Characterization of underwater optical turbulence caused by natural convection generated by Rayleigh-Bénard using machine learning

John D. Thomas; Owen O'Malley; Svetlana Avramov-Zamurovic; Nathaniel Ferlic; Joel Esposito; William Jarrett; K. Peter Judd

doi:10.1117/12.2663801

12 June 2023 Characterization of underwater optical turbulence caused by natural convection generated by Rayleigh-Bénard using machine learning

John D. Thomas, Owen O'Malley, Svetlana Avramov-Zamurovic, Nathaniel Ferlic, Joel Esposito, William Jarrett, K. Peter Judd

Author Affiliations +

Proceedings Volume 12543, Ocean Sensing and Monitoring XV; 125430Q (2023) https://doi.org/10.1117/12.2663801
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Characterization of the optical turbulence of complex media is important to designing resilient free-space optical communication systems. Previous studies have used machine learning algorithms to characterize optical turbulence in the atmospheric environment, but we propose to extend this concept to the underwater medium. Our experimental design propagates a Gaussian beam ~1.25 meters through a Rayleigh-Bénard (RB) turbulence tank, which creates realistic optical turbulence that is fully controllable and repeatable. The intensity and phase distortions of the Gaussian beam after propagation will be collected and used to train a convolutional neural network (CNN), for the purpose of the underwater optical turbulence characterization. The CNN will be trained to classify turbulence levels based on both intensity and phase measurements in varied levels of optical turbulence.

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John D. Thomas, Owen O'Malley, Svetlana Avramov-Zamurovic, Nathaniel Ferlic, Joel Esposito, William Jarrett, and K. Peter Judd "Characterization of underwater optical turbulence caused by natural convection generated by Rayleigh-Bénard using machine learning", Proc. SPIE 12543, Ocean Sensing and Monitoring XV, 125430Q (12 June 2023); https://doi.org/10.1117/12.2663801

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