3-D migration using rotated McClellan filters

Biondi, B. and Palacharla, G.

SEG 1994 Expanded abstracts: pp 1278-1285

The application of McClellan transformations considerably reduces the computational cost of 3-D wavefield depth extrapolation by explicit convolutional methods. The accuracy of migration methods based on McClellan transformation depends on how well the transformation filter \mbox{($\cosqkk$)} is approximated; errors in this approximation cause anisotropy in the extrapolation operator and frequency dispersion in the migrated results. This anisotropy can be greatly reduced by rotating the approximate filter by 45 degrees, and averaging the rotated filter with the original filter. The application of the rotated filter yields a migration method that images correctly very steep dips, with no or little additional computational cost. McClellan migration with the improved circular response enhanced the imaging of synthetic and real data.