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国土资源遥感  2018, Vol. 30 Issue (2): 202-207    DOI: 10.6046/gtzyyg.2018.02.27
     技术应用 本期目录 | 过刊浏览 | 高级检索 |
基于光谱磁化率模型的黄土剖面地层划分
崔静1(), 董新丰2, 丁锐1(), 张世民1, 王琮禾3, 鲁恒新1, 孙艳云2
1.中国地震局地壳应力研究所地壳动力学重点实验室,北京 100085
2.中国国土资源航空物探遥感中心,北京 100083
3.防灾科技学院,三河 065201
Stratigraphic division of loess along loess profile based on hyperspectral remote sensing
Jing CUI1(), Xinfeng DONG2, Rui DING1(), Shimin ZHANG1, Conghe WANG3, Hengxin LU1, Yanyun SUN2
1.Key Laboratory of Crustal Dynamics, Institute of Crustal Dynamics, China Earthquake Administration, Beijing 100085,China
2.China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China
3.Institute of Disaster Prevention, Sanhe 065201, China
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摘要 

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。

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崔静
董新丰
丁锐
张世民
王琮禾
鲁恒新
孙艳云
关键词 高光谱磁化率黄土剖面地层划分    
Abstract

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.

Key wordshyperspectral remote sensing    magnetic susceptibility    stratigraphic division of loess
收稿日期: 2016-12-09      出版日期: 2018-05-30
:  TP79  
基金资助:国家自然科学基金项目“基于成像光谱技术的黄土剖面隐性断层识别研究”(编号: 41602223);国家重点研发计划项目“基于红外遥感和电离层信息的地震监测预测技术研究”(编号: 2016YFE0122200);中国地震局基本科研业务专项“锦屏山—小金河断裂带晚第四纪运动学特征的河流地貌研究”和“高光谱技术在活动断层研究中的应用”(编号: ZDJ2014-10和ZDJ2015-01)
通讯作者: 丁锐
引用本文:   
崔静, 董新丰, 丁锐, 张世民, 王琮禾, 鲁恒新, 孙艳云. 基于光谱磁化率模型的黄土剖面地层划分[J]. 国土资源遥感, 2018, 30(2): 202-207.
Jing CUI, Xinfeng DONG, Rui DING, Shimin ZHANG, Conghe WANG, Hengxin LU, Yanyun SUN. Stratigraphic division of loess along loess profile based on hyperspectral remote sensing. Remote Sensing for Land & Resources, 2018, 30(2): 202-207.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2018.02.27      或      https://www.gtzyyg.com/CN/Y2018/V30/I2/202
Fig.1  探槽剖面与采样点位置(绿色点为影像光谱验证点)
Fig.2  不同磁化率光谱反射率变化
Fig.3  波段比值参数和磁化率的线性关系模型
Fig.4  实测磁化率和模型反演磁化率对比
Fig.5  UHD185影像反射率与实测反射率对比
Fig.6  探槽剖面磁化率强度
Fig.7  影像反演磁化率与实测磁化率对比
[1] 卫蕾华 . 基于高分辨率粒度、磁化率分析的黄土地层划分与古地震研究[D]. 北京:中国地震局地质研究所, 2015.
Wei L H . Loess Stratigraphy and Paleo-Earthquake Identification Based on High-Resolution Analysis of Granularity and Magnetic Susceptibility[D].Beijing:Institute of Geology, China Earthquake Administration, 2015.
[2] Balsam W, Ji J F, Chen J . Climatic interpretation of the Luochuan and Lingtai loess sections,China,based on changing iron oxide mineralogy and magnetic susceptibility[J]. Earth and Planetary Science Letters, 2004,223(3/4):335-348.
doi: 10.1016/j.epsl.2004.04.023
[3] Chen J, Ji J F, Balsam W , et al. Characterization of the Chinese loess-paleosol stratigraphy by whiteness measurement[J]. Palaeogeography,Palaeoclimatology,Palaeoecology, 2002,183(3/4):287-297.
doi: 10.1016/S0031-0182(02)00246-8
[4] Liu X M, Hesse P, Rolph T . Origin of maghaemite in Chinese loess deposits:Aeolian or pedogenic?[J]. Physics of the Earth and Planetary Interiors, 1999,112(3/4):191-201.
doi: 10.1016/S0031-9201(99)00002-3
[5] Maher B A, Thompson R . Mineral magnetic record of the Chinese loess and paleosols[J]. Geology, 1991,19(1):3-6.
doi: 10.1130/0091-7613(1991)019<0003:MMROTC>2.3.CO;2
[6] Maher B A . Magnetic properties of modern soils and Quaternary
loessic paleosols:Paleoclimatic implications[J]. Palaeogeography,Palaeoclimatology,Palaeoecology, 1998,137(1/2):25-54.
[7] Zhou L P, Oldfield F, Wintle A G , et al. Partly pedogenic origin of magnetic variations in Chinese loess[J]. Nature, 1990,346(6286):737-739.
doi: 10.1038/346737a0
[8] 季峻峰, 陈骏, 刘连文 , 等. 洛川黄土中绿泥石的化学风化与磁化率增强[J]. 自然科学进展, 1999,9(7):619-623.
doi: 10.1088/0256-307X/16/9/027
Ji J F, Chen J, Liu L W , et al. Chemical weathering of chlorite and enhancement of magnetic susceptibility in Luochuan Loess[J]. Progress in Natural Science, 1999,9(7):619-623.
[9] 甘甫平, 王润生 . 遥感岩矿信息提取基础与技术方法研究[M]. 北京: 地质出版社, 2004: 43-47.
Gan F P, Wang R S. Basis Theory and Technical Methods Study of Remote Sensing Rock and Mineral Information Extraction[M]. Beijing: Geological Publishing House, 2004: 43-47.
[10] Deaton B C, Balsam W L . Visible spectroscopy:A rapid method for determining hematite and goethite concentration in geological materials[J]. Journal of Sedimentary Petorology, 1991,61(4):628-632.
doi: 10.1306/D4267794-2B26-11D7-8648000102C1865D
[11] Ji J F, Balsam W, Chen J , et al. Rapid and quantitative measurement of hematite and goethite in the Chinese loess-epaleosol sequence by diffuse reflectance spectroscopy[J]. Clays and Clay Minerals, 2002,50(2):208-216.
doi: 10.1346/000986002760832801
[12] Scheinost A C, Chavernas A, Barrón V , et al. 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]. Clays and Clay Minerals, 1998,46(5):528-536.
doi: 10.1346/CCMN.1998.0460506
[13] Smith M J, Stevens T, MacArthur A ,et al.Characterising Chinese loess stratigraphy and past monsoon variation using field spectroscopy[J]. Quaternary International, 2011,234(1/2):146-158
doi: 10.1016/j.quaint.2010.04.011
[14] Hunt G R, Salisbury J W, Lenhoff C J . Visible and near-infrared spectra of minerals and rocks:III.Oxides and hydroxides[J]. Modern Geology, 1971,2:195-205.
[15] Cui J, Yan B K, Wang R S , et al. Regional-scale mineral mapping using ASTER VNIR/SWIR data and validation of reflectance and mineral map products using airborne hyperspectral CASI/SASI data[J]. International Journal of Applied Earth Observation and Geoinformation, 2014,33:127-141.
doi: 10.1016/j.jag.2014.04.014
[16] Kruse F A, Lefkoff A B, Boardman J W , et al. The spectral image processing system(SIPS)-interactive visualization and analysis of imaging spectrometer data[J]. Remote Sensing of Environment, 1993,44(2/3):145-163.
doi: 10.1063/1.44433
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