Efficient least-squares reverse time migration using local cross-correlation imaging condition

Abstract The data-domain least-squares reverse time migration (LSRTM), a promising imaging method for obtaining high-resolution reflectivity image, can be implemented by matching the de-migrated data to observed one. However, LSRTM requires expensive computational costs in its implementation due tremendous memory usage (direct storage on disk/memory) saving source wavefields and repeatable forward/backward wavefield simulations with iterations compared conventional RTM. Although reconstruction technique used reduce usage, additional cost is inevitable because it during backward simulation. To alleviate burden, we have developed an efficient scheme local cross-correlation condition (LSRTM-LC), which use pre-saved according window size. Because size much shorter than total recording extrapolation, storing into computer feasible. In addition, procedure only at first iteration background velocity fixed of LSRTM. validate feasibility LSRTM-LC scheme, carried out several numerical tests synthetic datasets. Numerical demonstrated that provide us equivalent from saved gradient vector obtained those As result, generate same quality models as LSRTM, however, less