Adaptive ground-roll attenuation using local nonlinear filtering

Abstract

Source-generated coherent noise suppression is a long-standing issue in land seismic data processing. Traditional methods—such as f–k filtering, τ-p transform, and median filtering—often fail to remove coherent noise in irregular data, especially in the case that the noise highly overlaps with the signal. To address this issue, we developed a method based on local nonlinear filtering (LNF) to attenuate coherent noise in seismic data. In the proposed method, a novel similarity coefficient algorithm is presented to determine the optimal velocity of coherent noise. An initial model of coherent noise is constructed using a mixed median–mean filter and is then refined by the proposed weighting correction algorithm. After modeling the noise, coherent noise in seismic data can be then suppressed by adaptively subtracting the estimated noise from the raw seismic data. To verify the effectiveness of the proposed method, we applied it to regular and irregular synthetic data and a 3D field dataset containing strong ground roll. The results demonstrate that the new method successfully attenuates the ground roll and obtains better results in ground-roll attenuation and the preservation of useful signals compared with the three commonly used methods. Synthetic and real data examples indicate that the method is applicable to the attenuation of coherent noise in irregular data.