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REMOTE SENSING FOR LAND & RESOURCES    1997, Vol. 9 Issue (2) : 27-31     DOI: 10.6046/gtzyyg.1997.02.06
Remote Sensing Application in the Jingjiu Governance Line Areas |
THE IMAGERY FEATURE OF ACTIVE TECTONIC ALONG THE JINGJIU RAILWAY IN HEBEI SECTION
Ma Yiuyi1, Lu Zhenjiu2, Hao Yaosheng3, Han Shining3
1. Geological Prospecting Bureau of Hebei Province, Shi Jiazhuang 050081;
2. Project Committee of Hebei Province, Shi Jiazhuang 050081;
3. Geological Prospecting Bureau of Hebei Province, Shi Jiazhuang 050081
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Abstract  

Jingjiu railway in Hebei section is situated in Jizhong plain, where is peripheral depression of the Cenozoic Era, and there are a lot of active tectonics. This paper gives the interpretive results of the active tectonic by using remote sensing image. In this time, a number of tectonics of NWWdirection in all region are interpreted. The paper puts the stress on the scattering feature of active tectonics and approach the active tectonic's controling function to stream system geomorphy as well as the depth of earthquake source. The active tectonics changing in the depth is infered and proved by using CTdata. At last, it points Hebei plain is one of the most important active tectonic unit where the earth's crust is thin and the earth thermal current value is high, the influence of active tectonic to the earth's steady must be considered when the industries was distributed.

Keywords  Kaixian county of Chongqing      Water-level fluctuating zone of Three Gorges Reservoir      Classificationsystem      RS      ArcGIS     
Issue Date: 02 August 2011
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ZHANG Hong
ZHU Ping
LI Yan-Sheng
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ZHANG Hong,ZHU Ping,LI Yan-Sheng. THE IMAGERY FEATURE OF ACTIVE TECTONIC ALONG THE JINGJIU RAILWAY IN HEBEI SECTION[J]. REMOTE SENSING FOR LAND & RESOURCES, 1997, 9(2): 27-31.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.1997.02.06     OR     https://www.gtzyyg.com/EN/Y1997/V9/I2/27


[1] 鞠紫云等.河北省北京市天津市区域地质志北京:地质出版社,1989

[2] 李祥根.京津及河北平原的最新活动构造带.见:中国活动断裂.北京:地震出版社,1982

[3] 马廷若等.京津唐地区活动断裂带现代运动特征.见:中国活动断裂.北京:地震出版社,1982

[4] 环文林等.中国大陆内部走滑型发展构造的构造变形场特征.地震学报,1995(2)

[5] 冯锐等.华北地区地壳构造与地震活动.地质学报,1989(2)

[6] 杨克绳.冀中拗陷断裂形成机理的探讨.见:国际大陆岩石圈构造演化与动力学讨论文集1.北京:地质出版社,1990

[7] 王同和.太行山以东沉积盆地与油气分布规律.华北地质矿产杂志,1995(3)

[8] 邢集普等.山西板内构造及其演化特征初探.山西地质,1991(1)

[9] 牛树银等.造山带与相邻盆地间物质的徽向迁移.地学前缘,1995(1)

[10 白文吉等.山系的形成与板块碰盆无关.地质论评,1993(2)

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