Robust rank-reduction filtering for erratic noise

Abstract

Rank-reduction filters operating on constant-frequency slices are highly effective at removing Gaussian random noise. Prestack seismic data, however, often contains spatially erratic noise which is far from Gaussian, sometimes causing these filters to give poor results. Here we describe new robust rank-reduction filters which can handle both Gaussian and erratic noise, and we present examples using synthetic and real data.