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Young generations are increasingly committed to understanding disasters, and are a key player in current and future disaster risk reduction activities. The availability of online tools opened new perspectives in the organization of risk-related educational activities, in particular in earthquake-prone areas. This is the case of CEDAS (building CEnsus for seismic Damage Assessment), a pilot training activity aimed at collecting risk-related information while educating high-school students about seismic risk. During this experimental activity, students collected and elaborated crowdsourced data on the main building typologies in the proximity of their homes. In a few months, students created a dataset of valuable risk-related information, while getting familiar with the area where they live. Data collection was performed both on-site, using smartphones, and online, based on remote sensing images provided by multiple sources (e.g., Google maps and street view). This allowed all students, including those with limited mobility, to perform the activity. The CEDAS experience pointed out the potential of online tools and remote sensing images, combined with practical activities and basic training in exploratory data analysis, to engage students in an inclusive way. The proposed approach can be naturally expanded in a multi-risk perspective, and can be adjusted, eventually increasing the technical content of collected information, to the specific training and expertise of the involved students, from high-school to university level.

The main purpose of this paper was to, for the first time, analyse the spatiotemporal features of the background seismicity of Northern Algeria and its vicinity, as identified by different declustering methods (specifically: the Gardner and Knopoff, Gruenthal, Uhrhammer, Reasenberg, Nearest Neighbour, and Stochastic Declustering methods). Each declustering method identifies a different declustered catalogue, namely a different subset of the earthquake catalogue that represents the background seismicity, which is usually expected to be a realisation of a homogeneous Poisson process over time, though not necessarily in space. In this study, a statistical analysis was performed to assess whether the background seismicity identified by each declustering method has the spatiotemporal properties typical of such a Poisson process. The main statistical tools of the analysis were the coefficient of variation, the Allan factor, the Markov-modulated Poisson process (also named switched Poisson process with multiple states), the Morisita index, and the L–function. The results obtained for Northern Algeria showed that, in all cases, temporal correlation and spatial clustering were reduced, but not totally eliminated in the declustered catalogues, especially at long time scales. We found that the Stochastic Declustering and Gruenthal methods were the most successful methods in reducing time correlation. For each declustered catalogue, the switched Poisson process with multiple states outperformed the uniform Poisson model, and it was selected as the best model to describe the background seismicity in time. Moreover, for all declustered catalogues, the spatially inhomogeneous Poisson process did not fit properly the spatial distribution of earthquake epicentres. Hence, the assumption of stationary and homogeneous Poisson process, widely used in seismic hazard assessment, was not met by the investigated catalogue, independently from the adopted declustering method. Accounting for the spatiotemporal features of the background seismicity identified in this study is, therefore, a key element towards effective seismic hazard assessment and earthquake forecasting in Algeria and the surrounding area.

The concept of the Unified Scaling Law for Earthquakes (USLE), which generalizes the Gutenberg-Richter relationship making use of the fractal distribution of earthquake sources in a seismic region, is applied to seismicity in the Friuli-Venezia Giulia region, FVG (Northeastern Italy) and its surroundings. In particular, the temporal variations of USLE coefficients are investigated, with the aim to get new insights in the evolving dynamics of seismicity within different tectonic domains of FVG. To this purpose, we consider all magnitude 2.0 or larger earthquakes that occurred in 1995–2019, as reported in the catalog compiled at the National Institute of Oceanography and Applied Geophysics (OGS catalog), within the territory of its homogeneous completeness. The observed variability of seismic dynamics for three sub-regions of the territory under investigation, delimited based on main geological and tectonic features, is characterized in terms of several moving averages, including: the inter-event time, τ; the cumulative Benioff strain release, Ʃ; the USLE coefficients estimated for moving six-years time intervals, and the USLE control parameter, η. We found that: 1) the USLE coefficients in FVG region are time-dependent and show up correlated; 2) the dynamical changes of τ, Ʃ, and η in the three sub-regions highlight a number of different seismic regimes; 3) seismic dynamics, prior and after the occurrence of the 1998 and 2004 Kobarid (Slovenia) strong main shocks, is characterized by different parameters in the related sub-region. The results obtained for the FVG region confirm similar analysis performed on a global scale, in advance and after the largest earthquakes worldwide. Moreover, our analysis highlights the spatially heterogeneous and non-stationary features of seismicity in the investigated territory, thus suggesting the opportunity of resorting to time-dependent estimates for improving local seismic hazard assessment. The applied methods and obtained parameters provide quantitative basis for developing suitable models and forecasting tools, toward a better characterization of future seismic hazard in the region.