Multichannel Semiblind Sparse Deconvolution of Seismic Signals

Seismic deconvolution is associated with recovering the reflectivity series from a seismic signal when the wavelet is known. In this paper, we address the problem of multichannel semiblind seismic deconvolution, where the wavelet is unknown and there is some uncertainty in the assumed wavelet. We present a novel, two-stage iterative algorithm that recovers both the reflectivity and the wavelet. While the reflectivity series is recovered using sparse modeling of the signal, the wavelet is recovered using ℓ2 minimization, exploiting the fact that all channels share the same wavelet. Presentation Date: Monday, September 25, 2017 Start Time: 4:45 PM Location: 370D Presentation Type: ORAL