The Full Waveform Inversion (FWI) is based on the optimization of a physical model in order to fit the generated synthetic data with the empirical data collected by the receivers used in the field. At each iteration of the FWI computational procedure, the wave equation is solved and an update direction of the model is computed. This process requires a large amount of memory, a critical step in the performance of the method. The compression of the wave field data can effectively be performed with the curvelet transform, a modern multi-scale tool. Because of their dependence on orientation, curvelets are suitable to represent the anisotropy of wave patterns. Besides, it has been demonstrated that the solution operators of a wide range of wave equations are optimally sparse. In this work, we explore curvelets for data compression in the seismic FWI context. We compare the memory use of standard FWI processing with similar FWI processing using curvelets decomposition.


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  1. Candes, E.J. and Demanet, L.
    [2005] The curvelet representation of wave propagators is optimally sparse. Communications on Pure and Applied Mathematics, 58(11), 1472–1528.
    [Google Scholar]
  2. Hennenfent, G. and Herrmann, F.J.
    [2006] Seismic denoising with nonuniformly sampled curvelets. Computing in Science & Engineering, 8(3), 16–25.
    [Google Scholar]
  3. Herrmann, F.J. and Hennenfent, G.
    [2008] Non-parametric seismic data recovery with curvelet frames. Geophysical Journal International, 173(1), 233–248.
    [Google Scholar]
  4. Herrmann, F.J., Moghaddam, P. and Stolk, C.C.
    [2008] Sparsity-and continuity-promoting seismic image recovery with curvelet frames. Applied and Computational Harmonic Analysis, 24(2), 150–173.
    [Google Scholar]
  5. Ma, J. and Plonka, G.
    [2010] A review of curvelets and recent applications. IEEE Signal Processing Magazine, 27(2), 118–133.
    [Google Scholar]
  6. Sanderson, C. and Curtin, R.
    [2016] Armadillo: a template-based C++ library for linear algebra. Journal of Open Source Software.
    [Google Scholar]
  7. Shan, H., Ma, J. and Yang, H.
    [2009] Comparisons of wavelets, contourlets and curvelets in seismic denoising. Journal of Applied Geophysics, 69(2), 103–115.
    [Google Scholar]
  8. Virieux, J. and Operto, S.
    [2009] An overview of full-waveform inversion in exploration geophysics. Geophysics, 74(6), WCC1–WCC26.
    [Google Scholar]
  9. Wang, Y. and Rao, Y.
    [2009] Reflection seismic waveform tomography. Journal of Geophysical Research: Solid Earth, 114(B3).
    [Google Scholar]

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