## 2 Detection Method of Microseisms in Coal Mines
### Microsesium Detection Method in Coal Mine
#### 2.2.2 Optimal Microseism Detection Results of Coal Mine  
The factors affecting the optimization ability of the SVR model include the error penalty factor \(D\) and kernel function parameters \(g\). In order to find the optimal parameter combination conveniently and reliably \((D,g)\), the particle swarm optimization (PSO) algorithm is introduced for optimization. When the PSO algorithm starts to run, it needs to set a group of random particles to be in the feasible solution space. During program operation, particles will obtain corresponding fitness values according to the fitness function during each iteration, and whether particles are within the scope of optimization solution will be determined [17]. Each iteration will find the particle closest to the optimal solution in this iteration, and the other particles will follow the direction of motion of the particle to search one by one until all particles are near the optimal solution. The particle is updated according to two extreme values in the iteration process: one is its own extreme value, which indicates the cognitive level of the particle itself to find the best position; the other is the global extremum, which represents the ability of all particles to find the optimal solution at the current position. At the same time, the small data optimization characteristics of the PSO algorithm can also solve complex optimization problems. At the beginning of the iteration, the optimization performance of the particle swarm optimization algorithm is better than that of other evolutionary algorithms, but with the increase of the number of iterations, its ability will also decline. Therefore, this paper used the improved particle swarm optimization algorithm based on passive aggregation to optimize the parameter combination of the SVR model \((D,g)\) and find the optimal solution through the PSO algorithm iteration. In the process of the parameter optimization of the SVR model using the PSO algorithm, the optimal SVR parameter combination can be found in the speed search model [18]. Initialize a group of particles in the 3D solution space, which are combined by parameters \((D,g)\)'s composition, including serial number \(i\)'s particle position:  
\[u_{i}=[u_{i1}u_{i2}u_{i3}]^{T} \tag{11}\]  
Its speed is:  
\[v_{i}=[v_{i1}v_{i2}v_{i3}]^{T} \tag{12}\]  
The individual extreme value at the current moment is recorded as \(E_{ibest}\), and the global extreme value is recorded as \(E_{gbest}\). During each iteration, the particle adjusts its direction and speed according to the global extreme value and its own extreme value, moving from the previous state to the next state. Because the traditional PSO algorithm easily falls into the local optimum and is not conducive to the global search, this paper proposes an improved particle swarm optimization algorithm based on passive aggregation to iterate and obtain the optimal value of the parameters [19]. In the process of optimization, the passive attraction term is introduced according to the passive aggregation algorithm, so that particles not only need to consider their own optimal position and the global optimal position, but also consider the interference of the attraction term to prevent falling into the local optimum. Obtain the best parameter combination of the SVR model, so as to obtain the best detection results of coal mine microseisms. The iteration formula of the particles is:  
\[\left\{ \begin{array}{l}v_{i}^{k+1}=\omega\cdot v_{i}^{k}+c_{1}r_{1}\cdot \left(E_{ibest}^{k}-u_{i}^{k}\right)+c_{2}r_{2}\cdot\left(E_{gbest}^{k}-u_{i}^ {k}\right)+c_{3}r_{3}\cdot\left(R_{i}^{k}-u_{i}^{k}\right)\\ u_{i}^{k+1}=u_{i}^{k}+\beta v_{i}^{k+1}\end{array} \right. \tag{13}\]  
where \(R_{i}^{k}\) is the attraction term that disturbs the particle position \(u_{i}^{k}\) in the \(k\) iteration.