全变分正则化非局部均值地震数据降噪

Total variational regularization for non-local mean seismic data denoising

在地震数据的采集中往往存在随机噪声,噪声会影响地震数据分析的准确性,针对地震数据中存在的高斯噪声,传统非局部均值降噪算法在对地震数据降噪后无法有效保持地震数据中的同相轴边缘。将全变分正则化非局部均值算法应用于地震数据降噪,通过计算噪声估计值,更新去抖动非局部均值算法的权值,将去抖动非局部均值降噪结果进行全变分正则化约束,得到最佳的地震数据降噪结果。在有效去除高斯噪声的同时,保留地震数据的同相轴边缘。通过在合成地震数据、海上叠前地震数据、陆上叠后地震数据上进行降噪实验,对比该算法与非局部均值算法、基于近邻法选择策略的非局部均值算法的峰值信噪比、均方误差、平均结构相似度,得出全变分正则化非局部均值降噪算法在有效降噪的同时,可以较完整地保留地震数据的同相轴边缘细节。

Random noise often exists in seismic data acquisition,which will affect the accuracy of seismic data analysis.For Gaussian noise existing in seismic data,traditional non-local mean denoising algorithms cannot effectively maintain the in-phase axis edge in seismic data after denoising the seismic data.The total variation regularization non-local mean algorithm is applied to seismic data noise reduction.By calculating the noise estimation value and then updating the weight value of the de-jitter non-local mean algorithm,the de-jitter non-local mean noise reduction result is subjected to the total variation regularization constraint so as to obtain the best seismic data noise reduction result.While Gaussian noise is effectively removed,the edge of the in-phase axis of seismic data is retained.Through noise reduction experiments on synthetic seismic data,offshore pre-stack seismic data and onshore post-stack seismic data,the peak signal-to-noise ratio,mean square error and average structural similarity of the algorithm are compared with non-local mean algorithm and non-local mean algorithm based on neighbor selection strategy.It is concluded that the total variation regularization non-local mean noise reduction algorithm can effectively reduce noise while retaining the in-phase axis edge details of seismic data.