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Sediment deposits are the only leftover records from paleo tsunami events.Therefore, inverse modeling method based on the information contained in thedeposit is an indispensable way of deciphering the quantitative characteristicsof the tsunamis, e.g., the flow speed and the flow depth. While several modelshave been proposed to perform tsunami inversion, i.e., to infer the tsunamicharacteristics based on the sediment deposits, the existing methods lackmathematical rigorousness and are not able to account for uncertainties in theinferred quantities. In this work, we propose an inversion scheme based onEnsemble Kalman Filtering (EnKF) to infer tsunami characteristics from sedimentdeposits. In contrast to traditional data assimilation methods using EnKF, anovelty of the current work is that we augment the system state to include boththe physical variables (sediment fluxes) that are observable and the unknownparameters (flow speed and flow depth) to be inferred. Based on the rigorousBayesian inference theory, the inversion scheme provides quantifieduncertainties on the inferred quantities, which clearly distinguishes thepresent method with existing schemes for tsunami inversion. Two test cases withsynthetic observation data are used to verify the proposed inversion scheme.Numerical results show that the tsunami characteristics inferred from thesediment deposit information have a favorable agreement with the truths, whichdemonstrated the merits of the proposed tsunami inversion scheme.

Inversion of Tsunamis Characteristics from Sediment Deposits Based on Ensemble Kalman Filtering