Random sampling: new insights into the reconstruction of coarsely-sampled wavefields

Random sampling: new insights into the reconstruction of coarsely-sampled wavefields

Gilles Hennenfent, EOS-UBC and Felix J. Herrmann, EOS-UBC

Abstract:

In this paper, we turn the interpolation problem of coarsely-sampled data into a denoising problem. From this point of view, we illustrate the benefit of random sampling at sub-Nyquist rate over regular sampling at the same rate. We show that, using nonlinear sparsity-promoting optimization, coarse random sampling may actually lead to significantly better wavefield reconstruction than equivalent regularly sampled data.