Iterative Least-Squares Migration: Standard migration images can suffer from migration artifacts arising from 1) poor source-receiver sampling, 2) weak amplitudes caused by geometric spreading, 3) attenuation, 4) defocusing, 5) poor resolution from a limited source-receiver aperture, and 6) an oscillatory source wavelet. To partially remedy these problems, least-squares migration (LSM), also known as linearized seismic inversion or migration deconvolution, often is proposed to invert seismic data for the reflectivity distribution. If the migration velocity model is suficiently accurate, then LSM can mitigate many of the above problems and lead to a more resolved migration image – sometimes with twice the spatial resolution. However, there are two problems with LSM: the cost can be an order of magnitude more than standard migration and the quality of the LSM image is no better than the standard image for modest velocity errors. I now show how to get the most from least-squares migration by reducing the cost and the sensitivity of LSM to velocity errors.