3D Seismic Data Reconstruction based on Weighted Fast Iterative Shrinkage Thresholding algorithm

Data reconstruction is a crucial step in seismic data preprocessing. To improve reconstruction speed and save memory, the commonly used three-dimensional (3D) seismic data reconstruction method divides the missing data into a series of time slices and independently reconstructs each time slice. However, when this strategy is employed, the potential correlations between two adjacent time slices are ignored, which degrades reconstruction performance. Therefore, this study proposes the use of a two-dimensional curvelet transform and the fast iterative shrinkage thresholding algorithm for data reconstruction. Based on the significant overlapping characteristics between the curvelet coefficient support sets of two adjacent time slices, a weighted operator is constructed in the curvelet domain using the prior support set provided by the previous reconstructed time slice to delineate the main energy distribution range, effectively providing prior information for reconstructing adjacent slices. Consequently, the resulting weighted fast iterative shrinkage thresholding algorithm can be used to reconstruct 3D seismic data. The processing of synthetic and field data shows that the proposed method has higher reconstruction accuracy and faster computational speed than the conventional fast iterative shrinkage thresholding algorithm for handling missing 3D seismic data.