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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2408.16491 |
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| _version_ | 1866913485509623808 |
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| author | Spassiani, I. Gentili, S. Console, R. Murru, M. Taroni, M. Falcone, G. |
| author_facet | Spassiani, I. Gentili, S. Console, R. Murru, M. Taroni, M. Falcone, G. |
| contents | Short-term earthquake clustering is one of the most important features of seismicity. Clusters are identified using various techniques, generally deterministic and based on spatio-temporal windowing. Conversely, the leading rail in short-term earthquake forecasting has a probabilistic view of clustering, usually based on the Epidemic Type Aftershock Sequence (ETAS) models. In this study we compare seismic clusters, identified by two different deterministic window-based techniques, with the ETAS probabilities associated with any event in the clusters, thus investigating the consistency between deterministic and probabilistic approaches. The comparison is performed by considering, for each event in an identified cluster, the corresponding probability of being independent and the expected number of triggered events according to ETAS. Results show no substantial differences between the cluster identification procedures, and an overall consistency between the identified clusters and the relative events' ETAS probabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16491 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reconciling the irreconcilable: window-based versus stochastic declustering algorithms Spassiani, I. Gentili, S. Console, R. Murru, M. Taroni, M. Falcone, G. Geophysics Short-term earthquake clustering is one of the most important features of seismicity. Clusters are identified using various techniques, generally deterministic and based on spatio-temporal windowing. Conversely, the leading rail in short-term earthquake forecasting has a probabilistic view of clustering, usually based on the Epidemic Type Aftershock Sequence (ETAS) models. In this study we compare seismic clusters, identified by two different deterministic window-based techniques, with the ETAS probabilities associated with any event in the clusters, thus investigating the consistency between deterministic and probabilistic approaches. The comparison is performed by considering, for each event in an identified cluster, the corresponding probability of being independent and the expected number of triggered events according to ETAS. Results show no substantial differences between the cluster identification procedures, and an overall consistency between the identified clusters and the relative events' ETAS probabilities. |
| title | Reconciling the irreconcilable: window-based versus stochastic declustering algorithms |
| topic | Geophysics |
| url | https://arxiv.org/abs/2408.16491 |