基于Morlet小波尺度参数寻优的匹配追踪时频分析
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摘要
与短时傅里叶变换、连续小波变换、广义S变换等时频分析方法相比,匹配追踪方法具有更高的时频分辨率,但传统的贪婪迭代算法计算效率较低。以Morlet小波作为时频原子进行匹配追踪,通过分析尺度因子不同时Morlet小波时频原子在时间域的形态,比较信号向频率、相位和延时相同,仅尺度因子不同的不同时频原子投影的投影值,认为尺度因子对时频原子的形态具有较强的控制作用,因而对时频原子和信号局部特征的匹配性能具有较强的控制作用。基于以上分析,在利用复地震道计算信号的瞬时信息作为时频原子频率、相位和时延等参数的基础上,对Morlet小波时频原子的尺度参数首先进行一维寻优,在得到最佳尺度因子基础上对时频原子参数进行微调,提高了计算效率。针对模型测试了算法的有效性及在去除噪声和薄层厚度求取等方面的应用前景。
Matching pursuit time-frequency analysis has better time-frequency resolution compared to short-time Fourier transform,continuous wavelet transform,and generalized S transform,but the traditional greed iterative algorithm has lower computation efficiency. The Morlet wavelet is chosen as timefrequency atoms to achieve the matching pursuit due to the good property of scale parameter. The scale parameter has strong control action on the form of time-frequency atom,thus has strong control action on the matching character between signal and time-frequency atom,through comparing and analyzing the forms of time-frequency atoms based on different scale parameters and the projection values of signal onto different time-frequency atoms with the same frequency,phase and time-delay parameters but different scale parameters. The 1D optimization for scale parameter is done with the frequency,phase and time-delay parameters calculated by Hilbert transform as the parameters of time-frequency atoms. Then the parameters are only needed to be fine-adjusted,and the computation efficiency is improved. The effectiveness of algorithm is tested by model data,and the algorithm is also tested on application of denosing and inversion for thin layer thickness.
引文
[1]ALLEN J B,RABINER L R.A unified approach to shorttime Fourier analysis and synthesis[J].Proceedings of the IEEE,1977,65(11):1558-1564.
    [2]GABOR D.Theory of communication[J].Journal of the IEEE,1946,93:429-441.
    [3]KOENIG W,DUNN H K,LACY L Y.The sound spectrograph[J].The Journal of the Acoustical Society of America,1946,18(1):19-49.
    [4]边海龙.非平稳信号联合时频分析方法的若干问题研究与应用[D].成都:电子科技大学,2008.
    [5]董建华,顾汉明,张星.几种时频分析方法的比较及应用[J].工程地球物理学报,2007(4):312-316.
    [6]吴勇.基于小波的信号去噪方法研究[D].武汉:武汉理工大学,2007.
    [7]刘丽娟.时频分析技术及其应用[D].成都:成都理工大学,2008.
    [8]吴伟龙.基于小波变换的地震信号瞬时参数提取方法研究[D].大庆:东北石油大学,2011.
    [9]张贤达,保铮.非平稳信号分析与处理[M].北京:国防工业出版社,1998:446.
    [10]姜镭.基于时频谱特征的薄互层分析[D].成都理工大学,2009.
    [11]高静怀,陈文超,李幼铭,等.广义S变换与薄互层地震响应分析[J].地球物理学报,2003,46(4):526-532.
    [12]杨阳.广义S变换时频分析的应用研究[D].哈尔滨:哈尔滨工程大学,2011.
    [13]STOCKWELL R G,MANSINHA L,LOWE R P.Localization of the complex spectrum:the S transform[J].IEEE Transactions on Signal Processing,1996,44(4):998-1001.
    [14]MALLAT S G,ZHANG Z F.Matching pursuits with time-frequency dictionaries[J].IEEE Transactions on Signal Processing,1993,41(12):3397-3415.
    [15]CASTAGNA J P,SUN S,SIEGFRIED R W.Instantaneous spectral analysis:detection of low-frequency shadows associated with hydrocarbons[J].The Leading Edge,2003,22(2):120-127.
    [16]孙万元,张会星,杜艺可.匹配追踪时频分析及其在油气检测中的应用[J].山东科技大学学报:自然科学版,2011,30(4):51-57.
    [17]PARTYKA G,GRIDLEY J,LOPEZ J.Interpretational applications of spectral decomposition in reservoir characterization[J].The Leading Edge,1999,18(3):353.
    [18]LIU J.Time-frequency decomposition based on Ricker wavelet[J].SEG Technical Program Expanded Abstracts,2004,23(1):1937.
    [19]LIU J.Matching pursuit decomposition using Morlet wavelets[J].SEG Technical Program Expanded Abstracts,2005,24(1):786.
    [20]张繁昌,李传辉.非平稳地震信号匹配追踪时频分析[J].物探与化探,2011,35(4):546-552.
    [21]张繁昌,李传辉,印兴耀.基于动态匹配小波库的地震数据快速匹配追踪[J].石油地球物理勘探,2010,45(5):667-673.
    [22]张繁昌,李传辉.基于正交时频原子的地震信号快速匹配追踪[J].地球物理学报,2012,55(1):277-283.
    [23]邵君.基于MP的信号稀疏分解算法研究[D].成都:西南交通大学,2006.
    [24]WANG Y.Seismic time-frequency spectral decomposition by matching pursuit[J].Geophysics,2007,72(1):V13-V20.

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