Reconstruction of seismic data with missing traces using normalized Gaussian weighted filter

Abstract

Missing traces complicate the seismic data processing and may cause difficulty in geological interpretation. We present a simple but efficient normalized Gaussian weighted filter (NGWF) method for seismic data interpolation that is suitable for reconstruction despite a large number of missing traces in the data, and has low computational complexity. The missing data are filled with locally retained pixel information via the Gaussian weight. Numerical tests show that the reconstructed result using the NGWF method is better than that using dictionary learning, total variation, partial differential equation, and economic orthogonal rank-one matrix pursuit. In addition, the proposed approach is also applied to pre-stack and post-stack seismic section, and the results indicate that the new approach is applicable to the recovery of seismic data with missing traces.