Obtaining machine learned surrogate underwater acoustics models to enable model reduction

Of great interest in underwater acoustics is the inverse problem to infer environmental seafloor parameters from acoustic data in a shallow ocean environment. One of the primary challenges with this "geoacoustic inversion" is model selection. Complex geoacoustic models have the potential of overfitting, and may include parameters that are unidentifiable, or not constrained by acoustic data. Information geometry provides tools for parameter identifiability analysis, including computational differential geometry tools like the Manifold Boundary Approximation Method (MBAM) for algorithmically finding unidentifiable model parameters to remove. These computational differential geometry tools require the ability to calculate accurate derivatives of model predictions with respect to model parameters. Automatic differentiation (AD) enables rapid evaluation of derivatives but faces implementation challenges with many underwater sound propagation models, such as use with models implemented in "legacy code" and having non-differentiable points in function space. We propose methods for obtaining machine learned (ML) surrogates of the model to which AD can be easily applied. Using a computational underwater acoustics model, surrogate and original model predictions will be compared. Additionally, the use of MBAM for model reduction will be demonstrated to enable next steps for geoacoustic inversion.