Self-attention summary networks for velocity model building from common-image gathers

This study develops a self-attention-based summary network designed to efficiently encode and compress seismic information residing in subsurface-offset Common Image Gathers (CIGs) into low-dimensional latent representations suitable for conditional machine-learning enabled velocity-model building. The proposed network functions as a compact seismic encoder that captures global spatial dependencies through self-attention mechanisms while preserving essential local structural features critical for accurate reconstruction. When integrated within a flow matching generative network, the summary network learns to effectively summarize seismic information from CIGs via a new multi-scale attention mechanisms. This integration enables efficient training and inference, facilitating fast and physically consistent generation of seismic velocity models conditioned on CIGs.