Enhanced Velocity Model Building For Low Quality Seismic Data

Determining velocities for seismic processing and imaging remains a big challenge, especially for seismic data with poor data quality or from complex geological settings. State-of-the-art methods to determine velocities for depth imaging can often not be applied to seismic data with poor signal/noise ratio(S/N) or data gaps due to restrictions from acquisition challenges. Multi-parameter stacking techniques simultaneously enhance the data quality and use the related wavefront attributes obtained by coherence analysis for velocity model building in the time and depth imaging workflow. We demonstrate examples using the Common Reflection Surface (CRS) method to efficiently derive velocity models for low quality and/or sparse acquisition data examples. Algorithms including CRS-Tomography, iterative Reverse Time Migration (iRTM) and Full Waveform Inversion (FWI) benefit from the CRS processing sequence, e.g. by addressing challenges in the application to onshore field data related to rugged topography as well as to poor data quality. Recently the CRS technology was extended to derive azimuth dependent wavefront attributes for anisotropic velocity model building. Furthermore, recent research on Diffraction-Tomography indicates the improvement of the resolution of CRS-Tomography using the diffracted wavefield energy.