Strategies for comparison of modern probabilistic seismic hazard models and insights from the Germany and France border region

The latest generation of national and regional probabilistic seismic hazard assessments (PSHAs) in Europe presents stakeholders with multiple representations of the hazard in many regions. This raises the question of why and by how much seismic hazard estimates between two or more models differ, not...

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Bibliographic Details
Published in:Natural hazards and earth system sciences 2024-11, Vol.24 (11), p.3755-3787
Main Authors: Weatherill, Graeme, Cotton, Fabrice, Daniel, Guillaume, Zentner, Irmela, Iturrieta, Pablo, Bosse, Christian
Format: Article
Language:English
Subjects:
Comparative analysis
Complexity
Decision making
Design engineering
Distribution (Probability theory)
Earthquakes
Epistemology
Geographical distribution
Geological hazards
Graben
Hazard assessment
Information theory
Probability distribution
Regional analysis
Regions
Seismic activity
Seismic hazard
Software upgrading
Spatial analysis
Spatial variations
Statistical analysis
Trees
Uncertainty analysis
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Summary:The latest generation of national and regional probabilistic seismic hazard assessments (PSHAs) in Europe presents stakeholders with multiple representations of the hazard in many regions. This raises the question of why and by how much seismic hazard estimates between two or more models differ, not only where models overlap geographically but also where new models update existing ones. As modern PSHA incorporates increasingly complex analysis of epistemic uncertainty, the resulting hazard is represented not as a single value or spectrum but rather as probability distribution. Focusing on recent PSHA models for France and Germany, alongside the 2020 European Seismic Hazard Model, we explore the differences in model components and highlight the challenges and strategy for harmonising the different models into a common PSHA calculation software. We then quantify the differences in the source model and seismic hazard probability distributions using metrics based on information theory, illustrating their application to the Upper Rhine Graben region. Our analyses reveal the spatial variation in and complexity of model differences when viewed as probability distributions and highlight the need for more detailed transparency and replicability of the models when used as a basis for decision-making and engineering design.
ISSN:1684-9981
1561-8633
1684-9981
DOI:10.5194/nhess-24-3755-2024