Search Results (130)

The abandoned goaf resulting from coal resource integration in China poses a significant threat to coal mine safety. The transient electromagnetic method (TEM) has emerged as a crucial technology for detecting goafs in coal mines due to its adaptable equipment and efficient implementation. In recent years, small-loop TEM has demonstrated high resolution and adaptability in challenging terrains with vegetation, such as coal mine ponding areas, karst regions, and reservoir seepage scenarios. By considering the sedimentary characteristics of coal seams and addressing the resistivity changes encountered in single-point inversion, a joint optimization inversion process incorporating lateral weighting factors and vertical roughness constraints has been developed to enhance the connectivity between adjacent survey points and improve the continuity of inversion outcomes. Through an OCCAM inversion approach, the regularization factor is dynamically determined by evaluating the norms of the data objective function and model objective function in each iteration, thereby reducing the reliance of inversion results on the initial model. Using the Xiaolong Coal Mine as a geological context, the impact of lateral and vertical weighting factors on the inversion outcomes of high- and low-resistivity structural models is examined through a control variable method. The analysis reveals that optimal inversion results are achieved with a combination of a lateral weighting factor of 0.5 and a vertical weighting factor of 0.1, ensuring both result continuity and accurate depiction of vertical and lateral electrical interfaces. The practical application of this approach validates its effectiveness, offering theoretical support and technical assurance for old goaf detection in coal mines, thereby holding significant engineering value. Full article

The applicability of the in-situ pyrolysis of oil-rich coal is highly dependent on regional geological conditions. In this study, six major geological factors and 19 key parameters influencing the in-situ pyrolysis of oil-rich coal were systematically identified. An analytic hierarchy process incorporating index classification and quantification was employed in combination with the geological features of the Tiaohu mining area to establish a feasibility evaluation index system suitable for in-situ development in the study region. Among these factors, coal quality parameters (e.g., coal type, moisture content, volatile matter, ash yield), coal seam occurrence characteristics (e.g., seam thickness, burial depth, interburden frequency), and hydrogeological conditions (e.g., relative water inflow) primarily govern pyrolysis process stability. Surrounding rock properties (e.g., roof/floor lithology) and structural features (e.g., fault proximity) directly impact pyrolysis furnace sealing integrity, while environmental geological factors (e.g., hazardous element content in coal) determine environmental risk control effectiveness. Based on actual geological data from the Tiaohu mining area, the comprehensive weight of each index was determined. After calculation, the southwestern, central, and southeastern subregions of the mining area were identified as favorable zones for pyrolysis development. A constraint condition analysis was then conducted, accompanied by a one-vote veto index system, in which the thresholds were defined for coal seam thickness (≥1.5 m), burial depth (≥500 m), thickness variation coefficient (≤15%), fault proximity (≥200 m), tar yield (≥7%), high-pressure permeability (≥10 mD), and high-pressure porosity (≥15%). Following the exclusion of unqualified boreholes, three target zones for pyrolysis furnace deployment were ultimately selected. Full article

The spontaneous combustion disaster of coal not only causes a waste of resources but also affects the safe production of coal mines. In order to accurately detect the range and location of the spontaneous combustion source of coal, this paper studies and summarizes previous research results, and based on the principles and research and development progress of existing detection technologies such as the surface temperature measurement method, ground temperature measurement method, wellbore temperature measurement method, and infrared remote sensing detection method, it briefly reviews the application of various detection technologies in engineering practice at this stage and briefly explains the advantages and disadvantages of each application. Research shows that the existing technologies are generally limited by the interference of complex environmental conditions (such as temperature measurement deviations caused by atmospheric turbulence and the influence of rock layer structure on ground temperature conduction) and the implementation difficulties of geophysical methods in mining applications (such as the interference of stray currents in the ground by electromagnetic methods and the fast attenuation speed of waves detected by geological radar methods), resulting in the insufficient accuracy of fire source location and difficulties in identifying concealed fire sources. In response to the above bottlenecks, the ”air–ground integrated” fire source location determination technology that breaks through environmental constraints and the location determination method of a CSC fire source based on a multi-physics coupling mechanism are proposed. By significantly weakening the deficiency in obtaining parameters through a single detection method, a new direction is provided for the detection of coal spontaneous combustion fire sources in the future. Full article

Predicting water inrush in coal mines faces significant challenges due to limited data, model generalization, and a lack of interpretability. Current approaches often neglect the inherent geometrical symmetries and structured patterns within the complex hydrological parameter space, rely on local parameter optimization, and struggle with interpretability, leading to insufficient predictive accuracy and engineering applicability under complex geological conditions. This study addresses these limitations by integrating Gaussian mixture modeling (GMM), manifold learning, and data augmentation to effectively capture multimodal hydrological data distributions and reveal their intrinsic symmetrical configurations and manifold structures, thereby reducing feature dimensionality. We then apply a whale optimization algorithm (WOA)-enhanced XGBoost model to forecast water inrush probabilities. Our model achieved an R² of 0.92, demonstrating a greater than 60% error reduction across various metrics. Validation at the Yangcheng Coal Mine confirmed that this balanced approach significantly enhances predictive accuracy, interpretability, and cross-scenario applicability. The synergy between high accuracy and transparency provides decision makers with reliable risk insights, enabling bidirectional validation with geological mechanisms and supporting the implementation of targeted, proactive safety measures. Full article

The efficiency of coalbed methane (CBM) extraction is closely related to the drilling response of coal seams, which is significantly influenced by natural fracture development of coal seams. This work investigated 11 coal samples from the Baode, Xinyuan, and Huolinhe mines, employing quantitative fracture characterization, acoustic wave testing, drilling experiments, and cuttings analysis to systematically reveal the relationships and mechanisms between fracture parameters and coal drilling response characteristics. The result found that acoustic parameters (average wave velocity v and drilling surface wave velocity v₀) exhibit significant negative correlations with fracture line density (ρ₁) and area ratio (ρ₂) (|r| > 0.7), while the geological strength index (GSI) positively correlates with acoustic parameters, confirming their utility as indirect indicators of fracture development. Fracture area ratio (ρ₂) strongly correlates with drilling cuttings rate q (r = 0.82), whereas GSI negatively correlates with drilling rate w, indicating that highly fractured coal is more friable but structural stability constrains drilling efficiency, while fracture parameters show limited influence on drill cuttings quantity Q. Cuttings characteristics vary with fracture types and density. Type I coal (low-density coexisting exogenous fractures and cleats) produces cuttings dominated by fine particles with concentrated size distribution (average particle size d ≈ 0.52 mm, crushability index n = 0.46–0.61). Type II coal (exogenous-fracture-dominant) exhibits coarser particle sizes in cuttings (d ≈ 0.8 mm, n = 0.43–0.53). Type III coal (dense-cleat-dominant) drill cuttings are mainly coarse particles and are concentrated in distribution (d ≈ 1.53 mm, n = 0.72–0.98). Additionally, drilling response differences are governed by the coupling effects of vitrinite reflectance (R_o), density, and firmness coefficient (f), with Huolinhe coal being easier to drill due to its lower R_o, f, and density. This study elucidates the mechanism by which fracture development affects coal drilling response through multi-parameter correlation analysis, while also providing novel insights into the optimization of fracturing sweet spot selection for CBM development. Full article

Sudden coal and gas outbursts pose a significant hazard in deep-seated coal seam extraction, necessitating reliable risk assessment methods. Traditionally, assessments focus on gas-dynamic parameters, but experience shows they must be supplemented with tectonic factors such as fault-related disturbances, weak interlayers, and increased fracturing. Even minor faults in the Karaganda Basin can weaken the coal massif and trigger outbursts. The integration of 3D modeling enhances risk evaluation by incorporating both dynamic (gas-related) and static (tectonic) parameters. Based on exploratory drilling and geophysical studies, these models map coal seam geometry, fault positioning, and high-risk structural zones. In weakened coal areas, stress distribution changes can lead to avalanche-like gas releases, even under normal gas-dynamic conditions. An expert scoring system was used to convert geological and gas-dynamic data into a comprehensive risk index guiding preventive measures. An analysis of Karaganda Basin incidents (1959–2021) shows all outbursts occurred in geological disturbance zones, with 43% linked to fault proximity, 30% to minor tectonic shifts, and 21% to sudden coal seam changes. Advancing 3D modeling, geomechanical analysis, and microseismic monitoring will improve predictive accuracy, ensuring safer coal mining operations. Full article

Seismic imaging in areas with complex geological structures, such as steeply dipping strata and lateral velocity variations, remains a significant challenge in geophysical exploration. In this paper, a Kirchhoff pre-stack depth and pre-stack time migration imaging method under the constraint of an initial model is proposed. By establishing the initial velocity model, the method is iteratively optimized under the horizon constraint, and the travel time difference is used to update the model. Finally, Kirchhoff pre-stack imaging is realized. Numerical simulations using a synthetic five-layer velocity model demonstrate that removing direct wave interference and incorporating horizon constraints significantly improve the signal-to-noise ratio and structural accuracy of the migration results. A field case study in a coalfield with monoclinic structures and high-angle faults further validates the method’s effectiveness. Comparative analysis with pre-stack time migration reveals that Kirchhoff pre-stack depth migration achieves superior fault delineation, diffraction wave homing, and event continuity, particularly in steeply dipping formations. The results highlight the method’s potential for improving seismic interpretation accuracy in complex structural settings, offering practical value for coal mine safety and resource exploration. Full article

Since geological structures around coal mines are complex, sudden coal mine water inflow is seriously threatening coal mining safety. To improve the accuracy of predicting coal mine water inflow, a multi-source dataset is collected to develop a coal mine water inflow prediction model based on multi-factor Pearson correlation analysis, where a convolutional neural network and bidirectional long short-term memory neural network are adopted to extract features from time-series data. To validate the performance of the present prediction model, a case study is conducted, where the predicted coal mine water inflow is close to the collected coal mine water inflow. Meanwhile, compared to other prediction models, the present prediction model can predict the magnitude and development trend of coal mine water inflow in the next 8 h more accurately, where the mean absolute percentage error is 5.76% and the correlation coefficient is 0.922. Full article

The hanging of hard roofs in coal seams poses a significant threat to the safe mining of coal. Hydraulic fracturing is an important method to achieve the pre-weakening of coal seam roofs. Clarifying the scope of hydraulic fracturing in coal seam roofs and its influencing factors is a prerequisite for ensuring the effectiveness of the pre-weakening process. In this paper, we developed a fluid–structure coupling numerical simulation model for hydraulic fracturing based on the element damage theory, and have systematically examined the effects of both engineering parameters and geological factors on the hydraulic fracture propagation behavior of the segmented fracturing of coal seam roofs. Results indicate that increasing the injection rate can significantly enhance fracture propagation length. A larger stress difference directs fractures along the maximum principal stress direction and effectively extends their length. Additionally, increasing the spacing between fracture stages reduces stress interference between clusters, leading to a transition from asymmetric to uniform fracture propagation. To validate the numerical simulation results, we conducted a field test on the hydraulic fracturing of the coal seam roof, and monitored the affected area by using transient electromagnetic and microseismic monitoring techniques. Monitoring results indicated that the effective impact range of field hydraulic fracturing was consistent with the numerical simulation results. Through the systematic monitoring of support resistance and coal body stress, the supporting resistance in the fractured zone decreased by 25.10%, and the coal seam stress in the fractured zone exhibited a 1 MPa reduction. Observations demonstrate the significant effectiveness of hydraulic fracturing in regional control of the coal seam roof. This study combines numerical simulation with engineering practice to investigate hydraulic fracturing performance under varying operational conditions, with the findings providing robust technical support for safe and efficient mining production. Full article

The transient electromagnetic (TEM) method is a widely used geophysical technique for detecting subsurface electrical structures. However, its inversion results are typically limited to resistivity parameters, making it challenging to directly infer key petrophysical properties, such as water saturation and porosity. This study proposes a petrophysics parameter inversion approach based on TEM data. By constructing multiple geoelectric models with varying porosities and water saturation values for numerical simulations, the results demonstrated that both the forward and inversion responses of the TEM field maintained errors within 5%. The inversion procedure begins with the reconstruction of the subsurface resistivity distribution, which reliably reflects the true geoelectric model. Based on the inverted resistivity, the water saturation and porosity parameters are subsequently estimated. The inversion results closely match the overall trend of the actual model and exhibit a clear response at the target layer. Finally, the proposed method is applied to a field test at the Tongxin Coal Mine. By integrating subsurface electrical responses with geological data, the spatial distributions of water saturation and porosity within the coal-bearing strata were delineated. This provides a scientific basis for the detailed characterization of the physical properties of coal and surrounding rock, as well as for understanding the development of pores and fractures in underground strata. Full article

In this article, we consider the roles of transcrustal magma- and fluid-conducting faults (TCMFCFs) in the formation of mineral deposits, showing the importance of deep sources of heat and hydrothermal solutions in the genesis and history of deposit formation. As a result of the impact on the lithosphere of mantle plumes rising along TCMFCFs, intense block deformations and tectonic movements are generated; rift systems, and volcanic–plutonic belts spatially combined with them, are formed; and intrusive bodies are introduced. These processes cause epithermal ore formation as a consequence of the impact of mantle plumes rising along TCMFCF to the lithosphere. At hydrocarbon fields, they play extremely important roles in conductive and convective heat, as well as in mass transfer to the area of hydrocarbon generation, determining the relationship between the processes of lithogenesis and tectogenesis, and activating the generation of hydrocarbons from oil and gas source rock. Detection of TCMFCFs was carried out using MMSS (the method of microseismic sounding) and MTSM (the magnetotelluric sounding method), in combination with other geological and geophysical data. Practical examples are provided for mineral deposits where subvertical transcrustal columns of increased permeability, traced to considerable depths, have been found; the nature of these unique structures is related to faults of pre-Paleozoic emplacement, which determined the fragmentation of the sub-crystalline structure of the Earth and later, while developing, inherited the conditions of volumetric fluid dynamics, where the residual forms of functioning of fluid-conducting thermohydrocolumns are granitoid batholiths and other magmatic bodies. Experimental modeling of deep processes allowed us to identify the quantum character of crystal structure interactions of minerals with “inert” gases under elevated thermobaric conditions. The roles of helium, nitrogen, and hydrogen in changing the physical properties of rocks, in accordance with their intrastructural diffusion, has been clarified; as a result of low-energy impact, stress fields are formed in the solid rock skeleton, the structures and textures of rocks are rearranged, and general porosity develops. As the pressure increases, energetic interactions intensify, leading to deformations, phase transitions, and the formation of chemical bonds under the conditions of an unstable geological environment, instability which grows with increasing gas saturation, pressure, and temperature. The processes of heat and mass transfer through TCMFCFs to the Earth’s surface occur in stages, accompanied by a release of energy that can manifest as explosions on the surface, in coal and ore mines, and during earthquakes and volcanic eruptions. Full article

The characterization of historical tailings bodies is crucial for optimizing environmental management and resource recovery efforts. This study investigated the Bielatal tailings dam (Altenberg, Germany), examining its internal structure, material distribution influenced by historical flushing technology, and the spatial distribution of valuable elements. To evaluate the tailings resource potential, drill core sampling was conducted at multiple points at a depth of 7 m. Subsequent analyses included geochemical characterization using sodium peroxide fusion, lithium borate fusion, X-ray fluorescence (XRF), and a scanning electron microscope with energy dispersive X-ray spectroscopy (SEM-EDX). Particle size distribution analysis via a laser particle size analyzer and wet sieving was conducted alongside milieu parameter (pH, Eh, EC) analysis. A theoretical assessment of the tailings’ potential for geopolymer applications was conducted by comparing them with other tailings used in geopolymer research and relevant European standards. The results indicated average concentrations of lithium (Li) of 0.1 wt%, primarily hosted in Li-mica phases, and concentrations of tin (Sn) of 0.12 wt%, predominantly occurring in cassiterite. Particle size analysis revealed that the tailings material is generally fine-grained, comprising approximately 60% silt, 32% fine sand, and 8% clay. These textural characteristics influenced the spatial distribution of elements, with Li and Sn enriched in fine-grained fractions predominantly concentrated in the dam’s central and western sections, while coarser material accumulated near injection points. Historical advancements in mineral processing, particularly flotation, had significantly influenced Sn distribution, with deeper layers showing higher Sn enrichment, except for the final operational years, which also exhibited elevated Sn concentrations. Due to the limitations of X-ray fluorescence (XRF) in detecting Li, a strong correlation between rubidium (Rb) and Li was established, allowing Li quantification via Rb measurements across varying particle sizes, redox conditions, and geological settings. This demonstrated that Rb can serve as a reliable proxy for Li quantification in diverse contexts. Geochemical and mineralogical analyses revealed a composition dominated by quartz, mica, topaz, and alkali feldspars. The weakly acidic to neutral conditions (pH 5.9–7.7) and reducing redox potential (Eh, 570 to 45 mV) of the tailings material indicated a minimal risk of acid mine drainage. Preliminary investigations into using Altenberg tailings as geopolymer materials suggested that their silicon-rich composition could serve as a substitute for coal fly ash in construction; however, pre-treatment would be needed to enhance reactivity. This study underscores the dual potential of tailings for element recovery and sustainable construction, emphasizing the importance of understanding historical processing techniques for informed resource utilization. Full article

Given the high proportion of global fossil energy consumption, the Ordovician karst water in the North China-type coalfield, as a green energy source that harnesses both water and heat, holds significant potential for mitigating environmental issues associated with fossil fuels. In this work, we collected geothermal water samples and conducted borehole temperature measurements at the Xinhu Coal Mine in the Huaibei Coalfield, analyzed the chemical composition of regional geothermal water, elucidated the characteristics of thermal storage, and explored the influence of regional structure on the karst geothermal system in the northern region. The results indicate that the geothermal water chemistry at the Xinhu Coal Mine is of the Na-K-Cl-SO₄ type, with its chemical composition primarily controlled by evaporation and concentration processes. The average temperature of the Ordovician limestone thermal reservoir is 48.2 °C, and the average water circulation depth is 1153 m, suggesting karst geothermal water undergoing deep circulation. The geothermal gradient at the Xinhu Coal Mine ranges from 22 to 33 °C/km, which falls within the normal range for ground-temperature gradients. A notable jump in the geothermal gradient at well G1 suggests a strong hydraulic connection between deep strata within the mine. The heat-accumulation model of the hydrothermal mine geothermal system is influenced by strata, lithology, and fault structures. The distribution of high ground-temperature gradients in the northern region is a result of the combined effects of heat conduction from deep strata and convection of geothermal water. The Ordovician limestone and extensional faults provide a geological foundation for the abundant water and efficient heat conduction of the thermal reservoirs. Full article

The water-conducting fracture zone (WCFZ) is a critical geological structure formed by the destruction of overburden during coal mining operations. Accurately predicting the height of the water-conducting fractured zone (HWCFZ) is essential for ensuring safe coal production. Based on more than 150 measured heights of fractured water-conducting zone samples from various mining areas in China, this study investigates the influence of five primary factors on the height: mining thickness, mining depth, length of the panel, coal seam dip, and the proportion coefficient of hard rock. The correlation degrees and relative weights of each factor are determined through grey relational analysis and principal component analysis. All five factors exhibit strong correlations with the height of the fractured water-conducting zone, with correlation degrees exceeding 0.79. Mining thickness is found to have the highest weight (0.256). A multiple nonlinear coordinated regression equation was constructed through regression analysis of the influencing factors. The prediction accuracy was compared with three other predictive models: the multiple nonlinear additive regression model, the BP neural network model, and the GA-BP neural network model. Among these models, the multiple nonlinear coordinated regression model was found to achieve the lowest error rate (7.23%) and the highest coefficient of determination (R² = 87.42%), indicating superior accuracy and reliability. The model’s performance is further validated using drill hole data and numerical simulations at the B-1 drill hole in the Fuda Coal Mine. Predictive results for the entire Fuda Coal Mine area indicate that as the No. 15 coal seam extends northwestward, the height of the fractured water-conducting zone increases from 52.1 m to 73.9 m. These findings have significant implications for improving mine safety and preventing geological hazards in coal mining operations. Full article

Coal is the main energy source in China, but coal mining is a high-risk industry, making the prevention and control of coal mining hazards an important topic. Constrained by the complexity and unpredictability of underground spaces, current research on coal mining disaster prevention and control technologies mainly focuses on the characteristics of overlying strata and the laws of mine pressure, resulting in significant deficiencies in accuracy. Given this, a data-driven pressure prediction method is proposed, which uses deep learning models to learn the patterns in existing data and generate the required predictions. This approach avoids the challenges of accurately extracting rock mass physical and mechanical parameters and geological structure modeling, thereby improving the accuracy of disaster prevention and control. The stage of working face pressure exertion is a period prone to disasters during coal mining. To achieve accurate prediction of working face pressure, the task is divided into three steps: the first step is to predict support resistance data ahead of the working face, the second step is to classify the pressure labels of coordinate units, and the third step is to predict the characteristic parameters of pressure exertion. Deep learning models were designed and trained separately for each of the three steps: For the first step, a deep Spatiotemporal sequence model was selected, and the trained model achieved a mean absolute error of 4.65 kN in prediction. For the second step, an image segmentation-based classification model was chosen, with the trained model reaching a classification accuracy of 97.77%. For the third step, a fusion model consisting of three LSTM (Long Short-Term Memory) networks was designed. The trained model achieved a mean absolute error of 0.17 for the dynamic pressure coefficient, a maximum resistance error of 810.93 kN during the pressure period, an error of 9.96 cycles for the pressure duration, and a classification accuracy of 92.35% for the pressure type. Simulating the actual situation of application scenarios, the input data for the second and third steps were set as the output data from the previous step, and the model was evaluated. The model achieved a mean absolute error of 1035.21 kN for the prediction of support resistance and classification accuracy of 82.90% for the pressure labels of coordinate units. In the simulated scenario, there were 9922 instances of pressure exertion, and the model predicted 10,336 instances, with 9046 of them matching the actual instances. The prediction of characteristic parameters was evaluated for 4946 instances of pressure exertion, which included three complete pressure exertion cycles. The mean absolute error for the dynamic pressure coefficient was 0.21, the maximum resistance error during the pressure period was 1218.31 kN, the error for the duration of the pressure cycle was 11.03 cycles, and the classification accuracy for the pressure exertion type was 91.75%. Full article