We adopted a multidisciplinary approach to investigate the seismotectonic scenario of the 30 October 2016, M_w 6.5, Norcia earthquake, the largest shock of the 2016–2017 central Italy earthquake sequence. First, we used seismological and geodetic data to infer the dip of the main slip patch of the seismogenic fault that turned out to be rather low‐angle (~37°). To evaluate whether this is an acceptable dip for the main seismogenic source, we modeled earthquake deformation using single‐ and multiple‐fault models deduced from aftershock pattern analyses. These models show that the coseismic deformation generated by the Norcia earthquake is coherent with slip along a rather shallow‐dipping plane. To understand the geological significance of this solution, we reconstructed the subsurface architecture of the epicentral area. As the available data are not robust enough to converge on a single fault model, we built three different models encompassing all major geological evidence and the associated uncertainties, including the tectonic style and the location of major décollement levels. In all models the structures derived from the contractional phase play a significant role: from controlling segmentation to partially reusing inherited faults, to fully reactivating in extension a regional thrust, geometrically compatible with the source of the Norcia earthquake. Based on our conclusions, some additional seismogenic sources falling in the eastern, external portions of the Apennines may coincide with inherited structures. This may be a common occurrence in this region of the chain, where the inception of extension is as recent as Middle‐Upper Pleistocene.

The 2012 Pollino (Calabria, Italy) seismic sequence, culminating in the Mw 5.2 earthquake of 25 October 2012, is investigated, exploiting data collected during a long-term continuous radon monitoring experiment performed in the epicentral area from late 2011 to the end of 2014. We analyse data collected both using a phenomenological approach based on quantitative evidence and a purely numerical analysis including the following: (i) correlation and cross-correlation investigations; (ii) an original approach aimed at limiting the impact of meteorological parameters variations on the interpretation of measured radon levels; (iii) a change point analysis; (iv) the implementation of an original detection algorithm aimed at highlighting the connections between radon emission variations and major seismic events occurrence. Results from both approaches suggest that radon monitoring stations can be subject to massive site effects, especially regarding rainfall, making data interpretation harder. The availability of long-term continuous measurements is crucial to precisely assess those effects. Nevertheless, statistical analysis shows a viable approach for quantitatively relating radon emanation variations to seismic energy release. Although much work is still needed to make radon time series analysis a robust complement to traditional seismological tools, this work has identified a characteristic variation in radon exhalation during the preparation process of large earthquakes.

We introduce the Italian Radon mOnitoring Network (IRON): a new nationwide permanent network for near real-time measurements of soil radon emissions in Italy. Deployed over the last 8 years, presently IRON consists of 26 stations mainly concentrated in the Central-Southern Apennines, but marginally covering the whole Italian peninsula. At present, most IRON stations have recorded radon concentration time-series for more than 4-5 years. With a standard sampling interval of about two hours, the whole IRON dataset consists of nearly 440,000 single radon concentration measurements. Here we present the network in terms of sites, installations types and collected time-series. The amount of data, together with the systematic methods of measurements, allowed us to evaluate some significant aspects related both to the measurement methodology and to the complex dynamics of soil radon emanations. Two case studies show, respectively, how different observational setups we implemented impact on the features of the recorded signal, and how observed fluctuations in radon concentration may be ascribed to geophysical processes taking place at depth in the crust. We discuss the potential suitability of IRON, in order to study the relation between radon variability and the preparation processes of strong earthquakes.

The identification of a source model for the catastrophic 1908 December 28 Messina earthquake (M_w = 7.2) has been the subject of many papers in the last decades. Several authors proposed different models on the basis of seismological, macroseismic and geodetic data sets; among these models, remarkable differences exist with regard to almost all parameters. We selected a subset of six models among those most cited in literature and used them to model the post-seismic sea level variation recorded at the tide gauge station of Messina (until 1923), to attempt an independent discrimination among them. For each model, we assumed a simple rheological structure and carried out a direct-search inversion of upper crust thickness and lower crust viscosity to fit the post-seismic sea level signal. This approach enabled us to identify a class of fault geometries which is consistent with the post-seismic signal at the Messina tide gauge and with the known structural and rheological features of the Messina strait.

We present an overview of the potential of active monitoring techniques to investigate the many factors affecting the concentration of radon in houses. We conducted two experiments measuring radon concentration in 25 apartments in Rome and suburban areas for two weeks and in three apartments in the historic center for several months. The reference levels of 300 and 100 Bq/m³ are overcome in 17% and 60% of the cases, respectively, and these percentages rise to 20% and 76% for average overnight radon (more relevant for residents’ exposure). Active detectors allowed us to identify seasonal radon fluctuations, dependent on indoor-to-outdoor temperature, and how radon travels from the ground to upper floors. High levels of radon are not limited to the lowest floors when the use of heating and ventilation produces massive convection of air. Lifestyle habits also reflect in the different values of gas concentration measured on different floors of the same building or in distinct rooms of the same apartment, which cannot be ascribed to the characteristics of the premises. However, the finding that high residential radon levels tend to concentrate in the historic center proves the influence of factors such as building age, construction materials, and geogenic radon.