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Instantaneous Physics-Based Ground Motion Maps Using Reduced-Order Modeling
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DOI：10.1029/2023JB026975.png)
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Abstract
En 中文
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 M-W 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 10(7)-10(8) 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.Plain Language Summary Computer simulations can be used to predict the intensity of ground shaking caused by earthquakes. However, these simulations require significant amounts of computing time to obtain accurate results, making them too slow for real-time systems. In this work, we apply a technique that allows us to obtain instantaneous and accurate approximations of a scenario earthquake simulation using simplified models. The simplified models are fast enough to evaluate this earthquake scenario on demand. We use the simplified models to predict shaking intensities for 1 million different fault planes at the location of the 2008 Chino Hills earthquake. By analyzing the predicted shaking intensities from these earthquakes, we identify possible areas of strong shaking, areas that might otherwise be unidentified if fewer earthquake scenarios were examined. These results suggest that these types of simplified models could be considered to assess regional earthquake hazards and to inform shaking intensity predictions in earthquake early warning.
Keywords:
reduced-order model
ground motion modeling
high-performance computing
machine learning
earthquake early warning
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