Stereotomography:: a semi-automatic approach for velocity macromodel estimation

Most methods for velocity macromodel estimation require considerable operator input, mainly concerning the regularization and the picking of events in the data set or in the migrated images. For both these aspects, slope tomography methods offer interesting solutions. They consider locally coherent events characterized by their slopes in the data cube. Picking is then much easier and consequently denser than in standard traveltime tomography. Stereotomography is the latest slope tomography method. In recent years it has been improved significantly, both from an algorithmic point of view and in terms of practical use. Robust and fast procedures are now available for 2D stereotomographic picking and optimization. Concerning the picking, we propose simple criteria for the selection of relevant data among the automatically picked events. This enables an accurate smooth velocity macromodel to be estimated quite rapidly and with very limited operator intervention. We demonstrate the method using a 2D line extracted from the Oseberg NH8906 data set.