海水背景下不同浓度的甲烷含量高光谱定量反演
Hyperspectral quantitative retrieval of methane content in different concentrations in the seawater background
- 2020年24卷第12期 页码:1525-1533
纸质出版日期: 2020-12-07
DOI: 10.11834/jrs.20209013
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纸质出版日期: 2020-12-07
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在油气资源遥感探测中,通过烃渗漏引起的海表面甲烷气浓度异常来探测海底气藏是最直接的方法之一。为了更好地识别海表甲烷异常,提高遥感反演精度,对海表甲烷气含量进行定量光谱分析研究。设计室内甲烷波谱测试平台,获取海水背景下不同含量甲烷高光谱数据为数据,对光谱数据预处理及进行比值导数光谱法,并提取光谱吸收特征参数,对甲烷含量与光谱参数之间进行相关性分析,构建甲烷含量的反演模型。比值导数光谱法确实抑制了海水背景信息,突出了甲烷特征。1650—1664 nm和2180—2210 nm波段范围的光谱参数与甲烷含量相关性显著;其中,波谷、波深、面积、斜率与甲烷含量显著相关。基于2180—2210 nm波段范围建立的波谷、波深、面积、斜率四元回归方程y=-14.356 - 5931.796x1 - 4325.081x2+241.481x3+7531.973x4拟合效果最好,R2为0.9817;且在此波段范围内基于波深建立的单变量甲烷反演模型y = 2047.571x- 9.758,R2为0.9741,比基于其他变量所建立的反演模型效果要好。成功获取了和海水背景下甲烷含量线性相关显著的对应波段和吸收特征,可为利用多/高光谱遥感预测勘探海表面甲烷气浓度提供一定的理论和技术依据。
In the remote sensing exploration of oil and gas resources, seabed gas reservoirs are usually detected through the anomaly of methane concentration on the sea surface caused by hydrocarbon seepage. The remote sensing exploration of hydrocarbon seepage in marine gas resources is currently mostly based on methane absorption characteristics and images. Quantitative spectral analysis of methane content on the sea surface is also insufficient. This study designs laboratory spectral response experiments of methane with different concentrations in seawater background to determine the methane anomaly on the sea surface better and improve the accuracy of remote sensing inversion. This study also attempts to establish an inversion model of methane concentration in seawater background.
A methane spectra laboratory test platform was designed to obtain hyperspectral data of different methane contents in seawater background. After spectra preprocessing and derivative of ratio spectroscopy, the spectral absorption characteristic parameters (the valley, wave depth, area, wave width, slope, and SAI) were extracted. The correlation between methane content and spectral parameters was analyzed to compare the ability of parameters to distinguish methane content. The correlation between spectral parameters with high correlation of methane content in selected bands was analyzed to further reduce the amount of data. Finally, the spectral parameters that were highly correlated with methane content and lowly correlated with each other were selected as independent variables and methane content was used as dependent variable to construct the methane content inversion model.
In the analysis of methane spectra in seawater background, the derivative of ratio spectroscopy can effectively suppress background information of seawater in the spectra and highlight the methane information. Thus, the curve characteristics of the spectra after derivative of ratio spectroscopy are only related to the content of methane. Moreover, higher methane content corresponds to more obvious characteristics. The correlation between spectral parameters in 1650—1664 nm and 2180—2210 nm with methane content is significantly correlated, which is apparently higher than that in 2300—2320 nm and 2350—2380 nm. The valley, wave depth, area, and slope are also significantly correlated with methane content. Meanwhile, wave width is generally correlated with methane content, and SAI is not correlated with methane content. The quadrivariate regression equation (y=-14.356 - 5931.796x1 - 4325.081x2+241.481x3+7531.973x4) in 2180—2210 nm has the best fitting effect, and R2 is 0.9817. The single variable methane inversion modely = 2047.571x - 9.758 is based on wave depth in this band, and R2 is 0.9741, which is better than that of the inversion model based on other spectral characteristic parameters.
The corresponding bands of 1650—1664 nm and 2180—2210 nm and corresponding absorption characteristics (valley, wave depth, area, and slope) with significant linear correlation of methane content in sea water background are successfully obtained. The methane content inversion models with good effect and regression significance are established. They can provide theoretical and technical basis for predicting methane concentration on sea surface by multispectral/hyperspectral remote sensing.
据多年研究与实践证明,全世界85%已发现的油气田都存在油气微渗漏现象(
遥感技术是探测烃渗漏的一种重要的方法(
不同于地表烃渗漏引起的地表蚀变,海洋气藏烃渗漏的光学遥感的目标主要集中在海表及近海表甲烷异常(
目前,遥感勘探海洋气藏烃渗漏大多是从甲烷的吸收特征和影像进行的,对海表的甲烷气含量的定量光谱分析研究不足,本文着重设计海水背景下不同浓度的甲烷室内光谱响应实验,尝试建立海水背景下甲烷浓度的反演模型。高光谱的一大特点是兼具图像维信息和光谱维信息,即其不仅能得到地表空间图像信息,还可以获取地物的连续光谱,能根据地物的光谱特征实现对目标地物的提取(
本文以实测的海水背景的不同浓度的甲烷高光谱数据为基础,进行比值导数光谱法,突出光谱中甲烷特征,再提取光谱吸收特征参数,基此分析光谱响应特征,并建立海水背景的甲烷浓度反演模型,为海表甲烷浓度异常探测提供理论基础,能较好地应用在多/高光谱遥感预测勘探海洋气藏中。
实验波谱仪为美国Spectral Evolution公司的PSR-3500 野外便携式地物波谱仪,波长范围35—2500 nm,数据采样间隔1 nm,光纤探头视场角25°。实验装置(
图1 实验装置示意图
Fig.1 Diagram of spectra measurement
(1)室内暗室,三脚架固定两个石英卤素灯,模拟太阳光源。
(2)容积为105 ml的锡箔纸包裹的竖直玻璃管内为待测气体,管上连有气压计,管中下部有3个开关,分别控制甲烷注气口、大气阀门与真空泵;玻璃管口径:26 mm,外直径:30 mm,长度:(200+20) mm(包括管塞)。
(3) 玻璃管上方连接光谱探头和温度计,下方烧杯内为海水,作为背景;探头垂直向下,离水面(180+80+40) mm(玻璃管长度+玻璃管底部到烧杯口+烧杯口到水面)。
设计室内海水背景下的甲烷含量检测实验,获取不同浓度的甲烷的光谱曲线。实验具体操作:
(1)玻璃管内全为空气,测量海水背景容器的光谱;
(2) 管内抽成真空,注入1 ml甲烷,再通入空气,使管内外处在一个大气压下,测量波谱值;
(3) 每次测量完后,重新将管内抽成真空,注入定量增加的甲烷,通空气使管内外保持在一个大气压下,再进行下一次的测量。
最终获得海水背景下甲烷含量分别为0 ml、1 ml、2 ml、3 ml、4 ml、5 ml、6 ml、7 ml、8 ml、9 ml、10 ml、15 ml、20 ml、25 ml、30 ml、35 ml、40 ml、45 ml、50 ml、55 ml、60 ml、65 ml、70 ml、75 ml、80 ml、85 ml、90 ml、95 ml、100 ml、105 ml的30组波谱数据。
由于波谱仪波段间对能量响应上的差异,以及周围环境、地物等的干扰,使得光谱曲线总存在一些噪声,即实测波谱实际上包含了地物波谱和噪声两部分。为了突出地物的波谱特征,需要通过平滑波形来去除包含在信号内的少量噪声。本文采用的是9点加权移动平均方法对光谱曲线进行平滑去噪处理,公式为
Ri=0.04Ri-4+0.08Ri-3+0.12Ri-2+0.16Ri-1+0.20Ri+0.16Ri+1+0.12Ri+2+0.08Ri+3+0.04Ri+4 | (1) |
式中, Ri是指第i波段的原始光谱反射率。
线性光谱混合模型的建立基于入射辐射与不同地物之间不存在多次散射的假设,即入射辐射与地表中的单类型地物相互作用后即被传感器接收。因此,像元在某一波段的反射率可以表示为占一定比例的各个基本端元组反射率的线性组合。基于此,建立如下模型:
r(λm)=t∑n=1Cnrn(λm)+ξ(λm) | (2) |
式中,m为光谱通道,r(λm)为在λm波长处的反射率,n为端元组分数目, Cn对应于混合物中第n个组分的丰度,ξ(λm)为第m个光谱通道的残余误差值。
当像元内仅包含两种组分时,可简化为
r(λm)=C1×r1(λm)+C2×r2(λm)+ξ(λm) | (3) |
在
r(λm)r1(λm)=C1+C2×r2(λm)r1(λm)+ξ'(λm) | (4) |
ddλ(r(λm)r1(λm))=C2×ddλ(r2(λm)r1(λm))+ξ″(λm) | (5) |
式中,
ξ'(λm)=ξ(λm)r1(λm) , ξ″(λm)=ddλ(ξ'(λm)r1(λm)) |
从
从上面的推导可以看出,基于线性光谱混合模型的比值导数法光谱解混算法,简洁清晰并且有严谨的数学推导证明,避免了复杂的穷举迭代运算,简化了光谱解混过程(
对海水背景的甲烷光谱进行比值导数处理,可以对混合光谱中作为除数的海水的波谱特征进行压制,突出光谱中的甲烷的影响。本文针对海水背景不同含量甲烷的光谱,建立甲烷含量的定量反演模型,需要去除海水的影响。因此用比值导数光谱法处理光谱,从而消除海水的影响,使得光谱值的变化只与甲烷含量的变化有关。
光谱吸收特征参数是高光谱区别于多光谱的重要特点,是反映光谱信息的重要参数(
(1)波谷(v):一个波长范围内的最小波幅值;
(2)波深(d):波谷的吸收能力;
(3)面积(a):波谷与肩宽围成的面积;
(4)肩宽(w):波谷张开的宽度;
(5)斜率(s):肩宽所在的直线相对于x轴的倾斜程度;
(6)光谱吸收性指数(SAI):非吸收基线的反射强度在谱带的波长位置处与谷底处之比,即以另一种方式度量谱带深度。
以上6个光谱吸收特征参数基本可以比较准确的表述光谱一个吸收波段的位置形态,不仅保留了光谱的特征信息,同时降低了数据维数,避免了原始数据中大量冗余数据对分类精度的干扰。
图2 平滑去噪后的甲烷波谱图
Fig.2 Methane spectra after smoothing
对30组不同浓度的海水背景下的甲烷反射率作比值导数处理,即每组数据除以同背景下甲烷含量0的波谱曲线后,进行一阶微分。可以得到抑制了海水背景信息,突出了甲烷信息的光谱曲线,曲线形态只与甲烷的含量相关(
图3 甲烷波谱比值导数曲线图
Fig.3 Methane spectra after derivative of ratio spectroscopy
甲烷含量越高,波谱比值导数曲线的变化特征越明显,峰值越高,谷值越深;而随着甲烷含量降低,波谱的变化特征也变小,波谱曲线趋于平缓。
结合甲烷的吸收特征,在甲烷波谱的比值导数曲线上选取相应的4个特征波段范围提取光谱吸收特征参数:(Ⅰ) 1650—1664 nm, (Ⅱ) 2180—2210 nm, (Ⅲ) 2300—2320 nm, (Ⅳ) 2350—2380 nm;每个波段范围对应一个波谷。
对4个特征波段提取对应特征参数,并与甲烷含量进行相关性分析(
波段范围 | 波谷 | 波深 | 面积 | 肩宽 | 斜率 | SAI |
---|---|---|---|---|---|---|
Ⅰ | -0.987** | 0.987** | 0.984** | -0.696** | 0.969** | 0.495** |
Ⅱ | -0.987** | 0.987** | 0.985** | -0.710** | 0.964** | 0.280 |
Ⅲ | -0.948** | 0.946** | 0.946** | -0.443* | -0.657** | 0.283 |
Ⅳ | -0.915** | 0.927** | 0.934** | 0.592** | -0.092 | 0.149 |
注: **为在0.01水平上(双侧)显著相关,*为在0.05水平上(双侧)显著相关。
除了SAI以外,其余光谱特征参数都与甲烷含量存在一定的相关性。各光谱参数中,波谷与甲烷含量呈最大负相关,相关系数最大为-0.987。波深与面积都与甲烷含量呈极大正相关,最大相关系数分别为0.987和0.985。波段范围Ⅰ、Ⅱ、Ⅲ的肩宽都与甲烷含量呈负相关,相关系数分别为-0.696,-0.710和-0.443,范围Ⅳ的肩宽则与甲烷含量正相关,相关系数为0.592;相关程度一般。波段范围Ⅰ、Ⅱ的斜率与甲烷含量呈较大正相关,相关系数分别为0.969和0.964;范围Ⅲ的斜率与甲烷含量一般负相关,相关系数为0.592;范围Ⅳ的斜率则与甲烷含量不相关。从各波段范围看,则波段范围Ⅰ、Ⅱ的各光谱参数与甲烷含量之间的相关系数都明显高于波段范围Ⅲ、Ⅳ。
基于光谱参数与甲烷含量之间的相关性,可选择波段范围Ⅰ、Ⅱ的波谷、波深、面积和斜率这4个参数对甲烷含量进行反演。为了进一步减少数据量,对选定的各波段范围内的光谱参数两两之间进行相关性分析(
Ⅰ | Ⅱ | |||||||
---|---|---|---|---|---|---|---|---|
波谷 | 波深 | 面积 | 斜率 | 波谷 | 波深 | 面积 | 斜率 | |
波谷 | 1 | -0.999** | -0.992** | -0.978** | 1 | -0.998** | -0.986** | -0.969** |
波深 | -0.999** | 1 | 0.996** | 0.982** | -0.998** | 1 | 0.993** | 0.973** |
面积 | -0.992** | 0.996** | 1 | 0.977** | -0.986** | 0.993** | 1 | 0.968** |
斜率 | -0.978** | 0.982** | 0.977** | 1 | -0.969** | 0.973** | 0.968** | 1 |
注: **为在0.01水平上(双侧)显著相关。
选取的Ⅰ、Ⅱ波段范围内,波谷、波深、面积和斜率相关性都比较大。波深相关性最大的2个参数是波谷和面积,且波谷、面积相关性最大的参数也是波深;选取波深、斜率作为反演甲烷含量的参数。
分别以Ⅰ、Ⅱ波段范围内的波深、斜率为自变量,甲烷的含量为因变量,建立回归方程(
图4 基于光谱参数的甲烷含量反演模型
Fig.4 Methane content inversion models based on spectral parameters
基于Ⅰ、Ⅱ波段范围内的波深、斜率反演的方程分别是: (1) y = 6155.541x- 11.061,决定系数R²为0.9736; (2) y = 253365.398x - 18.433,R²为0.9393; (3) y= 2047.571x- 9.758,R²为0.9741; (4) y = 83670.829x- 0.912,R²为0.9293。统计学中,决定系数R2(也称作拟合优度)是线性回归时回归平方和与总离差平方和的比值,可度量对所估计的回归方程的拟合优良情况。P值为假设检验中体现判断结果显著性的参数。4个反演模型的决定系数R²均大于0.9,相伴概率值p均小于0.001,表明4个模型都代表性强,且模型中的光谱参数与甲烷含量之间确实具有回归关系,方程有意义。4个模型中,根据波段范围Ⅱ的波深反演的模型决定系数R²最高,效果最好。
考虑到反演精度的问题,展开波段范围Ⅰ、Ⅱ内的波谷、波深、面积、斜率与甲烷含量的四元回归分析。根据
针对单变量回归方程中拟合最好的基于波段范围Ⅱ波深建立的甲烷含量高光谱定量反演模型,使用另一组实验室数据对其精度进行验证(
图5 反演模型的验证结果
Fig.5 Validation result of inversion model
对验证组光谱进行预处理,比值导数光谱处理,提取波谱在波段范围Ⅱ(2180—2210 nm)的光谱特征参数。用建立的回归模型对这批验证数据中光谱参数所对应的甲烷含量值进行预测,对照验证数据中真实甲烷含量,可计算两个评价指标:均方根误差RMSE和拟合度指标Rnew,计算式分别为
RMSE=√∑ni=1(yi-ˆyi)2n | (6) |
Rnew=1-∑ni=1(yi-ˆyi)2∑ni=1(yi-ˉyi)2 | (7) |
式中,yi为第i组验证波谱数据所对应的真实甲烷含量,ˆyi为通过回归模型得到的第i组数据的预测甲烷含量,ˉyi为验证组波谱的真是甲烷含量的均值,n为验证数据的样本数。RMSE越小,Rnew越接近1,模型的预测效果越好。计算结果为RMSE=6.757,Rnew=0.952,结合
同样地,也对效果更好的基于波段范围Ⅱ波谷、波深、面积、斜率建立的四元回归方程进行同样的精度验证。RMSE为5.278,Rnew为0.971,建立的模型效果良好,具有预测性和适用性,比单变量回归方程拟合效果更好一点。
本文设计海水背景下不同浓度的甲烷光谱响应室内实验,从高光谱角度研究海水背景下不同浓度甲烷的吸收特征,对此背景下的甲烷气含量进行定量光谱分析,并建立甲烷含量的反演模型。
在对海水背景上的甲烷光谱进行分析时,采用比值导数光谱法确实有效地抑制了光谱中的海水信息,突出了甲烷信息,使比值导数光谱曲线的曲线特征只与甲烷的含量有关,且甲烷含量越高,特征越明显。
在甲烷比值导数光谱曲线的4个特征波段范围(Ⅰ) 1650—1664 nm,(Ⅱ) 2180—2210 nm,(Ⅲ) 2300—2320 nm,(Ⅳ) 2350—2380 nm)上提取6个高光谱吸收特征参数:波谷、波深、面积、肩宽、斜率、SAI,并分析与甲烷含量的相关性:
(1) 波谷、波深、面积以及波段范围Ⅰ、Ⅱ的斜率都与甲烷含量显著相关,肩宽与甲烷含量相关性一般,SAI与甲烷含量不相关;
(2) 特征波段范围Ⅰ、Ⅱ的光谱参数与甲烷含量的相关性明显高于波段范围Ⅲ、Ⅳ。
基于波段范围Ⅱ内的波谷、波深、面积、斜率,建立四元回归方程:y= -14.356 - 5931.796x1- 4325.081x2+241.481x3+7531.973x4,R2为0.9817,拟合效果最好。以甲烷含量为因变量,选取与甲烷含量相关性高的光谱参数作为自变量,并通过分析光谱参数两两之间的相关性减少自变量数据量,最后得到甲烷含量反演效果最好的单变量模型——以波段范围Ⅱ的波深为因变量的方程:y=2047.571x- 9.758,R²为0.9741。
成功获得了和海水背景下甲烷含量线性相关显著的对应波段1650—1664 nm和2180—2210 nm,及对应的吸收特征:波谷、波深、面积、斜率,可为利用多/高光谱遥感预探海表面甲烷气浓度进行海洋油气勘探提供一定的理论依据和技术基础。
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