High-precision three-dimensional geological modeling of mining faces is crucial for intelligent coal mining and disaster prevention. Accurate spatial interpolation is essential for building high-quality models. This study focuses on the 25214 workface of the Hongliulin coal mine, addressing challenges in interpolating terrain elevation, stratum thickness, and coal seam thickness data. We evaluate eight interpolation methods (four kriging methods, an inverse distance weighting method, and three radial basis function methods) for terrain and stratum thickness, and nine methods (including the Bayesian Maximum Entropy method) for coal seam thickness, using cross-validation to assess their accuracy. Research results indicate that for terrain elevation data with dense and evenly distributed sampling points, linear kriging achieves the highest accuracy (MAE = 1.01 m, RMSE = 1.20 m). For the optimal interpolation methods of five layers of thickness data with sparse sampling points, the results are as follows: Q₄, spherical kriging (MAE = 2.13 m, RMSE = 2.83 m); N₂b, IDW (p = 2), MAE = 2.08 m, RMSE = 2.44 m; J₂y³, RS-RBF (MAE = 0.89 m, RMSE = 1.05 m); J₂y², TPS-RBF (MAE = 1.96 m, RMSE = 2.25 m); J₂y¹, HS-RBF (MAE = 2.36 m, RMSE = 2.71 m). A method for accurately delineating the zero line of strata thickness by assigning negative values to virtual thickness in areas of missing strata has been proposed. For coal seam thickness data with uncertain data (from channel wave exploration), a soft-hard data fusion interpolation method based on Bayesian Maximum Entropy has been introduced, and its interpolation results (MAE = 0.64 m, RMSE = 0.66 m) significantly outperform those of eight other interpolation algorithms. Using the optimal interpolation methods for terrain, strata, and coal seams, we construct a high-precision three-dimensional geological model of the workface, which provides reliable support for intelligent coal mining.

Coal mine disaster safety monitoring often employs microseismic technology for its high sensitivity and real-time capability. However, nonlinear, non-stationary, and multi-scale signals limit traditional time series models (e.g., ARMA, ARIMA). This paper proposes a hybrid deep learning model—VMD-SE-CEEMDAN-BO-CNNGRU—integrating variational mode decomposition, sample entropy, CEEMDAN, Bayesian optimization, and a CNN-GRU architecture. Microseismic data from the 08 working face in D mine (Weibei mining area) were used to predict daily maximum energy, average energy, and frequency. The model achieved high predictive performance with R² values of 0.93, 0.89, and 0.88, significantly outperforming baseline models lacking modal decomposition. Comparative experiments verified the superiority of the VMD-first, SE-reconstruction, and CEEMDAN-second decomposition strategy, yielding up to 13% greater accuracy than reverse-order schemes. The model maintained R² above 0.80 on another dataset from the 03 working face in W mine (Binchang mining area), demonstrating robust generalization. Although performance declined during fault disturbances, accuracy for average energy and frequency rebounded post-disturbance, indicating strong adaptability. Overall, the VSCB-CNNGRU model enhances both accuracy and stability in microseismic prediction, supporting dynamic risk assessment and early warning in coal mining.

Roof water inrush, induced by upward-developing water-conducting fracture zones (WCFZ) into aquifers, poses significant risks in coal mining. Conventional monitoring methods struggle with accurately capturing the complex 3D spatial structure and dynamic evolution of WCFZs. To address this, we propose an integrated early-warning framework combining microseismic spatial clustering and dynamic multi-indicator risk assessment. Our improved volume-corrected DBSCAN algorithm incorporates the spatial energy influence radius (apparent volume) of microseismic events, substantially enhancing dominant cluster identification. This improvement enables dynamic segmentation of WCFZ development into slow-growth, rapid-growth, and stable stages. We developed a Water-Risk Prediction Index (WRPI) model using twelve key indicators spanning spatial structure, energy characteristics, fault anomalies, and mining progress. A game-theoretic fusion optimizes indicator weighting, generating quantitative scores and four-tiered risk alerts. The model effectively identifies potential surge zones and fault-induced anomalies in real time. Field validation at Wenjiapo Coal Mine’s 3# and 6# workfaces confirmed the enhanced accuracy and adaptability of our method, demonstrating its significant potential for intelligent WCFZ recognition and proactive water hazard management under complex geological conditions.

The development height of the water-conducting fractured zone (H_WCFZ) serves as an important basis for the prevention of mine water disasters and assessment of water resource impacts. In recent years, with the increasing adoption of super-wide coal mining longwall panels with a width exceeding 300 m, studies on their H_WCFZ have been limited and relevant field measurement data remain sparse. This study aimed to determine the development H_WCFZ in super-wide longwall panels during the mining of medium-depth coal seams. The research area comprised the adjacent 01 and 02 fully mechanised longwall panels in Xiaobaodang No. 2 Coal Mine, China, with widths of 300 m and 450 m, respectively. The development H_WCFZ was determined via drilling exploration and numerical simulation. The results revealed that compared with the conventional longwall panel with a 300 m width, the H_WCFZ in the central area and near the boundary roadway area increased in the super-wide longwall panel with a 450 m width. The H_WCFZ increased by 6.4 m and 5.9 m, corresponding to growth rates of 11.3% and 8.4%, respectively. The H_WCFZ in the boundary areas of both longwall panels was greater than that in the central areas, exhibiting a saddle-shaped distribution characterised by lower values in the middle and higher values on both sides along the inclination direction. According to the numerical simulation results, the width of the coal mining face exhibited a nonlinear positive correlation with the maximum H_WCFZ. A width of 300 m represented the threshold at which the rate of increase of the H_WCFZ began to decline, whereas a width of ~450 m represented the critical threshold beyond which the H_WCFZ tended to stabilise. These findings offer valuable insights for the accurate prediction of the H_WCFZ in super-wide longwall panels, the prevention of mine water disasters and assessment of water resource impacts.