Authors / CoAuthors
Wang, T. | Griffin, J. | Brenna, M. | Fletcher, D. | Zeng, J. | Stirling, M.W. | Dillingham, P. | Kang, J.
Abstract
<div>Forecasting large earthquakes along active faults is of critical importance for seismic hazard assessment. Statistical models of recurrence intervals based on compilations of paleoseismic data provide a forecasting tool. Here we compare five models and use Bayesian model-averaging to produce time-dependent, probabilistic forecasts of large earthquakes along 93 fault segments worldwide. This approach allows better use of the measurement errors associated with paleoseismic records and accounts for the uncertainty around model choice. Our results indicate that although the majority of fault segments (65/93) in the catalogue favour a single best model, 28 benefit from a model-averaging approach. We provide earthquake rupture probabilities for the next 50 years and forecast the occurrence times of the next rupture for all the fault segments. Our findings suggest that there is no universal model for large earthquake recurrence, and an ensemble forecasting approach is desirable when dealing with paleoseismic records with few data points and large measurement errors. <b>Citation:</b> Wang, T., Griffin, J.D., Brenna, M. et al. Earthquake forecasting from paleoseismic records. <i>Nat Commun</i><b> 15</b>, 1944 (2024). https://doi.org/10.1038/s41467-024-46258-z
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document
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148627
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Keywords
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- earthquake hazard
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- statistics
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- forecasting
- theme.ANZRC Fields of Research.rdf
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- Natural Hazards
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- Published_External
Publication Date
2024-08-02T04:41:30
Creation Date
2023-07-20T08:00:00
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Scientific paper published in Nature Communications
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geoscientificInformation
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Nature Communications Volume 15, Issue 1944 (2024)
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<div>This is a scientific paper published in Nature Communications.</div><div>https://www.nature.com/articles/s41467-024-46258-z </div>
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[-54.75, -9.2402, 112.92, 159.11]
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Spatial Resolution
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