Statistics and pattern recognition applied to the spatio-temporal properties of seismicity
Co-organized by SM1
Convener: Stefania Gentili | Co-conveners: Rita Di Giovambattista, Álvaro GonzálezECSECS, Filippos Vallianatos
| Attendance Mon, 04 May, 14:00–15:45 (CEST)

Over the last years, significant progress has been made towards understanding spatio-temporal correlations of earthquake occurrence, scaling laws, earthquake clustering, and the emergence of seismicity patterns. Background and clustered seismicity occur with great spatio-temporal variability. New models being developed in statistical seismology and pattern recognition have direct implications for time-dependent seismic hazard assessment, probabilistic earthquake forecasting and for analyzing the evolution of seismicity clusters. In many regions where complex fault systems exist, clusters are characterized by multiple mainshock sequences, with large aftershocks, which increase the overall hazard.
In this session, we invite researchers to present their latest results and insights on the physical and statistical models (either theoretical or based on laboratory and numerical experiments on rock fracture and friction) for the occurrence of earthquakes, foreshocks and aftershocks. Particular emphasis will be placed on:

- physical and statistical models of earthquake occurrence;
- analysis of earthquake clustering;
- spatio-temporal properties of earthquake statistics;
- quantitative testing of earthquake occurrence models;
- implications for time-dependent hazard assessment;
- methods for earthquake forecasting;
- data analyses and requirements for model testing;
- pattern recognition in seismology;
- machine learning applied to seismic data.

Confirmed solicited speaker: Ilya Zaliapin (University of Nevada, Reno, USA)