基于Wigner-Ville的谱分解效果分析
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摘要
地震信号是典型的非平稳信号,地震谱分解实质上就是连续时频谱分析。谱分解不是惟一的过程,可供选用的算法很多。本次研究的是Wigner-Ville分布(WVD)算法,它不含任何窗函数,避免了时间分辨率和频率分辨率的互相牵制、难以兼顾的问题,获取了高精度的时频分辨率。首先介绍了WVD算法的基本原理和实现方法。把地震数据体从时间域转到频率域,求取每个地震道时间样点的频谱;然后按照频率重排产生共频率的剖面;再对各单一频率的剖面进行对比、分析和解释。理论分析和实际地震资料计算结果表明,WVD谱分解算法应用于地震资料解释中具有一定的可行性。
Seismic signal is a typical non-stationary signal. Spectral decomposition in essence is a continuous time-frequency analysis of a seismic trace. Since time-frequency mapping is a non-unique process, there exist various ways for time-frequency analysis of non-stationary signals. This paper studied WVD algorithm, which does not involve any window function that bypasses the interaction between time resolution and frequency resolution and achieves higher resolution in both time and frequency. Firstly, we transformed seismic data cube from time domain into frequency domain and computed the frequency spectrum of every time point for each seismic trace. Secondly, we generated different single frequency sections by rearranging frequency contents. Finally we compared, analyzed, and interpreted each single frequency section. It is shown by theoretical analysis and real seismic data that WVD algorithm is advisable.
Seismic signal is a typical non-stationary signal. Spectral decomposition in essence is a continuous time-frequency analysis of a seismic trace. Since time-frequency mapping is a non-unique process, there exist various ways for time-frequency analysis of non-stationary signals. This paper studied WVD algorithm, which does not involve any window function that bypasses the interaction between time resolution and frequency resolution and achieves higher resolution in both time and frequency. Firstly, we transformed seismic data cube from time domain into frequency domain and computed the frequency spectrum of every time point for each seismic trace. Secondly, we generated different single frequency sections by rearranging frequency contents. Finally we compared, analyzed, and interpreted each single frequency section. It is shown by theoretical analysis and real seismic data that WVD algorithm is advisable.
引文
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4Castagna J,Sun S.Comparison of spectral decomposi-tion methods[J].First Break,2006,24(3):75~79
5印兴耀,张奎,张广智.联合时频分布及其属性的应用[J].石油地球物理勘探,2003,38(5):522~526
6邹文,陈爱萍,顾汉明.联合时频分析技术在地震勘探中的应用[J].勘探地球物理进展,2004,27(4):246~250
7张奎凤,蓝晖.短时窗地震信号谱分析方法[J].石油物探,1995,34(2):94~98
8张宏.地震谱分解算法对比与局限性分析[J].勘探地球物理进展,2007,30(6):409~414
9徐伯勋,白旭滨.最大熵谱估计在油气检测中的应用[J].石油物探,1990,29(2):1~11
2Partyka G,Gridley J,Lopez J.Interpretational applica-tions of spectral decomposition in reservoir character-ization[J].The Leading Edge,1999,18(3):353~360
3黄绪德.薄层陷频法[J].勘探地球物理进展,2002,25(5):193~196
4Castagna J,Sun S.Comparison of spectral decomposi-tion methods[J].First Break,2006,24(3):75~79
5印兴耀,张奎,张广智.联合时频分布及其属性的应用[J].石油地球物理勘探,2003,38(5):522~526
6邹文,陈爱萍,顾汉明.联合时频分析技术在地震勘探中的应用[J].勘探地球物理进展,2004,27(4):246~250
7张奎凤,蓝晖.短时窗地震信号谱分析方法[J].石油物探,1995,34(2):94~98
8张宏.地震谱分解算法对比与局限性分析[J].勘探地球物理进展,2007,30(6):409~414
9徐伯勋,白旭滨.最大熵谱估计在油气检测中的应用[J].石油物探,1990,29(2):1~11
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