Gas concentration monitoring is an effective method for predicting gas disasters in mines. In response to the shortcomings of low efficiency and accuracy in conventional gas concentration prediction, a new method for gas concentration prediction based on Particle Swarm Optimization and Long Short-Term Memory Network (PSO-LSTM) is proposed. First, the principle of the PSO-LSTM fusion model is analyzed, and the PSO-LSTM gas concentration analysis and prediction model is constructed. Second, the gas concentration data are normalized and preprocessed. The PSO algorithm is utilized to optimize the training set of the LSTM model, facilitating the selection of the training data set for the LSTM model. Finally, the MAE, RMSE, and coefficient of determination R² evaluation indicators are proposed to verify and analyze the prediction results. Gas concentration prediction comparison and verification research was conducted using gas concentration data measured in a mine as the sample data. The experimental results show that: (1) The maximum RMSE predicted using the PSO-LSTM model is 0.0029, and the minimum RMSE is 0.0010 when the sample size changes. This verifies the reliability of the prediction effect of the PSO-LSTM model. (2) The predictive performance of all models ranks as follows: PSO-LSTM > SVR-LSTM > LSTM > PSO-GRU. Comparative analysis with the LSTM model demonstrates that the PSO-LSTM model is more effective in predicting gas concentration, further confirming the superiority of this model in gas concentration prediction.

Monitoring and providing warnings for coal mine rockburst disasters is a worldwide problem. Several rockburst accidents have occurred in a 1301 belt transport chute near a 1300 fully mechanized caving mine face. To address this issue, an empirical study of the occurrence mechanism of rockbursts in the adjacent area of the fully mechanized top-coal caving face was carried out. This paper mainly addresses the following issues: (1) based on microseismic monitoring technology, the distribution characteristics of the host-rock-supported pressure of the 1300 working face were measured, and the evolution and distribution of the deep-well caving working face host-rock-supported pressure were analyzed. It is revealed that the occurrence mechanism of rockburst in the adjacent area is actually caused by the evolution and superposition of the lateral abutment pressure of the 1300 stope, and the stress of the original rock along the 1301 belt transport down chute; (2) a theoretical calculation model of dynamic and static abutment pressure in longwall stope is built, and an example is tested. The results show that the peak position of lateral abutment pressure of the coal body outside the 1300 goaf is around 63 m, and the peak value of abutment pressure is around 47 MPa; (3) coal body stress monitoring, bolt dynamometer detection, and other means are compared and analyzed. At the same time, with the help of CT geophysical prospecting and drilling cutting measurements, it is concluded that the 1301 belt transport down chute is in the bearing pressure influence zone (superimposed zone), which further verifies the validity of microseismic analysis results and the accuracy of the above theoretical model. Based on this, the early warning system and prevention measures for rockburst based on microseismic monitoring are proposed. The engineering practice shows that the dynamic and static bearing pressure distribution and evolution law of the working face can be dynamically obtained by using microseismic technology, which provides a basis for the accurate prediction and treatment of rockbursts.

Under the background of intelligent construction of coal mines, gas extraction design is still based on manual design, which is complex, time–consuming, and error–prone, and its automation degree needs to be improved. In order to solve this problem, taking the 1302 working face of a mine in Shanxi Province as the research object, this paper carried out relevant research. Firstly, the influencing factors of gas extraction were determined, and the influence rules of different parameters on the extraction effect were studied by numerical simulation. Secondly, an intelligent optimization method of gas extraction drilling parameters based on deep mining called the PSO–LSTM model, is proposed. This model uses the PSO algorithm to optimize the parameters of the LSTM model, so as to improve the accuracy of the LSTM model results. Finally, a quantitative expression algorithm of 3D spatial information of gas extraction drilling holes based on Python is proposed, which can automatically generate 3D spatial models of bedding or through gas extraction drilling holes using optimized drilling parameters and known 3D information of coal seams. This study shows that the results obtained using the PSO–LSTM model are the same as the drilling parameters obtained using numerical simulation, which verifies the accuracy of the PSO–LSTM model. According to the optimized drilling parameters, a 3D model of gas extraction drilling is quickly generated, which greatly reduces the tedious work of drawing construction drawings for coal mine enterprises and improves the intelligence level of coal gas extraction drilling.

The purpose of denoising microseismic mine signals (MMS) is to extract relevant signals from background interference, enabling their utilization in wave classification, identification, time analysis, location calculations, and detailed mining feature analysis, among other applications. To enhance the signal-to-noise ratio (SNR) of single-channel MMS, a frequency-domain denoising method based on the Fourier transform, inverse transform, and singular value decomposition was proposed, along with its processing workflow. The establishment of key parameters, such as time delay, τ, reconstruction order, k, Hankel matrix length, n, and dimension, m, were introduced. The reconstruction order for SVD was determined by introducing the energy difference spectrum, E, and the denoised two-dimensional microseismic time series was obtained based on the SVD recovery principle. Through the analysis and processing of three types of typical microseismic waveforms in mining (blast, rock burst, and background noise) and with the evaluation of four indicators, SNR, ESN, RMSE, and STI, the results show that the SNR is improved by more than 10 dB after FSVD processing, indicating a strong noise suppression capability. This method is of significant importance for the rapid analysis and processing of microseismic signals in mining, as well as subsequently and accurately picking the initial arrival times and the exploration and analysis of microseismic signal characteristics in mines.