Earthquake forecasting from paleoseismic records

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.

A Bayesian Approach for Forecasting the Probability of Large Earthquakes Using Thermal Anomalies from Satellite Observations

“…Future research will focus on developing such a time-dependent earthquake forecast model founded upon sequential Bayesian inference, with explicit modeling of probability distribution functions for model parameters. This approach will leverage satellite data and potentially integrate fault information, wherein estimated earthquake rupture probabilities of fault segments can serve as background information for earthquake forecasts, employing newly updated thermal anomaly data [34,46].…”