基于混合优化算法的地震数据匹配追踪分解
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摘要
以5个参数(幅度、频率、相位移、尺度因子、时移)控制的Morlet小波作为匹配子波原子,在确定控制参数的过程中,提出应用具有全局优化能力的粒子群优化算法与具有局部优化能力的BFGS算法的混合优化算法,能够使得匹配追踪算法不再依赖于复数道分析确定子波原子的振幅、频率和相位的初值。控制子波时间延续长度的尺度因子是一个重要的参数。匹配追踪分解后,消除较小和较大的尺度因子和分解终止时的剩余信号能够有效地压制地震数据噪声。利用局部函数解析表达式和残差信号能量进行有效地控制算法的迭代次数可以提高计算效率。数值试验和实际资料的应用均表明:利用本文方法能够压制地震数据噪声,对地震信号快速地、精确地进行时频谱分析,为烃类检测和储层描述提供有效的手段。
Morlet wavelet with five parameters,including amplitude,frequency,phase,scale factor and time delay,as atoms in the matching pursuit decomposition was employed.In the processing of established controlled variable,hybrid optimization algorithm was introduced,including particle swarm optimization and BFGS method,so as to in-depend on complex-trace analysis to determine initial value of controlled variable,such as amplitude,frequency,and phase.The scale factor is an important adaptive parameter that controls the width of wavelet in time.After matching pursuit decomposition,removing wavelets with either very small or very large scale value and residual signal can suppress spikes and sinusoid functions,and rand noise effectively from seismic data.For fast matching pursuit algorithm,analytical expressions and the energy of the residual signal were employed which control effectively the iterating times.Synthetic data test and results of practical data application show that using method in the paper has good effect in the aspect of attenuating noise form seismic data,fleetly and accurately implementing time-frequency analysis,and provide an effective means for hydrocarbon detection and reservoir description.
引文
[1]Mallat S,Zhang Z.Matching pursuit with time-frequencydictionaries[J].IEEE Trans Signal Process,1993,41(12):3397 3421.
    [2]Qian S,Chen D.Signal representation via adaptive normalizedGaussian functions[J].Signal Processing,1994,36(1):1 11.
    [3]Rebollo-Neira L,Lowe D.Optimized orthogonal matchingpursuit approach[J].IEEE Signal Process Letters,2002,9(4):137 140.
    [4]Capobianco E.Independent multi-resolution component analysisand matching pursuit[J].Computational Statistics&DataAnalysis,2003,42(6):385 402.
    [5]Andrle M,Rebollo-Neira L,Sagianos E.Backward-optimizedorthogonal matching pursuit approach[J].IEEE Signal ProcessLetters,2004,11(9):705 708.
    [6]Andrle M,Rebollo-Neira L.A swapping-based refinement oforthogonal matching pursuit strategies[J].Signal Processing,2006,86(3):480 495.
    [7]Wang B,Pann K.Kirchhoff migration of seismic datacompressed by matching pursuit decomposition[C]//66th AnnualInternational Meeting.Denver,Colorado:SEG,ExpandedAbstracts,1996:1642 1645.
    [8]Castagna J P,Sun S J,Siegfried R W.Instantaneous spectralanalysis:Detection of low-frequency shadows associated withhydrocarbons[J].The Leading Edge,2003,22(2):120 127.
    [9]Liu J,Marfurt K J.Matching pursuit decomposition using Morletwavelet:[C]//75th Annual International Meeting.Houston,Texas:SEG,Expanded Abstracts,2005:786 789.
    [10]Liu J,Wu Y,Han D,et al.Time-frequency decomposition basedon Ricker wavelet[C]//74th Annual International Meeting.Denver,Colorado:SEG,Expanded Abstracts,2004:1937 1940.
    [11]Morlet J,Arens G,Fourgeau E,et al.Wave propagation andsampling theory:Part I.Complex signal and scattering inmultilayered media[J].Geophysics,1982,47(2):203 221.
    [12]Morlet J,Arens G,Fourgeau E,et al.Wave propagation andsampling theory:Part II.Sampling theory and complex waves[J].Geophysics,1982,47(2):222 236.
    [13]Wang Y H.Seismic time-frequency spectral decomposition bymatching pursuit[J].Geophysics,2007,72(1):v13 v20.
    [14]张繁昌,李传辉,印兴耀.基于动态匹配子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):667673.ZHANG Fanchang,LI Chuanhui,YIN Xingyao.Seismic datafast matching pursuit based on dynamic matching waveletlibrary[J].Oil Geophysical Prospecting,2010,45(5):667 673.
    [15]张显文,韩立国,王宇,等.地震信号谱分解匹配追踪快速算法及应用[J].石油物探,2010,49(1):16.ZHANG Xianwen,HAN Li-guo,WANG Yu,et al.Seismicspectral decomposition fast matching pursuit algorithm and itsapplication[J].Geophysical Prospecting for Petroleum,2010,49(1):1 6.
    [16]Gribonval R.Fast matching pursuit with a multi-scale dictionaryof Gaussian Chirps[J].IEEE Trans Signal Processing,2001,49(5):994 1001.
    [17]Davis G,Mallat S,Avellaneda M.Adaptive greedyapproximations[J].Constr Approx,1997,13(1):57 98.
    [18]Kennedy J,Eberhart R.Particle Swarm optimization[C]//Proceedings of IEEE International Conference on NeuralNetwork.Perth,Western Australia,1995:1942 1948.
    [19]Sun J,Xu W B.Particle swarm optimization with particleshaving quantum behavior[C]//IEEE Proceedings of Congress onEvolutionary Computation.Piscataway,New Jersey,2004:325 331.
    [20]袁亚湘,孙文瑜.最优化理论与方法[M].北京:科学出版社,2003:219 234.YUAN Yaxiang,SUN Wenyu.Optimization theory andmethods[M].Beijing:Science Press,2003:219 234.

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