Compressive acoustic sound speed profile estimation.

Area of Science:

• Ocean acoustics
• Signal processing
• Geophysical modeling

Background:

• Accurate sound speed profiles (SSPs) are crucial for ocean acoustic modeling.
• Traditional SSP estimation methods face challenges with limited observational data.
• Compressive sensing (CS) offers a potential solution for underdetermined inverse problems.

Purpose of the Study:

• To apply compressive sensing (CS) for estimating ocean acoustic sound speed profiles (SSPs).
• To investigate the capability of CS in resolving fine-scale SSP structures in shallow ocean environments.

Main Methods:

• Utilizing compressive sensing (CS) principles for signal recovery.
• Inverting a non-linear acoustic propagation model for SSP estimation.
• Applying the method to a range-independent shallow ocean scenario.

Main Results:

• Successful estimation of SSPs using CS with limited acoustic field observations.
• Demonstrated ability of CS to resolve fine-scale structures within the SSP.
• Validation of CS as a viable technique for acoustic inverse problems.

Conclusions:

• Compressive sensing (CS) is a powerful tool for high-resolution SSP estimation in shallow oceans.
• CS enables accurate recovery of detailed SSP features from sparse data.
• This approach advances acoustic field inversion techniques for oceanographic applications.