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.

Knowing the rate at which earthquakes happen along active faults is crucial to characterize the hazard that they pose. We present an approach (Paleoseismic EArthquake CHronologies, PEACH) to correlate and compute seismic histories using paleoseismic data, a type of data that characterizes past seismic activity from the geological record. Our approach reduces the uncertainties of the seismic histories and overall can improve the knowledge on fault rupture behavior for the seismic hazard.

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.

In weakly seismic or poorly monitored areas, the uncritical use of earthquake catalogues can be misleading. This is the case for a central sector in the Po Valley, where the Northern Apennines and Southern Alps collide. We collect and reprocess the available instrumental data of about 300 earthquakes from 1951 to 2019. The seismicity is weak, deeper than expected, and far from some existing human activities carried out underground. The potential tectonic causative sources are still unknown.

In order to evaluate the seismic risk, we first model the annual rate of occurrence of earthquakes on the faults near Istanbul. By using a novel modelling approach, we consider the fault system as a whole rather than each fault individually. We explore the hypotheses that are discussed in the scientific community concerning this fault system and compare the modelled results with local recorded data and a physics-based model, gaining new insights in particular on the largest possible earthquake.