Simultaneous reconstruction and denoising of 5-D seismic data using damped sparse representation based projection onto convex sets

Irregular landform and obstacles result in a irregular field data acquisition. However, the existing seismic data processing methods and techniques are almost based on the assumption and precondition of regularly input data. Besides, the observed seismic data always contains random noise because of impacts from acquisition equipments or the acquisition environment. Thus methods for reconstruction with the presence of noise are necessary. The simultaneous reconstruction and denoising problem can be effectively solved under the theory of compressed sensing (CS), and the projection onto convex sets (POCS) is one of the effective methods to solve the CS problem. In this abstract, we propose a damped sparse representation (DSR) based POCS method. By a introduced damping operator, the DSR based POCS method can obtain a more accurate estimation of signal, namely, a better result of simultaneous reconstruction and denoising. The feasibility of the proposed method is validated via both 5-D synthetic and field data examples.