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SUMMARYAlthough time-domain depth migration techniques have been successfully ported to run on modern hardware accelerators, their ultimate obstacle is the I/O overhead present during the imaging step. Frequency-domain depth migration algorithms overcome this limitation and can exploit the full potential of new computing technologies. In particular, our implementation of Phase Shift Plus Interpolation (PSPI) method is characterized by fast running time, good quality results under lowsignal-to-noise ratio conditions and excellent results for steep dips. We provide a novel computational dataflow scheme to perform acceleration of PSPI on a generic dataflow engine. We present speedup results obtained on the state-of-the-art dataflow technology for synthetic VTI datasets. Our measurements indicate that a dataflow approach can achieve high speedups despite larger and larger computational domains, increased complexity of the anisotropic approach and the I/O overhead during angle-gathers calculation.

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We analyze the depth wave eld extrapolation of a single dataset obtained by compressing sources and shotgathers. Our conclusion is that, running a sequence of M migrations, for a large class of random seismic data compressions, the noise variance of the average imaging condition decreases proportionally to M ?1. This Monte Carlo approach may render 3D wave eld prestack migration cost e ective.

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