协方差速度谱

Covariance velocity spectrum

协方差速度谱是基于采样数据可以排列成协方差矩阵的特征结构而设计的。为获取数据协方差矩阵,须对CDP道集记录进行初步动校正,然后引入时差扫描,进行剩余时差校正。利用协方差矩阵的特征值.能实现信号和噪声的分解。因为利用主特征可提取信号方差估计,利用次特征值可提取噪声方差估计。所以协方差速度谱与常规的叠加速度谱和相关速度谱以及相似系数谱相比,具有较高的分辨率.这种高分辨率下仅体现在l_o时间方向上,而且在叠加速度方向上改善分辨率的效果更为显著。由于此法是为了提高速度谱的分辨率,故时窗下应大于所分析子波主能量的延续时间,而且时窗移动增量不应大干半时窗长度,同时应尽量避免使用大的速度增量。

Covariance velocity spectrum is based on the fact that the sampled data can be arranged in eigen structure of covariance matrix. The data covariance matrix is obtained by performing NMO correction,moveout scan and residual moveout correction of CDP gathers. The eigenvalue of covariance matrix can be used to separate signal from noise. Signal covariance estimate may be derived from the dominant eigenvalue,and noise covariance estimate from subordinate eigenvalue, so that covariance velocity spectrum has higher resolution than conventional stack correlation and semblance coefficient velocity spectra. Such high resolution can be seen in t_o time direction,particularly in stack velocity direction. Because this method is used to improve the resolution of velocity spectrum,we must pay more attention to the following points. ·Time window is not longer than main energy duration of the wavelet which is analysed. ·Moving increment of time window is not longer than half time window, and ·Big velocity increment is avoided here.