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利用GPS数据反演震源参数的单纯形组合加权距离灰狼优化算法 被引量:1

Combinations of Simplex and Weighted Distance-Based Grey Wolf Algorithms for Seismic Source Parameter Inversion with GPS Measurements
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摘要 针对地震震源参数反演优化问题,提出了一种改进的灰狼优化(grey wolf optimizer, GWO)算法来反演震源参数。首先,采用基于余弦规律的非线性递减收敛因子策略的加权距离GWO(weighted distance GWO, wdGWO)算法来代替原来的线性递减算法。随后,配置了改进wdGWO算法和单纯形算法的组合方法,引入后者算法是为了稳定前者算法的性能。因此,组合算法(简称GWOS)在收敛性和稳定性方面都具有良好的优势。最后,通过实验测试来评估基本的wdGWO算法、遗传算法(genetic algorithm,GA)和GWOS的性能。仿真实验结果表明,GWOS对震源参数的估计优于wdGWO算法,具有良好的稳定性和准确性;GWOS既可以达到GA的反演精度,又表现出了更好的参数稳定性。将该算法应用于2014年纳帕地震和2017年博德鲁姆-科斯地震,不同类型地震的反演结果表明GWOS具有良好的实用性和可靠性。 Objectives:With the improvement of geodetic observation accuracy,higher requirements are put forward for the seismic inversion algorithm.Methods:In view of this problem,we successfully develop a novel grey wolf optimizer(GWO)algorithm to invert the seismic source parameters.The weighted dis-tance GWO(wdGWO)algorithm with the strategy of the nonlinear decreasing convergence factor based on the cosine law is proposed to instead that of the original linear decreasing.Subsequently,a combination ap-proach with the improved wdGWO algorithm and the simplex algorithm is configured and the introduction of the latter algorithm is to stabilize the performance of the proposed wdGWO algorithm.Thus,the combi-nation algorithm has better advantages for both convergence and stability.Finally,we achieve synthetic tests to evaluate the performance of the basic wdGWO algorithm,the genetic algorithm and the combina-tion algorithm.Results:The simulated experimental results show that the estimation of seismic source pa-rameters via the proposed algorithm is superior to the wdGWO algorithm,which expresses excellent stability and accuracy.On the other hand,the stability of seismic source parameters is validated between the combination algorithm and the genetic algorithm,and we find the superiority of the combination algorithm.Furthermore,the availability of the combination algorithm is tested by the 2014 Napa earthquake and the 2017 Bodrum-Kos earthquake.The results show that the combination algorithm can achieve the inversion precision of genetic algorithm,and exhibit better parameters stability.Conclusions:Considering the accuracy and stability of the inversion results is particularly important for the accurate determination of seismic source parameters,the combination algorithm has potential applications in the inversion of seismic source parameters.
作者 王乐洋 孙龙翔 许光煜 WANG Leyang;SUN Longriang;XU Guangyu(School of Surveying and Geoinformation Engineering,East China University of Technology,Nanchang 330013,China;School of Geodesy and Geomatics,Wuhan University,Wuhan 430079,China;Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake,Ministry of Natural Resources,Nanchang 330013,China)
出处 《武汉大学学报(信息科学版)》 EI CAS CSCD 北大核心 2024年第7期1140-1154,共15页 Geomatics and Information Science of Wuhan University
基金 国家自然科学基金(42174011,41874001,42104008) 江西省研究生创新基金(YC2020-S500)。
关键词 震源参数反演 灰狼优化算法 单纯形算法 组合算法 纳帕地震 博德鲁姆-科斯地震 source parameter inversion grey wolf optimizer algorithm simplex algorithm combination algorithm Napa earthquake Bodrum-Kos earthquake
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