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