融合谱元法与人工神经网络的漾濞地震宽频带地震动模拟

李春果; 王宏伟; 温瑞智; 任叶飞; 强生银

doi:10.3799/dqkx.2025.085

Volume 51 Issue 1

Jan. 2026

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Li Chunguo, Wang Hongwei, Wen Ruizhi, Ren Yefei, Qiang Shengyin, 2026. Simulation of Broadband Ground Motion in Yangbi Earthquake by Integrating Spectral Element Method and Artificial Neural Network. Earth Science, 51(1): 30-42. doi: 10.3799/dqkx.2025.085

Citation:

Li Chunguo, Wang Hongwei, Wen Ruizhi, Ren Yefei, Qiang Shengyin, 2026. Simulation of Broadband Ground Motion in Yangbi Earthquake by Integrating Spectral Element Method and Artificial Neural Network. Earth Science, 51(1): 30-42. doi: 10.3799/dqkx.2025.085

Citation:

Li Chunguo, Wang Hongwei, Wen Ruizhi, Ren Yefei, Qiang Shengyin, 2026. Simulation of Broadband Ground Motion in Yangbi Earthquake by Integrating Spectral Element Method and Artificial Neural Network. Earth Science, 51(1): 30-42. doi: 10.3799/dqkx.2025.085

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Simulation of Broadband Ground Motion in Yangbi Earthquake by Integrating Spectral Element Method and Artificial Neural Network

doi: 10.3799/dqkx.2025.085

Li Chunguo^{1, 2
,
,},
Wang Hongwei^{1, 2},
Wen Ruizhi^{1, 2
,
,
,},
Ren Yefei^{1, 2},
Qiang Shengyin^{1, 2}

1.
Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China
2.
Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China

Received Date: 2025-03-06
Publish Date: 2026-01-25

Abstract

Abstract

Broadband ground motion simulation is a critical issue in engineering seismology. As traditional methods often face issues of spectral mismatch and energy phase conflicts when combining low-frequency physics-based modeling with high-frequency stochastic components, this paper introduces a hybrid method that integrates artificial neural network (ANN) with spectral element method (SEM). The ANN is trained on the Strong Motion Flatfile of China to capture the nonlinear mapping of short-period response spectra, while the SEM is employed to simulate low-frequency ground motions. High-frequency stochastic components are optimized using scaling factors, and energy alignment is applied to synchronize low- and high-frequency time histories, ensuring stable broadband simulation results. Taking the Yangbi M_S6.4 earthquake as a case study, a finite fault model derived from inversion and a refined 3D velocity structure model are used to generate low-frequency time histories for monitors. The broadband method is then applied to produce corresponding broadband simulation time histories. The results demonstrate that the simulated broadband acceleration time histories exhibit strong consistency with observed records, providing reliable and realistic inputs for seismic hazard analysis.
- ground motion,
- broadband simulation,
- artificial neural network,
- spectral element method,
- Yangbi earthquake

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References(46)

References

Afanasiev, M., Boehm, C., van Driel, M., et al., 2019. Modular and Flexible Spectral-Element Waveform Modelling in Two and Three Dimensions. Geophysical Journal International, 216(3): 1675-1692. https://doi.org/10.1093/gji/ggy469

Ba, Z. N., Liu, Y., Zhao, J. X., et al., 2023. Near-Fault Broadband Ground-Motion Simulation of 2021 Yangbi M6.4 Earthquake: An Improved FK Method. Chinese Journal of Geotechnical Engineering, 45(4): 709-719 (in Chinese with English abstract).

Beresnev, I. A., Atkinson, G. M., 1997. Modeling Finite-Fault Radiation from the w_n Spectrum. Bulletin of the Seismological Society of America, 87(1): 67-84. https://doi.org/10.1785/bssa0870010067

Boore, D. M., 2003. Simulation of Ground Motion Using the Stochastic Method. Pure and Applied Geophysics, 160(3): 635-676. https://doi.org/10.1007/PL00012553

Bozorgnia, Y., Abrahamson, N. A., Al Atik, L., et al., 2014. NGA-West2 Research Project. Earthquake Spectra, 30(3): 973-987. https://doi.org/10.1193/072113eqs209m

Chen, J. L., Hao, J. L., Wang, Z., et al., 2022a. The 21 May 2021 Mw 6.1 Yangbi Earthquake—A Unilateral Rupture Event with Conjugately Distributed Aftershocks. Seismological Research Letters, 93(3): 1382-1399. https://doi.org/10.1785/0220210241

Chen, W. K., Wang, D., Zhang, C., et al., 2022b. Estimating Seismic Intensity Maps of the 2021 Mw 7.3 Madoi, Qinghai and Mw 6.1 Yangbi, Yunnan, China Earthquakes. Journal of Earth Science, 33(4): 839-846. https://doi.org/10.1007/s12583-021-1586-9

Clayton, R., Engquist, B., 1977. Absorbing Boundary Conditions for Acoustic and Elastic Wave Equations. Bulletin of the Seismological Society of America, 67(6): 1529-1540. https://doi.org/10.1785/bssa0670061529

Crempien, J. G. F., Archuleta, R. J., 2015. UCSB Method for Simulation of Broadband Ground Motion from Kinematic Earthquake Sources. Seismological Research Letters, 86(1): 61-67. https://doi.org/10.1785/0220140103

Fu, G. Y., Wang, Z. Y., Liu, J. S., et al., 2023. Lithospheric Equilibrium and Anisotropy around the 2021 Yangbi Ms 6.4 Earthquake in Yunnan, China. Journal of Earth Science, 34(4): 1165-1175. https://doi.org/10.1007/s12583-022-1607-3

Gatti, F., Touhami, S., Lopez-Caballero, F., et al., 2018. Broad-Band 3-D Earthquake Simulation at Nuclear Site by an All-Embracing Source-to-Structure Approach. Soil Dynamics and Earthquake Engineering, 115: 263-280. https://doi.org/10.1016/j.soildyn.2018.08.028

Graves, R. W., Pitarka, A., 2010. Broadband Ground- Motion Simulation Using a Hybrid Approach. Bulletin of the Seismological Society of America, 100(5A): 2095-2123. https://doi.org/10.1785/0120100057

Graves, R., Pitarka, A., 2016. Kinematic Ground-Motion Simulations on Rough Faults Including Effects of 3D Stochastic Velocity Perturbations. Bulletin of the Seismological Society of America, 106(5): 2136-2153. https://doi.org/10.1785/0120160088

Guo, W. X., Yao, X. X., Liu, Y., et al., 2025. Review of Research on Strong-Motion Record Flatfile. World Earthquake Engineering, 41(1): 14-25 (in Chinese with English abstract).

Ji, Z. W., Li, Z. C., Sun, J. Z., et al., 2023. Estimation of Broadband Ground Motion Characteristics Considering Source Parameter Uncertainty and Undetermined Site Condition in Densely Populated Areas of Pingwu. Frontiers in Earth Science, 10: 1081542. https://doi.org/10.3389/feart.2022.1081542

Jiang, Q. F., Rong, M. S., Wei, W., et al., 2024. A Quantitative Seismic Topographic Effect Prediction Method Based upon BP Neural Network Algorithm and FEM Simulation. Journal of Earth Science, 35(4): 1355-1366. https://doi.org/10.1007/s12583-022-1795-x

Liu, P., Archuleta, R. J., Hartzell, S. H., 2006. Prediction of Broadband Ground-Motion Time Histories: Hybrid Low/High-Frequency Method with Correlated Random Source Parameters. Bulletin of the Seismological Society of America, 96(6): 2118-2130. https://doi.org/10.1785/0120060036

Mai, P. M., Beroza, G. C., 2003. A Hybrid Method for Calculating Near-Source, Broadband Seismograms: Application to Strong Motion Prediction. Physics of the Earth and Planetary Interiors, 137(1-4): 183-199. https://doi.org/10.1016/S0031-9201(03)00014-1

Mazzieri, I., Stupazzini, M., Guidotti, R., et al., 2013. SPEED: SPectral Elements in Elastodynamics with Discontinuous Galerkin: A Non-Conforming Approach for 3D Multi-Scale Problems. International Journal for Numerical Methods in Engineering, 95(12): 991-1010. https://doi.org/10.1002/nme.4532

Olsen, K. B., 2003. Estimation of Q for Long-Period (> 2 Sec) Waves in the Los Angeles Basin. Bulletin of the Seismological Society of America, 93(2): 627-638. https://doi.org/10.1785/0120020135

Olson, M. H., Primps, S. B., 1984. Working at Home with Computers: Work and Nonwork Issues. Journal of Social Issues, 40(3): 97-112. https://doi.org/10.1111/j.1540-4560.1984.tb00194.x

Paolucci, R., Mazzieri, I., Smerzini, C., 2015. Anatomy of Strong Ground Motion: Near-Source Records and Three-Dimensional Physics-Based Numerical Simulations of the M_w 6.0 2012 May 29 Po Plain Earthquake, Italy. Geophysical Journal International, 203(3): 2001-2020. https://doi.org/10.1093/gji/ggv405

Paolucci, R., Gatti, F., Infantino, M., et al., 2018. Broadband Ground Motions from 3D Physics-Based Numerical Simulations Using Artificial Neural Networks. Bulletin of the Seismological Society of America, 108(3A): 1272-1286. https://doi.org/10.1785/0120170293

Sabetta, F., Pugliese, A., Fiorentino, G., et al., 2021. Simulation of Non-Stationary Stochastic Ground Motions Based on Recent Italian Earthquakes. Bulletin of Earthquake Engineering, 19(9): 3287-3315. https://doi.org/10.1007/s10518-021-01077-1

Sjögreen, B., Petersson, N. A., 2012. A Fourth Order Accurate Finite Difference Scheme for the Elastic Wave Equation in Second Order Formulation. Journal of Scientific Computing, 52(1): 17-48. https://doi.org/10.1007/s10915-011-9531-1

Smerzini, C., Vanini, M., Paolucci, R., et al., 2023. Regional Physics-Based Simulation of Ground Motion within the Rhne Valley, France, during the M_W 4.9 2019 Le Teil Earthquake. Bulletin of Earthquake Engineering, 21(4): 1747-1774. https://doi.org/10.1007/s10518-022-01591-w

Somerville, P., Irikura, K., Graves, R., et al., 1999. Characterizing Crustal Earthquake Slip Models for the Prediction of Strong Ground Motion. Seismological Research Letters, 70(1): 59-80. https://doi.org/10.1785/gssrl.70.1.59

Spudich, P., Xu, L. S., 2003. 85.14-Software for Calculating Earthquake Ground Motions from Finite Faults in Vertically Varying Media. In: Lee, W. H K., Kanamori, H., Jennings, P. C., et al., eds., International Geophysics, International Handbook of Earthquake and Engineering Seismology. Elsevier, Amsterdam, 1633-1634. https://doi.org/10.1016/s0074-6142(03)80293-0

Tarbali, K., Bradley, B. A., 2015. Ground Motion Selection for Scenario Ruptures Using the Generalised Conditional Intensity Measure (GCIM) Method. Earthquake Engineering & Structural Dynamics, 44(10): 1601-1621. https://doi.org/10.1002/eqe.2546

Wang, H. W., Li, H. R., Ren, Y. F., et al., 2025. A Comprehensive Earthquake Source Database for China's Strong-Motion Flatfile (2007-2020). Earthquake Research Advances, 5(2): 100346. https://doi.org/10.1016/j.eqrea.2024.100346

Wang, X. C., Wang, J. T., Zhang, C. H., 2023. Deterministic Full-Scenario Analysis for Maximum Credible Earthquake Hazards. Nature Communications, 14: 6600. https://doi.org/10.1038/s41467-023-42410-3

Wang, Y. M., Xu, L. H., Yang, H. Y., et al., 2023. Deep Deformation Mechanism and Active Tectonic along the Weixi-Qiaohou Fault Zone. Chinese Journal of Geophysics, 66(3): 1070-1085 (in Chinese with English abstract).

Whittaker, A., Atkinson, G., Baker, J., et al., 2011. Selecting and Scaling Earthquake Ground Motions for Performing Response-History Analyses, Grant/Contract Reports (NISTGCR). National Institute of Standards and Technology, Gaithersburg. https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=915482

Wu, J. P., Ming, Y. H., Wang, C. Y., 2004. Source Mechanism of Small-to-Moderate Earthquakes and Tectonic Stress Field in Yunnan Province. Acta Seismologica Sinica, 26(5): 457-465 (in Chinese with English abstract).

Yao, X. X., Ren, Y. F., Kishida, T., et al., 2025. A Post Processing Method of Strong Motion Record Filtering Satisfying the Compatibility. Engineering Mechanics, 42(1): 152-163 (in Chinese with English abstract).

Zhao, B., Gao, Y., Ma, Y. L., 2022. Relocations, Focal Mechanisms and Stress Inversion of the May 21th 2021 Yangbi M_S6.4 Earthquake Sequence in Yunnan, China. Chinese Journal of Geophysics, 65(3): 1006-1020 (in Chinese with English abstract).

Zhu, J. B., Liu, H. Y., Luan, S. C., et al., 2025. Prediction of On-Site Peak Ground Motion Based on Machine Learning and Transfer Learning. Earth Science, 50(5): 1842-1860 (in Chinese with English abstract).

Zhu, Y. J., Luo, Y., Zhao, L., et al., 2022. Rupture Process of Yunnan Yangbi M_S6.4 Earthquake Constrained by Regional Broadband Seismograms. Chinese Journal of Geophysics, 65(3): 1021-1031 (in Chinese with English abstract).

巴振宁, 刘悦, 赵靖轩, 等, 2023. 2021年漾濞6.4级近断层宽频地震动模拟: 一种改进的FK方法. 岩土工程学报, 45(4): 709-719.

郭文轩, 姚鑫鑫, 刘也, 等, 2025. 强震动记录Flatfile研究综述. 世界地震工程, 41(1): 14-25.

王一咪, 徐龙辉, 杨海燕, 等, 2023. 维西‒乔后断裂带的深部变形机制与构造活动特征. 地球物理学报, 66(3): 1070-1085.

吴建平, 明跃红, 王椿镛, 2004. 云南地区中小地震震源机制及构造应力场研究. 地震学报, 26(5): 457-465.

姚鑫鑫, 任叶飞, 岸田忠大, 等, 2025. 满足数据同一性的强震动记录去噪滤波后处理输出方法. 工程力学, 42(1): 152-163.

赵博, 高原, 马延路, 2022. 2021年5月21日云南漾濞M_S6.4地震序列重新定位、震源机制及应力场反演. 地球物理学报, 65(3): 1006-1020.

朱景宝, 刘赫奕, 栾世成, 等, 2025. 基于机器学习和迁移学习的现地地震动峰值预测. 地球科学, 50(5): 1842-1860. doi: 10.3799/dqkx.2024.071

朱音杰, 罗艳, 赵里, 等, 2022. 利用区域宽频地震数据反演2021年5月云南漾濞M_S6.4地震震源破裂过程. 地球物理学报, 65(3): 1021-1031.

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Simulation of Broadband Ground Motion in Yangbi Earthquake by Integrating Spectral Element Method and Artificial Neural Network

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