Gaussian process estimation of underwater acoustic fluctuations: Experimental validation on the Iceland–Faroe polar front

Forecasting underwater acoustic propagation in oceanic frontal areas is a difficult task due to their unstable dynamics. In this work, we propose to fit a Gaussian Process model, with a kernel derived from a structure model, to infer the position of the front from profiler data. Samples from the Gaussian Process can be used to generate sound-speed fields. Parabolic equation simulations on those samples show a good agreement with experimental acoustic data in propagation parallel to and across the front. As it can be intuitively expected, the discrepancy is a bit higher for across-front propagation due to strong range-dependence. However, these discrepancies are statistically due to Gaussian Process samples which proportion do not exceed 10% of the simulated data.