地震信号随机噪声压制的双树复小波域双变量方法

有效地压制地震信号中的噪声是地震信号解释和后续处理的重要环节之一.本文建立两种双树复小波域双变量模型对地震信号中的随机噪声进行压制.地震信号经双树复小波变换后,同一方向实部与虚部系数、实部(或虚部)系数与对应的模之间存在较强的相关性.鉴于此,对同一方向实部与虚部小波系数建立双变量模型,从含噪地震信号小波系数中估计原始信号的小波系数,再基于双树复小波逆变换重构得到降噪后的地震信号.进一步对同一方向实部(或虚部)系数与对应的模建立双变量模型,得到地震信号随机噪声压制的第二种双树复小波域双变量方法.最后对合成地震记录和实际地震资料中的随机噪声进行压制的实验结果证实本文两种方法都能够有效地压制地震信号中的随机噪声.

Dual-tree complex wavelet domain bivariate method for seismic signal random noise attenuation

Seismic signal noise attenuation is important in processing and interpreting seismic signal subsequently. Two dual-tree complex wavelet domain bivariate methods for seismic signal random noise attenuation are proposed. After the dual-tree complex discrete wavelet transform, the real and imaginary parts of the wavelet coefficients have dependency in the same direction. The real or imaginary parts and the corresponding magnitudes of the wavelet coefficients have dependency in the same direction. So we construct the bivariate model for the real and imaginary parts in the same direction. Using the model the wavelet coefficients of the original seismic signal are estimated. The denoised seismic signal is achieved based on the dual-tree complex wavelet inverse transform. The proposed algorithm is also extended to the real or imaginary parts and the corresponding magnitudes of dual-tree complex wavelet coefficients in the same direction. Through experiments on the synthetic seismic record and the field seismic data, the results demonstrate that the proposed two methods can attenuate random noise effectively.