
%0 Journal Article
%A F. Parrish, John 
%T Signature after predictive deconvolution
%J ASEG Extended Abstracts
%D 2003/08/01
%V 2003
%N 2
%P 1-4
%K 
%R https://doi.org/10.1071/ASEG2003AB127
%U https://www.publish.csiro.au/paper/ASEG2003AB127 
%X Conventional predictive deconvolution is very good for suppressing normal incidence water bottom reverberations. Classic papers, some published in Geophysics, have provided rules of thumb for conventional seismic deconvolution processing. These rules have been invaluable in shortening field wavelets enough to allow structural interpretation of the subsurface. However, concepts of seismic deconvolution processing have evolved. Merely shortening the interpretation wavelet is no longer enough. In order to interpret rock properties, it is necessary to know the interpretation wavelet shape and to maintain its amplitude and phase spectrum throughout a seismic volume. By utilizing an example seismogram synthesized with a finite impulse response (FIR) wavelet kernel, these rules can be exemplified and refined: 1. Predictive deconvolution can suppress reverberations as long as the lag is less than or equal to the minimum time of the water bottom reverberation sequence; 2. An isolated reflection?s signature is not distorted by predictive deconvolution, as long as the lag is larger than the length of the wavelet kernel; 3. The dereverberation filter changes shape whenever the lagged interval includes a significant portion of the wavelet kernel?s autocorrelation; 4. Placing the lag at the second zero crossing is a reasonable compromise but the reflection?s signature will be distorted and it can extend beyond the lag. The output signature can vary significantly with the value selected for the prediction distance (lag). Relative entropy deconvolution concepts can provide consistent dereverberation filters for lags shorter than the length of the wavelet kernel. Actual field signatures can be compensated to any convenient wavelet shape, including those with infinite impulse responses, before applying a relative entropy predictive deconvolution. 
