Anelastic sensitivity kernels with parsimonious storage for adjoint tomography and full waveform inversion
de Andrade, Elliott Sales
KAUST DepartmentExtreme Computing Research Center
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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.
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.
SponsorsWe 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.
PublisherOxford University Press (OUP)