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    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

    汪大明, 肖晨超, 李志忠, 倪国强, 吴小娟, 隋正伟, 2015. 高光谱遥感油气探测技术. 地球科学, 40(8): 1301-1309. doi: 10.3799/dqkx.2015.110
    引用本文: 汪大明, 肖晨超, 李志忠, 倪国强, 吴小娟, 隋正伟, 2015. 高光谱遥感油气探测技术. 地球科学, 40(8): 1301-1309. doi: 10.3799/dqkx.2015.110
    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
    Citation: 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

    高光谱遥感油气探测技术

    doi: 10.3799/dqkx.2015.110
    基金项目: 

    国家高技术研究发展计划(863计划)项目 2008AA121100

    国家高技术研究发展计划(863计划)项目 2012AA12A308

    国家自然科学基金项目 41402293

    详细信息
      作者简介:

      汪大明(1982-), 男, 博士, 高级工程师, 主要从事遥感卫星、通讯导航等技术在矿产和能源领域应用研究.E-mail: daming82@qq.com

    • 中图分类号: P627

    Hyperspectral Remote Sensing Technology in Oil and Gas Exploration

    • 摘要: 高光谱遥感探测技术已成为探测油气藏的前沿新技术之一.研究以油气微渗漏地表共生异常理论为基础, 采用基于小波主成份分析(principal component analysis, PCA)最大似然分类、端元提取分类、光谱库典型蚀变光谱分类和植被指数决策树分类方法, 对榆林典型稀疏植被地区的进行油气勘探, 提取了与烃异常相关的粘土、碳酸盐、植被异常等相关的专题信息产品, 得出综合异常区图.对照分析已知气井与油气异常区分布, 证明了油气微渗漏信息的提取与识别方法的有效性.

       

    • 图  1  研究区位置图

      Fig.  1.  Location of study area

      图  2  基于小波PCA分类填图

      a.细分结果;b.精分结果;c.各类均值曲线;d.各类标准差曲线

      Fig.  2.  Mineral mapping by wavelet-based principal components analysis classifications

      图  3  6种图像端元

      a.植被1端元;b.植被2端元;c.沙地端元;d.耕地边缘端元;e.水体端元;f.云端元

      Fig.  3.  Six end members

      图  4  图像端元光谱(a)与USGS标准光谱(b)

      Fig.  4.  End members spectrum (a) and standard spectrum in USGS (b)

      图  5  包络线去除后的图像端元光谱与USGS标准光谱

      a.端元1与蒙脱石;b.端元2与伊利石;c.端元3与方解石

      Fig.  5.  End members spectrum and standard spectrum in USGS after the continuum-removal

      图  6  基于端元提取矿物填图

      a.蒙脱石;b.伊利石;c.方解石

      Fig.  6.  Mineral mapping by endmember extraction classifications

      图  7  典型气田区光谱

      a.气田区光谱1;b.气田区光谱2

      Fig.  7.  Typical gas field spectrum

      图  8  基于光谱库典型蚀变光谱分类填图

      a.蒙脱石;b.伊利石;c.方解石

      Fig.  8.  Mineral mapping by typical alteration classification with spectral libraries

      图  9  研究区植被分类

      a.ML植被区域;b.NDVI植被区域;c.决策树植被异常区

      Fig.  9.  Alteration mineral vegetation classification

      图  10  蚀变矿物异常与植被异常综合分析

      a.选区1真彩色图像;b.选区1分类结果;c.选区1异常点分类;d.选区2真彩色图像;e.选区2分类结果;f.选区2异常点分类

      Fig.  10.  Distribution of vegetation abnormal area and exposed surface abnormal area

      图  11  高光谱数据油气异常区的综合圈定

      Fig.  11.  Comprehensive oil and gas anomaly area

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    出版历程
    • 收稿日期:  2015-09-15
    • 刊出日期:  2015-08-01

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