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Hyperspectral quantitative retrieval of methane content in different concentrations in the seawater background

  • role： First author第一作者
  • Affiliation：

    School of Earth Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China

  • Email：541176500@qq.com
  • Introduction：曾雅琦，1995年生，女，硕士研究生，研究方向为遥感地质与天然气应用。E-mail：541176500@qq.com
ZENG Yaqi1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Earth Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China

  • Email：wzhengh@mail.sysu.edu.cn
  • Introduction：王正海，1971年生，男，副教授，研究方向为地学信息技术与成矿预测。E-mail: wzhengh@mail.sysu.edu.cn
WANG Zhenghai1*,  
  • Affiliation：

    Research Institute of Petroleum Exploration and Development, Beijing 100083, China

XING Xuewen2,  
  • Affiliation：

    .Hefei Training and Testing Center of SINOPEC Petroleum Exploration and Production Research Institute, Hefei 230022, China

HU Bin3,  
  • Affiliation：

    Research Institute of Petroleum Exploration and Development, Beijing 100083, China

LIU Song2

résumé

In the remote sensing exploration of oil and gas resources, seabed gas reservoirs are usually detected through the anomaly of methane concentration on the sea surface caused by hydrocarbon seepage. The remote sensing exploration of hydrocarbon seepage in marine gas resources is currently mostly based on methane absorption characteristics and images. Quantitative spectral analysis of methane content on the sea surface is also insufficient. This study designs laboratory spectral response experiments of methane with different concentrations in seawater background to determine the methane anomaly on the sea surface better and improve the accuracy of remote sensing inversion. This study also attempts to establish an inversion model of methane concentration in seawater background.A methane spectra laboratory test platform was designed to obtain hyperspectral data of different methane contents in seawater background. After spectra preprocessing and derivative of ratio spectroscopy, the spectral absorption characteristic parameters (the valley, wave depth, area, wave width, slope, and SAI) were extracted. The correlation between methane content and spectral parameters was analyzed to compare the ability of parameters to distinguish methane content. The correlation between spectral parameters with high correlation of methane content in selected bands was analyzed to further reduce the amount of data. Finally, the spectral parameters that were highly correlated with methane content and lowly correlated with each other were selected as independent variables and methane content was used as dependent variable to construct the methane content inversion model.In the analysis of methane spectra in seawater background, the derivative of ratio spectroscopy can effectively suppress background information of seawater in the spectra and highlight the methane information. Thus, the curve characteristics of the spectra after derivative of ratio spectroscopy are only related to the content of methane. Moreover, higher methane content corresponds to more obvious characteristics. The correlation between spectral parameters in 1650—1664 nm and 2180—2210 nm with methane content is significantly correlated, which is apparently higher than that in 2300—2320 nm and 2350—2380 nm. The valley, wave depth, area, and slope are also significantly correlated with methane content. Meanwhile, wave width is generally correlated with methane content, and SAI is not correlated with methane content. The quadrivariate regression equation (y=-14.356 - 5931.796x1 - 4325.081x2+241.481x3+7531.973x4) in 2180—2210 nm has the best fitting effect, and R2 is 0.9817. The single variable methane inversion model y = 2047.571x - 9.758 is based on wave depth in this band, and R2 is 0.9741, which is better than that of the inversion model based on other spectral characteristic parameters.The corresponding bands of 1650—1664 nm and 2180—2210 nm and corresponding absorption characteristics (valley, wave depth, area, and slope) with significant linear correlation of methane content in sea water background are successfully obtained. The methane content inversion models with good effect and regression significance are established. They can provide theoretical and technical basis for predicting methane concentration on sea surface by multispectral/hyperspectral remote sensing.

mots-clés

remote sensing;hyperspectra;methane content;spectral characteristic parameter;derivative of ratio spectroscopy;inversion model

References

  1. 1.
    Almeida-Filho R, Miranda F P, Galvão L S and Freitas C C. 2002. Terrain characteristics of a tonal anomaly remotely detected in an area of hydrocarbon microseepage, Tucano basin, north-eastern Brazil. International Journal of Remote Sensing, 23(18): 3893-3898
  2. 2.
    Bradley E S, Leifer I, Roberts D A, Dennison P E and Washburn L. 2011. Detection of marine methane emissions with AVIRIS band ratios. Geophysical Research Letters, 38(10): L10702
  3. 3.
    Debba P, Carranza E J M, van der Meer F D and Stein A. 2006. Abundance estimation of spectrally similar minerals by using derivative spectra in simulated annealing. IEEE Transactions on Geoscience and Remote Sensing, 44(12): 3649-3658
  4. 4.
    Everett J R, Staskowski R J and Jengo C. 2002. Remote sensing and GIS enable future exploration success. World Oil, 223(11): 59-65
  5. 5.
    Gao L Z and Yang B L. 1991. A study on near infrared spectral characteristics of petroleum matter applied to remote sensing of oil and gas resources. Remote Sensing for Land and Resources, 3(4): 9-12, 29
  6. 6.
    Hu P, Tian Q J and Yan B K. 2009. The application of hyperspectral remote sensing to the identification of hydrocarbon alteration minerals in Qaidam Basin. Remote Sensing for Land and Resources, 21(2): 54-61
  7. 7.
    Li N and Zhou P. 2015. Hydrocarbon microleakage information extraction based on ASTER data and reflectance spectroscopy. Journal of Geomechanics, 21(2): 218-227
  8. 8.
    Lintz J. 1972. Remote sensing for petroleum. AAPG Bulletin, 56(3): 542-553
  9. 9.
    Lu Y C, Hu C M, Sun S J, Zhang M W, Zhou Y and Shi J. 2016. Overview of optical remote sensing of marine oil spills and hydrocarbon seepage. Journal of Remote Sensing, 20(5): 1259-1269
  10. 10.
    Lu Y C, Zhan W F and Hu C M. 2016. Detecting and quantifying oil slick thickness by thermal remote sensing: a ground-based experiment. Remote Sensing of Environment, 181: 207-217
  11. 11.
    Lv C C, Wang Z W and Qian S M. 2003. A review of pixel unmixing models. Remote Sensing Information, (3): 55-58, 60
  12. 12.
    Lv S Q, Yin Q L, Hou M L, Ma Q L, Hu Y G and Wu W T. 2017. Spectral unmixing of mineral pigment mixture by using derivative of ratio spectroscopy based on the mode. The Journal of Light Scattering, 29(3): 261-265
  13. 13.
    Ma L, Li Y and Liu Y. 2009. Oil spill monitoring based on its spectral characteristics. Environmental Forensics, 10(4): 317-323
  14. 14.
    Roberts D A, Bradley E S, Cheung R, Leifer I, Dennison P E and Margolis J S. 2010. Mapping methane emissions from a marine geological seep source using imaging spectrometry. Remote Sensing of Environment, 114(3): 592-606
  15. 15.
    Shen J L, Ding S B, Qi X P and Xing X W. 2010a. The progress of remote sensing technology in the detection of hydrocarbon micro-seepage. Remote Sensing for Land and Resources, 22(3): 7-11
  16. 16.
    Shen J L, Ding S B, Qi X P and Xing X W. 2010b. Preliminary study on detection of hydrocarbon seeps at the sea using Hyperion data. Remote Sensing Information, (4): 80-84
  17. 17.
    Solomon E A, Kastner M, MacDonald I R and Leifer I. 2009. Considerable methane fluxes to the atmosphere from hydrocarbon seeps in the Gulf of Mexico. Nature Geoscience, 2(8): 561-565
  18. 18.
    Thorpe A K, Roberts D A, Bradley E S, Funk C C, Dennison P E and Leifer I. 2013. High resolution mapping of methane emissions from marine and terrestrial sources using a Cluster-Tuned Matched Filter technique and imaging spectrometry. Remote Sensing of Environment, 134: 305-318
  19. 19.
    van der Meer F, van Dijk P, van der Werff H and Yang H. 2002. Remote sensing and petroleum seepage: a review and case study. Terra Nova, 14(1): 1-17
  20. 20.
    Wang X C, Tian Q J and Guan Z. 2007. The extraction of oil and gas information by using Hyperion imagery in the Sebei Gas Field. Remote Sensing for Land and Resources, 19(1): 36-40
  21. 21.
    Wang X Z, Fu J H, Li Z Z, Guo J M, Gao R M, Yao J L, Zhang L X, Ren L Y, He Y H, Ma F X, Meng W C and Bai X Y. 2015. Study on the hyperspectral information extraction of oil and gas microseepage in the Ordos Basin. Natural Gas Geoscience, 26(10): 1917-1924
  22. 22.
    Wang Y. 2010. Hydrocarbon microseepage information extracting through remote sensing technology in front range of Longmenshan. Coal Geology of China, 22(10): 10-16
  23. 23.
    Wang Y P, Geng A S and Liu D H. 1999. An investigation on remote sensing prospecting for the surface hydrocarbons in Eerduosi Basin. Natural Gas Industry, 19(6): 17-20
  24. 24.
    Yang D C, Chen J, Gao Z H and Han Y C. 2018. Extraction of hydrocarbon micro-seepage information based on TG-1 hyperspectral data. Remote Sensing for Land and Resources, 30(2): 107-113
  25. 25.
    Yang X L and Wan X X. 2017. Obtaining particle size information of mineral pigments from disturbed spectral reflectance. Spectroscopy and Spectral Analysis, 37(7): 2158-2164
  26. 26.
    Zhang G F, Shen X H, Zou L J, Li C J, Wu W Y, Zhang Z, Su N and Kong F L. 2009. Identifying hydrocarbon leakage induced anomalies through remote sensing techniques in the western slope of Songliao Basin, China. Journal of Remote Sensing, 13(2): 327-334
  27. 27.
    Zhang J K, Rivard B and Sanchez-Azofeifa A. 2004. Derivative spectral unmixing of hyperspectral data applied to mixtures of lichen and rock. IEEE Transactions on Geoscience and Remote Sensing, 42(9): 1934-1940
  28. 28.
    Zhang Y, Wang Y D, Li L, Zheng C T, An Y P and Song Z Y. 2008. The principle and technical analysis of methane detection using infrared absorption spectroscopy. Spectroscopy and Spectral Analysis, 28(11): 2515-2519.
  29. 29.
    Zhao C H, Cheng B Z and Yang W C. 2012. Algorithm for hyperspectral unmixing using constrained nonnegative matrix factorization. Journal of Harbin Engineering University, 33(3): 377-382
  30. 30.
    Zhao H Q, Zhang L F, Wu T X and Huang C P. 2013. Research on the model of spectral unmixing for minerals based on derivative of ratio spectroscopy. Spectroscopy and Spectral Analysis, 33(1): 172-176
  31. 31.
    Zhou Z Y. 2014. Progress in hyperspectral remote sensing for petroleum prospecting. Remote Sensing Technology and Application, 29(2): 352-361
  32. 32.
    Zhu F, Gong H L, Sun T L, Hou J, Guo X M and Guo L. 2013. Study on reflectance spectra morphological character of the hyperspectral mixed pixels at different component proportion. Spectroscopy and Spectral Analysis, 33(7): 1897-1902
  33. 33.
    Zhu M Q, Liu D C and Zhao Y J. 2007. Remote sensing detecting for the hydrocarbon micro-seepage and its implication on the East Yimeng Uplift, Ordos Basin, China. Journal of Remote Sensing, 11(6): 882-890

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