Bayesian seismic tomography based on velocity-space Stein variational gradient descent for physics-informed neural network

In this study, we propose a Bayesian seismic tomography inference method using physics-informed neural networks (PINN). PINN represents a recent advance in deep learning, offering the possibility to enhance physics-based simulations and inverse analyses. PINN-based deterministic seismic tomography uses two separate neural networks (NNs) to predict seismic velocity and travel time. Naive Bayesian NN (BNN) approaches are unable to handle the high-dimensional spaces spanned by the weight parameters of these two NNs. Hence, we reformulate the problem to perform the Bayesian estimation exclusively on the NN predicting seismic velocity, while the NN predicting travel time is used only for deterministic travel time calculations, with the help of the adjoint method. Furthermore, we perform BNN by introducing a function-space Stein variational gradient descent (SVGD), which performs particle-based variational inference in the space of the function predicted by the NN (i.e., seismic velocity), instead of in the traditional weight space. The result is a velocity-space SVGD for the PINN-based seismic tomography model (vSVGD-PINN-ST) that decreases the complexity of the problem thus enabling a more accurate and physically consistent Bayesian estimation, as confirmed by synthetic tests in one- and two-dimensional tomographic problem settings. The method allows PINN to be applied to Bayesian seismic tomography practically for the first time. Not only that, it can be a powerful tool not only for geophysical but also for general PINN-based Bayesian estimation problems associated with compatible NNs formulations and similar, or reduced, complexity

Consecutive ruptures on a complex conjugate fault system during the 2018 Gulf of Alaska earthquake

2018年アラスカ湾地震の複雑な破壊過程を解析 --間欠的に加速・減速する奇妙な巨大地震--. 京都大学プレスリリース. 2021-03-22.We developed a flexible finite-fault inversion method for teleseismic P waveforms to obtain a detailed rupture process of a complex multiple-fault earthquake. We estimate the distribution of potency-rate density tensors on an assumed model plane to clarify rupture evolution processes, including variations of fault geometry. We applied our method to the 23 January 2018 Gulf of Alaska earthquake by representing slip on a projected horizontal model plane at a depth of 33.6 km to fit the distribution of aftershocks occurring within one week of the mainshock. The obtained source model, which successfully explained the complex teleseismic P waveforms, shows that the 2018 earthquake ruptured a conjugate system of N-S and E-W faults. The spatiotemporal rupture evolution indicates irregular rupture behavior involving a multiple-shock sequence, which is likely associated with discontinuities in the fault geometry that originated from E-W sea-floor fracture zones and N-S plate-bending faults

Tsunami Analysis Method with High-Fidelity Crustal Structure and Geometry Model

Higher fidelity seafloor topography and crustal structure models have become available with accumulation of observation data. Previous studies have shown that the consideration of such high-fidelity models produces significant effects, in some cases, on crustal deformation results that are used as inputs for tsunami analysis. However, it is difficult to apply high-fidelity model of crustal deformation computations to tsunami computations because of large computational costs. In this paper, we propose a new crustal deformation computation method for estimating inputs for tsunami computations, which is based on a finite element analysis method with remarkable reduction of computation costs by efficient use of the arithmetic space and the solution space. This finite element analysis method enables us to conduct 102−3-times crustal deformation computations using high-fidelity models with a degree of freedom on the order of 108 for the 2011 Tohoku earthquake example. Tsunami computations with typical settings are conducted as an application example to present the advantages and characteristics of the proposed method. Comparisons between results of the proposed and the conventional method reveal that large shallow fault slip around the trench axis may lead to significant differences in tsunami waveforms and inundation height distributions in some cases

Numerical Verification Criteria for Coseismic and Postseismic Crustal Deformation Analysis with Large-scale High-fidelity Model

Numerical verification of postseismic crustal deformation analysis, computed using a large-scale finite element simulation, was carried out, by proposing new criteria that consider the characteristics of the target phenomenon. Specifically, pointwise displacement was used in the verification. In addition, the accuracy of the numerical solution was explicitly shown by considering the observation error of the data used for validation. The computational resource required for each analysis implies that high- performance computing techniques are necessary to obtain a verified numerical solution of crustal de- formation analysis for the Japanese Islands. Such verification in crustal deformation simulations should take on greater importance in the future, since continuous improvement in the quality and quantity of crustal deformation data is expected