基于优化领先狼群算法的微震源定位研究  

Research on microseismic source localization based on optimized leading wolfpack algorithm

作　　者：李晓燕 张明伟 宋雷 庞迎春[3] 张结如 LI Xiaoyan;ZHANG Mingwei;SONG Lei;PANG Yingchun;ZHANG Jieru(China University of Mining and Technology School of Mechanics and Civil Engineering,Xuzhou Jiangsu 221116,China;State Key Laboratory for Geomechanics&Deep Underground Engineering,Xuzhou Jiangsu 221116,China;Huaibei Mining Industry,Huaibei Anhui 235000,China)

机构地区：[1]中国矿业大学力学与土木工程学院,江苏徐州221116 [2]深地工程智能建造与健康运维全国重点实验室,江苏徐州221116 [3]淮北矿业集团有限责任公司,安徽准北235000

出　　处：《矿业科学学报》2024年第2期233-242,共10页Journal of Mining Science and Technology

摘　　要：为分析不同启发式方法对求解微震源定位精度问题的影响,提出一种优化领先狼群算法。该算法在领先狼群算法的基础上,调整搜索步长和围攻步长两个参数,提高了在搜索过程中跳出局部最优解的能力。通过理论模型反演和工程数据分析,验证了优化领先狼群算法的有效性。与常用的粒子群算法和模拟退火算法两种启发式算法相比,优化领先狼群算法收敛更快,精度更高,受P波波速误差影响更小。该算法为智能启发式算法应用于微震源定位提供了新思路。In order to analyze the impact of different heuristic methods on the precision of microseismic source localization,an optimized Dominant Wolf Pack Algorithm(DWPA)is proposed.This algorithm builds upon the Dominant Wolf Pack Algorithm and introduces adjustments to two parameters,namely the search step size and the siege step size,enhancing its ability to escape local optima during the search process.The effectiveness of the optimized DWPA is validated through theoretical model inversion and engineering numerical analysis.A comparative study with commonly used heuristic algorithms,Particle Swarm Optimization(PSO)and Simulated Annealing(SA),reveals that the optimized DWPA exhibits faster convergence,higher accuracy,and reduced sensitivity to P-wave velocity errors.This research provides new insights for the application of intelligent heuristic algorithms in microseismic source localization.

关 键 词：微震源定位 微震检测 领先狼群算法 粒子群算法 模拟退火算法 

分 类 号：TD76[矿业工程—矿井通风与安全]