Due to the catastrophic consequences of tsunamis, early warnings need to be
issued quickly in order to mitigate the hazard. Additionally, there is a need
to represent the uncertainty in the predictions of tsunami characteristics
corresponding to the uncertain trigger features (e.g. either position, shape
and speed of a landslide, or sea floor deformation associated with an
earthquake). Unfortunately, computer models are expensive to run. This leads to
significant delays in predictions and makes the uncertainty quantification
impractical. Statistical emulators run almost instantaneously and may represent
well the outputs of the computer model. In this paper, we use the Outer Product
Emulator to build a fast statistical surrogate of a landslide-generated tsunami
computer model. This Bayesian framework enables us to build the emulator by
combining prior knowledge of the computer model properties with a few carefully
chosen model evaluations. The good performance of the emulator is validated
using the Leave-One-Out method
