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

Xie Linghui; Wang Leyang; Han Shuhao; Xu Guangyu

doi:10.13203/j.whugis20220280

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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. DOI: 10.13203/j.whugis20220280

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

Xie Linghui^1,2,
Wang Leyang^1,2, ,,
Han Shuhao^1,2,
Xu Guangyu^1,2

1 Faculty of Geomatics, East China University of Technology, Nanchang 330013, China;
2 Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, Nanchang 330013, China

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Available Online: December 01, 2022

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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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[1]	Du Zhixing.Theory and Application of Geodesy Inversion Based on Mechanical Models[J]. Journal of Geodesy and Geoinformation Science,2002(01):94.(独知行.基于力学模式的大地测量反演理论及应用[J]. 测绘学报,2002(01):94.)
[2]	Li Haiyan. Research on inversion method of seismic dislocation model parameters[D]. Nan Chang:East China University of Technology,2016.(李海燕.震源位错模型参数反演方法研究[D].南昌:东华理工大学,2016.)
[3]	Xu G, Xu C, Wen Y, et al. Coseismic and postseismic deformation of the 2016 MW 6.2 lampa earthquake, southern peru, constrained by interferometric synthetic aperture radar[J]. Journal of Geophysical Research: Solid Earth, 2019, 124(4): 4250-4272.
[4]	Okada Y. Surface deformation due to shear and tensile faults in a half-space[J]. Bulletin of the seismological society of America, 1985, 75(4): 1135-1154.
[5]	Okada Y. Internal deformation due to shear and tensile faults in a half-space[J]. Bulletin of the seismological society of America, 1992, 82(2): 1018-1040.
[6]	Wang Leyang, Li Haiyan, Chen Hanqing.Source Parameters and Slip Distribution Inversion of 2013 Lushan Ms 7.0 Earthquake[J]. Geomatics and Information Science of Wuhan University,2019,44(03):347-354.(王乐洋,李海燕,陈汉清.2013年芦山Ms 7.0级地震断层参数模型反演[J]. 武汉大学学报(信息科学版),2019,44(03):347-354.)
[7]	Wright T J, Lu Z, Wicks C. Source model for the Mw 6.7, 23 October 2002, Nenana Mountain Earthquake (Alaska) from InSAR[J]. Geophysical Research Letters, 2003, 30(18).
[8]	Jónsson S, Zebker H, Segall P, et al. Fault slip distribution of the 1999 M w 7.1 Hector Mine, California, earthquake, estimated from satellite radar and GPS measurements[J]. Bulletin of the Seismological Society of America, 2002, 92(4): 1377-1389.
[9]	Nunnari G, Puglisi G, Guglielmino F. Inversion of SAR data in active volcanic areas by optimization techniques[J]. Nonlinear Processes in geophysics, 2005, 12(6): 863-870.
[10]	Xu Guangyu, Xu Caijun, Wen Yangmao, et al. Source Parameters of the 2016–2017 Central Italy Earthquake Sequence from the Sentinel-1, ALOS-2 and GPS Data[J]. Remote Sensing, 2017, 9(11): 1182.
[11]	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, 2021(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),2021.)
[12]	Feng Wanpeng, Li Zhenhong. A Novel Hybrid PSO/Simplex Algorithm for Determining Earthquake Source Parameter Using InSAR Data[J]. Progress in Geophysics, 2010, 25(4):1189-1196. (冯万鹏, 李振洪. InSAR资料约束下震源参数的PSO混合算法反演策略[J]. 地球物理学进展, 2010, 25(4): 1189-1196.)
[13]	Shi Xueming, Wang Jiaying. Lecture on Nonlinear Inverse Methods in Geophysics (3) Simulated Annealing Method[J]. Chinese Journal of Engineering Geophysics, 2007, 4(3):165-174. (师学明, 王家映. 地球物理资料非线性反演方法讲座(三)模拟退火法[J]. 工程地球物理学报, 2007, 4(3): 165-174.)
[14]	Shi Xueming, Wang Jiaying. Lecture on Nonlinear Inverse Methods in Geophysics (4) Genetic Algorithm Method[J]. Chinese Journal of Engineering Geophysics, 2008, 5(2):129-140. (师学明,王家映.地球物理资料非线性反演方法讲座(四)遗传算法[J].工程地球物理学报,2008(02):129-140.)
[15]	Wang Leyang, Jin Xibo, Xu Guangyu. Particle Swarm Optimization Algorithm with Dynamic Inertia Factors for Inversion of Fault Parameters[J]. Geomatics and Information Science of Wuhan University, 2021, 46(4):510-519. (王乐洋, 靳锡波, 许光煜. 断层参数反演的动态惯性因子的粒子群算法[J].武汉大学学报(信息科学版), 2021, 46(4): 510-519.)
[16]	Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm[J]. Journal of global optimization, 2007, 39(3): 459-471.
[17]	宁爱平,张雪英.人工蜂群算法的收敛性分析[J]. 控制与决策,2013,28(10):1554-1558.(Ning A P, Zhang X Y. Convergence analysis of artificial bee colony algorithm[J]. Control and Decision, 2013, 28(10): 1554-1558.)
[18]	Zhang Song. Research on Artificial Bee Colony Algorithm and its Application[D]. Xi'an University of Electronic Science and Technology,2019.(张松.人工蜂群算法研究及其应用[D].西安电子科技大学,2019.)
[19]	Su S, Zhou F, Yu H. An artificial bee colony algorithm with variable neighborhood search and tabu list for long-term carpooling problem with time window[J]. Applied Soft Computing, 2019, 85: 105814.
[20]	Yu J, Duan H. Artificial bee colony approach to information granulation-based fuzzy radial basis function neural networks for image fusion[J]. Optik-International Journal for Light and Electron Optics, 2013, 124(17): 3103-3111.
[21]	Zhang Chengrui, Ke Peng, YinMei. Improved Artificial Bee Colony Algorithm and Its Application in Edge Computing Offloading[J]. Conputer Engineering and Applications, 2022: 1-13.(章呈瑞,柯鹏,尹梅.改进人工蜂群算法及其在边缘计算卸载的应用[J].计算机工程与应用, 2022: 1-13.)
[22]	Li N, Zhu X F, Pan Y Z and Zhan P. 2018. Optimized SVM based on artificial bee colony algorithm for remote sensing image classification. Journal of Remote Sensing, 22(4): 559–569(李楠,朱秀芳,潘耀忠,詹培.人工蜂群算法优化的SVM遥感影像分类[J].遥感学报,2018,22(04):559-569.)
[23]	Qin Quande, Cheng Shi, Li Li, et al． Artificial bee colony algorithm: a survey[J]. CAAI Transactions on Intelligent Systems,2014,9( 2):127-135.(秦全德,程适, 李丽,史玉回.人工蜂群算法研究综述[J].智能系统学报,2014,9(02):127-135.)
[24]	Ding Haijun, Feng Qingxian. Artificial bee colony algorithm based on Boltzmann selection policy.Computer Engineering and Applications,2009,45(31):53-55.(丁海军,冯庆娴.基于boltzmann选择策略的人工蜂群算法[J].计算机工程与应用,2009,45(31):53-55.)
[25]	Zhang Yinxue, Tian Xuemin, Cao Yuping. Artificial bee colony algorithm with modified search strategy[J]. Journal of Computer Applications,2012,32(12):3326-3330+3342.(张银雪,田学民,曹玉苹.改进搜索策略的人工蜂群算法[J].计算机应用,2012,32(12):3326-3330+3342.)
[26]	Zhu G, Kwong S. Gbest-guided artificial bee colony algorithm for numerical function optimization[J]. Applied mathematics and computation, 2010, 217(7): 3166-3173.
[27]	Wang L, Ding R. Inversion and precision estimation of earthquake fault parameters based on scaled unscented transformation and hybrid PSO/Simplex algorithm with GPS measurement data[J]. Measurement, 2020, 153: 107422.
[28]	Xu X W, Wen X Z, Han Z J, et al. Lushan M S7. 0 earthquake: a blind reserve-fault event[J]. Chinese Science Bulletin, 2013, 58(28): 3437-3443.(徐锡伟,闻学泽,韩竹军,陈桂华,李传友,郑文俊,张世民,任治坤, 许冲,谭锡斌,魏占玉,王明明,任俊杰,何仲,梁明剑.四川芦山7.0级强震:一次典型的盲逆断层型地震[J].科学通报,2013,58(20):3437-3443.)
[29]	Wang W M, Hao J L, Yao Z X. Preliminary result for rupture process of Apr. 20, 2013, Lushan earthquake, Sichuan, China[J]. Chinese Journal of Geophysics, 2013, 56(4): 1412-1417.(王卫民,郝金来,姚振兴.2013年 4月 20日四川芦山地震震源破裂过程反演初步结果[J].地球物理学报,2013,56(04):1412-1417.)
[30]	Liu Y H, Wang C S, Shan X J, et al.2014. Result of SAR differential interferometry for the co-seismic deformation and source parameter of the Ms 7.0 Lushan Earthquake. Chinese J. Geophys. (in Chinese),57(8):2495-2506.(刘云华,汪驰升,单新建,张桂芳,屈春燕.芦山M_s7.0级地震InSAR形变观测及震源参数反演[J]. 地球物理学报,2014,57(08):2495-2506.)
[31]	Jiang Z, Wang M, Wang Y, et al. GPS constrained coseismic source and slip distribution of the 2013 Mw6. 6 Lushan, China, earthquake and its tectonic implications[J]. Geophysical Research Letters, 2014, 41(2): 407-413.
[32]	Parsons B, Wright T, Rowe P, et al. The 1994 Sefidabeh (eastern Iran) earthquakes revisited: new evidence from satellite radar interferometry and carbonate dating about the growth of an active fold above a blind thrust fault[J]. Geophysical Journal International, 2006, 164(1): 202-217.
[33]	Tiryakioğlu, İ., et al. "Slip distribution and source parameters of the 20 July 2017 Bodrum-Kos earthquake (Mw6. 6) from GPS observations." Geodinamica acta 30.1(2018): 1-14.
[34]	Karasözen, Ezgi, et al. "The 2017 July 20 M w 6.6 Bodrum–Kos earthquake illuminates active faulting in the Gulf of Gökova, SW Turkey." Geophysical Journal International 214.1(2018): 185-199.
[35]	Ganas A, Elias P, Valkaniotis S, et al.Co-seismic deformation and preliminary fault model of the July 20, 2017 M6.6 Kos earthquake, Aegean Sea[J]. EMSC, 2017.
[36]	Zhao Y, Xu C. Adaptive multistart Gauss–Newton approach for geodetic data inversion of earthquake source parameters[J]. Journal of Geodesy, 2020, 94(2): 1-18.

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

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