We present the BVAL method, designed to forecast potentially damaging earthquakes (Mw ≥ 5.0) in Italy based on temporal variations of the b-value of the Gutenberg–Richter frequency–magnitude distribution. The b-value is used as an indicator of stress within the Earth's crust, with lower b-values associated with higher stress levels and an increased likelihood of significant seismic events. This method issues alarms when the b-value falls below a critical threshold. It is optimized using the HOmogenized instRUmental Seismic catalogue data from 1990 to 2004 and validated pseudo-prospectively using data from 2005 to 2022. Our analysis uses the recently developed b-positive (b+) method to compute the b-value from magnitude differences, providing resilience against data incompleteness. We compare the performance of the BVAL method with two established models: the Epidemic Type Aftershock Sequence (ETAS) model, which forecasts earthquake rates based on the epidemic principle that each shock triggers subsequent shocks, and the FORE model, which relies on the occurrence of strong foreshocks. Additionally, we evaluate two ensemble models that combine BVAL and FORE through additive (EADD) and multiplicative (EMUL) strategies to balance false alarms and missed events. The EADD model declares an alarm when either BVAL or FORE signals it, while the EMUL model triggers alarms only when both methods agree. We assess the predictive efficiency of these models using the area skill score, derived from Molchan diagrams, which plot the miss rate against the fraction of space-time occupied by alarms. Our results demonstrate that BVAL is less effective than FORE and ETAS at high space-time fractions, but it outperforms ETAS at low fractions (⁠ τ < 2–4 per cent), indicating its potential utility in scenarios where minimizing false alarms is critical. This comprehensive comparison highlights the strengths and limitations of each method, suggesting that integrating multiple forecasting strategies can enhance the reliability of earthquake preparedness and response efforts in Italy.

The 2020 MW 6.4 Petrinja (Croatia) earthquake induced extensive and diversified liquefaction and lateral spreading phenomena within ≈ 20 km radius from the epicenter. A detailed investigation from field and Unmanned Aerial Vehicle (UAV) surveys was carried out by a European researcher team (EUTeam) in the months following the mainshock. This work focuses on 61 surveyed sites: field observations were coupled with laboratory tests for soil classification and sediment composition. The adopted procedure provides an in-depth geological and geotechnical characterization of the liquefied sites in the Petrinja region. The liquefaction evidences are mainly associated to alluvial plain environments, in particular to meander paleochannels, and the ejected material is predominantly siliciclastic, made up of very rounded quartz-rich lithics. Few sites are dominated by angular carbonate rock fragments, related to liquefaction in cataclastic deposits along tectonic fractures. The ejected sediment includes a wide range of grain-size from silt to gravel. The peculiar presence of gravel in the liquefied deposits (up to 28% in some samples) confirms the need of expanding the grain-size boundaries for liquefiable coarse-grained gravelly soils. The information gathered from the post-earthquake surveys and from the sedimentological and geotechnical analysis for each studied site were compiled in organized data sheets, providing a striking instrument for in-depth earthquake studies, both for geological and geotechnical engineering purposes. The format defined for the data sheet can be functional and applicable also in liquefaction studies from different geological and depositional settings.

Multi-hazard assessment aims at evaluating the potential impacts of various natural and humaninduced hazards in a given area of interest and time period. The analysis can include hazards of different nature – such as volcanic eruptions, earthquakes, floods, landslides, and industrial accidents – considering their interdependencies and cumulative effects. Multi-hazard assessments can provide critical insights into the potential impact of multiple hazards, enabling decision makers to adopt a wider view of the problem with respect to the approach of analyzing single hazards independently. Volcanoes are interesting targets for implementing multi-hazard analyses because they are intrinsically a multi-hazard source due to the variety of phenomena usually related to volcanic eruptions (e.g. volcano seismicity, lava flows, tephra fall, lahars, etc.). This paper presents a target-based approach for multi-hazard analysis at Etna volcano (Italy) in which the output of probabilistic single hazard assessment can be harmoniously integrated and used for assessing a wide number of scenarios. The findings underscore the advantages of adopting such a kind of approach for supporting decision makers when using the results of multiple probabilistic hazards assessments for performing tasks of planning, mitigation, or emergency preparedness. This work has been performed in the framework of the INGV project “Pianeta Dinamico” – PANACEA, a project developed for implementing multi-hazard and multi-risk assessments at Etna volcano.

Mount Etna is the largest active volcano in Europe and is renowned for its effusive and explosive eruptions, frequently accompanied by intense seismic activity. The densely urbanized area of Eastern Sicily (Italy), situated on the flanks of Mt. Etna, has been the focus of an innovative and comprehensive research project aimed at evaluating the potential volcano hazards and subsequent risks. Hazard scenarios were generated within the research project PANACEA (Probabilistic AssessmeNt of volCanorelated multi-hazard and multi-risk at Mount EtnA) and they have been effectively employed in risk assessment for built-up areas and lifelines. The risk analyses were conducted for lava flow, tephra fall and volcanic earthquake hazards. Risk scenarios were assessed at different spatial scales, from the local one (at the resolution of the census track) to the sub-regional scale, defined as the union of some municipalities. Probabilistic damage scenarios were calculated with the aim of conducting a multi-hazard risk analysis, estimating direct losses in terms of structural damage, casualties and loss of functionality. A few examples of risk assessment are presented here to test the last step of the whole process developed in PANACEA.

Mount Etna, in Italy, is one the most active volcanoes in the world. Over the past two decades, its explosive activity has intensified, producing high eruptive columns that rise up to about 15 km above sea level. The particles ejected during these eruptions have caused numerous challenges for the population living on the volcano’s slope, mainly due to difficulties in removing the deposits, but also in terms of health risks and mobility disruptions. The increase in Etna’s explosive activity has led in continuous improvements in the monitoring and forecasting system adopted by the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, since 2009, using new sensors and enhanced data collection and analysis. In this paper, we present a review of several activities carried out in the frame of the Work Package 2 (WP2) of the ‘Probabilistic AssessmeNt of volCano-related multi-hazard and multi-risk at Mount EtnA (PANACEA)’ project. While the PANACEA project aims at using accurate physics-based models and advanced probabilistic approaches to assess volcanic multi-hazards and identify at-risk zones, the WP2 objective is to improve previous studies on the tephra fallout hazards for Etna. In this context, various activities have been conducted such as: enrich the data collection of eruption source parameters by analysing previous studies and developing new methods for their quantification; improve hazard estimates using multi-model approaches; quantify the uncertainty in eruption source parameters. Additionally, progresses have been made in developing hazard maps that include ballistic impact analysis. These approaches may be extended to other active volcanoes where advanced monitoring and surveillance systems are in place

Although not specifically conceived for tackling short‐term aftershock incompleteness (STAI), earthquake detection methods such as template matching (TM) and machine learning (ML) can help mitigate the under‐reporting of aftershocks after large earthquakes by detecting low‐magnitude events hidden in seismic noise. So far, the ability of TM and ML to address STAI has not been evaluated against benchmark data sets reconstructed by independent methods. In this study, we use events reconstructed by RESTORE (REal catalogs STOchastic REplenishment), a Python toolbox specifically designed to tackle STAI, as a stochastic benchmark to assess the ability of TM and ML in recovering the bulk statistical properties of aftershocks missed during STAI period. Our results show overall good compatibility between the TM/ML detections and the RESTORE benchmark in the space–time–magnitude domain, though some discrepancies in detection rates and in the upper bounds of magnitudes are noted. This study also highlights the complementary use of stochastic and enhanced detection techniques. Stochastic algorithms like RESTORE can be implemented for immediate STAI mitigation in short‐term forecasting and operational earthquake forecasting, whereas enhanced detection techniques can be used over longer time scales to precisely recover unrecorded events.

Open-conduit basaltic volcanoes are susceptible to sudden transitions from mild activity to violent explosive eruptions with little to no warning. Such was the case at Stromboli in the summer of 2019, when two paroxysmal explosions occurred within approx- imately two months (July 3 and August 28). We apply coda wave interferometry to identify possible transitions in behavior in the build-up to these events, computing seismic velocity changes using five broadband seismic stations on the volcano between 2013–2022. This timeframe encompasses a range of volcanic activity including effusive activity, major explosions and parox- ysms. Cross-correlation functions are computed both between pairs of stations and single-station cross-components in multiple frequency bands that allow the sampling of different depths (between approximately 100–1000 m) within the plumbing system. Shallow velocity changes (1–2 Hz and 2–4 Hz) reveal mid-to-long term precursors prior to the paroxysms in 2019. For example, we observe that 2–4 Hz velocities recorded at the station closest to the active crater show an increase of 0.2–0.3 % relative to velocities recorded at other stations. This increase is largely accumulated from mid-2017, coinciding with previously observed heightened activity at the volcano, peaking approximately one month prior to the first paroxysm. A long-term decrease is also observed in deeper velocity changes (0.5–1.0 Hz) during the same time interval. It is hypothesized that these changes represent greater magma overpressure from increased volatile input from depth. The different response in the shallow subsurface may reflect a local response due to the same source within the vicinity close of the crater terrace. These findings illustrate how coda wave interferometry can provide meaningful insights into the evolving dynamics of open-conduit basaltic volcanoes.