引用本文: 谢灵惠, 王乐洋, 韩澍豪, 许光煜. 利用GPS观测数据反演震源参数的改进人工蜂群算法[J]. 武汉大学学报 ( 信息科学版).
Xie Linghui, Wang Leyang, Han Shuhao, Xu Guangyu. An improved artificial bee colony algorithm for inversion of seismic source parameters using GPS observation data[J]. Geomatics and Information Science of Wuhan University.
 Citation: Xie Linghui, Wang Leyang, Han Shuhao, Xu Guangyu. An improved artificial bee colony algorithm for inversion of seismic source parameters using GPS observation data[J]. Geomatics and Information Science of Wuhan University.

## An improved artificial bee colony algorithm for inversion of seismic source parameters using GPS observation data

• 摘要: 随着大地测量观测数据精度的提高，地震震源参数反演对优化算法的性能提出了更高的要求。针对地震震源参数优化问题，提出了一种新颖的人工蜂群算法来反演震源参数。随后，基于跟随蜂搜索模块的局限性，通过引入全局最优个体与经过雇佣蜂阶段更新后种群个体差值的变异分量以改进算法。为了验证算法改进的效果，通过实验测试来评估标准的人工蜂群算法、改进的人工蜂群算法和多峰值粒子群算法的性能； 8组不同类型断层的模拟地震仿真实验表明，改进的人工蜂群算法在精度和稳定性方面优于标准的人工蜂群算法及多峰值粒子群算法；最后将该算法应用到2013年芦山地震和2017年博得鲁姆-科斯地震的震源参数反演中，反演结果表明改进的人工蜂群算法具有良好的适用性和可靠性。

Abstract: Objectives: With the improvement of the accuracy of geodetic observation data, the inversion of seismic source parameters has put forward higher requirements on the performance of optimization algorithms. Methods: A novel artificial bee swarm algorithm is proposed to invert the seismic source parameters for the seismic source parameter optimization problem. Subsequently, based on the limitations of the following bee search module, the algorithm is improved by introducing the variance component of the difference between the global optimal individuals and the population individuals after the hiring bee stage update. To verify the effectiveness of the algorithm improvement, the performance of the standard artificial bee algorithm, the improved artificial bee algorithm and the multi-peak particle swarm algorithm are evaluated through experimental tests. Results: simulated earthquake simulation experiments for eight groups of different types of faults show that the improved artificial bee algorithm outperforms the standard artificial bee algorithm and the multi-peak particle swarm algorithm in terms of accuracy and stability; finally, the algorithm is applied to the 2013 Lushan earthquake and 2017 Bodrum-Kos earthquake. Conclusions: The results show that the improved artificial swarm algorithm has good practicality and reliability.

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