부산대학교 도서관

Recently, technical results from the theory of Compressive Sensing have been applied to the Fourier class of algorithms for solving the ever-increasing seismic data interpolation problem while handling potentially irregularly sampled geometries. The method we here propose makes use of the so-called Conjugate Gradient Pursuit with the Stagewise Selection Strategy, a novel general purpose algorithm for sparse representation, which brings advantages in terms of computational costs when compared to the well-known Matching Pursuit, while not affecting accuracy. The effectiveness and efficiency of the derived interpolation method is proved and compared to the performances of the Matching Pursuit-based interpolation method when applied on a real dataset showing, in turns, intrinsic sampling irregularity and heavy manual trace decimation.