Shearlet域基于非局部均值的地震信号去噪

由于采集环境及仪器性能的限制,采集的地震信号中含有较强的随机噪声,对后续的处理和解释带来很大困难.多尺度几何分析近年来受到关注,在Shearlet变换域中引入非局部均值(NLM,non-local mean algorithm)算法对地震信号进行去噪,该算法首先对地震信号进行非下采样Shearlet变换,对近似服从广义高斯分布的Shearlet系数进行主成分分析(PCA,principal component analysis),然后采用非局部均值处理Shearlet系数,最后对新的Shearlet系数进行Shearlet反变换,得到去噪之后的地震信号.实验结果表明,文中算法在低噪声情况下能够获得优于非局部均值算法的去噪效果,对地震信号去噪具有可行性.

Denoising of seismic signals based on non-local mean in Shearlet domain

Due to the limitations of the acquisition environment and instrument performance, the collected seismic signals contain strong random noise, which presents great challenges for subsequent processing and interpretation. Multi-scale geometric analysis has attracted attention in recent years. This paper introduces non-local mean algorithm (NLM) into the Shearlet transform domain to denoise seismic signals. The algorithm firstly performs non-subsampled Shearlet transform on seismic signals, and approximates the generalized Gaussian distribution. The Shearlet coefficients are subjected to principal component analysis (PCA), and then the non-local mean processing Shearlet coefficients are used. Finally, the new Shearlet coefficients are inversely transformed by Shearlet to obtain the denoised seismic signals. The experimental results show that under low noise the proposed algorithm can achieve better denoising effect than the non-local mean algorithm. Therefore, the proposed algorithm is feasible for denoising seismic signals.