Migration velocity analysis (MVA) is commonly performed inthe image domain in conjunction with ray-based tomography toupdate the velocity model. This approach can be challenging inthe presence of large velocity errors as it may require manyMVAiterations before converging to a model that can focus the eventsin the image domain. We introduced a downward continuationbaseddomain for carrying out MVA that is more flexible thanconventional domains. This approach consists of two steps:(1) forming the common image cube (CIC) and (2) modelingthe Green’s functions. In the first step, the cross-correlationimaging condition is relaxed to produce more than the zerolag common image gather (CIG). Slicing these data at differentlags forms a series of CIGs, whereas a conventional CIG can beobtained by slicing the cube at the zero lag. When the velocitymodel used for the migration differs from the true velocitymodel, properly flattened events may occur in CIGs other thanthe zero lag. In the second step, for each event on the CIG, wepicked the cross-correlation lag and depth at which it flattensbest. For each event, we modeled a Green’s function by seedinga source at the focusing depth using one-way waveequationmodeling. This process is then repeated for otherevents at different lateral positions. The result is a set ofGreen’s functions whose wavefield approximates the ones thatwould have been generated if the correct velocity model wasused to simulate these gathers. The updated Green functionsare easier to work with than the raw data as they have lessnoise. Wavefield tomography can then be applied on thesedata-driven, modeled Green’s functions to build the finalvelocity model. Tests on synthetic and real 2D data confirmthe method’s effectiveness in building velocity models in complexstructural areas with large lateral velocity variations.
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