Post-stack seismic data interpolation using a fast non-local similarity matching algorithm

In the process of seismic data acquisition, there are often missing seismic traces in seismic records, so it is necessary to reconstruct the missing data to provide high-quality data for subsequent seismic data migration and reservoir inversion. Traditional interpolation methods for post-stack seismic data are based on the sparse constraint in the frequency-wavenumber (f-k) domain. However, the data completed using the interpolation method usually leads to the loss of some weak signals when the dip of the post-stack seismic profile is complex. In this paper, the missing data could be regarded as the result of irregular noise with the same waveform and the original signal but with the opposite polarity. The non-local similarity in the denoising algorithm is introduced as a low-rank promoting transform of the low-rank regularization term, and an interpolation method based on non-local similarity is proposed (NLS-WNNM). Furthermore, a fast matching algorithm is developed to search and match the non-local similarity of missing seismic traces (abbreviation FNLS-WNNM), which reduces the loss of weak signals during interpolation. The traditional interpolation method based on f-k domain is compared with the NLS-WNNM to highlight the advancement of the method. Finally, the interpolation test applied to field data confirmes the robustness of the proposed method.