By Davide Zaccagnino, Ilaria Spassiani, Giuseppe Petrillo, Robert Shcherbakov & Jiancang Zhuang
SUBMIT YOUR ABSTRACT! Click here: https://agu.confex.com/agu/agu25/prelim.cgi/Session/247994
Statistical seismology has taken impressive advances during the last few decades thanks to the incorporation of empirical observations within modelling of seismic activity and the empowerment of new techniques and mathematical tools. However, our understanding of the
physics of seismicity is still poor and our ability to forecast major events is limited. This session is devoted to discussing original methods and integrated techniques combining different mathematical, physical and AI-enhanced approached for enhancing statistical earthquake forecasts and catalogue simulations. Research works about computational, physical and statistical approaches to tectonic, volcano and induced earthquake forecasting are welcome.
Our understanding of the spatio-temporal occurrence of seismicity is still poor and our ability to forecast major instabilities in fault systems is a long way off. Even the updated versions of the most informative models show only incremental improvements with respect to their basic versions, e.g., ETAS. Agreement has been progressively gathering that an intrinsic limit exists for the skilfulness of such statistical
techniques in quantifying time-dependent probabilities of occurrence of large earthquakes since they miss to grasp fundamental still hidden processes. Reducing epistemic uncertainty related to the seismic process as well as the incorporation and modelling of geophysical data and is of paramount importance to make further decisive improvements in our ability to forecast future strong events.
Our session is focused on original methods, integrated approaches, and analyses for enhancing statistical earthquake forecasts. Research works about the following topics are especially welcome:
- Mathematical modeling of large earthquake occurrences as extreme events.
- New techniques and approaches for stochastic earthquake simulations
- Stochastic and physics-based methods in the analysis of induced and volcano seismicity.
- Application ofmachine learning, specifically physics-informed neural networks (PINNs) to
earthquake forecasting. - Statistical and physics informed approaches to time-dependent earthquake forecasting.
- Early-warning and short-term earthquake forecasts based on statistical, physics-based
and AI-enhanced modeling. - Long-term recurrence of large earthquakes inferred from physics-based and statistical
analysis of paleoseismic, parametric and instrumental catalogs. - Earthquake hazard communication and new results in regional seismic hazard
assessment and forecasting.