We propose a least-squares reverse time migration (LSRTM) that uses a proper imaging condition to obtain faster convergence rates when compared with similar methods using conventional imaging conditions. The proposed modeling and migration operators use spatial and temporal derivatives that attenuate acquisition artifacts and deliver a better representation of the reflectivity and scattered wavefields. We apply the method to two Gulf of Mexico (GOM) field datasets: a 2D towed-streamer benchmark dataset and a 3D ocean-bottom node (OBN) dataset. We show the improvement in resolution of the LSRTM images, as well as the superior convergence rate.
Presentation Date: Tuesday, September 26, 2017
Start Time: 9:45 AM
Presentation Type: ORAL