Instantaneous Physics‐Based Ground Motion Maps Using Reduced‐Order Modeling
Instantaneous Physics‐Based Ground Motion Maps Using Reduced‐Order Modeling
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DOI：
10.1029/2023jb026975
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
J. Rekoske;A. Gabriel;David May
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文献类型：
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作者：
J. Rekoske;A. Gabriel;David May
基于物理的地震地面运动模拟对于补充记录的地面运动是有用的。然而，执行数值模拟的计算费用阻碍了它们在需要实时解决方案或不同震源解决方案集成的任务中的适用性。为了实现基于物理的快速解决方案，我们提出了一种基于插值正交分解（POD）的降阶建模方法来预测峰值地面速度（PGVs）。作为验证，我们使用具有不同深度和震源机制的双偶震源，在2008年MW 5.4奇诺山地震的区域三维波传播模拟中考虑了pgv。这些模拟解析频率≤1.0 Hz，包括地形、粘弹性衰减和S波速度≥500 m/ S。我们评估了插值POD降阶模型（ROM）作为近似方法的函数的准确性。比较径向基函数（RBF）、多层感知器神经网络、随机森林和k近邻，我们发现RBF插值在独立数据集上的误差最小（≈0.1 cm/s）。我们还发现，评估ROM比波传播模拟快107-108倍。我们使用ROM生成100万种不同震源机制的PGV地图，在这些地图中，我们识别出潜在的破坏性地面运动，并量化震源机制、深度和预测PGV精度之间的相关性。我们的研究结果表明，ROM可以快速准确地从不同震源性质、地形和复杂地下结构的波传播模拟中近似出PGV。
Physics‐based simulations of earthquake ground motion are useful to complement recorded ground motions. However, the computational expense of performing numerical simulations hinders their applicability to tasks that require real‐time solutions or ensembles of solutions for different earthquake sources. To enable rapid physics‐based solutions, we present a reduced‐order modeling approach based on interpolated proper orthogonal decomposition (POD) to predict peak ground velocities (PGVs). As a demonstrator, we consider PGVs from regional 3D wave propagation simulations at the location of the 2008 MW 5.4 Chino Hills earthquake using double‐couple sources with varying depth and focal mechanisms. These simulations resolve frequencies ≤1.0 Hz and include topography, viscoelastic attenuation, and S‐wave speeds ≥500 m/s. We evaluate the accuracy of the interpolated POD reduced‐order model (ROM) as a function of the approximation method. Comparing the radial basis function (RBF), multilayer perceptron neural network, random forest, and k‐nearest neighbor, we find that the RBF interpolation gives the lowest error (≈0.1 cm/s) when tested against an independent data set. We also find that evaluating the ROM is 107–108 times faster than the wave propagation simulations. We use the ROM to generate PGV maps for 1 million different focal mechanisms, in which we identify potentially damaging ground motions and quantify correlations between focal mechanism, depth, and accuracy of the predicted PGV. Our results demonstrate that the ROM can rapidly and accurately approximate the PGV from wave propagation simulations with variable source properties, topography, and complex subsurface structure.