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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

王乐洋 孙龙翔 许光煜

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. doi: 10.13203/j.whugis20210114
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. doi: 10.13203/j.whugis20210114
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. doi: 10.13203/j.whugis20210114
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. doi: 10.13203/j.whugis20210114

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

doi: 10.13203/j.whugis20210114
基金项目: 

国家自然科学基金(42174011,41874001,42104008);江西省研究生创新基金(YC2020-S500)

详细信息
    作者简介:

    王乐洋,博士,教授,主要研究方向为大地测量反演及大地测量数据处理。wleyang@163.com

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

Funds: 

The National Natural Science Foundation of China (Nos.42174011, 41874001, 42104008), the Innovation Found Designated for Graduate Students in Jiangxi Province (YC2020-S500).

  • 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。
  • [1] Bagnardi M, Hooper A. Inversion of Surface Deformation Data for Rapid Estimates of Source Parameters and Uncertainties:A Bayesian Approach[J]. Geochemistry Geophysics Geosystems, 2018, 19(7):2194-2211.
    [2] Wang Leyang, Zhao Xiong, Gao Hua. A Method for Determining the Regularization Parameter and the Relative Weight Ratio of the Seismic Slip Distribution with Multi-source Data[J]. Journal of Geodynamics, 2018, 118(7), 1-10.
    [3] Wang Leyang, Gao Hua, Feng Guangcai, Xu Wenbin. Source Parameters and Triggering Links of the Earthquake Sequence in Central Italy from 2009 to 2016 Analyzed with GPS and InSAR Data[J]. Tectonphysics, 2018, 744:285-295.
    [4] Wang Leyang, Zhao Xiong. Determination of Smoothing Factor for the Inversion of Co-seismic Slip Distribution[J]. Journal of Geodesy and Geo-information Science, 2020, 3(1):25-35.
    [5] Amey R M J, Hooper A, Walters R J. A Bayesian Method for Incorporating Self-similarity into Earthquake Slip Inversions[J]. Journal of Geophysical Research:Solid Earth, 2018, 123(7):6052-6071.
    [6] Xu Guangyu, Xu Caijun, Wen Yangmao, 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.
    [7] Okada, Y. Surface Deformation to Shear and Tensile Faults in a Halfspace[J]. Bulletin of the Seismological Society of America, 1985, 75(4):1135-1154.
    [8] Okada Y. Internal Deformation Due to Shear and Tensile Fault in a Half Space[J]. Bulletin of the Seismolog-ical Society of America, 1992, 92(2):1018-1040.
    [9] 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):381-398.
    [10] Jonsson S, Zebker H, Segall P, Amelung F. Fault Slip Distribution of the 1999 Mw71 Hector Mine, California, Earthquake, Estimated from Satellite Radar and GPS Measurements[J]. Bulletin of the Seismological Society of America, 2002, 92(4):1377-1389.
    [11] Pedersen R, Jónsson S, Árnadóttir T, et al. Fault Slip Distribution of Two June 2000 Mw6.5 Earthquakes in South Iceland Estimated from Joint Inversion of InSAR and GPS Measurements[J]. Earth and Planetary Science Letters, 2003, 213(3-4):487-502.
    [12] 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.
    [13] Marchandon M, Vergnolle M, Sudhaus H, et al. Fault Geometry and Slip Distribution at Depth of the 1997 Mw 7.2 Zirkuh Earthquake:Contribution of near-field displacement data[J]. Journal of Geophysical Research:Solid Earth, 2018, 123(2):1904-1924.
    [14] 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.
    [15] Mirjalili S, Mirjalili S M, Lewis A. Grey Wolf Optimizer[J]. Advances in Engineering Software, 2014, 69(3):46-61.
    [16] Tawhid M A, Ali A F. A Hybrid Grey Wolf Optimizer and Genetic Algorithm for Minimizing Potential Energy Function[J]. Memetic Computing, 2017, 9(4):347-359.
    [17] Rex C R E S, Beno M M, Annrose J. Optimal Power Flow-Based Combined Economic and Emission Dispatch Problems Using Hybrid PSGWO Algorithm[J]. Journal of Circuits, Systems, and Computers, 2019, 28(9):1-17.
    [18] ElGayyar M, Emary E, Sweilam N H, et al. A Hybrid Grey Wolf-bat Algorithm for Global Optimization[C]//International Conference on Advanced Machine Learning Technologies and Applications. Springer, Cham, 2018:3-12.
    [19] Routray A, Singh R K, Mahanty R. Harmonic Reduction in Hybrid Cascaded Multilevel Inverter Using Modified Grey Wolf Optimization[J]. IEEE Transactions on Industry Applications, 2019, 56(2):1827-1838.
    [20] Mahalingam T, Subramoniam M. A Hybrid Gray Wolf and Genetic Whale Optimization Algorithm for Efficient Moving Object Analysis[J]. Multimedia Tools and Applications, 2019, 78(12).
    [21] Devarapalli R, Bhattacharyya B. A Hybrid Modified Grey Wolf Optimization-Sine Cosine Algorithm-Based Power System Stabilizer Parameter Tuning in A Multimachine Power System[J]. Optimal Control Applications and Methods, 2020.
    [22] Malik M R S, Mohideen E R, Ali L. Weighted Distance Grey Wolf Optimizer for Global Optimization Problems[C].//2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2015:1-6.
    [23] Wang Leyang, Ding Rui. 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.
    [24] Brocher T M, Baltay A S, Hardebeck J L, et al. The Mw6.024 August 2014 South Napa Earthquake[J]. Ssmological Research Letters, 2015, 86(2A):309-326.
    [25] Dreger D S, Huang M H, Rodgers A, et al. Kinematic Finite-Source Model for the 24 August 2014 South Napa, California, Earthquake from Joint Inversion of Seismic, GPS, and InSAR Data[J]. Seismological Research Letters, 2015, 86(2A):327-334.
    [26] Feng Guangcai, Li Zhiwei, Shan Xinjian, et al. Source Parameters of the 2014 Mw 6.1 South Napa Earthquake Estimated from the Sentinel 1A, COSMO-SkyMed and GPS data[J]. Tectonophysics, 2015, 655:139-146.
    [27] Willmott C J, Ackleson S G, Davis R E, et al. Statistics for the evaluation and comparison of models[J]. Journal of Geophysical Research:Oceans, 1985, 90(C5):8995-9005.
    [28] Konca A O, Guvercin S E, Ozarpaci S, et al. Slip Distribution of the 2017 Mw6.6 Bodrum-Kos Earthquake:Resolving the Ambiguity of Fault Geometry[J]. Geophysical Journal International, 2019, 219(2):911-923.
    [29] USGS. Earthquake catalog released by U.S. Geological Survey[DB/OL].(2017-07-20)[2020-08-12)].https://earthquake.usgs.gov/earthquakes/eventpage/us20009ynd/executive
    [30] Tiryakioğlu İ, Aktuğ B, Yiğit C Ö, et al. Slip Distribution and Source Parameters of the 20 July 2017 Bodrum-Kos Earthquake (Mw6.6) from GPS Observations[J]. Geodinamica Acta, 2017, 30(1):1-14.
    [31] Aktug, B, Kaypak, B, Çelik, R. N. Source Parameters for the Mw=6.6, 03 February 2002, Çay Earthquake (Turkey) and Aftershocks from GPS, Southwestern Turkey[J]. Journal of Seismology, 2010, 14(3):445-456.
    [32] Karasözen E, Nissen E, Büyükakpınar P, et al. The 2017 July 20 Mw 6.6 Bodrum-Kos Earthquake Illuminates Active Faulting in the Gulf of Gökova, SW Turkey[J]. Geophysical Journal International, 2018, 214(1):185-199.
    [33] Zhao, Yingwen, Xu Caijun. Adaptive Multistart Gauss-Newton approach for Geodetic Data Inversion of Earthquake Source Parameters[J]. Journal of Geodesy, 2020, 94(2):17.
    [34] 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.
    [35] Ganas A, Elias P, Kapetanidis V, et al. The July 20, 2017 M6.6 Kos earthquake:seismic and geodetic evidence for an active north-dipping normal fault at the western end of the Gulf of Gökova (SE Aegean Sea)[J]. Pure and Applied Geophysics, 2019, 176(10):4177-4211.
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  • 收稿日期:  2021-12-13
  • 网络出版日期:  2022-01-14

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

doi: 10.13203/j.whugis20210114
    基金项目:

    国家自然科学基金(42174011,41874001,42104008);江西省研究生创新基金(YC2020-S500)

    作者简介:

    王乐洋,博士,教授,主要研究方向为大地测量反演及大地测量数据处理。wleyang@163.com

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。

English Abstract

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. doi: 10.13203/j.whugis20210114
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. doi: 10.13203/j.whugis20210114
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. doi: 10.13203/j.whugis20210114
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. doi: 10.13203/j.whugis20210114
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