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遥感技术与应用  2019, Vol. 34 Issue (5): 1016-1027    DOI: 10.11873/j.issn.1004-0323.2019.5.1016
模型与反演     
镁铁质―超镁铁质岩全谱段遥感识别模型
余沐瑶1(),张志2(),付杨康3
1. 广东省地图院, 广东 广州 510075
2. 中国地质大学(武汉) 地球物理与空间信息学院, 湖北 武汉 430074
3. 广州地理研究所,广东 广州 510070
Full-spectrum Remote Sensing Identification Model for Mafic-ultramafic Rocks
Muyao Yu1(),Zhi Zhang2(),Yangkang Fu3
1. Map Institute of Guangdong Province, Guangzhou 510075, China
2. Institute of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China
3. Guangzhou Institute of Geography, Guangzhou 510070, China
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摘要:

镁铁质―超镁铁质岩的遥感岩性识别研究一直是遥感岩石学领域的热点之一,其岩石信息对于岩浆型铜镍硫化物矿床的预测十分重要。提出一种综合利用Landsat 8、ASTER和PALSAR-2数据的镁铁质―超镁铁质岩石全谱段遥感识别模型:通过对野外采集岩石样品及光谱库中相关岩石矿物的可见光―热红外谱域的光谱测试与特性分析,结合岩石表面微波散射特性,应用特征空间,贝叶斯线性判别分析、逐步偏最小二乘回归分析等方法,创建镁铁质―超镁铁质岩性指数,并利用提出的岩性指数进行镁铁质―超镁铁质岩的初步提取,再经过基于贝叶斯决策理论的融合处理得到最终的镁铁质―超镁铁质岩石信息。结果表明:该模型能够对镁铁质―超镁铁质岩进行精准定位,识别精度达到94%以上。此外,根据遥感岩性、构造特征的解译与区内已知铜镍硫化物矿床的成矿岩体比较,推断出位于赤石山―小长山―中坡山一带以及小青山附近岩体具有一定的成矿潜力。

关键词: 全谱段遥感镁铁质―超镁铁质岩特征空间贝叶斯决策理论识别模型    
Abstract:

Lithological mapping of mafic-ultramafic rocks is a hotspot in remote sensing petrology, and this lithological information is significant for the prediction of magmatic Ni-Cu sulfide ore deposit. This paper proposed a full-spectrum remote sensing identification model based on Landsat-8, ASTER, and PALSAR-2 data to extract mafic-ultramafic rocks. Firstly, mafic-ultramafic rock indices were proposed by feature space model, Bayesian linear discriminant analysis, and stepwise partial least squares regression analysis, based on analysis of the laboratory reflectance and emissivity spectra of field rock samples measured by portable spectrometer and related rocks and minerals spectra from spectral library in the visible to thermal infrared region, combined with the microwave scattering properties of the rock surface. And then these mafic-ultramafic rock indices were used to preliminary obtain the mafic-ultramafic rock information. Finally, the final mafic-ultramafic rock information was obtained after multi-information fusion processing based on Bayesian decision theory. The results show that the full-spectrum remote sensing identification model can accurately locate mafic-ultramafic rocks, and the extraction accuracy of the mafic-ultramafic rock information is above 94%. Furthermore,Based on mafic-ultramafic rock extraction of this identification model and structure interpretation, mineral exploration prospect also infer around Chishishan-Xiaochangshan-Zhongposhan area and Xiaoqingshan area, comparing with metallogenic rock masses of known Ni-Cu sulfide ore deposits in study area.

Key words: Full-spectrum remote sensing    Mafic-ultramafic rock    Feature space    Bayesian decision theory    Identification model
收稿日期: 2018-05-04 出版日期: 2019-12-05
ZTFLH:  P585  
基金资助: 中国地质调查局项目“天山—北山重要成矿区带遥感调查”(12120113099900)
通讯作者: 张志     E-mail: yumuyaoo@163.com;171560655@qq.com
作者简介: 余沐瑶(1992—),女,福建沙县人,助理工程师,主要从事遥感信息提取与分析研究。 E?mail:yumuyaoo@163.com
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引用本文:

余沐瑶,张志,付杨康. 镁铁质―超镁铁质岩全谱段遥感识别模型[J]. 遥感技术与应用, 2019, 34(5): 1016-1027.

Muyao Yu,Zhi Zhang,Yangkang Fu. Full-spectrum Remote Sensing Identification Model for Mafic-ultramafic Rocks. Remote Sensing Technology and Application, 2019, 34(5): 1016-1027.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.5.1016        http://www.rsta.ac.cn/CN/Y2019/V34/I5/1016

图1  研究区概况
遥感卫星数据标识成像时间
Landsat-8LC81390322015234LGN002015/08/22
ASTERAST00144PRDAT02102003/08/20
AST00144PRDAT01182004/04/02
AST00152PRDAT0262004/08/15
AST00188PRDAT01102002/05/13
AST00188PRDAT01112002/05/13
AST00190PRDAT01122000/09/30
AST00190PRDAT01262000/10/23
PALSAR-2ALOS20484408102015/04/16
ALOS21184527902016/08/02
表1  研究中使用的遥感数据列表
图2  野外采集岩石样品的情况
图3  镁铁质―超镁铁质岩石信息遥感提取流程
图4  可见光-短波红外三维MSFS模型图
类别Landsat-8多光谱特征空间ASTER SWIR 多光谱特征空间
B7B6B4常数项B7B8B9常数项
镁铁质―超镁铁质岩337.791―173.178252.776―30.563―3.681―287.126813.231―52.381
其他类岩石588.153―347.371428.715―74.624―8.170―216.663921.509―89.585
表2  贝叶斯判别函数系数表
图5  SiO2含量与发射率光谱的相关性
图6  SiO2实际含量与预测含量散点图
图7  基于Cloude极化分解的岩石散射特征
图8  岩石训练样本的物理化学特征空间
图9  岩性指数协同图的生成示意图
ClassLMISMITMIFMI
11111
21110
31101
41011
50111
61100
71010
81001
90110
100101
110011
121000
130100
140010
150001
160000
表3  协同图的分组标准
图10  研究子区镁铁质-超镁铁质岩石信息初步提取结果
图11  Site1研究子区岩性指数协同子图
Class12345678
Site10.960.760.690.870.890.130.600.47
Site20.960.820.790.900.840.080.550.02
Class910111213141516
Site10.760.480.840.080.160.580.240.01
Site20.580.390.630.290.090.650.130.00
表4  研究子区的岩性指数协同子区的后验概率
图12  Site2研究区岩性指数协同子图
图13  研究子区镁铁质-超镁铁质岩最终提取结果
Site 1Site 2Site1和Site2
OA漏分误差错分误差OA漏分误差错分误差OA漏分误差错分误差
LMI指数74.42%25.58%14.44%85.88%14.12%11.96%85.03%14.97%12.14%
SMI指数71.13%38.87%8.60%78.19%21.81%10.89%77.66%22.34%10.72%
TMI指数77.86%22.14%9.62%90.62%9.38%10.73%89.67%10.33%10.65%
FMI指数79.88%20.12%8.78%92.40%7.60%10.73%91.46%8.54%15.33%
识别模型86.82%13.18%5.76%95.46%4.54%7.62%94.82%5.18%7.48%
表5  镁铁质―超镁铁质岩识别精度评价
图14  受碳酸盐岩坡积物覆盖的镁铁质-超镁铁质岩
图15  坡北—笔架山地区镁铁质—超镁铁质岩石信息最终提取结果
1 Chen Jiang,Xu Yongbo,Wang Fang,et al.Mafic-ultramafic Nickel⁃copper Metallogenic Regularity of Xinjiang Pobei-beacon Hill[J]. Xinjiang Geology, 2013,31(2): 184-189.陈疆,徐永波,王芳,等.新疆坡北―笔架山地区基性―超基性岩铜镍矿成矿规律[J].新疆地质,2013,31(2): 184-189.
2 Chai Fengmei, Xia Fang, Chen Bin, et al. Platinum Group Elements Geochemistry of Two Mafic-ultramafic Intrusions in the Beishan Block, Xinjiang,NW China[J].Acta Geologica Sinica,2013,87(4):474-485.柴凤梅,夏芳,陈斌,等.新疆北山地区两个含铜镍镁铁—超镁铁质岩体铂族元素地球化学研究[J].地质学报,2013,87(4):474-485.
3 Wang Heng, Wang Peng, Li Jian, et al. A Tentative Discussion on Features of Mafic-ultramafic Rocks and Exploration Methods in Pobei Area of Ruoqiang, Xinjiang[J].Geology in Chin, 2015,42(3):777-784.王恒,王鹏,李建,等.新疆若羌坡北地区镁铁-超镁铁质侵入岩含矿特征及找矿方法探讨[J].中国地质,2015,42(3):777-784.
4 Zheng Shuo,Fu Bihong.Lithoogical Mapping of Granitiods in the Western Junggar from ASTER SWIR-TIR Multispectral Data:Case Study in Karamay Pluton,Xinjiang[J].Acta Petrologica Sinica, 2013,29(8): 2936-2948.郑硕,付碧宏.基于ASTER SWIR-TIR多光谱数据的西准噶尔花岗岩类岩性信息提取与识别——以克拉玛依岩体为例[J].岩石学报,2013,29(8):2936-2948.
5 Tang Chao,Shao Longyi.Algorithm of Object Recognition from Hyperspectral Remote Sensing and Its Application in Lithologic Feature Extraction[J].Remote Sensing Technology and Application,2017,32 (4):691-697.唐超,邵龙义.高光谱遥感地物目标识别算法及其在岩性特征提取中的应用[J].遥感技术与应用,2017,32 (4):691-697.
6 Qi Xin, Liu Guangning, Huang Changsheng. Remote Sensing Investigation for Active Characteristics of Macheng—Tuanfeng Fault Zone Segmentation[J].Remote Sensing for Land and Resources,2018,30(1):121-127.齐信,刘广宁,黄长生.麻城—团风断裂带分段活动特征遥感调查[J].国土资源遥感,2018,30(1):121-127.
7 Wang Futao,Wang Shixin,Zhou Yi,et al.High Resolution Remote Sensing Monitoring and Assessment of Secondary Geological Disasters Triggered by the Lushan Earthquake[J].Spectroscopy and Spectral Analysis, 2016,36(1):181-185.王福涛,王世新,周艺,等.高分辨率多光谱的芦山地震次生地质灾害遥感监测与评估[J].光谱学与光谱分析,2016,36(1):181-185.
8 Hunt G R, Salisbury J W, Lenhoff C J. Visible and Near-infrared Spectra of Minerals and Rocks: IV. Sulphides and sulphates[J]. Modern Geology, 1971, 3:1-14.
9 Fu Binhong,Xiaowei Chou.Extraction and Recognition of Rock and Mineral Information by Using Thermal Infrared Multispectral Remote Sensing Technique[J]. Remote Sensing Technology and Application, 1994,9(1):56-61.付碧宏,丑晓伟.利用热红外多光谱遥感技术提取和识别岩石、矿物信息[J].遥感技术与应用,1994,9(1):56-61.
10 Ding C, Liu X, Liu W, et al. Mafic–ultramafic and Quartz-rich Rock Indices Deduced from ASTER Thermal Infrared Data Using a Linear Approximation to the Planck Function[J]. Ore Geology Reviews, 2014, 60(3):161- 173.
11 Wei J, Liu X, Ding C, et al. Developing a Thermal Characteristic Index for Lithology Identification Using Thermal Infrared Remote Sensing Data[J]. Advances in Space Research, 2017, 59(1):74-87.
12 Li Shu,Liu Yong.Land Use/Cover Classification of Remotely Sensed Imagery based on Multi-features at The Southeastern Marginal Area of the Tengle Desert[J]. Remote Sensing Technology and Application, 2006,21(2):154-158, 173.李述,刘勇.基于多特征的遥感影像土地利用/覆盖分类—以腾格里沙漠东南边缘地区为例[J].遥感技术与应用,2006,21(2):154-158, 173.
13 Duda R O, Hart P E, Stork D G. Pattern Classification(2nd Ed)[J].Pattern Analysis and Applications,2001,1(4):119-131.
14 Jing Z, Wang G, Zhang S, et al. Building Tianjin Driving Cycle based on Linear Discriminant Analysis[J]. Transportation Research Part D Transport & Environment, 2017, 53:78-87.
15 Harrington P. Machine Learning in Action[M]. Greenwich:Manning Publications, 2012:58-73.
16 Ding C, Liu X, Liu W, et al. Mafic–ultramafic and Quartz-rich Rock Indices Deduced from ASTER Thermal Infrared Data Using a Linear Approximation to the Planck Function[J]. Ore Geology Reviews, 2014, 60(3):161- 173.
17 Chen Jiang,Wang Anjian.The Pilot Study on Petrochemistry Components Mapping with ASTER Thermal Infrared Remote Sensing Data[J].Journal of Remote Sensing, 2007, 11(4):601-608.陈江, 王安建. 利用ASTER热红外遥感数据开展岩石化学成分填图的初步研究[J]. 遥感学报, 2007, 11(4):601-608.
18 Cooper B L, Salisbury J W, Killen R M, et al. Midinfrared Spectral Features of Rocks and Their Powders[J]. Journal of Geophysical Research, 2002, 107(E4):1-17.
19 Cloude S R, Pottier E. A Review of Target Decomposition Theorems in Radar Polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2):498-518.
20 Ninomiya Y, Fu B, Cudahy J. Detecting Lithology with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Multispectral Thermal Infrared “Radiance-at-sensor” Data[J]. Remote Sensing of Environment, 2005, 99(1-2):127-139.
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