Surface Waves Prediction Based on Long-Range Acoustic Backscattering in a Mid-Frequency Range

Underwater acoustic echosounding for surface roughness parameters retrieval is studied in a frequency band that is relatively new for such purposes. During the described 2-weeks sea experiment, 1–3 kHz tonal pulses were emitted from an oceanographic platform, located on the northern Black Sea shelf. Doppler spectra of the resulting reverberation were studied. The frequency band of the acoustic system, selected for this study, is chosen due to the fact that the sound propagation range is large enough for remote sensing in a coastal zone, and the resolution cell size does not limit the research. Backscattering of acoustical signals was received for distances around two nautical miles. However, it turned to be quite difficult to interpret the obtained data since backscattering spectrum shape was influenced by a series of effects, resulting in a complicated link to wind waves and currents’ parameters. Significant wave height and dominant wave frequency were estimated as the result of such signals processed 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

Surface Waves Prediction Based on Long-Range Acoustic Backscattering in a Mid-Frequency Range

Underwater acoustic echosounding for surface roughness parameters retrieval is studied in a frequency band that is relatively new for such purposes. During the described 2-weeks sea experiment, 1–3 kHz tonal pulses were emitted from an oceanographic platform, located on the northern Black Sea shelf. Doppler spectra of the resulting reverberation were studied. The frequency band of the acoustic system, selected for this study, is chosen due to the fact that the sound propagation range is large enough for remote sensing in a coastal zone, and the resolution cell size does not limit the research. Backscattering of acoustical signals was received for distances around two nautical miles. However, it turned to be quite difficult to interpret the obtained data since backscattering spectrum shape was influenced by a series of effects, resulting in a complicated link to wind waves and currents’ parameters. Significant wave height and dominant wave frequency were estimated as the result of such signals processed 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