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高山峡谷区1:50000地质填图技术方法探索与实践——以新疆乌什北山为例

辜平阳 陈瑞明 查显峰 庄玉军 胡朝斌 李培庆 查方勇 李林 郭亚鹏

辜平阳, 陈瑞明, 查显峰, 等, 2016. 高山峡谷区1:50000地质填图技术方法探索与实践——以新疆乌什北山为例. 地质力学学报, 22 (4): 837-855.
引用本文: 辜平阳, 陈瑞明, 查显峰, 等, 2016. 高山峡谷区1:50000地质填图技术方法探索与实践——以新疆乌什北山为例. 地质力学学报, 22 (4): 837-855.
GU Ping-yang, CHEN Rui-ming, CHA Xian-feng, et al., 2016. EXPLORATION AND PRACTICE OF 1: 50000 GEOLOGICAL MAPPING TECHNIQUES FOR ALPINE-GORGE AREA: A CASE STUDY IN BEISHAN AREA OF WUSHI, XINJIANG. Journal of Geomechanics, 22 (4): 837-855.
Citation: GU Ping-yang, CHEN Rui-ming, CHA Xian-feng, et al., 2016. EXPLORATION AND PRACTICE OF 1: 50000 GEOLOGICAL MAPPING TECHNIQUES FOR ALPINE-GORGE AREA: A CASE STUDY IN BEISHAN AREA OF WUSHI, XINJIANG. Journal of Geomechanics, 22 (4): 837-855.

高山峡谷区1:50000地质填图技术方法探索与实践——以新疆乌什北山为例

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

“新疆1:5万喀伊车山口等3幅艰险区试点” 12120114042701

国家青年基金项目 41002063

青海阿尔金1:5万打柴沟等6幅区调 1212011121193

详细信息
    作者简介:

    辜平阳(1982-), 男, 博士研究生, 主要从事区域地质、地球化学研究。E-mail:pingyang-322@163.com

  • 中图分类号: P546;P623

EXPLORATION AND PRACTICE OF 1: 50000 GEOLOGICAL MAPPING TECHNIQUES FOR ALPINE-GORGE AREA: A CASE STUDY IN BEISHAN AREA OF WUSHI, XINJIANG

  • 摘要: 新疆乌什北山填图试点项目充分发挥遥感技术的先导作用,探索1:50000高山峡谷区填图方法。不同分辨率遥感数据在岩性、构造解译等方面的差异表明多源遥感数据综合解译能有效提高解译程度。研究认为同一遥感数据最佳波段组合图像、Landsat-8和Worldview-2数据协同图像增强了对岩性和构造识别的能力。高光谱遥感矿物填图和岩性分类、基于ASTER热红外遥感数据的岩石化学成分填图等是高山峡谷区填图有效技术方法。利用ETM和ASTER数据开展矿化蚀变信息提取,结果表明ASTER较ETM数据在铁染异常、羟基异常等提取方面具有更大的优势。分析认为多元信息综合预测是区域找矿的重要途径。根据乌什北山地质地貌特征,选择其中有效技术方法或技术方法组合开展1:50000地质填图,结果显示在减少剖面测制和路线地质调查数量的同时,达到了填图精度,并取得了若干重要研究成果,为区域构造演化和成矿规律分析总结提供了资料支撑。

     

  • 图  1  研究区构造位置及地质简图

    Figure  1.  Tectonic location and geological map of the study area

    图  2  同一地区不同空间分辨率遥感数据岩性解译效果对比图

    a—Spot5遥感影像图; b—Spot6遥感影像图; c—Geoeye-1遥感影像图; d—Quickbird遥感影像图; e—Worldview-2遥感影像图; f—Worldview-3遥感影像局部放大图; g—高分一号遥感影像图; h—高分二号遥感影像图

    Figure  2.  Comparison of lithologic interpretation of remote sensing data with different spatial resolution in the same area

    图  3  不同空间分辨率遥感数据构造解译效果对比图

    a—斜歪倾伏褶皱影像; b—不对称褶皱影像; c—2褶皱构造影像(局部剥蚀露); d—断层构造影像; e—断层破碎带影像; f—两期节理构造影像; h—层理、劈理构造影像; g—两期劈理构造影像(a—f为Worldview-2数据; h—g为Worldview-3数据)

    Figure  3.  Comparison of structure interpretation of remote sensing data with different spatial resolution

    图  4  Worldview-2遥感影像图

    Figure  4.  Worldview-2 remote sensing image

    图  5  Landsat-8和Worldview-2协同影像图

    Figure  5.  The synergestic image of Landsat-8 and Worldview-2

    图  6  同一遥感数据不同波段组合图像解译效果对比

    Figure  6.  Comparison charts for different bands combined image of a remote sensing data

    图  7  ETM和ASTER遥感数据矿化蚀变信息对比图

    a—ETM比值法铁染异常信息; b—ETM主成分分析法铁染异常信息; c—ETM比值法羟基异常信息; d—ETM主成分分析法羟基异常信息; e—ASTER主成分分析法铁染异常信息; f—第一组羟基异常信息; g—第一组羟基异常信息; h—CO32-离子团异常信息

    Figure  7.  Images of alteration minerals produced from ETM and ASTER remote sensing data

    表  1  Spot5影像各波段数据统计特征值及各波段间相关系数矩阵

    Table  1.   Statistic eigenvalues and correlation coefficient matrix for bands of SPOT5

    波段最小值最大值均值标准差相关系数Band 1Band 2Band 3Band 4
    Band 1025577.48533.134Band 11.0000000.8972100.8865670.848133
    Band 2025556.16331.990Band 20.8972101.0000000.9943790.707432
    Band 3025556.58830.646Band 30.8865670.9943791.0000000.668707
    Band 41520187.08924.271Band 40.8481330.7074320.6687071.000000
    下载: 导出CSV

    表  2  Quickbird影像各波段数据统计特征值及各波段间相关系数矩阵

    Table  2.   Statistic eigenvalues and correlation coefficient matrix for bands of Quickbird

    波段最小值最大值均值标准差相关系数Band 1Band 2Band 3Band 4
    Band 101342507.711166.118Band 11.0000000.7994450.8175610.775338
    Band 201178384.326129.077Band 20.7994451.0000000.9868130.954843
    Band 301425482.756142.270Band 30.8175610.9868131.0000000.984581
    Band 40802294.57276.028Band 40.7753380.9548430.9845801.000000
    下载: 导出CSV

    表  3  Geoeye-1影像各波段数据统计特征值及各波段间相关系数矩阵

    Table  3.   Statistic eigenvalues and correlation coefficient matrix for bands of Geoeye-1

    波段最小值最大值均值标准差相关系数Band 1Band 2Band 3Band 4
    Band 103061246.776490.529Band 11.0000000.9901430.9792060.953965
    Band 201682117.880212.023Band 20.9901431.0000000.9934000.973462
    Band 302655262.247296.175Band 30.9792060.9934001.0000000.991808
    Band 402086233.266173.932Band 40.9539650.9734620.9918081.000000
    下载: 导出CSV

    表  4  ETM1, 3, 4, 5波段主成分变换特征向量矩阵

    Table  4.   Eigenvector statistics for ETM band 1, 3, 4 and 5

    主成分TM1TM3TM4TM5
    PC10.454600.551010.475590.51336
    PC2-0.55332-0.398520.247760.68821
    PC3-0.106040.38590-0.809480.42962
    PC4-0.689880.623420.23910-0.27974
    下载: 导出CSV

    表  5  ETM 1, 4, 5, 7波段主成分变换特征向量矩阵

    Table  5.   Eigenvector statistics for ETM band 1, 4, 5 and 7

    主成分TM1TM4TM5TM7
    PC10.466520.502980.554700.47083
    PC20.773100.16771-0.47247-0.38855
    PC3-0.405020.80432-0.02006-0.43431
    PC40.14367-0.268230.68460-0.66237
    下载: 导出CSV

    表  6  ASTER 1, 2, 3, 4波段主成分变换特征向量矩阵

    Table  6.   Eigenvector statistics for ASTER band 1, 2, 3 and 4

    主成分ASTER1ASTER2ASTER3ASTER4
    PC1-0.32791-0.36411-0.46219-0.73910
    PC20.522640.538430.20825-0.62736
    PC30.246230.37636-0.859510.24283
    PC4-0.747460.660200.06524-0.03442
    下载: 导出CSV

    表  7  ASTER 1, 3, 4, (5+6)/2主成分变换特征向量矩阵

    Table  7.   Eigenvector statistics for ASTER band 1、3、4 and (5+6)/2

    主成分ASTER1ASTER3ASTER4ASTER(5+6)/2
    PC10.291820.418100.681450.52503
    PC20.631200.57799-0.30069-0.42084
    PC3-0.514420.359680.47487-0.61685
    PC4-0.501800.60146-0.468730.40833
    下载: 导出CSV

    表  8  ASTER 1, 3, 4, 8主成分变换特征向量矩阵

    Table  8.   Eigenvector statistics for ASTER band 1、3、4 and 8

    主成分ASTER1ASTER3ASTER4ASTER8
    PC10.291660.414010.681700.52803
    PC20.499620.64353-0.17682-0.55225
    PC30.70933-0.30017-0.461860.43982
    PC4-0.402700.56953-0.539180.47197
    下载: 导出CSV

    表  9  ASTER1、ASTER3、ASTER4、ASTER5主成分变换特征向量矩阵

    Table  9.   Eigenvector statistics for ASTER band 1、3、4 and 5

    主成分ASTER1ASTER3ASTER4ASTER5
    PC10.280500.398820.655790.57637
    PC20.579720.64566-0.27923-0.41119
    PC3-0.705210.530740.29824-0.36338
    PC40.29652-0.377330.63484-0.60554
    下载: 导出CSV
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  • 收稿日期:  2016-09-15
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