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In the presence of complex structures and/or strong lateral velocity variations, extracting velocity information in the data space is both inaccurate and time-consuming. In such situations, the image space is a more appropriate domain for extracting kinematic information because migration focuses and greatly simplifies the events. Even when the migration velocity is far from the true velocity, incomplete focusing of the reflections is a step in the right direction. Velocity-estimation methods that use the focusing capabilities of migration to extract kinematic information more reliably are known commonly as migration velocity analysis (MVA) methods.

MVA is an iterative process in which each iteration comprises two distinct steps: (1) The data are imaged by prestack migration and (2) the velocity function is updated on the basis of the migration results. The global convergence of this process is a first-order concern. The relationship between the velocity model and the focusing quality of the image is highly nonlinear, and consequently, the objective function optimized during MVA is non-convex and has several local minima. The quality of the starting velocity model is crucial in assuring global convergence. The starting model usually is estimated by using one of the data-domain velocity-estimation methods presented in Chapter 6. Therefore, development of MVA methods has not made the data-domain methods obsolete. On the contrary, datadomain velocity-estimation methods are a necessary preamble to MVA.

The first challenge of MVA is extraction of kinematic information from the migrated image cube. In the data domain, the accuracy of the velocity

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