Joint Least-Squares RTM of Surface Seismic and Seismic-While-Drilling Datasets

Abstract

Least-squares migration can, in theory, reduce the acquisition footprint and improve the illumination of the subsurface structures. However, in complex subsurface structures, rays or the wave energy will penetrate poorly in some regions, e.g., subsalt region, and that region will be a shadow zone to a typical surface seismic acquisition. The shadow zone is in the null space of the migration operator and the subsurface information in that region will not be recovered even by posing imaging as an inverse problem. To rectify this, we use another set of data, along with surface seismic dataset, whose ray paths are different from the surface seismic. Seismic-while-drilling (SWD) dataset are complementary to surface data, and it brings an opportunity to address seismic illumination issue by adding new measurements into the imaging problem. Accordingly, in this research, we formulate the joint least-squares reverse time migration of surface seismic and SWD datasets and explore its potential in imaging the parts of the model that is in the shadow zone of the surface seismic acquisition. Presented results on the BP-Model94 show that the joint least-squares migration outperforms the single surface and single SWD least-squares migrations in improving the illumination of subsurface structures.