Focused blind deconvolution
Pawan Bharadwaj, Laurent Demanet, and Aim\'e Fournier

TL;DR
This paper presents a new multichannel blind deconvolution method called focused blind deconvolution (FBD) that extracts sparse, front-loaded impulse responses without support restrictions, useful in seismic inversion.
Contribution
FBD introduces focusing constraints based on whiteness and front-loading to resolve indeterminacy in blind deconvolution without support restrictions.
Findings
FBD effectively separates source signatures from Green's functions in seismic data.
FBD improves interpretability of noisy seismic recordings.
Numerical experiments demonstrate FBD's practical benefits in seismic-while-drilling applications.
Abstract
We introduce a novel multichannel blind deconvolution (BD) method that extracts sparse and front-loaded impulse responses from the channel outputs, i.e., their convolutions with a single arbitrary source. A crucial feature of this formulation is that it doesn't encode support restrictions on the unknowns, unlike most prior work on BD. The indeterminacy inherent to BD, which is difficult to resolve with a traditional L1 penalty on the impulse responses, is resolved in our method because it seeks a first approximation where the impulse responses are: "maximally white" -- encoded as the energy focusing near zero lag of the impulse-response auto-correlations; and "maximally front-loaded" -- encoded as the energy focusing near zero time of the impulse responses. Hence we call the method focused blind deconvolution (FBD). The focusing constraints are relaxed as the iterations progress. Note…
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