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Synthetic head-wave correlations from ocean ambient noise on a drifting vertical line array.
Jie Li1, Peter Gerstoft2, Martin Siderius3
1Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
The Journal of the Acoustical Society of America
|May 1, 2025
Summary
Weak head-wave correlations from ocean noise were observed over a large area using a vertical line array. These signals, dependent on noise energy, offer a robust method for estimating geoacoustic parameters and enhancing environmental characterization.
Area of Science:
- Ocean acoustics
- Geophysics
- Environmental sensing
Background:
- Head-wave correlations from ocean surface noise are typically observed using fixed arrays and short averaging times.
- The long-term and spatially variable presence of these weak correlations remains uncertain.
Purpose of the Study:
- To analyze head-wave correlations under varying ocean noise conditions and over extended distances.
- To investigate the conditions influencing the detection of synthetic head-wave correlations and passive fathometer returns.
Main Methods:
- Analysis of BOUNDARY2003 data from a vertical line array over a 13-hour drift.
- Examination of head-wave correlations at four distinct time points with different noise power levels.
- Utilizing conventional beamforming to assess noise source energy variations with frequency and time.
Main Results:
- Synthetic head-wave correlations from the water-sediment interface were detected across most of the drift track.
- The presence and characteristics of these signals were found to depend on the energy of stationary phase noise sources.
- Frequency-dependent variations in synthetic head-wave correlations were observed.
Conclusions:
- The robustness of synthetic head-wave correlations provides an effective method for estimating waveguide and geoacoustic parameters.
- This approach significantly enhances environmental characterization capabilities over large spatial scales.
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