1887

Abstract

Summary

The simultaneous-source shooting technique can accelerate field acquisition and improve spatial sampling but will cause strong interferences in the recorded data. Direct imaging of blended simultaneous-source data has been demonstrated to be an promising research field since there can be no need for the separation for blended sources before the subsequent traditional processing and imaging. The key issue in direct imaging of blended data is the strong artifacts in the migrated image. Although the least-squares migration can help reduce some artifacts, there are still residual artifacts in the image. Those artifacts mainly appears on the shallow part of the image and appears as spatially incoherent noise. We propose to apply the singular spectrum analysis (SSA) operator to attenuate the such artifacts during least-squares inversion. Considering that global SSA cannot deal with over-complicated data well, we propose to use local SSA in order to better remove noise and preserve steeply dipping components. The local SSA operator corresponds to a local low-rank constraint applied in the inversion process. The migration operator used in the study is the reverse time migration (RTM) operator. We use the Marmousi model example to show the superior performance of the proposed algorithm.

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/content/papers/10.3997/2214-4609.201601200
2016-05-31
2020-07-11
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References

  1. Abma, R.L. and Yan, J.
    [2009] Separating simultaneous sources by inversion. 71st Annual International Conference and Exhibition, EAGE, Extended Abstracts.
    [Google Scholar]
  2. Beasley, C.J., Chambers, R.E. and Jiang, Z.
    [1998] A new look at simultaneous sources. 68th Annual International Meeting, SEG, Expanded Abstracts, 133–135.
    [Google Scholar]
  3. Berkhout, A.J.
    [2008] Changing the mindset in seismic data acquisition. The Leading Edge, 27, 924–938.
    [Google Scholar]
  4. Chen, Y.
    [2015] Iterative deblending with multiple constraints based on shaping regularization. IEEE Geoscience and remote sensing letters, 12, 2247–2251.
    [Google Scholar]
  5. Chen, Y., Fomel, S. and Hu, J.
    [2014] Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization. Geophysics, 79, V179–V189.
    [Google Scholar]
  6. Chen, Y., Yuan, J., Zu, S., Qu, S. and Gan, S.
    [2015] Seismic imaging of simultaneous-source data using constrained least-squares reverse time migration. Journal of Applied Geophysics, 114, 32–35.
    [Google Scholar]
  7. Dai, W. and Schuster, G.T.
    [2011] Least-squares migration of multisource data with a deblur-ring filter. Geophysics, 76, R135–R146.
    [Google Scholar]
  8. Mahdad, A.
    [2012] Deblending of seismic data. PhD thesis, Delft University of Technology.
    [Google Scholar]
  9. Oropeza, V. and Sacchi, M.
    [2011] Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis. Geophysics, 76, V25–V32.
    [Google Scholar]
  10. Xue, Z., Chen, Y., Fomel, S. and Sun, J.
    [2016] Seismic imaging of incomplete data and simultaneous-source data using least-squares reverse time migration with shaping regular-ization. Geophysics, 81, S11–S20.
    [Google Scholar]
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