1887

Abstract

Summary

I propose an application of Spectral Decomposition using Regularizaed Nonstationary Autoregression (SDRNAR) to random noise attenuation. SDRNAR is a recently proposed method which aims at decomposing the seismic signal into several spectral components, each of which has a smoothly variable frequency and smoothly variable amplitude. According to the unpredictive property of random noise in SDRNAR, I present a new way to subtract random noise in the t-x domain. In the new method, random noise is deemed to be the residual part of decomposed sepctral components becasue it is unpredictively distributed. The new scheme adapts to those seismic profiles where reflection impulses are distributed relative averagely along a seismic trace, which means the local frequency is relative constant or smoothly varing. I use a real data set to demostrate the performance of the proposed method.

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/content/papers/10.3997/2214-4609.201317930
2013-04-08
2024-04-18
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References

  1. Bekara, M. and van der Baan, M.
    [2009] Random and coherent noise attenuation by empirical mode decomposition. Geophysics, 74(5), V89–V98.
    [Google Scholar]
  2. Durrani, T.S. and Bisset, D.
    [1984] The radon transform and its properties. Geophysics, 49, 1180.
    [Google Scholar]
  3. Fomel, S.
    [2012] Seismic data decomposition into spectral components using regularized non-stationary autoregression. 82nd Annual International Meeting, SEG, Expanded Abstracts, doi:http://dx.doi.org/10.1190/segam2012-1416.1.
    [Google Scholar]
  4. Fomel, S. and Liu, Y.
    [2010] Seislet transform and seislet frame. Geophysics, 75, V25–V38.
    [Google Scholar]
  5. Herrmann, F.J. and Hennenfent, G.
    [2008] Non-parametric seismic data recovery with curvelet frames. Geophysical Journal International, 173, 233.
    [Google Scholar]
  6. Huang, N.E. et al.
    [1998] The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceeding of the Royal Society of London Series A, 454, 903–995.
    [Google Scholar]
  7. Liu, Y. and Fomel, S.
    [2011] Seismic data interpolation beyond aliasing using regularized nonstationary autoregression. Geophysics, 76, V69–V77.
    [Google Scholar]
  8. Zhang, R. and Ulrych, T.
    [2003] Physical wavelet frame denosing. Geophysics, 68, 225.
    [Google Scholar]
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