KAUST DepartmentEarth Science and Engineering Program
Extreme Computing Research Center
Physical Science and Engineering (PSE) Division
Online Publication Date2017-07-05
Print Publication Date2017
Permanent link to this recordhttp://hdl.handle.net/10754/626099
MetadataShow full item record
AbstractIn this work, we investigate global seismic tomographic models obtained by spectral-element simulations of seismic wave propagation and adjoint methods. Global crustal and mantle models are obtained based on an iterative conjugate-gradient type of optimization scheme. Forward and adjoint seismic wave propagation simulations, which result in synthetic seismic data to make measurements and data sensitivity kernels to compute gradient for model updates, respectively, are performed by the SPECFEM3D-GLOBE package   at the Oak Ridge Leadership Computing Facility (OLCF) to study the structure of the Earth at unprecedented levels. Using advances in solver techniques that run on the GPUs on Titan at the OLCF, scientists are able to perform large-scale seismic inverse modeling and imaging. Using seismic data from global and regional networks from global CMT earthquakes, scientists are using SPECFEM3D-GLOBE to understand the structure of the mantle layer of the Earth. Visualization of the generated data sets provide an effective way to understand the computed wave perturbations which define the structure of mantle in the Earth.
CitationPugmire D, Bozdağ E, Lefebvre M, Tromp J, Komatitsch D, et al. (2017) Pillars of the Mantle. Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact - PEARC17. Available: http://dx.doi.org/10.1145/3093338.3104170.
SponsorsThis work was done using resources generously made available by the Oak Ridge Leadership Computing Facility located at the Oak Ridge National Laboratory.
JournalProceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact - PEARC17
Conference/Event name2017 Practice and Experience in Advanced Research Computing, PEARC 2017