Motivation Migration generally does not correctly recover the amplitudes. Least-squares migration is computationally unfeasible. Amplitude recovery (e.g. AGC) lacks robustness w.r.t. noise. Existing diagonal amplitude-recovery methods  do not always correct for the order (1 -2D) of the Hessian [see Symes '07]  do not invert the scaling robustly Moreover, these (scaling) methods assume that there  are no conflicting dips (conormal) in the model  is infinite aperture  are infinitely-high

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... uencies  etc. Curvelets & seismology Wish list A transform that  detects the reflectors without prior information on the geologic dips  is sparse, i.e. the magnitude-sorted coefficients decay fast  is relative invariance under the demigrationmigration, i.e. sparse on migrated images Curvelets  were "born" from studying high-frequency solution operators for wave propagation*  diagonalization of migration operators**