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Abstract

Summary

In this work, we present a proof of concept for Bayesian full-waveform inversion (FWI) in 2-D. This is based on approximate Langevin Monte Carlo sampling with a gradient-based adaptation of the posterior distribution. We apply our method to the Marmousi model, and it reliably recovers important aspects of the posterior, including the statistical moments, and 1-D and 2-D marginals. Depending on the variations of seismic velocities, the posterior can be significantly non-Gaussian, which directly suggest that using a Hessian approximation for uncertainty quantification in FWI may not be sufficient.

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/content/papers/10.3997/2214-4609.202112443
2021-10-18
2023-03-20

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References

  1. Fichtner, A. and Zunino, A.
    [2019] Hamiltonian Nullspace Shuttles. Geophysical Research Letters, 46(2), 644–651.
     [Google Scholar]
  2. Fichtner, A., Zunino, A. and Gebraad, L.
    [2019] Hamiltonian Monte Carlo solution of tomographic inverse problems. Geophysical Journal International, 216(2), 1344–1363.
     [Google Scholar]
  3. Gebraad, L., Boehm, C. and Fichtner, A.
    [2020] Bayesian Elastic Full-Waveform Inversion Using Hamiltonian Monte Carlo. Journal of Geophysical Research: Solid Earth, 125(3), e2019JB018428.
     [Google Scholar]
  4. Izzatullah, M., Van Leeuwen, T. and Peter, D.
    [2020] Langevin Dynamics Markov Chain Monte Carlo Solution for Seismic Inversion. 2020(1), 1–5.
     [Google Scholar]
  5. Kingma, D.P. and Ba, J.
    [2015] Adam: A Method for Stochastic Optimization. In: Bengio, Y. and LeCun, Y. (Eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings.
     [Google Scholar]
  6. Nemeth, C. and Fearnhead, P.
    [2020] Stochastic gradient Markov chain Monte Carlo. Journal of the American Statistical Association, 0(ja), 1–47.
     [Google Scholar]
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Approximate Langevin Monte Carlo with Adaptation for Bayesian Full-Waveform Inversion
Conference Proceedings 2021, 1 (2021); https://doi.org/10.3997/2214-4609.202112443
/content/papers/10.3997/2214-4609.202112443
/content/papers/10.3997/2214-4609.202112443
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