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dc.contributor.authorLiu, Qiancheng
dc.contributor.authorPeter, Daniel
dc.contributor.authorTape, Carl
dc.date.accessioned2019-05-21T12:10:50Z
dc.date.available2019-05-21T12:10:50Z
dc.date.issued2019-05-17
dc.identifier.citationLiu Q, Peter D, Tape C (2019) Square-Root Variable Metric based elastic full-waveform inversion – Part 1: Theory and validation. Geophysical Journal International. Available: http://dx.doi.org/10.1093/gji/ggz188.
dc.identifier.issn0956-540X
dc.identifier.issn1365-246X
dc.identifier.doi10.1093/gji/ggz188
dc.identifier.urihttp://hdl.handle.net/10754/652934
dc.description.abstractFull-waveform inversion (FWI) has become a powerful tool in inverting subsurface geophysical properties. The estimation of uncertainty in the resulting Earth models and parameter trade-offs, although equally important to the inversion result, has however often been neglected or became prohibitive for large-scale inverse problems. Theoretically, the uncertainty estimation is linked to the inverse Hessian (or posterior covariance matrix), which for massive inverse problems becomes impossible to store and compute. In this study, we investigate the application of the square-root variable metric (SRVM) method, a quasi-Newton optimisation algorithm, to FWI in a vector version. This approach allows us to reconstruct the final inverse Hessian at an affordable storage memory cost. We conduct SRVM based elastic FWI on several elastic models in regular, free-surface and practical cases. Comparing the results with those obtained by the state-of-the-art L-BFGS algorithm, we find that the proposed SRVM method performs on a similar, highly-efficient level as L-BFGS, with the advantage of providing additional information such as the inverse Hessian needed for uncertainty quantification.
dc.description.sponsorshipThe authors are grateful to editor Jean Virieux and two anonymous reviewers for improving the initial manuscript. This work was supported by the King Abdullah University of Science & Technology (KAUST) Office of Sponsored Research (OSR) under award No. UAPN#2605-CRG4. Computational resources were provided by the Information Technology Division and Extreme Computing Research Center (ECRC) at KAUST.
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggz188/5491280
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectcomputational seismology
dc.subjectseismic tomography
dc.subjectinverse theory
dc.subjectwaveform inversion
dc.titleSquare-Root Variable Metric based elastic full-waveform inversion – Part 1: Theory and validation
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalGeophysical Journal International
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUniversity of Alaska Fairbanks, Geophysical Institute and Department of Geosciences, Fairbanks, Alaska, USA
kaust.personLiu, Qiancheng
kaust.personPeter, Daniel
kaust.grant.numberUAPN#2605-CRG4
refterms.dateFOA2019-05-21T12:31:20Z
dc.date.published-online2019-05-17
dc.date.published-print2019-08-01


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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.