Based on the statistical analysis of coal occurrence characteristics, and dynamic phenomena of coal and rock in Qianjiaying coal mine, China, an area–local outburst early warning system based on outburst key factors and early warning indicators was constructed. Statistical analysis of anomaly features of gas emission rate prior to outburst determined that the early warning index of the heading-face featured characteristic values of gas emission rate, including variance, peak difference, and fluctuation slope. Based on the entropy-weight method, the weight of indicators in the early warning process was determined, and the membership degree of each early warning grade under the synergistic effect of multiple indicators was calculated using Bayesian theory to determine the early warning grade. An outburst early warning model for Qianjiaying coal mine was constructed. The application client for an early warning system was developed, including a real-time gas data acquisition system and a visual early warning system. During the application of the early warning system in Qianjiaying Mine, it detected abnormal early warning indicators and issued early warning signals 6 hours in advance, avoiding casualties and equipment losses.

Using Markov chain Monte Carlo (MCMC) random sampling, a Gaussian mixture model (GMM) of the overpressure of a blast shock wave based on parameter optimization of an expectation-maximization (EM) algorithm is proposed to improve the accuracy of sampling. The probability of an explosion caused by gas accumulation under different conditions is obtained from statistics of gas explosion accidents. The explosion equivalent and shock wave overpressure are estimated by using field gas data. The data sets of different types of gas explosions and their corresponding density distribution functions are established. The EM algorithm is used for iterative calculation, and the optimal distribution of each gas explosion data set is obtained. The parameters are built according to a posteriori optimization. A state transition matrix is used to achieve numerical inversion of the overpressure of an MCMC gas explosion shock wave. The inversion results are based on the actual conditions of the mine. On the premise of improving the accuracy of the random simulation, the overpressure value of shock wave is more in line with the law of disaster change, which provides theoretical support for safety protection during a disaster.