## 3 Experimental Analysis
Combining the missed detection rate and misjudment rate of the original detection method of the group in the detection of the 9 operation points in the experiment, it was found that the system was in a fault state when the original method of the group detected 3 operation points and 7 operation points, indicating that the overall operation of the method is unstable; in the 9 coal mine operation points detected by the method, no long-term stability test was carried out to assess the performance of the method in continuous operation, and the number of samples involved in the experiment may appear to be unable to fully represent the overall situation. There is insufficient research on whether the effect of this paper's method on leakage detection rate control is relatively good or universally applicable, and there is a certain degree of uncertainty in the experimental results. However, the leakage rate of this paper's method was lower than the standard value when detecting the nine coal mine operating points, and it can control the leakage rate below 3%, which indicates that this paper's method can effectively control the leakage rate when detecting the microseismicity of coal mines and ensure the stability of the overall detection operation. The experiment was conducted with nine coal mine workings in the group, and a knocking simulation was performed on each of the workings. Under different inertia weights, the method detected the amplitude produced by knocking. Combining the simulated knocking amplitudes with the inertial mass coefficients, the sensitivity of this paper's method in coal mine microseism detection can be calculated as follows:  
\[\aleph=\frac{\xi}{\omega} \tag{18}\]  
where \(\aleph\) represents the detection sensitivity and \(\xi\) the amplitude. It is well known that the denser the received amplitude \(\xi\) is, the larger the amplitude fluctuation is and the more sensitive the reception is. The inertia weights \(\omega\) were set to 0.3, 0.5, 0.7, and 0.9, respectively, and the specific experimental data are shown in Figure 5.  
It can be seen from Figure 5 that the wave spectrum presented by this method is different when the inertia weight is set differently. From the comparison of the amplitude fluctuation degree, when the inertia weight was set at 0.7 and 0.9, the amplitude fluctuation was larger; from the aspect of the amplitude fluctuation frequency, when the inertia weight was set at 0.5, 0.7, and 0.9, the amplitude fluctuation frequency was higher, but when the inertia weight was 0.5, the amplitude frequency gradually decreased with the increase of time. When the inertia weights were set to 0.9, the amplitude fluctuation frequency was the highest, and the amplitude reached a maximum value of 50 mV, which was combined with  
Figure 5: Spectrogram of impact experiment. Equation (18) to calculate such that, when the inertia weights of the method were set to 0.9, the detection sensitivity of the method reached a maximum of 55.6 mV. Usually, the number of particles in the PSO algorithm affects the parameter optimization of the SVR model. In this experiment, when testing the number of particles set by this method, the average fitness of the population was closest to the optimal fitness, and the number of iterations was the least, i.e., the parameter optimization effect was the best. It is known that the optimal fitness is 10, the number of particles is set to 15, 30, 45, and 60, the acceleration factor is 2, the position range is restricted to \([0,\ 60]\), and the self-learning factor is 2.