2015/11/10 by Jian-Xun Wang, Jianxun Wang, Wang, Jian-Xun +6
Earth and Planetary Sciences · Physics and Astronomy · #FOS: Physical sciences #Geophysics (physics.geo-ph) #Geophysics and Gravity Measurements #Seismic Imaging and Inversion Techniques #earthquake and tectonic studies #physics.geo-ph
paper · pdf · doi:10.48550/arxiv.1511.03307
38 pages, 10 figures
openalex publication_date 2015/11/10 · arxiv created 2016/01/15 · arxiv updated 2016/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Sediment deposits are the only leftover records from paleo tsunami events. Therefore, inverse modeling method based on the information contained in the deposit is an indispensable way of deciphering the quantitative characteristics of the tsunamis, e.g., the flow speed and the flow depth. While several models have been proposed to perform tsunami inversion, i.e., to infer the tsunami characteristics based on the sediment deposits, the existing methods lack mathematical rigorousness and are not able to account for uncertainties in the inferred quantities. In this work, we propose an inversion scheme based on Ensemble Kalman Filtering (EnKF) to infer tsunami characteristics from sediment deposits. In contrast to traditional data assimilation methods using EnKF, a novelty of the current work is that we augment the system state to include both the physical variables (sediment fluxes) that are observable and the unknown parameters (flow speed and flow depth) to be inferred. Based on the rigorous Bayesian inference theory, the inversion scheme provides quantified uncertainties on the inferred quantities, which clearly distinguishes the present method with existing schemes for tsunami inversion. Two test cases with synthetic observation data are used to verify the proposed inversion scheme. Numerical results show that the tsunami characteristics inferred from the sediment deposit information have a favorable agreement with the truths, which demonstrated the merits of the proposed tsunami inversion scheme.