Time-Dependent Probabilistic Tsunami Inundation Assessment Using Mode Decomposition to Assess Uncertainty for an Earthquake Scenario

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Abstract

This study presents an innovative probabilistic tsunami inundation assessment for an earthquake scenario to randomly generate tsunami inundation depth distributions by quantitatively evaluating the spatial correlation of tsunami inundation depths using singular value decomposition (SVD) derived from proper orthogonal decomposition and to evaluate the tsunami inundation depths considering the imminent occurrence of an earthquake. We found a good agreement between the evaluation results of the proposed surrogate model and the numerical results of the nonlinear long wave equations for the tsunami inundation depth distribution in Kamakura city, Japan, due to the Sagami Trough megathrust earthquake. Evaluating the spatial correlation using SVD has the advantage that the covariance matrix does not need to be defined in advance but can be defined from the data itself. We also achieved a significant reduction in the number of required tsunami propagation simulations for the probabilistic assessment and attained higher computational efficiency by extracting spatial correlations with SVD. Furthermore, we conducted a probabilistic tsunami inundation assessment focusing on a relatively short period (i.e., 50 years) considering the time-dependent occurrence probability of the target earthquake. The proposed probabilistic assessment method with mode decomposition is applicable to the general probabilistic tsunami hazard assessment by integrating it with physical stochastic slip models.

Original languageEnglish
Article numbere2021JC017250
JournalJournal of Geophysical Research: Oceans
Volume126
Issue number7
DOIs
Publication statusPublished - 2021 Jul

Keywords

  • Gaussian process regression
  • inundation simulation
  • Kamakura from Kanto earthquake
  • probabilistic tsunami hazard assessment
  • singular value decomposition
  • time-dependent hazard analysis

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geophysics
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science
  • Oceanography

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