引用本文: 王乐洋, 孙龙翔, 许光煜. 利用GPS观测数据反演震源参数的单纯形组合加权距离灰狼新算法[J]. 武汉大学学报 ( 信息科学版).
Wang Leyang, Sun Longxiang, Xu Guangyu. Combinations of the simplex and weighted distance-based grey wolf algorithms for the seismic source parameter inversion with GPS measurements[J]. Geomatics and Information Science of Wuhan University.
 Citation: Wang Leyang, Sun Longxiang, Xu Guangyu. Combinations of the simplex and weighted distance-based grey wolf algorithms for the seismic source parameter inversion with GPS measurements[J]. Geomatics and Information Science of Wuhan University.

## Combinations of the simplex and weighted distance-based grey wolf algorithms for the seismic source parameter inversion with GPS measurements

• 摘要: 随着大地测量观测精度的提高，对地震反演算法也提出了更高的要求。针对地震震源参数反演优化问题，改进了一种新颖的灰狼优化算法来反演震源参数，提出了基于余弦规律的非线性递减收敛因子策略的加权距离灰狼优化算法来代替原来的线性递减算法。随后，配置了改进加权距离灰狼优化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加权距离灰狼优化算法、遗传算法和组合算法的性能。仿真实验结果表明，组合算法对震源参数的估计优于加权距离灰狼优化算法，具有良好的稳定性和准确性；组合算法既可以达到遗传算法的反演精度，又表现出了更好的参数稳定性。并将该算法应用于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组合算法具有良好的实用性和可靠性。考虑反演结果的精度和稳定性对震源参数的准确确定尤为重要，因此组合算法在震源参数反演中具有潜在的应用价值。

Abstract: 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 Optimization (GWO) algorithm to invert the seismic source parameters. The weighted distance Grey Wolf Optimization (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 approach 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 combination 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 combination algorithm.   Results:  The simulated experimental results show that the estimation of seismic source parameters 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.

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