Forecasting the magnitude of the largest expected earthquake
- The majority of earthquakes occur unexpectedly and can trigger subsequent sequences of events that can culminate in more powerful earthquakes. This self-exciting nature of seismicity generates complex clustering of earthquakes in space and time. Therefore, the problem of constraining the magnitude of the largest expected earthquake during a future time interval is of critical importance in mitigating earthquake hazard. We address this problem by developing a methodology to compute the probabilities for such extreme earthquakes to be above certain magnitudes. We combine the Bayesian methods with the extreme value theory and assume that the occurrence of earthquakes can be described by the Epidemic Type Aftershock Sequence process. We analyze in detail the application of this methodology to the 2016 Kumamoto, Japan, earthquake sequence. We are able to estimate retrospectively the probabilities of having large subsequent earthquakes during several stages of the evolution of this sequence.
Author details: | Robert ShcherbakovORCiD, Jiancang ZhuangORCiD, Gert ZöllerORCiDGND, Yosihiko Ogata |
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DOI: | https://doi.org/10.1038/s41467-019-11958-4 |
ISSN: | 2041-1723 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/31492839 |
Title of parent work (English): | Nature Communications |
Publisher: | Nature Publishing Group |
Place of publishing: | London |
Publication type: | Article |
Language: | English |
Date of first publication: | 2019/09/06 |
Publication year: | 2019 |
Release date: | 2020/11/09 |
Volume: | 10 |
Number of pages: | 11 |
Funding institution: | NSERCNatural Sciences and Engineering Research Council of Canada; Japan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science [19H04073]; DFG Collaborative Research Centre 1294 (Data Assimilation-The seamless integration of data and models) |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik |
DDC classification: | 5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik |
Peer review: | Referiert |
Publishing method: | Open Access |
Open Access / Gold Open-Access | |
DOAJ gelistet |