Bayesian interface detection in very shallow chirp seismic data

In this paper, we consider an approach to the problems of processing very high resolution, very shallow, seismic data. We have developed a processing strategy based on a Bayesian model of the basebanded, matched filtered, signal. We have found this model to be robust in detecting close reflector wavelets (overlapping by up to 80%) and in adapting to local conditions within the data under suitable stochastic a priori constraints. In addition, the use of Reversible-Jump Markov chain Monte Carlo techniques allow us to address the issue of model selection directly. After developing the requirements for the model, and describing the processing methodology, we show results in synthetic and real data sets. We show that under realistic operational conditions, the algorithm is capable of resolving subtle layers, making subsequent interpretation simpler.