| Popis: |
In marine applications, the value of accurate modeling and learning for stochastic acoustic propagation in uncertain ocean environments cannot be overstated. In this work, we derive stochastic theory and schemes for (i) modeling of high frequency acoustic propagation in uncertain ocean environments and (ii) joint Bayesian assimilation of ocean-acoustic measurements to infer fields, parameters, and uncertain model functions. We first obtain the Dynamically Orthogonal (DO) wavefront equations to solve for the stochastic extension of the Liouville Equation that governs the dynamics of acoustic wavefront in an augmented phase space. These DO wavefront equations provide the prior for the Gaussian Mixture Model—DO (GMM-DO) filter that completes joint physics-acoustics Bayesian inference using sparse observations. Specifically, given a set of receivers, the Eulerian nature of the DO wavefront equations allows for the efficient extraction of arrival time prior probability distributions. The GMM-DO Waverfront filter then combines these joint priors with arrival time measurements using Bayes rule, jointly inferring environmental properties (e.g., unknown source location and/or sound speed field), the acoustic wavefront distribution, and the arrival time distribution itself. We evaluate results using high-frequency applications, illustrating the estimation of mean fields and properties, but also of probability density distributions and model parameterizations. |
