Real-time probabilistic tsunami forecasting in Cascadia from sparse offshore pressure observations

TL;DR

This paper demonstrates a real-time Bayesian tsunami forecasting method in Cascadia using sparse offshore pressure sensors, enabling rapid and probabilistic predictions for different earthquake scenarios.

Contribution

It introduces a Bayesian inversion framework with offline precomputation and online data assimilation for fast tsunami forecasting from limited pressure observations.

Findings

Forecast errors are below 23% for both rupture scenarios.

Forecasts can be computed in less than a second on standard hardware.

The method effectively distinguishes between different rupture scenarios within minutes.

Abstract

Near-field tsunami early warning in the Cascadia Subduction Zone is limited by sparse offshore observations. We show that a hypothetical network of 175 seafloor pressure sensors can support real-time Bayesian inference of tsunamigenic seafloor motion and probabilistic tsunami forecasts for two fully-coupled Cascadia earthquake dynamic rupture--tsunami scenarios, a partial rupture and a margin-wide rupture. The complex oceanic acoustic, Rayleigh, and tsunami wavefields in both scenarios are similar during the first two minutes and then diverge. Using an acoustic--gravity inversion with offline precomputation and online assimilation of pressure data, tsunami forecasts are obtained in less than a second. We leverage a Bayesian inversion-based framework that splits the computations into an offline precomputation phase performed with large-scale computing facilities, and an online phase that computes forecasts from real-time data and can be executed on a laptop. Forecast errors remain low at 22.1% for the margin-wide rupture and 19.6% for the partial rupture.