3-D wavefield depth extrapolation by rotated McClellan filters

Biondo Biondi and Gopal Palacharla

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ABSTRACT

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 ($\cos\mid\vec k\mid$) is approximated; errors in this approximation cause anisotropy in the extrapolator operator. 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, without additional computational cost.