3-D migration via McClellan transformations on hexagonal grids

Abstract

Two‐dimensional (2-D) spatially varying convolutional filters for three‐dimensional (3-D) depth extrapolation are efficiently designed and implemented by using McClellan transformations. Although efficient, McClellan transformations only approximate circularly symmetric depth extrapolation filters. The accuracy of the extrapolation filters can be increased only at an increase in the computational cost of implementing those filters. By applying McClellan transformations on a hexagonal sampling grid, the accuracy of the extrapolation filters can be increased at a reduction in cost.