1887
Volume 32 Number 5
  • E-ISSN: 1365-2478

Abstract

A

A synthetic seismogram that closely resembles a seismic trace recorded at a well may not be at all reliable for, say, stratigraphic interpretation around the well. The most accurate synthetic seismogram is, in general, not the one that displays the smallest errors of fit to the trace but the one that best estimates the noise on the trace. If the match is confined to a short interval of interest or if the seismic reflection wavelet is allowed to be unduly long, there is considerable danger of forcing a spurious fit that treats the noise on the trace as part of the seismic reflection signal instead of making a genuine match with the signal itself. This paper outlines tests that allow an objective and quantitative evaluation of the accuracy of any match and illustrates their application with practical examples.

The accuracy of estimation is summarized by the normalized mean square error (NMSE) in the estimated reflection signal, which is shown to be

(/)(/)

where / is the signal‐to‐noise power ratio and is the spectral smoothing factor. That is, the accuracy varies directly with the ratio of the power in the signal (taken to be the synthetic) to that in the noise on the seismic trace, and the smoothing acts to improve the accuracy of the predicted signal. The construction of confidence intervals for the NMSE is discussed. Guidelines for the choice of the spectral smoothing factor are given.

The variation of wavelet shape due to different realizations of the noise component is illustrated, and the use of confidence intervals on wavelet phase is recommended.

Tests are described for examining the normality and stationarity of the errors of fit and their independence of the estimated reflection signal.

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2006-04-27
2024-04-20
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References

  1. Bloomfield, P.1976, Fourier Analysis of Time Series: An Introduction, Wiley, New York .
    [Google Scholar]
  2. Bunch, A. W. H.1984, Predicting the optimal least squares filter using adaptations of standard statistical theory, BP Report Ext. 25628.
  3. Cleveland, W. and Kleiner, B.1975, A graphical technique for enhancing scatterplots with moving statistics, Technometrics17, 447–454.
    [Google Scholar]
  4. Goodman, N. R.1957, On the joint estimation of the spectra, co‐spectrum and quadrature spectrum of a two‐dimensional stationary Gaussian process, Scientific Paper No. 10, Engineering Statistics Laboratory, New York University, also University of Princeton thesis.
  5. Jenkins, G. M. and Watts, D. G.1968, Spectral Analysis and its Applications, Holden‐Day, San Francisco .
    [Google Scholar]
  6. Johnson, N. L. and Kotz, S.1970, Continuous Univariate Distributions—2, Wiley, New York .
    [Google Scholar]
  7. Papoulis, A.1973, Minimum‐bias windows for high resolution spectral estimates, IEEE Transactions on Information Theory IT‐19, 9–12.
  8. Patnaik, P.1949, The non‐central χ2 and F‐distributions and their applications, Biometrika36, 202–232.
    [Google Scholar]
  9. Stephens, M.1974, EDF statistics for goodness‐of‐fit and some comparisons, Technometrics69, 730–737.
    [Google Scholar]
  10. Stephens, M.1976, Asymptotic results for goodness‐of‐fit statistics with unknown parameters, Annals of Statistics4, 357–369.
    [Google Scholar]
  11. Walden, A. T.1984, Confidence intervals on gain and phase of frequency response functions, BP Report Ext. 25477.
  12. White, R. E.1980, Partial coherence matching of synthetic seismograms with seismic traces, Geophysical Prospecting28, 333–358.
    [Google Scholar]
  13. Wilk, M. B. and Gnanadesikan, R.1968, Probability plotting methods for the analysis of data, Biometrika55, 1–17.
    [Google Scholar]
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  • Article Type: Research Article

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