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Abstract

Summary

The regular reconstruction of seismic data is one of the most important and fundamental aspects of seismic data processing. The deep reflection seismic profile is mostly arranged in a complicated geological tectonic section and more complicated wave group characteristics on the deep reflection seismic profile are resultant due to the complicated geological structure. Consequently, the internal structure characteristics of data cannot be represented effectively by using a single sparse transformation. The morphological component analysis (MAC) method decomposes a signal into several components with distinguished morphological features to approximate the complex internal structure of data, however, the various characteristics of the complex data still cannot be described effectively through a simple addition of the signal components. We improved the MCA method, which is weighted by sparse dictionaries. By making use of the morphological differences among signal components, the seismic signal was divided into two forms. In the process of reconstruction, the weight of the two components is constantly adjusted to achieve the recovery of the complex missing seismic data. Experimental results shows that the weighted MCA method can be used to reconstruct the irregular and large span data and eliminate the spatial aliasing.

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/content/papers/10.3997/2214-4609.201801277
2018-06-11
2024-04-19

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References

  1. RonenJ.
    , 1987, Wave Equation Trace Interpolation: Geophysics, 52, 973–984.
     [Google Scholar]
  2. Sacchi, M. D. and T. J.Ulrych
    , 1996, Estimation of the Discrete Fourier Transform, a linear inversion approach: Geophysics, 61, 1128–1136.
     [Google Scholar]
  3. Sacchi, M. D., D. J.Verschuur, and P. M.Zwartjes
    , 2004, Data reconstruction by generalized deconvolution: SEG, Expanded Abstracts, 23, 1989–1992.
     [Google Scholar]
  4. Zwartjes, P. and A.Gisolf
    , 2006, Fourier reconstruction of marine-streamer data in four spatial coordinates: Geophysics, 71, V171–V186.
     [Google Scholar]
  5. Hennenfent, G. and F. J.Herrmann
    , 2006, Seismic denoising with nonuniformly sampled curvelets: IEEE Trans. on Computing in Science and Engineering, 8, 16–25.
     [Google Scholar]
  6. Liang, J.W., Ma, J. W. and Zhang, X. Q.
    , 2014, Seismic data restoration via data-driven tight frame: Geophysics, 79, V65–V74.
     [Google Scholar]
  7. StarckJ. L., M.Elad, D.Donoho
    , 2004, Redundant multiscale transforms and their application for morphological component separation: Advances in Imaging and Electron Physics, 132, 287–348.
     [Google Scholar]
  8. StarckJ. L., MurtaghF., FadiliJ. M.
    , 2010, Sparse image and signal processing: wavelets, curvelets, morphological diversity: Cambridge university press.
     [Google Scholar]
  9. Mostafa, N. and MauricioD. S.
    , 2010, Beyond alias hierarchical scale curvelet interpolation of regularly and irregularly sampled seismic data: Geophysics, 75(6), WB189–WB202.
     [Google Scholar]
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Seismic Data Restoration by the Weighted MCA
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201801277
/content/papers/10.3997/2214-4609.201801277
/content/papers/10.3997/2214-4609.201801277
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