高光谱遥感油气探测技术

汪大明; 肖晨超; 李志忠; 倪国强; 吴小娟; 隋正伟

doi:10.3799/dqkx.2015.110

Volume 40 Issue 8

Aug. 2015

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Wang Daming, Xiao Chenchao, Li Zhizhong, Ni Guoqiang, Wu Xiaojuan, Sui Zhengwei, 2015. Hyperspectral Remote Sensing Technology in Oil and Gas Exploration. Earth Science, 40(8): 1301-1309. doi: 10.3799/dqkx.2015.110

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Wang Daming, Xiao Chenchao, Li Zhizhong, Ni Guoqiang, Wu Xiaojuan, Sui Zhengwei, 2015. Hyperspectral Remote Sensing Technology in Oil and Gas Exploration. Earth Science, 40(8): 1301-1309. doi: 10.3799/dqkx.2015.110

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Hyperspectral Remote Sensing Technology in Oil and Gas Exploration

doi: 10.3799/dqkx.2015.110

1.
Oil & Gas Survey, China Geological Survey, Beijing 100029, China
2.
China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China
3.
School of Opte-electronics, Beijing Institute of Technology, Beijing 100081, China
4.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430027, China
5.
China Centre for Resources Satellite Data and Application, Beijing 100094, China

Received Date: 2015-09-15
Publish Date: 2015-08-01

Abstract

Abstract

Hyperspectral remote sensing technology is new in oil and gas exploration. Based on the theory of abnormal surface symbiosis with oil and gas microseepage, four typical classifications, maximum likelihood classification in the wavelet-based principal component analysis, endmember extraction, typical alteration classification with spectral libraries and decision tree classification based on vegetation indices are chosen to carry a case study of Hyperion images in Yulin, to obtain the related thematic maps such as clay, carbonate, vegetation and determine six comprehensive anomalous areas. A comprehensive analysis of the distribution of existing gas well and oil-gas anomalous areas show that the information extraction method of oil and gas microseepage is valid.
- hyperspectral remote sensing,
- oil and gas exploration,
- wavelet-based PCA,
- endmember extraction,
- spectral library,
- vegetation index,
- remote sensing,
- decision tree classification

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References(22)

References

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