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Resolving wave propagation in anisotropic poroelastic media using graphical processing units (GPUs)
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  • Yury Alkhimenkov,
  • Ludovic Räss,
  • Lyudmila Khakimova,
  • Beatriz Quintal,
  • Yury Podladchikov
Yury Alkhimenkov
University of Lausanne

Corresponding Author:yury.alkhimenkov@unil.ch

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Ludovic Räss
ETH Zurich
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Lyudmila Khakimova
Lomonosov Moscow State University
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Beatriz Quintal
University of Lausanne
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Yury Podladchikov
University of Lausanne
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Biot's equations describe the physics of hydro-mechanically coupled systems establishing the widely recognized theory of poroelasticity. This theory has a broad range of applications in Earth and biological sciences as well as in engineering. The numerical solution of Biot's equations is challenging because wave propagation and fluid pressure diffusion processes occur simultaneously but feature very different characteristic time scales. Analogous to geophysical data acquisition, high resolution and three dimensional numerical experiments lately re-defined state of the art. Tackling high spatial and temporal resolution requires a high-performance computing approach. We developed a multi-GPU numerical application to resolve the anisotropic elastodynamic Biot's equations that relies on a conservative numerical scheme to simulate, in a few seconds, wave fields for spatial domains involving more than 1.5 billion grid cells. We present a comprehensive dimensional analysis reducing the number of material parameters needed for the numerical experiments from ten to four. Furthermore, the dimensional analysis emphasizes the key material parameters governing the physics of wave propagation in poroelastic media. We perform a dispersion analysis as function of dimensionless parameters leading to simple and transparent dispersion relations. We then benchmark our numerical solution against an analytical plane wave solution. Finally, we present several numerical modeling experiments, including a three-dimensional simulation of fluid injection into a poroelastic medium. We provide the Matlab, symbolic Maple, and GPU CUDA C routines to reproduce the main presented results. The high efficiency of our numerical implementation makes it readily usable to investigate three-dimensional and high-resolution scenarios of practical applications.
Jul 2021Published in Journal of Geophysical Research: Solid Earth volume 126 issue 7. 10.1029/2020JB021175