MULTICHANNEL SEISMIC DECONVOLUTION

This paper deals with Bayesian estimation of two-dimensional (2-D) stratified structures from echosounding signals. This problem is of interest in seismic exploration, but also for nondestructive testing or medical imaging. The proposed approach consists of a multichannel Bayesian deconvolution method of the 2-D reflectivity based upon a theoretically sound prior stochastic model. The Markov-Bernoulli random field representation introduced in [1] is used to model the geometric properties of the reflectivity, and the emphasis is placed on representation of the amplitudes and on deconvolution algorithms. It is shown that the algorithmic structure and computational complexity of the proposed multichannel methods are similar to those of single channel B-G deconvolution procedures, but that explicit modeling of the stratified structure results in significantly better performances. Simulation results and examples of real-data processing illustrate the performances and the practicality of the multichannel approach.