​Approximate images of the earth's subsurface structures areusually obtained by migrating surface seismic data. Leastsquaresmigration, under the single-scattering assumption, isused as an iterative linearized inversion scheme to suppress migrationartifacts, deconvolve the source signature, mitigate theacquisition fingerprint, and enhance the spatial resolution of migratedimages. The problem with least-squares migration of primaries,however, is that it may not be able to enhance events thatare mainly illuminated by internal multiples, such as vertical andnearly vertical faults or salt flanks. To alleviate this problem, weadopted a linearized inversion framework to migrate internallyscattered energy. We apply the least-squares migration of firstorderinternal multiples to image subsurface vertical faultplanes. Tests on synthetic data demonstrated the ability ofthe proposed method to resolve vertical fault planes, whichare poorly illuminated by the least-squares migration of primariesonly. The proposed scheme is robust in the presence of whiteGaussian observational noise and in the case of imaging thefault planes using inaccurate migration velocities. Our resultssuggested that the proposed least-squares imaging, under thedouble-scattering assumption, still retrieved the vertical faultplanes when imaging the scattered data despite a slight defocusingof these events due to the presence of noise or velocityerrors.