Least-Squares Reverse Time Migration Using the Inverse Scattering Imaging Condition

The formulation of conventional least-squares reverse time migration (LSRTM) starts with the forward modeling process; as a result, the migration operator of it presents as a migration process with a cross correlation imaging condition (CCIC). Since the imaging results produced by CCIC usually contain undesirable components (e.g., strong backscattering noise), it can be assumed that the primary target of the conventional LSRTM is to fit input data rather than produce high-quality imaging results; therefore, conventional LSRTM can be considered as a modeling-driven algorithm. To mitigate the desirable component in the imaging results, additional efforts should be spent in the process of modeling-driven LSRTM. To improve the performance of the LSRTM, we develop a migration-driven LSRTM by formulating the migration process using the inverse scattering imaging condition (ISIC) first. To guarantee the convergence of the algorithm, an adjoint modeling operator and a data precondition operator are incorporated in this migration-driven LSRTM. Since the ISIC can effectively eliminate the backscattering noise, this migration-driven LSRTM can produce high-quality images without the influence of that. After two synthetic data tests, this approach is applied to a 2-D streamer field dataset from the Gulf of Mexico. These tests indicate that, compared to the modeling-driven LSRTM, the migration-driven LSRTM approach can solve the inversion problem more robustly and efficiently.

Automated summary

The conventional least-squares reverse time migration (LSRTM) is a modeling-driven algorithm that aims to fit input data rather than produce high-quality imaging results. To overcome the limitations of the conventional LSRTM, a migration-driven LSRTM approach is proposed by formulating the migration process using an inverse scattering imaging condition (ISIC) to eliminate backscattering noise. Synthetic data tests and real data application demonstrate that the migration-driven LSRTM approach can solve the inversion problem more robustly and efficiently compared to the modeling-driven LSRTM.