SeisLFMFlow: Seismic Common Image Gathers Enhancement Using Self-Supervised Optical Flow Estimation Based on Local Feature Matching

Seismic imaging technology, which analyzes seismic wave propagation and reflection to gather data on underground geological structures, is vital for geological exploration. Due to factors such as the anisotropy of subsurface media, migration velocity errors, and drift of seismic streamers in marine environments, observation points at the same position exhibit horizontal and vertical displacements in different common offset gathers (COGs), thereby diminishing stacking coherence and compromising imaging quality. Consequently, the nonflattened seismic events in common image gathers (CIGs) extracted from COGs can lead to false amplitude variations with offset. Traditional CIG enhancement methods like cross-correlation matching encounter challenges such as slow inference speed, limited accuracy, the capability to predict only a single directional displacement, and difficulty in parameter tuning. Therefore, based on optimizing local normalized cross-correlation matching, an interpretable deep learning method to enhance CIGs using a self-supervised optical flow estimation network is proposed. Experiments performed using both synthetic and field data demonstrate the validity and effectiveness of the method.