基于多源遥感卫星数据的青海东昆仑沟里地区线性构造识别及找矿预测

王晓云; 井国正; 李文君; 何俊江; 王艺龙; 刘晓阳; 谭俊; 石文杰

doi:10.19509/j.cnki.dzkq.2022.0268

基于多源遥感卫星数据的青海东昆仑沟里地区线性构造识别及找矿预测

doi: 10.19509/j.cnki.dzkq.2022.0268

王晓云^1,,
井国正¹,
李文君¹,
何俊江¹,
王艺龙^{2, 3},
刘晓阳⁴,
谭俊⁴,
石文杰^4, ,

1.
青海省有色第三地质勘查院, 西宁 810012
2.
辽宁省地质勘查院有限责任公司, 辽宁大连 116100
3.
高分辨率对地观测系统辽宁地质资源环境应用与服务中心, 辽宁大连 116100
4.
中国地质大学(武汉)资源学院, 武汉 430074

基金项目:

青海省有色第三地质勘查院、青海省有色地质矿产勘查局和中国地质大学(武汉)联合科技攻关项目"青海省东昆仑东段金银多金属成矿系列研究与关键勘查技术应用示范项目" 青色地[2021]34号

详细信息

作者简介:
王晓云, E-mail: 1072393654@qq.com

通讯作者:
石文杰, E-mail: swjhaoo@126.com

中图分类号: P627
计量
- 文章访问数: 330
- PDF下载量: 45
- 被引次数: 0
出版历程
- 收稿日期: 2022-06-13
- 录用日期: 2022-09-16
- 修回日期: 2022-07-31

Lineament mapping and deposit prospecting in the Gouli area, East Kunlun, Qinghai Province: Using multisource remote sensing data

1.
The Third Nonferrous Geological Exploration Institute of Qinghai Province, Xining 810012, China
2.
Geological Exploration Institute of Liaoning Province, Dalian Liaoning 116100, China
3.
Liaoning Province High-Resolution Observation System Application and Service Center of Geological Resources and Environmental, Dalian Liaoning 116100, China
4.
School of Earth Resources, China University of Geosciences(Wuhan), Wuhan 430074, China

More Information

Author Bio:
WANG Xiaoyun, E-mail: 1072393654@qq.com

Corresponding author: SHI Wenjie, E-mail: swjhaoo@126.com

摘要

摘要:
沟里地区是青海东昆仑金及多金属成矿带重要组成部分之一，矿产资源丰富，具有良好的找矿前景，目前区内尚无大面积基于多源遥感卫星数据的线性构造识别及找矿应用方面的研究。选取Landsat8 OLI、GF-2不同空间分辨率遥感数据，运用最佳波段选择、主成分分析、图像融合、线性拉伸、定向滤波等遥感图像处理技术，结合DEM衍生产品和多元地学信息综合研究方法，实现了沟里地区线性构造识别和找矿预测。结果表明：（1）研究区构造格架以卡可特尔-色日德一线为界，可分为南部、北部2个区域，北部区域线性构造以NNE向最为发育，近EW向次之，NW、NE向分布较少，南部区域受昆中断裂带控制作用，线性构造主要发育近EW向和少量NW向、NE向；（2）已知矿床（点）全部位于线性构造内及其附近，构造控矿作用明显；（3）线性构造密集分布和交汇处以及遥感影像上不同色调连接区是寻找金矿化的有利地区。最后根据多元地学信息与遥感解译构造信息综合对比分析，在研究区圈定找矿靶区4处并进行了野外验证。研究结果表明，基于多源遥感卫星数据能够较好地识别地表构造空间结构特征，结果较为客观和准确，能够为该地区和外围找矿预测提供参考和依据。
- GF-2 /
- 高分辨率 /
- 遥感 /
- 线性构造 /
- 沟里地区 /
- 找矿预测
Abstract: Objective
The Gouli area is one of the important gold and polymetallic belt in the East Kunlun Orogen. There is no research on lineament identification and mineral exploration based on multisource remote sensing data in the area.
Methods
In this paper, various enhancement techniques were employed on Landsat 8 OLI, GF-2 and DEM data to delineate structural lineaments. Meanwhile, information on geological, mineral and geochemical anomalies was combined to define prospecting targets in the Gouli area.
Results
The results show that (1) The tectonic framework of the study area can be divided into two regions by the boundary of Kaketeer-Seride. The lineament in the northern region is most developed in the NNE direction, followed by the near-EW direction, and less distributed in the NW and NE directions. The lineament in the southern region is mainly developed in the near-EW direction, and the NW to NE directions are less distributed. (2) The gold ore is controlled by lineaments. (3) The areas of densely distributed and intersected lineaments, as well as the areas connected by different colors on remote sensing images, are favorable locations for gold mineralization. Finally, based on the comprehensive analysis of multiple geological information and remote sensing interpreted tectonic information, four prospecting targets were identified in the study area and verified in the field.
Conclusion
This study proves that multisource remote sensing data can better identify the spatial structure characteristics of surface tectonics, which can provide a reference and basis for prospecting new deposits in the Gouli area.
- GF-2 /
- high spatial resolution /
- remote sensing /
- lineament /
- Gouli area /
- deposit prospecting
注释:

所有作者声明不存在利益冲突。

HTML全文

图 1 东昆仑构造位置图(a)(据文献[30]修编)和东昆仑地区地质矿产图(b)(据文献[25]修编)

Figure 1. Geotectonic framework of the East Kunlun (a), geological and mineral regional map of the East Kunlun (b)

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图 2 沟里地区地质矿产图

Figure 2. Geological and mineral regional map of the Gouli area

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图 3 线性构造识别与分析方法流程图

Figure 3. Methodology flowchart of the lineament identification

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图 4 融合数据B7B5B4波段组合假彩色合成图

Figure 4. False color composite image of the fused data B7B5B4

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图 5 融合数据PC453假彩色合成图

Figure 5. False color composite image of the fused data PC453

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图 6 定向滤波结果RGB(45°，90°，135°)合成图

Figure 6. RGB color composite of the directional filtered images at 45°, 90° and 135°

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图 7 DEM衍生产品坡度分析图(a)和山体阴影渲染图(b)

Figure 7. DEM-derived products, slope analysis map (a) and shaded relief image (b)

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图 8 变质岩内线性构造遥感影像特征

Figure 8. Remote sensing image characteristics of lineaments in metamorphic rocks

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图 9 果洛龙洼金矿床控矿构造遥感解译标志

Figure 9. Remote sensing interpretation markers of the mineral control structure in the Guoluolongwa gold deposit

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图 10 侵入岩内线性构造遥感影像特征

Figure 10. Remote sensing image characteristics of lineaments in intrusive rocks

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图 11 色日金矿床控矿构造遥感解译标志

Figure 11. Remote sensing interpretation markers of the mineral control structure in the Seri gold deposit

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图 12 香日德-德龙断裂遥感解译标志

Figure 12. Remote sensing interpretation markers of the Xiangride-Delong fault

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图 13 沟里地区遥感解译线性构造分布图

Figure 13. Distribution of remote sensing interpreted lineaments in the Gouli area

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图 14 沟里地区线性构造走向玫瑰花图

Figure 14. Rose diagram of the lineament orientations in the Gouli area

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图 15 沟里地区不同期次线性构造遥感影像特征

Figure 15. Remote sensing images of lineaments at different periods in the Gouli area

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图 16 沟里地区线性构造密度等值线图

Figure 16. Contour map of lineament density in the Gouli area

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图 17 沟里地区线性构造强度等值线图

Figure 17. Contour map of lineament intensity in the Gouli area

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图 18 沟里地区遥感找矿靶区预测图

Figure 18. Remote sensing interpretation map showing prospecting targets in the Gouli area

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图 19 遥感解译线性构造和找矿靶区野外验证照片

Figure 19. Field photographs of lineaments and prospecting target areas

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表 1 Landsat8 OLI和GF-2数据主要技术参数

Table 1. Main technical parameters of Landsat8 OLI and GF-2 data

卫星名称	波段号	波段名称	光谱范围/μm	空间分辨率/m
Landsat8 OLI	Band 1	Coastal	0.43~0.45	30
	Band 2	Blue	0.45~0.52	30
	Band 3	Green	0.53~0.60	30
	Band 4	Red	0.63~0.68	30
	Band 5	NIR	0.85~0.89	30
	Band 6	SWIR 1	1.56~1.66	30
	Band 7	SWIR 2	2.10~2.30	30
	Band 8	Pan	0.50~0.68	15
	Band 9	Cirrus	1.36~1.39	30
GF-2	—	Pan	0.45~0.90	1
	Band 1	Blue	0.45~0.52	4
	Band 2	Green	0.52~0.59	4
	Band 3	Red	0.63~0.69	4
	Band 4	NIR	0.77~0.89	4

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表 2 融合数据波段标准差、均值及相关系数统计

Table 2. Statistics of band standard deviation, average and correlation coefficient of fusion data

统计值	波段
统计值	Band2	Band3	Band4	Band5	Band6	Band7
标准差	441.89	621.01	755.79	928.60	1 026.76	888.55
均值	1 088.84	1 608.33	1 934.36	2 832.55	3 109.43	2 524.66
相关系数矩阵	1
	0.994 213	1
	0.989 288	0.997 255	1
	0.881 185	0.706 360	0.692 695	1
	0.925 156	0.947 292	0.942 235	0.863 996	1
	0.958 154	0.974 140	0.974 103	0.736 571	0.986 125	1

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表 3 融合数据多波段组合及OIF值

Table 3. Different band combinations and OIF values of the fusion data

波段组合	OIF	波段组合	OIF
754	1 070.56	654	1 084.93
753	1 008.73	653	1 023.32
752	876.99	652	897.73

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表 4 融合数据主成分特征向量矩阵

Table 4. PCA eigenvectors matrix for the fusion data

特征向量	B2	B3	B4	B5	B6	B7	百分比/%
PC1	-0.220 294	-0.314 994	-0.381 546	-0.466 080	-0.529 065	-0.457 747	82.70
PC2	-0.315 577	-0.358 446	-0.498 597	0.650 092	0.272 462	-0.162 707	12.58
PC3	0.278 662	0.299 848	0.223 719	0.532 057	-0.462 089	-0.534 582	3.72
PC4	-0.767 893	-0.110 960	0.593 397	0.108 054	-0.178 726	0.047 847	0.78
PC5	0.058 847	-0.073 131	-0.229 271	0.250 207	-0.632 746	0.689 675	0.21
PC6	-0.425 566	0.815 307	-0.388 700	-0.052 788	0.004 247	0.016 595	0.01

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