An Efficient Self-supervised Seismic Data Reconstruction Method Based on Self-Consistency Learning

ArXi:2411.00911v2 Announce Type: replace-cross Seismic exploration remains the most critical method for characterizing subsurface structures in geophysics. However, complex surface conditions often cause a non-uniform distribution of seismic receivers along survey lines, leading to irregularly acquired seismic data, which affects subsequent processing and inversion. Prior deep learning-based seismic data reconstruction methods typically rely on datasets for supervised