Element-by-element parallel spectral-element methods for 3-D teleseismic wave modeling
KAUST DepartmentPhysical Science and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/625887
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AbstractThe development of an efficient algorithm for teleseismic wave field modeling is valuable for calculating the gradients of the misfit function (termed misfit gradients) or Fréchet derivatives when the teleseismic waveform is used for adjoint tomography. Here, we introduce an element-by-element parallel spectral-element method (EBE-SEM) for the efficient modeling of teleseismic wave field propagation in a reduced geology model. Under the plane-wave assumption, the frequency-wavenumber (FK) technique is implemented to compute the boundary wave field used to construct the boundary condition of the teleseismic wave incidence. To reduce the memory required for the storage of the boundary wave field for the incidence boundary condition, a strategy is introduced to efficiently store the boundary wave field on the model boundary. The perfectly matched layers absorbing boundary condition (PML ABC) is formulated using the EBE-SEM to absorb the scattered wave field from the model interior. The misfit gradient can easily be constructed in each time step during the calculation of the adjoint wave field. Three synthetic examples demonstrate the validity of the EBE-SEM for use in teleseismic wave field modeling and the misfit gradient calculation.
CitationLiu S, Yang D, Dong X, Liu Q, Zheng Y (2017) Element-by-element parallel spectral-element methods for 3-D teleseismic wave modeling. Solid Earth 8: 969–986. Available: http://dx.doi.org/10.5194/se-8-969-2017.
SponsorsWe greatly appreciate the detailed suggestions from Michal Afanasiev and the anonymous reviewer. Their valuable suggestions greatly improved the quality of the paper. This study was supported by the National Natural Science Foundation of China (grant nos. 41230210 and 41604034). Shaolin Liu was financially supported by the China Postdoctoral Science Foundation (grant no. 2015M580085).
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