Surface waves prediction based on long-range acoustic backscattering in a mid-frequency range

TL;DR

This study explores long-range acoustic backscattering at mid-frequencies (1-3 kHz) in the Black Sea, demonstrating its potential for sea surface wave prediction using machine learning, with promising results for practical coastal applications.

Contribution

It introduces a novel application of acoustic backscattering in the 1-3 kHz range for sea surface wave prediction and employs machine learning for data interpretation.

Findings

Backscattering signals can estimate significant wave height and wave frequency.

Machine learning models accurately predict wind waves, aligning with direct measurements.

The 1-3 kHz band offers a practical range for coastal sea surface monitoring.

Abstract

New data was obtained for a frequency band that had not been so well-studied for sea surface probing applications before. During the described 2-weeks sea experiment 1-3 kHz tonal pulses were emitted from a platform, located on the northern Black Sea shelf, and Doppler spectrum of reverberation was studied. We believe that this band is worth further studying due the sound propagation range is large enough to meet practical needs in coastal zone while the angle-distance resolution is quite moderate. However it is quite difficult to interpret the obtained data since backscattering spectrum shape is influenced by a series of effects and has a complicated link to wind waves and currents parameters. Backscattering of acoustical signals was received for distances around 2 nautical miles. Significant wave height, dominant wave frequency were estimated as the result of such signals processing with the use of machine learning tools. A decision-tree-based mathematical regression model was trained to solve the inverse problem. Wind waves prediction is in a good agreement with direct measurements, made on the platform, and machine learning results allow physical interpretation.