In this work, we present a total-variation (TV)-norm minimization method to perform model-free low-frequency data extrapolation for the purpose of assisting full-waveform inversion. Low-frequency extrapolation is the process of extending reliable frequency bands of the raw data towards the lower end of the spectrum. To this end, we propose to solve a TV-norm based convex optimization problem that has a global minimum and is equipped with a fast solver. The approach takes into account both the sparsity of the reflectivity series associated with a single trace, as well as the inter-trace correlations. A favorable byproduct of this approach is that it allows one to work with coarsely sampled trace along time (as coarse as 0:02s per sample), hence substantially reduces the size of the proposed optimization problem. Last, we show the effectiveness of the method for frequency-domain FWI on the Marmousi model. Presentation Date: Thursday, October 20, 2016 Start Time: 11:00:00 AM Location: 162/164 Presentation Type: ORAL
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