Articles | Volume 24, issue 4
https://doi.org/10.5194/nhess-24-1401-2024
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https://doi.org/10.5194/nhess-24-1401-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Apr 2024
Research article |
| 24 Apr 2024
| 24 Apr 2024
Scoring and ranking probabilistic seismic hazard models: an application based on macroseismic intensity data
Istituto Nazionale di Geofisica e Vulcanologia, 56125 Pisa, Italy
Francesco Visini
Istituto Nazionale di Geofisica e Vulcanologia, 56125 Pisa, Italy
Andrea Rovida
Istituto Nazionale di Geofisica e Vulcanologia, 20133 Milan, Italy
Warner Marzocchi
Department of Earth Sciences, Environment and Resources, University of Naples Federico II, 80126 Naples, Italy
Carlo Meletti
Istituto Nazionale di Geofisica e Vulcanologia, 56125 Pisa, Italy
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This study investigates rupture directivity in intensity data of Italian earthquakes, adapting the residual decomposition framework used in ground-motion studies. The analysis is based on 213 earthquakes and over 18,000 intensity data, decomposed into between-event, locality-to-locality, and aleatory variability terms. The aleatory residuals are investigated for 31 earthquakes with uniform azimuthal coverage: 8 exhibit directional patterns consistent with fault strikes and rupture propagation.
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Andrea Antonucci, Andrea Rovida, Vera D'Amico, and Dario Albarello
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Francesco Visini, Carlo Meletti, Andrea Rovida, Vera D'Amico, Bruno Pace, and Silvia Pondrelli
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As new data are collected, seismic hazard models can be updated and improved. In the framework of a project aimed to update the Italian seismic hazard model, we proposed a model based on the definition and parametrization of area sources. Using geological data, seismicity and other geophysical constraints, we delineated three-dimensional boundaries and activity rates of a seismotectonic zoning and explored the epistemic uncertainty by means of a logic-tree approach.
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Eruption forecasting and volcanic hazard analysis are pervaded by uncertainty of different kinds, such as the natural randomness, our lack of knowledge, and the so-called unknown unknowns. After discussing the limits of how classical probabilistic frameworks handle these uncertainties, we put forward a unified probabilistic framework which unambiguously defines uncertainty of different kinds, and it allows scientific validation of the hazard model against independent observations.
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Short summary
We propose a scoring strategy to rank multiple models/branches of a probabilistic seismic hazard analysis (PSHA) model that could be useful to consider specific requests from stakeholders responsible for seismic risk reduction actions. In fact, applications of PSHA often require sampling a few hazard curves from the model. The procedure is introduced through an application aimed to score and rank the branches of a recent Italian PSHA model according to their fit with macroseismic intensity data.
We propose a scoring strategy to rank multiple models/branches of a probabilistic seismic hazard...
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