Strong mine pressure has a great impact upon the safety production of coal mines. Microseismic information provides a more advanced technical means for overburden fracture dynamic identification and mine pressure mechanism research, since it contains rich information on rock fracture sources. In this study, the isolated LW8102 working face in Tongxin Mine was investigated in order to propose a spatio-temporal microseismic event data analysis method based on the k-means clustering algorithm. This algorithm can handle dynamic identification of overburden fractures constrained by spatiotemporally discrete distributions of microseismic events. This provided the dynamic extension process and the fracture distribution pattern of the overburden: eight fracture extensions were formed in the overburden. In each extension, vertical fractures connected the low and high rock layers in the LW8102 and LW8103 goafs, and through fractures connected the LW8102 and LW8103 goafs in their high, middle, and low levels. Some extensions had fractures that were connected to form a closed loop structure. In the vertical fracture, there was a tendency for one or two layers of the stratum to fail first, and then extend to one or both sides. The process of through and vertical fracture propagation followed a certain temporal sequence, reflected primarily in two forms: firstly, as the vertical fracture extended to a certain layer, it provided the initial rupture space for through fracture spreading; secondly, the through fracture first broke, and then extended to the vertical fracture until it intersected with the vertical fracture or provided the initial rupture space for the expansion of the vertical fracture. By matching the overburden fracture to the mine pressure that responded to the support resistance, we analyzed the mechanism of mine pressure at the working face. Through fracture at the high level was found to be the primary cause of the occurrence of mining pressure. It was precisely placed that the formation of multiple adjacent high through fractures 110 m from the floor, triggering simultaneous instability motion of the lower multi-layer level rock; this was the main reason for the phenomenon of strong mine pressure at the working face. Meanwhile, high through fracture at 80 m from the floor was the main reason for the phenomenon of large mine pressure at the working face.

Aiming at the problem of large deformations and difficult maintenance of cross-mining floor roadways, taking the track transportation roadway of the cross-mining east wing floor in Qinan Coal Mine as the engineering background, the stress field distribution of mining stress in floor strata and surrounding rock of floor roadway during the cross-mining process of the working face is studied by combining theoretical analysis with numerical simulation. The results show that the influence of mining stress on the vertical stress of floor strata is reflected in the stress-increasing area in front of the coal wall and the stress-decreasing area in the rear of the coal wall. With the increase in the depth of the floor strata, the peak value of the vertical stress gradually decreases, and the distance from the peak value of the vertical stress to the coal wall and the influence range of the vertical stress gradually increases. When the width of the coal pillar is greater than the influence range of advance abutment pressure of the working face, the development speed of the plastic zone is slow. When the roadway is located in the influence range of advance abutment pressure, the plastic zone of the roadway’s surrounding rock develops rapidly. When the working face crosses the floor roadway more than 10 m, the depth of the plastic zone of the surrounding rock of the roadway is no longer increased; the siltstone above the roadway is the key layer of fracturing, and the deformation of the roadway has been effectively improved after hydraulic fracturing. Through the analysis of numerical simulation results, the fracturing scheme has a significant effect on the stability control of the surrounding rock of the cross-mining floor roadway. This study has certain guiding significance for the maintenance and sustainable utilization of floor roadways in the cross-mining process, which is conducive to ensuring the sustainable mining of underground coal and the safety of personnel and equipment and is of great significance to the sustainable development of the coal mining industry.

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%.

Coal is the primary energy source in China and has long dominated energy consumption, serving as both the cornerstone for safeguarding national energy security and the backbone of stable energy supply. Despite the gradual improvement in the level of fully mechanized and intelligent mining in recent years, as well as the remarkable progress achieved in safe and efficient mining technologies, significant challenges are still encountered in the horizontal slicing mining of steeply inclined coal seams. This study was conducted against the engineering backdrop of the steeply inclined extra-thick coal seam in the Yimen Coal Mine, Sichuan Province. A combination of theoretical analysis, FLAC3D numerical simulation, and on-site monitoring was employed to investigate the support technology for mining roadways. Considering the geological occurrence conditions, roadway dimensions, and service life, the bolt (cable) + steel strip + metal mesh system was selected as the basic support method, with shed supports supplemented for reinforcement in areas with special geological structures or fractured surrounding rock. A non-uniform roadway support technology for horizontal slicing mining of steeply inclined extra-thick coal seams was proposed. The optimal support parameters of the roadways were determined through numerical simulation, and favorable support effects were verified by field measurements.

Rock mechanics serves as the fundamental theoretical pillar supporting the safe, efficient and sustainable implementation of geotechnical engineering in construction, transportation, water conservancy, mining and urban underground space development