Image-domain wavefield tomography (WT) exploits focusing characteristics of extended images for updating the velocity field. To make good use of this information, one must understand how such images behave if the migration velocity is accurate. This is not trivial because focusing depends on not only the model error, but also on the acquisition setup, the data bandwidth, and illumination variation caused by the overburden. We address this problem by constructing penalty functions based on the point spread functions of the imaging operator that characterize focusing in extended images. Moreover, instead of sampling the extended images at preset distances along the surface, we sample the image by constructing common-image-point gathers, which are more economical from a computational point of view and also measure the spatial and temporal focusing of the wavefield. Coupled with image residuals exploiting illumination-based penalty functions, we construct robust wavefield tomographic updates from sparse locations of the image in areas of poor or uneven illumination. Models obtained with this type of methodology are a good starting point for more sensitive, but less robust, waveform inversion methods. Starting with the velocity model obtained by illumination-based tomography (where important low-wavenumber information is added to the model), we perform an inversion using waveform tomography in the data domain. We describe an application of the method to synthetic examples and to a marine 2D data set. We complement the low-resolution WT resolution with a high-resolution data-domain inversion, which has the potential to add details to the reconstructed model. We describe the tomographic implementation with a synthetic data set and a marine field data example.

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