Multiple problems, including high computational cost, spurious local minima, and solutions with no geologic sense, have prevented widespread application of full waveform inversion (FWI), especially FWI of seismic reflections. These problems are fundamentally related to a large number of model parameters and to the absence of low frequencies in recorded seismograms. Instead of inverting for all the parameters in a dense model, image-guided full waveform inversion inverts for a sparse model space that contains far fewer parameters. We represent a model with a sparse set of values, and from these values, we use image-guided interpolation (IGI) and its adjoint operator to compute finely and uniformly sampled models that can fit recorded data in FWI. Because of this sparse representation, image-guided FWI updates more blocky models, and this blockiness in the model space mitigates the absence of low frequencies in recorded data. Moreover, IGI honors imaged structures, so image-guided FWI built in this way yields models that are geologically sensible.

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Velocity/depth ambiguity in the conventional premigration (time domain) velocity analysis has been studied by Bickel (1990) and Lines (1993) for the horizontal reflector case. Rathor (1997) extended this analysis to the dipping reflector case. These studies answered some important questions that arose in traditional common midpoint (CMP) time‐domain velocity analysis. However, it is not clear that their conclusions apply to migration velocity analysis (MVA) in the prestack‐migrated depth domain.

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