非正交小波谱分解技术的研究和应用
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摘要
谱分解技术利用薄层的调谐效应,在频率域成像出薄层的厚度及不连续性等特征,是一项薄储层识别和描述的有效技术。目前常见的谱分解技术包括短时傅里叶变换、小波变换、最大熵等。短时傅里叶变换技术存在窗口问题,限制了垂向分辨率,而且不能适应地层厚度的变化;常规小波变换使用尺度参数,与频率参数难以直接对应,地质含义不够明确。非正交小波变换避免了上述方法的局限,直接使用频率控制,可以指定频带和频率分布密度,适应薄层识别的需求和地震层序分析的规律,更有利于薄层的识别和描述。
According to tuning effect of thin bed reflection, spectral-decomposition technique images the thickness and discontinuity of thin bed in frequency domain. It's a good and effective tool for identification and characterization of thin bed. Usually, common spectral- decomposition methods include short-time window Fourier transform (STFT), wavelet transform (WT), maximum entropy(ME), etc. STFT method has the window problem, which limits the vertical resolution, and cannot adapt the thickness transverse change of layers. Normal WT methods use scale parameter, which cannot correspond directly with frequency with ambiguity geological means. Non- orthogonal WT avoids upper limitations under the control of frequency parameter directly. It can specify frequency band and distribution density in order to satisfy the identification requirement of thin bed and seismic sequence analysis, and benefit to characterize thin beds.
According to tuning effect of thin bed reflection, spectral-decomposition technique images the thickness and discontinuity of thin bed in frequency domain. It's a good and effective tool for identification and characterization of thin bed. Usually, common spectral- decomposition methods include short-time window Fourier transform (STFT), wavelet transform (WT), maximum entropy(ME), etc. STFT method has the window problem, which limits the vertical resolution, and cannot adapt the thickness transverse change of layers. Normal WT methods use scale parameter, which cannot correspond directly with frequency with ambiguity geological means. Non- orthogonal WT avoids upper limitations under the control of frequency parameter directly. It can specify frequency band and distribution density in order to satisfy the identification requirement of thin bed and seismic sequence analysis, and benefit to characterize thin beds.
引文
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[5] Chakraborty Avijit,Okaya David. Frequency-time decomposition of seismic data using wavelet-based methods. Geophysics, 1995, Vol.60,No.6: 1906～1916
[6] Sun Shengjie, Castagna J P,Siegfried. Examples of enhanced spectral processing in direct hydrocarbon detection. AAPG Annual Meeting,March 10-13,2002,Houston Texas
[7] Castagna J P, Sun S,Siegfried R W. Instantaneous spectral analysis: Detection of low-frequency shadows associated with hydrocarbons. The Leading Edge,2003,Vol.22,No.2:120～127
[8] Gribonval Rém i. Fast matching pursuit with a multiscale dictionary of Gaussian Chirps. IEEE Transactions on Signal Processing, 2001,Vol.49,No.5:994～1001
[9] Stockwell R G, Mansinha L,Lowe R P. Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing, 1996,Vol.44,No.4:998～1001
[10] Pinnegar C R,Mansinha L. The S-transform with windows of arbitrary and varying shape.Geophysics,2003,Vol.68,No.1:381～385
[11] Pinnegar C R,Mansinha L. Time-local spectral analysis for non-stationary time series: the S-transform for noisy signals. Flunctuation and Noise Letters, 2003,Vol.3,No.3:357～364
[12] Ebrom Dan. The low-frequency gas shadow on seismic sections. The Leading Edge, 2004,Vol.23,No.8:772
[2] 刘传虎,等. 时频分析方法及在储层预测中的应用[J].石油地球物理勘探(增刊1),1996,31(1):11～20
[3] 张贤达. 现代信号处理[M]. 北京:清华大学出版社,2002
[4] Mallat Stéphane G,Zhang Zhifeng. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 1993, Vol.41,No.12: 3397～3415
[5] Chakraborty Avijit,Okaya David. Frequency-time decomposition of seismic data using wavelet-based methods. Geophysics, 1995, Vol.60,No.6: 1906～1916
[6] Sun Shengjie, Castagna J P,Siegfried. Examples of enhanced spectral processing in direct hydrocarbon detection. AAPG Annual Meeting,March 10-13,2002,Houston Texas
[7] Castagna J P, Sun S,Siegfried R W. Instantaneous spectral analysis: Detection of low-frequency shadows associated with hydrocarbons. The Leading Edge,2003,Vol.22,No.2:120～127
[8] Gribonval Rém i. Fast matching pursuit with a multiscale dictionary of Gaussian Chirps. IEEE Transactions on Signal Processing, 2001,Vol.49,No.5:994～1001
[9] Stockwell R G, Mansinha L,Lowe R P. Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing, 1996,Vol.44,No.4:998～1001
[10] Pinnegar C R,Mansinha L. The S-transform with windows of arbitrary and varying shape.Geophysics,2003,Vol.68,No.1:381～385
[11] Pinnegar C R,Mansinha L. Time-local spectral analysis for non-stationary time series: the S-transform for noisy signals. Flunctuation and Noise Letters, 2003,Vol.3,No.3:357～364
[12] Ebrom Dan. The low-frequency gas shadow on seismic sections. The Leading Edge, 2004,Vol.23,No.8:772
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