双密度双树复小波域统计模型的地震信号降噪

Double density dual tree complex wavelet domain statistical model for seismic signal denoising

随机噪声的存在降低了地震信号的信噪比,淹没了有效信号,影响后续的地质解释。文章根据随机噪声的特性以及地震信号道间相关性,建立双密度双树复小波域统计模型压制地震信号中的随机噪声。首先对含噪地震信号进行双密度双树复小波变换,分别对不同尺度、不同方向上的噪声方差和含噪地震信号方差进行估计,计算阈值;再运用最大后验概率估计方法从含噪地震信号小波系数中估计出源地震信号的小波系数;最后利用双密度双树复小波逆变换对源地震信号的小波系数估计值进行重构,得到降噪后的地震信号。仿真实验和对实际地震信号的处理结果表明该方法能够有效地压制随机噪声,提高了信噪比,较好地保留了有效信号。

The random noise reduces the signal-to-noise ratio of the seismic signal,submerges the effective signal and affects the geological interpretation.The double density dual tree complex wavelet domain statistical model is established to suppress random noise in the seismic signal according to the characteristics of random noise and seismic signal channel correlation.Firstly,the noisy seismic signal is decomposed by double density dual tree complex wavelet.The noise variance and noisy seismic signal variance at different scales and directions are estimated,and the threshold is calculated.The wavelet coefficients of the source seismic signal are estimated from the noisy seismic signal using the maximum a posteriori probability estimation method.Finally,the denoised seismic signal is achieved based on double density dual tree complex wavelet inverse transform.The experimental results show that the proposed method can suppress the random noise effectively,enhance the signal-to-noise ratio and retain the effective signal very well.