An unsplit convolutional perfectly matched layer improved at grazing incidence for the seismic wave equation

By Dimitri Komatitsch, Roland Martin. 2007

768 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering, Geophysics

View Paper: https://www.semanticscholar.org/paper/8f09cd312bc3ab8cab2ee530a290573a3e76598f

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In Atomic and Molecular Physics, and Optics

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Citations in the corpus

Unsplit complex frequency-shifted PML implementation using auxiliary differential equations for seismic wave modeling (2010, methodology): “This technique has been used in seismic wave modeling in elastic media Komatitsch and Martin, 2007; Drossaert and Giannopoulos, 2007b and poroelastic media Martin et al., 2008a .”
An unsplit convolutional perfectly matched layer technique improved at grazing incidence for the viscoelastic wave equation (2009, methodology): “…αmax = π f 0, where f 0 is the dominant frequency of the source defined above, and we compare the results with αmax = 0 and κ = 1 which is very similar to the classical PML case of Collino & Tsogka (2001) because the Butterworth filter is then suppressed, as mentioned in Komatitsch & Martin (2007).”
An unsplit convolutional perfectly matched layer improved at grazing incidence for seismic wave propagation in poroelastic media (2008, methodology): “s b a s n t i E a © r T m e d e c b p c o r c c t c s d t d G e r m m t w p f e c ceived 1 t d’Ima atitsch iques A INTRODUCTION In Komatitsch and Martin 2007 , we presented an improved aborbing boundary technique for the purely elastic wave equation ased on an unsplit convolutional perfectly matched”
Comparison of two algorithms for isotropic elastic P and S vector decomposition (2015, methodology): “…at each grid point within the model including the edges; there will not be any significant edge reflections because convolutional perfectly matched layer absorbing boundary conditions (Komatitsch and Martin, 2007) with a width of 20 grid points inside the model are used on all four grid edges.”
Deep learning for denoising (2018, methodology): “A convolutional perfectly matched layer (CPML) absorbing boundary conditions (Komatitsch and Martin, 2007) are used on the left, right, and bottom grid edges to reduce unwanted reflections.”
Time Domain 2D Elastic Full Waveform Tomography (2011, background): “This can be achieved by a coordinate stretch of the wave equations in the frequency domain (Komatitsch and Martin (2007)).”
Forward and adjoint simulations of seismic wave propagation on fully unstructured hexahedral meshes (2011, methodology): “It would be more efficient to use a Perfectly Matched Layer (PML) (see e.g. Komatitsch & Martin 2007, 2008c; Martin & Komatitsch 2009), but a parallel implementation with good loadbalancing properties is challenging because additional equations need to be solved.”
An hp-adaptive discontinuous Galerkin finite-element method for 3-D elastic wave modelling (2010, methodology): “Here, we present a low-order DG-FEM formulation with the convolutional perfectly matched layer (CPML) absorbing boundary condition (Roden & Gedney 2000; Komatitsch & Martin 2007) that is suitable for large-scale 3-”
Seismic metasurfaces: Sub-wavelength resonators and Rayleigh wave interaction (2016, methodology): “Perfectly matched layers (Komatitsch and Martin, 2007), (PML) are applied on the bottom and vertical boundaries of the computational domain containing the half-space; the remaining boundaries (top surface and resonators) are traction free.”