Improvements to seismicity forecasting based on a Bayesian spatio-temporal ETAS model
Abstract The epidemic-type aftershock sequence (ETAS) model provides an effective tool for predicting the spatio-temporal evolution of aftershock clustering in short-term. Based on this model, a fully probabilistic procedure was previously proposed by the first two authors for providing spatio-tempo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-24080-1 |
| _version_ | 1811189002780278784 |
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| author | Hossein Ebrahimian Fatemeh Jalayer Behnam Maleki Asayesh Sebastian Hainzl Hamid Zafarani |
| author_facet | Hossein Ebrahimian Fatemeh Jalayer Behnam Maleki Asayesh Sebastian Hainzl Hamid Zafarani |
| author_sort | Hossein Ebrahimian |
| collection | DOAJ |
| description | Abstract The epidemic-type aftershock sequence (ETAS) model provides an effective tool for predicting the spatio-temporal evolution of aftershock clustering in short-term. Based on this model, a fully probabilistic procedure was previously proposed by the first two authors for providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval. This procedure exploited the versatility of the Bayesian inference to adaptively update the forecasts based on the incoming information provided by the ongoing seismic sequence. In this work, this Bayesian procedure is improved: (1) the likelihood function for the sequence has been modified to properly consider the piecewise stationary integration of the seismicity rate; (2) the spatial integral of seismicity rate over the whole aftershock zone is calculated analytically; (3) background seismicity is explicitly considered within the forecasting procedure; (4) an adaptive Markov Chain Monte Carlo simulation procedure is adopted; (5) leveraging the stochastic sequences generated by the procedure in the forecasting interval, the N-test and the S-test are adopted to verify the forecasts. This framework is demonstrated and verified through retrospective early forecasting of seismicity associated with the 2017–2019 Kermanshah seismic sequence activities in western Iran in two distinct phases following the main events with Mw7.3 and Mw6.3, respectively. |
| first_indexed | 2024-04-11T14:27:40Z |
| format | Article |
| id | doaj.art-41d16ab18b514e86a578c82de304ed8f |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T14:27:40Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-41d16ab18b514e86a578c82de304ed8f2022-12-22T04:18:47ZengNature PortfolioScientific Reports2045-23222022-12-0112112710.1038/s41598-022-24080-1Improvements to seismicity forecasting based on a Bayesian spatio-temporal ETAS modelHossein Ebrahimian0Fatemeh Jalayer1Behnam Maleki Asayesh2Sebastian Hainzl3Hamid Zafarani4Department of Structures for Engineering and Architecture, University of Naples Federico IIDepartment of Structures for Engineering and Architecture, University of Naples Federico IIInstitute of Geosciences, University of PotsdamGFZ German Research Center for GeosciencesInternational Institute of Earthquake Engineering and Seismology (IIEES)Abstract The epidemic-type aftershock sequence (ETAS) model provides an effective tool for predicting the spatio-temporal evolution of aftershock clustering in short-term. Based on this model, a fully probabilistic procedure was previously proposed by the first two authors for providing spatio-temporal predictions of aftershock occurrence in a prescribed forecasting time interval. This procedure exploited the versatility of the Bayesian inference to adaptively update the forecasts based on the incoming information provided by the ongoing seismic sequence. In this work, this Bayesian procedure is improved: (1) the likelihood function for the sequence has been modified to properly consider the piecewise stationary integration of the seismicity rate; (2) the spatial integral of seismicity rate over the whole aftershock zone is calculated analytically; (3) background seismicity is explicitly considered within the forecasting procedure; (4) an adaptive Markov Chain Monte Carlo simulation procedure is adopted; (5) leveraging the stochastic sequences generated by the procedure in the forecasting interval, the N-test and the S-test are adopted to verify the forecasts. This framework is demonstrated and verified through retrospective early forecasting of seismicity associated with the 2017–2019 Kermanshah seismic sequence activities in western Iran in two distinct phases following the main events with Mw7.3 and Mw6.3, respectively.https://doi.org/10.1038/s41598-022-24080-1 |
| spellingShingle | Hossein Ebrahimian Fatemeh Jalayer Behnam Maleki Asayesh Sebastian Hainzl Hamid Zafarani Improvements to seismicity forecasting based on a Bayesian spatio-temporal ETAS model Scientific Reports |
| title | Improvements to seismicity forecasting based on a Bayesian spatio-temporal ETAS model |
| title_full | Improvements to seismicity forecasting based on a Bayesian spatio-temporal ETAS model |
| title_fullStr | Improvements to seismicity forecasting based on a Bayesian spatio-temporal ETAS model |
| title_full_unstemmed | Improvements to seismicity forecasting based on a Bayesian spatio-temporal ETAS model |
| title_short | Improvements to seismicity forecasting based on a Bayesian spatio-temporal ETAS model |
| title_sort | improvements to seismicity forecasting based on a bayesian spatio temporal etas model |
| url | https://doi.org/10.1038/s41598-022-24080-1 |
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