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Bayesian algorithm to jointly estimate wavelet, seismic noise level and correlation

This paper describes how a Bayesian framework can be modeled and applied on seismic data to estimate the wavelet. The method works on poststack and pre-stack data, in both, the convolutional forward model is considered, but it differ in how the reflectivity is calculated from the well log. We expanded the method to estimate the seismic noise correlation range jointly with the wavelet, the seismic noise level and uncertainties. The method is applied in synthetic and real post-stack seismic data. The Gaussian assumption for the likelihood models enables to obtain the analytical expressions for the conditioned distributions, which allows sampling from the posterior distribution via Gibbs Sampling Algorithm.