Bayesian matched-field geoacoustic inversion
This paper describes a Bayesian approach to matched-field inversion (MFI) of ocean acoustic data for seabed geoacoustic properties. In a Bayesian formulation, the unknown environmental and experimental parameters are considered random variables constrained by noisy data and prior information, and the goal is to interpret the multi-dimensional posterior probability density (PPD). The PPD is typically characterized in terms of point estimates, marginal distributions, and posterior correlations...[Show more]
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