基于布里渊散射的海水参数测量反演算法研究 
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Real-time and accurate remote sensing of ocean temperature and salinity information are of great significance for understanding ocean properties and biodiversity. At the same time, it can forecast weather or temperature changes based on ocean temperature and salinity data. Brillouin scattering frequency shift is not easily affected by background noise due to its temperature sensitivity and narrow scattering spectrum, allowing for active remote sensing of ocean temperature and salinity.
Compared with the traditional Fabry Perot interferometer technique, the edge detection technique has faster measurement time, nothing to do with the incident angle while detecting signal intensity, and the parameters of the molecular absorption cell are also easy to control. It is suitable for airborne, satellite, and other large-scale ocean detection needs, and has become a popular technique for detecting ocean temperature and salinity information using the Brillouin scattering method. The edge detection technique has a limitation in that it converts Brillouin frequency shift into signal intensity change based on the characteristic absorption lines of molecules. It must extract Brillouin frequency shift information from the detected signal intensity. The two edge lines of molecular absorption used for detection are not necessarily symmetrical, and changing the laser frequency in the system will directly affect the change in output signal intensity, so laser frequency stability is extremely important. But with the development of laser frequency stabilization technology, laser frequency stability has been greatly improved. Simultaneously, both the Fabry Perot interferometer technique and the molecular absorption cell technique based on Brillouin scattering require the salinity information to be assumed in advance, and then use the empirical formula of Brillouin frequency shift, salinity, and temperature to inverse the temperature. The salinity of the ocean profile differs from the salinity of the sea surface depending on the season, region, and so on (Fig.7). If the fixed salinity is substituted into the temperature inversion formula, some systematic temperature measurement error will inevitably result, so the salinity variation factor must be considered in the temperature measurement technique. To obtain ocean temperature and salinity information efficiently on airborne, satellite, and other mobile platforms, the random jitter of the sea surface urgently needs the detection system to have a larger receiving field of view. In this paper, we make full use of laser development achievements, take the technical route based on iodine molecular absorption cell, combine with the absorption line of iodine cell, stabilize the laser frequency at 532.2334112 nm, which is the strong absorption line of iodine molecule, and effectively filter out elastic scattering. After passing through the absorption cell, the steep absorption lines on both sides of the band cause the frequency shift and full width at half maximum change due to signal intensity change caused by temperature change. The relationship between temperature and normalized signal intensity is obtained by fitting the relationship between temperature and full width at half maximum, and the widely used empirical formula of temperature and salinity with Brillouin frequency shift (Fig.3). To avoid temperature measurement errors caused by laser intensity jitter, the system employs a three-iodine cell design scheme, so that the laser intensity jitter can be converted into common-mode noise and removed (Fig.4). It is discovered that in the temperature range discussed, the signal intensity ratio curve and difference curve of an iodine molecular cell with different salinity can maintain monotonicity and disjointness (Fig.6). As a result, a set of signal intensity ratio and ratio difference data can only be determined from a pair of temperature and salinity data.
An innovative algorithm is proposed (Fig.8). The algorithm can inverse the temperature and salinity information repeatedly after the actual measurement of two groups of ratio data. Simulation is used to validate the program’s dependability. This set of inversion algorithm does not need to assume salinity information in advance, and instead uses an iterative algorithm based on the intensity ratio and ratio difference of the detector output to achieve accurate temperature and salinity inversion at the same time (Fig.10). The allowable intensity ratio random jitter is 1.3‰ to ensure that the inversion temperature error is less than 0.2 K from 5 ℃ to 30 ℃. The temperature inversion error between 10 ℃ and 20 ℃ is small. The temperature inversion error of 0.2 K can still be satisfied when the random jitter of intensity ratio is 2.3‰ in the temperature range of 10 ℃ to 20 ℃ (Fig.11).
A new edge detection technique based on iodine molecular absorption cells is proposed. This technical path has the advantages of signal intensity detection independent of incident angle, fast measurement speed, not being easily affected by laser intensity jitter, and not requiring salinity information. It is expected to be used on airborne, spaceborne, and other large mobile platforms, and it has a promising future application.
1 引言
实时准确地遥感海洋温度、盐度信息对于了解海洋性质[1-2]、海洋中的生物多样性[3-4]具有重要意义[5-6]，同时也能通过海洋温度剖面信息来预测天气或者气温变化[7]。目前，大范围的海洋温度信息主要是海表温度信息，来源于被动遥感卫星，温度剖面信息比较缺乏。布里渊散射具有频移量对温度敏感、散射谱线窄、不易受背景噪声影响等特点，为海洋温度剖面的主动遥感提供了可能[6，8]。该方面的研究始于20世纪60年代[7，9]，典型代表是德克萨斯A&M大学的Fry教授团队。该团队在实验中先采用注入式锁模脉冲激光器以及高精度扫描法布里-珀罗干涉仪获得高精度布里渊散射光谱[10]，然后利用布里渊频移与温度、盐度的经验公式，在海水盐度已知的条件下，以0.1 ℃的理论精度反演了海表下100 m以内的温度。除了温度测量外，该团队还将布里渊散射技术用于水下声速和水下物体的探测，并对理论误差进行了详细的推导和分析[7]。北京师范大学的刘大禾教授团队和南昌航空大学的何兴道教授团队采用基于法布里-珀罗干涉法的布里渊散射方法在实时测量水中声速[9-13]、水体的黏滞系数[14]、水下物体探测以及受激布里渊[15]等方面进行了大量工作，他们获得的声速的理论精度可达0.22 m/s，温度的理论精度可达到0.1 ℃。华中科技大学的梁琨教授团队[16]对基于边缘探测技术的海表温度的精度及误差进行了分析，结果表明，在7.2～7.9 GHz的布里渊频移范围内，布里渊频移的测量误差约为0.04～0.33 MHz。法布里-珀罗技术作为布里渊散射谱的检测技术，其优势在于能获取完整的布里渊散射谱线[17]，局限性在于系统对入射视场的要求十分严格，极小的入射角变化就会导致较大的光谱精度变化[18]；同时，该技术对入射光的平行度要求很高，信号接收视场小，响应速度不够快，因此难以在机载、星载等移动平台上应用。北京师范大学[19]在国内首次报道了将基于分子吸收池的边缘探测技术用于布里渊散射频移的提取，该技术方案的优势在于：实时性好，测量时间较之传统的法布里-珀罗干涉仪法减少了约34%[20]；信号强度的探测与入射角无关，分子吸收池参数易控制，适合机载、星载等大范围海洋探测的需求。局限性在于，该方法基于分子的特征吸收谱线将布里渊频移变化转化为强度变化进行探测，需要从探测得到的信号强度信息中提取布里渊频移信息，而用于检测的分子吸收双边缘线不一定对称，系统中激光频率的变化会直接影响输出光强的变化，所以对激光器的频率稳定性有十分严格的要求[18]。可喜的是，随着激光器稳频技术的发展，激光器的频率稳定性已得到大幅提升。采用高增益磷酸盐玻璃光纤作为增益介质，得到的频率长期稳定性可达2.5 MHz/h[21]。这为基于气体吸收池的布里渊散射测温技术注入了生机[21-22]。在温度反演方面，基于法布里-珀罗干涉仪以及基于分子吸收池的布里渊散射测温技术路线均需事先假定海水的盐度信息，然后利用参考文献[11]给出的布里渊频移、盐度与温度的经验公式来反演温度。而不同季节、区域的海洋剖面盐度与海表盐度存在一定差异[23]，若将固定的海水盐度代入温度反演公式，则势必带来一定的系统测温误差。基于机载、星载等移动平台高效获取海洋温度剖面信息时，海表的随机抖动要求探测系统具有较大的接收视场。鉴于此，本文充分利用激光器的发展成果，采用基于分子吸收池的技术路线，结合碘分子吸收池的吸收谱线，将激光器稳频于532.2334112 nm这一碘分子强吸收谱线上，从而将弹性散射有效滤除。该波段双侧的陡峭吸收线使得温度变化带来的频移及半峰全宽变化可以被转化为激光束透过吸收池后的强度变化。另外，为避免激光器自身的光强抖动给系统带来测温误差，系统采用了多个碘分子吸收池的设计方案，从而将激光器光强抖动转化为共模噪声并加以去除；在温度反演算法方面，系统无需事先假定海水盐度的具体数值，信号处理系统可以通过迭代算法确定温度和盐度信息；最终，以测温精度0.2 K为设计目标，给出了系统容许的强度测量比值误差。
2 原理
2.1 仿真原理
基于分子吸收池的边缘探测技术的基本原理是将被测光的频率定位于特定气体吸收线的边缘。由于在吸收线的边缘，透过率谱线具有很陡的斜率，因此被测光频率的微小变化就会导致其通过分子吸收池后的信号强度发生显著变化，从而实现被测光频率的精确测量。碘分子吸收池的吸收谱线可以通过仿真软件获取。碘分子吸收谱线在532.2334112 nm处有极高的吸收率，在该波长左右两侧(距离中心7.5 GHz±0.5 GHz)的吸收谱线可以最大限度地透过布里渊散射光谱，且吸收谱线斜率陡峭，故采用一个碘分子吸收池既可以滤除瑞利散射光又能有效提取布里渊散射光。仿真中使用碘分子127号同位素。在碘分子吸收池温度为343 K、压强为2128 Pa、碘分子吸收池长度为10.16 cm、低波数为18787.4 cm-1、高波数为18790.4 cm-1、盐度为35‰的条件下的模拟探测过程如
在布里渊散射光谱中，布里渊频移的定义为
另一个重要的参数为布里渊散射的半峰全宽。根据盐度为35‰时测得的实验图像[14]，按照经验规律[24]拟合得到了半峰全宽(FWHM)与温度的关系，如
图 2. 温度与半峰全宽的关系
Fig. 2. Relationship between temperature and full width at half height (FWHM)
由
基于分子吸收池的边缘探测技术的基本原理是通过信号强度的变化来反推温度信息。若采用单一的碘分子吸收池，则激光器本身的强度波动也会耦合进入温度误差，而采用两个不同碘分子吸收池输出信号强度之比的形式则可以避免光强抖动带来的误差。另外，温度、盐度会同时影响布里渊频移及布里渊光谱的半峰全宽，从而影响碘分子吸收池的透过强度，因此本系统设计了三个压强不同的碘分子吸收池，可得到两个信号强度比值信息，用于反演温度、盐度信息。系统框图如
在
因本系统根据比值及比值差来反推被测区域的温度和盐度，故三个碘分子吸收池压强的选择原则是两个比值及比值差随温度的变化曲线必须具有单调性，且在不同盐度下比值和比值差曲线不相交。曲线斜率越大，越有利于温度的精密探测。假设采用的三个碘分子吸收池长度均为10.16 cm，压强分别为2128，1064，3192 Pa。由于通过压强为1064 Pa的碘分子吸收池的归一化光强斜率较小，所以采用该碘分子吸收池作为I2可以最大限度地提高I1/I2和I3/I2曲线的斜率，则I1、I3对应的碘分子吸收池压强分别为2128 Pa和3192 Pa。在盐度为35‰，I1、I2、I3分别对应碘分子吸收池压强为2128，1064，3192 Pa时，由理论计算得到的探测器输出强度比值、比值差与温度的关系如
图 5. 盐度为35‰时，探测器输出强度比值、强度比值差与温度的关系。(a)强度比值与温度的关系；(b)强度比值差与温度的关系
Fig. 5. Relationship between detector output intensity ratio or intensity ratio difference with temperature when salinity is 35‰. (a) Relationship between intensity ratio with temperature; (b) intensity ratio difference with temperature
由
图 6. 盐度为30‰～40‰时，探测器输出强度比值曲线和强度比值差曲线。(a)强度比值曲线；(b)强度比值差曲线
Fig. 6. Detector output intensity ratio and intensity ratio difference curves when salinity is 30‰40‰. (a) Intensity ratio curves; (b) intensity ratio difference curves
为清晰地展示不同盐度下强度比值、强度比值差的区别，
2.2 反演算法
WOA13网站提供的海洋剖面盐度信息显示，绝大多数海域的盐度范围为30‰～40‰。
图 7. 中国渤海、东海盐度剖面变化图。(a)渤海；(b)东海
Fig. 7. Salinity profiles of Bohai Sea and East Sea in China. (a) Bohai Sea; (b) East Sea
从
由
3 仿真结果
3.1 算法可靠性验证
根据上述原理，三个碘分子吸收池的压强分别选定为2128，1064，3192 MPa，反演温度误差Terror的阈值γ设定为0.05 K。在强度比值和比值差不添加抖动、盐度范围为30‰～40‰，变化步长为0.1‰的条件下，根据反演算法得到的反演温度误差如
由
以输入比值I3/I2、比值差I1/I2-I3/I2分别选取0.6477、0.1230为例(对应的理论温度、盐度分别为14.26 ℃和35‰)，迭代算法的收敛过程如
图 10. 迭代算法的收敛过程。(a)温度收敛曲线；(b)盐度收敛曲线
Fig. 10. Convergence of iterative algorithm. (a) Temperature convergence curve; (b) salinity convergence curve
3.2 信号强度抖动对反演精度的影响
反演的温度、盐度与碘分子吸收池输出的强度比值密切相关。除共模噪声外，一些随机抖动也会影响探测器的输出强度，从而影响两个比值。仍以
图 11. 在强度比值中添加不同程度抖动后的温度反演误差。(a)添加1.3‰随机抖动；(b)添加1‰随机抖动；(c)0.2 K误差容忍度
Fig. 11. Temperature inversion error when adding different degrees of jitter to intensity ratio. (a) Adding 1.3 per 1000 jitter; (b) adding 1 per 1000 jitter; (c) 0.2 K error tolerance
4 结论
本文基于分子吸收池的边缘探测技术，利用布里渊散射及三个压强不同的碘分子吸收池来反演海洋水体的温度、盐度信息。仿真过程中充分考虑了不同温度、盐度对布里渊散射频移及布里渊光谱带宽的影响。反演算法无需事先假定盐度信息，只需根据探测器输出的强度比值和比值差，就可以利用迭代算法实现温度和盐度的同时精确反演。为在5～30 ℃内保证反演温度误差小于0.2 K，允许的强度比值随机抖动程度为1.3‰。其中10～20 ℃内的反演误差较小，此温度范围内强度比值随机抖动程度为0.23%时，仍可以满足0.2 K的温度反演误差。本技术具有信号强度探测与入射角无关、测量速度快、不易受激光器光强抖动影响、无需假定盐度信息的优势，有望应用于机载、星载等大移动平台上，具有广阔的应用前景。
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