Multi-scale Multi-directional Prediction-error Filtering for Seismic Data Reconstruction

The interpolation method based on prediction filter is one of the most effective approaches recently proposed for seismic data reconstruction. However, the number of effective regression equations for estimating the filter coefficients will be less with missing much seismic data. The number of effective regression equations can increase much more using multi-directional and multi-scale prediction-error filter, the accuracy of filter coefficients can increase much more. We developed a new approach to interpolate missing much seismic data based on multi-scale multi-directional prediction-error filtering method. For seismic data which is partial regular missing and partial irregular missing, the proposed method can be used to interpolate missing traces to obtain more accurate results conveniently. The proposed method improves the calculation accuracy and conveniences by applying more effective regression equations from different directions and scales. The applicability and effectiveness of the proposed method are examined by synthetic and field data examples.