基于经验模态分解的属性优化方法
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摘要
本文提出了一种基于经验模态分解的属性优化新方法,其具体步骤为:首先对某一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;其次分别对每一组固有模态分量的各个分量的权重进行归一化,并将前若干分量分别乘以相应的归一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。
This paper proposes a new attribute optimization method based on empirical mode decomposition.Firstly we decompose every kind of seismic attributes by the empirical mode decomposition and obtain some intrinsic mode functions.Then we normalize the weight coefficients of the top three components and multiply the three attribute value by the normalized weight coefficient and get the attribute components.Finally,we add the attribute components together.The empirical mode decomposition method and the principal component analysis method are applied in the same actual data.The result shows that the empirical mode decomposition method can obtain more information of the original attributes with less principal components(intrinsic mode functions),and improve the resolution of the original attribute section.This could be very helpful for fluid identification when attribute values change dramatically.
This paper proposes a new attribute optimization method based on empirical mode decomposition.Firstly we decompose every kind of seismic attributes by the empirical mode decomposition and obtain some intrinsic mode functions.Then we normalize the weight coefficients of the top three components and multiply the three attribute value by the normalized weight coefficient and get the attribute components.Finally,we add the attribute components together.The empirical mode decomposition method and the principal component analysis method are applied in the same actual data.The result shows that the empirical mode decomposition method can obtain more information of the original attributes with less principal components(intrinsic mode functions),and improve the resolution of the original attribute section.This could be very helpful for fluid identification when attribute values change dramatically.
引文
[1]Barnes A E.Weighted average seismic attributes.Geophysics,2000,65(1):275～285
[2]印兴耀,周静毅.地震属性优化方法综述.石油地球物理勘探,2005,40(4):482～489Yin Xingyao,Zhou Jingyi.Summary of optimum methodsof seismic attributes.OGP,2005,40(4):482～489
[3]印兴耀,孔国英,张广智.基于核主成分分析的地震属性优化方法及应用.石油地球物理勘探,2008,43(2):179～183Yin Xingyao,Kong Guoying,Zhang Guangzhi.Seismicattributes optimization based on kernel principal compo-nent analysis(KPCA)and application.OGP,2008,43(2):179～183
[4]赵加凡,陈小宏.基于灰关联分析与K-L变换的双重属性优化方法.石油地球物理勘探,2004,39(6):661～665Zhao Jiafan,Chen Xiaohong.Dual optimization of seis-mic attributes based on grey association analysis and K-Ltransform.OGP,2004,39(6):661～665
[5]王永刚,刘伟,黄国平.地震属性的GA-BP优化方法.石油地球物理勘探,2002,37(6):606～611Wang Yonggang,Liu Wei,Huang Guoping.GA-BP op-timization of seismic attribution.OGP,2002,37(6):606～611
[6]殷文,印兴耀,张繁昌.基于并行遗传算法的地震属性优化研究.吉林大学学报(自然科学版),2005,35(5):672～676Yin Wen,Yin Xingyao,Zhang Fanchang.A Study onseismic attribute optimization based on parallel genetic al-gorithm.Journal of Jilin University(Natural ScienceEdition),2005,35(5):672～676
[7]Huang N E,Long S R,Shen Z.The mechanism for fre-quency downshift in nonlinear wave evolution.Adv AplMech,1996,32:59～111
[8]Huang N E,Shen Z.The empirical mode decompositionand the Hilbert spectrum for nonlinear and non-stationarytime series analysis.Proc Roy Soc London A,1998,454:903～995
[9]Huang N E,Shen Z,Long S R.A new view of nonlin-ear water waves:the Hilbert spectrum.Annu Rev FluidMech,1999,31:417～457
[10]Wu Z,Huang N E.A study of the characteristics ofwhite noise using the empirical mode decompositionmethod.Proc Roy Soc London,2004,460A:1597～1611
[11]陈林,宋海斌.基于经验模态分解的地震瞬时属性提取.地球物理学进展,2008,23(4):1179～1185Chen Lin,Song Haibin.Seismic instantaneous attributeextraction based on empirical mode decomposition.Pro-gress in Geophysics,2008,23(4):1179～1185
[12]龚志强,邹明玮,高新全等.基于非线性时间序列分析经验模态分解和小波分解异同性的研究.物理学报,2005,54(8):3947～3957Gong Zhiqiang,Zou Mingwei,Gao Xinquan et al.On thedifference between empirical mode decomposition andwavelet decompositon in the nonlinear time series.ActaPhysica Sinica,2005,54(8):3947～3957
[13]Hotelling H.Analysis of a complex of statistical varia-bles into principal components.Education Psychology,1933,24(3):417～444
[14]Carreira-Perpian M A.A review of Dimension Re-duction Techniques.Technical Report CS-96-09,1997:5～20,Department of Computer Science,University ofSheffield
[2]印兴耀,周静毅.地震属性优化方法综述.石油地球物理勘探,2005,40(4):482～489Yin Xingyao,Zhou Jingyi.Summary of optimum methodsof seismic attributes.OGP,2005,40(4):482～489
[3]印兴耀,孔国英,张广智.基于核主成分分析的地震属性优化方法及应用.石油地球物理勘探,2008,43(2):179～183Yin Xingyao,Kong Guoying,Zhang Guangzhi.Seismicattributes optimization based on kernel principal compo-nent analysis(KPCA)and application.OGP,2008,43(2):179～183
[4]赵加凡,陈小宏.基于灰关联分析与K-L变换的双重属性优化方法.石油地球物理勘探,2004,39(6):661～665Zhao Jiafan,Chen Xiaohong.Dual optimization of seis-mic attributes based on grey association analysis and K-Ltransform.OGP,2004,39(6):661～665
[5]王永刚,刘伟,黄国平.地震属性的GA-BP优化方法.石油地球物理勘探,2002,37(6):606～611Wang Yonggang,Liu Wei,Huang Guoping.GA-BP op-timization of seismic attribution.OGP,2002,37(6):606～611
[6]殷文,印兴耀,张繁昌.基于并行遗传算法的地震属性优化研究.吉林大学学报(自然科学版),2005,35(5):672～676Yin Wen,Yin Xingyao,Zhang Fanchang.A Study onseismic attribute optimization based on parallel genetic al-gorithm.Journal of Jilin University(Natural ScienceEdition),2005,35(5):672～676
[7]Huang N E,Long S R,Shen Z.The mechanism for fre-quency downshift in nonlinear wave evolution.Adv AplMech,1996,32:59～111
[8]Huang N E,Shen Z.The empirical mode decompositionand the Hilbert spectrum for nonlinear and non-stationarytime series analysis.Proc Roy Soc London A,1998,454:903～995
[9]Huang N E,Shen Z,Long S R.A new view of nonlin-ear water waves:the Hilbert spectrum.Annu Rev FluidMech,1999,31:417～457
[10]Wu Z,Huang N E.A study of the characteristics ofwhite noise using the empirical mode decompositionmethod.Proc Roy Soc London,2004,460A:1597～1611
[11]陈林,宋海斌.基于经验模态分解的地震瞬时属性提取.地球物理学进展,2008,23(4):1179～1185Chen Lin,Song Haibin.Seismic instantaneous attributeextraction based on empirical mode decomposition.Pro-gress in Geophysics,2008,23(4):1179～1185
[12]龚志强,邹明玮,高新全等.基于非线性时间序列分析经验模态分解和小波分解异同性的研究.物理学报,2005,54(8):3947～3957Gong Zhiqiang,Zou Mingwei,Gao Xinquan et al.On thedifference between empirical mode decomposition andwavelet decompositon in the nonlinear time series.ActaPhysica Sinica,2005,54(8):3947～3957
[13]Hotelling H.Analysis of a complex of statistical varia-bles into principal components.Education Psychology,1933,24(3):417～444
[14]Carreira-Perpian M A.A review of Dimension Re-duction Techniques.Technical Report CS-96-09,1997:5～20,Department of Computer Science,University ofSheffield
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