Himoto Keisuke
Risk Anal. 2020 Jun;40(6):1124-1138. doi: 10.1111/risa.13455. Epub 2020 Feb 13.
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.
地震后火灾是具有巨大破坏潜力的高后果事件。它们也是低频事件，因此其本质仍未得到充分研究。模拟地震后起火概率的一个困难在于减少由于源数据稀缺而导致的模型不确定性。通过汇总从多次地震中不加区分收集的数据，数据稀缺问题已得到解决。然而，这种方法忽略了区域和季节特征方面的地震间异质性，而这对于未来地震后火灾的风险评估是必不可少的。因此，本研究采用分层贝叶斯方法分析了1995年至2016年日本五次大地震（1995年神户地震、2003年十胜冲地震、2004年新潟中越地震、2011年东北地震和2016年熊本地震）后的起火概率。由于地震的起火原因具有一定的共性，因此为参数分配了共同的先验分布，并通过马尔可夫链蒙特卡罗模拟从参数的目标后验分布中抽取样本。将分层模型的结果与汇总模型和独立模型的结果进行了比较分析。尽管汇总模型和分层模型与独立模型相比都很稳健，但汇总模型低估了数据样本较少的地震的起火概率。在测试的模型中，分层模型受数据中源到源变异性的影响最小。不同区域和季节特征的地震后起火的异质性长期以来一直是地震后起火概率建模中所期望的，但在现有方法中尚未得到妥善考虑。所提出的分层贝叶斯方法提供了一个系统且合理的框架来有效应对这一问题，从而提高了估计地震后起火概率的统计可靠性和稳定性。
