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Volume 61 Number 1
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

We present a singular value decomposition (SVD) filtering method for the enhancement of coherent reflections and for attenuation of noise. The method is applied in two steps. First normal move‐out (NMO) correction is applied to shot or CMP records, with the purpose of flattening the reflections. We use a spatial SVD filter with a short sliding window to enhance coherent horizontal events. Then the data are sorted in common‐offset panels and the local dip is estimated for each panel. The next SVD filtering is performed on a small number of traces and a small number of time samples centred around the output sample position. Data in a local window are corrected for linear moveout corresponding to the dips before SVD. At the central time sample position, we sum over the dominant eigenimages of a few traces, corresponding to SVD dip filtering. We illustrate the method using land seismic data from the Tacutu basin, located in the north‐east of Brazil. The results show that the proposed method is effective and is able to reveal reflections masked by ground‐roll and other types of noise.

© 2012 European Association of Geoscientists & Engineers
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2012-04-03
2024-04-27

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References

  1. BekaraM. and van der BaanM.2007. Local singular value decomposition for signal enhancement of seismic data. Geophysics 72, 59–65.
    [Google Scholar]
  2. ChiuS.K. and HowellJ.E.2008. Attenuation of coherent noise using localized‐adaptive eigenimage filter. SEG, Expanded Abstracts 27, 2541–2545.
    [Google Scholar]
  3. FreireS.L.M. and UlrychT.1988. Application of singular value decomposition to vertical seismic profiling. Geophysics 53, 778–785.
    [Google Scholar]
  4. GolubG.H. and van LoanC.F.1996. Matrix Computations. Johns Hopkins University Press.
    [Google Scholar]
  5. LiuX.1999. Ground roll suppression using the Karhunen‐Loeve transform. Geophysics 64, 564–566.
    [Google Scholar]
  6. MeloP., PorsaniM. and SilvaM.2009. Ground‐roll attenuation using a 2D time‐derivative filter. Geophysical Prospecting 57, 343–353.
    [Google Scholar]
  7. PorsaniM., SilvaM. , MeloP. and UrsinB.2009. Ground‐roll attenuation based on SVD filtering. SEG Expanded Abstracts 28, 3381–3385.
    [Google Scholar]
  8. PorsaniM., SilvaM. , MeloP. and UrsinB.2010a. SVD filtering applied to ground‐roll attenuation. Journal of Geophysics and Engineering 7, 284–289.
    [Google Scholar]
  9. PorsaniM., SilvaM. , MeloP. and UrsinB.2010b. An adaptive local‐slope SVD filtering approach to enhance events on seismic sections. SEG Expanded Abstracts 29, 3717–3722.
    [Google Scholar]
  10. SchleicherJ., CostaJ.C. , SantosL.T. , NovaisA. and TygelM.2009. On the estimation of local slopes. Geophysics 74, 25–33.
    [Google Scholar]
  11. TyapkinY.K., MarmalyevskyyN.Y. and GornyakV.2003. Source‐generated noise attenuation using the singular value decomposition. SEG Expanded Abstract 22, 2044–2047.
    [Google Scholar]
  12. Van HuffelS. and VandewalleJ.1991. The total least squares problems: Computational aspects and analysis . SIAM, Philadelphia .
    [Google Scholar]
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Dip‐adaptive singular‐value decomposition filtering for seismic reflection enhancement
Geophysical Prospecting 61, 42 (2013); https://doi.org/10.1111/j.1365-2478.2012.01059.x
/content/journals/10.1111/j.1365-2478.2012.01059.x
/content/journals/10.1111/j.1365-2478.2012.01059.x
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  • Article Type: Research Article
Keyword(s): Eigenimage; Local dip; SVD filtering

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