Signal and noise estimation from seismic reflection data using spectral coherence methods

Spectral coherence analysis is unrivaled as a quantitative tool over a range of practical problems in seismic interpretation, data processing, quality assessment for data acquisition, and research. Its great virtue is its ability to supply the detailed error information necessary for a thorough interpretation of results. Ordinary coherence analysis is employed in line intersection analysis and the design of filters to cross-equalize differently acquired seismic sections in a given area; both ordinary and partial coherence methods are indispensable in matching synthetic seismograms and seismic data; and multiple coherences are used to estimate the coherent signal and incoherent noise content of seismic sections and gathers. The precise meaning of the signal and noise estimates output by coherent analysis has to be related to the particular technique employed and the type of data input to it. The principles and procedures for analyzing seismic data with these methods are reviewed and illustrated with practical examples.

[1]  Andrew T. Walden,et al.  An investigation of the spectral properties of primary reflection coefficients , 1985 .

[2]  P. Newman,et al.  CONTINUOUS CALIBRATION OF MARINE SEISMIC SOURCES , 1985 .

[3]  Andrew T. Walden,et al.  On errors of fit and accuracy in matching synthetic seismograms and seismic traces , 1984 .

[4]  Marcello Pagano,et al.  Simultaneous confidence bands for autoregressive spectra , 1984 .

[5]  J. C. Samson,et al.  The reduction of sample‐bias in polarization estimators for multichannel geophysical data with anisotropic noise , 1983 .

[6]  R. R. Hocking.  Developments in Linear Regression Methodology: 1959–l982 , 1983 .

[7]  Walt Lynn,et al.  Coherent noise in marine seismic data , 1983 .

[8]  D. B. Preston.  Spectral Analysis and Time Series , 1983 .

[9]  J. C. Samson,et al.  Pure states, polarized waves, and principal components in the spectra of multiple, geophysical time-series , 1983 .

[10]  D. Thomson,et al.  Robust-resistant spectrum estimation , 1982, Proceedings of the IEEE.

[11]  D. Thomson,et al.  Spectrum estimation and harmonic analysis , 1982, Proceedings of the IEEE.

[12]  S.M. Kay,et al.  Spectrum analysis—A modern perspective , 1981, Proceedings of the IEEE.

[13]  C. Van Schooneveld,et al.  Spectral analysis: On the usefulness of linear tapering for leakage suppression , 1981 .

[14]  P. Lee,et al.  An algorithm for computing the cumulative distribution function for magnitude squared coherence estimates , 1981 .

[15]  W. E. Lerwill,et al.  WAVELET DECONVOLUTION USING A SOURCE SCALING LAW , 1980 .

[16]  E. Rietsch.  ESTIMATION OF THE SIGNAL‐TO‐NOISE RATIO OF SEISMIC DATA WITH AN APPLICATION TO STACKING* , 1980 .

[17]  R. White,et al.  Partial coherence matching of synthetic seismograms with seismic traces , 1980 .

[18]  F. K. Levin,et al.  The effect of subsurface sampling on one-dimensional synthetic seismograms , 1979 .

[19]  D. Thomson,et al.  Robust Estimation of Power Spectra , 1979 .

[20]  D. N. Swingler,et al.  A comparison between burg's maximum entropy method and a nonrecursive technique for the spectral analysis of deterministic signals , 1979 .

[21]  S. Deregowski.  SELF‐MATCHING DECONVOLUTION IN THE FREQUENCY DOMAIN * , 1978 .

[22]  R. E. White,et al.  THE PERFORMANCE OF OPTIMUM STACKING FILTERS IN SUPPRESSING UNCORRELATED NOISE , 1977 .

[23]  A. Nuttall,et al.  Bias of the estimate of magnitude-squared coherence , 1976 .

[24]  T. Ulrych,et al.  Time series modeling and maximum entropy , 1976 .

[25]  R. White,et al.  ESTIMATION OF THE PRIMARY SEISMIC PULSE , 1974 .

[26]  M. Schoenberger,et al.  Apparent attenuation due to intrabed multiples; II , 1974 .

[27]  R. White,et al.  THE ESTIMATION OF SIGNAL SPECTRA AND RELATED QUANTITIES BY MEANS OF THE MULTIPLE COHERENCE FUNCTION , 1973 .

[28]  A. Nuttall,et al.  Statistics of the estimate of the magnitute-coherence function , 1973 .

[29]  N. R. Goodman,et al.  Probability distributions for estimators of the frequency-wavenumber spectrum , 1970 .

[30]  V. Benignus.  Estimation of the coherence spectrum and its confidence interval using the fast Fourier transform , 1969 .

[31]  E. J. Hannan,et al.  Time series regression of sea level on weather , 1968 .

[32]  M. Foster,et al.  THE COEFFICIENT OF COHERENCE: ITS ESTIMATION AND USE IN GEOPHYSICAL DATA PROCESSING , 1967 .

[33]  W. Munk,et al.  Tidal spectroscopy and prediction , 1966, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[34]  N. R. Goodman.  MEASUREMENT OF MATRIX FREQUENCY RESPONSE FUNCTIONS AND MULTIPLE COHERENCE FUNCTIONS , 1965 .

[35]  C. Khatri.  Classical Statistical Analysis Based on a Certain Multivariate Complex Gaussian Distribution , 1965 .

[36]  N. R. Goodman.  Statistical analysis based on a certain multivariate complex Gaussian distribution , 1963 .

[37]  T. Teichmann,et al.  The Measurement of Power Spectra , 1960 .

[38]  Hirotugu Akaike,et al.  TIME SERIES ANALYSIS AND CONTROL THROUGH PARAMETRIC MODELS , 1978 .

[39]  Athanasios Papoulis,et al.  Minimum-bias windows for high-resolution spectral estimates , 1973, IEEE Trans. Inf. Theory.