Abstract

Marine micro-bubbles are one of those important constituents that influence scattering characteristics of water column. Monte Carlo Based simulations show that a water entrained bubble cloud generate a characteristic backscatter of incident laser light [M. Xia, J. Opt. A: Pure Appl. Opt. 8, 350 (2006)]. This characteristic can be used to detect and localize bubble clouds, leading to wide ranging applications, especially in optical remote sensing. This paper describes tests of an underwater lidar system applied to detecting cloud of micro-bubbles. Laboratory experiments demonstrate that the system is capable of detecting bubbles ranging from diameter 10 μm ~200 μm, over a distance of 7-12m from the detector. The dependence of the lidar return signal on size distribution of bubbles, concentration, thickness and location of bubble clouds is studied and compared with simulation results.

©2009 Optical Society of America

1. Introduction

Gas bubbles in the ocean are important in a variety of subjects: breaking waves and whitecaps influence air-sea gas exchange [1], aerosol formation, sea surface chemistry [2], fractionation of organic and inorganic materials and cavitations. Recent researches show that oceanic bubbles greatly influence the scattering property of waters [3,4].Typically, such scatter is treated as noise that degrades the ability to image objects of interest [5]. However, bubble cloud themselves could also be a target in various applications, such as the monitoring of ship traffic by ship wakes [6], and obtaining information about fish from bubbles left by schools [7].

Some remote sensing systems based on optical scattering have been developed to detect bubbles of ranges from several millimeters to hundreds of millimeters [8] however fail to precisely locate bubble clouds or detect micro-bubbles. In this paper, we present the application of lidar to the detection of bubble clouds. Previously, we did some Monte Carlo based simulations for a lidar system [9]. The results show that an additional backscatter peak appears when the laser beam travels through bubble entrained water as compared to bubble-free water. Figure 1 is a representative simulation result under the condition that the distance between bubble cloud and laser emitter (collocated with detector), L = 8m; attenuation coefficient of water is 0.3m−1; concentration of bubbles, ρ is in order of 108 m−1, and the thickness of the bubble cloud Lcloud=2m. The backscatter lidar signal is normalized over the interval [0,-1] as a PMT is usually used as the detector in lidar system.

 

Fig. 1 Simulated backscatter of laser pulse in water

Download Full Size | PPT Slide | PDF

The amplitude of this additional peak caused by a bubble cloud is determined by distance L, thickness Lcloud, concentration ρ, and size distribution of bubbles, while the pulse width changes with thickness Lcloud. The distance between the bubble cloud and laser/detector can be calculated by Eq. (1)

L=tc2nw.
Here, t is the two-way travel time of light from laser source to bubble cloud and back to the detector, labeled in Fig. 1. c is the speed of light, and nw is the index of refraction of water. The above theoretical analysis implies that it is possible to get information about the bubble cloud by detecting the additional backscatter peak in Fig. 1.

In this paper we present an underwater lidar system to detect underwater mirco-bubble clouds, and demonstrate the results of experiments to study the dependency of the lidar return signal on size distribution of bubbles, concentration, thickness and location of bubble clouds. The experiment results will be compared with previous Monte Carlo simulation. Although some experiments were conducted for bubbles as big as 1-10 cm in diameter, the main emphasis was placed on micro-bubble detection, since firstly, small bubbles may persist for an extended period of time due to the presence of surfactants or particle attachment [10]; secondly, they are more difficult to detect than large bubbles, and observation of bubbles in this size range is currently accomplished by acoustic methods, holography and microphotography.

2. Experiment setup

2.1 Underwater lidar configuration

The lidar system is a non-scanning, radiometric lidar system, shown as Fig. 2 . The system consists of three parts: (1) laser (2) receiver optics, filters and detector, (3) data collection and display. All the components were packed in a water-proof metal box with two optical windows; one for the laser emission and one for receiving the backscatter light.

 

Fig. 2 Experiment setup: underwater lidar system and bubble generator

Download Full Size | PPT Slide | PDF

A frequency-doubled, Q-switched Nd: YAG laser (CFR200, manufactured by BigSky) was used to produce linear polarized green light, wavelength 532nm, in a 12ns pulse at a rate of 10 pulses/s. The beam diameter was 7mm with a divergence smaller than 1.0mrad. The pulse energy was 100mJ. The backscattered light was collected by receiver optics and transformed to voltage by a high speed Hamamatsu PMT. A high speed oscilloscope (Lecroy wave Runner 6000A) was used to acquire the voltage signal. A Q-switch signal from the laser controller was used to externally trigger the oscilloscope to synchronize the experiment data. The data we recorded were averaged over 200 acquisitions to minimize random shot noise.

The receiver optics comprises a 10cm aperture telescope with field of view (FOV) of 2°. It was placed 20cm from laser, with the optical axis of the emitter and receiver optics forming an angle of 1° in a horizontal plane. The two optical axes crossed at 10m away, while the distance between lidar and bubble cloud (labeled as L in Fig. 2) ranged from 7 to 12m. An interference filter (central wavelength of 532nm,bandwidth of 1nm) was placed in front of the PMT to achieve better contrast by blocking stray light. Due to the depolarizing effects of random particles in the water [11,12], a polarizer was used to select component of the return signal that is co-polarized with the laser [13,14].

2.2 Bubble generator

An electrolytic micro-bubble generator, Fig. 3 , was located at the bottom of the pool. It comprises a 2m × 1m rectangular aluminum plate with molybdenum wires suspended above it in a parallel configuration. Six groups of molybdenum wires, spaced 40cm from each other, whose diameters are in a cycle of 50 μm, 100 μm or 200 μm. The thickness of the bubble cloud is 2m if all the wires were connected to cathode of a power supply. The rectangular volume above the metal plate, shown as Eq. (2), is regarded as volume of the bubble cloud.

V=AH,
where H represents height of the water surface above bottom of the pond, A is the area of the metal plate.The diameter range of bubbles is related to diameter of the molybdenum wire along which those bubbles generated. Generally speaking, thinner wires produce smaller bubbles, and diameter of a bubble is a little less than diameter of the wire which induced it. Figure 4 . is a photo of bubbles created by 50μm wires, observed under microscope. The black shade in the middle of the photo is a molybdenum wire; other spheres around it are bubbles. Bubbles obtained from this generator are of diameter between 10μm to 200μm as ascertained via observation under microscope. The bubbles are almost invisible to the naked eyes.

 

Fig. 3 Layout of bubble generator

Download Full Size | PPT Slide | PDF

 

Fig. 4 Small-scale bubbles induced by electrolysis reaction under microscope

Download Full Size | PPT Slide | PDF

The concentration of bubbles is determined by the circuit current while other parameters, such as dimension and number of electrolysis wires, are constant. From the chemistry Eq. (3), we know that 2mol electron are needed for introducing 1mol H2,

2H2O+2eH2+2OH
Suppose a bubble rises with speed w and disappears when it gets to the water surface after time period t. t can be obtained by Eq. (4) and Eq. (5).
t=Hw,
w=118gvd2,
where d is the approximate average diameter of bubbles, which equals the diameter of the wire that induces the bubble; g, the acceleration of gravity is 9.8m2/s, and the viscosity coefficient of water v is 1×106m3.

The volume of gas generated during this period of time can be calculated from Faraday's 1st Law of Electrolysis - The mass of a substance altered at an electrode during electrolysis is directly proportional to the quantity of electricity transferred at that electrode. The quantity of electricity refers to electrical charge, typically measured in coulombs, can be calculated from electrical current I (unit: A). The electrical charge of 1 mol electrons is 96485Coulombs where the volume of 1mol H2 is22.4×103m3, so the volume of the gas V is

V=22.4×1032×96485It,
The quantity of bubbles generated during this time period is indicated by Eq. (7), Where 4π3(d/2)3is average volume of a single bubble.
N=V4π3(d/2)3,
The concentration of bubbles is the quantity of bubbles per volume(unit: m−3). so Eq. (8), derived from Eq. (2) - Eq. (7), can be expressed as
CN=NAH=4.074×1013Id5A.
This equation is used to calculate theoretical concentration of bubbles, and choose suitable current accordingly during experiment.

3. Experiment and results

The experiment was accomplished in a 22m long, 4m wide and 2m deep indoor pool. The pool is filled with clear tap water, of which empirical attenuation coefficient is about 0.27m-1. The lidar system, contained in a 0.8m × 0.8m × 1m (Height × Width × Length) waterproof box, was immersed in water at one end of the pool. A bubble generator was placed at the bottom of the pool; the long side of the metal plate along the optical path of laser. The distance between lidar and bubble cloud, denote as L, is 7-12m, is measured from the front end of the box that contains lidar system to the front end of bubble cloud, shown as Fig. 2. Repetitive experiments were carried out during daylight and night and the results show no obvious difference. This is easy to understand: there is no intense sunlight indoor, so the background noise caused by solar light is negligible.

3.1 Experiment results and simulation results

The return signal of lidar system was displayed on an oscilloscope, where the vertical axis is the voltage of the PMT, and the horizontal axis is time. The start point of time domain is determined by Q-switch signal. Figure 5 is a plot of average data for the case L = 8m, Lcloud = 2m, ρ = 5.0 × 108m−3. The dotted line shows a typical backscatter lidar signal of bubble-free water, regarded as baseline. The laser emits a pulse at 105ns. A backscatter peak at 293ns results from reflection off the wall at the far end which is 21m away from detector/laser. The measured distance agrees with results obtained from lidar signal by Eq. (1), L=(293105)×109×3×1082×1.33=21.2m. The solid curve shows the backscatter of water containing a bubble cloud. The peak for Reflection from the pool wall disappeared on the solid curve, while an additional peak induced by the backscattered light from bubble cloud appears at 189ns, corresponding to a target that is 11.7m from the far end of the pond by Eq. (1), e.g., 9.4m away from detector/laser. This is in good agreement with the known location of the bubble cloud (8-10m). One can conclude from the experimental result that the underwater bubble cloud indeed causes a slight backscatter peak, as predicted by Monte Carlo simulation, and suppresses the reflection from other under water targets beyond the location of the bubble cloud. The location of the bubble cloud could be inferred from the time of the backscattered peak appears.

 

Fig. 5 Backscatter lidar signal of bubble-free water and water contains bubble that are 10-200 μm in diameter

Download Full Size | PPT Slide | PDF

As a comparison, we also detected much larger bubbles generated by continually pouring gas into a customized porcelain tube with small holes on it. When the gas gets through the small holes in the porcelain, bubbles are formed in water. The size of those bubbles, determined by gas pressure, is about 1-10mm in diameter,~10-100 times bigger than bubbles generated by the electrolysis wires. Experiment results show that big bubbles cause a more remarkable backscatter peak, that can even saturate the detector when a bubble cloud gets too close to the detector. Figure 6 shows an experimental result for a bubble cloud that is ~17m from the detector.

 

Fig. 6 Backscatter lidar signal of bubble-free water and water contains bubbles that are 1-10mm in diameter

Download Full Size | PPT Slide | PDF

In order to explore influence of location L, thickness Lcloud, and concentration of bubble cloud ρ, on characteristic backscatter peak, the experimental data was processed by subtracting the baseline from the backscatter of the water containing bubble cloud. Figure 7 is the processed data of Fig. 6. An obvious peak caused by bubble cloud can be observed in the figure. Amplitude of the peak Vmax and pulse width tw are denoted in Fig. 7. Here, pulse-width is defined as time interval between the leading edge and trailing edge of a pulse at a point where the amplitude is 50% of the peak value. The influences of L, Lcloud, ρ, will be discussed in the following section. All the experiments were done using the electrolysis bubble generator shown in Fig. 3 since this makes it easier to control the parameters of bubble clouds.

 

Fig. 7 Processed experiment data (corresponding to curves in Fig. 5)

Download Full Size | PPT Slide | PDF

3.2 Relationship between return signal and Location of the bubble cloud

To study the relationship between Vmax and L, we conducted experiments to generate bubble cloud of thickness 2m, and concentration 5.0×108m3, located at L = 7m-12m. The shape and concentration of bubble cloud remain the same when L varies from 7 to 12m since the whole bubble cloud is in the lidar FOV. Results are shown in Fig. 8 . The full lidar return signal of bubble cloud entrained water is denoted as raw signal in Fig. 8, while the extracted backscatter peak of bubble cloud is denoted as processed signal. Experiment results show that the location of bubble cloud not only determines the location of the peak of the return signal, but also influences the peak value Vmax. The amplitude of the peak Vmax decays exponentially with L shown as Fig. 9 , with the attenuation coefficient being approximately equal to the attenuation coefficient of water. The equation fitted to the data, shown in Fig. 9, is Vmax(L)=2.365e-0 .28L. This experiment result agrees with the Monte Carlo simulation results [9], and can be explained in that the light intensity exponential decays with its traveling distance before it reaches bubble cloud, as does the backscatter signal if the thickness and concentration of bubble cloud remains constant.

 

Fig. 8 Influence of location of bubble cloud on backscatter lidar signal

Download Full Size | PPT Slide | PDF

 

Fig. 9 Relationship between amplitude of bubble backscatter peak and location of bubble cloud

Download Full Size | PPT Slide | PDF

3.3 Relationship between return signal and Concentration of bubbles

The concentration of the bubble cloud is another factor that influences Vmax . Figure 10 . shows the results of an experiment conducted to study the dependence of Vmax on bubble concentration. Table 1 shows five groups of current value we used in experiment and the theoretical concentration of bubbles obtained from Eq. (8). Total concentration ρ is the sum of the theoretical concentration for each type of wire. The bubble generator was located at 8m. Figure 10 indicates that Vmax increases linearly with the concentration of bubbles. The function Vmax(ρ) reduces to zero at a concentration of 2.5 × 108 m -3, which represents the theoretical detection limit of our lidar system.

 

Fig. 10 Relationship between amplitude of bubble backscatter peak and concentration of bubbles

Download Full Size | PPT Slide | PDF

Tables Icon

Table 1. Current and corresponding concentrations of bubble cloud

3.4 Relationship between return signal and thickness of the bubble cloud

The thickness of bubble clouds is one of the important characteristics that influences lidar return signal. Theoretically, if the thickness of the bubbles increases, laser will be scattered by more bubbles when it passes through bubble clouds, thus, the intensity of backscattered light should increase. In the meantime, it takes more time for the light to travel through bubble clouds, so pulse width of backscatter signal should also increase. As such, an experiment was conducted to explore the influence of thickness Lcloud. The thickness of the bubble cloud is changed by changing the number of wires that participates in electrolysis. In order to keep constant concentration, current should be changed accordingly. The thickness of bubbles, current, number of wires we used is listed in Table 2 . Experiment result shows that peak value Vmax increases with thickness (Fig. 11 ) and pulse width(tw) exponentially increases, as shown as Fig. 12 . Results are in agreement with our theoretical analysis and Monte Carlo simulation.

Tables Icon

Table 2. Thickness of bubble and the corresponding wires and current

 

Fig. 11 Relationship between amplitude of the bubble backscatter peak and thickness of bubble cloud

Download Full Size | PPT Slide | PDF

 

Fig. 12 Relationship between pulse width and thickness of bubble cloud

Download Full Size | PPT Slide | PDF

4. Conclusion and discussion

The scattering properties of bubble clouds in water is usually regarded as inevitable noise, which distorts lidar returns or underwater images of those objects we are interested. However, bubble clouds can also be a valuable target in various remote sensing applications. In situ long range sensing of bubble clouds can be achieved based on their optical property. In this paper we describe the fabrication and results obtained from an underwater lidar system for micro-bubble clouds detection. A peak of the backscatter lidar signal corresponds to backscatter of the bubble cloud is detected and its characteristic is in good agreement with previous Monte Carlo simulation

In addition, we tested the performance of this lidar system by detecting bubbles in the size range of several millimeters as well as tens of micrometers. Results show that backscatter of the bubble cloud that consists of bubbles that are millimeter in diameter has similar characteristic of reflection from other solid objects, but backscatter from micro-bubbles that are micrometers in diameter is relatively small. This does not only mean that the backscatter intensity is influenced by the size distribution of bubbles, but also that detection ability of a lidar system will be challenged when bubble size gets smaller. In order to get improved detection capability, some particular technology should be deployed in the lidar system design.

Through our experimental results, it is concluded that the location of a bubble cloud can be inferred from the location of a corresponding backscatter peak, which can then be used as a criterion for location of a bubble cloud. Other information about the bubble cloud is explored by processing experiment results. Some property of a bubble cloud such as location, concentration, size distribution and thickness influences amplitude and width of this peak. For bubbles of constant size distribution, the backscatter peak increase linearly with respect to the increasing of concentration of bubbles, and exponential decay with distance between the bubble cloud and lidar system under the assumption that thickness of bubble cloud stays the same. An increase in the thickness of the bubble cloud does not only cause an increase in amplitude of backscatter peak, but also widens the pulse-width exponentially. Those methods can be used to characterize bubble clouds in the future and has potential applications in remote sensing.

Although the results were constrained by the operational limitations on range and bubble concentration posed by the experimental setup, this lidar configuration could be developed for at-sea deployment. In this paper, some common characteristics of bubble clouds that influence backscatter signal were considered, however, more factors that influences the performance of the lidar, such as water quality, have not been discussed. These factors would be more meaningful if applied in the context of field experiments in natural waters.

Acknowledgments

The authors would like to thank J. S. Jaffe (UCSD) and an anonymous reviewer for a careful review of the manuscript and their helpful advice.

References and links

1. A. Stigebrandt, “Computations of oxygen fluxes through the sea surface and the net production of organic matter with application to the Baltic and adjacent seas,” Limnol. Oceanogr. 36, 444–454 (1991). [CrossRef]  

2. H. Loisel, X. Meriaux, J. F. Berthon, and A. Poteau, “Investigation of the optical backscattering to scattering ratio of marine particles in relation to their biogeochemical composition in the eastern English Channel and southern North Sea,” Limnol. Oceanogr. 52, 739–752 (2007). [CrossRef]  

3. D. Stramski, “Gas microbubbles: an assessment of their significance to light scattering in quiescent seas,” Proc. SPIE 2258, 704–710 (1994). [CrossRef]  

4. X. D. Zhang, M. Lewis, W. P. Bissett, B. Johnson, and D. Kohler, “Optical influence of ship wakes,” Appl. Opt. 43(15), 3122–3132 (2004). [CrossRef]   [PubMed]  

5. M. M. Krekova, G. M. Krekov, and V. S. Shamanaev, “Influence of air bubbles in seawater on the formation of lidar returns,” J. Atmos. Ocean. Technol. 21(5), 819–824 (2004). [CrossRef]  

6. Y. Liu, “Automatic detecting system for ship wakes in SAR images,” in Fifth World Congress on Intelligent Control and Automation (IEEE, 2004), pp. 3032–3036.

7. B. D. Johnson and R. C. Cooke, “Generation of Stabilized Microbubbles in Seawater,” Science 213(4504), 209–211 (1981). [CrossRef]   [PubMed]  

8. L. P. Su, W. J. Zhao, X. Y. Hu, D. M. Ren, and X. Z. Liu, “Simple lidar detecting wake profiles,” J. Opt. A, Pure Appl. Opt. 9(10), 842–847 (2007). [CrossRef]  

9. M. Xia, K. C. Yang, X. H. Zhang, J. H. Rao, Y. Zheng, and D. Tan, “Monte Carlo simulation of backscattering signal from bubbles under water,” J. Opt. A, Pure Appl. Opt. 8(3), 350–354 (2006). [CrossRef]  

10. P. J. Mulhearn, “Distribution of Microbubbles in Coastal Waters,” J. Geophys. Res. 86(C7), 6429–6434 (1981). [CrossRef]  

11. K. Sassen, H. J. Zhao, and G. C. Dodd, “Simulated Polarization Diversity Lidar Returns from Water and Precipitating Mixed Phase Clouds,” Appl. Opt. 31(15), 2914–2923 (1992). [CrossRef]   [PubMed]  

12. X. D. Wang, L. V. Wang, C. W. Sun, and C. C. Yang, “Polarized light propagation through scattering media: time-resolved Monte Carlo simulations and experiments,” J. Biomed. Opt. 8(4), 608–617 (2003). [CrossRef]   [PubMed]  

13. K. C. Yang, X. Zhu, Y. Li, J. Z. Lin, H. Yang, and Z. G. Li, “Polarization compression of large dynamic range laser returned signals from water surface and underwater,” J. Phys. D Appl. Phys. 35(9), 935–938 (2002). [CrossRef]  

14. G. D. Gilbert and J. C. Pernicka, “Improvement of Underwater Visibility by Reduction of Backscatter with a Circular Polarization Technique,” Appl. Opt. 6(4), 741–746 (1967). [CrossRef]   [PubMed]  

References

  • View by:
  • |
  • |
  • |

  1. A. Stigebrandt, “Computations of oxygen fluxes through the sea surface and the net production of organic matter with application to the Baltic and adjacent seas,” Limnol. Oceanogr. 36, 444–454 (1991).
    [Crossref]
  2. H. Loisel, X. Meriaux, J. F. Berthon, and A. Poteau, “Investigation of the optical backscattering to scattering ratio of marine particles in relation to their biogeochemical composition in the eastern English Channel and southern North Sea,” Limnol. Oceanogr. 52, 739–752 (2007).
    [Crossref]
  3. D. Stramski, “Gas microbubbles: an assessment of their significance to light scattering in quiescent seas,” Proc. SPIE 2258, 704–710 (1994).
    [Crossref]
  4. X. D. Zhang, M. Lewis, W. P. Bissett, B. Johnson, and D. Kohler, “Optical influence of ship wakes,” Appl. Opt. 43(15), 3122–3132 (2004).
    [Crossref] [PubMed]
  5. M. M. Krekova, G. M. Krekov, and V. S. Shamanaev, “Influence of air bubbles in seawater on the formation of lidar returns,” J. Atmos. Ocean. Technol. 21(5), 819–824 (2004).
    [Crossref]
  6. Y. Liu, “Automatic detecting system for ship wakes in SAR images,” in Fifth World Congress on Intelligent Control and Automation (IEEE, 2004), pp. 3032–3036.
  7. B. D. Johnson and R. C. Cooke, “Generation of Stabilized Microbubbles in Seawater,” Science 213(4504), 209–211 (1981).
    [Crossref] [PubMed]
  8. L. P. Su, W. J. Zhao, X. Y. Hu, D. M. Ren, and X. Z. Liu, “Simple lidar detecting wake profiles,” J. Opt. A, Pure Appl. Opt. 9(10), 842–847 (2007).
    [Crossref]
  9. M. Xia, K. C. Yang, X. H. Zhang, J. H. Rao, Y. Zheng, and D. Tan, “Monte Carlo simulation of backscattering signal from bubbles under water,” J. Opt. A, Pure Appl. Opt. 8(3), 350–354 (2006).
    [Crossref]
  10. P. J. Mulhearn, “Distribution of Microbubbles in Coastal Waters,” J. Geophys. Res. 86(C7), 6429–6434 (1981).
    [Crossref]
  11. K. Sassen, H. J. Zhao, and G. C. Dodd, “Simulated Polarization Diversity Lidar Returns from Water and Precipitating Mixed Phase Clouds,” Appl. Opt. 31(15), 2914–2923 (1992).
    [Crossref] [PubMed]
  12. X. D. Wang, L. V. Wang, C. W. Sun, and C. C. Yang, “Polarized light propagation through scattering media: time-resolved Monte Carlo simulations and experiments,” J. Biomed. Opt. 8(4), 608–617 (2003).
    [Crossref] [PubMed]
  13. K. C. Yang, X. Zhu, Y. Li, J. Z. Lin, H. Yang, and Z. G. Li, “Polarization compression of large dynamic range laser returned signals from water surface and underwater,” J. Phys. D Appl. Phys. 35(9), 935–938 (2002).
    [Crossref]
  14. G. D. Gilbert and J. C. Pernicka, “Improvement of Underwater Visibility by Reduction of Backscatter with a Circular Polarization Technique,” Appl. Opt. 6(4), 741–746 (1967).
    [Crossref] [PubMed]

2007 (2)

H. Loisel, X. Meriaux, J. F. Berthon, and A. Poteau, “Investigation of the optical backscattering to scattering ratio of marine particles in relation to their biogeochemical composition in the eastern English Channel and southern North Sea,” Limnol. Oceanogr. 52, 739–752 (2007).
[Crossref]

L. P. Su, W. J. Zhao, X. Y. Hu, D. M. Ren, and X. Z. Liu, “Simple lidar detecting wake profiles,” J. Opt. A, Pure Appl. Opt. 9(10), 842–847 (2007).
[Crossref]

2006 (1)

M. Xia, K. C. Yang, X. H. Zhang, J. H. Rao, Y. Zheng, and D. Tan, “Monte Carlo simulation of backscattering signal from bubbles under water,” J. Opt. A, Pure Appl. Opt. 8(3), 350–354 (2006).
[Crossref]

2004 (2)

X. D. Zhang, M. Lewis, W. P. Bissett, B. Johnson, and D. Kohler, “Optical influence of ship wakes,” Appl. Opt. 43(15), 3122–3132 (2004).
[Crossref] [PubMed]

M. M. Krekova, G. M. Krekov, and V. S. Shamanaev, “Influence of air bubbles in seawater on the formation of lidar returns,” J. Atmos. Ocean. Technol. 21(5), 819–824 (2004).
[Crossref]

2003 (1)

X. D. Wang, L. V. Wang, C. W. Sun, and C. C. Yang, “Polarized light propagation through scattering media: time-resolved Monte Carlo simulations and experiments,” J. Biomed. Opt. 8(4), 608–617 (2003).
[Crossref] [PubMed]

2002 (1)

K. C. Yang, X. Zhu, Y. Li, J. Z. Lin, H. Yang, and Z. G. Li, “Polarization compression of large dynamic range laser returned signals from water surface and underwater,” J. Phys. D Appl. Phys. 35(9), 935–938 (2002).
[Crossref]

1994 (1)

D. Stramski, “Gas microbubbles: an assessment of their significance to light scattering in quiescent seas,” Proc. SPIE 2258, 704–710 (1994).
[Crossref]

1992 (1)

1991 (1)

A. Stigebrandt, “Computations of oxygen fluxes through the sea surface and the net production of organic matter with application to the Baltic and adjacent seas,” Limnol. Oceanogr. 36, 444–454 (1991).
[Crossref]

1981 (2)

P. J. Mulhearn, “Distribution of Microbubbles in Coastal Waters,” J. Geophys. Res. 86(C7), 6429–6434 (1981).
[Crossref]

B. D. Johnson and R. C. Cooke, “Generation of Stabilized Microbubbles in Seawater,” Science 213(4504), 209–211 (1981).
[Crossref] [PubMed]

1967 (1)

Berthon, J. F.

H. Loisel, X. Meriaux, J. F. Berthon, and A. Poteau, “Investigation of the optical backscattering to scattering ratio of marine particles in relation to their biogeochemical composition in the eastern English Channel and southern North Sea,” Limnol. Oceanogr. 52, 739–752 (2007).
[Crossref]

Bissett, W. P.

Cooke, R. C.

B. D. Johnson and R. C. Cooke, “Generation of Stabilized Microbubbles in Seawater,” Science 213(4504), 209–211 (1981).
[Crossref] [PubMed]

Dodd, G. C.

Gilbert, G. D.

Hu, X. Y.

L. P. Su, W. J. Zhao, X. Y. Hu, D. M. Ren, and X. Z. Liu, “Simple lidar detecting wake profiles,” J. Opt. A, Pure Appl. Opt. 9(10), 842–847 (2007).
[Crossref]

Johnson, B.

Johnson, B. D.

B. D. Johnson and R. C. Cooke, “Generation of Stabilized Microbubbles in Seawater,” Science 213(4504), 209–211 (1981).
[Crossref] [PubMed]

Kohler, D.

Krekov, G. M.

M. M. Krekova, G. M. Krekov, and V. S. Shamanaev, “Influence of air bubbles in seawater on the formation of lidar returns,” J. Atmos. Ocean. Technol. 21(5), 819–824 (2004).
[Crossref]

Krekova, M. M.

M. M. Krekova, G. M. Krekov, and V. S. Shamanaev, “Influence of air bubbles in seawater on the formation of lidar returns,” J. Atmos. Ocean. Technol. 21(5), 819–824 (2004).
[Crossref]

Lewis, M.

Li, Y.

K. C. Yang, X. Zhu, Y. Li, J. Z. Lin, H. Yang, and Z. G. Li, “Polarization compression of large dynamic range laser returned signals from water surface and underwater,” J. Phys. D Appl. Phys. 35(9), 935–938 (2002).
[Crossref]

Li, Z. G.

K. C. Yang, X. Zhu, Y. Li, J. Z. Lin, H. Yang, and Z. G. Li, “Polarization compression of large dynamic range laser returned signals from water surface and underwater,” J. Phys. D Appl. Phys. 35(9), 935–938 (2002).
[Crossref]

Lin, J. Z.

K. C. Yang, X. Zhu, Y. Li, J. Z. Lin, H. Yang, and Z. G. Li, “Polarization compression of large dynamic range laser returned signals from water surface and underwater,” J. Phys. D Appl. Phys. 35(9), 935–938 (2002).
[Crossref]

Liu, X. Z.

L. P. Su, W. J. Zhao, X. Y. Hu, D. M. Ren, and X. Z. Liu, “Simple lidar detecting wake profiles,” J. Opt. A, Pure Appl. Opt. 9(10), 842–847 (2007).
[Crossref]

Loisel, H.

H. Loisel, X. Meriaux, J. F. Berthon, and A. Poteau, “Investigation of the optical backscattering to scattering ratio of marine particles in relation to their biogeochemical composition in the eastern English Channel and southern North Sea,” Limnol. Oceanogr. 52, 739–752 (2007).
[Crossref]

Meriaux, X.

H. Loisel, X. Meriaux, J. F. Berthon, and A. Poteau, “Investigation of the optical backscattering to scattering ratio of marine particles in relation to their biogeochemical composition in the eastern English Channel and southern North Sea,” Limnol. Oceanogr. 52, 739–752 (2007).
[Crossref]

Mulhearn, P. J.

P. J. Mulhearn, “Distribution of Microbubbles in Coastal Waters,” J. Geophys. Res. 86(C7), 6429–6434 (1981).
[Crossref]

Pernicka, J. C.

Poteau, A.

H. Loisel, X. Meriaux, J. F. Berthon, and A. Poteau, “Investigation of the optical backscattering to scattering ratio of marine particles in relation to their biogeochemical composition in the eastern English Channel and southern North Sea,” Limnol. Oceanogr. 52, 739–752 (2007).
[Crossref]

Rao, J. H.

M. Xia, K. C. Yang, X. H. Zhang, J. H. Rao, Y. Zheng, and D. Tan, “Monte Carlo simulation of backscattering signal from bubbles under water,” J. Opt. A, Pure Appl. Opt. 8(3), 350–354 (2006).
[Crossref]

Ren, D. M.

L. P. Su, W. J. Zhao, X. Y. Hu, D. M. Ren, and X. Z. Liu, “Simple lidar detecting wake profiles,” J. Opt. A, Pure Appl. Opt. 9(10), 842–847 (2007).
[Crossref]

Sassen, K.

Shamanaev, V. S.

M. M. Krekova, G. M. Krekov, and V. S. Shamanaev, “Influence of air bubbles in seawater on the formation of lidar returns,” J. Atmos. Ocean. Technol. 21(5), 819–824 (2004).
[Crossref]

Stigebrandt, A.

A. Stigebrandt, “Computations of oxygen fluxes through the sea surface and the net production of organic matter with application to the Baltic and adjacent seas,” Limnol. Oceanogr. 36, 444–454 (1991).
[Crossref]

Stramski, D.

D. Stramski, “Gas microbubbles: an assessment of their significance to light scattering in quiescent seas,” Proc. SPIE 2258, 704–710 (1994).
[Crossref]

Su, L. P.

L. P. Su, W. J. Zhao, X. Y. Hu, D. M. Ren, and X. Z. Liu, “Simple lidar detecting wake profiles,” J. Opt. A, Pure Appl. Opt. 9(10), 842–847 (2007).
[Crossref]

Sun, C. W.

X. D. Wang, L. V. Wang, C. W. Sun, and C. C. Yang, “Polarized light propagation through scattering media: time-resolved Monte Carlo simulations and experiments,” J. Biomed. Opt. 8(4), 608–617 (2003).
[Crossref] [PubMed]

Tan, D.

M. Xia, K. C. Yang, X. H. Zhang, J. H. Rao, Y. Zheng, and D. Tan, “Monte Carlo simulation of backscattering signal from bubbles under water,” J. Opt. A, Pure Appl. Opt. 8(3), 350–354 (2006).
[Crossref]

Wang, L. V.

X. D. Wang, L. V. Wang, C. W. Sun, and C. C. Yang, “Polarized light propagation through scattering media: time-resolved Monte Carlo simulations and experiments,” J. Biomed. Opt. 8(4), 608–617 (2003).
[Crossref] [PubMed]

Wang, X. D.

X. D. Wang, L. V. Wang, C. W. Sun, and C. C. Yang, “Polarized light propagation through scattering media: time-resolved Monte Carlo simulations and experiments,” J. Biomed. Opt. 8(4), 608–617 (2003).
[Crossref] [PubMed]

Xia, M.

M. Xia, K. C. Yang, X. H. Zhang, J. H. Rao, Y. Zheng, and D. Tan, “Monte Carlo simulation of backscattering signal from bubbles under water,” J. Opt. A, Pure Appl. Opt. 8(3), 350–354 (2006).
[Crossref]

Yang, C. C.

X. D. Wang, L. V. Wang, C. W. Sun, and C. C. Yang, “Polarized light propagation through scattering media: time-resolved Monte Carlo simulations and experiments,” J. Biomed. Opt. 8(4), 608–617 (2003).
[Crossref] [PubMed]

Yang, H.

K. C. Yang, X. Zhu, Y. Li, J. Z. Lin, H. Yang, and Z. G. Li, “Polarization compression of large dynamic range laser returned signals from water surface and underwater,” J. Phys. D Appl. Phys. 35(9), 935–938 (2002).
[Crossref]

Yang, K. C.

M. Xia, K. C. Yang, X. H. Zhang, J. H. Rao, Y. Zheng, and D. Tan, “Monte Carlo simulation of backscattering signal from bubbles under water,” J. Opt. A, Pure Appl. Opt. 8(3), 350–354 (2006).
[Crossref]

K. C. Yang, X. Zhu, Y. Li, J. Z. Lin, H. Yang, and Z. G. Li, “Polarization compression of large dynamic range laser returned signals from water surface and underwater,” J. Phys. D Appl. Phys. 35(9), 935–938 (2002).
[Crossref]

Zhang, X. D.

Zhang, X. H.

M. Xia, K. C. Yang, X. H. Zhang, J. H. Rao, Y. Zheng, and D. Tan, “Monte Carlo simulation of backscattering signal from bubbles under water,” J. Opt. A, Pure Appl. Opt. 8(3), 350–354 (2006).
[Crossref]

Zhao, H. J.

Zhao, W. J.

L. P. Su, W. J. Zhao, X. Y. Hu, D. M. Ren, and X. Z. Liu, “Simple lidar detecting wake profiles,” J. Opt. A, Pure Appl. Opt. 9(10), 842–847 (2007).
[Crossref]

Zheng, Y.

M. Xia, K. C. Yang, X. H. Zhang, J. H. Rao, Y. Zheng, and D. Tan, “Monte Carlo simulation of backscattering signal from bubbles under water,” J. Opt. A, Pure Appl. Opt. 8(3), 350–354 (2006).
[Crossref]

Zhu, X.

K. C. Yang, X. Zhu, Y. Li, J. Z. Lin, H. Yang, and Z. G. Li, “Polarization compression of large dynamic range laser returned signals from water surface and underwater,” J. Phys. D Appl. Phys. 35(9), 935–938 (2002).
[Crossref]

Appl. Opt. (3)

J. Atmos. Ocean. Technol. (1)

M. M. Krekova, G. M. Krekov, and V. S. Shamanaev, “Influence of air bubbles in seawater on the formation of lidar returns,” J. Atmos. Ocean. Technol. 21(5), 819–824 (2004).
[Crossref]

J. Biomed. Opt. (1)

X. D. Wang, L. V. Wang, C. W. Sun, and C. C. Yang, “Polarized light propagation through scattering media: time-resolved Monte Carlo simulations and experiments,” J. Biomed. Opt. 8(4), 608–617 (2003).
[Crossref] [PubMed]

J. Geophys. Res. (1)

P. J. Mulhearn, “Distribution of Microbubbles in Coastal Waters,” J. Geophys. Res. 86(C7), 6429–6434 (1981).
[Crossref]

J. Opt. A, Pure Appl. Opt. (2)

L. P. Su, W. J. Zhao, X. Y. Hu, D. M. Ren, and X. Z. Liu, “Simple lidar detecting wake profiles,” J. Opt. A, Pure Appl. Opt. 9(10), 842–847 (2007).
[Crossref]

M. Xia, K. C. Yang, X. H. Zhang, J. H. Rao, Y. Zheng, and D. Tan, “Monte Carlo simulation of backscattering signal from bubbles under water,” J. Opt. A, Pure Appl. Opt. 8(3), 350–354 (2006).
[Crossref]

J. Phys. D Appl. Phys. (1)

K. C. Yang, X. Zhu, Y. Li, J. Z. Lin, H. Yang, and Z. G. Li, “Polarization compression of large dynamic range laser returned signals from water surface and underwater,” J. Phys. D Appl. Phys. 35(9), 935–938 (2002).
[Crossref]

Limnol. Oceanogr. (2)

A. Stigebrandt, “Computations of oxygen fluxes through the sea surface and the net production of organic matter with application to the Baltic and adjacent seas,” Limnol. Oceanogr. 36, 444–454 (1991).
[Crossref]

H. Loisel, X. Meriaux, J. F. Berthon, and A. Poteau, “Investigation of the optical backscattering to scattering ratio of marine particles in relation to their biogeochemical composition in the eastern English Channel and southern North Sea,” Limnol. Oceanogr. 52, 739–752 (2007).
[Crossref]

Proc. SPIE (1)

D. Stramski, “Gas microbubbles: an assessment of their significance to light scattering in quiescent seas,” Proc. SPIE 2258, 704–710 (1994).
[Crossref]

Science (1)

B. D. Johnson and R. C. Cooke, “Generation of Stabilized Microbubbles in Seawater,” Science 213(4504), 209–211 (1981).
[Crossref] [PubMed]

Other (1)

Y. Liu, “Automatic detecting system for ship wakes in SAR images,” in Fifth World Congress on Intelligent Control and Automation (IEEE, 2004), pp. 3032–3036.

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (12)

Fig. 1
Fig. 1 Simulated backscatter of laser pulse in water
Fig. 2
Fig. 2 Experiment setup: underwater lidar system and bubble generator
Fig. 3
Fig. 3 Layout of bubble generator
Fig. 4
Fig. 4 Small-scale bubbles induced by electrolysis reaction under microscope
Fig. 5
Fig. 5 Backscatter lidar signal of bubble-free water and water contains bubble that are 10-200 μm in diameter
Fig. 6
Fig. 6 Backscatter lidar signal of bubble-free water and water contains bubbles that are 1-10mm in diameter
Fig. 7
Fig. 7 Processed experiment data (corresponding to curves in Fig. 5)
Fig. 8
Fig. 8 Influence of location of bubble cloud on backscatter lidar signal
Fig. 9
Fig. 9 Relationship between amplitude of bubble backscatter peak and location of bubble cloud
Fig. 10
Fig. 10 Relationship between amplitude of bubble backscatter peak and concentration of bubbles
Fig. 11
Fig. 11 Relationship between amplitude of the bubble backscatter peak and thickness of bubble cloud
Fig. 12
Fig. 12 Relationship between pulse width and thickness of bubble cloud

Tables (2)

Tables Icon

Table 1 Current and corresponding concentrations of bubble cloud

Tables Icon

Table 2 Thickness of bubble and the corresponding wires and current

Equations (8)

Equations on this page are rendered with MathJax. Learn more.

L=tc2nw.
V=AH,
2H2O+2eH2+2OH
t=Hw,
w=118gvd2,
V=22.4×1032×96485It,
N=V4π3(d/2)3,
CN=NAH=4.074×1013Id5A.

Metrics