1887

Abstract

Summary

Surface-related multiple elimination (SRME) is a solid and effective approach for primary estimation. However, due to the imperfections in data and method (e.g. coarsely-sampled dataset and balancing effect of adaptive subtraction) multiple energy leakage is commonly seen in the results of SRME-predicted primaries. Assuming that the primaries and multiples do not correlate locally in the time-space domain, we are able to extract the leaked multiples from the initially estimated primaries using local primary-and-multiple orthogonalization. The proposed framework consists of two steps: an initial primary/multiple estimation step and a multiple-leakage extraction step. The initial step corresponds to SRME, which produces the initial estimated primary and multiple models. The second step is based on local primary-and-multiple orthogonalization to retrieve the leaked multiples, which can be seen as a remedy for correcting the initial estimated primary and multiple models. Thus, we can obtain a better primary output which has much less leaked multiple energy. We demonstrate a good performance of our proposed framework on both synthetic and field data, where it repairs the leakage of standard adaptive subtraction.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201901199
2019-06-03
2024-03-29
Loading full text...

Full text loading...

References

  1. Berkhout, A.J.
    [1982] Seismic migration, imaging of acoustic energy by wave field extrapolation, A: theoretical aspects. Elsevier (second edition).
    [Google Scholar]
  2. [2014] Review Paper: An outlook on the future of seismic imaging, Part I: Forward and reverse modelling. Geophysical Prospecting, 62, 911–930.
    [Google Scholar]
  3. Berkhout, A.J. and Verschuur, D.J.
    [1997] Estimation of multiple scattering by iterative inversion, Part I: Theoretical considerations. Geophysics, 62(5), 1586–1595.
    [Google Scholar]
  4. Chen, Y.
    [2015] Iterative deblending with multiple constraints based on shaping regularization. IEEE Geoscience and Remote Sensing Letters, 12(11), 2247–2251.
    [Google Scholar]
  5. Chen, Y. and Fomel, S.
    [2015] Random noise attenuation using local signal-and-noise orthogonalization. Geophysics, 80(6), WD1–WD9.
    [Google Scholar]
  6. Chen, Y., Jiao, S., Ma, J., Chen, H., Zhou, Y. and Gan, S.
    [2015] Ground-roll noise attenuation using a simple and effective approach based on local band-limited orthogonalization. IEEE Geoscience and Remote Sensing Letters, 12(11), 2316–2320.
    [Google Scholar]
  7. Fomel, S.
    [2007] Shaping regularization in geophysical-estimation problems. Geophysics, 72(2), R29–R36.
    [Google Scholar]
  8. van Groenestijn, G.J.A. and Verschuur, D.J.
    [2009] Estimating primaries by sparse inversion and application to near-offset data reconstruction. Geophysics, 74, A23–A28.
    [Google Scholar]
  9. Lopez, G.A. and Verschuur, D.J.
    [2015] Closed-loop surface-related multiple elimination and its application to simultaneous data reconstruction. Geophysics, 80(12), V189–V199.
    [Google Scholar]
  10. Verschuur, D.J.
    [2006] Seismic multiple removal techniques - past, present and future. EAGE Publications BV.
    [Google Scholar]
  11. Verschuur, D.J. and Berkhout, A.J.
    [1997] Estimation of multiple scattering by iterative inversion, Part II: Practical aspects and examples. Geophysics, 62(5), 1596–1611.
    [Google Scholar]
  12. Verschuur, D.J., Berkhout, A.J. and Wapenaar, C.P.A.
    [1992] Adaptive surface-related multiple elimination. Geophysics, 57(9), 1166–1177.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201901199
Loading
/content/papers/10.3997/2214-4609.201901199
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