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Area of Science:

  • Oceanography
  • Acoustics
  • Machine Learning

Background:

  • Deep-sea currents are crucial for understanding ocean dynamics.
  • Measuring these currents at great depths presents significant challenges.
  • Acoustic noise can be influenced by water flow.

Purpose of the Study:

  • To investigate the relationship between near-bottom current speed and acoustic noise intensity at deep-sea seamounts.
  • To develop a method for quantifying flow noise and distinguishing it from ambient sound.
  • To utilize machine learning for estimating deep-sea current speeds from acoustic data.

Main Methods:

  • Deployment of moored autonomous acoustic noise recorders (MANRs) at depths >2500m.
  • Analysis of acoustic data, focusing on infrasonic frequencies (<20 Hz).
  • Development and application of a regression tree machine learning model.

Main Results:

  • Strong correlation observed between current speed and acoustic noise intensity.
  • Flow noise and ambient sound (including shipping noise) were identified as comparable contributors with distinct spectral properties.
  • Machine learning model successfully estimated current speed with 1-min resolution using only acoustic data.

Conclusions:

  • Acoustic noise measurements, particularly at infrasonic frequencies, provide a viable method for estimating deep-sea current speeds.
  • The developed machine learning approach offers a novel way to monitor deep-sea currents remotely.
  • This technique can enhance our understanding of oceanographic processes in data-sparse deep-sea environments.