Seismic random noise attenuation based on adaptive nonlocal median filter

Abstract

Abstract The accurate image of underground medium is determined by the quality of the seismic data, which can be improved by random noise attenuation and structural continuity enhancement. We proposed an adaptive nonlocal median filter that can protect geological structure while attenuating random noise. We combine the nonlocal idea with the weighted median filter and design the appropriate weights of the nonlocal median filter based on seismic data characteristics. The local structure is represented by the neighborhood around the center point. The directional difference of spatial vectors in the neighborhood is considered when computing the similarity. According to the local dip attribute of seismic data, the anisotropic Gaussian window is adaptively adjusted to increase the constraint along the structural direction. The proposed method can search more precisely for points with similar local structure to the filtered points and effectively attenuate seismic random noise. The continuity of events is enhanced while the goal of protecting fault information is achieved. The experimental results of the theoretical model and field data show that the adaptive nonlocal median filter can strike a balance between preserving structure information and attenuating seismic random noise.