Selective-correlation velocity analysis

Abstract

The need for increased resolution in velocity spectra is clear when one wishes to distinguish between neighboring primary events from reflectors with conflicting dip, or to identify primaries in the presence of multiples. The transformation in velocity analysis from the offset and reflection-time domain to the stacking velocity and zero-offset-time domain can be achieved using any of several coherence measures based on the crosscorrelations between traces in a collection such as a common-midpoint (CMP) gather or common-image gather (CIG). Use of selected subsets of crosscorrelations, rather than all possible ones in a gather, however, can improve both the reliability and resolution of velocity analysis. In selective-correlation velocity analysis, we include in the summation only those crosscorrelations for whose pair of traces the relative differential moveout of reflections exceeds a chosen threshold value. Comparisons of the performances on synthetic CMP gathers show that selective-correlation velocity analysis considerably enhances the resolving power of velocity spectra over that of conventional crosscorrelation sum (whether normalized or unnormalized) in the presence of closely interfering reflections, statics distortions and random noise, at no sacrifice in the quality of results, and does so at computational cost that is comparable to that for conventional velocity analysis.

High-resolution bootstrapped differential semblance

“…Correlation-based methods use zero-lag crosscorrelation between traces inside the time gate to produce a coherency estimator that better resists contaminating noise than does the semblance coefficient (Sherwood and Poe, 1972;Fuller and Kirlin, 1992;Larner and Celis, 2007). Eigenstructure-based estimators can also efficiently resolve the parameter-estimation problem at the expense of the additional computing time required to perform an eigenvalue decomposition of the covariance matrix computed for the tentative time gates.…”

“…We next compare the relative effectiveness of these estimators with those of other methods aimed at enhancing resolution of velocity spectra. First is the selectivecorrelation coherency estimator, a normalized sum of crosscorrelations based on computing zero-lag correlations between traces in the time gate with a predefined offset difference divided by a normalization factor (Larner and Celis, 2007) …”

“…The semblance coefficient is the most widely used estimator (Taner and Koehler, 1969;Neidell and Taner, 1971) because of its small sensitivity to amplitude variations with offset (AVO), variable seismic amplitude with increasing time, and the computing efficiency, which allows building velocity spectra for highfold supergathers in 3D data with a dense spatial sampling or for nonhyperbolic parameter search that tracks the moveout velocity and the effective anellipticity (Alkhalifah, 1997;Abbad et al, 2009Abbad et al, , 2010. Nevertheless, several estimators have been suggested, with the aim to build high-resolution velocity spectra with better resistance to noise, handling of phase changes with offset, and the power to discriminate among several parameters that produce comparable moveout curves (Schneider and Backus, 1968;Sherwood and Poe, 1972;Douze and Laster, 1979;Kirlin et al, 1984;Key et al, 1987;Biondi and Kostov, 1989;Key and Smithson, 1990;Fuller and Kirlin, 1992;Sacchi, 1998;Larner and Celis, 2007).…”

Characterisation of shallow marine sediments using high‐resolution velocity analysis and genetic‐algorithm‐driven 1D elastic full‐waveform inversion

“…(4) Sguazzero and Vesnaver 1987;Biondi and Kostov 1989;Key and Smithson 1990;Spagnolini et al 1993;Sacchi 1998;Grandi et al 2007;Larner and Celis 2007;Abbad, Ursin, and Rappin 2009;Abbad and Ursin 2012;Tognarelli et al 2013). Each of them differs in terms of resolution and capability to discriminate between signal and random and non-random noise (Ashton et al 1994;Jones 2010).…”

Horizon-based semiautomated nonhyperbolic velocity analysis

“…Swan (2001) is one of the first researchers to develop a high-resolution velocity-spectra algorithm that accounts for amplitude variation with offset (AVO). Larner and Celis (2007) improve the resolution and reliability of the velocity spectra by just using selected subsets of crosscorrelation rather than all possible ones in the gathers. To minimize the effect of AVO phenomenon that exists in prestack gathers, Fomel (2009) proposes a generalized "AB semblance" that is particularly attractive for velocity analysis of class II AVO anomalies in which the polarity of the reflections changes.…”