Ground roll attenuation using non-stationary matching filtering

Abstract

Conventional approaches based on adaptive subtraction for ground roll attenuation first predict an initial model for ground rolls and then adaptively subtract it from the original data using a stationary matching filter (MF). Because of the non-stationary property of seismic data and ground rolls, the application of a traditional stationary MF is not physically plausible. Thus, in the case of highly non-stationary seismic reflections and ground rolls, a stationary MF cannot obtain satisfactory results. In this paper, we apply a non-stationary matching filter (NMF) to adaptively subtract the ground rolls. The NMF can be obtained by solving a highly under-determined inversion problem using non-stationary autoregression. We apply the proposed approach to one synthetic example and two field data examples, and demonstrate a much improved performance compared with the traditional MF approach.