f A semblance-guided median filter

We describe and implement a slowness selective median filter based on information from a local set of traces. The filter is constructed in two steps, the first being an estimation of a preferred slowness and the second the selection of a median or trimmed mean value to replace the original data point. A symmetric window of traces is selected about each trace to be filtered and the filter repeatedly applied to each time point. The preferred slowness is determined by scanning a range of linear moveouts within the user specified slowness passband. Semblance is computed for each trial slowness and the preferred slowness selected from the peak semblance value.