玉龙斑岩铜矿带北段ASTER遥感蚀变信息提取与优选
投稿时间:2013-05-08  修订日期:2013-11-21  点此下载全文
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作者单位E-mail
别小娟 成都理工大学旅游学院 bxj_sophia@qq.com 
孙传敏 成都理工大学地球科学学院  
张廷斌 成都理工大学地球科学学院  
易桂花 成都理工大学管理科学学院
成都理工大学管理科学学院 
 
郭娜 成都理工大学管理科学学院
成都理工大学管理科学学院 
 
吴华 西藏自治区地质调查院  
基金项目:国家自然科学基金“西藏典型斑岩型铜矿床遥感蚀变信息重现性机理研究”(编号:41202233)、中国地质调查局“西藏自治区矿产资源潜力评价项目”(编号:1212010813025)及成都理工大学“青年科学”(编号:2010QJ19)
中文摘要:玉龙铜矿带是我国迄今已发现的最大的斑岩型铜(钼)矿带,发现了多个大型斑岩铜(钼)矿床。蚀变矿物是斑岩型铜矿重要的找矿标志,利用遥感数据可定量获取蚀变信息。论文选取玉龙铜矿带北段恒星错-玉龙-莽总一带为研究区,基于ASTER数据采用常用的比值法、主成分分析法提取的蚀变信息存在多解性,而采用波谱特征拟合的方法提取的蚀变信息具有综合性,结合矿床围岩、与中酸性岩体有关的环形构造和蚀变信息分布的环带特征,对提取结果进行优化和筛选,认为位于甲丕拉组、波里拉组地层、花岗斑岩岩体上,或位于环形构造周围,环带特征明显的围岩蚀变信息,指示矿产存在或成矿有利部位的可靠性更高。
中文关键词:关 键 词:玉龙  斑岩型铜矿  ASTER  遥感蚀变  波谱特征拟合(SFF)
 
Extraction and optimization of ASTER remote sensing alteration information in the north of Yulong porphyry
Abstract:Yulong porphyry copper belt is the biggest in China up to now, and several large porphyry Cu (Mo) ore deposits have been discovered nearby. Altered mineral is one of the crucial indicators for deposit of porphyry Cu ore, alteration information of which can be obtained quantitatively by analysising the remote sensing data. Taking the Hengxingcuo-Yulong-Mangzong belt as the study area, the difference in the results by using ratio method and principal component analysis has been discovered in extracting alteration information which been based on the ASTER data. Through the study found information extraction by the method of spectral characteristics fitting will be provided with the comprehensive. The extraction results can be optimized and screened well by integrating the information of wall rock of deposit, zonal characteristics of circular structure and distribution of alteration information, then those information which were located on the Jiapila and Bolila formation, granite porphyry, in the surrounding of circle structure and alteration information with the characteristics of atolls and band will be more reliable in indicating mineralization.
keywords:Yulong  Porphyry copper belt  ASTER  Remote sensing alteration  Spectral Feature Fitting(SFF)
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