复数子波匹配追踪算法识别薄层砂体
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摘要
针对东营凹陷滩坝砂岩薄砂体的预测识别问题,研究了复小波匹配追踪方法.从地震信号morlet小波分解及重构分析入手,讨论了重构地震信号时关于频率、振幅、相位及时移参数的选取方法及其具体的选取变量.并就关键的振幅参数,研究了改善的计算方法.常规的匹配追踪算法扫描地震信号的所有时间点和频率.在本文中,为了改善时频分辨率,采用局部扫描主频和子波的时间延迟.在具体实现过程中,采用计算地震信号的平均瞬时频率和地震信号的瞬时包络处的时间,做为采样频率和时间采样点,得到与地震信号最佳相关的子波.对研究的方法从理论模型和实际资料进行了论证分析,证明理论上正确合理,实际应用效果显著.
For the problem of prediction and identification bed sandstone in bar and beach sand reservoir,a new method of matching and tracing complex wavelet is studied.We discuss the parameters about frequency,amplitude,phase and time-shift with reconstruction the seismic signal,and extensional various measure selection through analyses Morlet wavelet composition and reconstruction of seismic signal.We research improve algorithm for the key parameters.All time and frequency points are traced by normal marching algorithm.In order to modify time-frequency distinguish,the part scanning dominant frequency and wavelet time delay are adapt in this paper.In extensional realization,we regard mean instantaneous frequency and time of instantaneous envelop of the seismic signal as sample frequency and time points to obtain the best correlation wavelet.For the studied method,we conduct to verify and analyze through theory and actual data,and verify it is right and reasonably in theory,application effect is obvious.
For the problem of prediction and identification bed sandstone in bar and beach sand reservoir,a new method of matching and tracing complex wavelet is studied.We discuss the parameters about frequency,amplitude,phase and time-shift with reconstruction the seismic signal,and extensional various measure selection through analyses Morlet wavelet composition and reconstruction of seismic signal.We research improve algorithm for the key parameters.All time and frequency points are traced by normal marching algorithm.In order to modify time-frequency distinguish,the part scanning dominant frequency and wavelet time delay are adapt in this paper.In extensional realization,we regard mean instantaneous frequency and time of instantaneous envelop of the seismic signal as sample frequency and time points to obtain the best correlation wavelet.For the studied method,we conduct to verify and analyze through theory and actual data,and verify it is right and reasonably in theory,application effect is obvious.
引文
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[18]范虹,孟庆丰,张优云.用混合编码遗传算法实现匹配追踪算法[J].西安交通大学学报,2005,39(3):295~299.
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[21]刘代志,王仁明,李夕海,刘志刚.基于小波包分解及AR模型的单通道地震波信号初至点检测[J].地球物理学报,2005,48(5):1098~1102.
[2]王真理,李衍达.基于小波的信号部分重构与插值[J].清华大学学报(自然科学版),1995,35(5):67~72.
[3]Miao Y,et al.Fast subspace tracking and NN learning by a no-vel information criterion[J].IEEE Trans Signal Processing,1998,46:1967~1979.
[4]Barbes A E.Instantaneous spectral bandwidth and dominantfrequency with applications to seismic reflection data[J].Geo-physics,1993,58:419~428.
[5]Wang Y H.Seismic time-frequency spectral decomposition bymatching pursuit[J].GEOPHYSICS,2007.
[6]Zhao B.Spectral decomposition of 4D seismic data SEG neworleans 2006 Annual Meeting.
[7]彭玉华.小波变换与工程应用[M].北京:科学出版社,2003.
[8]何建军,任震,黄雯莹,等.电力系统奇异信号的复值小波分析[J].中国电机工程学报,1999,19(11):1~4.
[9]Vetterli M,Herley C.Wavelets and filter banks:theory anddesign[J].IEEE Trans on SP,1992,40(9):2207~2232.
[10]Gaouda A M,Sultan M M A,Sultan M R,et al.Powerquality detection and classification using wavelet-multiresolu-tion signal decomposition[J].IEEE Trans on PD,1999,14(4):1469~1476.
[11]Lawton W.Applications of complex valued wavelet trans-forms to subband decomposition[J].IEEE Trans on SP,1993,41(12):3566~3568.
[12]Kingsbury N G.The dual tree complex wavelet transform:anew technique for shift invarianceand filtersdirectional[A].Proceedings of 8th IEEE Digit Signal Processing Workshop[C].bry-ce Canyon,USA:[s.n.],1998.86~89.
[13]Kingsbury N G.Image processing with complex wavelets[J].Philosophical Transactions:Mathem-etical,PhysicalandEngi-neeringSciences,1999,35(160):2543~2560.
[14]张春梅,张太镒,陆从德.基于双密度双树复数小波变换的图像去噪[J].西安交通大学学报,2006,40(2):246~248.
[15]耿云玲,王群,何怡刚.基于复数小波相位信息的电能质量扰动的检测、定位与分类[J].电工技术学报,2006,21(8):112~116.
[16]陈文超,高静怀.基于改进的最佳匹配地震子波的地震资料衰减特性分析[J].地球物理学报,2007,50(3):837~843.
[17]王文延,曾庆宁,李琴.匹配追踪时频分解算法的端点检测方法[J].声学技术,2007,26(1):117~120.
[18]范虹,孟庆丰,张优云.用混合编码遗传算法实现匹配追踪算法[J].西安交通大学学报,2005,39(3):295~299.
[19]李宏亮,黄润华.基于遗传算法的匹配追踪小波分析及其在非平稳故障信号中的应用[J].机械强度,2001,23(1):268~272.
[20]肖燕,黄成军,郁惟镛,江秀臣.波形匹配追踪算法在多局放脉冲提取中的应用[J].中国电机工程报,2005,26(11):157~162.
[21]刘代志,王仁明,李夕海,刘志刚.基于小波包分解及AR模型的单通道地震波信号初至点检测[J].地球物理学报,2005,48(5):1098~1102.
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