Room: Exhibit Hall
Date: 4/15/2025
Session Time: 8:00 AM to 5:45 PM (local time)
Improving the State of the Art of Earthquake Forecasting Through Models, Testing and Communication
Current earthquake forecasting models utilize only a fraction of the existing knowledge about earthquakes, thereby lacking important information on seismogenesis. With the advent of increased computational power and high-resolution geophysical datasets, including fault information, interseismic strain data, highly detailed machine-learning-based catalogs, laboratory observations of microseismicity, etc., our understanding of the physical processes involved in earthquake nucleation is continuously growing. Yet, translating this theoretical knowledge into practical, informative earthquake forecasts remains a significant hurdle. Furthermore, testing these forecasts against observations and communicating them to non-scientific audiences similarly require innovative solutions so earthquake forecasts can realize their potential for seismic risk reduction.
In this session, we welcome contributions that seek to improve the state of the art of earthquake forecasting by bridging the gap between theoretical advancements and real-world applications. We invite submissions that integrate our growing understanding of earthquake processes with the creation of generalizable, statistically robust and interdisciplinary models that are more informative than the currently widely-used empirical clustering models - both for natural and induced seismicity, and across scales from micro-scale in the laboratory to continental catalog analysis. Complementarily, we seek contributions that explore tests or metrics that better characterize model performance and thus identify promising areas for their improvement. We also encourage the submission of new communication and visualization strategies that turn earthquake probability estimates into practical, actionable and societally relevant information.
Conveners
José A. Bayona, University of Bristol (jose.bayona@bristol.ac.uk)
Kélian Dascher-Cousineau, University of California Berkeley (kdascher@berkeley.edu)
Pablo Iturrieta, GFZ German Research Centre for Geosciences (pciturri@gfz-potsdam.de)
Leila Mizrahi, ETH Zurich (leila.mizrahi@sed.ethz.ch)
Berman Neri, Tel Aviv University (neriberman@gmail.com)
Max Schneider, U.S. Geological Survey (mschneider@usgs.gov)
Poster Presentations
| Participant Role | Details | Action |
|---|---|---|
| Submission | Towards Operational Earthquake Forecasting in Switzerland | View |
| Submission | Evaluating the Forecasting Performance of U.S. Geological Survey Aftershock Forecasts | View |
| Submission | A Deep Learning Application to Model the Full Distribution of Higher-order Aftershock Numbers in the ETAS Framework | View |
| Submission | Forecasting Ground Motion Intensity Time Series with a Generative Pre-trained Transformer | View |
| Submission | The Influence of Magnitude Determinations on b-Values | View |
Improving the State of the Art of Earthquake Forecasting Through Models, Testing and Communication [Poster]
Description