1887

Abstract

Summary

Reverse-time migration (RTM) with the conventional cross-correlation imaging condition suffers from low-frequency artifacts that result from backscattered energy in the background velocity models. This problem translates to least-squares reverse-time migration (LS-RTM), where these artifacts slow down the convergence, as many of the initial iterations are spent on removing them. In RTM, this problem has been successfully addressed by the introduction of the so-called inverse scattering imaging condition, which naturally removes these artifacts. In this work, we derive the corresponding linearized forward operator of the inverse scattering imaging operator and incorporate this forward/adjoint operator pair into a sparsity-promoting (SPLS-RTM) workflow. We demonstrate on a challenging salt model, that LS-RTM with the inverse scattering imaging condition is far less prone to low-frequency artifacts than the conventional cross-correlation imaging condition, improves the convergence and does not require any type of additional image filters within the inversion. Through source subsampling and sparsity promotion, we reduce the computational cost in terms of PDE solves to a number comparable to conventional RTM, making our workflow applicable to large-scale problems.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201701125
2017-06-12
2020-09-29
Loading full text...

Full text loading...

References

  1. Baysal, E., Kosloff, D.D., and Sherwood, J.W.C.
    [1983] Reverse time migration. Geophysics, 48(11), 1514–1524.
    [Google Scholar]
  2. Brandsberg-Dahl, S., Chemingui, N., Whitmore, D., Crawley, S., Klochikhina, E. and Valenciano, A.
    [2013] 3D RTM angle gathers using an inverse scattering imaging condition. 83rd Annual International Meeting, SEG, Expanded Abstracts, 3958–3962.
    [Google Scholar]
  3. Clearbout, J.F.
    [1985] Imaging the earth’s interior. Blackwell Scientific Publications.
    [Google Scholar]
  4. Guitton, A., Kaelin, B. and Biondi, B.
    [2007] Least-squares attenuation of reverse-time-migration artifacts. Geophysics, 72(1).
    [Google Scholar]
  5. Herrmann, F.J., Tu, N. and Esser, E.
    [2015] Fast “online” migration with Compressive Sensing. 77th EAGE Conference & Exhibition.
    [Google Scholar]
  6. Op’t Root, T.J., Stolk, C.C. and de Hoop, M.V.
    [2012] Linearized inverse scattering based on seismic reverse time migration. Journal de Mathematiques Pures et Appliquees, 98(2), 211–238.
    [Google Scholar]
  7. Pestana, R., Santos, A. and Araujo, E.
    [2013] RTM Imaging Condition Using Impedance Sensitivity Kernel Combined with Poynting Vector. 75th EAGE Conference & Exhibition.
    [Google Scholar]
  8. Whitmore, N.D. and Crawley, S.
    [2012] Applications of RTM inverse scattering imaging conditions. 82nd Annual International Meeting, SEG, Expanded Abstracts.
    [Google Scholar]
  9. Yin, W.
    [2010] Analysis and Generalizations of the Linearized Bregman Method. SIAM Journal on Imaging Sciences, 3(4), 856–877.
    [Google Scholar]
  10. Yoon, K. and Marfurt, K.J.
    [2004] Accurate simulations of pure quasi-P-waves in complex anisotropic media. Geophysics, 79(6), 341–348.
    [Google Scholar]
  11. Zhang, Y. and Sun, J.
    [2009] Practical issues in reverse time migration: true amplitude gathers, noise removal and harmonic source encoding. First break, 26, 843–852.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201701125
Loading
/content/papers/10.3997/2214-4609.201701125
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