Automatic versions of migration velocity analysis provide velocity background models that<br>can serve as a starting point for migration or least-squares inversion. When dealing with<br>primary reflections, current methods perform well. They tend to break down, however, when<br>strong multiples are present. This is due to the single scattering approximation that underlies<br>most migration algorithms. When multiples are interpreted as primaries, they may end up at<br>the wrong depth with the wrong apparent velocity. In the data-domain, it should in principle<br>be possible to match observed with predicted multiples and use them for velocity estimation.<br>We present a method for performing the velocity analysis in the data domain and include tests<br>on multiple-free synthetic data and on real data, using the convolutional model to model the<br>data. We also outline ideas on how the method is expected to behave in the presence of<br>multiples.


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