Salmanidou, DM;
Beck, J;
Pazak, P;
Guillas, S;
(2021)
Probabilistic, high-resolution tsunami predictions in northern Cascadia by exploiting sequential design for efficient emulation.
Natural Hazards and Earth System Sciences
, 21
(12)
pp. 3789-3807.
10.5194/nhess-21-3789-2021.
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Abstract
The potential of a full-margin rupture along the Cascadia subduction zone poses a significant threat over a populous region of North America. Previous probabilistic tsunami hazard assessment studies produced hazard curves based on simulated predictions of tsunami waves, either at low resolution or at high resolution for a local area or under limited ranges of scenarios or at a high computational cost to generate hundreds of scenarios at high resolution. We use the graphics processing unit (GPU)-accelerated tsunami simulator VOLNA-OP2 with a detailed representation of topographic and bathymetric features. We replace the simulator by a Gaussian process emulator at each output location to overcome the large computational burden. The emulators are statistical approximations of the simulator's behaviour. We train the emulators on a set of input–output pairs and use them to generate approximate output values over a six-dimensional scenario parameter space, e.g. uplift/subsidence ratio and maximum uplift, that represent the seabed deformation. We implement an advanced sequential design algorithm for the optimal selection of only 60 simulations. The low cost of emulation provides for additional flexibility in the shape of the deformation, which we illustrate here considering two families – buried rupture and splay-faulting – of 2000 potential scenarios. This approach allows for the first emulation-accelerated computation of probabilistic tsunami hazard in the region of the city of Victoria, British Columbia.
| Type: | Article |
|---|---|
| Title: | Probabilistic, high-resolution tsunami predictions in northern Cascadia by exploiting sequential design for efficient emulation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.5194/nhess-21-3789-2021 |
| Publisher version: | https://doi.org/10.5194/nhess-21-3789-2021 |
| Language: | English |
| Additional information: | © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10141318 |
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