Testing the forecasting skills of aftershock models using a Bayesian framework
The Epidemic Type Aftershock Sequence (ETAS) model and the modified Omori law (MOL) are two aftershock rate models that are used for operational earthquake/aftershock forecasting. Previous studies have investigated the relative performance of the two models for specific case studies. However, a rigo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2023.1126511/full |
| _version_ | 1797804471581933568 |
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| author | Elisa Dong Robert Shcherbakov Robert Shcherbakov Katsuichiro Goda |
| author_facet | Elisa Dong Robert Shcherbakov Robert Shcherbakov Katsuichiro Goda |
| author_sort | Elisa Dong |
| collection | DOAJ |
| description | The Epidemic Type Aftershock Sequence (ETAS) model and the modified Omori law (MOL) are two aftershock rate models that are used for operational earthquake/aftershock forecasting. Previous studies have investigated the relative performance of the two models for specific case studies. However, a rigorous comparative evaluation of the forecasting performance of the basic aftershock rate models for several different earthquake sequences has not been done before. In this study, forecasts of five prominent aftershock sequences from multiple catalogs are computed using the Bayesian predictive distribution, which fully accounts for the uncertainties in the model parameters. This is done by the Markov Chain Monte Carlo (MCMC) sampling of the model parameters and forward simulation of the ETAS or MOL models to compute the aftershock forecasts. The forecasting results are evaluated using five different statistical tests, including two comparison tests. The forecasting skill tests indicate that the ETAS model tends to perform consistently well on the first three tests. The MOL fails the same tests for certain forecasting time intervals. However, in the comparison tests, it is not definite whether the ETAS model is the better performing model. This work demonstrates the use of forecast testing for different catalogs, which is also applicable to catalogs with a higher magnitude of completeness. |
| first_indexed | 2024-03-13T05:37:40Z |
| format | Article |
| id | doaj.art-94e6fe77ec0c483a9e9e534ade683540 |
| institution | Directory Open Access Journal |
| issn | 2297-4687 |
| language | English |
| last_indexed | 2024-03-13T05:37:40Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj.art-94e6fe77ec0c483a9e9e534ade6835402023-06-14T05:42:40ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872023-06-01910.3389/fams.2023.11265111126511Testing the forecasting skills of aftershock models using a Bayesian frameworkElisa Dong0Robert Shcherbakov1Robert Shcherbakov2Katsuichiro Goda3Department of Earth and Space Science Engineering, York University, Toronto, ON, CanadaDepartment of Earth Sciences, Western University, London, ON, CanadaDepartment of Physics and Astronomy, Western University, London, ON, CanadaDepartment of Earth Sciences, Western University, London, ON, CanadaThe Epidemic Type Aftershock Sequence (ETAS) model and the modified Omori law (MOL) are two aftershock rate models that are used for operational earthquake/aftershock forecasting. Previous studies have investigated the relative performance of the two models for specific case studies. However, a rigorous comparative evaluation of the forecasting performance of the basic aftershock rate models for several different earthquake sequences has not been done before. In this study, forecasts of five prominent aftershock sequences from multiple catalogs are computed using the Bayesian predictive distribution, which fully accounts for the uncertainties in the model parameters. This is done by the Markov Chain Monte Carlo (MCMC) sampling of the model parameters and forward simulation of the ETAS or MOL models to compute the aftershock forecasts. The forecasting results are evaluated using five different statistical tests, including two comparison tests. The forecasting skill tests indicate that the ETAS model tends to perform consistently well on the first three tests. The MOL fails the same tests for certain forecasting time intervals. However, in the comparison tests, it is not definite whether the ETAS model is the better performing model. This work demonstrates the use of forecast testing for different catalogs, which is also applicable to catalogs with a higher magnitude of completeness.https://www.frontiersin.org/articles/10.3389/fams.2023.1126511/fullearthquake forecastingforecast performance testingBayesian predictive distributionETAS modelmodified Omori law |
| spellingShingle | Elisa Dong Robert Shcherbakov Robert Shcherbakov Katsuichiro Goda Testing the forecasting skills of aftershock models using a Bayesian framework Frontiers in Applied Mathematics and Statistics earthquake forecasting forecast performance testing Bayesian predictive distribution ETAS model modified Omori law |
| title | Testing the forecasting skills of aftershock models using a Bayesian framework |
| title_full | Testing the forecasting skills of aftershock models using a Bayesian framework |
| title_fullStr | Testing the forecasting skills of aftershock models using a Bayesian framework |
| title_full_unstemmed | Testing the forecasting skills of aftershock models using a Bayesian framework |
| title_short | Testing the forecasting skills of aftershock models using a Bayesian framework |
| title_sort | testing the forecasting skills of aftershock models using a bayesian framework |
| topic | earthquake forecasting forecast performance testing Bayesian predictive distribution ETAS model modified Omori law |
| url | https://www.frontiersin.org/articles/10.3389/fams.2023.1126511/full |
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