基于多源遥感数据的高山峡谷区岩性信息提取研究——以新疆乌什县北山1:50000填图试点为例

孟鹏燕; 孙杰; 于长春; 穆超; 帅爽; 谢菲; 孟丹

基于多源遥感数据的高山峡谷区岩性信息提取研究——以新疆乌什县北山1:50000填图试点为例

孟鹏燕^1,,
孙杰^1, ,,
于长春²,
穆超³,
帅爽³,
谢菲³,
孟丹³

1.
中国地质大学(武汉)地球科学学院, 武汉 430074
2.
中国地质调查局国土资源航空物探遥感中心, 北京 100083
3.
湖北省国土测绘院, 武汉 430010

基金项目:

中国地质调查局地质调查项目“特殊地质地貌区填图试点” DD20160060

详细信息

作者简介:
孟鹏燕(1989-), 男, 硕士, 研究方向为遥感地质、国土资源遥感。E-mail:meng_pengyan@sina.com

通讯作者:
孙杰(1982-), 男, 博士, 研究方向为遥感数据处理。E-mail:sunjie_cug@163.com

中图分类号: P623
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- 文章访问数: 229
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- 被引次数: 0
出版历程
- 收稿日期: 2000-09-16
- 刊出日期: 2016-12-01

LITHOLOGICAL INFORMATION EXTRACTION IN MOUNTAIN CANYON REGION BASED ON MULTI-SOURCE REMOTE SENSING DATA: A CASE STUDY OF 1: 50000 PILOT GEOLOGICAL MAPPING IN BEISHAN AREA IN WUSHI, XINJIGAN

1.
School of Earth Sciences, China University of Geoscience, Wuhan 430074, China
2.
China Aero Geophysical Survey & Remote Sensing Center for Land and Resources, Beijing 100083, China
3.
Hubei Institute of Land Surveying And Mapping, Wuhan 430010, China

摘要

摘要: 新疆乌什县北山1：50000填图试点项目位于塔里木盆地西北边缘和西南天山交接部位，海拔较高，地形切割较深，属于典型的高山峡谷区。利用ASTER、SPOT6、GF-2等多源遥感数据，基于典型岩性光谱吸收特征，进行岩性差异信息增强与提取研究，总结出一套基于多源遥感数据进行岩性单元边界划分的方法。以ASTER数据、ASTER与SPOT6协同数据、ASTER与GF-2协同数据等为基础影像数据，并选择最佳波段组合进行RGB彩色合成，从而增强影像差异，结合已有研究区地质资料，初步圈定不同影像单元边界；继而利用矿物丰度指数、SMACC端元丰度提取等方法识别研究区内主要岩性的分布位置和范围；最后结合野外实际调查数据，依据实际地质背景和影像质量进行筛选，获得最终的岩性单元解译图。研究结果为该区进一步进行地层优化划分及对比提供了参考资料。
- 高山峡谷区 /
- 多源数据协同 /
- 矿物丰度指数 /
- SMACC /
- 岩性信息提取
Abstract: The location of 1:50000 pilot geological mapping program in Beishan, Wushi County, lies at the junction point of north-west margin of Tarim basin and south-west of Tianshan. According to the geomorphic characteristics of high altitude and deep negative relief, the study area belongs to alpine valley region. Based on the typical lithology spectral absorption characteristics, we carried out some research on the enhancement and absorption of lithologic differences information, and summarized a series of methods to divide the lithological units margins according to multi-source remote sensing data. On the foundation of ASTER, ASTER and SPOT6 cooperative data, ASTER and GF-2 cooperative data, we choose the best wave combination to synthesize RGB color and enhance the difference between images. And we preliminarily mark the boundaries of different image units according to the known geological data of study area. Then, the final lithology units can be interpreted by combining field survey data, realistic geological background, and geomorphic images. Thus, the study results provide reference for further optimized stratum division and comparison.
- alpine valley region /
- multi source remote sensing data /
- mineral abundance index /
- SMACC /
- lithological information extraction

HTML全文

图 1 研究区地理位置和地质构造简图

1, 2—第四系冲洪积物(Q^apl_3-4); 3—安吉然组砾岩夹钙质粗砂岩((N₂²—Q₁)ad); 4—阿依里下亚组石灰岩、纺锤灰岩、砾状灰岩及钙质砂岩夹铝土矿层(C₃ai^a); 5—阿克恰衣群薄层状砂岩、粉砂岩夹砾岩、灰岩(C_1-2ak); 6—阿帕达尔康下亚组大理岩化灰岩、鲕状灰岩夹凝灰砾岩泥质粉砂岩薄层(D₂ap^b); 7—不整合接触界线;
8—正常接触界线; 9—断层; 10—研究区

Figure 1. Location and geological sketch of the study area

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图 2 ASTER原始影像(R:8, G:4, B:1)

Figure 2. Original image of ASTER (R:8, G:4, B:1)

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图 3 ASTER与SPOT6协同影像(R:8, G:4, B:1)

Figure 3. Cooperative image of ASTER and SPOT6 (R:8, G:4, B:1)

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图 4 ASTER与GF-2协同影像(R:8, G:4, B:1)

Figure 4. Cooperative image of ASTER and GF-2 (R:8, G:4, B:1)

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图 5 碳酸盐矿物和典型黏土类矿物光谱曲线

Figure 5. Spectral curves of carbonate minerals and typical clay minerals

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图 6 矿物丰度指数法彩色合成图

Figure 6. Synthesized color image according to mineral abundance index

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图 7 中SMACC算法所提取端元波谱

Figure 7. The spectral extracted by SMACC algorithm

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图 8 SMACC端元丰度合成图

Figure 8. Synthesis image according to SMACC end member abundance

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图 9 研究区遥感解译岩性构造图

1—冲洪积物; 2—灰色半固结砾石层; 3—灰色厚-巨厚层状粗砾岩夹巨砾岩、中细砾岩、砂岩; 4—灰白色、灰色厚层状含生物碎屑粉晶、亮晶灰岩; 5—深灰色厚层状与薄层状生物碎屑灰岩互层; 6—灰-灰黄色钙质粉砂岩、灰色粉砂质灰岩, 夹灰黑色薄层; 7—灰色薄层状与厚层状泥晶灰岩不等厚互层, 夹钙质粉砂岩; 8—灰黑色厚层状泥晶灰岩, 夹浅灰绿色薄层状钙质粉砂岩, 局部见褐铁矿化黄铁矿颗粒; 9—深灰色-浅灰绿色中薄层状粉砂质灰岩、钙质粉砂岩; 10—灰白色与灰色中厚层状含生物碎屑灰岩; 11—深灰色与浅灰色中厚层状含生物碎屑灰岩; 12—灰白色厚层状含生物碎屑灰岩; 13—深灰色夹灰白色中厚层、中薄层状含生物碎屑灰岩

Figure 9. Lithology and structure map interpreted by remote sensing image of study area

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图 10 矿物丰度指数图、SMACC端元丰度合成图与遥感解译岩性构造叠加图

Figure 10. Stacking chart of mineral abundance index image, synthesis image of SMACC end member abundance and lithology-structure map

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图 11 岩性单元8和单元9遥感地质特征及野外地质特征

a—矿物丰度指数图; b—SMACC端元丰度合成图; c—ASTER与SPOT6协同影像; d—野外照片; 8—灰黑色厚层状泥晶灰岩, 夹浅灰绿色薄层状钙质粉砂岩, 局部见褐铁矿化黄铁矿颗粒; 9—深灰色-浅灰绿色中薄层状粉砂质灰岩、钙质粉砂岩

Figure 11. The remote sensing and field outcrop geological characteristics of lithology unit 8 and unit 9

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图 12 岩性单元野外照片

Figure 12. Outcrop photos of lithology units

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表 1 GF-2、SPOT6、ASTER、ETM+遥感数据基本特征

Table 1. Characteristics of GF2, SPOT6, ASTER and ETM+ remote sensing data

数据源	波段	波长/nm	空间分辨率/m
GF-2	1	450~520	3.2
	2	520~590	3.2
	3	630~690	3.2
	4	770~890	3.2
	Pan	450~900	0.8
ASTER	VNIR1	520~600	15
	VNIR2	630~690	15
	VNIR3N, 3B	780~860	15
	SWIR4	1600~1700	30
	SWIR5	2145~2185	30
	SWIR6	2185~2225	30
	SWIR7	2235~2285	30
	SWIR8	2295~2365	30
	SWIR9	2360~2430	30
SPOT6	1	425~525	6.0
	2	530~590	6.0
	3	625~695	6.0
	4	760~890	6.0
	Pan	455~745	1.5
ETM+	1	450~520	28.50
	2	520~600	28.50
	3	630~690	28.50
	4	760~900	28.50
	5	1550~1750	28.50
	7	23080~2350	28.50
	8	500~900	14.25

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表 2 相关系数矩阵

Table 2. Correlation index matrix

相关性	B1	B2	B3	B4	B5	B6	B7	B8	B9
B1	1.000	0.983	0.734	0.775	0.832	0.835	0.854	0.861	0.863
B2	0.983	1.000	0.739	0.819	0.876	0.877	0.892	0.894	0.899
B3	0.734	0.739	1.000	0.857	0.749	0.768	0.764	0.751	0.751
B4	0.775	0.819	0.857	1.000	0.960	0.966	0.955	0.925	0.939
B5	0.832	0.876	0.749	0.960	1.000	0.996	0.991	0.970	0.982
B6	0.835	0.877	0.768	0.966	0.996	1.000	0.992	0.972	0.982
B7	0.854	0.892	0.764	0.955	0.991	0.992	1.000	0.988	0.991
B8	0.861	0.894	0.751	0.925	0.970	0.972	0.988	1.000	0.988
B9	0.863	0.899	0.751	0.939	0.982	0.982	0.991	0.988	1.000

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参考文献(21)

[1]	王润生, 熊盛青, 聂洪峰, 等.遥感地质勘查技术与应用研究[J].地质学报, 2011, 85(11):1699~1743. http://www.cnki.com.cn/Article/CJFDTOTAL-SDGJ201617057.htm WANG Run-sheng, XIONG Sheng-qing, NIE Hong-feng, et al. Remote sensing technology and its application in geological exploration[J]. Acta Geologica Sinica, 2011, 85(11):1699~1743. http://www.cnki.com.cn/Article/CJFDTOTAL-SDGJ201617057.htm
[2]	Hunt G R, Salisbury J W. Visible and near infrared spectra of minerals and rocks Ⅰ:Silieate minerals[J]. Modern Geology, 1970, 1:238~300.
[3]	Hunt G R, Salisbury J W. Visible and Near Infrared Spectra of Minerals and Rocks Ⅱ:Carbonates[J]. Modern Geology, 1971, 2:23~30.
[4]	燕守勋, 武晓波, 周朝宪, 等.遥感和光谱地质进展及其对矿产勘查的实践应用[J].地球科学进展, 2011, 26(1):13~29. http://www.cnki.com.cn/Article/CJFDTOTAL-MTJS201111060.htm YAN Shou-xun, WU Xiao-bo, ZHOU Chao-xian, et al. Remote sensing and spectral geology and their applications to mineral exploration[J]. Advances in Earth Science, 2011, 26(1):13~29. http://www.cnki.com.cn/Article/CJFDTOTAL-MTJS201111060.htm
[5]	甘甫平, 王润生, 马蔼乃, 等.光谱遥感岩矿识别基础与技术研究进展[J].遥感技术与应用, 2002, 17(3):140~147. doi: 10.11873/j.issn.1004-0323.2002.3.140 GAN Fu-ping, WANG Run-sheng, MA Ai-nai, et al. The development and tendency of both basis and techniques of discrimination for minerals and rocks using spectral remote sensing data[J]. Remote Sensing Technology and Application, 2002, 17(3):140~147. doi: 10.11873/j.issn.1004-0323.2002.3.140
[6]	王晋年, 李志忠, 张立福, 等. "光谱地壳"计划——探索新一代矿产勘查技术[J].地球信息科学学报, 2012, 14(3):344~351. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXX201203010.htm WANG Jin-nian, LI Zhi-zhong, ZHANG Li-fu, et al. "Spectral Crust" project:Research on new mineral exploration technology[J]. Journal of Geo-Information Science, 2012, 14(3):344~351. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXX201203010.htm
[7]	刘超群, 马祖陆, 莫源富.遥感岩性识别研究进展与展望[J].广西科学院学报, 2007, 23(2):120~124. http://www.cnki.com.cn/Article/CJFDTOTAL-GXKX200702016.htm LIU Chao-qun, MA Zu-lu, MO Yuan-fu. Progress and prospect of study on remote sensing lithologic identification[J]. Journal of Guangxi Academy of Sciences, 2007, 23(2):120~124. http://www.cnki.com.cn/Article/CJFDTOTAL-GXKX200702016.htm
[8]	Loughlin W. Principal component analysis for alteration mapping[J]. Photogrammetric Engineering & Remote Sensing, 1991, 57(9):1163~1169.
[9]	丑晓伟, 傅碧宏.干旱区TM图像岩石地层信息提取与分析方法研究[J].沉积学报, 1995, (s1):164~170. http://www.cnki.com.cn/Article/CJFDTOTAL-CJXB5S1.021.htm CHOU Xiao-wei, FU Bi-hong. Extraction and analysis of lithostratigraphic information from Landsat Thematic Mapper Imagery in arid region[J]. Acta Sedimentologica Sinica, 1995, (s1):164~170. http://www.cnki.com.cn/Article/CJFDTOTAL-CJXB5S1.021.htm
[10]	Rowan L C, Mars J C, Simpson C J. Lithologic mapping of the Mordor, NT, Australia ultramafic complex by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)[J]. Remote Sensing of Environment, 2005, 99(1/2):105~126.
[11]	时丕龙, 付碧宏, 二宫芳树.基于ASTER VNIR-SWIR多光谱遥感数据识别与提取干旱地区岩性信息——以西南天山柯坪隆起东部为例[J].地质科学, 2010, 45(1):333~347. http://www.cnki.com.cn/Article/CJFDTOTAL-DZKX201001029.htm SHI Pi-long, FU Bi-hong, Ninomiya Y. Detecting lithologic features from ASTER VNIR-SWIR multispectral data in the arid region:A case study in the eastern Kalpin uplift, southwest Tianshan[J]. Chinses Journal of Geology, 2010, 45(1):333~347. http://www.cnki.com.cn/Article/CJFDTOTAL-DZKX201001029.htm
[12]	刘本培.西南天山构造格局与演化[M].武汉:中国地质大学出版社, 1996. LIU Ben-pei. Tectonic pattern and evolution of southwest Tianshan[M]. Wuhan:China University of Geosciences Press, 1996.
[13]	梁群峰, 杨克俭, 杨运军, 等.西南天山梅尔盖西地区成矿地质条件及成矿预测[J].西北地质, 2013, 46(1):91~102. http://www.cnki.com.cn/Article/CJFDTOTAL-XBDI201301013.htm LIANG Qun-feng, YANG Ke-jian, YANG Yun-jun, et al. Metallogenic prognosis in the Meiergaixi region, southwest Tianshan orogeny[J]. Northwestern Geology, 2013, 46(1):91~102. http://www.cnki.com.cn/Article/CJFDTOTAL-XBDI201301013.htm
[14]	赵仁夫, 杨建国, 王满仓, 等.西南天山地区矿产资源潜力综合评价报告[R].西安:西安地质矿产研究所, 2003. ZHAO Ren-fu, YANG Jian-guo, WANG Man-cang, et al. Comprehensive evaluation report on mineral resources potential in southwest Tianshan region[R]. Xi'an:Xi'an Institute of Geology and Mineral Resources, 2003.
[15]	Itami H, Roehl T W. Mobilizing Invisible Assets[M]. Harvard University Press, 1991.
[16]	马艳华.高空间分辨率和高光谱分辨率遥感图像的融合[J].红外, 2003, (10):11~16. doi: 10.3969/j.issn.1672-8785.2003.10.003 MA Yan-hua. Fusion of high spatial resolution and high spectral resolution remote sensing image[J]. Infrared, 2003, (10):11~16. doi: 10.3969/j.issn.1672-8785.2003.10.003
[17]	Hunt G R. Spectral signatures of particulate minerals in the visible and near infrared[J]. Geophysics, 1977, 42(3):501. doi: 10.1190/1.1440721
[18]	Amer R, Kusky T, Ghulam A. Lithological mapping in the central eastern desert of Egypt using ASTER data[J]. Journal of African Earth Sciences, 2010, 56(2):75~82.
[19]	杨可明, 刘士文, 王林伟, 等.光谱最小信息熵的高光谱影像端元提取算法[J].光谱学与光谱分析, 2014, 34(8):2229~2233. http://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201408049.htm YANG Ke-ming, LIU Shi-wen, WANG Lin-wei, et al. An algorithm of Spectral Minimum Shannon Entropy on extracting endmember of hyperspectral image[J]. Spectroscopy and Spectral Analysis, 2014, 34(8):2229~2233. http://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201408049.htm
[20]	褚海峰, 翟中敏, 赵银娣, 等.一种多/高光谱遥感图像端元提取的凸锥分析算法[J].遥感学报, 2007, 11(4):460~467. doi: 10.11834/jrs.20070464 CHU Hai-feng, ZHAI Zhong-min, ZHAO Yin-di, et al. A Convex Cone Analysis Method for end member selection of multispectral and hyperspectral images[J]. Journal of Remote Sensing, 2007, 11(4):460~467. doi: 10.11834/jrs.20070464
[21]	高晓惠, 相里斌, 魏儒义, 等.基于光谱分类的端元提取算法研究[J].光谱学与光谱分析, 2011, 31(7):1995~1998. http://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201107065.htm GAO Xiao-hui, XIANG Li-bin, WEI Ru-yi, et al. Research on endmember extraction algorithm based on spectral classification[J]. Spectroscopy and Spectral Analysis, 2011, 31(7):1995~1998. http://www.cnki.com.cn/Article/CJFDTOTAL-GUAN201107065.htm