Connections Between Least-Squares Migration, Capon Beamforming, and Phase Correction for Seismic Resolution Enhancement

Seismic imaging is essential for subsurface exploration, yet achieving high-resolution images remains a significant challenge. This paper investigates the theoretical connections between three resolution-enhancement methods—least-squares migration (LSM), Capon beamforming, and phase correction—within a unified framework based on the Gauss-Markov theorem. This framework reveals their underlying equivalence in enhancing seismic image resolution. LSM iteratively refines seismic images by minimizing data mismatches, but it can be computationally expensive for large-scale datasets. In contrast, Capon beamforming improves resolution by suppressing misaligned noise, while phase correction improves coherence by aligning phase information, both operating as cost-efficient post-processing methods in common image gathers. The theoretical derivations demonstrate the intrinsic equivalence of these methods despite differences in principles and implementations. Synthetic and field data experiments validate the theoretical derivations, demonstrating their interconnections and paving the way for advanced seismic imaging approaches that balance image resolution and computational efficiency.