Geophysical datasets are rarely stationary over long distances in either time or space, so recursive filtering is particularly helpful in statistical estimation. The purpose of most filters is to make possible the observation of important weak events that are obscured by strong events. One -dimensional filters can do this only by the selection or rejection of frequency components. In two dimensions, a different criterion is possible, namely, selection by dip. Steep dips are often ground-roll noise.
Horizontal dips can also be noise.
For example, weak fault diffractions carry valuable information, but they may often be invisible because of the dominating presence of flat layers. Pie-slice filters thus offer complete control over the filter response in dip space. The causality property offers an interesting opportunity during data recording.