Statistics and Forecasting of Aftershocks During the 2019 Ridgecrest, California, Earthquake Sequence

The 2019 Ridgecrest, California, earthquake sequence represents a complex pattern of seismicity that is characterized by the occurrence of a well-defined foreshock sequence followed by a mainshock and subsequent aftershocks. In this study, a detailed statistical analysis of the sequence is performed. Particularly, the parametric modeling of the frequency-magnitude statistics and the earthquake occurrence rate is carried out. It is shown that the clustering of earthquakes plays an important role during the evolution of this sequence. In addition, the problem of constraining the magnitude of the largest expected aftershocks to occur during the evolution of the sequence is addressed. In order to do this, two approaches are considered. The first one is based on the extreme value theory, whereas the second one uses the Bayesian predictive framework. The latter approach has allowed to incorporate the complex earthquake clustering through the Epidemic Type Aftershock Sequence (ETAS) process and the uncertainties associated with the model parameters into the computation of the corresponding probabilities. The results indicate that the inclusion of the foreshock sequence into the analysis produces higher probabilities for the occurrence of the largest expected aftershocks after the M7.1 mainshock compared to the approach based on the extreme value distribution combined with the Omori-Utsu formula for the earthquake rate. Several statistical tests are applied to verify the forecast. Plain Language Summary Strong earthquakes typically trigger the subsequent sequence of events known as aftershocks. Among those, the largest aftershocks can pose significant hazard and result in additional damage to infrastructure already weakened by the mainshock. Therefore, the estimation of the magnitude of the largest expected aftershock is of critical importance. This problem can be addressed within the statistical modeling of the occurrence of earthquakes. In this study, the 2019 Ridgecrest, California, earthquake sequence is chosen to illustrate and compare several approaches to constrain the magnitudes of the largest expected aftershocks during the evolution of the sequence. The first approach uses the extreme value theory and the modeling of the earthquake rate based on the Omori-Utsu formula. Whereas, the second approach uses a recently formulated method based on the Bayesian predictive analysis and the Epidemic Type Aftershock Sequence (ETAS) model to approximate the earthquake rate. The obtained results indicate that the latter approach produces statistically accurate forecast for the magnitudes of the largest expected earthquakes. This is verified by applying several statistical tests.

Automated summary

The study conducted a detailed statistical analysis of the 2019 Ridgecrest, California, earthquake sequence, revealing the important role of earthquake clustering in the sequence evolution and comparing two approaches to constrain the magnitudes of the largest expected aftershocks.