Source Localization and Acoustic Inversion Using Bayesian Optimization with Local Gaussian Processes

We demonstrate Bayesian optimization (BO), an efficient global optimization method for acoustic inversion in which an ocean acoustic source is localized while simultaneously estimating propagation environment properties. This problem requires Monte Carlo techniques with thousands of forward model evaluations to solve; however, BO can approximate the optimal solution within dozens or hundreds of evaluations. BO performs a sequential search of the parameter space for the global optimum of an objective function, here defined as the correlation between predicted and measured data. At each step, a Gaussian process (GP) surrogate model is fit to the observed data and a new point to evaluate is chosen using an acquisition function. Conventionally, the GP is fit to the entire parameter space. We adopt trust regions based on observed data to enable fitting of GPs on local subsets of the parameter space, leading to improved optimization results. The method is demonstrated on simulated and experimental data over a 7-dimensional search space encompassing source location and four geoacoustic parameters.