Markovian modeling for Bayesian multi-channel deconvolution
Two dimensional (2D) Bayesian restoration of layered media and its application to seismic deconvolution are addressed. The Bernoulli-Gaussian (B-G) approach is suboptimal in a 2D setting, since it does not account for spatial continuity. To compensate for this deficiency, a class of Markov random fields which suitably extends 1D Bernoulli processes to two dimensions is built. It provides a manageable model for 2D stratified structures. A maximum a posteriori detection-estimation algorithm is proposed. Compared to standard B-G algorithms, it performs much better, without an increase in computational load.<<ETX>>