IEEE ASA/SPS Lecture - Machine Learning Approaches for Ocean Acoustic Tomography
Abstract: Underwater sound propagation is primarily driven by a non-linear forward model relating variability of the ocean sound speed profile (SSP) to the acoustic observations (e.g. eigenray arrival times). Ocean acoustic tomography (OAT) methods aim at reconstructing SSPs variations (with respect to a reference environment) from changes of the acoustic measurements between multiple source-receiver pairs. This presentation will review the performance of three different OAT methods: 1) model-based methods (i.e. classical ray-based OAT using a linearized forward model), 2) data-driven methods (such as deep learning) to directly learn the inverse model and 3) a hybrid solution (i.e. the Neural Adjoint-NA-method) which combines deep learning of the forward model followed by a standard recursive optimization to estimate SSPs. Additionally, synthetic SSPs were generated to augment the variability of the training set. These methods were tested with modeled ray arrivals calculated for a downward refracting environment with mild fluctuations of the thermocline. Idealized towed and fixed source configurations are considered. Results indicate that merging data-driven and model-based methods can benefit OAT predictions depending on the selected sensing configurations and actual ray coverage of the water column. But ultimately, the robustness of OAT predictions depends on the dynamics of the SSP variations.
Biography: Dr. Sabra began at Tech in 2007 as an Assistant Professor. He emphasizes an interdisciplinary approach to research problems in different areas (acoustics, structural health monitoring, biomechanics and seismology) based on common wave propagation physics features. His research combines both theoretical and experimental studies for the exploration of these research problems.
Food: Pizza lunch to be provided.
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- 771 Ferst Dr NW
- Atlanta, Georgia
- United States 30332
- Building: MRDC
- Room Number: 3403
- Contact Event Host
- Co-sponsored by Kevin Berman; Lezheng Fang
- Starts 11 November 2022 06:30 PM UTC
- Ends 14 November 2022 05:00 PM UTC
- No Admission Charge
Speakers
Dr. Karim Sabra
Machine Learning Approaches for Ocean Acoustic Tomography
Abstract. Underwater sound propagation is primarily driven by a non-linear forward model relating variability of the ocean sound speed profile (SSP) to the acoustic observations (e.g. eigenray arrival times). Ocean acoustic tomography (OAT) methods aim at reconstructing SSPs variations (with respect to a reference environment) from changes of the acoustic measurements between multiple source-receiver pairs. This presentation will review the performance of three different OAT methods: 1) model-based methods (i.e. classical ray-based OAT using a linearized forward model), 2) data-driven methods (such as deep learning) to directly learn the inverse model and 3) a hybrid solution (i.e. the Neural Adjoint-NA-method) which combines deep learning of the forward model followed by a standard recursive optimization to estimate SSPs. Additionally, synthetic SSPs were generated to augment the variability of the training set. These methods were tested with modeled ray arrivals calculated for a downward refracting environment with mild fluctuations of the thermocline. Idealized towed and fixed source configurations are considered. Results indicate that merging data-driven and model-based methods can benefit OAT predictions depending on the selected sensing configurations and actual ray coverage of the water column. But ultimately, the robustness of OAT predictions depends on the dynamics of the SSP variations.
Biography. Dr. Sabra began at Tech in 2007 as an Assistant Professor. He emphasizes an interdisciplinary approach to research problems in different areas (acoustics, structural health monitoring, biomechanics and seismology) based on common wave propagation physics features. His research combines both theoretical and experimental studies for the exploration of these research problems.
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