Learning a surrogate Pekeris waveguide model

Abstract

Underwater sound propagation models predict transmission loss (TL) given environmental parameters, such as seafloor sediment parameters. Also of interest is the reverse: Can seafloor parameters, such as sediment density, sound speed, and attenuation, be inferred from ocean acoustic data? This inverse problem can be addressed using information geometry tools for parameter identifiability analysis and model reduction, where we identify parameters that can be removed from a model without sacrificing accuracy. Critical to these methods for model reduction is the ability to evaluate derivatives of the model’s TL predictions with respect to model parameters. Automatic differentiation (AD) allows for rapid evaluation of a model’s derivatives but may face some challenges for implementation with sound propagation models, including models being written in “legacy code” and having non-differentiable points in the function space. Our solution is to train a surrogate machine learning (ML) model which can circumvent these challenges, and which we can apply AD to. We demonstrate this method using the Pekeris waveguide model of the ocean. We compare the surrogate and original model outputs and demonstrate the process of model reduction using a ML surrogate model. [Work supported by Office of Naval Research.]