Abstract: To TTI or not to TTI? Semi-Automated 3D Tomographic Velocity Analysis in the Canadian Foothills: A Case History; #90211 (2015)

In this case study, we evaluate how velocity model building strategy affects imaging in the Canadian Foothills Thrust Belt. Two depth imaging processing techniques using different near surface weathering static calculations and different velocity model building strategies are compared. In the first processing, we tackled the depth-imaging problem with the usual highly interpretive, “model driven” approach. We used an unconstrained diving-wave traveltime tomography to take into account the near surface effects. We then depth migrated the data from the topography, interpreted the seismic data to extract the main velocity boundaries and used a layer stripping approach (manual tomography) to build the anisotropic (TTI) velocity model. The differences between the pre-stack time migration image and the anisotropic pre-stack depth migration image were disappointingly small. With the second processing, we used a Tau-P refraction tomography, with constraints derived from up-hole times. The Tau-P model provided us with a more convincing near-surface velocity model that matched known geology and improved the resulting depth image. The data were depth migrated from the base of the weathering layer instead of the topography, decoupling the near surface effects from the depth imaging migration velocity analysis. Lastly, we used a “data driven” automated tomography velocity analysis to build the final velocity model. With this dataset, the isotropic “data driven” automated global tomography gave a superior image over the anisotropic “model driven” manual tomography. Using scanning to determine the anisotropic parameters has shown us that anisotropy varies spatially within the 3D volume. Defining variable anisotropic parameters via scanning is very difficult in areas with significantly varying dip. A global approach that would integrate dip meter data, well formation tops and velocities would make the estimation of spatially varying TTI anisotropy easier.