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REMOTE SENSING FOR LAND & RESOURCES    2014, Vol. 26 Issue (2) : 148-153     DOI: 10.6046/gtzyyg.2014.02.24
Technology Application |
Analysis of relationship between belts of concentrated remote sensing linear structures and gold deposits as well as prospecting prognosis in Xiaoqinling region
ZHONG Jiangwen, PENG Yi
Henan Institute of Geological Survey, Zhengzhou 450001, China
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Abstract  Xiaoginling region has densely-distributed magama hydrothermal gold deposits. The structural framework in this region is the Neoarchean gneiss dome,where intense magmatic activities occurred in Yanshanian period. The gold deposits are closely related to the distribution of quartz veins and altered mylonite. In the ETM+ remote sensing image,the deposits often find expression in linear, parallel linear intensive belts and flexible-brittle shear zones. Consequently,the distribution of remote sensing linear structures and the intensive belts of linear structures are very significant for the gold ore prediction in this region. On such a basis and with the ETM+ remote sensing image as the basic map,the authors made interpretation of the linear structure intensive belts and circular structure of Yanshanian granite bodies in Xiaoginling region. The gold deposit distribution and metallogenic regularity were analyzed based on remote sensing interpretation,the maps showing the favorableness of the metallogenic remote sensing characteristic conditions for gold deposits in this region were compiled, and the remote sensing prospecting prediction of gold resources in this region was made. These results will provide some reference for the prospecting of gold deposits in Xiaoqinling region and the assessment of mineral resource potential in Henan Province.
Keywords ZY-1 02C satellite      water information extraction      NDVI      NDWI      decision tree     
:  TP753  
Issue Date: 28 March 2014
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HU Weiguo
MENG Lingkui
ZHANG Dongying
FAN Zhiwei
CHENG Jianguo
LI Xiaohui
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HU Weiguo,MENG Lingkui,ZHANG Dongying, et al. Analysis of relationship between belts of concentrated remote sensing linear structures and gold deposits as well as prospecting prognosis in Xiaoqinling region[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 148-153.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2014.02.24     OR     https://www.gtzyyg.com/EN/Y2014/V26/I2/148
[1] 陈毓川,李兆鼐,毋瑞身,等.中国金矿床及其成矿规律[M].北京:地质出版社,2001:138-139. Chen Y C,Li Z N,Wu R S,et al.Chinese gold deposit and its metallogenic regularity[M].Beijing:Geological Publish House,2001:138-139.
[2] 冯建之.河南小秦岭金矿构造控矿规律及控矿模式[J].矿产与地质,2009,23(4):302-307. Feng J Z.Ore-controlling structure and model in Xiaoqinling gold deposit,Henan[J].Mineral Resources and Geology,2009,23(4):302-307.
[3] 彭翼,何玉良,钟江文,等.河南省金矿资源潜力评价成果报告[R].郑州:河南省地质调查院,2011. Peng Y,He Y L,Zhong J W,et al.The assessment result report of gold mineral resource potentials in Henan Province[R].Zhengzhou:Henan Institute of Geological Survey,2011.
[4] 白万成,卿敏,王春宏,等.小秦岭金矿田韧性剪切带特征及与金矿成矿的关系[J].黄金地质,1995,1(1):27-33. Bai W C,Qing M,Wang C H,et al.The features of ductile-shear zone of Xiaoqinling gold orefield and its relationship with gold mineralization[J].Gold Geology,1995,1(1):27-33.
[5] 彭大明.北秦岭剪切带型金矿的成矿规律[J].黄金地质,1998,4(1):57-62. Peng D M.Metallogenic regularity of shear zone type gold deposit in north Qinling[J].Gold Geology,1998,4(1):57-62.
[6] 王力群,陈昌明.陕西小秦岭金矿成矿地质条件刍议[J].陕西地质,2002,20(2):66-74. Wang L Q,Chen C M.Metallogenetic geology of gold mineralization in Xiaoqinling,Shaanxi Province[J].Geology of Shaanxi,2002,20(2):66-74.
[7] 况忠,龙胜清,曾禹人,等.黔西南地区遥感构造与金矿的关系及找矿预测[J].国土资源遥感,2012,24(1):160-165. Kuang Z,Long S Q,Zeng Y R,et al.The relationship between remote sensing structures and gold deposits and ore-prospecting prognosis in southwest Guizhou[J].Remote Sensing for Land and Resources,2012,24(1):160-165.
[8] 余凤鸣,何龙清,王磊.武当地区韧性剪切带遥感解译[J].国土资源遥感,2012,24(4):124-131. Yu F M,He L Q,Wang L.Remote sensing interpretation of ductile shear zone in Wudang area[J].Remote Sensing for Land and Resources,2012,24(4):124-131.
[9] 李明.利用遥感等手段圈定紫金山铜金矿床外围找矿有利区[J].国土资源遥感,2012,24(1):137-142. Li M.The delineation of potential ore-prospecting areas in the Zijinshan copper-gold deposit and its outskirts by using remote sensing and other means[J].Remote Sensing for Land and Resources,2012,24(1):137-142.
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