Migration velocity analysis (MVA) is commonly performed in the image domain in conjunction with ray-based tomography to update the velocity model. This approach can be challenging in the presence of large velocity errors as it may require many MVA iterations before converging to a model that can focus the events in the image domain. We introduced a downward continuation-based domain for carrying out MVA that is more flexible than conventional domains. This approach consists of two steps: (1) forming the common image cube (CIC) and (2) modeling the Green’s functions. In the first step, the cross-correlation imaging condition is relaxed to produce more than the zero lag common image gather (CIG). Slicing these data at different lags forms a series of CIGs, whereas a conventional CIG can be obtained by slicing the cube at the zero lag. When the velocity model used for the migration differs from the true velocity model, properly flattened events may occur in CIGs other than the zero lag. In the second step, for each event on the CIG, we picked the cross-correlation lag and depth at which it flattens best. For each event, we modeled a Green’s function by seeding a source at the focusing depth using one-way wave-equation modeling. This process is then repeated for other events at different lateral positions. The result is a set of Green’s functions whose wavefield approximates the ones that would have been generated if the correct velocity model was used to simulate these gathers. The updated Green functions are easier to work with than the raw data as they have less noise. Wavefield tomography can then be applied on these data-driven, modeled Green’s functions to build the final velocity model. Tests on synthetic and real 2D data confirm the method’s effectiveness in building velocity models in complex structural areas with large lateral velocity variations.