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Room: 203

Date: 4/19/2023

Session Time: 8:00 AM to 11:45 AM (local time)

New Methods and Models for More Informative Earthquake Forecasting
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 that underpin probabilistic seismic hazard analyses (PSHA). Beyond PSHA, new methods developed by the statistical and machine learning (ML) communities have been shown to add predictive skill for forecasting large earthquakes and aftershock activity. These new methods, hypotheses and models can be prospectively tested and compared within the framework of the Collaboratory for the Study of Earthquake Predictability (CSEP).

We invite contributions that develop novel methodology or applications in analyzing and modeling seismicity datasets. In particular, we encourage contributions from researchers who are developing and testing models for long-term earthquake forecasting, Operational Earthquake Forecasting (OEF) and Operational Aftershock Forecasting (OAF). Example submissions may include models based on ML-derived catalogs, new hypotheses explaining what controls earthquake probabilities, quantitative analyses evaluating the predictive abilities of seismicity models or new approaches to evaluating probabilistic earthquake forecasts.

Conveners
Jose A. Bayona, University of Bristol (jose.bayona@bristol.ac.uk)
William H. Savran, Southern California Earthquake Center (wsavran@usc.edu)
Max Schneider, United States Geological Survey (mschneider@usgs.gov)
Leila Mizrahi, ETH Zurich (leila.mizrahi@sed.ethz.ch)
Nicholas J. van der Elst, United States Geological Survey (nvanderelst@usgs.gov)

Oral Presentations