Real-time Tsunami Force Prediction by Mode Decomposition-Based Surrogate Modeling

Abstract

Abstract. This study presents a framework for real-time tsunami force predictions by the application of mode decomposition based surrogate modelling with 2D-3D coupled numerical simulations. A limited number of large-scale numerical analyses are performed for a selection scenarios with variations in fault parameters to capture the distribution tendencies of the target risk indicators. Then, the proper orthogonal decomposition (POD) is applied to the analysis results to extract the principal modes that represent the temporal and spatial characteristics of tsunami forces. A surrogate model is then constructed by a linear combination of these modes, whose coefficients are defined as functions of the selected input parameters. A numerical example is presented to demonstrate the applicability of the proposed framework to one of the tsunami-affected areas during the Great East Japan Earthquake of 2011. Combining 2D and 3D versions of the stabilized finite element method, we carry out a series of high precision numerical analyses with different input parameters to obtain a set of time history data of the tsunami forces acting on buildings and the inundation depths. POD is applied to the data set to construct the surrogate model that is capable of providing the predictions equivalent to the simulation results almost instantaneously. Based on the acceptable accuracy of the obtained results, it was confirmed that the proposed framework is a useful tool for evaluating time series data of hydrodynamic force acting on buildings.

Sequential Bayesian Update to Detect the Most Likely Tsunami Scenario Using Observational Wave Sequences

“…For more details on the relationship between the eigenvalue decomposition of C and SVD, refer to appropriate references; see, for example, Weiss (2019), Fukutani et al (2021), Otake et al (2021) andTozato et al (2021).…”