We describe the main structure and outcomes of the new probabilistic seismic hazard model for Italy, MPS19 [Modello di Pericolosità Sismica, 2019]. Besides to outline the probabilistic framework adopted, the multitude of new data that have been made available after the preparation of the previous MPS04, and the set of earthquake rate and ground motion models used, we give particular emphasis to the main novelties of the modeling and the MPS19 outcomes. Specifically, we (i) introduce a novel approach to estimate and to visualize the epistemic uncertainty over the whole country; (ii) assign weights to each model components (earthquake rate and ground motion models) according to a quantitative testing phase and structured experts’ elicitation sessions; (iii) test (retrospectively) the MPS19 outcomes with the horizontal peak ground acceleration observed in the last decades, and the macroseismic intensities of the last centuries; (iv) introduce a pioneering approach to build MPS19_cluster, which accounts for the effect of earthquakes that have been removed by declustering. Finally, to make the interpretation of MPS19 outcomes easier for a wide range of possible stakeholders, we represent the final result also in terms of probability to exceed 0.15 g in 50 years.
Earthquake catalogs describe the distribution of earthquakes in space, time, and magnitude, which is essential information for earthquake forecasting and the assessment of seismic hazard and risk. Available high-resolution (HR) catalogs raise the expectation that their abundance of small earthquakes will help better characterize the fundamental scaling laws of statistical seismology. Here, we investigate whether the ubiquitous exponential-like scaling relation for magnitudes (Gutenberg–Richter [GR], or its tapered version) can be straightforwardly extrapolated to the magnitude–frequency distribution (MFD) of HR catalogs. For several HR catalogs such as of the 2019 Ridgecrest sequence, the 2009 L’Aquila sequence, the 1992 Landers sequence, and entire southern California, we determine if the MFD agrees with an exponential-like distribution using a statistical goodness-of-fit test. We find that HR catalogs usually do not preserve the exponential-like MFD toward low magnitudes and depart from it. Surprisingly, HR catalogs that are based on advanced detection methods depart from an exponential-like MFD at a similar magnitude level as network-based HR catalogs. These departures are mostly due to an improper mixing of different magnitude types, spatiotemporal inhomogeneous completeness, or biased data recording or processing. Remarkably, common-practice methods to find the completeness magnitude do not recognize these departures and lead to severe bias in the b-value estimation. We conclude that extrapolating the exponential-like GR relation to lower magnitudes cannot be taken for granted, and that HR catalogs pose subtle new challenges and lurking pitfalls that may hamper their proper use. The simplest solution to preserve the exponential-like distribution toward low magnitudes may be to estimate a moment magnitude for each earthquake.
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