| Summary: | In this paper, we recover the amplitude of a seismic
image by approximating the normal (demigrationmigration)
operator. In this approximation, we make
use of the property that curvelets remain invariant under
the action of the normal operator. We propose a seismic
amplitude recovery method that employs an eigenvalue
like decomposition for the normal operator using
curvelets as eigen-vectors. Subsequently, we propose
an approximate non-linear singularity-preserving solution
to the least-squares seismic imaging problem with
sparseness in the curvelet domain and spatial continuity
constraints. Our method is tested with a reverse-time
’wave-equation’ migration code simulating the acoustic
wave equation on the SEG-AA salt model.
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