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• W2028092617 abstract "This paper develops a self-parametrized Bayesian inversion to infer the spatio-temporal evolution of tsunami sources (initial sea state) due to megathrust earthquakes. To date, tsunami-source uncertainties are poorly understood, and the effect of choices such as discretization have not been studied. The approach developed here is based on a trans-dimensional self-parametrization of the sea surface, avoids regularization constraints and provides rigorous uncertainty estimation that accounts for model-selection ambiguity associated with the source discretization. The sea surface is parametrized using self-adapting irregular grids, which match the local resolving power of the data and provide parsimonious solutions for complex source characteristics. Source causality is ensured by including rupture-velocity and obtaining delay times from the Eikonal equation. The data are recorded on ocean-bottom pressure and coastal wave gauges and predictions are based on Green-function libraries computed from ocean-basin scale tsunami models for cases that include/exclude dispersion effects. The inversion is applied to tsunami waveforms from the great 2011 Tohoku-Oki (Japan) earthquake. The tsunami source is strongest near the Japan trench with posterior mean amplitudes of ~5 m. In addition, the data appear sensitive to rupture velocity, which is part of our kinematic source model." @default.
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• W2028092617 date "2014-10-01" @default.
• W2028092617 modified "2023-10-17" @default.
• W2028092617 title "Bayesian tsunami-waveform inversion with trans-dimensional tsunami-source models" @default.
• W2028092617 doi "https://doi.org/10.1121/1.4899490" @default.
• W2028092617 hasPublicationYear "2014" @default.
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