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The objective is to make inference about reservoir properties from seismic reflection data. The inversion problem is cast in a Bayesian framework, and bi-modal prior models are defined in order to honor the bi-modal behavior of the saturation variable. By using a Gauss-linear likelihood model the explicit expressions for the posterior models are obtained by the convenient properties of the family of Gaussian distributions. The posterior models define computationally efficient inversion methods that can be used to make predictions of the reservoir variables while providing an uncertainty assessment. The inversion methodologies are tested on synthetic seismic data with respect to porosity and water saturation at two time steps. Encouraging results are obtained under realistic signal-noise ratios.

Bayesian Inversion of Time-lapse Seismic Data Using Bimodal Prior Models