Mining-induced earthquakes (MIEs) in underground coal mines have been a common phenomenon that easily triggers rock bursts, but the mechanism is not understood clearly. This research investigates the laws of focal mechanism and source parameters based on focal mechanism and source parameters analysis of MIEs in three frequent rock burst areas. The relative moment tensor inversion (MTI) method was introduced, and the way to construct the inversion matrix was modified. The minimum ray and source number conditions were calculated, and an optimized identification criterion for source rupture type was proposed. Results show that the geological structure, stress environment, and source horizon influence the focal mechanism. The tensile type sources can distribute in the roof and coal seam, while the shear types are primarily located in the coal seam. In the typical fold structure area, the difference in source rupture strength and stress adjustment between tensile and shear types is negligible, while the disturbance scale of tensile types is distinct. The shear types have higher apparent volume and seismic moment in the deep buried fault area but lower source energy. The apparent stress of the tensile types is higher than that of the shear types, representing that the stress concentration still exists in the roof after the MIEs, but the stress near the faults could be effectively released. In the high-stress roadway pillar area, the primary fracture of the coal pillar easily produces a continuous shear rupture along the dominant stress direction under the extrusion of the roof and floor. The source parameters (except apparent stress) of shear types are higher than tensile types and have higher dynamic risk. The results contribute to expanding the understanding of rock burst mechanisms and guide MIEs’ prevention.

The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production, and it has become a challenging task to enhance the accuracy of coal burst disaster prediction. To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction, this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory (Bi-LSTM) network. The method involves three main modules, including microseismic spatio-temporal characteristic indicators construction, temporal prediction model, and spatial prediction model. To validate the effectiveness of the proposed method, engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia, focusing on 13 high-energy microseismic events with energy levels greater than 10⁵ J. In terms of temporal prediction, the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions, and there is no false alarm detected throughout the entire testing period. Moreover, compared to the traditional threshold-based coal burst temporal prediction method, the accuracy of the proposed method is increased by 38.5%. In terms of spatial prediction, the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions, 3 medium hazard predictions, and 4 weak hazard predictions.

Strong mining tremors (SMTs) frequently occur in super-thick strata near the goaf when mining. Since 2021, there have been three consecutive SMTs with magnitude greater than 2.0 in longwall 1208 of the Shilawusu Coal Mine. These SMTs caused mine production to be suspended for more than 290 days and affected over 100 households located on the shaking ground, and seriously threatened the safety of underground workers and restricted production capacity. Therefore, it is essential to investigate the occurrence mechanism of SMTs in super-thick strata goaf mining in order to understand the phenomenon, how the disaster of mining tremors occurs, and the prevention and control of mining tremor disasters. In this study, field observation, numerical analysis, and theoretical calculation were used to study the occurrence mechanism of three SMTs in the Shilawusu Coal Mine. The results show that the super-thick strata fracture induced by the SMTs is generally higher by one to three orders of magnitude in some of the source mechanical parameters compared to other mining tremors, and so is more likely to cause ground shaking. Field observations revealed that before and after the occurrence of SMTs, the maximum surface subsidence suddenly increased by about 0.1 m and showed a “stepped” increase, and the super-thick strata began to experience fractures. The following theoretical mechanics model of super-thick strata was established: at the goaf stage of mining, with the increase in the area of the hanging roof, the super-thick strata will experience initial and periodic fractures, which can easily induce SMTs. The relative moment tensor inversion method was used to calculate the source mechanism of SMTs, which was found to be caused by the tensile rupture resulting from the initial and periodic ruptures of super-thick strata, in addition to the shear rupture generated by the adjustment of unstable strata structures. As the mining continues on the longwall face, there is still a possibility of SMT occurrence. This paper provides some insights into the mechanism and prevention of SMT in underground coal mines.

Coal bursts are one of the formidable hazards in underground coal mines, yet it is still not fully explored due to the complex stress environment that exists during mining. To better understand the bursting mechanism of coal under real-time mining conditions, it is necessary to develop an experimental method capable of reproducing in situ stress and loading-unloading paths of coal in in situ conditions. In this study, a self-developed true triaxial testing system was used to investigate the damage and failure characteristics of coal samples under true triaxial loading and dynamic unloading conditions. Acoustic Emission (AE) monitoring was used to capture the fracturing of the loaded coal. Passive Velocity Tomography (PVT) and Cumulative AE Energy Density (CAEED) were used to analyse damage evolution characteristics of the coal samples under true triaxial loading conditions. A high-speed camera was used to record the failure of the coal samples when the minimum principal stress $σ3$ was suddenly unloaded. It was found that continuous coal damage occurred primarily during the true triaxial loading period. The peak number of AE events in the coal samples increased and then dropped as $σ1$ levels increased. High and low wave velocity zones in the coal samples represent regenerations of the high-density zone and fracture emergence, respectively. Significant energy release zones transferred and expanded as the triaxial loading level increased. Under significant triaxial loads, the coal samples failed and were severely damaged, and the dynamic unloading of $σ3$ caused ejections of coal fragments at low velocities. The outcome of this study provides in-depth understanding of the failure mechanism of coal under in situ conditions.

Seismic hazards are typical mining hazards causing dynamic failure of coal and rock mass, which greatly threatens the safety of personnel and equipment. At present, various seismic analysis methods are used to assess seismic risks but their accuracy is significantly limited by the incompleteness of seismic data. The probability of detecting earthquakes (PDE) method has been proven as a powerful means for retrieving missed seismic events and enhancing the seismic data integrity in mines. However, to date, the reliability of the results of the PDE method has not been assessed and the highly integrated seismic data have not been linked with the actual hazard potential. To fill these gaps, this paper investigated the impacts of the seismic data volume used for calculation and the modification of the layout of sensors on the reliability and robustness of the PDE method. The event counts and seismic energy were compensated using the PDE method, correlated with strong seismic events. The results indicated that the compensated seismic data presented higher accuracy in locating future hazardous events than before. This research provides references on enhancing the performance of seismic analysing methods for seismic risk assessments.