1887
ASEG2003 - 16th Geophysical Conference
  • ISSN: 2202-0586
  • E-ISSN:

Abstract

Conventional predictive deconvolution is very good for suppressing normal incidence water bottom reverberations. Classic papers, some published in Geophysics, have provided rules of thumb for conventional seismic deconvolution processing. These rules have been invaluable in shortening field wavelets enough to allow structural interpretation of the subsurface. However, concepts of seismic deconvolution processing have evolved. Merely shortening the interpretation wavelet is no longer enough. In order to interpret rock properties, it is necessary to know the interpretation wavelet shape and to maintain its amplitude and phase spectrum throughout a seismic volume.

By utilizing an example seismogram synthesized with a finite impulse response wavelet kernel, these rules can be exemplified and refined:

  1. Predictive deconvolution can suppress reverberations as long as the lag is less than or equal to the minimum time of the water bottom reverberation sequence.
  2. An isolated reflection's signature is not distorted by predictive deconvolution, as long as the lag is larger than the length of the wavelet kernel.
  3. The dereverberation filter changes shape whenever the lagged interval includes a significant portion of the wavelet kernel's autocorrelation.
  4. xsPlacing the lag at the second zero crossing is a reasonable compromise but the reflection's signature will be distorted and it can extend beyond the lag.

The output signature can vary significantly with the value selected for the prediction distance (lag). Relative entropy deconvolution concepts can provide consistent dereverberation filters for lags shorter than the length of the wavelet kernel. Actual field signatures can be compensated to any convenient wavelet shape, including those with infinite impulse responses, before applying a relative entropy predictive deconvolution.

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2003-08-01
2026-01-25
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References

  1. Backus, M. M., 1959, Water reverberations�their nature and elimination: Geophysics, v. 24, p. 233-261.
  2. Burg, J. P., 1975, Maximum entropy spectral analysis, Ph.D. dissertation, Stanford University, Stanford, CA. (University Microfilms No. 75-25, 499)
  3. Burg, J. P., 1967, Maximum entropy spectral analysis, Society of Exploration Geophysicists International Exposition and 37th Annual Meeting.
  4. Parrish, John F., 1997, Relative entropy spectrum deconvolution, Society of Exploration Geophysicists International Exposition and 67th Annual Meeting, Dallas, Expanded Abstracts.
  5. Parrish, John F., 1999, Applying minimum relative entropy spectrum deconvolution, Society of Exploration Geophysicists International Exposition and 69th Annual Meeting, Houston, Expanded Abstracts.
  6. Peacock, K. L., and Treitel, S., 1969, Predictive deconvolution: theory and practice: Geophysics, v. 34, p. 155-169.
  7. Shore, John E., 1979, Minimum cross-entropy spectral analysis, NRL-MR 3921, Naval Research Laboratory, Washington, D. C, Jan.
  8. Shore, J. E. and Johnson, R. W., 1980, Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy, IEEE Trans. Inform. Theory, rf 26, 26 37, Jan.
  9. Shore, J. E., 1981, Minimum cross-entropy spectral analysis, IEEE Trans. Acous. Speech Signal Processing, ASSP 29, 230 237, Apr.
  10. Shore, John E. and Johnson, Rodney W., 1983, Properties of cross-entropy minimization, IEEE Trans. Inform. Theory, FT 27, 472 482, July 1981. See also: comments and corrections, IEEE Trans. Inform. Theory, FT 29, Nov.
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  • Article Type: Research Article
Keyword(s): Deconvolution; dereverberation; relative-entropy; wavelet
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