Multichannel Seismic Deconvolution Using Markov–Bernoulli Random-Field Modeling

In this paper, we present an algorithm for multichannel blind deconvolution of seismic signals, which exploits lateral continuity of Earth layers based on Markov-Bernoulli random-field modeling. The reflectivity model accounts for layer discontinuities resulting from splitting, merging, starting, or terminating layers within the region of interest. We define a set of reflectivity states and legal transitions between the reflector configurations of adjacent traces and subsequently apply the Viterbi algorithm for finding the most likely sequences of reflectors that are connected across the traces by legal transitions. The improved performance of the proposed algorithm and its robustness to noise, compared with a competitive algorithm, are demonstrated using simulated and real seismic data examples, in blind and nonblind scenarios.

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