Selective-correlation velocity analysis

Abstract

Increased resolution in computed velocity spectra aids in distinguishing between neighboring primary events from reflectors with conflicting dip and in identifying primaries in the presence of multiples. The transformation 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 crosscorrelations among traces in a common-midpoint (CMP) gather or a common-image gather (CIG). Use of just selected subsets of crosscorrelations rather than all possible ones in a gather can improve both the reliability and resolution of velocity analysis. In selective-correlation velocity analysis, we include in the summation only crosscorrelations for those pairs of traces with relative differential moveout of reflections exceeding a chosen threshold value. Comparisons of performance on CMP gathers, both synthetic and field-data, show that selective-correlation velocity analysis considerably enhances the resolving power of velocity spectra over that of conventional crosscorrelation sum (normalized or unnormalized) in the presence of closely interfering reflections, statics distortions, and random noise, at no sacrifice in quality of results, and does so at computational cost comparable to that for conventional velocity analysis.