Seismic data interpolation beyond aliasing using regularized nonstationary autoregression

Conclusions

We have introduced a new approach to adaptive prediction-error filtering for seismic data interpolation. Our approach uses regularized nonstationary autoregression to handle time-space variation of nonstationary seismic data. We apply this method to interpolating seismic traces beyond aliasing and to reconstructing data with missing and decimated traces. Experiments with benchmark synthetic examples and field data tests show that the proposed filters can depict nonstationary signal variation and provide a useful description of complex wavefields having multiple curved events. These properties are useful for applications such as seismic data interpolation and regularization. Other possible applications may include seismic noise attenuation.