SVD-Constrained MWNI With Shaping Theory

Observed seismic data are mostly irregularly sampled and seismic data interpolation is an essential procedure to provide accurate complete data for seismic data analysis, such as amplitude-versus-offset analysis, multiple suppression, and wave-equation migration. The well-known minimum weighted norm interpolation (MWNI) method could achieve a relatively good result. However, the algorithm needs many iterations and thus the total calculation is expensive. In this letter, we propose a fast interpolation algorithm. Instead of wavenumber spectrum, singular spectrum can give a more accurate description of the sampled data. We use shaping regularization to control the smoothness of the singular values matrix. Compared with the conventional MWNI method, we test the proposed method on both synthetic and field data sets. The results confirm that our proposed method is more effective.