基于大逻辑树的国家地震危险性模型计算:在NZ NSHM 2022中的应用

IF 2.6 3区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS
Christopher J. DiCaprio, Chris B. Chamberlain, Sanjay S. Bora, Brendon A. Bradley, Matthew C. Gerstenberger, Anne M. Hulsey, Pablo Iturrieta, Marco Pagani, Michele Simionato
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引用次数: 0

摘要

具有大型逻辑树的国家级地震灾害模型难以用传统的地震灾害软件进行计算。为了计算2022年新西兰国家地震灾害模型(te Tauira Matapae Pūmate ri Aotearoa)的完整修订,包括认知不确定性,我们开发了一种方法,将计算分为两个独立的阶段。这种方法利用了由多个独立逻辑树组成的逻辑树结构,这些逻辑树通过组合形成完整的实现。在第一阶段,我们预先计算逻辑树的独立实现。在第二阶段,我们通过组合第一阶段的组件来组装逻辑树实现的完整集合。一旦计算了完整逻辑树的所有实现,我们就可以计算模型的汇总统计信息。这种方法的好处在于减少了必要的计算量和它的并行性。除了便于大型地震灾害模型的计算外,所描述的方法还可用于模型组件的灵敏度测试,以及加快逻辑树结构和权重的实验速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Calculation of National Seismic Hazard Models with Large Logic Trees: Application to the NZ NSHM 2022
Abstract National-scale seismic hazard models with large logic trees can be difficult to calculate using traditional seismic hazard software. To calculate the complete 2022 revision of the New Zealand National Seismic Hazard Model—Te Tauira Matapae Pūmate Rū i Aotearoa, including epistemic uncertainty, we have developed a method in which the calculation is broken into two separate stages. This method takes advantage of logic tree structures that comprise multiple, independent logic trees from which complete realizations are formed by combination. In the first stage, we precalculate the independent realizations of the logic trees. In the second stage, we assemble the full ensemble of logic tree realizations by combining components from the first stage. Once all realizations of the full logic tree have been calculated, we can compute aggregate statistics for the model. This method benefits both from the reduction in the amount of computation necessary and its parallelism. In addition to facilitating the computation of a large seismic hazard model, the method described can also be used for sensitivity testing of model components and to speed up experimentation with logic tree structure and weights.
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来源期刊
Seismological Research Letters
Seismological Research Letters 地学-地球化学与地球物理
CiteScore
6.60
自引率
12.10%
发文量
239
审稿时长
3 months
期刊介绍: Information not localized
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