Wave simulation of symmetric V-shaped canyon based on physics-informed deep learning method

LUAN Shaokai; CHEN Su; DING Yi; JIN Liguo; WANG Juke; LI Xiaojun

doi:10.11779/CJGE20230263

Volume 46 Issue 6

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Chinese Journal of Geotechnical Engineering > 2024 > 46(6): 1246-1253. > DOI: 10.11779/CJGE20230263

LUAN Shaokai, CHEN Su, DING Yi, JIN Liguo, WANG Juke, LI Xiaojun. Wave simulation of symmetric V-shaped canyon based on physics-informed deep learning method[J]. Chinese Journal of Geotechnical Engineering, 2024, 46(6): 1246-1253. DOI: 10.11779/CJGE20230263

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Wave simulation of symmetric V-shaped canyon based on physics-informed deep learning method

LUAN Shaokai^1,,
CHEN Su^1, ,,
DING Yi¹,
JIN Liguo²,
WANG Juke²,
LI Xiaojun^{1, 2}

1.
Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China
2.
Institute of Geophysics, China Earthquake Administration, Beijing 100081, China

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Received Date: March 26, 2023
Available Online: June 04, 2024

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Abstract

Abstract

The seismic effects of alpine and canyon sites are a research hotspot in the field of earthquake engineering. The series solution of the two-dimensional scattering and diffraction wave functions of cylindrical SH waves caused by V-shaped canyons is relatively mature and provides reasonable and scientific ground motion input for many major projects in river valleys. In this study, the physics-informed deep learning method combined with the comparative analysis of the analytical results is used to further clarify the seismic response characteristics and complex wave field spatial distribution of the V-shaped river valley. The method mainly focuses on sparse samples and interpretable artificial intelligence, and establishes a deep neural network to realize the semi-infinite seismic propagation model by combining the strong formal automatic differentiation with the soft constraint boundary condition embedding, and realizes high-precision prediction of V-shaped river valleys under different given wave field conditions by adopting the time domain decomposition strategy. By comparing with the analytical solution, the accuracy and efficiency of the proposed physics-driven artificial intelligence method are evaluated. The results show that the physics-driven artificial intelligence method can be applied to the analysis of terrain effects, and the cylindrical SH waves are significantly attenuated at the bottom of the V-shaped canyon, and the edge area shows an amplification effect.
- physics-informed deep learning,
- river valley earthquake,
- wave simulation,
- AI for science

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References

[1]	WONG H L, TRIFUNAC M D. Scattering of plane sh waves by a semi-elliptical canyon[J]. Earthquake Engineering & Structural Dynamics, 1974, 3(2): 157-169.
[2]	TRIFUNAC M D. Surface motion of a semi-cylindrical alluvial valley for incident plane SH waves[J]. Bulletin of the Seismological Society of America, 1971, 61(6): 1755-1770. doi: 10.1785/BSSA0610061755
[3]	WONG H L, TRIFUNAC M D. Surface motion of a semi-elliptical alluvial valley for incident plane SH waves[J]. Bulletin of the Seismological Society of America, 1974, 64(5): 1389-1408. doi: 10.1785/BSSA0640051389
[4]	YUAN X M, LIAO Z P. Scattering of plane SH waves by a cylindrical canyon of circular-arc cross-section[J]. Soil Dynamics and Earthquake Engineering, 1994, 13(6): 407-412. doi: 10.1016/0267-7261(94)90011-6
[5]	TSAUR D H, CHANG K H. Scattering of SH waves by truncated semicircular canyon[J]. Journal of Engineering Mechanics, 2009, 135(8): 862-870. doi: 10.1061/(ASCE)0733-9399(2009)135:8(862)
[6]	GAO Y, ZHANG N, LI D, et al. Effects of topographic amplification induced by a U-shaped canyon on seismic waves[J]. Bulletin of the Seismological Society of America, 2012, 102(4): 1748-1763. doi: 10.1785/0120110306
[7]	GAO Y F, ZHANG N. Scattering of cylindrical SH waves induced by a symmetrical V-shaped canyon: near-source topographic effects[J]. Geophysical Journal International, 2013, 193(2): 874-885. doi: 10.1093/gji/ggs119
[8]	XU J X, ZHANG W, CHEN X F. An optimized finite difference method based on a polar coordinate system for regional-scale irregular topography[J]. Earthquake Science, 2021, 34(4): 334-343. doi: 10.29382/eqs-2021-0022
[9]	KONG X, LI H. Surface motion evaluation of terrace-shaped hill subjected to incident plane SV wave using spectral element method[C]// 16 th World Conference on Earthquake Engineering. Chile, 2017.
[10]	RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 2019, 378: 686-707. doi: 10.1016/j.jcp.2018.10.045
[11]	SMITH J D, AZIZZADENESHELI K, ROSS Z E. EikoNet: solving the eikonal equation with deep neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(12): 10685-10696. doi: 10.1109/TGRS.2020.3039165
[12]	MOSELEY B, MARKHAM A, NISSEN-MEYER T. Solving the wave equation with physics-informed deep learning[EB/OL]. https://arxiv.org/abs/2006.11894.pdf. 2020.
[13]	RASHT-BEHESHT M, HUBER C, SHUKLA K, et al. Physics-informed neural networks (PINNs) for wave propagation and full waveform inversions[J]. Journal of Geophysical Research: Solid Earth, 2022, 127(5): e2021JB023120. doi: 10.1029/2021JB023120
[14]	SHUKLA K, LEONI P C, BLACKSHIRE J, et al. Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks[J]. Journal of Nondestructive Evaluation, 2020, 39(3): 1-20.
[15]	唐明健, 唐和生. 基于物理信息的深度学习求解矩形薄板力学正反问题[J]. 计算力学学报, 2022, 39(1): 120-128. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJG202201018.htm TANG Mingjian, TANG Hesheng. A physics-informed deep learning method for solving forward and inverse mechanics problems of thin rectangular plates[J]. Chinese Journal of Computational Mechanics, 2022, 39(1): 120-128. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJG202201018.htm
[16]	HAGHIGHAT E, RAISSI M, MOURE A, et al. A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics[J]. Computer Methods in Applied Mechanics and Engineering, 2021, 379: 113741. doi: 10.1016/j.cma.2021.113741
[17]	SUN L N, GAO H, PAN S W, et al. Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data[J]. Computer Methods in Applied Mechanics and Engineering, 2020, 361: 112732. doi: 10.1016/j.cma.2019.112732
[18]	GUO H W, ZHUANG X Y, LIANG D, et al. Stochastic groundwater flow analysis in heterogeneous aquifer with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning[EB/OL]. https://arxiv.org/abs/2010.12344.pdf. 2020.
[19]	RAO C P, SUN H, LIU Y. Physics-informed deep learning for incompressible laminar flows[J]. Theoretical and Applied Mechanics Letters, 2020, 10(3): 207-212. doi: 10.1016/j.taml.2020.01.039
[20]	JIN X W, CAI S Z, LI H, et al. NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations[J]. Journal of Computational Physics, 2021, 426: 109951. doi: 10.1016/j.jcp.2020.109951
[21]	陆至彬, 瞿景辉, 刘桦, 等. 基于物理信息神经网络的传热过程物理场代理模型的构建[J]. 化工学报, 2021, 72(3): 1496-1503. https://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ202103031.htm LU Zhibin, QU Jinghui, LIU Hua, et al. Surrogate modeling for physical fields of heat transfer processes based on physics-informed neural network[J]. CIESC Journal, 2021, 72(3): 1496-1503. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HGSZ202103031.htm
[22]	赵暾, 周宇, 程艳青, 等. 基于内嵌物理机理神经网络的热传导方程的正问题及逆问题求解[J]. 空气动力学学报, 2021, 39(5): 19-26. https://www.cnki.com.cn/Article/CJFDTOTAL-KQDX202105003.htm ZHAO Tun, ZHOU Yu, CHENG Yanqing, et al. Solving forward and inverse problems of the heat conduction equation using physics-informed neural networks[J]. Acta Aerodynamica Sinica, 2021, 39(5): 19-26. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KQDX202105003.htm
[23]	KARNIADAKIS G E, KEVREKIDIS I G, LU L, et al. Physics-informed machine learning[J]. Nature Reviews Physics, 2021, 3(6): 422-440. doi: 10.1038/s42254-021-00314-5
[24]	LU L, JIN P, KARNIADAKIS G E. Deeponet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators[EB/OL]. https://arxiv.org/abs/1910.03193.pdf. 2019.
[25]	BELBUTE-PERES F A, CHEN Y, SHA F. HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks[EB/OL]. https://arxiv.org/bs/111.01008.pdf. 2021.
[26]	LEUNG W T, LIN G, ZHANG Z C. NH-PINN: neural homogenization-based physics-informed neural network for multiscale problems[J]. Journal of Computational Physics, 2022, 470: 111539. doi: 10.1016/j.jcp.2022.111539
[27]	BRUNTON S L, PROCTOR J L, KUTZ J N. Discovering governing equations from data by sparse identification of nonlinear dynamical systems[J]. Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(15): 3932-3937.
[28]	GAO H, SUN L N, WANG J X. PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain[J]. Journal of Computational Physics, 2021, 428: 110079. doi: 10.1016/j.jcp.2020.110079
[29]	MOSELEY B, MARKHAM A, NISSEN-MEYER T. Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations[J]. Advances in Computational Mathematics, 2023, 49(4): 62. doi: 10.1007/s10444-023-10065-9
[30]	陈苏, 丁毅, 孙浩, 等. 物理驱动深度学习波动数值模拟方法及应用[J]. 力学学报, 2023, 55(1): 272-282. https://www.cnki.com.cn/Article/CJFDTOTAL-LXXB202301022.htm CHEN Su, DING Yi, SUN Hao, et al. Methods and applications of physical information deep learning in wave numerical simulation[J]. Chinese Journal of Theoretical and Applied Mechanics, 2023, 55(1): 272-282. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-LXXB202301022.htm