Efficient and accurate monitoring and early warning of coal dynamic disaster and other disasters can provide guarantee for the efficient operation of mine transportation system. However, the traditional threshold early warning method often fails to warning some accidents. To address above issues, a new early warning method was proposed based on "quiet period" phenomenon of AE and EMR during fracture. It is found that, a "quiet period" of AE and EMR was present before the load reaches the peak stress, which could be used as one of the precursors to warn the imminent failure of coal and rock specimens. MS and AE signals increased abnormally followed by the phenomenon of "quiet period" before the occurrence of coal dynamic disaster on site, and the decrease of MS events in the "quiet period" is about 57%–88% compared with that in previous abnormal increase stage. During the damage evolution of coal and rock, "quiet period" phenomenon usually occurred at 85%–90% of the peak stress, where the slope of damage parameter curve is almost zero. The "quiet period" of the AE-EMR signals and the low change rate of damage parameter before failure provide a theoretical foundation for the coal dynamic disaster warning based on the "quiet period" precursor found in MS-AE-EMR monitoring system. These findings will help reduce the number of under-reported events and improve early warning accuracy.

Coal and rock dynamic disasters have been the main concern in underground engineering because these seriously threaten the safety of miners and industrial production. Aiming to improve the EMR and AE monitoring technology, the refined nonlinear characteristics of EMR and AE during coal splitting failure are studied using Hilbert-H and multifractal theory, and valuable information pertaining to coal fracture law contained in EMR and AE waveform was revealed. The results show that the EMR and AE of coal splitting failure are related to the process of coal crack propagation. They possess the same initiation time and frequency band, however, the signal duration of EMR is comparatively longer than AE, and the main frequency of AE is higher than EMR. The EMR of coal splitting failure has the same excitation source as AE; nonetheless, the excited forms display different behavior. In terms of signal duration, the distribution of EMR signal is relatively uniform, the proportion of large-signal is less, the amount of information is more than that of AE, and the multifractal characteristics are more complicated. During the coal splitting failure, AE is mainly generated in the process of wall vibration caused by crack propagation, while the generation of EMR includes piezoelectric effect, charge separation, free charge vibration, charge neutralization and other processes, making EMR more complicated than AE and has a relatively low frequency. The research provides an effective method for studying nonlinear refinement characteristics of coal EMR and AE, and can provide an important basis for the study of the mechanism of EMR generation.

Response law and indicator selection of seismic wave velocity for coal seam outburst risk

In order to accurately predict the gas concentration, find out the gas abnormal emission in advance, and take effective measures to reduce the gas concentration in time, this paper analyzes multivariate monitoring data and proposes a new dynamic combined prediction method of gas concentration. Spearman’s rank correlation coefficient is applied for the dynamic optimization of prediction indicators. The time series and spatial topology features of the optimized indicators are extracted and input into the combined prediction model of gas concentration based on indicators dynamic optimization and Bi-LSTMs (Bi-directional Long Short-term Memory), which can predict the gas concentration for the next 30 min. The results show that the other gas concentration, temperature, and humidity indicators are strongly correlated with the gas concentration to be predicted, and Spearman’s rank correlation coefficient is up to 0.92 at most. The average R² of predicted value and real value is 0.965, and the average prediction efficiency R for gas abnormal or normal emission is 79.9%. Compared with the other models, the proposed dynamic optimized indicators combined model is more accurate, and the missing alarm of gas abnormal emission is significantly alleviated, which greatly improves the early alarming accuracy. It can assist the safety monitoring personnel in decision making and has certain significance to improve the safety production efficiency of coal mines.

In order to realize dynamic, continuous, and real-time prediction of coal and gas outburst risk in real time in blasting driving face, an outburst risk prediction method based on the characteristics of gas emission after blasting is proposed. In this study, the causes of abnormal gas concentration in blasting driving face are analyzed, and the identification method of abnormal gas concentration based on weighted K-nearest neighbor (weighted KNN) is proposed. The correlation between gas emission characteristics after blasting and K₁ value is analyzed, and the prediction model of outburst risk based on convolutional neural networks (CNN) is established and applied in Jinjia coal mine in China. The results show that the causes of abnormal gas concentration mainly include ventilation stop, blasting operation, sensor adjustment, and other abnormalities. The accuracy of the identification method is 86.1. Especially, the identification accuracy of blasting operation is 92. There are strong correlations between the growth rate, peak value, and decay rate of gas concentration after blasting and K₁ value, and the maximum correlation coefficient is 0.92. Using the prediction model, 28 times of jet holes and 1 small outburst event are predicted successfully, and the efficiency of the prediction model is 76.39. By this technology, the utilization rate of gas information is improved, and the relationship between the change characteristics of gas concentration after blasting and the risk of coal seam outburst is established, which is of significant for improving the prediction accuracy and risk management ability of coal and gas outburst.

In China, coal‐gas outburst is seriously affecting safety of the coal mine. To improve the safety status of underground coal mining, this work investigated the evolution process and occurrence mechanism of coal‐gas outburst under the coupling action of stress and gas. Results show that increasing either gas pressure or in‐situ stress can make coal destroy and destabilize, and the contribution of gas pressure to coal failure is twice that of in‐situ stress. In ultradeep coal mining, coal‐gas outburst may occur even under the condition of low gas pressure due to large in‐situ stress. Moreover, the larger the mining depth is, the lower the gas index is required for disaster occurrence. The results have certain guiding significance for coal energy mining and the control of coal‐gas outburst in deep coal mining.