Preprints
https://doi.org/10.5194/nhess-2023-203
https://doi.org/10.5194/nhess-2023-203
24 Nov 2023
 | 24 Nov 2023
Status: a revised version of this preprint is currently under review for the journal NHESS.

Scoring and ranking probabilistic seismic hazard models: an application based on macroseismic intensity data

Vera D’Amico, Francesco Visini, Andrea Rovida, Warner Marzocchi, and Carlo Meletti

Abstract. A probabilistic seismic hazard model consists of a set of weighted models/branches that describes the center, the body, and the range of seismic hazard. Owing to the intrinsic nature of this kind of analysis, the weight of each model/branch represents its scientific credibility. However, practical uses of this model may sometimes require the selection of one or a few hazard curves that are sampled from the whole model, that often consists of thousands of branches. Here we put forward an innovative procedure that facilitates the scoring, ranking and selection of those hazard curves to account for the requirements of a specific application. The approach consists of a careful quality check of the data used for scoring and the adoption of a proper scoring rule. To show the applicability of this approach, we present an example that consists of scoring and ranking a set of multiple models/branches constituting a recent seismic hazard model of Italy. To score these branches, hazard estimates produced by each of them are compared with time-series of macroseismic observations available in the Italian macroseismic database for a carefully selected set of localities deemed sufficiently representative, homogeneously distributed in space and complete with respect to time and intensity levels. The proper scoring parameter used for such a comparison is the logarithmic score, which can be always applied independently from the distribution of the data.

Vera D’Amico, Francesco Visini, Andrea Rovida, Warner Marzocchi, and Carlo Meletti

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-203', Dario Albarello, 08 Dec 2023
    • AC1: 'Reply on RC1', Vera D'Amico, 14 Jan 2024
  • RC2: 'Comment on nhess-2023-203', Anonymous Referee #2, 23 Dec 2023
    • AC2: 'Reply on RC2', Vera D'Amico, 14 Jan 2024
Vera D’Amico, Francesco Visini, Andrea Rovida, Warner Marzocchi, and Carlo Meletti
Vera D’Amico, Francesco Visini, Andrea Rovida, Warner Marzocchi, and Carlo Meletti

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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. Actually, applications of PSHA often require sampling 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.
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