Curvelet-Domain Least-Squares Migration with Sparseness Constraints

G027 CURVELET-DOMAIN LEAST-SQUARES MIGRATION WITH SPARSENESS CONSTRAINTS FELIX J. HERRMANN AND PEYMAN MOGHADDAM Department of Earth and Ocean Sciences University of British Columbia Vancouver BC Canada Abstract A non-linear edge-preserving solution to the least-squares migration problem with sparseness constraints is introduced. The applied formalism explores Curvelets as basis functions that by virtue of their sparseness and locality not only allow for a reduction of the dimensionality of the imaging problem but which also naturally lead to a non-linear solution with significantly improved signalto-noise ratio. Additional conditions on the image are imposed by solving a constrained optimization problem on the estimated