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A joint modeling approach for spatial earthquake risk variations

机译：空间地震风险变化的联合建模方法

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摘要

Modeling spatial patterns and processes to assess the spatial variations of data over a study region is an important issue in many fields. In this paper, we focus on investigating the spatial variations of earthquake risks after a main shock. Although earthquake risks have been extensively studied in the literatures, to our knowledge, there does not exist a suitable spatial model for assessing the problem. Therefore, we propose a joint modeling approach based on spatial hierarchical Bayesian models and spatial conditional autoregressive models to describe the spatial variations in earthquake risks over the study region during two periods. A family of stochastic algorithms based on a Markov chain Monte Carlo technique is then performed for posterior computations. The probabilistic issue for the changes of earthquake risks after a main shock is also discussed. Finally, the proposed method is applied to the earthquake records for Taiwan before and after the Chi-Chi earthquake.
机译：对空间模式和过程进行建模以评估研究区域中数据的空间变化是许多领域中的重要问题。在本文中，我们专注于调查主震后地震风险的空间变化。尽管在文献中已经对地震风险进行了广泛研究，但据我们所知，尚不存在合适的空间模型来评估该问题。因此，我们提出了一种基于空间分层贝叶斯模型和空间条件自回归模型的联合建模方法，以描述研究区域在两个时期内地震风险的空间变化。然后执行基于马尔可夫链蒙特卡洛技术的一系列随机算法进行后验计算。还讨论了主震后地震风险变化的概率问题。最后，将所提出的方法应用于台湾集集地震前后的地震记录。

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