Hierarchical Bayesian Modeling of Post‐Earthquake Ignition Probabilities Considering Inter‐Earthquake Heterogeneity

Post‐earthquake fires are high‐consequence events with extensive damage potential. They are also low‐frequency events, so their nature remains underinvestigated. One difficulty in modeling post‐earthquake ignition probabilities is reducing the model uncertainty attributed to the scarce source data. The data scarcity problem has been resolved by pooling the data indiscriminately collected from multiple earthquakes. However, this approach neglects the inter‐earthquake heterogeneity in the regional and seasonal characteristics, which is indispensable for risk assessment of future post‐earthquake fires. Thus, the present study analyzes the post‐earthquake ignition probabilities of five major earthquakes in Japan from 1995 to 2016 (1995 Kobe, 2003 Tokachi‐oki, 2004 Niigata–Chuetsu, 2011 Tohoku, and 2016 Kumamoto earthquakes) by a hierarchical Bayesian approach. As the ignition causes of earthquakes share a certain commonality, common prior distributions were assigned to the parameters, and samples were drawn from the target posterior distribution of the parameters by a Markov chain Monte Carlo simulation. The results of the hierarchical model were comparatively analyzed with those of pooled and independent models. Although the pooled and hierarchical models were both robust in comparison with the independent model, the pooled model underestimated the ignition probabilities of earthquakes with few data samples. Among the tested models, the hierarchical model was least affected by the source‐to‐source variability in the data. The heterogeneity of post‐earthquake ignitions with different regional and seasonal characteristics has long been desired in the modeling of post‐earthquake ignition probabilities but has not been properly considered in the existing approaches. The presented hierarchical Bayesian approach provides a systematic and rational framework to effectively cope with this problem, which consequently enhances the statistical reliability and stability of estimating post‐earthquake ignition probabilities.