1887

Abstract

Summary

Simultaneous source shooting, also known as blending, is now an accepted practice in seismic acquisition. Most seismic processing algorithms, however, can still not handle blended data directly as input. Deblending, a procedure which separates the wavefields from each individual source, becomes then necessary. Deblending is an ill-posed problem, but often prior information can be incorporated to the problem in the form of constraints. The proposed algorithm utilizes the fact that the deblended data are expected to have a sparse representation in the focal transform domain, by casting deblending as a basis pursuit denoising problem. The novelty of the algorithm lies on the fact that by means of the focal transform, available subsurface information is used to construct the sparsifying basis. The algorithm is assessed by testing its ability to deblend numerically blended synthetic data, in both cases of exact and inexact knowledge of subsurface information.

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/content/papers/10.3997/2214-4609.20141456
2014-06-16
2020-04-09
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References

  1. Abma, R., Manning, T., Tanis, M., Yu, J. and Foster, M.
    [2010] High quality separation of simultaneous sources by sparse inversion. 72nd EAGE Conference & Exhibition.
    [Google Scholar]
  2. Ayeni, G., Almomin, A. and Nichols, D.
    [2011] On the separation of simultaneous-source data by inversion. 2011 SEG Annual Meeting.
    [Google Scholar]
  3. Beasley, C.J., Chambers, R.E. and Jiang, Z.
    [1998] A new look at simultaneous sources. SEG Technical Program Expanded Abstracts, vol. 17, 133–135.
    [Google Scholar]
  4. Berkhout, A.J.
    [2008] Changing the mindset in seismic data acquisition. The Leading Edge, 27(7), 924–938.
    [Google Scholar]
  5. Berkhout, A.J. and Verschuur, D.J.
    [2010] Parameterization of seismic data using gridpoint responses. 2010 SEG Annual Meeting.
    [Google Scholar]
  6. Berkhout, A.J.
    [1984] Seismic Migration: Imaging of Acoustic Energy by Wave Field Extrapolation: Imaging of Acoustic Energy by Wave Field Extrapolation. Access Online via Elsevier.
    [Google Scholar]
  7. Hampson, G., Stefani, J. and Herkenhoff, F.
    [2008] Acquisition using simultaneous sources. The Leading Edge, 27(7), 918–923.
    [Google Scholar]
  8. Huo, S., Luo, Y. and Kelamis, P.G.
    [2012] Simultaneous sources separation via multidirectional vector-median filtering. Geophysics, 77(4), V123–V131.
    [Google Scholar]
  9. Kutscha, H. and Verschuur, D.J.
    [2012] Data reconstruction via sparse double focal transformation: An overview. Signal Processing Magazine, IEEE, 29(4), 53–60.
    [Google Scholar]
  10. Kutscha, H., Verschuur, D.J. and Berkhout, A.J.
    [2010] High resolution double focal transformation and its application to data reconstruction. 90th Annual International Meeting, SEG, Expanded Abstracts, 3589–3593.
    [Google Scholar]
  11. Lin, T. and Herrmann, F.
    [2009] Designing simultaneous acquisitions with compressive sensing. 71st EAGE Conference & Exhibition.
    [Google Scholar]
  12. Mahdad, A., Doulgeris, P. and Blacquière, G.
    [2011] Separation of blended data by iterative estimation and subtraction of blending interference noise. Geophysics, 76(3), Q9–Q17.
    [Google Scholar]
  13. Moore, I., Dragoset, B., Ommundsen, T., Wilson, D., Eke, D. and Ward, C.
    [2008] Simultaneous source separation using dithered sources. 2008 SEG Annual Meeting.
    [Google Scholar]
  14. Spitz, S., Pica, A. and Hampson, G.
    [2008] Simultaneous source separation: A prediction-subtraction approach. SEG Technical Program Expanded Abstracts 2008, 2811–2815.
    [Google Scholar]
  15. van den Berg, E. and Friedlander, M.P.
    [2007] SPGL1: A solver for large-scale sparse reconstruction. Http://www.cs.ubc.ca/labs/scl/spgl1.
    [Google Scholar]
  16. [2008] Probing the pareto frontier for basis pursuit solutions. SIAM Journal on Scientific Computing, 31(2), 890–912, doi:10.1137/080714488.
    https://doi.org/10.1137/080714488 [Google Scholar]
  17. Wason, H., Herrmann, F.J. and Lin, T.T.
    [2011] Sparsity-promoting recovery from simultaneous data: a compressive sensing approach. 2011 SEG Annual Meeting.
    [Google Scholar]
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