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植被覆盖地区的高光谱油气勘探技术研究现状 被引量:2

CURRENT STATUS OF HYPERSPECTRAL TECHNIQUES FOR OIL AND GAS EXPLORATION IN VEGETATION COVERING AREA
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摘要 高光谱遥感油气勘探技术利用油气微渗漏所致的地表蚀变矿物异常和地植物异常在光谱上的异常表现提取油气微渗漏信息,是油气勘探的辅助手段。针对高光谱遥感油气勘探技术在植被覆盖区应用中存在的植被遮挡地表真实情况的问题开展研究,总结相关领域的国内外研究现状,指出其存在的问题,提出了与植被反射率相关的指数作为油气微渗漏的指示标志,确定了适用于植被覆盖区的高光谱遥感油气微渗漏信息大面积普查的工作流程,研究成果可为建立更加广泛、适用的高光谱遥感油气勘探模式提供新的参考依据。 The technology of oil-gas exploration using hyperspectral remote sensing is based on the spectrum anomalies of alteration minerals and plants to extract the information of hydrocarbon microseepage,which is an auxiliary means of oil and gas exploration. But in the study area covered by vegetation,the real situation of the surface is occluded by the existence of vegetation,which will affect the application efficiency to the technology of oil-gas exploration using hyperspectral remote sensing. In order to explore the solution of this block,the real situation of the related domestic and foreign researches is summarized and the existing problems are pointed out. The vegetation reflectance indices which can be used as the indication sign of hydrocarbon microseepage are summed up, and the work flow which can apply to the large area census of hydrocarbon microseepage using hyperspectral remote sensing is put forwarded. The research result of this paper provide a new reference for the establishment of the more extensive and applicable work model of oil and gas exploration using hyperspectral remote sensing.
作者 李倩倩 许宁
出处 《地质力学学报》 CSCD 北大核心 2015年第2期142-150,共9页 Journal of Geomechanics
基金 中国地质调查局地质调查项目"地质勘查遥感系统集成与综合应用示范"(1212011120226)
关键词 高光谱 遥感 油气微渗漏 植被 hyperspectral remote sensing oil and gas microseepage vegetation
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  • 1高志强,刘纪远.基于遥感和GIS的中国植被指数变化的驱动因子分析及模型研究[J].气候与环境研究,2000,5(2):155-164. 被引量:71
  • 2张仁华,饶农新,廖国男.植被指数的抗大气影响探讨[J].Acta Botanica Sinica,1996,38(1):53-62. 被引量:107
  • 3Kauth R J, Thomas G S. 1976. The tasseled cap-a graphic description of the spectral-temporal development of agriculture crops as seen by Landsat[A] . Pros Symposium on Machine Processing of Remotely Sensed Data[C].Purdure University, West Lafayette, Indiana: 41-51.
  • 4Wheeler S G, and Misra P N. 1976. Linear dimensionality of landsat agricultural data with implications for classifications[A]. Pros Symposium on Machine Processing of Remotely Sensed Data[C]. West Lafayette, Indiana.Laboratory for the Applications of Remote Sensing.
  • 5Jackson, R D, Slater P N , and Pinter P J. 1983.Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres[J]. Remote Sens. Environ, 13:187-208.
  • 6Huete A R. 1988. A soil-adjusted vegetation index (SAVI)[J]. Remote Sens. Environ, 25: 295-309.
  • 7Elvidge C D, and Z Chen. 1995. Comparison of broad-band and narrow-band red and near-infrared vegetation indices[J].Remote Sens. Environ, 54: 38-48.
  • 8Qi J A. 1994. Modified soil adjusted vegetation index[J].Remote Sens. Environ, 48: 119-126.
  • 9Baret F, Guyot G, Major D J. 1989. TSAVI: A vegetation index which minimize soil brightness effects on LAI and APAR estimation[A]. Proceedings of the 12th Canadian Symposium on Remote sensing and IGARSS'89[C],Vancouver, Canada, 3:1355-1358.
  • 10Major D J, Baret F, and Guyot G. 1990. A ratio vegetation index adjusted for soil brightness[J]. Int. J. Remote Sens,11: 727-740.

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