GA-PSO Algorithm for Microseismic Source Location

• Published in: Applied sciences Vol. 15; no. 4; p. 1841
• Main Authors: Han, Yaning, Zeng, Fanyu, Fu, Liangbin, Zheng, Fan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2025
• Subjects: Accuracy; adaptive inertia weight; Data compression; dynamic learning factor; Earthquakes; GA-PSO algorithm; Genetic algorithms; Heuristic; Localization; Methods; microseismic source location; Mutation; Optimization; Propagation; Velocity
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15041841

Accurate source location is a critical component of microseismic monitoring and early warning systems. To improve the accuracy of microseismic source location, this manuscript proposes a GA-PSO algorithm that combines the Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). The GA-PSO algorithm enhances the PSO algorithm by dynamically adjusting the balance between global exploration and local exploitation through a sinusoidal function for the nonlinear adjustment of both learning factors, and an adaptive inertia weight that decreases quadratically with iterations. Additionally, the precision of the solutions is further improved through the crossover and mutation operations of the GA. In the simulated location model, the GA-PSO algorithm demonstrated the smallest error value, outperforming both the GA and PSO algorithm in terms of accuracy. Furthermore, the GA-PSO algorithm exhibited minimal sensitivity to wave speed fluctuations of ±1%, ±3%, and ±5%, maintaining the error within 0.5 m. The validation through the blasting experiment at the Shizhuyuan mine further confirmed the enhanced accuracy of the GA-PSO algorithm, with a location error of 20.08 m, representing an improvement of 59% over the GA and 43% over the PSO algorithm.