Modern recording methods insure that seismic data are adequately sampled in time. However, economic and practical considerations dictate that data are gathered at surface locations which are often far enough apart that the data are spatially aliased. This aliasing can adversely effect the performance of migration algorithms as well as other processing and imaging techniques. One low cost method of increasing the performance of imaging methods is to interpolate the data in space. The effectiveness of this type of interpolation is demonstrated by Spitz 1991, who shows that using an f-x prediction method to interpolate data results in an improved migrated section. To this end, Claerbout 1992a presents a t-x prediction filter method of interpolation. Indeed, the subject is receiving so much attention at the Stanford Exploration Project that whole sections in this and the last report are devoted to interpolation.
I describe the implementation of the interpolation scheme suggested by Muir 1991 and show that it works for real and synthetic data. The nice thing about this method is that it is simple and easy to understand. Regularly sampled data sets which require trace interpolation and have an adequate low-frequency portion of their spectra unaliased are candidates for this scheme.
First I describe the algorithm in detail, and then I present interpolations of synthetic and real data.