TSUFLIND-EnKF inversion model applied to tsunami deposits for estimation of transient flow depth and speed with quantified uncertainties

TL;DR

This study introduces the TSUFLIND-EnKF inversion model that combines physical modeling and data assimilation to estimate tsunami flow characteristics from deposits, accounting for uncertainties, demonstrated on idealized and real tsunami deposits.

Contribution

The paper presents a novel inversion approach integrating TSUFLIND with Ensemble Kalman Filtering to quantify tsunami flow parameters and uncertainties from deposit data.

Findings

Sampling methods significantly affect inversion accuracy.

More samples do not necessarily improve robustness.

The model successfully estimates flow depth and speed with quantified uncertainties.

Abstract

Tsunami deposits are recordings of tsunami events that contain information about flow conditions. Deciphering quantitative information from tsunami deposits is especially important for analyzing paleo-tsunami events in which deposits comprise the only leftover physical evidence. The physical meaning of the deciphered quantities depends on the physical assumptions that are applied. The aim of our study is to estimate the characteristics of tsunamis and quantify the errors and uncertainties that inherent within them. To achieve this goal, we apply the TSUFLIND-EnKF inversion model to study the deposition of an idealized deposit created by a single tsunami wave and one real case from 2004 India ocean tsunami. TSUFLIND-EnKF model combines TSUFLIND for the deposition module with the Ensemble Kalman Filtering (EnKF) method. In our modeling, we assume that grain-size distribution and thickness from the idealized deposits at different depths can be used as an observational variable. Our tentative results indicate that sampling methods and sampling frequencies of tsunami deposits influence not only the magnitude of the inverted variables, but also their errors and uncertainties. An interesting result of our technique is that a larger number of samples from a given tsunami deposit does not automatically mean that the inversion results are more robust with smaller errors and decreased uncertainties.