Gioacchino Fertitta

Mud volcanoes (MVs) are geological structures emitting a mixture of gas (mostly methane), water, clayey sediments and rock fragments. Their background activity is sometimes interrupted by paroxysmal events, consisting of violent expulsions of huge amounts of these materials, determining geohazard conditions. Although MV eruptions have been reported to have caused damages and casualties worldwide, their surveillance protocols are poorly developed, and multiparametric instrumental recordings of paroxysmal phases have never been documented. Here we show the first-ever multiparametric dataset capturing the pre-, syn-, and post-eruptive evolution of a paroxysmal event at Maccalube di Aragona (Italy) on 29 August 2025. Seismic, geodetic, geomorphological, hydro-meteorological and geochemical data, acquired before, during and after the event allowed to reconstruct the eruption dynamics, identifying indicators for the implementation of monitoring protocols. In particular, variations of physical-chemical parameters of mud pools, soil electric conductivity, seismic tremor and ground deformation shed light on possible precursors, from days to minutes, of the paroxysmal phase.

Mud volcanoes (MVs) are geological structures emitting a mixture of gas (mostly methane), water, clayey sediments and rock fragments. Their background activity is sometimes interrupted by paroxysmal events, consisting of violent expulsions of huge amounts of these materials, determining geohazard conditions. Although MV eruptions have been reported to have caused damages and casualties worldwide, their surveillance protocols are poorly developed, and multiparametric instrumental recordings of paroxysmal phases have never been documented. Here we show the first-ever multiparametric dataset capturing the pre-, syn-, and post-eruptive evolution of a paroxysmal event at Maccalube di Aragona (Italy) on 29 August 2025. Seismic, geodetic, geomorphological, hydro-meteorological and geochemical data, acquired before, during and after the event allowed to reconstruct the eruption dynamics, identifying indicators for the implementation of monitoring protocols. In particular, variations of physical-chemical parameters of mud pools, soil electric conductivity, seismic tremor and ground deformation shed light on possible precursors, from days to minutes, of the paroxysmal phase.

Mud volcanoes (MVs) are geological structures emitting a mixture of gas (mostly methane), water, clayey sediments and rock fragments. Their background activity is sometimes interrupted by paroxysmal events, consisting of violent expulsions of huge amounts of these materials, determining geohazard conditions. Although MV eruptions have been reported to have caused damages and casualties worldwide, their surveillance protocols are poorly developed, and multiparametric instrumental recordings of paroxysmal phases have never been documented. Here we show the first-ever multiparametric dataset capturing the pre-, syn-, and post-eruptive evolution of a paroxysmal event at Maccalube di Aragona (Italy) on 29 August 2025. Seismic, geodetic, geomorphological, hydro-meteorological and geochemical data, acquired before, during and after the event allowed to reconstruct the eruption dynamics, identifying indicators for the implementation of monitoring protocols. In particular, variations of physical-chemical parameters of mud pools, soil electric conductivity, seismic tremor and ground deformation shed light on possible precursors, from days to minutes, of the paroxysmal phase.

Recent advances in Micro-Electro-Mechanical Systems (MEMSs) have enabled the development of accelerometers increasingly suitable for seismological and structural engineering applications. Quartz MEMS (QMEMS) sensors combine low self-noise, wide dynamic range, excellent thermal stability, and compact dimensions, providing a cost-effective alternative to conventional force-balance and piezoelectric accelerometers. This study presents the development and validation of a complete QMEMS-based sensing platform for seismic monitoring and Structural Health Monitoring (SHM), integrating the recently introduced Epson M-A370 accelerometer, a Smart Sensor Box with precise timing synchronization, and embedded acquisition and edge-processing capabilities. The platform was evaluated through comprehensive laboratory and field experiments. The M-A370 was experimentally compared with the M-A352 and a reference force-balance accelerometer, while complementary M-A352 tests included representative MEMS and piezoelectric accelerometers. The experimental results indicate that accelerometer self-noise is a primary factor governing the reliability of Operational Modal Analysis (OMA) and long-term SHM. Self-noise densities below 1 μg/√Hz, preferably below 0.5 μg/√Hz, and, for the most demanding applications, approaching or below 0.1 μg/√Hz, represent practical performance targets for robust modal identification and reliable tracking of structural dynamic properties under weak ambient excitation or in very quiet environments. These values, however, are not exclusion thresholds, as higher-noise accelerometers can reliably record ground motions sufficiently above their instrumental noise floor. The ultra-low-noise M-A370 (0.02 μg/√Hz) delivers data quality comparable to engineering-grade force-balance accelerometers. The proposed platform, combining ultra-low-noise QMEMS technology, precise timing synchronization, and embedded processing, provides a scalable framework for Urban Seismic Observatories, distributed SHM, OMA, and impact-based Earthquake Early Warning (EEW) across buildings, bridges, and heritage structures.

Recent advances in Micro-Electro-Mechanical Systems (MEMS) have enabled the development of accelerometers increasingly suitable for seismological and structural engineering applications. Quartz MEMS (QMEMS) sensors combine low self-noise, wide dynamic range, excellent thermal stability, and compact dimensions, providing a cost-effective alternative to conventional force-balance and piezoelectric accelerometers. This study presents the development and validation of a complete QMEMS-based sensing platform for seismic monitoring and Structural Health Monitoring (SHM), integrating the recently introduced Epson M-A370 accelerometer, a Smart Sensor Box with precise timing synchronization, embedded acquisition and edge-processing capabilities, and comprehensive laboratory and field validation. The M-A370 is evaluated against the previous-generation M-A352 and representative MEMS, piezoelectric, and force-balance accelerometers. Experimental results demonstrate that accelerometer self-noise is a primary factor governing the reliability of Operational Modal Analysis (OMA) and long-term SHM. Self-noise densities below 1 μg/√Hz, and preferably below 0.5 μg/√Hz, are shown to be necessary for robust modal identification and long-term tracking of structural dynamic properties under weak ambient excitation. The ultra-low-noise M-A370 (0.02 μg/√Hz) provides data quality comparable to engineering-grade force-balance accelerometers while enabling continuous monitoring of buildings, bridges, and heritage structures. The proposed sensing platform, combining ultra-low-noise QMEMS technology, precise timing synchronization, and embedded processing, provides a scalable framework for Urban Seismic Observatories, Operational Modal Analysis, distributed SHM systems and impact-based Earthquake Early Warning.

A fully integrated tsunami monitoring unit, funded by the Italian National Civil Protection Department, was installed in July 2025 by INGV, offshore the Sciara del Fuoco, Stromboli, under the coordination of two INGV centres, the CME (Centro Monitoraggio Eolie) and CAT (Centro Allerta Tsunami), and has been operating reliably since.The seafloor module, anchored to a reinforced-concrete deadweight, hosts multiple sensors, including a pressure sensor to detect rapid sea-level changes, a hydrophone to capture volcanic and underwater activity and an accelerometer to observe seafloor dynamics.Information is exchanged bidirectionally between the buoy and the seafloor module through an elastic electromechanical cable, also supplying energy to the seafloor module, and simultaneously providing elastic mooring. The elastic cable can extend up to 2.5 times its unstretched length while maintaining its mechanical and electrical integrity. To reach the installation depth, two cable segments were connected using an intermediate connection buoy. A mechanical release system can be triggered acoustically to release the seafloor module from the deadweight.The depth of the seafloor module, approximately 88 meters, was chosen so that pressure data can be converted into tsunami height values to limit as much as possible the deep-water high-wavenumber attenuation effects, as tsunamis induced by mass flows over the Sciara del Fuoco feature shorter periods signals than earthquake-generated tsunamis. Data streams are transmitted via radio and Wi-Fi to the shore station on Stromboli Island, then forwarded to the INGV-Osservatorio Etneo data center at the COA (Centro Operazioni Avanzate) in Stromboli. From there, the data are transmitted to the INGV-CAT. An STA/LTA algorithm, first introduced by Ripepe & Lacanna (2024) for the tsunami warning at Stromboli from the elastic beacons at a similar location, is applied experimentally to the data stream. The data will become in the future an important source of information for the tsunami warning system in Stromboli.

The INGV-PROMUD is a 3-years (2023-2025) multidisciplinary project (https://progetti.ingv.it/it/promud), aimed to investigate the indicators of the activity of MVs, with the ultimate goal of individuating possible precursors of paroxysmal events. Its two main target areas, both in Italy, are the ‘Salse di Nirano’ (Northern Apennines) and the ‘Maccalube di Aragona’ (Sicily).Among the multidisciplinary research activities carried out in the scopes of the project, particular attention is focused on the analysis of the background seismic noise wavefield, and its role in the identification and monitoring of degassing sources and conduits, and of the hydrological regime, and its influence on MVs activity.Seismic data have been recorded at Aragona, since July 2024, by six seismometers, four of which are part of a seismic array located close to the vents, with the aim of investigating the characteristics of the recorded background seismic noise, e.g., spectral properties, H/V spectral ratios, energy (RMS) and polarization temporal pattern. Preliminary results of data recorded from July to September 2024 show that the main frequency content of the wavefield is below 5 Hz. H/V spectral ratios are almost flat, indicating the absence of amplification effects, at least in the investigated sites. The temporal pattern of the RMS amplitude is affected by some fluctuations, which need to be further investigated to look for evidences of a possible periodic behaviour. The polarization parameters (azimuth, incidence angle and rectilinearity) appear to be stable over the whole analysed time interval. At the array site, in the 0.2-1 Hz frequency band, the noise wavefield is polarized along the NS direction, while polarization azimuths 1-5 Hz band are more disperse.Seismic monitoring has been coupled to near real time acquisition of meteorological data and temperature, volumetric water content and electric conductivity of soil, integrated by monthly surveys of number and position of active mud emitting vents, and apparent soil moisture content distribution.First analyses of the acquired data indicate that the hydrological cycle exerts a strong control on both number and distribution of vents, and on the rheological properties of the emitted fluids, playing a potential role in determining pressure accumulation, prodromic to paroxysmal events.

This study presents the results of an experimental investigation conducted on a 2:3 scale model of a two-story stone masonry building. We tested the model on the UniKORE L.E.D.A. lab shake table, simulating the Mw 6.3 earthquake ground motion that struck L’Aquila, Italy, on 6 April 2009, with progressively increasing peak acceleration levels. We installed a network of accelerometric sensors on the model to capture its structural behaviour under seismic excitation. Medium-to lower-cost MEMS accelerometers (classes A and B) were compared with traditional piezoelectric sensors commonly used in Structural Health Monitoring (SHM). The experiment assessed the structural performance and damage progression of masonry buildings subjected to realistic earthquake inputs. Additionally, the collected data provided valuable insights into the effectiveness of different sensor types and configurations in detecting key vibrational and failure patterns. All the sensors were able to accurately measure the dynamic response during seismic excitation. However, not all of them were suitable for Operational Modal Analysis (OMA) in noisy environments, where their self-noise represents a crucial factor. This suggests that the self-noise of MEMS accelerometers must be less than 1 µg/√Hz, or preferably below 0.5 µg/√Hz, to obtain good results from the OMA. Therefore, we recommend ultra-low-noise sensors for detecting differences in the structural behaviour before and after seismic events. Our findings provide valuable insights into the seismic vulnerability of masonry structures and the effectiveness of sensors in detecting damage. The management of buildings in earthquake-prone areas can benefit from these specifications.