Earthquake forecasting from paleoseismic records

Ting Wang (), Jonathan D. Griffin, Marco Brenna, David Fletcher, Jiaxu Zeng, Mark Stirling, Peter W. Dillingham and Jie Kang
Additional contact information
Ting Wang: University of Otago
Jonathan D. Griffin: Geoscience Australia
Marco Brenna: University of Otago
David Fletcher: David Fletcher Consulting Limited
Jiaxu Zeng: Otago Medical School, University of Otago
Mark Stirling: University of Otago
Peter W. Dillingham: University of Otago
Jie Kang: Beef + Lamb New Zealand Genetics

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41467-024-46258-z

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