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REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (3) : 191-195     DOI: 10.6046/gtzyyg.2017.03.28
Delineation of iron formation in Wenquangou Group along Heiqia Pass in West Kunlun metallogenic belt
YANG Jinzhong1, CHEN Wei1, WANG Hui2
1. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China;
2. Remote Sensing Application Institute of ARSC, Xi’an 710054, China
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Abstract  Using middle and high resolution remote sensing data such as WorldView2, IKONOS, QuickBird, ASTER and ETM+, and their processing methods such as de-relatedcalculation, ratio calculation, principal component analysis and image fusion, the authors delineated a siderite-hematite mineralization belt along Heiqia Pass in West Kunlun metallogenic belt on the basis of the field survey. The belt occurs in the Lower Silurian Wenquangou Group and stretches 120 km long northwestward, and has been eroded by the rock mass in the northwest part and truncated by Kalatage fault in the southeast part. Its ore-bearing layers remain stable, and its continuity in formation strike and dip direction is very good, so the belt is favorable for mineral resources investigation. The results of the survey show that geological survey with remote sensing technology is one of indispensable methods in regional geological and mineral resources survey, and will play an important role in the geological prospecting in western metallogenic belts, especially in the complex and dangerous regions.
Keywords high resolution      remote sensing      GF-1      residential area     
Issue Date: 15 August 2017
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LI Jinxiang
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LI Jinxiang,LI Zhiqiang,LI Shuai, et al. Delineation of iron formation in Wenquangou Group along Heiqia Pass in West Kunlun metallogenic belt[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 191-195.
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[1] 王润生,熊盛青,聂洪峰,等.遥感地质勘查技术与应用研究[J].地质学报,2011,85(11):1699-1743.
Wang R S,Xiong S Q,Nie H F,et al.Remote sensing technology and its application in geological exploration[J].Acta Geologica Sinica,2011,85(11):1699-1743.
[2] 楼性满,葛榜军.遥感找矿预测方法[M].北京:地质出版社,1994.
Lou X M,Ge B J.Remote Sensing Prospecting Prediction Method[M].Beijing:Geological Publishing House,1994.
[3] 杨金中,孙延贵,秦绪文,等.高分辨率遥感地质调查[M].北京:测绘出版社,2013.
Yang J Z,Sun Y G,Qin X W,et al.High Resolution Remote Sen-sing Technology on Geological Survey[M].Beijing:SinoMaps Press,2013.
[4] 杨金中,赵玉灵.遥感技术的特点及其在地质矿产调查中的作用[J].矿产勘查,2015,6(5):529-534.
Yang J Z,Zhao Y L.Technical features of remote sensing and its application in the geological survey and mineral resources survey[J].Mineral Exploration,2015,6(5):529-534.
[5] 中国地质调查局.DD2011-05矿产资源遥感调查技术要求(1:50000、1:250000)[S].北京:中国地质调查局,2011.
China Geological Survey.DD2011-05 Regulation on remote sen-sing surveying of mineral resources[S].Beijing:China Geological Survey,2011.
[6] 李荣社,计文化,杨永成,等.昆仑山及邻区地质[M].北京:地质出版社,2008.
Li R S,Ji W H,Yang Y C,et al.Geology in Kunlun Mountain and its Adjacent Area[M].Beijing:Geological Publishing House,2008.
[7] 何凯涛,甘甫平,王永江.高空间分辨率卫星遥感地质微构造及蚀变信息识别[J].国土资源遥感,2009,21(1):97-99.doi:10.6046/gtzyyg.2009.01.22"> doi:10.6046/gtzyyg.2009.01.22.
He K T,Gan F P,Wang Y J.The extraction of geological micro-structure and altered rock information with high-resolution satellite images in a small range[J].Remote Sensing for Land and Resources,2009,21(1):97-99.doi:10.6046/gtzyyg.2009.01.22"> doi:10.6046/gtzyyg.2009.01.22.
[8] 陈 玲,张 微,周 艳,等.高分辨率遥感影像在新疆塔什库尔干地区沉积变质型铁矿勘查中的应用[J].地质与勘探,2012,48(5):1039-1048.
Chen L,Zhang W,Zhou Y,et al.Application of high-resolution remote sensing images to searching for sedimentary-metamorphic type iron deposits in the Taxkorgan area,Xinjiang[J].Geology and Exploration,2012,48(5):1039-1048.
[9] 张 微,张 伟,刘世英,等.基于核PCA方法的高分辨率遥感图像自动解译[J].国土资源遥感,2011,23(3):82-87.doi:10.6046/gtzyyg.2011.03.15"> doi:10.6046/gtzyyg.2011.03.15.
Zhang W,Zhang W,Liu S Y,et al.Automatic interpretation of high resolution remotely sensed images by using kernel method[J].Remote Sensing for Land and Resources,2011,23(3):82-87.doi:10.6046/gtzyyg.2011.03.15"> doi:10.6046/gtzyyg.2011.03.15.
[10] 杨金中,方洪宾,张玉君,等.中国西部重要成矿带遥感找矿异常提取的方法研究[J].国土资源遥感,2003,15(3):50-53.doi:10.6046/gtzyyg.2003.03.12"> doi:10.6046/gtzyyg.2003.03.12.
Yang J Z,Fang H B,Zhang Y J,et al.Remote sensing anomaly extraction in important metallogenic belts of western China[J].Remote Sensing for Land and Resources,2003,15(3):50-53.doi:10.6046/gtzyyg.2003.03.12"> doi:10.6046/gtzyyg.2003.03.12.
[11] 张玉君,曾朝铭,陈 薇.ETM + (TM)蚀变遥感异常提取方法研究与应用——方法选择和技术流程[J].国土资源遥感,2003,15(2):44-49.doi:10.6046/gtzyyg.2003.02.11"> doi:10.6046/gtzyyg.2003.02.11.
Zhang Y J,Zeng Z M,Chen W.The methods for extraction of alteration anomalies from the ETM + (TM) data and their application:Method selection and technological flow chart[J].Remote Sensing for Land and Resources,2003,15(2):44-49.doi:10.6046/gtzyyg.2003.02.11"> doi:10.6046/gtzyyg.2003.02.11.
[12] 金谋顺,王 辉,张 微,等.高分辨率遥感数据铁染异常提取方法及其应用[J].国土资源遥感,2015,27(3):122-127.doi:10.6046/gtzyyg.2015.03.20"> doi:10.6046/gtzyyg.2015.03.20.
Jin M S,Wang H,Zhang W,et al.Method for extraction of ferric contamination anomaly from high-resolution remote sensing data and its application[J].Remote Sensing for Land and Resources,2015,27(3):122-127.doi:10.6046/gtzyyg.2015.03.20"> doi:10.6046/gtzyyg.2015.03.20.
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