Non-stationary predictive deconvolution is necessary for inverse filtering with variable wavelet. In most cases we mean the variation of wavelet shape related with frequency-dependent attenuation. The procedure is also suitable for elimination of reverberation on the marine shallow data with variable interval between arrival times. This procedure is alternative to the multi-windows deconvolution, which limitations are well-known and related to the insufficient volume of selections for statistical estimations. The model of non-stationary, which describes the wide class of variable signal processes is used in non-stationary deconvolution approach.
Example of data from Western Siberia (common offset 862 m). Data is affected by complex surface, static shifts and some other factors. Result of non-stationary deconvolution is shown on the left. This kind of deconvolution is stronger than preliminary fil
Example representing the conventional application of deconvolution to the task of water reverberation elimination. Result of standard deconvolution is shown on the left, result of non- stationary predictive deconvolution is shown on the right.
Initial data is shown on the left. Stack after non-stationary predictive deconvolution, computed with corrected velocities is shown on the right. It is easy to see, that increase of quality is controlled not only by reverberation elimination, but also usi
