最优化广义S变换及其在油气检测中的应用  

Optimal generalized S-transform and its application to hydrocarbon detection

作　　者：史燕红[1] 边立恩[1] 贺振华[1] 

机构地区：[1]成都理工大学"油气藏地质及开发工程"国家重点实验室,四川成都610059

出　　处：《石油与天然气地质》2009年第2期236-239,共4页Oil & Gas Geology

基　　金：国家高技术研究发展计划(863)项目(2006AA0AA102-12);国家自然科学基金项目(40774064)

摘　　要：时频分析是直接应用地震资料进行烃类检测的重要手段之一,基于实际地震信号的非平稳特征及短时傅立叶变换、Gabor变换、小波变换等常规时频分析方法的缺点和不足,通过在S变换中引入新的参数p来调节窗函数的标准差,并结合Jones和Parks等提出的时频聚集性度量准则来选取最优的p值,得到了一种新的时频分析方法,即最优化广义S变换。合成信号试算结果表明,该方法比常规时频分析方法具有更好的时频聚集性。在实际生产,此方法的应用结果显示由它生成分频数据体对于地层的含油气性检测和识别具有比较明显的效果。Time-frequency analysis is one of the important means for direct hydrocarbon detection with seismic data. Real seismic signals are non-stationary and shortcomings and deficiencies exist in conventional time-frequency analysis methods such as short-time Fourier transform, Gabor transform and wavelet transform. To tackle these problems, a new time-frequency analysis method-optimal generalized S-transform-is achieved by adding a new parameter ＂p＂ in S-transform to adjust the standard deviation of window function and selecting the optimal value of p according to the measurement criteria of the time-frequency aggregation proposed by Jones and Parks. Trial calculation with synthetic signals shows that this method has a better time-frequency aggregation than conventional time-frequency analysis methods. Application of this method to actual seismic data shows that the frequency-division data volume generated by this method is effective in hydrocarbon detection and identification.

关 键 词：时频分析 非平稳信号 最优化广义S变换 分频剖面 油气检测 

分 类 号：P631.4[天文地球—地质矿产勘探]