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Abstract

Summary

The sparse inversion based random noise elimination methods utilize the soft threshold operation to realize denoising on the basis that seismic signals have sparsity expression in a transformed domain. The threshold values are very crucial for the final inversion results, they should match the energy of noise. However, the energy of noise is hard to achieve, the popular method to get proper threshold values is try many times manually which will cost much computation resource and labor. This paper proposed an adaptive random noise elimination method without any a priori information using the curvelet transform as the sparse transform, the inner relation between the sparsity of solution and residual with the iteration can be used to decide a proper threshold value, thus suitable denoising results can be provide based on this idea. Numerical experiment demonstrate that the proposed method can protect valid signal, eliminate random noise, render the events clear and improve signal-noise-ratio and resolution of seismic data.

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/content/papers/10.3997/2214-4609.201700576
2017-06-12
2024-04-27

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References

  1. Candes, E. J. and Donoho, D. L.
    [2004] New tight frames of curvelets and optimal representation of objects with piecewise C2 singularities. Communications on Pure and Applied Mathematics, 57, 219–66.
     [Google Scholar]
  2. Cao, J. J., Wang, Y. F., Zhao, J. T. and Yang, C. C.
    [2011] A review on restoration of seismic wavefields based on regularization and compressive sensing. Inverse Problems in Science and Engineering, 19, 679–704.
     [Google Scholar]
  3. Cao, J., Zhao, J., Hu, Z.
    [2015]. 3D seismic denoising based on a low-redundancy curvelet transform. Journal of Geophysics and Engineering, 12, 566–76.
     [Google Scholar]
  4. Hennenfent, G. and Herrmann, F.
    [2006] Seismic denoising with non-uniformly sampled curvelets. Computing in Science and Engineering, 8, 16–25.
     [Google Scholar]
  5. Kumar, V., Oueity, J., Clowes, R. M. and Herrmann, F.
    [2011] Enhancing crustal reflection data through curvelet denoising. Tectonophysics, 508, 106–16.
     [Google Scholar]
  6. Naghizadeh, M.
    [2012] Seismic data interpolation and denoising in the frequency-wavenumber domain. Geophysics, 77, V71–V80.
     [Google Scholar]
  7. Shan, H., Ma, J. W. and Yang, H. Z.
    [2009] Comparisons of wavelets,contourlets and curvelets in seismic denoising. Journal of Applid Geophysics, 69, 103–15.
     [Google Scholar]
  8. Wang, Y.
    [2002] Antialiasing conditions in the delay-time Radon transform. Geophysical Prospecting, 50, 665–72.
     [Google Scholar]
  9. Yuan, S., Wang, S. and Li, G.
    [2012]. Random noise reduction using Bayesian inversion. Journal of Geophysics and Engineering, 9, 60–68.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201700576
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Adaptive Seismic Random Noise Attenuation Using Curvelet Transform
Conference Proceedings 2017, 1 (2017); https://doi.org/10.3997/2214-4609.201700576
/content/papers/10.3997/2214-4609.201700576
/content/papers/10.3997/2214-4609.201700576
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