Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform

According to the compressive sensing (CS) theory in the signal-processing field, we proposed a new CS approach based on a fast projection onto convex sets (POCS) algorithm with sparsity constraint in the seislet transform domain. The seislet transform appears to be the sparest among the state-of-the-art sparse transforms. The FPOCS can obtain much faster convergence than conventional POCS (about two thirds of conventional iterations can be saved), while maintaining the same recovery performance. The FPOCS can obtain faster and better performance than FISTA for relatively cleaner data but will get slower and worse performance than FISTA, which becomes a reference to decide which algorithm to use in practice according the noise level in the seismic data. The seislet transform based CS approach can achieve obviously better data recovery results than f−k transform based scenarios, considering both signal-to-noise ratio (SNR), local similarity comparison, and visual observation, because of a much sparser structure in the seislet transform domain. We have used both synthetic and field data examples to demonstrate the superior performance of the proposed seislet-based FPOCS approach. •We proposed a novel fast projection onto convex sets (POCS) algorithm using the seislet transform as the sparsity-promoting transform.•The seislet is shown to be the sparsest among well-known transforms.•The selection of FPOCS or FISTA depends on the noise level of the data.•The seislet based FPOCS can obtain better and faster seismic data recovery.•The local similarity is also used to measure the data recovery performance.