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Lateral velocity deviations as small as three percent from the
background can cause visible focusing. The FEAVA detector performs
well both in the presence and in the absence of multiples. FEAVA
removal by migration
works when the migration operator is the adjoint of the
modeling one and when no multiples are present. When internal
multiples are present and imaging is performed with an
algorithm of a lower order than the one used for modeling, FEAVA is removed
only partially by migration. This is most likely caused by
multiples not being defocused by a migration with the velocity of the
primary reflections, regardless of the order of the algorithm. To verify
this conjecture, one would need to model with an amplitude-preserving
two-way algorithm two similarly complex datasets - one multiple-free
and one multiple-affected, and then migrate each of them with the
operator adjoint to the one used in modeling. If the conjecture is
true, FEAVA will be removed completely from the multiple-free dataset,
but only partially from the multiple-affected one. I also conjecture
that, for a synthetic dataset, FEAVA removal is possible if the algorithm used
for migration has the same accuracy or greater than the one used for
modeling, and that exact adjointness of migration and modeling
operators is not important. To verify this I will need to image with a
higher-order algorithm a multiple-free dataset generated with a
lower-order algorithm. I plan to verify these assertions in the near future.

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Stanford Exploration Project

10/23/2004