March 3, 2026Open Access

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

Key Points

Potential acoustic propagation forecasts improve using Gaussian Process modeling in ocean environments, especially in frontal areas.
Good agreement between simulations and experimental data was observed in parallel propagation, while across-front discrepancies are slightly higher.
Analysis involved fitting a Gaussian Process model to profiler data to infer front positions through derived kernels and sound-speed estimations.
Statistical analysis indicates discrepancies remain below 10% in simulated data generated from Gaussian Process samples.

Abstract

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.

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

Key Points

Abstract

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