Open Access
Add to BibSonomyAbstract
An efficient and simple and convenient technique for transparent samples thickness measurements by Raman spectroscopy is suggested. The elastic scattering can be effectively used for sample border indication if the refractive index changes more than 3%, while it fails to detect an ice-to-water border of floating ice. The alternative is to use Raman spectroscopy to detect the interface between different layers of transparent materials. The difference between the Raman spectra of poly methyl methacrylate (PMMA) and water, and between ice and liquid water were employed to locate the PMMA-water and ice-water interfaces, while elastic scattering was used for air-solid surface detection. This approach yields an error of 2%–5% indicating that it is promising to express a remote and noninvasive thickness measurement technique in field experiments.
© 2015 Optical Society of America
1. Introduction
The Arctic region is of growing interest for both science and technology due to global climate change monitoring and increased industrial activity. Climatologists are focused on ice properties study for two reasons. First, ice-covered ocean areas are good indicators of global warming. Second, ice greatly influences solar radiation absorption and serves as an important interface between ocean and atmosphere by restricting mass and heat transfer [1–3]. The reliable diagnostics of global climate change and prediction of future changes can be tracked if different parameters of ice and water are available for Polar regions: ice thickness, salinity, temperature, and optical properties. Industrial companies involved in exploration and exploitation of hydrocarbons are also interested in ice properties affecting ice loads on offshore structures [4]. Ice loads depend on thermomechanical properties of sea ice such as thickness, temperature, and salinity. Monitoring of ice conditions in the vicinity of the offshore structures may prevent associated damage possibly resulting in oil/gas pollution.
Conventional techniques of ice thickness measurements can be classified as contact and remote sensing methods (air or space borne) [1,2]. These methods include drilling, thermostring measurements, electromagnetic sounding, differential GPS surveying, pulsed X-ray sensing, and satellite image processing. Different techniques greatly vary within the accuracy, spatial and time resolution, cost and capabilities to perform measurements on large Polar areas under heavy meteorological conditions. Drilling is a single-point measurement method with high accuracy (in cm range); however, it is a man-power and time consuming technique [1]. Ice thickness measurement can be performed in long-term range applications by deployment of thermistors. But this method is rarely used in practice due to high man-power consumption for retrieving equipment from ice; also, it can hardly resolve the interface between the melting ice and water due to their equal temperatures [5]. Electro-magnetic sounding provides a greater sampling rate, while its accuracy strongly depends on ice salinity and porosity and needs calibration [1]. Upward-looking sonars measure long-term statistical data; however, the ice velocity drift must be measured at the same time to interpret the data correctly [6]. Pulsed radars installed on helicopters can be used for ice thickness estimation, but these measurements are of low accuracy [7]. Pulsed X-ray sensing provides accuracy within tenths of centimeters, but it is rarely used in practice due to high costs. Satellite measurements of ice thickness suffer from low accuracy and are strongly influenced by temperature and salinity of seawater (20% error for ice thickness below 20 cm and 100% error for layer) [8,9]. Therefore, new express and rapid techniques for ice thickness measurements are needed.
We suggested a new alternative technique for express ice thickness measurements using laser remote sensing methods. Laser remote sensing is an optical pump-probe method where the short laser pulse is scattered by a remote object, resulting in a spectra. These spectra reveal the internal structure of the interested sample and thus provide information about the sample’s chemical composition, temperature, and pressure [10,11]. Laser remote sensing was successfully used in a variety of applications for seawater property studies [12–14]: seawater chlorophyll concentration measurements, water surface temperature measurements, and seawater pollution monitoring.
Laser remote sensing can be successfully used for ice thickness measurements. A laser altimetry can be used to detect ice-to-air interfaces, while detection of an ice-to-water border in a nutshell is due to similar refraction indexes for ice and water. Raman spectra for OH-bond differs substantially for ice and water. This feature can be a used as an indicator of an ice-to-water border. Temperature dependence of Raman OH-band profile on water temperature was successfully used as a remote sensing tool [14–19]. Raman spectroscopy was used for thin film thickness measurements [20–22], but to the best of our knowledge bulk sample thickness measurements are not published in the literature.
In this paper we verify the optical materials thickness measurements by Raman scattering. The two types of optic transparent materials were used in the study: ice and poly methyl methacrylate (PMMA). The first sample type was chosen due to the importance of ice thickness measurements, while the last sample was selected because of simplicity of results interpretation: CH and OH-bands are not spectrally overlapped.
2. Experiment
We used a compact Raman LIDAR system that was described in detail in our previous paper [13,23] (Fig. 1). The solid state YVO4:Nd laser with diode pumping (527 nm, 5 ns, 1 kHz, 200 μJ/pulse, beam quality ; beam diameter at level 1.8 mm) was used as a source. Laser pulse was scattered by remote object, and elastic and inelastic scattered irradiation was collected by a quartz lens () to the input slit of the spectrograph. The detection system consists of a spectrograph (Spectra Physics, MS127i) equipped with gated detector (ICCD, Andor iStar). We chose a broad spectral window 500–750 nm to register several signals simultaneously: elastic scattering (527 nm) (Mie and Rayleigh scattering) and Raman scattering (OH-band center 640 nm). The signals for elastic and Raman scattering were determined as the integral of the corresponding band with background correction. The spectra were summed by 100 laser pulses to improve reproducibility of measurements. The gated detector was able to obtain images with 5 ns duration, while time jitter between laser pulse and detection gate was less than 3 ns that was insufficient to perform time-to-flight thickness measurements: the effective depth resolution was estimated as 1.2 m in air. The experimental setup sketch is presented in Fig. 2. We suggested an alternative approach for ice thickness measurement with lens-to-sample distance variation and the detection of signals from different laser beam waist positions. The choice of optimal focal length for the lens used in the experiments was formulated by two conditions: (1) lens focal length should be greater than sample thickness to measure both sample borders (air-sample and sample-water), and (2) lens focal length should be as small as possible to minimize beam waist and improve spatial resolution. We used a quartz lens (, diameter 5 cm) mounted on a movable stage. The laser beam waist at level was 65 μm in diameter ( in Fig. 2) and the estimated Rayleigh length () was 0.55 mm. Raman spectra were measured at fixed lens-to-sample distances with a step of 1 mm.
Fig. 2. Experiment setup for transparent materials thickness measurements by Raman scattering. (The detailed description of the LIDAR is given in the text.)
Fig. 3. Raman spectra of PMMA (cyan), ice (light blue), and water (blue). A detailed view of OH- and CH-stretching bands are presented in the inset.
We used two types of samples: PMMA and ice bricks. A commercially available 100% PMMA parallelepiped of 80 mm thickness was used. The ice brick was prepared in laboratory: the ice of good optical quality was frozen from distilled water in a refrigerator for 24 h at , and then a cube was cut. PMMA and ice samples were placed on the top of a tank filled with distilled water at : ice was floating, while the PMMA sample was adjusted to a side wall of the water tank.
3. Results
The typical Raman spectra detected for a sample (PMMA, ice or water) located 2 m from Lidar are presented in Fig. 3.
The strongest bands in ice/water spectra were stretching vibration and elastic scattering, while a low intensity peak of OH-bending vibrations () can be also observed. For the PMMA sample the three molecular bands were detected: stretching and bending vibrations bands and stretching band. The low intensity of elastic scattering for PMMA compared with ice or water was explained by the probing laser beam waist position located in the brick center. The detailed spectra of CH- and OH-stretching bands (inset in Fig. 3) have a small spectral interference, thus these bands can be easily used to detect the PMMA-to-water interface. In the case of an ice-to-water border detection, this is a more complex task; it is much more difficult to quantify small differences on the OH-stretching band profile for an ice-to-water border. The possible approaches to solve this problem will be discussed further in the text.
A. PMMA Thickness Measurements
We have detected a series of Raman spectra for different positions of laser beam waist by varying the lens-to-sample distance. The refractive index of PMMA ice () will result in continuous increase in lens to beam waist position when the lens is moving toward the brick surface. The actual position of the laser beam waist was estimated, and the corresponding correction was made for the y axis in Fig. 4(b).
Fig. 4. Raman scattering for PMMA thickness measurements: (a) the detailed spectrum of CH-stretching vibrations for laser beam waist located in PMMA brick; (b) Raman spectra (z–x cross section) at different lens-to-sample positions (y coordinate) for PMMA sample in distilled water; (c) the detailed spectrum of OH-stretching vibrations for laser beam waist located in water.
Three scattered signals were used for thickness measurements: elastic scattering, Raman scattering for OH and CH stretching vibrations. The signals were calculated as corresponding band integral with background correction. The results of PMMA thickness measurements are shown in Figs. 4 and 5. The intensity of elastic scattering can be successfully used for the detection of air-PMMA and PMMA-water interfaces due to a large difference of refractive indexes as presented in Fig. 5 (1.450 and 1.333 for PMMA and water, respectively). Still, any optical defect located near the surface in sample brick will dramatically change elastic scattering, thus leading to a fault positive measurement of thickness. In Fig. 5(a), such case is used for illustration: a small defect intentionally was made inside brick at a distance of 30 mm from the surface.
Fig. 5. Signals for PMMA brick thickness measurements: (a) elastic scattering; (b) Raman scattering.
Raman CH and OH signals can be used as an alternative indicator of PMMA sample thickness. Optical defects could influence both CH- and OH-signals, but this impact will be eliminated by comparison of 1/2 amplitude change for two cases: if optical defects are absent then these values should coincide; otherwise, an OH-signal will be a real indicator of the PMMA-to-water interface.
B. Floating Ice Thickness Measurements
Floating ice thickness measurement is more complex task compared with PMMA. An elastic scattering of laser irradiation can be used to detect the top border of ice (air-to-ice), while the ice bottom interface (ice-to-water) in a nutshell is due to two reasons. The first one is a refraction index coincidence for ice and water (1.312 and 1.333, respectively). The second reason is that the bottom ice border (ice-to-water) is of high optical quality (air bubbles or defects are almost absent) due to slow growth of ice in water at equilibrium conditions. Consequently, elastic scattering is not a good choice for distinguishing an ice-to-water interface. The Raman OH-band profile spectra () differ substantially for ice and water (Fig. 3), and this feature can be used for ice-to-water interface detection. But a sensitive method quantifying OH-band profile change is needed for reliable ice thickness measurements.
The same task has already been solved for remote measurements of water temperature by Raman scattering. The Raman OH-band profile for water undergoes significant changes with temperature variation, but these changes are less than for the water-to-ice phase transition. Several methods quantifying OH-band profile changes for temperature measurements are illustrated in Fig. 6 [15–18,24–26]. A two-color model is a simple method used in the first experiments. It is based on detecting left and right amplitudes of the OH-band profile: left and right Raman shift positions are chosen where the profile changes are largest. After that, the ratio of these arms are fitted as a linear function of temperature [24]. Another method was used in the late 1990s and is based on the OH-band profile fit with the two (or more) Gaussian peaks [25]. By using the complex metric as a function of temperature, a good linear dependence was obtained. A weighting technique was suggested to improve the accuracy of temperature measurements [15,25]. The basic idea is to determine the center of mass for the OH-band profile with improved accuracy. This technique uses a single Gaussian peak to fit the OH-band profile, and then a fitted peak center is used as a metric for temperature measurements. In other words, if the chosen symmetric peak function fits the analyzed curve (OH-band), then a minor change of this curve will result in the change of peak center [25]. In this study we used a weighting technique because of algorithm stability, simplicity, and high sensitivity (good accuracy for water temperature measurements).
Fig. 6. Water temperature estimation by Raman OH-band profile measurements (see text for details): (a) OH-band profiles for seawater sample at 3 and 81°C; (b) two-color technique metric for temperature measurements, where and are left and right arms of changing OH-band profile; (c) peak fitting technique metric , where and are high and full width at half-maximum for corresponding peaks; (d) weighting technique metric , where is a mass center of the OH-band profile.
The results of ice thickness measurements are presented in Fig. 7. The examples of the detailed Raman spectra for ice and water are shown in Fig. 7(a). It is clearly observed that two phases can be easily distinguished using the OH-band profile. The water spectrum was detected through the ice layer by adjusting probing laser beam waist 30 mm below the ice-to-water interface. The refractive index of ice and water changed the effective focal length of the lens that was corrected (Fig. 7).
Fig. 7. Ice thickness measurements by elastic and Raman scattering: (a) the detailed Raman OH-band profiles for ice and water; (b) elastic signal (black squares, log scale) and Raman OH-band centers (blue rounds) as a function of lens-to-sample distance.
The elastic scattering signal was successfully used for detection of the air-to-ice boundary, while it failed to determine the ice-to-water interface (note log scale in Fig. 7(b). Similarity in refractive indexes (1.312 and 1.333 for ice and water, respectively), and slow ice growth at thermodynamic equilibrium conditions resulted in low elastic scattering. The ice-to-water interface was estimated by the weighting technique. The centers for the Raman OH-band profiles measured at different lens-to-sample positions were plotted as a function of distance, and results are presented in Fig. 7(b). The air-to-ice interface was estimated at the maximum value of the elastic scattering signal. The ice-to-water interface was measured at half-maximum change of OH-band centers. The thickness measurements accuracy was estimated as full width at half-maximum for the air-to-ice interface and as a confidence band interval width near the inflection point for OH-band centers dependence. The accuracy of 81 mm thick ice sample was estimated to be 2 mm.
4. Summary
A new simple express procedure for optical noncontact thickness measurements of transparent materials by Raman scattering was suggested. The Raman spectral difference for PMMA and ice samples (the CH- and OH- stretching vibrations) was used to indicate sample-to-water border, while elastic scattering was used for air-to-sample interface detection. The combination of elastic and Raman scattering prevents fault-positive errors for thickness measurements. The estimated accuracy of the sample thickness measurement was estimated as being less than 5%.
The disadvantage of the time consuming moving lens system can be overcome by two options. The first option is to use the technique for simultaneous measurements of Raman spectra at multiple distances suggested in [27]. The second option is to use new generation detectors (e.g., single-photon avalanche diode, SPAD) that allow time-of-light measurements with up to a few hundred picosecond time resolution when working in a single-photon counting mode [28]. These approaches can be used for development of a compact lidar system without movable parts.
Compact Raman LIDAR for remote sensing of oceans is of growing interest because these systems can provide reliable information on sea water and floating ice properties in heavy cloud conditions. For example, unique applications can be made: iceberg evolution study during its motion in the open ocean can be carried out if compact LIDAR is installed on an air/underwater unmanned vehicle. Laser remote sensing will provide 3D images of icebergs including valuable parameters of ice (e.g., temperature, salinity, porosity, mechanical properties), thus giving reliable information for iceberg thermodynamics. From a practical point of view, an underwater unmanned vehicle with installed LIDAR can provide real time automatic monitoring of ice thickness that is of great importance for preventing oil/gas floating platform damage in the Arctic region. Additionally, such systems also can be used for express diagnosis of oil leaks at early stages in the immediate vicinity to the platform.
FUNDING INFORMATION
Russian Academy of Sciences (RAS) (supervisor academician Konovalov A.I., suvervisor academician Lagarkov A.N.); Russian Foundation for Basic Research (RFBR) (14-02-00018, 14-02-00748, 15-03-09154); Russian President Program for Leading Scientific Schools (4484.2014.2).
References
1. C. Haas and M. Druckenmiller, Ice Thickness and Roughness Measurements, Field Techniques for Sea-Ice Research, H. Eicken, ed. (University of Alaska, 2009).
2. P. Wadhams, “Sea ice thickness changes and their relation to climate,” in The Polar Oceans and Their Role in Shaping the Global Environment, O. M. Johannessen, R. D. Muench, and J. E. Overland, eds. (American Geophysical Union, 1994).
3. S. M. Pershin, A. F. Bunkin, V. K. Klinkov, V. N. Lednev, D. Lushnikov, E. G. Morozov, and R. N. Yul’metov, “Remote sensing of Arctic fjords by Raman Lidar: heat transfer screening by layer of glacier’s relict water,” Phys. Wave Phenom. 20, 212–222 (2012).
4. O. T. Gudmestad, A. I. Alhimenko, S. Løset, K. N. Shkhinek, A. Tørum, and A. Jensen, “Engineering aspects related to Arctic offshore developments,” in Student’s Book for Institutes of Higher Education (LAN, 2007).
5. K. V. Hoyland, “Ice thickness, growth and salinity in Van Mijenfjorden, Svalbard, Norway,” Polar Research 28, 339–352 (2009).
6. C. Haas and P. Jochmann, “Continuous EM and ULS thickness profiling in support of ice force measurements,” in Proceedings of the 17th International Conference on Port and Ocean Engineering under Arctic Conditions (Norwegian University of Science and Technology, 2003).
7. K. J. Campbell and A. S. Orange, “A continuous profile of sea ice and freshwater ice thickness by impulse radar,” Polar Record 17, 31–41 (1974).
8. M. Huntemann, G. Heygster, L. Kaleschke, T. Krumpen, M. Mäkynen, and M. Drusch, “Empirical sea ice thickness retrieval during the freeze up period from SMOS high incident angle observations,” Cryosphere Discuss. 7, 4379–4405 (2013).
9. L. Kaleschke, X. Tian-Kunze, N. Maaß, M. Mäkynen, and M. Drusch, “Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period,” Geophys. Res. Lett. 39, L05501 (2012). [CrossRef]
10. R. M. Measures, Laser Remote Sensing: Fundamentals and Applications (Wiley, 1985).
11. A. F. Bunkin and K. I. Voliak, Laser Remote Sensing of the Ocean: Methods and Applications (Wiley, 2001), p. 256.
12. K.-J. Lee, Y. Park, A. Bunkin, R. Nunes, S. Pershin, and K. Voliak, “Helicopter-based Lidar system for monitoring the upper ocean and terrain surface,” Appl. Opt. 41, 401–406 (2002). [CrossRef]
13. A. F. Bunkin, V. K. Klinkov, V. N. Lednev, D. L. Lushnikov, A. V. Marchenko, E. G. Morozov, S. M. Pershin, and R. N. Yulmetov, “Remote sensing of seawater and drifting ice in Svalbard fjords by compact Raman LIDAR,” Appl. Opt. 51, 5477–5485 (2012). [CrossRef]
14. A. F. Bunkin, V. K. Klinkov, V. A. Lukyanchenko, and S. M. Pershin, “Ship wake detection by Raman lidar,” Appl. Opt. 50, A86–A89 (2011). [CrossRef]
15. S. M. Pershin and A. F. Bunkin, “A jump in the position and width of the Raman band envelope of O–H valence vibrations upon phase transitions of the first and second kinds in water,” Opt. Spectrosc. 85, 190–193 (1998).
16. S. H. Park, Y. G. Kim, D. Kim, H. D. Cheong, W. S. Choi, and J. I. Lee, “Selecting characteristic Raman wavelengths to distinguish liquid water, water vapor, and ice water,” Appl. Opt. 14, 209–214 (2010).
17. R. Barbini, F. Colao, R. Fantoni, L. Fiorani, A. Palucci, E. Artamonov, and M. Galli, “Remotely sensed primary production in the western Ross Sea: results of in situ tuned models,” Antarctic Science 15, 77–84 (2003). [CrossRef]
18. A. F. Bunkin and S. M. Pershin, “Observation of rotational resonances of ortho and para spin isomers of the H2O molecule in hexagonal ice using four-photon laser spectroscopy,” Phys. Wave Phenom. 18, 237–239 (2010).
19. F. Rulla, A. Vegas, A. Sansano, and P. Sobron, “Analysis of Arctic ices by remote Raman spectroscopy,” Spectroch. Acta A 80, 148–155 (2011).
20. W. A. England, S. N. Jenny, and D. A. Greenhalgh, “Chromium oxide film thickness measurements using spontaneous Raman scattering,” J. Raman Spectrosc. 15, 156–159 (1984). [CrossRef]
21. K. F. McCarty, “Raman scattering as a technique of measuring film thickness: interference effects in thin growing films,” Appl. Opt. 26, 4482–4486 (1987). [CrossRef]
22. A. O. Kivioja, A. S. Jääskeläinena, V. Ahteeb, and T. Vuorinen, “Thickness measurement of thin polymer films by total internal reflection Raman and attenuated total reflection infrared spectroscopy,” Vibrational Spectroscopy 61, 1–9 (2012). [CrossRef]
23. S. M. Pershin, V. N. Lednev, V. K. Klinkov, R. N. Yulmetov, and A. F. Bunkin, “Ice thickness measurements by Raman scattering,” Opt. Lett. 39, 2573–2575 (2014). [CrossRef]
24. D. A. Leonard, B. Caputo, and F. E. Hoge, “Remote sensing of subsurface water temperature by Raman scattering,” Appl. Opt. 18, 1732–1745 (1979). [CrossRef]
25. S. M. Pershin, A. F. Bunkin, and V. A. Luk’yanchenko, “Evolution of the spectral component of ice in the OH band of water at temperatures from 13 to 99°C,” Quantum Electron. 40, 1146–1148 (2011). [CrossRef]
26. M. Becucci, S. Cavalieri, R. Eramo, L. Fini, and M. Materazzi, “Raman spectroscopy for water temperature sensing,” Laser Phys. 9, 422–425 (1999).
27. B. Lienert, J. Porter, and S. K. Sharma, “Simultaneous measurement of spectra at multiple ranges using a single spectrometer,” Appl. Opt. 48, 4762–4766 (2009). [CrossRef]
28. S. M. Pershin, A. N. Lyash, V. S. Makarov, K. Hamal, I. Prochazka, and B. Sopko, “Multilayer cloud monitoring by micro-Joule lidar based on photon counting receiver and diode laser,” Proc. SPIE 7355, 73550S (2009).
