本文提出了-种基于经验模态分解的属性优化新方法,其具体步骤为:首先对某-种地震属性的各个列向量分别进行经验模态分解,得到各个列向量的固有模态分量组;其次分别对每-组固有模态分量的各个分量的权重进行归-化,并将前若干分量分别乘以相应的归-化权重系数后再线性相加得到新的列向量,进而构成优化后的属性矩阵;然后对其他几种参与优化的属性也实行前面两个步骤,分别得到相应的新的地震属性矩阵;最后将得到的新地震属性矩阵进行线性叠加,得到优化后的地震属性矩阵。将经验模态分解方法和主成分分析方法分别应用于相同的实际资料,试验证明前者在保证运行效率的情况下能用更少的主成分(固有模态分量)刻画出更多的原始属性信息,并且提高了原始属性剖面的分辨率。
Abstract
This paper proposes a new attribute optimiza-tion method based on empirical mode decomposi-tion. Firstly we decompose every kind of seismicattributes by the empirical mode decomposition andobtain some intrinsic mode functions.Then wenormalize the weight coefficients of the top threecomponents and multiply the three attribute valueby the normalized weight coefficient and get the at-tribute components.Finally，we add the attributecomponents together.The empirical mode decom-position method and the principal component anal-ysis method are applied in the same actual data.The result shows that the empirical mode decom-position method can obtain more information of theoriginal attributes with less principal components(intrinsic mode functions)，and improve the reso-lution of the original attribute section. This couldbe very helpful for fluid identification when attrib-ute values change dramatically.
关键词
经验模态分解(EMD) /
属性优化 /
固有模态分量(IMF) /
主成分分析(PCA)
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Key words
intrinsic mode function(IMF) /
principal component analysis(PCA) /
empirical mode decomposition(EMD) /
attributeoptimization
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中图分类号：
P631
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参考文献
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基金
国家重点基础研究发展规划项目“非均质油气藏地球物理探测的基础研究”(2007CB209600)资助
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