| 光学遥感在识别花岗伟晶岩型锂矿床中的应用 |
| Received:January 18, 2021 Revised:June 29, 2021 点此下载全文 |
| 引用本文：JIANG Qi,DAI JingJing,WANG DengHong,and TIAN ShuFang.2021.Application of optical remote sensing to identifying granite pegmatite lithium deposits[J].Mineral Deposits,40(4):793~804 |
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| Author Name | Affiliation | E-mail | | JIANG Qi | China University of Geosciences, Beijing 100083, China | | | DAI JingJing | Key Laboratory of Metallogeny and Mineral Assessment, Ministry of Natural Resources, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, Chinas | daijingjing863@sina.com | | WANG DengHong | Key Laboratory of Metallogeny and Mineral Assessment, Ministry of Natural Resources, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, Chinas | | | and TIAN ShuFang | China University of Geosciences, Beijing 100083, China | |
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| 基金项目:本文得到中国地质调查二级项目（编号：DD20190173、DD20190379）资助 |
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| 中文摘要:花岗伟晶岩型锂矿是一种重要的锂矿资源，近年来，光学遥感技术在花岗伟晶岩型锂矿找矿应用中效果显著，开启了遥感找锂矿的研究热潮。本文基于对全球及国内花岗伟晶岩型锂矿空间分布特征、成矿地质特征的综合分析，归纳总结了目前伟晶岩型锂矿识别的光学遥感数据源及技术方法： ASTER、Landsat-8等中等分辨率影像具有较高的光谱分辨率，可利用锂矿独特的光谱特征，通过信息增强方法来识别锂矿；而WorldView-2、WorldView-3等高分辨率影像具有较高的空间分辨率，可以通过图像色彩增强方法提取到细小的伟晶岩型锂矿露头。提取技术方法包括RGB组合、波段比值、主成分分析、辐射增强等，RGB组合方法虽然能在锂矿提取中起到一定作用，但主观因素影响较大，且提取出的伟晶岩是否含有锂矿需要进一步研究；主成分变换以及波段比值主要依据锂矿的光谱特征信息，得出的结果具有客观性和科学性，但提取精度还需要提高；辐射增强通过改变影像的像元灰度值来突出花岗伟晶岩型锂矿信息，能够很好的解决花岗伟晶岩型锂矿和围岩光谱信息差异细微的技术瓶颈。最后，本文指出了多源遥感数据协同应用、自动化及智能化算法的引入等将是开展花岗伟晶岩型锂矿遥感找矿的发展趋势。 |
| 中文关键词:地质学 花岗伟晶岩型锂矿 遥感技术 找矿 资源调查 |
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| Application of optical remote sensing to identifying granite pegmatite lithium deposits |
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| Abstract:Granite pegmatite-type lithium deposits constitute a kind of important lithium mineral resources. In recent years, optical remote sensing technology has been effectively applied to the prospecting of granite pegmatite-type lithium deposits, which has opened up the research boom of remote sensing for lithium deposits. Based on the comprehensive analysis of the global and domestic granite pegmatite-type lithium deposits' spatial distribution characteristics and metallogenic geological characteristics, this paper summarizes the current optical remote sensing data sources and technical methods for the identification of pegmatite-type lithium deposits:ASTER and Landsat-8 and other medium-resolution images have high spectral resolution, and unique spectral characteristics of lithium ore can be used to identify lithium ore through information enhancement methods, while high-resolution images such as WorldView-2 and WorldView-3 have the higher space. The resolution can be extracted from the tiny pegmatite-type lithium ore outcrops through the image color enhancement method. The extraction methods include such means as RGB combination, band ratio, principal component analysis and radiation enhancement. Although the RGB combination method can play a certain role in the extraction of lithium ore, its subjective factors have a great unfavorable impact, and hence the problem as to whether the extracted pegmatite contains lithium ore or not needs further research. The principal component transformation and the band ratio are mainly based on the spectral characteristic information of the lithium ore, and the results obtained are objective and scientific, but the extraction accuracy needs to be improved. Radiation enhancement is achieved by changing the image's pixel gray value. Highlighting the information of the granite pegmatite-type lithium ore can well solve the technical bottleneck of the subtle difference in spectral information between the granite pegmatite-type lithium ore and the surrounding rock. Finally, this paper points out that the collaborative application of multisource remote sensing data, the introduction of automation and intelligent algorithms and some other means will be the development trend of remote sensing in search for granite pegmatite lithium deposits in the future. Keywords:geology, granite pegmatite type lithium ore, remote sensing technology, prospecting, survey of resources |
| keywords:geology granite pegmatite type lithium ore remote sensing technology prospecting survey of resources |
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