Seismic data filtering using non-local means algorithm based on structure tensor

Abstract

AbstractNon-Local means algorithm is a new and effective filtering method. It calculates weights of all similar neighborhoods’ center points relative to filtering point within searching range by Gaussian weighted Euclidean distance between neighborhoods, then gets filtering point’s value by weighted average to complete the filtering operation. In this paper, geometric distance of neighborhood’s center point is taken into account in the distance measure calculation, making the non-local means algorithm more reasonable. Furthermore, in order to better protect the geometry structure information of seismic data, we introduce structure tensor that can depict the local geometrical features of seismic data. The coherence measure, which reflects image local contrast, is extracted from the structure tensor, is integrated into the non-local means algorithm to participate in the weight calculation, the control factor of geometry structure similarity is added to form a non-local means filtering algorithm based on structure tensor. The experimental results prove that the algorithm can effectively restrain noise, with strong anti-noise and amplitude preservation effect, improving PSNR and protecting structure information of seismic image. The method has been successfully applied in seismic data processing, indicating that it is a new and effective technique to conduct the structure-preserved filtering of seismic data.