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dc.contributor.authorPisupati, Pawan Bharadwaj
dc.contributor.authorDemanet, Laurent
dc.contributor.authorFournier, Aime
dc.date.accessioned2020-03-20T15:24:58Z
dc.date.available2020-03-20T15:24:58Z
dc.date.issued2019-04
dc.identifier.issn1941-0476
dc.identifier.issn1053-587X
dc.identifier.urihttps://hdl.handle.net/1721.1/124161
dc.description.abstractWe 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. Unlike most prior work on BD, a crucial feature of this formulation is that it does not encode support restrictions on the unknowns, except for fixing their duration lengths. The indeterminacy inherent to BD, which is difficult to resolve with a traditional l-1 penalty on the impulse responses, is resolved in our method because it seeks a first approximation where the impulse responses are: 'maximally white' over frequency - encoded as the energy focusing near zero lag of the impulse-response temporal autocorrelations; and 'maximally front-loaded' - encoded as the energy focusing near zero time of the impulse responses. Hence, we call the method focused BD (FBD). It partitions BD into two separate optimization problems and uses the focusing constraints in succession. The respective constraints in both these problems are removed as the iterations progress. A multichannel BD problem whose physics calls for sparse and front-loaded impulse responses arises in seismic inversion, where the impulse responses are the Green's function evaluations at different receiver locations, and the operation of a drill bit inputs the noisy and correlated source signature into the subsurface. We demonstrate the benefits of FBD using seismic-while-drilling numerical experiments, where the noisy data recorded at the receivers are hard to interpret, but FBD can provide the processing essential to separate the drill-bit (source) signature from the interpretable Green's function. ©2019 keywords: blind source separation; deconvolution; geophysical signal processing; iterative methods; optimisation; seismology; front-loaded impulse responses; impulse-response temporal autocorrelations; multichannel blind deconvolution method; multichannel BD problem; seismic inversion; Green function evaluations; receiver locations; drill-bit signature; Deconvolution; Noise measurement; Receivers; Green's function methods; Focusing; Interferometry; Optimization; Blind deconvolution; seismic interferometry; phase retrieval; channel identification; dereverberation; front-loaded; coprimeen_US
dc.description.sponsorshipUnited States. Air Force. Office of Scientific Research (Grant FA9550-17-1-0316)en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant: DMS-1255203)en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/TSP.2019.2908911en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleFocused blind deconvolutionen_US
dc.title.alternativeIEEE Transactions on Signal Processingen_US
dc.typeArticleen_US
dc.identifier.citationP. Bharadwaj, L. Demanet and A. Fournier, "Focused Blind Deconvolution," in IEEE Transactions on Signal Processing 67, 12 (June 2019): 3168-3180. ©2019 Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mathematicsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciencesen_US
dc.relation.journalIEEE Transactions on Signal Processingen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-11-12T14:18:06Z
dspace.date.submission2019-11-12T14:18:14Z
mit.journal.volume67en_US
mit.journal.issue12en_US
mit.metadata.statusComplete


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