Quantitative analysis of alteration mineral content and characteristic spectra of Hyperion image at oil and gas microseepage area

Na Liu; Xiaomei Chen; Qianqian Li

doi:10.1117/12.2193077

5 August 2015 Quantitative analysis of alteration mineral content and characteristic spectra of Hyperion image at oil and gas microseepage area

Na Liu, Xiaomei Chen, Qianqian Li

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Proceedings Volume 9622, 2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology; 962212 (2015) https://doi.org/10.1117/12.2193077
Event: International Conference on Optical Instruments and Technology 2015, 2015, Beijing, China

Abstract

With Sanhu region of Qaidam Basin as the test area and the mineral compositions and hyperspectral remote sensing images as test data, the present paper sets up the quantitative relationships between clay and carbonate of altered minerals caused by oil and gas microseepage and the characteristic parameters from hyperspectral remote sensing image. To get the quantitative relationships between these characteristic parameters and contents, the statistical regression method is used after the spectral characteristics extraction from Hyperion image. The research results show the contents of clay and carbonate have a high degree fitting with the depth of spectral absorption peak, while there are low correlations between other characteristic parameters and the contents. This conclusion provides references for using the hyperspectral remote sensing information to explore the oil and gas direct and lessening or even getting rid of the groundwork, and provides a statistical basis for inversing the surface mineral contents with the hyperspectral remote sensing image.

Citation Download Citation

Na Liu, Xiaomei Chen, and Qianqian Li "Quantitative analysis of alteration mineral content and characteristic spectra of Hyperion image at oil and gas microseepage area", Proc. SPIE 9622, 2015 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 962212 (5 August 2015); https://doi.org/10.1117/12.2193077

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KEYWORDS

Minerals

Absorption

Carbonates

Hyperspectral imaging

Remote sensing

Aerospace engineering

Feature extraction

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