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Room: Exhibit Hall

Date: 5/2/2024

Session Time: 8:00 AM to 5:45 PM (local time)

The increasing availability and quality of geophysical datasets, including high-resolution earthquake catalogs, fault information and interseismic strain data, has enabled the creation of statistical and physics-based seismicity models, some of which underpin probabilistic seismic hazard analyses. New machine learning (ML) techniques have also improved data acquisition and analysis for seismicity modelling. Forecasts produced by such models can be tested and compared prospectively, e.g., within the framework of the Collaboratory for the Study of Earthquake Predictability, paving the way for potentially more informative earthquake forecasts. In turn, forecast models are being operationalized by public and private agencies to provide a range of audiences with reliable information on the occurrence of earthquakes. This poses communication challenges that require solutions from the social sciences. We welcome contributions that help us elucidate the main advantages and limitations of current seismicity models, identify the most informative forecasting methods, improve our understanding of the earthquake generation process, and facilitate the communication and visualization of earthquake forecasts. Submissions may include models based on ML-derived earthquake catalogs, new hypotheses explaining what controls earthquake potential, quantitative analyses evaluating the predictive skills of seismicity forecasts, or studies on the effective communication of earthquake forecast information. 

Conveners:
Jose Bayona, University of Bristol (jose.bayona@bristol.ac.uk)
Kelian Dascher-Cousineau, University of California, Berkeley (kdascher@berkeley.edu)
Leila Mizrahi, Swiss Seismological Service (leila.mizrahi@sed.ethz.ch)
William Savran, University of Nevada, Reno (wsavran@unr.edu)
Max Schneider, U.S. Geological Survey (mschneider@usgs.gov)
 

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