amirmohammad Nasrollahzadeh bafti; Laya Shakib Mehr; Esmaeel Darezereshki; Mohsen Akhoundi parizi; Hossein pour Shahnazari
Abstract
Copper smelting slag (CSS) represents a significant secondary resource containing valuable metals such as copper and molybdenum. However, its complex mineralogy and the glassy nature of the slag limit the efficiency of conventional flotation processes and require high reagent consumption. In this study, ...
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Copper smelting slag (CSS) represents a significant secondary resource containing valuable metals such as copper and molybdenum. However, its complex mineralogy and the glassy nature of the slag limit the efficiency of conventional flotation processes and require high reagent consumption. In this study, a native halophilic bacterium, Halomonas lutescens, was investigated as an eco-friendly bio-reagent to improve the flotation performance of CSS. Laboratory-scale experiments were conducted under controlled conditions to determine the optimal bacterial dosage, evaluate reusability, and analyze kinetic behavior. The results demonstrated that adding 40 mL of bacterial suspension (conditioned for 5 min) significantly enhanced copper and molybdenum recoveries compared to chemical flotation. Based on previous research on the adhesion of halophilic bacteria, supportive FTIR, SEM–EDS, and adhesion schematic analyses indicate that hydroxyl, carboxyl, and amine groups in bacterial EPS can coordinate with Cu²⁺/Fe³⁺ surface sites, thereby enhancing mineral hydrophobicity and improving Cu–Mo recoveries. Total copper recovery increased from 58.98% to 71.11%, and molybdenum recovery rose markedly from 4.50% to 28.51%, while maintaining similar concentrate grades. Kinetic modeling revealed higher rate constants and better fitting with bacterial presence, confirming enhanced flotation kinetics. Moreover, bacteria remained viable and reusable over multiple flotation cycles, indicating strong potential for process sustainability. Overall, H. lutescens acts as a bio-frother and collector aid, enabling more efficient and environmentally friendly flotation of copper smelting slag.
Original Research Paper
Environment
Ali Najmeddin; Taha Salahjou; Kimia Zendehdel
Abstract
Porphyry copper mining generates substantial volumes of tailings, which pose considerable environmental and public health hazards due to their capacity for acid generation and the release of potentially toxic elements (PTEs). This study provides an integrated environmental and human health risk assessment ...
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Porphyry copper mining generates substantial volumes of tailings, which pose considerable environmental and public health hazards due to their capacity for acid generation and the release of potentially toxic elements (PTEs). This study provides an integrated environmental and human health risk assessment of tailings from the Sungun porphyry copper mine in northwestern Iran. A comprehensive and multidisciplinary approach was employed, combining physicochemical, mineralogical and geochemical analyses with statistical methods. Chemical speciation was done by employing a modified procedure suggested by the BCR (European Community Bureau of Reference) which has also been used in numerous studies to assess the geochemical fractionation and mobility of elements. The main goal was to advance from total concentration analysis to a more precise, bioavailability-based risk evaluation utilizing the USEPA framework for both children and adults. Mineralogical investigation indicated a net acid-generating capability, with pyrite content (~4%) typically surpassing that of the principal neutralizing mineral, calcite (~2%). Geochemical analyses verified that the tailings exhibit significant enrichment in Cu and Mo, along with moderate enrichment of As and Co. Among the studied elements, the highest mobility factors belonged to Cu (81.49%), Pb (76.71%), Zn (71.65%) and Mo (59.27%), respectively. The non-carcinogenic hazard index (HI) for children was 2.04, exceeding the safety threshold of 1.0, with bioavailable vanadium recognized as the principal risk factor. These findings highlight that relying solely on total PTE concentrations can be misleading, reinforcing the need for speciation-based assessments to accurately characterize the environmental behavior and health risks of mine tailings.
Original Research Paper
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.
Original Research Paper
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.
Original Research Paper
Rock Mechanics
Mohammad-Taghi Hamzaban; Alireza Chehreghan; Roozbeh Geraili Mikola
Abstract
Back analysis of tunnel excavation plays a fundamental role in calibrating geomechanical parameters using field monitoring data. However, conventional direct back analysis procedures remain computationally demanding and highly dependent on operator supervision. This study presents an integrated Finite ...
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Back analysis of tunnel excavation plays a fundamental role in calibrating geomechanical parameters using field monitoring data. However, conventional direct back analysis procedures remain computationally demanding and highly dependent on operator supervision. This study presents an integrated Finite Difference Method–Genetic Algorithm (FDM–GA) framework for automated tunnel back analysis, implemented entirely within the FLAC environment using the embedded FISH programming language. The proposed approach eliminates the need for external optimization software and data transfer between numerical and artificial intelligence platforms. A simplified genetic algorithm is coupled directly with finite difference simulations to iteratively minimize the discrepancy between measured and computed tunnel convergences. The framework incorporates constrained parameter optimization, automated handling of non-convergent models, and a robust convergence-based stopping criterion that avoids predefined error thresholds. Verification is performed using two synthetic plane-strain tunnel models representing stiff cohesive soil and dense granular material. Six unknown parameters (ρ, E, ν, c, φ, and K0) are back-calculated using only three convergence measurements. Results from multiple independent runs demonstrate stable convergence toward very small error values (on the order of 10-6–10-5) and consistent reproduction of synthetic monitoring data. The method successfully narrows broad initial parameter ranges and produces multiple acceptable parameter sets, explicitly acknowledging the non-uniqueness inherent in back analysis problems. The developed FDM–GA framework provides an efficient, self-contained, and adaptable tool for practical tunnel back analysis applications.
Original Research Paper
Rock Mechanics
Reza Mohseni Afkham; Erfan Rafiei; Adel Taheri
Abstract
Concrete and rock-like materials play a crucial role in civil and mining engineering due to their favorable mechanical performance, design flexibility, and relatively low production costs. However, the coupled and simultaneous assessment of mechanical properties and energy-based characteristics in this ...
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Concrete and rock-like materials play a crucial role in civil and mining engineering due to their favorable mechanical performance, design flexibility, and relatively low production costs. However, the coupled and simultaneous assessment of mechanical properties and energy-based characteristics in this materials has received comparatively limited attention. In this study, the mechanical behavior and energy-based responses of rock-like specimens prepared using seven mix designs (Types 1–7), categorized into three main groups, were investigated through uniaxial compressive strength (UCS) tests, triaxial compression tests, and Brazilian tensile strength tests. Group 1 specimens were designed to evaluate the effect of cement-to-fine sand ratio, Group 2 specimens investigated gypsum-containing cementitious mixes without sand at a constant water-to-cement ratio, and Group 3 specimens were used to assess the effect of water-to-cement ratio in cement–sand mixes with a constant cement-to-sand ratio. Energy components were quantified using Python-based numerical analysis. The results show that increasing the cement-to-fine sand ratio enhances compressive strength and promotes a more brittle failure response. Group 1 specimens exhibited the highest compressive strength and fracture energy. In contrast, Group 2 specimens showed reduced strength and a more ductile mechanical behavior, while Group 3 specimens displayed intermediate mechanical and energy characteristics. Triaxial compression test results indicated that Group 1 specimens possessed higher cohesion and internal friction angles compared to the other groups, while Group 2 specimens showed a reduction in cohesion and load-bearing capacity. Overall, this study fully demonstrates the significant influence of the mixture composition on the mechanical and energy-based behavior of rock-like materials.
Original Research Paper
Exploitation
Abbas Khajouei Sirjani; Farhang Sereshki; Mohammad Ataei; Mohammad Amiri Hossaini
Abstract
Rock fragmentation induced by blasting plays a critical role in the productivity and cost efficiency of open-pit mining operations. Among blast design strategies, air-deck blasting has been proposed as a technique to improve energy utilization by modifying the pressure–time characteristics within ...
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Rock fragmentation induced by blasting plays a critical role in the productivity and cost efficiency of open-pit mining operations. Among blast design strategies, air-deck blasting has been proposed as a technique to improve energy utilization by modifying the pressure–time characteristics within the blasthole. However, its performance under production-scale conditions and the reliability of numerical predictions remain insufficiently validated. This study presents an integrated field–numerical investigation of air-deck blasting at the Gol-e-Gohar Iron Ore Mine No. 1, Iran. Fragmentation characteristics were quantified using image-based particle size distribution (PSD) analysis of muck piles processed with Split-Desktop software. Characteristic fragmentation indices (D20, D50, and D80) were extracted to evaluate blast performance. Three-dimensional numerical simulations were performed using the LS-DYNA explicit finite element code to model stress-wave propagation, damage evolution, and fragmentation development for both conventional blasting and air-deck blasting configurations. Numerical models were calibrated using site-specific blasting geometry, explosive properties, and rock mass parameters derived from field measurements. The results show strong agreement between numerical predictions and field observations, with coefficients of determination exceeding 0.95 and RMSE values below 10%. Compared with conventional blasting, air-deck blasting produced finer and more uniform fragmentation, reducing D50 by approximately 10–15% and D80 by up to 18%. The improvement is primarily attributed to stress-wave reflection at the air gap and enhanced tensile crack propagation. The proposed field-validated numerical framework provides a practical tool for blast design optimization and demonstrates the potential of air-deck blasting to improve fragmentation efficiency in large-scale open-pit mining operations.
Original Research Paper
Exploration
Feridon Ghadimi; Arman Ghadimi
Abstract
The Geochemical Mineralization Probability Index (GMPI) represents a phase weight assigned to each geochemical stream sediment sample for individual indicator components. In this framework, the weights of the evidence map classes are determined based on the factor scores (FS) derived from factor analysis ...
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The Geochemical Mineralization Probability Index (GMPI) represents a phase weight assigned to each geochemical stream sediment sample for individual indicator components. In this framework, the weights of the evidence map classes are determined based on the factor scores (FS) derived from factor analysis for each indicator component. A wide range of direct and indirect approaches has been proposed to delineate prospective zones for mineral exploration; however, many of these methods are associated with substantial time and financial costs. Accordingly, this study aims to evaluate the performance of an Artificial Neural Network (ANN) model and to compare its predictive capability with that of conventional model-architecture-based approaches. In this research, a hybrid Artificial Neural Network–Biogeography-Based Optimization (ANN–BBO) model is developed to estimate the GMPI. The dataset used for model training and validation comprises geochemical concentrations of Au, Cu, Pb, Zn, Ag, Mo, W, and Sn obtained from 109 stream sediment samples collected in the Zaghar area. Biogeography-Based Optimization (BBO) is employed to optimize the ANN training process by adaptively adjusting its parameters to enhance predictive performance. The proposed ANN–BBO model achieved a Mean Squared Error (MSE) of 0.0221 and a coefficient of determination (R²) of 0.8244, indicating satisfactory predictive accuracy. Furthermore, the model demonstrated robust generalization capability, maintaining reliable predictive performance despite the considerable geochemical variability observed within the stream sediment dataset. The results of the sensitivity analysis reveal that Pb and Ag exert the most significant influence on the prediction of geochemical anomalies within the study area.
Case Study
Mineral Processing
khadijeh farokhi nasab; Ali Imam Ali Pour; Amin Hossein-Morshedy
Abstract
Accurate mineral resource estimation is the cornerstone of effective mine planning, especially within geologically intricate deposits like the Janja copper-gold system in southeastern Iran. This study introduces a comprehensive approach that synergizes high-resolution 3D geological modeling with robust ...
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Accurate mineral resource estimation is the cornerstone of effective mine planning, especially within geologically intricate deposits like the Janja copper-gold system in southeastern Iran. This study introduces a comprehensive approach that synergizes high-resolution 3D geological modeling with robust geostatistical techniques, Ordinary Kriging and Inverse Distance Weighting, while leveraging advanced computational methods to enhance grade estimation precision and resource confidence. An extensive dataset from 108 drill holes informs a detailed 3D model integrating lithological diversity, alteration patterns, and assay results. Employing dynamic anisotropic variogram modeling, the framework adeptly captures spatial continuity and complex structural heterogeneity, surpassing conventional stationary models. Rigorous validation through variogram analysis and swath plotting confirms consistent spatial patterns and dependable copper and gold grade predictions, addressing inherent spatial heterogeneity and uneven sampling issues through optimized mineralized boundary delineation and ore-waste differentiation. Conforming to JORC reporting standards, resource classification into Measured, Indicated, and Inferred categories is anchored on spatial variance metrics, culminating in a robust estimate of approximately 482 million tonnes at cutoff grades of 0.1% Cu and 0.2 ppm Au. Geological insights underline the pivotal roles of structural controls and alteration zones in governing mineralization, providing strategic guidance for exploration and mine development. While the dataset is comprehensive, peripheral zones with sparse drilling highlight areas for future investigation to reduce uncertainty. This integrated, replicable methodology offers a scalable blueprint for resource estimation in complex porphyry deposits worldwide, advancing predictive accuracy and fostering sustainable mining practices aligned with responsible resource stewardship.
Original Research Paper
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.
Original Research Paper
Exploitation
Hamidreza Ramazi; Moslem Jahantigh
Abstract
The present study aims to compare data-driven and knowledge-driven methods in weighting exploration witness layers in porphyry copper potential in the study area of Shahr-e-Babak, Iran. Initially, eight exploration layers including geochemical, geological, airborne magnetic, and remote sensing data were ...
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The present study aims to compare data-driven and knowledge-driven methods in weighting exploration witness layers in porphyry copper potential in the study area of Shahr-e-Babak, Iran. Initially, eight exploration layers including geochemical, geological, airborne magnetic, and remote sensing data were preprocessed and prepared. Then, these layers were transferred to an equal distance of zero and one using a fuzzy function. The weighting of exploration witness layers in the study area was done using three methods: AHP, random forest algorithm, and area prediction method. Then, the weighted layers were combined using the MABAC multivariate decision-making method and three mineral potential models were formed for the study area. The produced models were compared using the main characteristic curve diagram. It was found that weighting using a data-driven method has more desirable results than a knowledge-driven method. Also, weighting exploration layers by random forest algorithm has more desirable results than the area prediction method. The better results of the random forest algorithm are because in this algorithm, weighting is based on mineralized and non-mineralized points, while in the area prediction method, weighting is only based on outcrops in the study area. This method can be implemented with appropriate accuracy in areas with limited training data, along with knowledge-based methods for weighting exploratory evidential layers.
Original Research Paper
Mineral Processing
Sasan Mirshekari; Mehdi iranajad; Hossein Kamran Haghighi
Abstract
In recent years, the use of microorganisms in the flotation of sulfide minerals has gained significant attention, particularly in processes requiring reduced chemical consumption and the utilization of saline water. The targeted application of microorganisms can improve flotation process conditions. ...
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In recent years, the use of microorganisms in the flotation of sulfide minerals has gained significant attention, particularly in processes requiring reduced chemical consumption and the utilization of saline water. The targeted application of microorganisms can improve flotation process conditions. Halophilic bacteria, as salt tolerant species, can be beneficial in selectively depressing pyrite during flotation. This study aimed to investigate the effect of the halophilic bacterium Bacillus mojavensis on enhancing the separation of chalcopyrite from pyrite in froth flotation using seawater. Experiments were conducted at five pH levels, and changes in recovery, flotation kinetics, and contact angle were evaluated. The results showed that the presence of the bacterium improved process selectivity and enhanced chalcopyrite flotation behavior at pH values between 6 and 10, while bacterial performance decreased at pH 4 and pH 12. The maximum recovery improvement was observed at pH 8, where chalcopyrite recovery increased from 55.11% to 62.04%, and the kinetic constant K was higher than that of the bacteria free sample. Contact angle measurements indicated that the bacterium significantly reduced pyrite hydrophobicity (from 73.73° to 46.7°) without causing noticeable changes in chalcopyrite (~75.8°); this behavior was attributed to the selective adsorption of EPS and bacterial cells on the pyrite surface, leading to its surface deactivation. The findings suggest that Bacillus mojavensis, due to its salt tolerance and stability in neutral to alkaline pH ranges, is a suitable candidate for use in seawater-based flotation circuits and low chemical consumption processes involving pH regulators.
Original Research Paper
Exploration
Ali Aalianvari; Shirin Jahanmiri
Abstract
The increasing environmental risks associated with mining operations demand real-time, accurate risk assessment frameworks to prevent ecological damage and ensure operational safety. This study proposes an integrated real-time environmental risk assessment system utilizing multi-source sensor data and ...
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The increasing environmental risks associated with mining operations demand real-time, accurate risk assessment frameworks to prevent ecological damage and ensure operational safety. This study proposes an integrated real-time environmental risk assessment system utilizing multi-source sensor data and advanced machine learning techniques. Sensor arrays monitoring parameters such as turbidity, pH, conductivity, temperature, and geotechnical vibrations provide continuous high-frequency data streams, which are preprocessed and analyzed using feature engineering methods to handle noise and heterogeneity. A comparative evaluation of several supervised and unsupervised models—namely XGBoost, Random Forest, Long Short-Term Memory (LSTM), Autoencoder, and Isolation Forest—was conducted. The XGBoost model outperformed others with an accuracy of 95%, precision of 94%, recall of 94%, F1-score of 94%, and an AUC-ROC of 0.97, while maintaining a low inference time of 18.7 ms per instance, suitable for real-time deployment. Autoencoder models achieved the highest anomaly detection rate of 92%, indicating their effectiveness in identifying rare environmental hazards. Feature importance analysis highlighted turbidity, pH, and conductivity as the most influential predictors, corroborating environmental science insights. This framework demonstrates a robust, scalable, and interpretable solution for mining environmental risk management, enabling prompt hazard detection and facilitating proactive interventions
