SuitSparseQR

A MATLAB-based frequency-domain finite difference package for solving 2D visco-acoustic wave equation. Frequency-domain finite-difference (FDFD) modelling is widely used for multi-source experiments modelling and full waveform tomography. In this paper, a frequency-domain finite difference package written in MATLAB is presented which solves 2D visco-acoustic wave equation. The mixed-grid stencil is used as a state-of-the-art finite differencing approach and SuitSparseQR solver is utilised for solving the large linear system of equations. Because of the independence of frequency components and the use of TBB-enabled SuitsparseQR solver, the package benefits from parallel computation in multi-core machines. Using MATLAB, codes became more readable and using different visualisation facilities inside MATLAB made this package very useful for research purposes. This package uses a PML absorbing boundary and supports anti-time aliasing and reduction velocity technique. Different attenuation mechanisms can easily be implemented. The performance of codes are examined on simple and complicated models which proved satisfactory in terms of accuracy and required CPU time, both in single and multi-source cases.


References in zbMATH (referenced in 39 articles )

Showing results 1 to 20 of 39.
Sorted by year (citations)

1 2 next

  1. Al Daas, Hussam; Jolivet, Pierre; Scott, Jennifer A.: A robust algebraic domain decomposition preconditioner for sparse normal equations (2022)
  2. Brenner, Holger; Steinbuch, Jonathan: Deciding stability of sheaves on curves (2022)
  3. Brust, Johannes J.; Marcia, Roummel F.; Petra, Cosmin G.; Saunders, Michael A.: Large-scale optimization with linear equality constraints using reduced compact representation (2022)
  4. Gnanasekaran, Abeynaya; Darve, Eric: Hierarchical orthogonal factorization: sparse square matrices (2022)
  5. Hanke, Michael; März, Roswitha: Towards a reliable implementation of least-squares collocation for higher index differential-algebraic equations. II: The discrete least-squares problem (2022)
  6. Hanke, Michael; März, Roswitha: Towards a reliable implementation of least-squares collocation for higher index differential-algebraic equations. I: Basics and ansatz function choices (2022)
  7. Jiao, Xiangmin; Chen, Qiao: Approximate generalized inverses with iterative refinement for (\epsilon)-accurate preconditioning of singular systems (2022)
  8. Sobczyk, Aleksandros; Gallopoulos, Efstratios: Estimating leverage scores via rank revealing methods and randomization (2021)
  9. Vanderstukken, Jeroen; De Lathauwer, Lieven: Systems of polynomial equations, higher-order tensor decompositions, and multidimensional harmonic retrieval: a unifying framework. Part I: the canonical polyadic decomposition (2021)
  10. Agnese, Marco; Nürnberg, Robert: Fitted front tracking methods for two-phase ncompressible Navier-Stokes flow: Eulerian and ALE finite element discretizations (2020)
  11. Buttari, Alfredo; Hauberg, Søren; Kodsi, Costy: Parallel \textitQRfactorization of block-tridiagonal matrices (2020)
  12. Lundquist, Tomas; Malan, Arnaud G.; Nordström, Jan: Efficient and error minimized coupling procedures for unstructured and moving meshes (2020)
  13. Cullen, Andrew C.; Clarke, Simon R.: A fast, spectrally accurate homotopy based numerical method for solving nonlinear differential equations (2019)
  14. Roininen, Lassi; Girolami, Mark; Lasanen, Sari; Markkanen, Markku: Hyperpriors for Matérn fields with applications in Bayesian inversion (2019)
  15. Essid, Montacer; Solomon, Justin: Quadratically regularized optimal transport on graphs (2018)
  16. Grigori, Laura; Cayrols, Sebastien; Demmel, James W.: Low rank approximation of a sparse matrix based on LU factorization with column and row tournament pivoting (2018)
  17. Roininen, Lassi; Lasanen, Sari; Orispää, Mikko; Särkkä, Simo: Sparse approximations of fractional Matérn fields (2018)
  18. Maier, Matthias; Margetis, Dionisios; Luskin, Mitchell: Dipole excitation of surface plasmon on a conducting sheet: finite element approximation and validation (2017)
  19. Scott, Jennifer: On using Cholesky-based factorizations and regularization for solving rank-deficient sparse linear least-squares problems (2017)
  20. Agullo, Emmanuel; Buttari, Alfredo; Guermouche, Abdou; Lopez, Florent: Implementing multifrontal sparse solvers for multicore architectures with sequential task flow runtime systems (2016)

1 2 next