NH4.4 | Multi-parametric Short-Term Seismic Hazard monitoring and Physical and Statistical Models for Earthquake Risk assessment
EDI
Multi-parametric Short-Term Seismic Hazard monitoring and Physical and Statistical Models for Earthquake Risk assessment
Co-organized by EMRP1/ESSI2/GI6, co-sponsored by JpGU and EMSEV
Convener: Valerio Tramutoli | Co-conveners: Pier Francesco Biagi, Antonella Peresan, Carolina Filizzola, Nicola Genzano, Katsumi Hattori, Rajesh Rupakhety

Mitigating earthquake disasters involves several key components and stages, from identifying and assessing risk to reducing their impact. These components include: a) Long-term and time-dependent analysis of hazards: anticipating the space-time characteristics of ground shaking and its cascading events. b) Vulnerability and exposure assessment c) Risk management: preparedness, rescue, recovery, and overall resilience. A variety of seismic hazard and risk models can be adopted, at different spatial and temporal scale, that incorporate diverse observations and require multi-disciplinary input. Testing and validating these methodologies, for all risk components, is essential for effective disaster mitigation.
From the real-time integration of multi-parametric observations is expected the major contribution to the development of operational time-Dependent Assessment of Seismic Hazard (t-DASH) systems, suitable for supporting decision makers with continuously updated seismic hazard scenarios. A very preliminary step in this direction is the identification of those parameters (seismological, chemical, physical, etc.) whose space-time dynamics and/or anomalous variability can be, to some extent, associated with the complex process of preparation of major earthquakes.
This session includes studies on various aspects of seismic risk research and assessment, observations and/or data analysis methods within the t-DASH and Short-term Earthquakes Forecast perspectives:
- Studies on time-dependent seismic hazard and risk assessments
- Development of physical/statistical models and studies based on long-term data analyses, including different conditions of seismic activity
- Application of AI to assess earthquake risk factors (hazard, exposure, and vulnerability). Exploring innovative data collection and processing techniques, such as statistical machine learning
- Estimating earthquake hazard and risk across different temporal and spatial scales and assessing the accuracy of these models against available observations
- Earthquake-induced cascading effects such as landslides and tsunamis, and multi-risk assessments
- Studies devoted to the description of genetic models of earthquake’s precursory phenomena
- Infrastructures devoted to maintain and further develop our present observational capabilities of earthquake related phenomena also contributing to build a global multi-parametric Earthquakes Observing System (EQuOS) to complement the existing GEOSS initiative