Statistical characterization of full-margin rupture recurrence for Cascadia subduction zone using event time resampling and Gaussian mixture model

Earthquake occurrence modeling of large subduction events involves significant uncertainty, stemming from the scarcity of geological data and inaccuracy of dating techniques. The previous research on statistical modeling of full-margin ruptures of the Cascadia subduction zone attempted to address these issues. However, the adopted resampling method to account for the uncertain marine turbidite age data from the Cascadia subduction zone was not sufficient in the sample size. This study presents a statistical approach based on the Gaussian mixture model applied to significantly larger resampled Cascadia age data. The results suggest that the 3-component Gaussian mixture model outperforms the 2-component Gaussian mixture model and the 1-component renewal models by capturing the long gap and short-term clustering. The developed Gaussian mixture model is well suited to apply to probabilistic seismic and tsunami hazard analysis and the calculation of long-term probability of the future full-margin Cascadia events by considering the elapsed time since the last event.

Automated summary

Modeling the occurrence of large subduction earthquakes involves significant uncertainty due to the lack of geological data and inaccurate dating techniques. This study proposes a statistical approach based on the Gaussian mixture model, which outperforms previous methods by capturing both long gaps and short-term clustering. The developed model is suitable for probabilistic seismic and tsunami hazard analysis and can calculate the long-term probability of future events.