Bayesian near‐field tsunami forecasting with uncertainty estimates

Tsunami waveforms can be observed at offshore locations such as sea‐bottom pressure gauges or GPS‐mounted buoys. Recent work has focused on using these observations to make near‐field tsunami forecasts in real time. However, existing forecasting methods are limited in that they do not provide uncertainty estimates. This study develops a near‐field tsunami forecasting method with uncertainty estimates. The method embeds a conditional autoregressive model in a hierarchical Bayesian inverse model. Since we sample from the posterior distribution of interest using a Markov Chain Monte Carlo algorithm, not only the mean but also the variance for forecasts can be readily obtained. The proposed method is validated through simulation‐based experiments for four historical earthquakes in the Nankai Trough, Japan.