一种新的遥感蚀变信息编图方法

李建国; 于岩; 高学生; 陈圣波; 滕学建

doi:10.3799/dqkx.2015.116

一种新的遥感蚀变信息编图方法

doi: 10.3799/dqkx.2015.116

李建国^{1, 2,},
于岩³,
高学生²,
陈圣波^3, ,,
滕学建²

1.
中国地质大学地球科学与资源学院, 北京 100083
2.
天津地质矿产研究所, 天津 300170
3.
吉林大学地球探测科学与技术学院, 吉林长春 130026

基金项目:

中国地质调查局项目 1212011220469

中国地质调查局项目 1212011121029

国家高技术研究发展计划(863计划)项目 2012AA12A308

国家高技术研究发展计划(863计划)项目 2008AA121100

国家自然科学基金项目 41402293

详细信息

作者简介:
李建国(1980-), 男, 高级工程师, 主要从事遥感地质与定量资源预测相关研究工作.E-mail: jianguo_lee@126.com

通讯作者:
陈圣波, E-mail: chensb0408@126.com

中图分类号: P627
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- 被引次数: 0
出版历程
- 收稿日期: 2015-04-11
- 刊出日期: 2015-08-01

A New Mapping Method of Remote Sensing Alteration Information

Li Jianguo^{1, 2
,},
Yu Yan³,
Gao Xuesheng²,
Chen Shengbo^{3
, ,},
Teng Xuejian²

1.
School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
2.
Tianjin Institute of Geology and Mineral Resources, Tianjin 300170, China
3.
College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China

摘要

摘要: 利用遥感蚀变信息进行成矿预测已经得到普遍应用, 但是并没有系统的编图方法, 提出遥感蚀变信息等密度制图法以进行改进.它借鉴了地球化学场的编图思路, 通过统计单位面积内各蚀变信息所占的面积百分比, 进而生成面密度等值线图.利用该方法并结合线、环构造信息在内蒙古东乌旗地区圈定了8个成矿远景区.这种新的编图方法, 既能有效增强多种蚀变同时出现时小面积蚀变信息, 又能更直观的表达蚀变信息的总体空间分布特征和浓集中心, 对圈定成矿远景区和优选野外验证点十分有利.
- 蚀变信息 /
- 编图方法 /
- 等密度 /
- 成矿远景区 /
- 野外验证 /
- 遥感 /
- 内蒙东乌旗
Abstract: Metallogenic prediction using remote sensing alteration information has been widely applied, but there is no systematic mapping method. This paper suggests a new method of isodensity map compilation of remote sensing alteration information. Following the compilation pratice of geochemical field maps, the percentage of the area of each alteration information per unit area is statistically calculated, and the contour map of the areal density is generated. During the remote sensing analysis and geological survey in Dongwuqi region of Inner Mongolia, eight metallogenic prospective areas were successfully delineated by means of this method with the inferred lineation and circular structures based on remote sensing interpretation. Compared to the traditional compilation methods based on pixel units, this new map compilation method can not only effectively enhance the small area information occurring simultaneously on various alterations, but also provide a more visual presentation of the overall spatial distribution and the concentration center of alteration information, favorable for delineation of metallogenic prospective areas and selection of places for further field investigation.
- alteration information /
- mapping method /
- isodensity /
- metallogenic prospective area /
- field verification /
- remote sensing /
- Dongwuqi Inner Mongolia

HTML全文

图 1 研究区地质简图

1.全新统；2.下更新统复台堆积；3.砖红色粉砂质泥岩；4.大磨拐河组；5.满克头鄂博组上端；6.满克头鄂博组一段；7.安格尔音乌拉组二段；8.安格尔音乌拉组一段；9.塔尔巴格特组；10.泥鳅河组二段；11.泥鳅河组一段；12.细粒似斑状黑云母二长花岗岩；13.中粒白云母二长花岗岩；14.中细粒白云母二长花岗岩；15.中细粒正长花岗岩；16.中粒正长花岗岩；17.细粒二长花岗岩；18.中细粒花岗闪长岩；19.中细粒二长花岗岩；20.中细粒正长花岗岩；21.细粒正长花岗岩；22.二长斑岩

Fig. 1. Geological sketch of the study area

下载: 全尺寸图片幻灯片

图 2 几种典型矿物在ASTER波段上的光谱反射特征

Fig. 2. Several typical alteration mineral on ASTER band spectral features

下载: 全尺寸图片幻灯片

图 3 研究区主要蚀变信息分布

Fig. 3. The main alteration information distribution in the study area

下载: 全尺寸图片幻灯片

图 4 遥感蚀变信息等密度图及成矿预测

Fig. 4. Remote sensing abnormal density sketch and metallogenic prediction

下载: 全尺寸图片幻灯片

表 1 PC1、3、4和6的特征向量

Table 1. The feature vectors table of principal component analysis of PC1, 3, 4 and 6

主成分	Band 1	Band 3	Band 4	Band6
PC1	0.307 6	0.522 0	0.520 7	0.601 3
PC2	0.705 9	0.434 7	-0.341 7	-0.442 5
PC3	-0.633 2	0.722 6	-0.032 6	-0.275 1
PC4	-0.077 2	0.127 5	0.781 6	-0.605 6

下载: 导出CSV

表 2 PC1、3、4和8的特征向量

Table 2. The feature vectors table of principal component analysis of PC1, 3, 4 and 8

主成分	Band 1	Band 3	Band 4	Band 8
PC1	0.297 7	0.474 8	0.540 5	0.627 3
PC2	0.393 9	0.614 0	0.036 2	-0.682 9
PC3	0.491 9	0.122 2	-0.786 8	0.351 9
PC4	0.717 0	-0.618 4	0.295 5	-0.126 7

下载: 导出CSV

表 3 PC1、2、3和4的特征向量

Table 3. The feature vectors table of principal component analysis of PC1, 2, 3 and 4

主成分	Band 1	Band 2	Band 3	Band 4
PC1	-0.374 8	-0.508 6	-0.559 0	-0.536 9
PC2	0.041 9	0.091 7	-0.744 9	0.659 4
PC3	0.521 0	0.567 9	-0.363 6	-0.523 1
PC4	-0.765 6	0.640 5	-0.014 6	-0.057 0

下载: 导出CSV

表 4 PC1、3、4和5的特征向量

Table 4. The feature vectors table of principal component analysis of PC1, 3, 4 and 5

主成分	Band 1	Band 3	Band 4	Band 5
PC1	0.339 2	0.471 6	0.537 5	0.611 1
PC2	0.146 2	0.758 6	-0.037 4	-0.633 7
PC3	0.608 4	0.067 4	-0.744 8	0.265 3
PC4	0.702 3	-0.444 2	0.393 4	-0.393 0

下载: 导出CSV

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