Achmad Choiruddin, Annisa Auliya Rahman, Christopher Andreas
{"title":"Algorithms for Fitting the Space-Time ETAS Model to Earthquake Catalog Data: A Comparative Study","authors":"Achmad Choiruddin, Annisa Auliya Rahman, Christopher Andreas","doi":"10.1007/s13253-024-00650-w","DOIUrl":null,"url":null,"abstract":"<p>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. <span>ETAS</span> and <span>etasFLP</span> are the two <span>R</span> 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 <span>etasFLP</span> outperforms <span>ETAS</span> 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.</p>","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"105 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Agricultural Biological and Environmental Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s13253-024-00650-w","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.