| 基于GF-5、Landsat8与GF-2遥感数据的蚀变信息提取研究——以四川宁南铅锌矿集区为例 |
| Received:December 10, 2021 Revised:July 10, 2022 点此下载全文 |
| 引用本文：DU XiaoChuan,LOU DeBo,ZHANG ChangQing,XU LinGang,LIU Huan,FAN YingLin,ZHANG Lin,HU JinMeng,and LI Biao.2022.Study on extraction of alteration information from GF-5, Landsat8 and GF-2 remote sensing data: A case study of Ningnan lead-zinc ore concentration area in Sichuan Province[J].Mineral Deposits,41(4):839~858 |
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| Author Name | Affiliation | E-mail | | DU XiaoChuan | School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China | | | LOU DeBo | MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China | llddbb_e@126.com | | ZHANG ChangQing | MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China | | | XU LinGang | School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China | | | LIU Huan | MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China | | | FAN YingLin | School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China | | | ZHANG Lin | School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China | | | HU JinMeng | School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China | | | and LI Biao | The Fifth Geology Company of Hebei Geology and Minerals Bureau, Tangshan 063000, Hebei, China | |
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| 基金项目:本文得到国家自然科学基金(编号:41602103)和中国地质调查局项目(编号:DD20190182)、企业横向项目"四川重点铅锌矿成矿区矿产资源潜力评价"联合资助 |
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| 中文摘要:四川宁南铅锌矿集区是中国重要的铅锌矿成矿区之一，区内铅锌矿床广泛分布，其围岩蚀变主要为白云岩化、方解石化、黏土矿化、硅化、黄铁矿化及赤铁矿化，是重要的找矿标志，同时也为遥感提取铁染、羟基、碳酸根离子蚀变信息提供了重要的理论依据。由于遥感卫星传感器类型的不同，同一研究区内不同种类影像所得到的数据会有所差异，且采用不同的提取方法所得到的蚀变信息结果也会有所不同，因此，对同一区域进行单一影像或单一提取方法的研究往往存在偶然性与不充分性。据此，文章基于对研究区内Landsat8与GF-5两种影像数据的详细解读，采用3种有效方法进行蚀变信息提取。①波段比值法，对影像数据采用波段比值计算，并对波段比值进行密度分割，从中突出铁染，羟基、碳酸根离子蚀变信息；②主成分分析法和主分量密度分割法对铁染蚀变信息，羟基、碳酸根离子蚀变信息进行提取并作分级处理，结合GF-2高空间分辨率影像以及依据同谱异物、同物异谱、混合像元等原理剔除伪异常，以得到有效的蚀变信息；③光谱角匹配法，通过提取GF-5高光谱影像数据的纯净端员，结合光谱角匹配技术提取有效的蚀变信息矿物。对2种影像数据分别用3种提取方法所得到的蚀变信息结果进行对比，讨论其差异性，并通过叠合已知矿产地信息的方法筛选出效果较好的蚀变信息，将选取出的结果进行综合比对，最终在宁南矿集区圈定出预测靶区，经过室内化探数据验证及野外实地工作验证，证实其真实性，同时也为该地区下一步工作提供真实可靠的资料。 |
| 中文关键词:地质学 GF-5 Landsat8 GF-2 蚀变信息 波段比值 主成分分析 光谱角匹配 宁南 |
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| Study on extraction of alteration information from GF-5, Landsat8 and GF-2 remote sensing data: A case study of Ningnan lead-zinc ore concentration area in Sichuan Province |
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| Abstract:The Ningnan lead-zinc ore concentration area in Sichuan Province is one of the most important lead-zinc metallogenic Province in China, and lead-zinc deposits are widely distributed in the area. The wall rock alteration in this area is mainly characterized by dolomitization, calcite, clay mineralization, silicification, pyritization and hematite, which is an important prospecting indicator and provides an important theoretical basis for extracting iron-stained alteration information, hydroxyl and carbonate ion alteration information by means of remote sensing. Due to the different types of remote sensing satellite sensor, the data obtained are different over the same research area, and the results of alteration information obtained by different extraction methods will be different too. Therefore, the study of single image or single extraction method for the same area is often accidental and inadequate. Based on the detailed interpretation of Landsat8 and GF-5 satellite data, three effective methods were used in this paper. One of the methods is the band ratio, which calculates the band ratio information of the image data and grades the band ratio, highlighting iron-stained, hydroxyl and carbonate ion alteration information. The other is principal component analysis, which uses mask, principal component analysis and principal component threshold method to extract iron staining alteration information, hydroxyl and carbonate ion alteration information and make classification processing, which uses the mask-principal component analysis and main component threshold method to extract iron-stained alteration information, hydroxyl and carbonate ion alteration information and make classification processing, and then the false-anomalies will be removed by combining with the GF-2 high spatial-resolution images and the principles of foreign objects in the same spectrum, different spectra of the same object and mixed pixels, so as to obtain effective alteration information. Finally, the spectral angle matching method extracts the pure end elements of GF-5 hyperspectral image data, and extracts the effective information of alteration minerals combined with the spectral angle matching technology. Compare the alteration information extracted by the two images and the three extraction methods, superimpose with the information of known mineral occurrences to screen out the better alteration information and comprehensively compare the results, and then delineate the prediction target area. The verification of geochemical data and field work proves the reliability of the method. At the same time, the method also provides reliable information for further work in the area. |
| keywords:geology GF-5 Landsat8 GF-2 alteration information band ratio principal component analysis spectral angle match Ningnan |
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