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

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

1.
长安大学地球科学与资源学院, 西部矿产资源与地质工程教育部重点实验室, 西安 710054
2.
中国地质调查局西安地质调查中心, 国土资源部岩浆作用成矿与找矿重点实验室, 西安 710054

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
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出版历程
- 收稿日期: 2016-09-15
- 刊出日期: 2016-12-01

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

1.
Earth Science & Resources College of Chang'an University, Key Laboratory of Western China's Mineral Resources and Geological Engineering, Ministry of Education, Xi'an 710054, China
2.
Key Laboratory for the Study of Focused Magmatism and Giant Ore Deposits, MLR, Xi'an Geological Survey Center, Xi'an 710054, China

摘要

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。
- 高山峡谷区 /
- 岩性和构造解译 /
- 矿化蚀变信息提取 /
- 有效填图方法
Abstract: The 1:50000 geological mapping project makes sufficient use of remote sensing technologies to explore new methods of geological mapping in alpine-gorge area of Wushi, Xinjiang. Different spatial resolution remote sensing data can reveal different characteristics of lithology and structures. Integrated interpretations of multi-source remote sensing data can enhance the ability of interpretation effectively. Higher accuracy interpretations of lithologic classification can be obtained by combination of different bands of the same remote sensing image according to the optimum index factor (OIF) and synergestic images of Landsat-8 and worldview-2. Hyperspectral mineral mapping and lithology identification, petrochemistry components mapping using ASTER thermal infrared remote sensing data are useful technologies of geological mapping in alpine-gorge area. Both of the ETM and ASTER data were used to extract alteration information. The result shows that the ASTER data is more useful than ETM data in extracting alteration anomalies, such as ferric contamination anomaly and hydroxyl anomaly. Therefore, it is an important way of multivariate information comprehensive analysis to prospect. Based on the geological and geomorphological features, effective techniques have been selected to complete 1:50000 geological mapping of the North Mountain, Wushen area, and important research results have been obtained. The sufficient use of remote sensing data can reduce the amount of geologic section and geological survey routes, and achieve the mapping precision.
- alpine-gorge area /
- interpretations of lithology and structure /
- extraction of mineralized alteration informations /
- effective mapping methods

HTML全文

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

图 4 Worldview-2遥感影像图

Figure 4. Worldview-2 remote sensing image

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图 5 Landsat-8和Worldview-2协同影像图

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

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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

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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—CO₃^2-离子团异常信息

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

下载: 全尺寸图片幻灯片

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 1	Band 2	Band 3	Band 4
Band 1	0	255	77.485	33.134	Band 1	1.000000	0.897210	0.886567	0.848133
Band 2	0	255	56.163	31.990	Band 2	0.897210	1.000000	0.994379	0.707432
Band 3	0	255	56.588	30.646	Band 3	0.886567	0.994379	1.000000	0.668707
Band 4	15	201	87.089	24.271	Band 4	0.848133	0.707432	0.668707	1.000000

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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 1	Band 2	Band 3	Band 4
Band 1	0	1342	507.711	166.118	Band 1	1.000000	0.799445	0.817561	0.775338
Band 2	0	1178	384.326	129.077	Band 2	0.799445	1.000000	0.986813	0.954843
Band 3	0	1425	482.756	142.270	Band 3	0.817561	0.986813	1.000000	0.984581
Band 4	0	802	294.572	76.028	Band 4	0.775338	0.954843	0.984580	1.000000

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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 1	Band 2	Band 3	Band 4
Band 1	0	3061	246.776	490.529	Band 1	1.000000	0.990143	0.979206	0.953965
Band 2	0	1682	117.880	212.023	Band 2	0.990143	1.000000	0.993400	0.973462
Band 3	0	2655	262.247	296.175	Band 3	0.979206	0.993400	1.000000	0.991808
Band 4	0	2086	233.266	173.932	Band 4	0.953965	0.973462	0.991808	1.000000

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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	TM1	TM3	TM4	TM5
PC1	0.45460	0.55101	0.47559	0.51336
PC2	-0.55332	-0.39852	0.24776	0.68821
PC3	-0.10604	0.38590	-0.80948	0.42962
PC4	-0.68988	0.62342	0.23910	-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	TM1	TM4	TM5	TM7
PC1	0.46652	0.50298	0.55470	0.47083
PC2	0.77310	0.16771	-0.47247	-0.38855
PC3	-0.40502	0.80432	-0.02006	-0.43431
PC4	0.14367	-0.26823	0.68460	-0.66237

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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	ASTER1	ASTER2	ASTER3	ASTER4
PC1	-0.32791	-0.36411	-0.46219	-0.73910
PC2	0.52264	0.53843	0.20825	-0.62736
PC3	0.24623	0.37636	-0.85951	0.24283
PC4	-0.74746	0.66020	0.06524	-0.03442

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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	ASTER1	ASTER3	ASTER4	ASTER(5+6)/2
PC1	0.29182	0.41810	0.68145	0.52503
PC2	0.63120	0.57799	-0.30069	-0.42084
PC3	-0.51442	0.35968	0.47487	-0.61685
PC4	-0.50180	0.60146	-0.46873	0.40833

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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	ASTER1	ASTER3	ASTER4	ASTER8
PC1	0.29166	0.41401	0.68170	0.52803
PC2	0.49962	0.64353	-0.17682	-0.55225
PC3	0.70933	-0.30017	-0.46186	0.43982
PC4	-0.40270	0.56953	-0.53918	0.47197

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	ASTER1	ASTER3	ASTER4	ASTER5
PC1	0.28050	0.39882	0.65579	0.57637
PC2	0.57972	0.64566	-0.27923	-0.41119
PC3	-0.70521	0.53074	0.29824	-0.36338
PC4	0.29652	-0.37733	0.63484	-0.60554

下载: 导出CSV

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