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Abstract

Summary

Seismic Full Waveform Inversion (FWI) is a powerful tool for subsurface estimation, but its results are inherently non-unique. By quantifying the uncertainty in FWI results, this non-uniqueness can be better characterized, leading to more reliable and insightful interpretations of the subsurface. Bayesian inference, applied through the Metropolis-Hastings MCMC algorithm, addresses this need but is computationally intensive, especially for complex forward simulations. This study proposes a deep learning-assisted workflow to improve MCMC efficiency, replacing the traditional physics-based solver (SPECFEM2D) with a Convolutional Neural Network (CNN) for data misfit calculations. Results show the CNN approach offers a 4-fold speed increase while maintaining accuracy in posterior distribution estimation, underscoring its potential as a fast, reliable alternative for complex seismic inversion tasks.

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/content/papers/10.3997/2214-4609.202510306
2025-06-02
2026-02-14
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References

  1. Fichtner, A., Zunino, A., & Gebraad, L. (2019). Hamiltonian Monte Carlo solution of tomographic inverse problems. Geophysical Journal International, 216(2), 1344–1363.
    [Google Scholar]
  2. Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 7(4), 457–472.
    [Google Scholar]
  3. Kotsi, M., Malcolm, A., & Ely, G. (2020). Uncertainty quantification in time-lapse seismic imaging: a full-waveform approach. Geophysical Journal International, 222(2), 1245–1263.
    [Google Scholar]
  4. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
    [Google Scholar]
  5. Mosegaard, K., & Tarantola, A. (1995). Monte Carlo sampling of solutions to inverse problems. Journal of Geophysical Research: Solid Earth, 100(B7), 12431–12447.
    [Google Scholar]
  6. Sobol’, I. Y. M. (1967). On the distribution of points in a cube and the approximate evaluation of integrals. Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, 7(4), 784–802.
    [Google Scholar]
  7. Tromp, J., Komatitsch, D., & Liu, Q. (2008). Spectral-element and adjoint methods in seismology. Communications in Computational Physics, 3(1), 1–32.
    [Google Scholar]
  8. Zhang, X., & Curtis, A. (2020). Seismic tomography using variational inference methods. Journal of Geophysical Research: Solid Earth, 125(4), e2019JB018589.
    [Google Scholar]
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