07 Nov 2023
 | 07 Nov 2023
Status: this preprint is currently under review for the journal NHESS.

Strategies for Comparison of Modern Probabilistic Seismic Hazard Models and Insights from the Germany and France Border Region

Graeme Weatherill, Fabrice Cotton, Guillaume Daniel, Irmela Zentner, Pablo Iturrieta, and Christian Bosse

Abstract. The latest generation of national and regional probabilistic seismic hazard assessments (PSHA) 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 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 Upper Rhine Graben region. Our analyses reveal the spatial variation 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.

Graeme Weatherill et al.

Status: open (until 19 Dec 2023)

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Graeme Weatherill et al.

Graeme Weatherill et al.


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Short summary
New generations of seismic hazard models are developed with sophisticated approaches to quantify uncertainties in our knowledge of earthquake process. To understand why and how recent state-of-the-art seismic hazard models for France, Germany and Europe differ despite similar underlying assumptions, we present a systematic approach to investigate model-to-model differences and to quantify and visualise them while accounting for their respective uncertainties.