Reconstruction of a sparse spike train from a portion of its spectrum and application to high-resolution deconvolution

Abstract An algorithm is proposed for the reconstruction of a sparse spike train from an incomplete set of its Fourier components. It is shown that as little as 20-25 percent of the Fourier spectrum is sufficient in practice for a high-quality reconstruction. The method employs linear programming to minimize the L 1 -norm of the output, because minimization of this norm favors solutions with isolated spikes.Given a wavelet, this technique can be used to perform deconvolution of noisy seismograms when the desired output is a sparse spike series. Relative reliability of the data is assessed in the frequency domain, and only the reliable spectral data are included in the calculation of the spike series. Equations for the unknown spike amplitudes are solved to an accuracy compatible with the uncertainties in the reliable data. In examples with 10 percent random noise, the output is superior to that obtained using conventional least-squares techniques.