Toward Real-Time Forecasting of Earthquake Occurrence and Ground-Shaking Intensity Using ETAS and GMM: Insights from Recent Large Earthquakes in Taiwan

Earthquake forecasting, combined with precise ground-shaking estimations, plays a pivotal role in safeguarding public safety, fortifying infrastructure, and bolstering the preparedness of emergency services. This study introduces a comprehensive workflow that integrates the epidemic-type aftershock sequence (ETAS) model with a preselected ground-motion model (GMM), facilitating accurate short-term forecasting of ground-shaking intensity (GSI), which is crucial for adequate earthquake warning for earthquake-prone regions like Taiwan. First, an analysis was conducted on a Taiwanese earthquake catalog from 1994 to 2022 to optimize the ETAS parameters. The dataset used in this analysis allowed for the further calculation of total, background, and clustering seismicity rates, which are crucial for understanding spatiotemporal earthquake occurrence. Subsequently, short-term earthquake activity simulations were performed using these up-to-date seismicity rates to generate synthetic catalogs. The ground-shaking impact on the target sites from each synthetic catalog was assessed by determining the maximum intensity using a selected GMM. This simulation process was repeated to enhance the reliability of the forecasts. Through this process, a probability distribution was created, serving as a robust forecasting for GSI at sites. The performance of the forecasting model was validated through an example of the Taitung, Taiwan earthquake sequence in September 2022, showing its effectiveness in forecasting earthquake activity and site-specific GSI. The other example is the Hualien, Taiwan earthquake sequence from April 2024, which serves as an excellent demonstration of a workflow designed to provide real-time aftershock forecasting following an M7.2 event. The proposed forecasting model can quickly deliver short-term seismic hazard curves and warning messages, facilitating timely decision-making.