## 3 Experimental Analysis  
Take the fully mechanized caving face of a coal mine of a group as the on-site test base. The strike length of the working face is 2365 m, and the dip length is 695 m. Inclined longwall mining and \"U\" negative pressure ventilation were adopted. There are nine coal mine operation points distributed in the working face with an average coal thickness of 13.64 m, a mechanized mining of 4.3 m, an average coal caving height of 6.98 m, and an average spacing between points of 164 m.  
At 1:33 p.m. on 8 January 2023, the group conducted a microseismic simulation accident experiment in the 296-549 m section of the lower roadway of the mine. The LS-DYNA nonlinear dynamics finite element program with the LS-PrePost 6.0 processing software and the D\({}_{3}\)PLOT visualization and analysis tool were mainly utilized in the simulation experiments. According to the original coal mine microseism detection system of the group and the method in this paper, the microseismic energy detected in six time periods before and after the accident simulation experiment was detected, and the detection performance of the method in this paper for coal mine microseisms was tested by comparative analysis. The specific experimental results are shown in Table 1.  
Figure 2: PSO optimization detection process. As can be seen from Table 1, compared with the microseismic energy before and after the actual accident, the energy detected by the original microseismic monitoring system before the accident was not much different from the actual energy. However, when the accident occurred, the original system did not detect the microseismic energy at the time of the accident in a timely and accurate manner, but detected a higher microseismic energy one minute after the accident. This is inconsistent with the actual microseismic energy value, indicating that the original detection system is not accurate enough and has a certain degree of hysteresis in the detection process of coal mine microseisms; however, the method in this paper had little difference between the six microseismic energy values measured before and after the accident and the actual microseismic energy values and accurately detected the abnormal situation of microseismic energy increase when the accident occurred, which shows that the method in this paper can accurately detect in real-time the microseismic situation in coal mines in the mining area. The method in this paper was used to test the clarity of microseism reception in different forms that lead to microseisms in coal mines. This experiment was divided into 10 levels. The larger the number is, the higher the level is and the clearer the received microseisms in the coal mines are. Four forms are designed to test the microseism level in coal mines, namely knocking on pipes, knocking on bolts, knocking on walls, and shouting. The specific experimental results are shown in Figure 3.  
As can be seen from Figure 3, when the distance from the pit mouth is within 20 m, the clarity level of the coal mine microseisms received by calling is the highest, but with the increase of distance, the clarity level of coal mine microseisms drops sharply, and when the distance from the pit mouth is 90 m, the clarity level is almost 0; compared with the way of knocking on the wall and the way of knocking on the pipe and the anchor bolt, the clarity level is relatively higher when the distance is closer to the pit mouth, but the clarity level gradually weakens with the increase of distance and is lower than the clarity level