# Microsseismic Source Location Method and Application Based on NM-PSO Algorithm
###### Abstract  
Microseismic source location is the core of microseismic monitoring technology in coal mining; it is also the advantage of microseismic monitoring technology compared with other monitoring methods. The source location method directly determines the accuracy and stability of the source location results. Based on the problem of non-benign arrays of microseismic monitoring sensors in the coal mining process, a fast location method of microseismic source in coal mining based on the NM-PSO algorithm is proposed. The core idea of the NM-PSO algorithm is to use the particle swarm optimization (PSO) algorithm for global optimization, reduce the size of the solution space and provide the optimized initial value for the Nelder Mead simplex algorithm (NM), and then use the fast iteration characteristics of the NM algorithm to accelerate the convergence of the model. The NM-PSO algorithm is analyzed by an example and verified by the microseismic source location engineering. The NM-PSO algorithm has a significant improvement in the source location accuracy. The average location errors in all directions are (5.65 m, 5.01 m, and 7.21 m), all Within the acceptable range, and they showed good universality and stability. The proposed NM-PSO algorithm can provide a general fast seismic source localization method for different sensor array deployment methods, which significantly improves the stability and result in the accuracy of the seismic source localization algorithm and has good application value; this method can provide new ideas for research in microseismic localization in coal mining. microseismic monitoring; source location; Nelder Mead simplex algorithm; particle swarm optimization algorithm +
Footnote †: journal: Applied Sciences  
the source location in Tibet. Some scholars also use microseismic to conduct positioning research on rock hydraulic seepage [8; 9; 10]. Many scholars have also combined different methods to locate the source, using the solution of linear positioning and the least square method as the initial value of the Geiger algorithm, which improves the accuracy and speed of the source solution [11; 12]. In order to solve the limitation of the local search method, the global search method is applied to the objective function solution process of the source location. [PERSON] et al. [13] used the Powell algorithm for earthquake location, which simplifies the solution process. [PERSON] et al. [14] obtained the plane interval velocity model between wells that realized a very fast simulated annealing (VFSA) inversion. [PERSON] et al. [15] proposed a layered velocity positioning model SV, which was optimized by a genetic algorithm. Many scholars also use a combination of two or more positioning algorithms for microseismic positioning to make up for the shortcomings of a single algorithm and improve positioning accuracy. [PERSON] et al. [16] combined the robust simulated annealing algorithm with the simplex algorithm to improve positioning accuracy and speed effectively. [PERSON] et al. [17] proposed a mixed location method of mine microseismic sources based on multi-objective particle swarm optimization simulated annealing (MOPSO-SA). [PERSON] et al. [18; 19; 20] proposed a positioning method without pre-velocity measurement in order to reduce the impact of the wave velocity model on the source positioning accuracy. DAVUT et al. [21; 22; 23; 24] obtained a series of optimized hybrid algorithms based on the NM algorithm and using its excellent local search capability. In addition, some hybrid algorithms, such as GA-PSO [25], GA-SOP [26], hybridizing PSO [27], and AGSA-PS [28], show high accuracy in terms of arithmetic results. In terms of improved algorithms, IWSA [29], a new modified particle swarm optimization algorithm [30], has also obtained more significant results. h-BOASOS algorithm Used to optimize weight and cost of cantilever retaining wall [31; 32], MPBOA was designed borrowing concepts of mutualism and commensalism from the SOS algorithm.