1887

Abstract

Summary

Traditional coherence algorithms most rely on the basic assumption that the relationship between seismic traces is linear and obeys Gaussian distribution. However, in practice, correlation between seismic traces is usually nonlinear, and the seismic traces are non-Gaussian signals. The canonical correlation analysis (CCA) cannot describe the similarity between adjacent seismic traces in detail. To overcome this problem and improve the resolution and robustness of the coherence algorithm, we introduce the kernelized correlation instead of the linear correlation in the C3 algorithm. Note that the kernelized correlation is a generalized correlation with various kernel functions. Then, we discuss how to choose the appropriate kernel function in detail. To demonstrate the validity of the proposed algorithm, we apply it to field data using different kernels. The results demonstrate the effectiveness of the proposed algorithm to describe geological discontinuity and heterogeneity, such as fluvial channels and faults.

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/content/papers/10.3997/2214-4609.201901172
2019-06-03
2024-03-29
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References

  1. Bahorich, M., and Farmer, S.
    , [1995] 3-D seismic discontinuity for faults and stratigraphic features: The coherence cube. The Leading Edge, 14(10), 1053–1058.
    [Google Scholar]
  2. Liu, N., Gao, J. H., Zhang, B., Li, F., and Wang, Q.
    [2017] Time-Frequency Analysis of Seismic Data Using a Three Parameters S Transform. IEEE Geoscience & Remote Sensing Letters, 15(1), 142–146.
    [Google Scholar]
  3. Marfurt, K. J., Farmer, S. L., and Bahorich, M. S.
    [1998] 3-D seismic attributes using a semblance-based coherency algorithm. Geophysics, 63(4), 1150–1165.
    [Google Scholar]
  4. Marfurt, K. J., Sudhaker, V., Gersztenkorn, A., and Crawford, K. D.
    [1999] Coherency calculations in the presence of structural dip. Geophysics, 64(1), 104–111.
    [Google Scholar]
  5. Schölkopf, B., and Smola, A. J.
    [2003] Learning with kernels : support vector machines, regularization, optimization, and beyond. MIT Press.
    [Google Scholar]
  6. Wang, Z. G., Gao, J. H., Wang, P., and Jiang, X.D.
    [2016] The analytic wavelet transform with generalized Morse wavelets to detect fluvial channels in the Bohai Bay Basin China. Geophysics, 81(4), 1–9.
    [Google Scholar]
  7. Yilmaz, Ö.
    [2001] Seismic data analysis: processing, inversion, and interpretation of seismic data /-(2nd ed.). Society of Exploration Geophysicists.
    [Google Scholar]
  8. Yuan, S., Wang, S., Ma, M., Ji, Y., and Deng, L.
    [2017] Sparse Bayesian Learning-Based Time-Variant Deconvolution. IEEE Transactions on Geoscience & Remote Sensing, 55(11), 6182–6194.
    [Google Scholar]
  9. Ziolkowski, A.
    [1991] Why don't we measure seismic signatures?. Geophysics, 56(1), 190–201.
    [Google Scholar]
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