Iterative frequency-domain seismic wave solvers based on multi-level domain-decomposition preconditioners

Frequency-domain full-waveform inversion (FWI) is suitable for long-offset stationary-recording acquisition, since reliable subsurface models can be reconstructed with a few frequencies and attenuation is easily implemented without computational overhead. In the frequency domain, wave modelling is a Helmholtz-type boundary-value problem which requires to solve a large and sparse system of linear equations per frequency with multiple right-hand sides (sources). This system can be solved with direct or iterative methods. While the former are suitable for FWI application on 3D dense OBC acquisitions covering spatial domains of moderate size, the later should be the approach of choice for sparse node acquisitions covering large domains (more than 50 millions of unknowns). Fast convergence of iterative solvers for Helmholtz problems remains however challenging due to the non definiteness of the Helmholtz operator, hence requiring efficient preconditioners. In this study, we use the Krylov subspace GMRES iterative solver combined with a multi-level domain-decomposition preconditioner. Discretization relies on continuous finite elements on unstructured tetrahedral meshes to comply with complex geometries and adapt the size of the elements to the local wavelength ($h$-adaptivity). We assess the convergence and the scalability of our method with the acoustic 3D SEG/EAGE Overthrust model up to a frequency of 20~Hz and discuss its efficiency for multi right-hand side processing.

[1]  Victorita Dolean,et al.  An introduction to domain decomposition methods - algorithms, theory, and parallel implementation , 2015 .

[2]  E. A. Spence,et al.  Recent Results on Domain Decomposition Preconditioning for the High-Frequency Helmholtz Equation Using Absorption , 2017 .

[3]  Pierre Jolivet,et al.  Block Iterative Methods and Recycling for Improved Scalability of Linear Solvers , 2016, SC16: International Conference for High Performance Computing, Networking, Storage and Analysis.

[4]  Pierre-Henri Tournier,et al.  A two-level domain-decomposition preconditioner for the time-harmonic maxwell’s equations , 2017, CSE 2018.

[5]  Frédéric Nataf,et al.  Scalable domain decomposition preconditioners for heterogeneous elliptic problems , 2013, 2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[6]  Eric de Sturler,et al.  Recycling Krylov Subspaces for Sequences of Linear Systems , 2006, SIAM J. Sci. Comput..

[7]  René-Édouard Plessix Some Computational Aspects of the Time and Frequency Domain Formulations of Seismic Waveform Inversion , 2017 .

[8]  R. Pratt Seismic waveform inversion in the frequency domain; Part 1, Theory and verification in a physical scale model , 1999 .

[9]  Théo Mary,et al.  Block Low-Rank multifrontal solvers: complexity, performance, and scalability. (Solveurs multifrontaux exploitant des blocs de rang faible: complexité, performance et parallélisme) , 2017 .

[10]  Y. Erlangga,et al.  ON A MULTILEVEL KRYLOV METHOD FOR THE HELMHOLTZ EQUATION PRECONDITIONED BY SHIFTED LAPLACIAN , 2008 .

[11]  Patrick R. Amestoy,et al.  Fast 3D frequency-domain full-waveform inversion with a parallel block low-rank multifrontal direct solver: Application to OBC data from the North Sea , 2016 .

[12]  Allan Ross,et al.  Field design and operation of a novel deepwater, wide-azimuth node seismic survey , 2007 .

[13]  Yousef Saad,et al.  Iterative methods for sparse linear systems , 2003 .