1887

Abstract

Summary

Attenuating noise from seismic recordings is one of the main objectives during processing, and if executed successfully it improves the interpreter’s ability to characterize the geology. Anisotropic diffusion is a noise attenuation method that focuses on reducing various forms of incoherent noise that obscures the signal by smoothing in predefined directions following the texture. Constraints can be applied to the smoothing by the dip field itself or corresponding attributes to ensure preservation of meaningful edges in the seismic section, such as variations in the temporal reflectivity strength related to reflectors or the spatial terminations of said reflectors. Such constraints can include magnitude and reliability of the local dip, and degree on anisotropy or coherency. The amount of smoothing can also be controlled by the number of diffusion steps, meaning the same diffusion applied several times for a clearer result, but with caution to avoid over smoothing. In this work, anisotropic diffusion was formulated as a line integral convolution and results were investigated when applied on post-stack seismic data, along a dip field estimated by a gradient structure method. The results showed a decrease in random noise with characteristic salt and pepper features, causing more distinct reflectors.

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/content/papers/10.3997/2214-4609.201801664
2018-06-11
2020-07-11
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References

  1. Cabral, B. and Leedom, L. C.
    [1993] Imaging Vector Fields Using Line Integral Convolution. Proceedings of the 20th annual conference on Computer graphics and interactive techniques. SIGGRAPH ’93. Anaheim, California. 263–270.
    [Google Scholar]
  2. Claerbout, J.F.
    [1992] Earth sounding analysis: Processing versus inversion. Blackwell Scientific Publications, Inc. 315.
    [Google Scholar]
  3. Fehmers, G. and Höcker, C.
    [2002] Fast structural interpretation with structure-oriented filtering. Geophysics, 68(4), 1286–1293.
    [Google Scholar]
  4. Marfurt, K., Sudhaker, V, Gersztenkorn, A., Crawford, K. and Nissen, S.
    [1999] Coherency calculations in the presence of structural dip. Geophysics, 64(1). 104–111.
    [Google Scholar]
  5. Perona, P. and Malik, J.
    [1987] Scale-space and edge detection using anisotropic diffusion. Proceedings of the IEEE Workshop on Computer Vision, Miami. 16–22.
    [Google Scholar]
  6. Sobel, I. and FeldmanG.
    [1968]. A 3×3 isotropic gradient operator for image processing. Presented at a talk at the Stanford Artificial Project.
    [Google Scholar]
  7. Solberg, A. and Gelius, L.
    [2011] New texture attributes from local 2D Fourier spectra. Extended Abstract, Society of Exploration Geophysics: 81st Annual Meeting, San Antonio, Texas.
    [Google Scholar]
  8. Vliet, L. J. van and Verbeek, P. W.
    [1995] Estimators for orientation and anisotropy in digitized images. ASCI’95, Proc. First Annual Conference of the Advanced School for Computer and Imaging, Heijen, ASCI. 442–450.
    [Google Scholar]
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