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Velocity model building remains a crucial step in seismic depth imaging. A general drawback of conventional tomographic approaches is that the estimated velocity models do not conform enough to structures. We present several applications of an innovative high resolution tomography that inverts densely picked residual move-out data to reveal detailed structurally conformable velocities. The application to the synthetic 2D Marmousi II dataset offers the possibility to carefully assess the method. It demonstrates the ability to produce structurally conformable velocity models with a level of detail that promotes velocity attributes as an aid to geological interpretation. As such it can offer an alternative to full waveform inversion for the interpretation of reflected waves. Finally we show an application to a 3D land dataset where the obtained higher resolution velocity model results in improved focusing of migrated images and improved match to well velocities.

Conventional imaging does not deal adequately with absorption, especially in the case of strong anomalies. Over recent years, many authors have proposed to compensate the absorption loss effects inside of the migration through the use of an attenuation model. Q tomography has been developed for estimating this attenuation model but is generally limited to estimating attenuation in predefined anomalies. In this paper, we explain how we developed a high-resolution volumetric Q tomography to attain an accurate volumetric estimation of the attenuation model. A key component of our workflow is the estimation of effective attenuation in the pre-stack data domain through accurate picking of the frequency peak. Finally we present a case study where our approach has been used to reveal shallow gas pockets and compensate for absorption in the migration.

Tomography algorithms using gridded model description and ray tracing have made continuous progress in terms of resolution and efficiency. However one strong limitation is the difficulty to recover strong velocity contrasts encountered in presence of salt bodies, chalk, basalt, carbonates… The conventional solution for velocity model building in such a context is to proceed in a top-down manner from one velocity contrast to the next one. In such a layer stripping approach velocities and horizons are updated layer after layer recursively from top to bottom. Such a workflow is time consuming and prone to velocity errors being propagated into deeper layers as the model building progresses. We present here a solution to remedy these drawbacks. Our solution involves a non-linear tomographic approach combining dense dip and residual move-out picks with horizons describing the main velocity contrasts. While dip and RMO picks are used to update 3D velocity grids inside each layer by non-linear slope tomography, the picked horizons describing layer boundaries are kinematically de-migrated and re-migrated recursively from top to bottom to reposition the major velocity contrasts after each velocity update. We present applications of the method to a marine North Sea dataset and to a land dataset with salt structures and compare the results with the layer stripping approach.

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