Data Assimilation Seminar
Prof. Serge Guillas (May 31, 2019, 15:00- * joint seminar with R-CCS cafe *)
|Affiliation||University College London|
|Title||Statistical emulation to quantify uncertainties in tsunami modelling using high performance computing|
|Abstract||In this talk, we present solutions to the investigation of uncertainties in tsunami impacts in three settings.
First, we consider landslides as a source of tsunamis from the Indus Canyon in the Western Indian Ocean. We employ statistical emulation, i.e. surrogate modelling, to efficiently quantify uncertainties associated with slump-generated tsunamis at the slopes of the canyon. We simulated 60 slump scenarios to train the emulator and predict 500,000 trial scenarios in order to study probabilistically the tsunami hazard over the near field. The results show that the most likely tsunami amplitudes and velocities can potentially impact vessels and maritime facilities. We demonstrate that the emulator-based approach is an important tool for probabilistic hazard analysis since it can generate thousands of tsunami scenarios in few seconds, compared to days of computations on High Performance Computing facilities for a single run of the dispersive tsunami solver that we use here.
We then examine future tsunami hazard from the Makran subduction zone in the Western Indian Ocean. Since tsunamis present a high risk to ports in the form of high velocities and vorticity, we capture these phenomena in high resolution (down to 10m) using carefully constructed unstructured meshes for the port of Karachi. The seabed deformations triggered by the earthquake sources vary in magnitude. A parametrization of these sources is done via geometric descriptions and a newly introduced amplification parameter of the vertical deformation due the sediments. A emulator approximates the functional relationship between inputs and outputs maximum velocity and free surface elevation. A hazard assessment is performed using the emulator.
Finally, we create emulators that respect the nature of time series outputs. We introduce here a novel statistical emulation of the input-output dependence of these computer models: functional registration and Functional Principal Components techniques improve the predictions of the emulator. Our phase registration method captures fine variations in amplitude. Smoothness in the time series of outputs is modelled, and we are thus able to select more representative, and more parsimonious, regression functions than a fixed basis method such as a Fourier basis. We apply this approach to the high resolution tsunami wave propagation and coastal inundation for the Cascadia region in the Pacific Northwest.