Background

Probabilistic seismic hazard analysis (PSHA) is a framework that integrates information from multiple areas of geophysics, geology and engineering seismology to provide models quantifying the expected ground shaking at a location and its probability of being exceeded in a certain timespan. It comprises two core elements: the seismogenic source model, which describes the location, magnitude and rate of occurrence of future earthquakes originating from a particular fault or similar seismic source, and the ground motion model, which describes the expected strength of ground shaking at a location and its corresponding uncertainty given the properties of a particular earthquake scenario. Probabilistic seismic risk analysis develops this concept further to predict the impacts of the shaking on the built environment. It does so by combining the predictions of ground shaking with models describing the location, type and value of buildings in a region (the exposure) with engineering- or observation-based numerical models to predict the level of damage to each building type when subjected to strong shaking (the fragility) and the corresponding impact in terms of economic or human loss (the vulnerability). One of the crucial aspect of probabilistic seismic hazard and risk analysis is how we capture and quantify uncertainty in each component of the process, which critical for stakeholders to make the most informed decisions and take effective measures for risk mitigation. While the general framework of probabilistic seismic hazard and risk analysis is well established, there are many challenges and important questions that drive ongoing developments and research. 

Scientific key questions

• How can we calibrate models of ground shaking that capitalise on the growing volume of data in order to make better predictions of shaking and reduce uncertainties in PSHA?
• How do we best incorporate sequencies of earthquakes and other temporal dependencies into the modelling process and what are the implications of doing so?
• Can new sources of big data and insights from other disciplines help advance our understanding of the earthquake process to make better predictions of the impacts of future earthquakes? 

Related projects

• METIS | Seismic Risk Assessment for Nuclear Safety
• DT-Geo | A Digital Twin for GEOphysical extremes
• Geotwins | Digital Twin Components for Deep Geothermal Energy Power and Heat Generation
• Geo-INQUIRE | Geosphere INfrastructures for QUestions into Integrated Research