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

• Published in: Geomechanics and geophysics for geo-energy and geo-resources. Vol. 7; no. 3
• Main Authors: Zhou, Jian, Shen, Xiaojie, Qiu, Yingui, Li, Enming, Rao, Dijun, Shi, Xiuzhi
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2021; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Computational efficiency; Computing time; Efficiency; Energy; Engineering; Environmental Science and Engineering; Foundations; Genetic algorithms; Geoengineering; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Heuristic; Heuristic methods; Hydraulics; Identification; Locating; Microseisms; Objective function; Optimization; Original Article; Parameters; Particle swarm optimization; Sensors; Source location; Virtual field optimization method (VFOM); Microseism; Heuristic algorithm; Geophysics
• DOI: 10.1007/s40948-021-00285-y
• ISSN: 2363-8419

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. Article highlights Two heuristic algorithms (i.e., GA and PSO) were used to increase the location speed of the VFOM; Heuristic algorithms can effectively enhance the VFOM resistance to local optimal values; PSO achieves a higher location accuracy and computational efficiency for the VFOM than GA.