A High-Efficiency and Robust Compressive Sensing Reconstruction and Regularization Technique

Abstract Seismic data acquisition is a crucial procedure in seismic exploration. Compressive sensing (CS) is a new signal sampling framework and breaks through the limitation of Nyquist theorem (Donoho, 2006). The application of CS in seismic data acquisition (Mosher et al. 2012) can potentially improve exploration efficiency and lower costs. CS helps the reconstruction of seismic data before subsequent data processing stages. Numerous researchers have made contributions to the advancement of techniques for reconstructing regularly sampled data. The majority of these methods fall under the category of prediction error filter techniques (Li et al. 2017; Spitz 1991; Zheng et al. 2022), sparse transformation approaches (Fomel Gong et al. 2016), and low-rank constraint approaches (Sacchi 2009). In recent years, numerous deep learning-based reconstruction methods have been proposed (Chen et al. 2023; Liu Herrmann et al., 2008) is selected as sparse operator to boost the iterative convergence speed. In order to decrease the computational redundancy of 3D curvelet transform, a parallel patching strategy is utilized to segment a large volume of data into small patches, following to parallel-compute the 3D curvelet coefficient of each small patch to further improve the computational efficiency. Finally, we demonstrate the effectiveness of the proposed method by massive field datasets.