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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.
Hyperspectral remote sensing detection technology has become one of the forefront new technologies for detecting oil and gas reservoirs.Based on the theory of surface coexistence abnormalities of microleakage of oil and gas and using the maximum likelihood classification based on principal component analysis (PCA) Extraction classification, classification of typical spectral changes of spectral library and classification method of vegetation index decision tree, the oil and gas exploration is carried out in the typical sparse vegetation area of Yulin, and the special thematic information products such as clay, carbonate and vegetation anomalies related to hydrocarbon anomalies are extracted , A comprehensive anomaly map is obtained.Considering the distribution of anomalous zones of gas wells and oil-gas anomaly, it is proved that the method of extracting and identifying oil-gas micro-leakage information is effective.