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Physics-informed and machine learning-enabled retrieval of ocean current speed from flow noisea).
Tsuwei Tan1, Oleg A Godin2, Matthew W Walters2,3
1Department of Marine Science, Republic of China (ROC) Naval Academy, 813 Kaohsiung, Taiwan.
The Journal of the Acoustical Society of America
|February 12, 2025
Summary
Researchers used acoustic noise recorders to measure deep-sea currents over the Atlantis II Seamounts. They found a strong correlation between current speed and infrasonic acoustic noise, enabling accurate current speed estimation using machine learning.
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.
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