Alice-Agnes Gabriel

2papers

2 Papers

NAFeb 18, 2018
On energy stable discontinuous Galerkin spectral element approximations of the perfectly matched layer for the wave equation

Kenneth Duru, Alice-Agnes Gabriel, Gunilla Kreiss

We develop a provably energy stable discontinuous Galerkin spectral element method (DGSEM) approximation of the perfectly matched layer (PML) for the three and two space dimensional (3D and 2D) linear acoustic wave equations, in first order form, subject to well-posed linear boundary conditions. First, using the well-known complex coordinate stretching, we derive an efficient un-split modal PML for the 3D acoustic wave equation. Second, we prove asymptotic stability of the continuous PML by deriving energy estimates in the Laplace space, for the 3D PML in a heterogeneous acoustic medium, assuming piece-wise constant PML damping. Third, we develop a DGSEM for the wave equation using physically motivated numerical flux, with penalty weights, which are compatible with all well-posed, internal and external, boundary conditions. When the PML damping vanishes, by construction, our choice of penalty parameters yield an upwind scheme and a discrete energy estimate analogous to the continuous energy estimate. Fourth, to ensure numerical stability when PML damping is present, it is necessary to systematically extend the numerical numerical fluxes, and the inter-element and boundary procedures, to the PML auxiliary differential equations. This is critical for deriving discrete energy estimates analogous to the continuous energy estimates. Finally, we propose a procedure to compute PML damping coefficients such that the PML error converges to zero, at the optimal convergence rate of the underlying numerical method. Numerical experiments are presented in 2D and 3D corroborating the theoretical results.

61.2GEO-PHMar 16
Real-time probabilistic tsunami forecasting in Cascadia from sparse offshore pressure observations

Stefan Henneking, Fabian Kutschera, Sreeram Venkat et al.

Near-field tsunami early warning in the Cascadia Subduction Zone is limited by sparse offshore observations. We show that a hypothetical network of 175 seafloor pressure sensors can support real-time Bayesian inference of tsunamigenic seafloor motion and probabilistic tsunami forecasts for two fully-coupled Cascadia earthquake dynamic rupture--tsunami scenarios, a partial rupture and a margin-wide rupture. The complex oceanic acoustic, Rayleigh, and tsunami wavefields in both scenarios are similar during the first two minutes and then diverge. Using an acoustic--gravity inversion with offline precomputation and online assimilation of pressure data, tsunami forecasts are obtained in less than a second. We leverage a Bayesian inversion-based framework that splits the computations into an offline precomputation phase performed with large-scale computing facilities, and an online phase that computes forecasts from real-time data and can be executed on a laptop. Forecast errors remain low at 22.1% for the margin-wide rupture and 19.6% for the partial rupture.