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REMOTE SENSING FOR LAND & RESOURCES    1994, Vol. 6 Issue (1) : 29-36     DOI: 10.6046/gtzyyg.1994.01.05
Applied Research |
THE APPLICATION AND EFFECT OF DATA FROM IMAGE LINEAMENTS IN EXPLORATION FOR OIL AND GAS IN NORTHERN TARIM BASIN
Li Bing
Comprehensive Research Institute of petroleum Geology Ministry of Geololgy and Mineral Resources
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Abstract  

It is effective to make use of data from image lineaments in search for ail and gas in Northern Tarim basin. The strike of lineaments, dominated by NE, NWand near EW. Confirms with the regional structure in the area. The density of lineaments striking NEhas a fine coherence with hydrocarbon traps.The coherent coefficient between density of lineament and known ail and gas fieldis and commercial wells is 62 .5%.

Keywords  Canopy reflectance      Leaf area index (LAI)      PROSPECT      SAIL      Red edge      Principal component analysis     
Issue Date: 02 August 2011
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YANG Yan
TIAN Qing-Jiu
HU Ming-Shun
PAN Dong-Ming
XU Hong-Li
ZHAO Li-Gui
Cite this article:   
YANG Yan,TIAN Qing-Jiu,HU Ming-Shun, et al. THE APPLICATION AND EFFECT OF DATA FROM IMAGE LINEAMENTS IN EXPLORATION FOR OIL AND GAS IN NORTHERN TARIM BASIN[J]. REMOTE SENSING FOR LAND & RESOURCES, 1994, 6(1): 29-36.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.1994.01.05     OR     https://www.gtzyyg.com/EN/Y1994/V6/I1/29


[1] 地质矿产部情报研究所,遥感专辑(三).地质出版社,1986年

[2] 黎兵等,《国土资源遥感》,1990年第2期第29页

[3] 庄培仁、赵不亿等,遥感技术及地质应用研究,地质出版社,1985年

[4] Johnl. Beny et al,《Remote sensing for Exploration Gcology》,Vol. 1 pp. 99-63, 1988年

[5] Darcy L. Uixo and J. Gregory Bryan. 《Remote Sensing for Exploraton Geology》,Vol. 1,pp. 229-247,1984年

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