Improving the efficiency of microseismic source locating using a heuristic algorithm-based virtual field optimization method

Fast and accurate microseismic locating methods, such as the virtual field optimization method (VFOM), are increasingly used by researchers and mine management personnel. The VFOM can accurately locate a microseismic source under a large picking error. However, due to the complexity of the objective function of the VFOM, especially when a large number of sensors are involved, this method may require substantial time for the locating process. To overcome this problem, heuristic algorithms were used to increase the locating speed of the VFOM, and the performances of two heuristic algorithms (particle swarm optimization algorithm (PSO) and genetic algorithm (GA)) for the VFOM were evaluated. In general, the performances of these algorithms are affected by many factors, such as the Number of generations (NG) and Number of populations (NP). To enhance the performance of heuristic algorithms, a parameter tuning method was used to determine the relevant parameters for these algorithms. In contrast to the traditional gradient-based algorithm, heuristic algorithms can greatly improve the location efficiency of the VFOM with almost no loss of accuracy and can avoid falling into the local optimal value. The results showed that the PSO can provide better location accuracy and computational efficiency for the VFOM than those obtained with the GA. Furthermore, the VFOM and traditional methods were compared to discuss the influence of the number of sensors and positioning of the source on the location identification and the superiority of the VFOM-based location identification was verified.

Automated summary

The translation provides insights into the use of heuristic algorithms to improve the speed and efficiency of the Virtual Field Optimization Method, with the PSO algorithm outperforming the GA algorithm. By determining relevant parameters, the performance of heuristic algorithms can be further enhanced.