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REMOTE SENSING FOR LAND & RESOURCES    1997, Vol. 9 Issue (2) : 32-37     DOI: 10.6046/gtzyyg.1997.02.07
Remote Sensing Application in the Jingjiu Governance Line Areas |
APPLICATION OF REMOTE SENSING ON EVALUATION OF SEISMOGEOLOGICAL ENVIRONMENT FOR MAJOR ENGINEERING SITES
Li Faxiang, Liu Zhongwen, Xu Guilin, Zhang Kangfu, Zhu Xiugang, Bian Zhaoyin
Institute of Crustal Dynamics, State Bureau of Seismology
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Abstract  

This paper discusses the method of using remote sensing to assist evaluation of seismogeological environment of the Zhuhai—lingdinyang bridge engineering sites. First, active faults information was extracted from remote sensing images,second,seismicity in the area was studied,finally,evaluation of seismogeological environment of the site was made.

Keywords ETM      Remote sensing      Geological mapping      Xinjiang      Balikun     
Issue Date: 02 August 2011
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ZHOU Jun
GAO Peng
TIAN Qin-Hu
LIU Lei
LI De-Cheng
LIU Ye-Xuan
LI Qing-Xia
DONG Yong-Sheng
SUN Xiao-Ling
ZHANG Qin
Cite this article:   
ZHOU Jun,GAO Peng,TIAN Qin-Hu, et al. APPLICATION OF REMOTE SENSING ON EVALUATION OF SEISMOGEOLOGICAL ENVIRONMENT FOR MAJOR ENGINEERING SITES[J]. REMOTE SENSING FOR LAND & RESOURCES, 1997, 9(2): 32-37.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.1997.02.07     OR     https://www.gtzyyg.com/EN/Y1997/V9/I2/32


[1] 陈上福等.从陆地卫星影像探讨活动断裂的判读标志.遥感地震地质文集。北京:地震出版社.1985

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