提出了一种基于W加权核范数最小化的地震信号盲去噪算法。采用主成分分析法估计地震信号噪声水平,借助加权核范数最小化(WNNM)实现去噪。在去噪中通过权值分配控制矩阵奇异值的收缩程度,提升了算法性能。分别对三种地震信号进行去噪,并与双树复小波变换、曲波变换、WNNM算法进行了性能对比。研究结果表明,该算法在噪声水平未知的情况下,能有效去除地震信号所含噪声,去噪效果优于传统去噪算法。

A seismic signal blind denoising algorithm is proposed based on W-weighted nuclear norm minimization. The noise level of seismic signals is estimated by principal component analysis and the denoising is realized by weighted nuclear norm minimization (WNNM). In denoising, the shrinkage degree of singular values of a matrix is controlled by weight assignment, and the performances of the algorithm is improved. Three kinds of seismic signals are denoised, respectively. The performance is compared with double tree complex wavelet transform, curvelet transform and the WNNM algorithm. The research results show that the proposed algorithm can effectively remove the noises contained in seismic signals when the noise level is unknown. Moreover, the denoising effect is superior to those of the traditional denoising algorithms.