Hyperspectral Remote Sensing Technology in Oil and Gas Exploration
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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.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.
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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 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 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
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