Parametric Approaches for Refractivity-from-Clutter Inversion

We have considerable experience in carrying out refractivity estimation from ocean clutter data [Gerstoft et al., 2003a, 2003b, Gerstoft et al., 2004; Rogers et al., 2004]. Little has been done to indicate the quality of the solution for each parameter, either with the variance of the parameter estimate or preferably the complete a posteriori distribution. We have already done much work on this in an ocean acoustic context, but this has not been explored in our refractivity from clutter (RFC) processing to date. This will entail developing likelihood formulations and importance sampling algorithms. This inversion approach will show the information content in the data, the importance of each parameter, and the quality of the inversions. Another related topic is that in RFC inversions, we commonly invert each data block independently. When these inversions are close in time (i.e., successive looks in time at the same azimuth) or space (i.e. adjacent azimuths), it should be beneficial to use the results of the previous inversion as a starting condition for the next inversion. A natural framework for this is a Bayesian approach where the posterior of the last inversion becomes the prior for the current inversion. For this investigation, the Bayesian approach will be implemented using a Metropolis-Hastings Gibbs sampler.