Square-root variable metric based elastic full-waveform inversion—Part 2: uncertainty estimation
Qiancheng Liu,Daniel Peter +1 more
TL;DR: This work was supported by the King Abdullah University of Science & Technology (KAUST) Office of Sponsored Research (OSR) under award No.
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Abstract: The authors are grateful to editor Jean Virieux and reviewer Andreas Fichtner and an anonymous reviewer for improving the initial manuscript. The authors are grateful to Carl Tape for inspiring discussions and valuable inputs to improve the manuscript. This work was supported by the King Abdullah University of Science & Technology (KAUST) Office of Sponsored Research (OSR) under award No. UAPN#2605-CRG4. Computational resources were provided by the Information Technology Division and Extreme Computing Research Center (ECRC) at KAUST.
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Global adjoint tomography—model GLAD-M25
W. Lei,Y. Ruan,Y. Ruan,Ebru Bozdag,Daniel Peter,Matthieu Lefebvre,Dimitri Komatitsch,Jeroen Tromp,Judith Hill,Norbert Podhorszki,David Pugmire +10 more
TL;DR: In this paper, a transversely isotropic global adjoint tomography model (GLAD-M25) is presented, which is the result of 10 quasi-Newton tomographic iterations with an earthquake database consisting of 1480 events.
Seismic wavefield imaging of Earth’s interior across scales
Jeroen Tromp
- 01 Jan 2020
TL;DR: The use of full waveform inversion (FWI) for imaging Earth's interior was introduced in the late 1970s and has become feasible for a wide range of applications and is currently used across nine orders of magnitude in frequency and wavelength.
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Bayesian Elastic Full-Waveform Inversion Using Hamiltonian Monte Carlo
TL;DR: In this article, a proof of concept for Bayesian elastic full-waveform inversion in 2D is presented based on Hamiltonian Monte Carlo sampling of the posterior distribution, and the computation of misfit derivatives using adjoint techniques.
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Ensemble-based uncertainty estimation in full waveform inversion
TL;DR: A joint full waveform inversion and ensemble data assimilation scheme, allowing local Bayesian estimation of the solution that brings uncertainty estimation to the tomographic problem.
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