We introduce a computationally efficient and robust method to regularize acquisition geometries of 3-D prestack seismic data before prestack migration. The proposed method is based on a formulation of the geometry regularization problem as a regularized leastsquares problem. The model space of this least-squares problem is composed of uniformly sampled common offset-azimuth cubes. The regularization term fills the acquisition gaps by minimizing inconsistencies between cubes with similar offset and azimuth. To preserve the resolution of dipping events in the final image, the regularization term includes a transformation by Azimuth Moveout (AMO) of the common offset-azimuth cubes. The method is computationally efficient because we applied the AMO operator in the Fourier-domain and we precondition the least-squares problem. Therefore, no iterative solution is needed and excellent results are obtained by applying the adjoint operator followed by a diagonal weighting in the model domain. We tested the method on a 3-D land data set. Subtle reflectivity features are better preserved after migration when the proposed method is employed as compared to more standard geometry regularization methods.