Poststack deconvolution often is considered for several reasons. First, a residual wavelet almost always is present on the stacked section. This is because none of the underlying assumptions for deconvolution is completely met in real data; therefore, deconvolution never can completely compress the basic wavelet contained in prestack data to a spike. Second, since a CMP stack is an approximation to the zero-offset section, predictive deconvolution aimed at removing multiples may be a viable process after stack. Figure 2.5-18 is an example of poststack deconvolution applied to marine data. After deconvolution, the spectrum is flattened, albeit incompletely, the wavelet is compressed further and the marker horizons are better characterized. Again, as the prediction lag is increased, the flatness character of the spectrum and thus vertical resolution is increasingly compromised (Figure 2.5-19).
Figure 2.5-20 shows poststack deconvolution applied to land data. Note the significant improvement in vertical resolution as it can be verified by the autocorrelogram and average amplitude spectrum of the data.
Figure 2.5-19 A portion of a CMP-stacked section as in Figure 2.5-7a after predictive deconvolution using an operator length of 320 ms and a prediction lag of: (a) 8 ms, (b) 12 ms, (c) 24 ms, (d) 32 ms, and (e) 48 ms. The amplitude spectra (top) averaged over the CMP stack, and the autocorrelograms (bottom) are used to to choose deconvolution parameters and evaluate the data after the application of deconvolution.
Figure 2.5-20 (a) A portion of a CMP-stacked section, and (b) after spiking deconvolution using an operator length of 240 ms. The amplitude spectra (top) averaged over the CMP stack, and the autocorrelograms (bottom) are used to choose deconvolution parameters and evaluate the data after the application of deconvolution.
- Prestack deconvolution
- Signature deconvolution
- Vibroseis deconvolution
- Field data examples