Applications of Bayesian optimization with a Gaussian process surrogate model in ocean acoustics

Abstract

In this study, we present a method that samples geoacoustic parameter space with a Bayesian approach that uses a Gaussian process as a surrogate model of the objective function. The objective function is defined as a Bartlett processor whose output measures the match between a received and replica pressure field on a vertical line array. Replica fields are obtained using a normal mode propagation model whose geoacoustic parameters are selected from the parameter search space. The surrogate model represents the posterior on the objective function and is updated with each model evaluation. Optimization is performed with sequential model evaluations, with an acquisition function guiding the next point in parameter space to be evaluated. Various use cases and parameterizations are discussed, including the effects of the choice of acquisition function and covariance function of the Gaussian process. Results indicate that Bayesian optimization using a Gaussian process surrogate model converges rapidly on an approximated optimal solution.