Light Propagation in a Turbulent Ocean

Korotkova, Olga

doi:10.1016/bs.po.2018.09.001

Cited by 58 publications

(33 citation statements)

References 120 publications

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“…More importantly, we observe that with increase in the average water temperature the coherence radius increases as well. The dependence of the coherence radius on other parameters have been previously explored (see review [21]). As we now show, this dependence is carried over to the second-order statistics of the finite beams.…”

Section: Second-order Statistics Of Optical Beams In Oceanic Turbulenmentioning

confidence: 99%

“…[20] where a very accurate numerical Hill's model 4 [20] was analytically fitted within the wide range of the Prandtl/Schmidt numbers [3,3000] making it possible to consider the effects of average water temperatures in the range [0 o C, 30 o C], which covers practically all possible situations occurring within the oceanic portion of the boundary layer of Earth. Single-pass propagation of optical waves through the water turbulence with the Nikishovs' power spectrum has been explored in depth [21]. In particular, the changes in the average intensity [22], spectrum [23], polarization [24], coherence state [25], scintillation [26], structure functions [27], [28], beam wander [29], etc.…”

Section: Introductionmentioning

confidence: 99%

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Bi-static LIDAR systems operating in the presence of oceanic turbulence

Korotkova¹,

Yao²

2020

Optics Communications

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Optical turbulence occurring in the oceanic waters may be detrimental for light beams used in the short-link communication and sensing systems, and, in particular, in underwater LIDARs. We develop a theory capable of predicting the passage of light beams through the bi-static LIDAR systems, for a wide variety of optical waves, including partially coherent and partially polarized, and for a wide family of targets. Our theoretical framework is based on the Huygens-Fresnel integral adopted to random media and optical systems described by the 4 × 4 ABCD matrices. The treatment of oceanic turbulence relies on the recently introduced power spectrum model of the fluctuating refractive-index [Opt. Express 27, 27807 (2019)] capable of accounting for different average temperatures of water. We first analyze the evolution of the second-order beam statistics such as the spectral density and the degree of coherence of the beam on its single pass propagation and then incorporate this knowledge into the analysis of the bi-static LIDAR returns.inverse problem of finding the parameters of the target embedded in the turbulent medium, from comparison of the incident and the returned waves statistics, has also been tackled for the atmospheric propagation setting [7], [8] and can also be readily adjusted for oceanic propagation problems.

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Section: Second-order Statistics Of Optical Beams In Oceanic Turbulenmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bi-static LIDAR systems operating in the presence of oceanic turbulence

Korotkova¹,

Yao²

2020

Optics Communications

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“…Let us now analytically derive the expression for the scintillation index of the spherical wave in the water channel described by spectrum in Eqs. (9) and (10). According to the Rytov perturbation theory it takes form…”

Section: The Scintillation Index Of a Spherical Wavementioning

confidence: 99%

“…Such effects can be characterized with the help of the parabolic equation, the Rytov approximation, the extended Huygens-Fresnel integral and other classic methods as long as the refractive-index power spectrum of turbulent fluctuations is established [6][7][8][9]. As applied to oceanic propagation, the theoretical predictions for the major light statistics of the optical waves have been established (see recent review [10]) on the basis of the widely known power spectrum developed by Nikishovs [11]. In particular, the spectral density and the beam spread for coherent and random beams have been analyzed [12], the spectral shifts in random beams were revealed [13], the polarimetric changes of the random electromagnetic beams have been discussed [14], the loss of coherence of initially coherent beams has been addressed [15], the scintillation analysis was provided in Refs.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, it can also be employed for optical characterization of other turbulent media, with one or two advected quantities, for example, fresh turbulent water contaminated by a chemical. The ratio of kinematic viscosity to diffusivity is orders of magnitude larger for temperature and salinity fluctuations in water than for temperature and humidity fluctuations in air [22], resulting in much larger Prandtl numbers and Schmidt number (averaging at Pr T = 7 for temperature and Pr S = 700 for salinity [10,11,21,23,26]. More importantly, these numbers can exhibit variation.…”

Section: Introductionmentioning

confidence: 99%

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Wide-range Prandtl/Schmidt number power spectrum of optical turbulence and its application to oceanic light propagation

et al. 2019

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Light influenced by the turbulent ocean can be fully characterized with the help of the power spectrum of the water's refractive index fluctuations, resulting from the combined effect of two scalars, temperature and salinity concentration advected by the velocity field. The Nikishovs' model [Fluid Mech. Res. 27, 8298 (2000)] frequently used in the analysis of light evolution through the turbulent ocean channels is the linear combination of the temperature spectrum, the salinity spectrum and their co-spectrum, each being described by an approximate expression developed by Hill [J. Fluid Mech. 88, 541562 (1978)] in the first of his four suggested models. The fourth of the Hill's models provides much more precise power spectrum than the first one expressed via a non-linear differential equation that does not have a closed-form solution. We develop an accurate analytic approximation to the fourth Hill's model valid for Prandtl/Schmidt numbers in the interval [3, 3000] and use it for the development of a more precise oceanic power spectrum. To illustrate the advantage of our model, we include numerical examples relating to the spherical wave scintillation index evolving in the underwater turbulent channels with different average temperatures, and, hence, different Prandtl numbers for temperature and different Schmidt numbers for salinity. Since our model is valid for a large range of Prandtl number (or/and Schmidt number), it can be readily adjusted to oceanic waters with seasonal or extreme average temperature and/or salinity or any other turbulent fluid with one or several advected quantities.

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Computationally efficient processing of in situ underwater digital holograms

Cotter

Fischell

Lavery

2021

Limnology & Ocean Methods

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Underwater digital in-line holography can provide high-resolution, in situ imagery of marine particles and offers many advantages over alternative measurement approaches. However, processing of holograms requires computationally expensive reconstruction and processing, and computational cost increases with the size of the imaging volume. In this work, a processing pipeline is developed to extract targets from holograms where target distribution is relatively sparse without reconstruction of the full hologram. This is motivated by the desire to efficiently extract quantitative estimates of plankton abundance from a data set (>300,000 holograms) collected in the Northwest Atlantic using a large-volume holographic camera. First, holograms with detectable targets are selected using a transfer learning approach. This was critical as a subset of the holograms were impacted by optical turbulence, which obscured target detection. Then, target diffraction patterns are detected in the hologram. Finally, targets are reconstructed and focused using only a small region of the hologram around the detected diffraction pattern. A search algorithm is employed to select distances for reconstruction, reducing the number of reconstructions required for 1 mm focus precision from 1000 to 31. When compared with full reconstruction techniques, this method detects 99% of particles larger than 0.1 mm 2 , a size class which includes most copepods and larger particles of marine snow, and 85% of those targets are sufficiently focused for classification. This approach requires 1% of the processing time required to compute full reconstructions, making processing of long time-series, large imaging volume holographic data sets feasible.

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Light Propagation in a Turbulent Ocean

Cited by 58 publications

References 120 publications

Bi-static LIDAR systems operating in the presence of oceanic turbulence

Bi-static LIDAR systems operating in the presence of oceanic turbulence

Wide-range Prandtl/Schmidt number power spectrum of optical turbulence and its application to oceanic light propagation

Computationally efficient processing of in situ underwater digital holograms

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