Real-time probabilistic tsunami forecasting in Cascadia from sparse offshore pressure observations
This provides a practical solution for tsunami early warning in Cascadia, though it is incremental as it builds on existing Bayesian inversion methods.
The paper tackles the problem of near-field tsunami early warning in the Cascadia Subduction Zone by using a network of seafloor pressure sensors for real-time Bayesian inference, achieving forecast errors of 22.1% for a margin-wide rupture and 19.6% for a partial rupture.
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.