Seismic processing techniques, such as migration, have strict requirements on information content in the input seismic data. Although not a substitute for well-sampled field data, interpolation can provide useful data preconditioning that allows migration to work better.<br>Seismic data interpolation has been around for long time, but only recently have we been able to use complex multidimensional and global algorithms that have the capability to infill large gaps in 3D land surveys. This innovation offers great potential for improvement, but for this technology to become useful, many questions still need answers. What are the best domains in which to interpolate? What is the optimal size of operators given a particular level of structural complexity? Should we pursue an ideal geometry for migration or should we stay close to the input geometry in order to minimize distortions? How does sampling in multiple dimensions affect our traditional aliasing constraints? How can we infill large gaps without using a model for our data? Are irregularities in sampling beneficial? Understanding land data interpolation may help to solve many problems in seismic processing.<br>In this paper, we address some of these issues and show some examples of multidimensional land data interpolation.<br>


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