Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion

Handle URI:
http://hdl.handle.net/10754/621157
Title:
Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion
Authors:
Komatitsch, Dimitri; Xie, Zhinan; Bozdağ, Ebru; de Andrade, Elliott Sales; Peter, Daniel ( 0000-0002-3397-5379 ) ; Liu, Qinya; Tromp, Jeroen
Abstract:
We introduce a technique to compute exact anelastic sensitivity kernels in the time domain using parsimonious disk storage. The method is based on a reordering of the time loop of time-domain forward/adjoint wave propagation solvers combined with the use of a memory buffer. It avoids instabilities that occur when time-reversing dissipative wave propagation simulations. The total number of required time steps is unchanged compared to usual acoustic or elastic approaches. The cost is reduced by a factor of 4/3 compared to the case in which anelasticity is partially accounted for by accommodating the effects of physical dispersion. We validate our technique by performing a test in which we compare the Kα sensitivity kernel to the exact kernel obtained by saving the entire forward calculation. This benchmark confirms that our approach is also exact. We illustrate the importance of including full attenuation in the calculation of sensitivity kernels by showing significant differences with physical-dispersion-only kernels.
KAUST Department:
Extreme Computing Research Center
Citation:
Komatitsch D, Xie Z, Bozdağ E, Sales de Andrade E, Peter D, et al. (2016) Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion. Geophysical Journal International 206: 1467–1478. Available: http://dx.doi.org/10.1093/gji/ggw224.
Publisher:
Oxford University Press (OUP)
Journal:
Geophysical Journal International
Issue Date:
13-Jun-2016
DOI:
10.1093/gji/ggw224
Type:
Article
ISSN:
0956-540X; 1365-246X
Sponsors:
We thank Mark Asch, Didier Auroux, Cedric Bellis, Elie Bretin,Andreas Fichtner, Josselin Garnier, Thomas Guillet, Ioannis G.Kevrekidis, Bruno Lombard, Vadim Monteiller and William W.Symes for fruitful discussion, and the Computational Infrastructure for Geodynamics (CIG) and Marie Cournille for support. We thank Heiner Igel and an anonymous reviewer for useful comments that improved the manuscript. Part of this work was funded by the Si-mone and Cino del Duca/Institut de France/French Academy of Sciences Foundation under grant no. 095164, by the European UnionHorizon 2020 Marie Curie Action no. 641943 project ‘WAVES’of call H2020-MSCA-ITN-2014, by U.S. NSF grant 1112906 and by China NSFC grant 51378479. ZX thanks the China Scholarship Council for financial support during his stay at LMA CNRS and the continuous support from Prof Liao Zhenpeng. ES andQL were supported by the NSERC G8 Research Councils Initiative on Multilateral Research grant no. 490919 and Discovery grant no. 487237. This work was granted access to the Euro-pean Partnership for Advanced Computing in Europe (PRACE)under allocation TGCC CURIE no. ra2410, to the French HPCresources of TGCC under allocation no. 2015-gen7165 made byGENCI and of the Aix-Marseille Supercomputing Mesocenter under allocations nos 14b013 and 15b034, to the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, USA,which is supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC05-00OR22725, and to the Sandybridge cluster at the SciNet HPC Consortium funded by the Canada Foundation for Innovation, the Ontario Research Fund and the University of Toronto Startup Fund.
Additional Links:
http://gji.oxfordjournals.org/content/206/3/1467
Appears in Collections:
Articles; Extreme Computing Research Center

Full metadata record

DC FieldValue Language
dc.contributor.authorKomatitsch, Dimitrien
dc.contributor.authorXie, Zhinanen
dc.contributor.authorBozdağ, Ebruen
dc.contributor.authorde Andrade, Elliott Salesen
dc.contributor.authorPeter, Danielen
dc.contributor.authorLiu, Qinyaen
dc.contributor.authorTromp, Jeroenen
dc.date.accessioned2016-10-24T13:47:14Z-
dc.date.available2016-10-24T13:47:14Z-
dc.date.issued2016-06-13en
dc.identifier.citationKomatitsch D, Xie Z, Bozdağ E, Sales de Andrade E, Peter D, et al. (2016) Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion. Geophysical Journal International 206: 1467–1478. Available: http://dx.doi.org/10.1093/gji/ggw224.en
dc.identifier.issn0956-540Xen
dc.identifier.issn1365-246Xen
dc.identifier.doi10.1093/gji/ggw224en
dc.identifier.urihttp://hdl.handle.net/10754/621157-
dc.description.abstractWe introduce a technique to compute exact anelastic sensitivity kernels in the time domain using parsimonious disk storage. The method is based on a reordering of the time loop of time-domain forward/adjoint wave propagation solvers combined with the use of a memory buffer. It avoids instabilities that occur when time-reversing dissipative wave propagation simulations. The total number of required time steps is unchanged compared to usual acoustic or elastic approaches. The cost is reduced by a factor of 4/3 compared to the case in which anelasticity is partially accounted for by accommodating the effects of physical dispersion. We validate our technique by performing a test in which we compare the Kα sensitivity kernel to the exact kernel obtained by saving the entire forward calculation. This benchmark confirms that our approach is also exact. We illustrate the importance of including full attenuation in the calculation of sensitivity kernels by showing significant differences with physical-dispersion-only kernels.en
dc.description.sponsorshipWe thank Mark Asch, Didier Auroux, Cedric Bellis, Elie Bretin,Andreas Fichtner, Josselin Garnier, Thomas Guillet, Ioannis G.Kevrekidis, Bruno Lombard, Vadim Monteiller and William W.Symes for fruitful discussion, and the Computational Infrastructure for Geodynamics (CIG) and Marie Cournille for support. We thank Heiner Igel and an anonymous reviewer for useful comments that improved the manuscript. Part of this work was funded by the Si-mone and Cino del Duca/Institut de France/French Academy of Sciences Foundation under grant no. 095164, by the European UnionHorizon 2020 Marie Curie Action no. 641943 project ‘WAVES’of call H2020-MSCA-ITN-2014, by U.S. NSF grant 1112906 and by China NSFC grant 51378479. ZX thanks the China Scholarship Council for financial support during his stay at LMA CNRS and the continuous support from Prof Liao Zhenpeng. ES andQL were supported by the NSERC G8 Research Councils Initiative on Multilateral Research grant no. 490919 and Discovery grant no. 487237. This work was granted access to the Euro-pean Partnership for Advanced Computing in Europe (PRACE)under allocation TGCC CURIE no. ra2410, to the French HPCresources of TGCC under allocation no. 2015-gen7165 made byGENCI and of the Aix-Marseille Supercomputing Mesocenter under allocations nos 14b013 and 15b034, to the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, USA,which is supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC05-00OR22725, and to the Sandybridge cluster at the SciNet HPC Consortium funded by the Canada Foundation for Innovation, the Ontario Research Fund and the University of Toronto Startup Fund.en
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttp://gji.oxfordjournals.org/content/206/3/1467en
dc.rights© The Authors 2016. Published by Oxford University Press on behalf of The Royal Astronomical Society.en
dc.subjectNumerical solutionsen
dc.subjectTomographyen
dc.subjectSeismic attenuationen
dc.subjectSeismic tomographyen
dc.subjectComputational seismologyen
dc.subjectWave propagationen
dc.titleAnelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversionen
dc.typeArticleen
dc.contributor.departmentExtreme Computing Research Centeren
dc.identifier.journalGeophysical Journal Internationalen
dc.eprint.versionPre-printen
dc.contributor.institutionLMA, CNRS UPR 7051, Aix-Marseille University, Centrale Marseille, F-13453 Marseille Cedex 13, Franceen
dc.contributor.institutionInstitute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, Chinaen
dc.contributor.institutionGéoazur, University of Nice Sophia Antipolis, 250 rue Albert Einstein, F-06560 Valbonne, Franceen
dc.contributor.institutionDepartment of Physics and Department of Earth Sciences, University of Toronto, Toronto, Ontario, M5S 1A7, Canadaen
dc.contributor.institutionDepartment of Geosciences and Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USAen
kaust.authorPeter, Danielen
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