ESC 2026 Abstracts Submission - Session S43 ”Statistical Seismology and AI: from theory to operational forecasting and risk communication"

[Peresan, Antonella]

Dear Colleague,

we would like to remind you that abstract submission to ESC 2026 GA, the 40th General Assembly of the European Seismological Commission (Istanbul, Türkiye, 6-11 September 2026), will close in few days (DEADLINE: Sunday, March 15, 2026)

We warmly invite you to consider submitting your abstract to the following session:
S43 - ”Statistical Seismology and AI: from theory to operational forecasting and risk communication"
https://www.esc2026.org/medya/session-43.pdf

Abstracts must be submitted via the ESC 2026 General Assembly website,
with guidelines and registration information available at:
https://www.esc2026.org/abstract-submission.html
https://arkonmice.digiabstract.com/esc2026/

We look forward to receiving your contributions.

The Session Conveners,
Antonella Peresan
Elisa Varini
Ioanna Triantafyllou


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S43 - ”Statistical Seismology and AI: from theory to operational forecasting and risk communication"

Conveners:
Antonella Peresan, Elisa Varini, Ioanna Triantafyllou

Session description:
Recent advances in earthquake understandings and emerging technologies in monitoring and modelling are driving a new era of earthquake forecasting. This session focuses on deterministic and probabilistic methods for modelling earthquake occurrence, clustering, and spatiotemporal evolution of seismicity. Special attention is given to the integration of statistical and physical models to assess earthquake related hazards such as tsunamis, landslides, and cascading risks. The increasing availability of high- resolution seismic catalogues, real-time GNSS data, and machine learning techniques is reshaping the field of statistical seismology, creating opportunities for more accurate and timely forecasts. However, the transition from scientific models to actionable forecasts remains complex and challenging. This session will explore how statistical rigor can coexist with practical usability, fostering dialogue between model developers, operational agencies, and end users.
Emphasis will be placed on:
• Statistical models of earthquake occurrence (e.g., Poisson, ETAS, renewal, etc.).
• Advances in Bayesian inference, machine learning, and data assimilation for earthquake forecasting.
• Evaluation metrics for forecast skill, uncertainty quantification, and reliability testing.
• Integration of seismic and other observational data (e.g. geodetic, geochemical, etc.) into statistical frameworks.
• Operational Earthquake Forecasting (OEF) systems and their use in civil protection and communication strategies.
• Case studies and lessons learned from operational systems in different countries.
• Ethical, societal, and communication challenges associated with probabilistic earthquake forecasting.