A B S T R A C TMost 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. I N T R O D U C T I O NPrestack depth migration remains the best way of imaging the subsurface, in particular in areas of complex geology. This process is, however, very sensitive to the velocity macromodel and therefore great care must be taken at this very important step (Fagin 1998). Since standard approaches based on Dix's formula are not acceptable in complex media, two main types of method remain available: migration-based velocity analysis (MVA) and traveltime tomography. Tomography is particularly interesting in the case of 3D applications for which MVA is expensive, although traveltime picking in 3D tomography is a difficult and time-consuming process, requiring significant expertise.In order to facilitate the picking, a slope tomography method, known as stereotomography, was proposed for es-

S U M M A R YWe present the extension of stereotomography to P-and S-wave velocity estimation from PP-and PS-reflected/diffracted waves. In this new context, we greatly benefit from the use of locally coherent events by stereotomography. In particular, when applied to S-wave velocity estimation from PS-data, no pairing of PP-and PS-events is a priori required. In our procedure the P-wave velocity model is obtained first using stereotomography on PP-arrivals. Then the S-wave velocity model is obtained using PS-stereotomography on PS-arrivals fixing the Pwave velocity model. We present an application to an 'ideal' synthetic data set demonstrating the relevance of the approach, which allows us to recover depth consistent P-and S-waves velocity models even if no pairing of PP-and PS-events is introduced. Finally, results to a real data set from the Gulf of Mexico are presented demonstrating the potential of the method in a noisy data context.

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A B S T R A C TDepth velocity model building remains a difficult step within the seismic depth imaging sequence. Stereotomography provides an efficient solution to this problem but was limited until now to a picking of seismic data in the prestack time un-migrated domain. We propose here a method for stereotomographic data picking in the depth migrated domain. Picking in the depth migrated domain exhibits the advantage of a better signal-to-noise ratio and of a more regular distribution of picked events in the model, leading to a better constrained tomographic inverse problem. Moreover, any improvement on the velocity model will improve the migrated results, again leading to improved picking. Our strategy for obtaining a stereotomographic dataset from a prestack depth migration is based on migration of attributes (and not on a kinematic demigration approach!). For any locally coherent event in the migrated image, migration of attributes allows one to compute ray parameter attributes corresponding to the specular reflection angle and dip. For application to stereotomography, the necessary attributes are the source/receiver locations, the traveltime and the data slopes. For the data slope, when the migration velocity model is erroneous, some additional corrections have to be applied to the result of migration of the attributes. Applying these corrections, our picking method is theoretically valid whatever the quality of the migration velocity model. We first present the theoretical aspects of the method and then validate it on 2D synthetic and real seismic reflection data sets.