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Integrating Earthquake Clustering Into Probabilistic Seismic Hazard and Risk Assessments

Description: 

Conventional probabilistic seismic hazard analysis (PSHA) and risk assessment often do not fully capture the dynamic nature of earthquake clustering, including phenomena such as foreshocks, aftershocks, extended sequences, and the cumulative impacts of multiple seismic events. Recent earthquakes, such as the 2010/2012 Canterbury sequence in New Zealand, the 2019 Ridgecrest sequence in the USA, and the 2023 Turkey-Syria earthquakes, highlight the critical need to refine these methodologies to address the complexities of earthquake behaviour.

PSHA has long been a cornerstone for governments and industries, guiding the development of hazard maps, building codes, earthquake insurance policies, and safety standards for critical infrastructure. However, seismic hazard models must evolve to incorporate the full dynamic nature of earthquake clustering to advance disaster risk reduction and enhance seismic resilience.

We examine advancements in time-dependent probabilistic seismic hazard analysis (PSHA) and risk assessment, emphasising clustering effects such as spatiotemporal variations in earthquake activity. This includes methodologies that incorporate time-dependent fragility curves and damage accumulation to capture the evolving vulnerabilities of structures exposed to multiple seismic events. We aim to review state-of-the-art methodologies in time-dependent seismic hazard analysis and risk assessment, address implementation challenges, and examine real-world applications.

Incorporating time-dependent factors into PSHA and risk assessment is critical to advancing disaster risk reduction strategies and strengthening community resilience. Although challenges such as model complexity, data limitations, and computational demands persist, these approaches offer significant benefits by providing valuable insights for more effective risk management.

Session: Advancing Time-dependent PSHA and Seismic Risk Assessment: Accounting for Short- to Medium-term Clustering [Poster]

Type: Poster

Date: 4/15/2025

Presentation Time: 08:00 AM (local time)

Presenting Author: Kenny

Student Presenter: No

Invited Presentation: 

Poster Number: 74

Authors