Journal of Jilin University(Earth Science Edition) ›› 2016, Vol. 46 ›› Issue (5): 1589-1597.doi: 10.13278/j.cnki.jjuese.201605307

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Application of Hyperion Hyperspectral Image for Studying on the Distribution of Oil Sands

You Jinfeng1,2, Xing Lixin1, Pan Jun1, Shan Xuanlong3, Fan Ruixue1, Cao Hui4   

  1. 1. College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China;
    2. Aviation University of Air Force, Changchun 130022, China;
    3. College of Earth Sciences, Jilin University, Changchun 130061, China;
    4. No.1 Gold Geological Party of CAPF, Mudanjiang 157021, Heilongjiang, China
  • Received:2016-01-13 Online:2016-09-26 Published:2016-09-26
  • Supported by:

    Supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China (2011ZX05028-002);Science and Technology Project of Petro China Company Limited (2013E-050102) and China Geological Survey Program (1212010761502)

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Abstract:

The research was mainly based on the principles of hydrocarbon microseepage and spectral response of oil sands composition characteristics. The spectral information related to oil sands distribution was extracted and identified from the hyperspectral image to predict the favorable reservoir for oil sands. Based on the analysis of ground characteristics of hydrocarbon microseepage caused by oil sands, the anomalous feature from low plantation coverage was primarily in mineralization anomaly, the main identification features of medium and high vegetation covering areas were vegetation anomalies. Normalized difference vegetation index (NDVI) was used to represent the different vegetation coverage degree. When NDVI is [0.0,0.4), SAM (spectral angle method) was used to extract mineralization anomaly information. When NDVI are [0.4，0.7] and （0.7，1.0], the vegetation anomaly information were taken by using LIC(lichenthaler index) and CTR(carter indices)respectively. Meanwhile, in order to ensure the extraction of mineralization and vegetation abnormal information caused by the leakage of hydrocarbons from oil sands, the spectral reflectance of oil sands was encouraged to be the endmember to get oil sands spatial information by using SAM. Finally, spatial superimposed analysis was applied to integrate oil sands composition spatial information with mineralization and vegetation exception information for delineating the prospective areas of oil sands distribution. The results showed that a combination of field measurement hyperspectral data and hyperspectral image could predict the distribution of oil sands reservoir. So hyperspectral image plays an important role in prediction of the oil sands bearing reservoir prospective areas, it could also provide some useful information for researching into recoverable reserves evaluation of oil sands by using remote sensing technology.

Key words: oil sands, hyperion image data, hydrocarbon microseepage, oil content, short wave infrared

CLC Number: 

  • P627

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