SCEC Award Number 24186
Proposal Category Collaborative Research Project (Multiple Investigators / Institutions)
Proposal Title Knowledge Informed Neural Temporal Point Process for Earthquake Forecast
Investigator(s)
Name Organization
Yan Liu University of Southern California Yehuda Ben-Zion University of Southern California
SCEC Milestones D1-1, D2-2, D3-1 SCEC Groups EFP, Seismology
Report Due Date 03/15/2025 Date Report Submitted No report submitted
Project Abstract
 In recent years, notable progress has been made in earthquake forecasting, particularly with the development of deep learning models that integrate temporal point processes. However, one of the primary limitations of these models is their focus on forecasting seismicity rates which are dominated by the smallest events that have little impact on hazard. The Epidemic Type Aftershock Sequence (ETAS) model can model the distribution of seismic events in both time and space. While ETAS provides valuable insight into the temporal and spatial clustering of aftershocks, it fails to address an important aspect of earthquake forecasting: the magnitude of future events. To address this, we investigate a novel AI model, referred to as the Spatio-Temporal Earthquake Forecasting with Informed Point-process Optimization (STEF-IPO), which combines the strengths of both neural networks and temporal point processes. Our experiment analysis shows that the proposed STEF-IPO model achieved an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 78.67% for predicting events with a magnitude greater than 4.5.
Intellectual Merit XX
Broader Impacts XX
Project Participants XX
Exemplary Figure Figure 1
Linked Publications

Add missing publication or edit citation shown. Enter the SCEC project ID to link publication.