Robert T. Baumel

Velocity analysis in the presence of amplitude variation

Conventional semblance velocity analysis is equivalent to modeling prestack seismic data with events that have hyperbolic moveout but no amplitude variation with offset (AVO). As a result of its assumption that amplitude is independent of offset, this method might be expected to perform poorly for events with strong AVO—especially for events with polarity reversals at large offset, such as reflections from tops of some class 1 and class 2 sands. We find that substantial amplitude variation and even phase change with offset do not compromise the conventional semblance measure greatly. Polarity reversal, however, causes conventional semblance to fail.The semblance method can be extended to take into account data with events that have amplitude variation, expressed by AVO intercept and gradient (i.e., the Shuey approximation). However, because of the extra degrees of freedom introduced in AVO‐sensitive semblance, resolution of the estimated velocities is decreased. This is because the data can be modeled acc...

Analytic correction for wavelet stretch due to imaging

The presence of wavelet stretch due to imaging presents serious difficulty in AVO or inversion analysis, especially for 3-term wide-angle analysis. Wavelet stretch significantly alters the gradient and wide-angle coefficient and reduces resolution of stacks. In this paper we present a method for correcting wavelet stretch that is exact for any v(z) (layered) medium. It does not depend on an underlying AVO/AVA approximation and is therefore applicable for 2or 3-term AVA analysis. The required input is an extracted wavelet from any known reflection angle. The resulting correction operator is stationary over the time coordinate of the angle domain and is robustly implemented by a Weiner-Levinson method. This filter corrects angle gathers for wavelet stretch, producing improved resolution in subsequent angle stacks or gradient computations. Wavelet stretch correction is essential for linear inversion for density.

Inversion for source wavelet and AVA parameters from prestack seismic data

It is difficult to establish the influence of the seismic wavelet on reflection data away from well control. In this paper we present an inversion methodology to extract a source wavelet from a prestack gather. This inversion estimates and deconvolves the effective source contribution, at the same time estimating reflectivity with high resolution. Discrimination of source from reflectivity depends on differential, that is, non-parallel, moveout over offset.

Calibration of prestack inversions using Bayes' theorem

The ability of seismic processing centers to invert data already exceeds our capacity to interpret the inversion output. Many seismic interpreters still ask “What should we do with this additional, new information?” Here we apply Bayes’ Theorem to calibrate the inversion output to lithologic well data. Calibration to lithology clarifies inversion interpretation. Oftentimes it also substantiates the need for prestack, rather than poststack, inversion.