1887

Abstract

Summary

Full waveform inversion (FWI) is a waveform matching procedure which provides subsurface model with a resolution of the order of the seismic wavelength. However, this high resolution potential also makes the inverse problem underlying FWI highly nonlinear and ill-posed and highlights the role of regularization. To stabilize FWI while preserving its resolution power, frequency-domain FWI is recasted as a constrained total-variation (TV) minimization of the subsurface parameters where the inequality constraints require the modelled wavefields to fit the data and satisfy the wave equation with prescribed errors. The wavefield and subsurface parameters are updated through an alternating optimization technique based on split Bregman iterations. At each iteration of the optimization, the wavefield is updated via a penalty method to best fit the data and satisfy the wave equation, while the constrained total-variation optimization provides subsurface parameter updates which conciliate blockiness and good signal-to-noise ratio. The resilience of the method to noise and cycle skipping as well as its resolution power are illustrated with two synthetic examples performed with a toy example and the 2004 Bp salt model. Starting from crude initiam model, we show how the proposed method manages sharp velocity contrasts generated by salt bodies and improves sub-salt imaging accordingly.

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/content/papers/10.3997/2214-4609.201801033
2018-06-11
2024-04-19
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References

  1. Aghamiry, H., Gholami, A. and Operto, S.
    [2018] Improving full-waveform inversion based on wavefield reconstruction via iterative update of data and sources. In: Expanded Abstracts, 80th Annual EAGE Meeting (Copenhagen).
    [Google Scholar]
  2. Boyd, S., Parikh, N., Chu, E., Peleato, B. and Eckstein, J.
    [2010] Distributed optimization and statistical learning via the alternating direction of multipliers. Foundations and trends in machine learning, 3(1), 1–122.
    [Google Scholar]
  3. Esser, E.
    [2009] Applications of Lagrangian-based alternating direction methods and connections to split Breg-man. Tech. rep., UCLA.
    [Google Scholar]
  4. Goldstein, T. and Osher, S.
    [2009] The split Bregman method for L1-regularized problems. SIAM Journal on Imaging Sciences, 2(2), 323–343.
    [Google Scholar]
  5. Marfurt, K.
    [1984] Accuracy of finite-difference and finite-element modeling of the scalar and elastic wave equations. Geophysics, 49, 533–549.
    [Google Scholar]
  6. Peters, B. and Herrmann, F.J.
    [2017] Constraints versus penalties for edge-preserving full-waveform inversion. The Leading Edge, 36(1), 94–100.
    [Google Scholar]
  7. Rudin, L., Osher, S. and Fatemi, E.
    [1992] Nonlinear total variation based noise removal algorithms. Physica D, 60, 259–268.
    [Google Scholar]
  8. van Leeuwen, T. and Herrmann, F.
    [2016] A penalty method for PDE-constrained optimization in inverse problems. Inverse Problems, 32(1), 1–26.
    [Google Scholar]
  9. van Leeuwen, T. and Herrmann, F.J.
    [2013] Mitigating local minima in full-waveform inversion by expanding the search space. Geophysical Journal International, doi:10.1093/gji/ggt258.
    https://doi.org/10.1093/gji/ggt258 [Google Scholar]
  10. Yin, W., Osher, S., Goldfarb, D. and Darbon, J.
    [2008] Bregman iterative algorithms for l1-minimization with applications to compressed sensing. SIAM Journal Imaging Sciences, 1(1), 143–168.
    [Google Scholar]
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