Structure‐enhancing nonlinear filtering of seismic images

Abstract

Attenuation of random noise and enhancement of structural continuity can significantly improve the quality of seismic interpretation. We present a novel filtering method, which aims at reducing random noise while protecting seismic structures. The method is based on combining predictive flattening with similarity-mean filtering. We use predictive flattening to form a structural prediction of seismic traces from neighboring traces. We apply a nonlinear similarity-mean filter to select the best samples from different predictions. Parameters of the nonlinear filter allow us to control the balance between eliminating random noise and protecting structural information. Numerical tests using synthetic and field data confirm the effectiveness of proposed structure-enhancing filtering.