基于光谱磁化率模型的黄土剖面地层划分
Stratigraphic division of loess along loess profile based on hyperspectral remote sensing
通讯作者: 丁 锐(1982-),男,硕士,助理研究员,主要从事活动构造与构造地貌方面的研究。Email:reiding@hotmail.com。
第一联系人: 第一作者: 崔 静(1986-),女,博士,副研究员,主要从事高光谱遥感方面研究。Email:
收稿日期: 2016-12-9 修回日期: 2017-04-19 网络出版日期: 2018-06-15
基金资助: |
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Received: 2016-12-9 Revised: 2017-04-19 Online: 2018-06-15
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黄土剖面地层划分对于古地震研究具有重要意义,当前黄土地层的精细划分是一个薄弱环节。磁化率是土壤和沉积物的一个重要参数,能反映一定的沉积环境变化,常用来作为地层层序划分的标记。但离散的磁化率在反映黄土剖面地层结构空间展布特征时,会出现以点带面、以偏概全的问题。本研究选取平原区一处剖面为例,利用高光谱遥感具有图谱合一,光谱分辨率高,可以定量反演地表物理化学参数,分析地表物理化学过程的特点,探索建立光谱与反映地层韵律变化的磁化率之间的光谱模型,并将其应用到黄土剖面上,进行黄土地层结构特征分析。研究结果表明,基于光谱特征建立的磁化率模型精度较高(R 2﹥0.95),其得到的剖面磁化率强度分布图较好地展示了地层结构空间展布特征,为黄土剖面地层划分提供了依据。
关键词:
Invisible fault identifying in loess area is a difficult problem in active fault study in northern China. Detailed stratigraphic division of loess area by the naked eye is very difficult due to the insignificant difference of the granularities and the colors, which would affect the identification of the obscured fault and paleo-seismic event. Spectral technique has been used for magnetic susceptibility estimation. Magnetic susceptibility (MS) has been considered to be a measure of the degree of pedogenic activity and excellent proxies for terrestrial climatic fluctuations. In this study, multiple linear regression was used to build MS estimation models based on the spectral features. A model was built and was applied to hyperspectral image. Test of datasets indicates that this model is very successful. The applying of this model to hyperspectral image shows that the intensity distribution of MS could be used for stratigraphic division.
Keywords:
本文引用格式
崔静, 董新丰, 丁锐, 张世民, 王琮禾, 鲁恒新, 孙艳云.
CUI Jing, DONG Xinfeng, DING Rui, ZHANG Shimin, WANG Conghe, LU Hengxin, SUN Yanyun.
0 引言
黄土地层的划分对于古地震研究具有重要的意义。特别是在我国地震危险性较强、黄土分布较广的西部黄土高原区以及华北平原和东北的南部,古地震研究都无法避开黄土。由于黄土的粒度与颜色差别小、古生物化石稀少,沉积学参数提取难,采用传统的目视分层、生物地层划分和同位素地层学等方法都很难实现黄土地层的划分对比。长期以来,黄土地层的划分问题一直是值得深入研究的课题。
在一定的沉积环境下和等时间段内,沉积物地层化学特征具有某些相似性或具有某些特有的、区别于其他沉积环境和其他时间段内沉积的标志。因此,根据沉积物成分变化对地层剖面进行分层是合理的。磁化率是土壤和沉积物的一个重要参数,能够很好地指示地层韵律变化[1]。但磁化率样本的采样位置、数量等在很大程度上制约了其在地层层序识别的深度,其离散的数据也很难说明复杂的地质现象,特别是在进行黄土剖面的地层结构空间展布特征分析时,会出现以点代面、以偏概全的问题。
磁化率的高低主要与铁磁性矿物有关,磁赤铁矿、磁铁矿以及风化成壤过程中的一些含铁的硅酸盐矿物(如绿泥石等)都与土壤磁化率强度有关[2,3,4,5,6,7,8]。而铁氧化物和氢氧化物在可见光/近红外谱段具有诊断性光谱特征[9],且被用来识别土壤和沉积物中的铁氧化物,并对其含量进行估算[10,11,12]。由此推测磁化率和反射光谱之间可能存在一定关系。Smith等首次分析了洛川黄土剖面磁化率和光谱特征参量之间的关系,结果表明磁化率和光谱的反射率、一阶导数、吸收深度、吸收深度面积具有较高的相关系数,并提出具有图谱合一特性的高光谱影像是未来开展黄土堆积区地层划分的一个研究方向[13]; 但并没有利用关系模型进行磁化率反演或应用到高光谱影像上验证其可行性。
本研究基于高光谱吸收特征参数,探讨光谱和磁化率之间的关系,尝试使用高光谱磁化率模型对地层剖面进行分层。将该模型应用到高光谱影像上,获得二维空间上连续的磁化率数据,从而为地层结构空间展布分析提供重要依据。
1 数据和方法
本研究以山西口泉一处黄土剖面为例。通过对剖面进行土壤采样和高光谱影像采集,在实验室对土壤样品进行磁化率和光谱测试。基于光谱特征分析,寻找特征谱段,利用多元逐步线性回归的方法建立特征谱段与磁化率之间的关系模型。将建立的模型应用到影像上,通过对磁化率强度的分类,分析其在地层划分的有效性。采样位置和影像获取范围示意图见图1。
图1
图1
探槽剖面与采样点位置(绿色点为影像光谱验证点)
Fig.1
Photograph of the studied section with the profiles labeled
1.1 数据采集
该剖面位于山西省怀仁县西北部一处黄土台地上,剖面中心位置经纬度坐标为113° 1'13.10″E,39° 51'33.04″N。该剖面自上而下分别为耕植土(L0)、全新世灰黑色黑垆土(S0)、上更新统马兰黄土(L1)和砾石层。从古土壤顶层开始,设计3条测线自上而下每隔10 cm进行样品采集,3条测线分别命名为C1,C2和C3。其中C1和C2的样品用来建模,样品数分别为52个和35个,总数为87个; C3的样品用来做模型验证,样品总数为55个。低频磁化率采用美国AGICPOI公司生产的Kappabridge MFK1-FA各向磁化率仪进行样品测试,选取的频率为976 Hz,得到的磁化率单位为10-11m3·kg-1。反射率光谱采用美国ASD公司生产的FieldSpec光谱仪对样品进行测试。野外波谱测试时,要求每个测试点地物尽量均一,面积大于等于17.8 cm2,每个测点都选取5个位置进行测量。为保证仪器的稳定性和尽可能多地去除仪器噪声,每次测量记录30条连续波谱,求取30条波谱的均值作为该样品的测量值。在剖面上选取4验证个点进行高光谱影像的光谱验证,4个验证点分别从暗色地物向亮色地物过渡(图1)。
2015年5月20日,天气晴朗无风,北京时间11:00—14:00,采用德国Cubert公司生产的UHD185机载光谱成像仪进行光谱影像采集。视场角范围为27°,光谱范围为450~950 nm,光谱分辨率为4 nm。传感器垂直探槽剖面,距离为9 m,空间分辨率为4.22 cm。
1.2 模型建立与验证
图2为光谱反射率和磁化率之间的关系。
图2
图2
不同磁化率光谱反射率变化
Fig.2
Sepctral features of samples and their magnetic susceptibility values
选择b600/b500(x1)、b750/b650(x2)、 b880/b810(x3)为特征光谱参数,发现三者与磁化率线性关系良好(图3),特别是x2和x3相关系数均大于0.96。
图3
图3
波段比值参数和磁化率的线性关系模型
Fig.3
Linear regression between band ratios and magnetic susceptibility
因此将这2个特征参数作为自变量,采用多元逐步线性回归的方法建立模型,模型表达式为
y=156.031x2+611.195x3-720.957 78, R2=0.984, RMSE=3.875 09。 (1)
图4
图4
实测磁化率和模型反演磁化率对比
Fig.4
Comparison of the instrumentally measured and spectrally estimated magnetic susceptibility of the test data
定量评价主要采用波谱角分析方法(spectral angle mapper,SAM)和相关系数法。其中SAM法是光谱分析的一种手段,即用光谱匹配程序对预测磁化率与实测磁化率曲线形态进行定量比较,用以评价波谱质量[15]。该算法是将N个样本点的磁化率看做N维空间向量,通过计算与实测磁化率曲线之间的夹角判定2个磁化率曲线的相似度,夹角越小,说明越相似。相似度用一个得分来表示,得分越接近于1,说明相似度越高[16]。55个测试样本点的磁化率曲线与对应的实测磁化率曲线相似度的平均得分为0.896,相关系数R2>0.97,均方根误差RMSE=4.934 47,证明预测磁化率曲线与实测磁化率曲线匹配度较高,该模型的精度较高,能够很好地应用于磁化率预测。
2 模型应用
2.1 影像质量评价
图5
图5
UHD185影像反射率与实测反射率对比
Fig.5
Reflectance spectra from the UHD185 image and filed reflectance spectra
从图5中可以明显看出ASD实测波谱与UHD185影像波谱整体趋势上比较一致,吸收位置也比较对应。但是900~950 nm波段范围的反射率整体是下降的,与实际不符,说明这些通道的数据不可信。
定量评价主要是采用光谱分析手段——SAM法和相关系数法对影像光谱和实测波谱进行定量比较。由前文分析可知,900~950 nm的影像波谱数据不可信,因此在分析时需要去除该谱段数据,去除后影像光谱曲线和实测光谱曲线的相似度SAM>0.9,相关系数R2>0.995。
磁化率模型中使用波段比值作为参数为了进一步对影像数据进行评估,本研究对模型中用到的波段比值b750/b650(x2)和b880/b810(x3)开展了定量评价。精度用相关系数R2和平均比值差来评价,平均比值差计算公式为
$\bar{D}=\sum^{N}_{n=1}\frac{UHD185_{n}(b_{i}/b_{j})-ASDn(b_{i}/b_{j})}{N}$, (2)
P=
式中: n为样品点个数; i和j为UHD185对应的波长;
2.2 模型应用
将模型应用到UHD185影像上,得到了磁化率强度(图6)。为了便于肉眼识别,本研究使用ArcMap自带的标准差法选择1倍标准差间隔进行分级显示。磁化率强度可以明显地将地层分为6层,自上而下表现为: 蓝绿混合层、橙色层、黄色层、绿色层、蓝色层和绿色层。将肉眼划分的剖面层序分界点(图6粉色虚线和黑色虚线)与磁化率强度分级点(图6蓝色实线)显示的层序对比,可以看出磁化率强度分级将S0和L1之间划分为2层,而且这2层的分界点对应于磁化率曲线的转折点。这表明磁化率强度在纵向上的波动特征与地层层序的旋回性有较好的对应关系。相较于单测线测试磁化率进行地层划分,二维磁化率强度可以直观地展现地层结构空间展布特征,避免局部由于生物效应等引起的磁化率变化异常,造成误判。例如图6中A和B部分磁化率曲线有所不同,若利用其对应的磁化率划分地层,A和B的结果将会不同。但是从磁化率强度上,可以宏观地识别出A和B为同一地层。需要注意的是,本次研究中C处有一个树根,磁化率强度呈现出局部高值,并不能代表整个地层的特征。
图6
图6
探槽剖面磁化率强度
Fig.6
Magnetic susceptibilty estimates with the regression models
为了精确评价基于影像的磁化率填图效果,用C2的实测磁化率(35个,描述见2.1)与影像反演的磁化率进行对比(图7)。
图7
图7
影像反演磁化率与实测磁化率对比
Fig.7
Comparison of the UHD185 image estimated and measured magnetic susceptibilty
图7显示影像预测的磁化率整体曲线特征与实测一致,且模型反演的影像磁化率与实测磁化率具有良好的线性关系,相关系数均大于0.95。但是影像反演的磁化率和实测磁化率存在一定的偏移,模型反演的值表现为高估实际的磁化率。一种可能是对目标物观测的方式不同,造成了这些差异,但是仍然需要对其原因做进一步的研究。整体上本文的磁化率光谱模型表现出具有相对较高的精度。
3 结论与讨论
本文基于高光谱磁化率模型对黄土剖面地层划分进行了探索,建立了光谱与指示地层韵律变化的磁化率之间的关系模型。该模型可以有效地反演磁化率,具有较高的精度: 实验室测试样品估计磁化率与实测磁化率的相关系数R2>0.97,剖面高光谱影像估计磁化率和实测值相关系数R2>0.95。反演得到的磁化率强度分布不仅可以识别出肉眼识别的地层,还能将肉眼无法识别的黄土与古土壤的过渡层识别出来,其在纵向上的波动特征与地层层序的旋回性有较好的对应关系。因此,基于光谱磁化率模型的高光谱影像有利于实现黄土地层的精细划分。
通常情况下,古地震事件分析对应的剖面需要较高的空间分辨率,剖面影像需要多幅影像拼接而成。然而影像获取的条件很难保证完全一致,高光谱影像多个波段的镶嵌融合是首先要解决的问题。利用高光谱影像反演得到的磁化率是单波段影像,镶嵌融合技术要求较低,分类方法也较成熟。因此,本研究并没有直接使用反射波谱进行地层的直接划分。同时不同时间尺度的地层层序尺度不同,即还有分层阈值的设定问题,本研究并没有将地层层序和时间关联起来,只是探索了利用高光谱进行地层划分的可行性。与时间关联的层序划分对于古地震定量分析具有重要作用,因此后续研究可以根据不同的应用需求,开展层序序列分析。
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对最近130ka来,洛川黄土-古土壤剖面中绿泥石和云母的分布特征及其与磁化率的关系进行了研究。发现在黄土和古土壤中,云母/绿泥石比值随粒级的减小而增大;在大于8μm粒级部分,黄土和古土壤样品的地云母/绿泥石比值相近,而在小于8μm的各粒级中,古土壤样品的云母/绿泥石比值较同粒级黄土样品为高,并且这种差别随粒级的减小而加大;洛川黄土剖面中云母/绿泥石比值曲线与磁化率分布曲线十分相似,两者之间存在密切
Chemical weathering of chlorite and enhancement of magnetic susceptibility in Luochuan Loess
[J].
Visible spectroscopy:A rapid method for determining hematite and goethite concentration in geological materials
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DOI:10.1306/D4267794-2B26-11D7-8648000102C1865D
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[本文引用: 1]
Abstract Application of reflectance spectra to geologic samples requires a set of standards for materials in concentrations and matrixes similar to those which occur naturally on the earth's surface. Reports the VIS spectra reflectance response to hematite and goethite in geologically meaningful matrixes and compares spectral results to an established analytical technique, XRD. -from Authors
Rapid and quantitative measurement of hematite and goethite in the Chinese loess-epaleosol sequence by diffuse reflectance spectroscopy
[J].DOI:10.1346/000986002760832801 URL [本文引用: 2]
Use and limitation of second-derivative diffuse reflectance spectroscopy in the visible to near-infrared range to identify and quantify Fe oxide minerals in soils
[J].
DOI:10.1346/CCMN.1998.0460506
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[本文引用: 1]
We measured the visible to near-infrared (IR) spectra of 176 synthetic and natural samples of Fe oxides, oxyhydroxides and an oxyhydroxysulfate (here collectively called Fe oxides), and of 56 soil samples ranging widely in goethite/hematite and goethite/lepidocrocite ratios. The positions of the second-derivative minima, corresponding to crystal-field bands, varied substantially within each group of the Fe oxide minerals. Because of overlapping band positions, goethite, maghemite and schwertmannite could not be discriminated. Using the positions of the
Characterising Chinese loess stratigraphy and past monsoon variation using field spectroscopy
[J].
DOI:10.1016/j.quaint.2010.04.011
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[本文引用: 1]
The loess record in China constitutes one of the most important archives of past environmental change and specifically, the East Asian monsoon system. Changes in summer monsoon driven pedogenesis are commonly inferred from magnetic susceptibility of loess. However, there is still controversy as to the signal origin and the uncertain effects of sediment accumulation rates. This is linked to a wider problem that is emerging from recent work; that of the relative importance on proxy records such as magnetic susceptibility of both regional climate patterns (i.e. the monsoon) and local site-specific influences, particularly modulated through site-specific sedimentation rate. At present this is poorly constrained and significantly increases the degree of uncertainty over the wider-scale applicability of climate reconstructions from individual sites. To resolve this issue for sub-orbital timescales, a rapidly deployable technique is needed that can be used to test multiple sites and differentiate between local and regional signals. This research develops the first use of full spectrum reflectance spectroscopy in studying loess in the field at one site on the south of the Loess Plateau, and utilises statistical analyses to compare such data with magnetic susceptibility records. Establishment of loess reflectance as a proxy for climate variability will potentially allow extension of the technique from point measurements to an imaging system and so enable the compilation of large data sets in order to investigate lateral facies variations in loess profiles. This may allow the extraction of a broad scale climate record. Field spectra were obtained from 350 to 1100 nm, with red edge reflectance spectra indicating the presence of iron-oxides, previously demonstrated to be indicators of pedogenesis through laboratory measurements. An off the shelf camera was also tested with visible wavelength spectra being useful for rapid but general profile characterisation. In order to mitigate the effects of bidirectional reflectance distribution function (a potential problem in field measurements), further laboratory measurement was made of powdered samples (400 2400 nm). Absorption features indicative of montmorillonite and illite/muscovite were noted and stepwise regression modelling utilising absolute reflectance, first derivative spectra and continuum removed spectra indicated strong predictive relationships with magnetic susceptibility, particularly against the presence of montmorillonite. The abundance of such clay minerals could be used to infer weathering rates and hence be used as a proxy for pedogensis. Reconstructions for the studied site are presented and demonstrate the potential power of loess reflectance environmental reconstruction.
Visible and near-infrared spectra of minerals and rocks:III.Oxides and hydroxides
[J].Mn(OH)2 kommt als Mineral Pyrochroit vor. Unter Ausschlu08 von Sauerstoff kann es durch F01llung von Mangan(II)-Salzl02sungen mit Alkali erhalten werden. Mn(OH)2 besitzt eine Schichtstruktur vom CdJ2(I)-Typ1) und ist isostrukturell mit Ca(OH)2. Vermutlich existiert auch eine orthorhombische Modifikation von Pyrochroit und es liegen Angaben über die Synthese einer orthorhombischen Modifikation vor2).
Regional-scale mineral mapping using ASTER VNIR/SWIR data and validation of reflectance and mineral map products using airborne hyperspectral CASI/SASI data
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DOI:10.1016/j.jag.2014.04.014
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[本文引用: 1]
ASTER data have been widely and successfully used in lithological mapping and mineral exploration for decades. The errors due to atmospheric water vapor and the characteristics of the photoelectric sensor could lead to the anomalous characteristics of band 5 and 9 in the ASTER standard reflectivity product. These anomalies could result in the spectroscopic misidentification of minerals. This study proposed a simple method of atmospheric correction for converting radiance-at-sensor to ground reflectance. The ASTER VNIR/SWIR reflectance correction factor was derived to correct the spectral shape bias resulting from the radiometric calibration error using airborne hyperspectral CASI_SASI data. The ASTER VNIR/SWIR reflectance correction factor was derived to correct the spectral shape bias resulting from the radiometric calibration error. After applying the reflectance factor to the atmospheric-corrected ASTER L1B data, a band combination mapping method was proposed for identifying minerals more quickly and accurately. The results indicate that this method for atmospheric correction of ASTER data produces very good results in the arid and bare areas. It is still unknown whether the method is suitable for humid and rainy areas where atmospheric water vapor varies spatially more than in arid and bare areas. After applying the reflectance factor to the atmospheric-corrected ASTER L1B data, the mean error of all reflectance bands decreased from 0.0256 to 0.002, and the standard deviation decreased from 0.04251 to 0.0007. The errors of the 2/1, 5/6 and 9/8 band ratios decreased from 2.38%, 4.102%, and 4.28% to 1.26%, 0.162%, and 0.31%, respectively. The radiometric calibration error of the ASTER band 1 9 data can lead to the overestimation of kaolinite. A band index of 2/1 for retrieving Fe3+ cannot produce a reliable Fe3+ distribution map, and a new index should be developed.
The spectral image processing system(SIPS)-interactive visualization and analysis of imaging spectrometer data
[J].
DOI:10.1063/1.44433
URL
[本文引用: 1]
The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the ‘‘Spectral Image Processing System (SIPS)’’ using ‘‘IDL’’ (the Interactive Data Language) on UNIX‐based workstations. SIPS is designed to take advantage of the combination of high spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to interact with entire datasets in real‐time. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X‐windows‐based, user friendly, and provides ‘‘point and click’’ operation. SIPS is being used for multidisciplinary research concentrating on the use of physically‐based analysis methods to enhance scientific results from imging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).
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