Resolution of seismic waveform inversion: Bayes versus Occam

.<F3.733e+05> In Bayesian inference, probabilistic information about models is posited<F3.174e+05> a priori<F3.733e+05> . This information, which may very well include features in the null space of the forward problem, affects both the computed models and the resulting resolution estimates. In Occam's inversion, on the other hand, the goal is to construct the smoothest model consistent with the data. This is not to say that one believes<F3.174e+05> a priori<F3.733e+05> that models are really smooth, but rather that a more conservative interpretation of the data ought to be made by eliminating features of the model that are not required to fit the data. The length scale associated with the smoothing is an indirect measure of resolution. In some cases the mathematical machinery of Bayesian inference resembles that of Occam's inversion, but the goals and interpretations of the two methods are rather different. To understand better the similarities and differences of these two approaches, ...