根据地震目录数据拟合时空 ETAS 模型的算法:比较研究

Achmad Choiruddin, Annisa Auliya Rahman, Christopher Andreas
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引用次数: 0

摘要

时空流行型余震序列(时空 ETAS)是地震目录分析的标准模型。该模型考虑了由半参数背景率和参数余震率组成的半参数条件烈度函数。在估计过程中,优化采用了一种迭代算法,分别使用核密度估计和最大似然技术对非参数和参数部分进行迭代估计。ETAS 和 etasFLP 这两个 R 软件包采用不同的技术实现了这种程序,用于估计非参数成分和参数成分。这两个软件包从不同的方向进行了研究,还没有一起进行过评估。本研究探讨了这两个软件包生成的模型和算法的共同特点,然后通过模拟研究和苏门答腊地震的应用评估了它们的性能。对于涉及少量或中等数量地震的分析,etasFLP 在参数估计和计算时间方面优于 ETAS。在应用方面,我们确定了三个主要的高地震风险地区:Simeulue 岛、尼亚斯岛和西伯鲁特岛东南部。
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Algorithms for Fitting the Space-Time ETAS Model to Earthquake Catalog Data: A Comparative Study

The space-time epidemic-type aftershock sequence (space-time ETAS) is a standard model for the analysis of earthquake catalogs. The model considers a semi-parametric conditional intensity function consisting of a semi-parametric background rate and a parametric aftershock rate. For the estimation procedure, the optimization employs an iterative algorithm where the nonparametric and parametric components are estimated iteratively using, respectively, kernel density estimation and maximum likelihood technique. ETAS and etasFLP are the two R packages that implement such a procedure with different techniques for estimating both the nonparametric and parametric components. The two packages have been studied from different directions and have not been evaluated together. This study examines the common features of the models and algorithms generated from the packages, and then evaluates their performance through simulation study and application to the Sumatran earthquake. For the analysis involving small or medium number of earthquakes, the etasFLP outperforms ETAS in terms of parameter estimation and computing time. For the application, we identify three main areas of high seismic risk: Simeulue Island, Nias Island, and southeast of Siberut Island.

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来源期刊
CiteScore
2.70
自引率
7.10%
发文量
38
审稿时长
>12 weeks
期刊介绍: The Journal of Agricultural, Biological and Environmental Statistics (JABES) publishes papers that introduce new statistical methods to solve practical problems in the agricultural sciences, the biological sciences (including biotechnology), and the environmental sciences (including those dealing with natural resources). Papers that apply existing methods in a novel context are also encouraged. Interdisciplinary papers and papers that illustrate the application of new and important statistical methods using real data are strongly encouraged. The journal does not normally publish papers that have a primary focus on human genetics, human health, or medical statistics.
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