1887

Abstract

Summary

Overcoming cycle-skipping in Full Waveform Inversion (FWI) is a significant step toward enabling automation in velocity model building. This reduces the demand of acquiring very low frequency data and/or starting the inversion procedure from kinematically accurate models. We present a new FWI method that uses time-warping as the extension domain to overcome cycle-skipping. The warping function dynamically transports the recorded field data to the modeled data and is imposed to represent the actual physical time. Thus, the derived objective function allows the inversion of the two parameters involved, model and time-warping extension, in a single optimization problem, whose solution is provided by the Alternate Direction Method (ADM). The novel FWI objective function enables automatic transition from a pure time-shift problem to a conventional least-squares one. We successfully apply the new FWI method to both synthetic and field data sets to demonstrate its effectiveness starting from inaccurate initial models. Results show the benefits of the new FWI approach in reducing the turnaround time for building high-resolution models from very simple initial velocity models.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202113271
2021-10-18
2024-04-29
Loading full text...

Full text loading...

References

  1. Biondi, B. and Almomin, A.
    [2013] Tomographic full-waveform inversion (TFWI) by combining FWI and wave-equation migration velocity analysis. The Leading Edge, 32(9), 1074–1080.
    [Google Scholar]
  2. Hale, D.
    [2013] Dynamic warping of seismic images. Geophysics, 78(2), S105–S115.
    [Google Scholar]
  3. Huang, G., Nammour, R. and Symes, W. W.
    [2017] Full-waveform inversion via source-receiver extension. Geophysics, 82(3) R153–R171.
    [Google Scholar]
  4. Luo, Y. and Schuster, G.T.
    [1991] Wave-equation traveltime inversion. Geophysics, 56(5), 645–653.
    [Google Scholar]
  5. Katzaros, G., Burrell, A., Colsoul, J. A. and Cahumba Quengue, N. R.
    [2020] Unlocking Hydrocarbon Prospectivity on the Angola Kwanza Shelf. Geoexpro, 17(5), 23–24.
    [Google Scholar]
  6. Ma, Y. and Hale, D.
    [2013] Wave-equation reflection travel-time inversion with dynamic warping and full-waveform inversion. Geophysics, 78(6), R223–R233.
    [Google Scholar]
  7. Métivier, L. and Brossier, R.
    [2020] A receiver-extension approach to robust full waveform inversion. 90th Annual International Meeting, SEG, Expanded Abstracts, 641–645.
    [Google Scholar]
  8. Sakoe, H., and S.Chiba
    [1978] Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26, 43–49.
    [Google Scholar]
  9. Symes, W. W.
    [2008] Migration velocity analysis and waveform inversion. Geophysical prospecting, 56(6), 765–790.
    [Google Scholar]
  10. Tarantola, A.
    [1984] Inversion of seismic reflection data in the acoustic approximation. Geophysics, 49, 1259–1266.
    [Google Scholar]
  11. Virieux, J. and Operto, S.
    [2009] An overview of full-waveform inversion in exploration geophysics. Geophysics, 74(6), WCC1–WCC26.
    [Google Scholar]
  12. Wang, M., Xie, Y., Xu, W.Q., Loh, F.C., Xin, K., Chuah, B.L., Manning, T. and Wolfarth, S.
    [2016] Dynamic-warping full-waveform inversion to overcome cycle skipping. 86th Annual International Meeting, SEG, Expanded Abstracts, 1273–1277.
    [Google Scholar]
  13. Yao, G., da Silva, N., Warner, M., Wu, D. and Yang, C.
    [2019] Tackling cycle skipping in full-waveform inversion with intermediate data. Geophysics, 84(3), R411–R427.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202113271
Loading
/content/papers/10.3997/2214-4609.202113271
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error