Statistical emulation of a tsunami model for sensitivity analysis and uncertainty quantification

TL;DR

This paper develops a statistical emulator for a tsunami model to enable rapid predictions and uncertainty quantification, crucial for early warning systems and hazard mitigation.

Contribution

It introduces the Outer Product Emulator within a Bayesian framework to efficiently approximate a landslide-generated tsunami model using limited evaluations.

Findings

The emulator performs well validated by Leave-One-Out method.

It provides near-instantaneous predictions of tsunami characteristics.

The approach effectively quantifies uncertainty in tsunami modeling.

Abstract

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.