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Title: Uncertainty quantification of dynamic earthquake rupture simulations
Authors: Daub, EG
Arabnejad, H
Mahmood, I
Groen, D
Keywords: uncertainty quantification;earthquake mechanics;model calibration;simulation management
Issue Date: 29-Mar-2021
Publisher: The Royal Society Publishing
Citation: Daub, E.G., Arabnejad, H., Mahmood, I. and Groen, D. (2021) 'Uncertainty quantification of dynamic earthquake rupture simulations', Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 379 (2197), 20200076, pp. 1 - 17. doi: 10.1098/rsta.2020.0076.
Abstract: © 2021 The Authors. We present a tutorial demonstration using a surrogate-model based uncertainty quantification (UQ) approach to study dynamic earthquake rupture on a rough fault surface. The UQ approach performs model calibration where we choose simulation points, fit and validate an approximate surrogate model or emulator, and then examine the input space to see which inputs can be ruled out from the data. Our approach relies on the mogp_emulator package to perform model calibration, and the FabSim3 component from the VECMA toolkit to streamline the workflow, enabling users to manage the workflow using the command line to curate reproducible simulations on local and remote resources. The tools in this tutorial provide an example template that allows domain researchers that are not necessarily experts in the underlying methods to apply them to complex problems. We illustrate the use of the package by applying the methods to dynamic earthquake rupture, which solves the elastic wave equation for the size of an earthquake and the resulting ground shaking based on the stress tensor in the Earth. We show through the tutorial results that the method is able to rule out large portions of the input parameter space, which could lead to new ways to constrain the stress tensor in the Earth based on earthquake observations. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.
ISSN: 1364-503X
Appears in Collections:Dept of Computer Science Research Papers

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