Seismic data random noise attenuation using DBM filtering

Seismic random noise attenuation is a very important step in seismic processing, resulting in seismic interpretation enhancement. Decision-based median (DBM) filtering is proposed here for seismic random noise attenuation to improve the signal to noise ratio (S/N) and the quality of the seismic images. Unlike conventional median filtering, where both the signal and noise samples are affected, a DBM filter predominantly affects the noise samples using a selection criterion based on a threshold. In other words, using a DBM filter, noise samples are initially detected, and then the filtering operation is applied to them. When the DBM filter is applied to the synthetic and observed pre/post stack seismic data sets, its superiority over the conventional median filter in suppressing random noise and improving S/N of seismic data is demonstrated.