Sub-basalt imaging: modeling and demultiple

Seismic imaging of sub-basalt sedimentary layers is difficult due to high impedance of the basalt layer, the roughness of the top and bottom of the basalt layer and sometimes the heterogeneities within the basalt layer. In this thesis we identify specific problems within the modern imaging technology which limit sub-basalt imaging. The basic framework for the identification of this limitation is that we are able to group most basalt layers into the following four categories: A basalt layer having smooth top and bottom surfaces. A basalt layer having rough top and bottom surfaces. Small-scale heterogeneities within the basalt layer. Intra-basalt velocity variation due to different basalt flows. All the above models of basalt layers obviously have high impedance with respect to the surrounding sedimentary layers. These four models encapsulate all the possible heterogeneities of basalt layers seen in areas like the Voring and More basins off mid- Norway, basins in the Faroes, W. Greenland, Angola and Brazil margins, and the Deccan Traps of India. In this work, problems in seismic processing and imaging specific to these models have been presented. For instance, we have found that the application of the multiple attenuation technique, which first predicts the multiples and then subtracts them from the data, using least-squares criteria, can be effective for all the models except for the model, which has intra-bedded layers within the basalt. The failure in the second case is due to the destructive interference of multiple scattering from the intra-bedded layers within the basalt and the multiples located below the primary associated with the top of the basalt layer. This interference degrades the signal-to-noise (S/N) ratio of the multiples contained in the data, whereas the predicted multiples, which are constructed from the reflectors above the basalt, have a much higher signal-to-noise ratio. Our recommendation is to subtract the predicted multiples from the data using either leastabsolute- value criteria or any other higher-order-statistics-based criteria.