Towards a probabilistic risk analysis due to volcanic‑hazards at Mount Etna

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. Hazardscenarios 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 thelocal 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

Evaluation of Ground Motion Models for Volcanic Areas in Italy: Advancing ShakeMap Implementation

In Italy, volcanic earthquakes are crucial for assessing seismic hazard, as several volcanoes pose significant risks to densely populated areas (e.g., Catania and its surroundings, Phlegraean Fields, Vesuvius). Recently, the increase in seismic activity in the Phlegraean Fields, characterized by swarms of low-magnitude earthquakes concomitant with bradyseism, has prompted a revision of the configuration of the Ground Motion Models (GMMs) used in the U.S. Geological Survey (USGS) ShakeMap software implemented in Italy for volcanic areas. In this study, we applied a cross-validation technique to evaluate the effectiveness of various GMMs available in the literature for volcanic regions, aiming to the identification of the most appropriate configuration to update and improve the ShakeMap service provided by the Istituto Nazionale di Geofisica e Vulcanologia (INGV). We compared the performance of the currently adopted GMMs [a combination of Tusa and Langer (2016) for earthquakes down to 5 km depth and Bindi et al. (2010) for deeper earthquakes] in predicting ground shaking with those developed by Tusa et al. (2020) and Lanzano and Luzi (2020). To conduct the tests, we used 100 Italian earthquakes that occurred in volcanic areas between February 2019 and May 2024, with magnitudes in the range 3.0‑4.5 and depths ≤ 35 km. Among the tested models, the GMM proposed by Tusa et al. (2020) was found to provide the most accurate predictions and will be adopted for generating the INGV shakemaps

Spatiotemporal evolution of broadband seismological networks in the Netherlands and the added value of the NARS-DICTUM array

Passive seismic networks play critical roles in both seismicity monitoring and subsurface imaging. The seismic network in the Netherlands has continuously been expanded, both to monitor induced seismicity related to gas exploitation in the north and to monitor natural (tectonic related) earthquakes in the south of the country. Aside monitoring seismicity, the data delivered by theseismological networks is required for imaging the subsurface to improve our knowledge of the composition and structure. This serves as a crucial layer of information for decision-makers in regional planning, locating resources, and risk assessment regarding current and future projects utilising the Dutch subsurface. Over the years, there has been an increase in the number of geophonesand accelerometers for monitoring mostly the high frequency seismic signals (induced seismicity) and imaging the near surface structure. However, the network of broadband seismometers for measuring tectonic events (usually lower frequency signals) and imaging deep subsurface is still limited. We describe an overview of previous temporary and permanent broadband seismometerdeployments in the Netherlands, with a focus on the latest NARS-DICTUM array of 25 broadband seismometers, underscoring its design, instruments, installation, and some preliminary subsurface information delivered by the network

Investigation of Potential Structures in the Southeastern Aegean Region Using Seismological and Gravity Data

This study investigates potential structure distribution in the southeastern Aegean Region, a seismically active and complex region of the Eastern Mediterranean. Using gravity data from theTOPEX v29.1 database, the Fast Sigmoid (FSED) and Tilt Angle of the Horizontal Gradient (TAHG) methods were applied to detect potential structural boundaries. Earthquake and focal mechanism catalogs compiled from various databases were also used to interpret the identified structural edges. A total of 69 structure boundaries were detected, some of which are potential faults related to existing faults. The results of this study provide new insights into the region’s tectonic framework, revealing both the continuity of known faults and the identification of new potential faults, thus contributing valuable information on the region’s seismotectonic characteristics

Integrated Earthquake Risk Assessment Using PSHA-Based Microzonation and Soil Vulnerability Index Maps of Urban Kathmandu Valley

The Kathmandu Valley, located in a seismically active region, has a long history of enduring significant earthquakes. Despite existing efforts, previous studies often lack comprehensive integration of seismic hazard data with vulnerability assessment, which is crucial for detailed earthquake risk evaluations. This study develops a comprehensive seismic risk assessment by integrating probabilisticseismic hazard analysis (PSHA) and soil vulnerability index map using Arc GIS. The PSHA, conducted with the R-CRISIS tool, estimates peak ground acceleration (PGA) across the region, while the soil vulnerability index is derived from nonlinear ground response analysis using DEEPSOIL. The seismic hazard analysis, reveals maximum peak ground accelerations (PGA) ranging from 0.345 g to 0.382 g over 50 years at a 10% probability of exceedance, corresponding to a return period of 475 years. The soil vulnerability index extending from 0.21 to 21.72, is applied here to assess the risk. Among the metropolitan cities and municipalities, Madhyapur Thimi emerged as the safest area, with 99.38% of its region classified as low-risk, while Shankharapur municipality was identified as the mostvulnerable, with 88.01% of its area classified as high-risk. This study provides critical insights for seismic risk assessment in the Kathmandu Valley; however, further research should consider dynamic land use and urban development changes to continually refine and update these assessments

How Artificial Intelligence and Earth Observation Satellites are re-shaping volcano monitoring

A growing number of satellite missions offer data at various spatial resolutions and revisit times.There are plans for new missions to maintain near-continuous monitoring of volcanic activity globally. Artificial Intelligence (AI) techniques, particularly Machine Learning (ML) and Deep Learning (DL) models, offer distinct advantages in extracting information and knowledge from these vast datasets. Here, the potential of Artificial Intelligence techniques, satellite data, and cloud computing in addressing some of the most critical challenges and questions related to volcanic hazards is shown. A description of how the data-driven science paradigm is used to solve volcanic hazard problems is provided highlighting how earth observation satellite data are able to drive the entire estimation process through advanced AI techniques. An overview of the most important concepts and techniques to assist in interpreting satellite data for volcano monitoring is provided. From feature engineering methods enhancing the input signal for AI models, to convolution filters that can strategically be used in Convolutional Neural Network (CNN) architectures to find patterns, to the concept of “attention” in neural networks and the powerful abilities it brings to briefly discuss strategies from unsupervised, self-supervised and transfer learning to reduce the need for large labeled datasets. The objective of this work is dual, providing the basic concepts of EarthObservation (EO) and AI and showing how they have re-shaped volcano monitoring

Methana Magmatic Observational Experiment (MeMaX) – seismological monitoring of magmatic and tectonic activity in the western Saronic Gulf region, Greece

Large-scale historical volcanic eruptions caused significant destruction in the Mediterranean region (e.g., Thera/Santorini explosion circa 1600 BCE). The South Aegean Volcanic Arc remains active, and in addition to primary volcanic hazards such as ashfall and lava flows, active submarine and coastal volcanoes have the potential to trigger tsunamis as secondary volcanic hazard. These tsunamis pose a threat even to far-distant coastlines. With increasing population density, infrastructure development, and seasonal tourism, both primary and secondary volcanic risks along the Aegean coasts are increasing, even with respect to smaller, more frequent eruptions. Our focus is on the western Saronic Gulf region within the Aegean Sea, as possible impacts may even extend into the greater Athens metropolitan area. There, the dormant volcanoes of the Methana volcanic system, which last erupted in 230 BCE, and the submarine Pausanias Volcanic Field pose an underappreciated hazard. We search for evidence of yet undetected magmatic activity through the identification of related microseismic events and describe the design of the related MeMaX experiment. Since 2019, the National Observatory of Athens and the University of Patras operate six seismic stations on Methana and the nearby Peloponnese mainland. In March 2024, an additional 15 seismic recording stations were deployed for a two-years period across Methana, Aegina, Agistri, Kyra, and Poros islands and the mainland Peloponnese. This network configuration provides a dense and good azimuthal coverage of seismic ray paths for earthquake location and structural analysis. The continuous recordings enhance the observational capacities for earthquake detection, e.g. the first results indicate that the noise at the recording sites is quite low and that low magnitude events to ML ca. 0 can be recorded with a very good signal-to-noise ratio. This geophysical experiment is partof the MULTI-MAREX initiative

Near-Field PSHA with Directivity – Dorud

This study investigates the probabilistic seismic hazard in Dorud city, located near the active Dorud fault, with a specific focus on incorporating near-field and rupture directivity effects into the hazard modeling framework. Near-field ground motions – particularly those influenced by rupture directivity – can generate long-period velocity pulses, posing serious risks to long-period structures such as bridges and tall buildings near faults. To realistically capture these effects, this study integrates empirical directivity models (Somerville et al., 1997; Abrahamson, 2000) into the probabilistic seismic hazard assessment (PSHA). The seismicity parameters were derived using the Kijko (2004) method based on a carefully declustered earthquake catalog; the suitability of this catalog for PSHA was statistically confirmed through a Kolmogorov‑Smirnov (K‑S) test, validating the Poissonian nature of inter-event times.Seismic hazard calculations were performed for vibration periods of 0.75, 1, 2, 3, and 4 seconds and return periods of 50, 475, and 2475 years. The study further includes deaggregation analysis to examine how near-field and directivity effects influence magnitude and distance contributions to hazard. The results show that the influence of directivity increases with both return period and vibration period. The most significant amplification – a 17.16% increase in acceleration – occurs when directivity is included for a 2475-year return period at a 4-second vibration period. A regional comparison of seismicity parameters with previous PSHA studies supports the robustness of the selected input values. This study demonstrates the importance of explicitly incorporating directivity in PSHA for fault-adjacent urban areas, especially for engineering design of critical long-period structures

Seismic structure at the test site for wind energy research, WINSENT, Southwest Germany

The subsurface at the Wind Science and Engineering Test Site in Complex Terrain (WINSENT) in SW Germany is studied to derive its underground structure and 3‑D seismic velocity distribution. These parameters are important for further geotechnical studies to better understand the soil‑structure interaction of wind turbines and their underground. This knowledge is needed for the saveconstruction of modern wind turbines on land whose nacelles reach altitudes of more than 150 m above the ground. Another issue are ground motions which are emitted from wind turbines and can be measured up to distances of several kilometers. We describe the fieldwork at the wind energy test site and the seismic inversion models. The seismic velocities are low compared to other studies due to the weathering and karstification of the Jurassic limestone at the site. We derive 3‑D compressional and shear wave velocity models with minor lateral variation which can be used as input for numerical modelling of wave propagation to explore vibrating wind turbines and their emissions

Multidisciplinary investigation of the Salse di Regnano mud volcanoes (Northern Italy) using remote sensing and historical data

This study presents a multidisciplinary analysis of the Salse di Regnano, a significant mud volcanic area in the Emilia-Romagna Apennines, aiming to develop a comprehensive research strategy to investigate its morphological evolution and fluid emission dynamics. High-resolution 3D models generated through UAV-based photogrammetric surveys enabled detailed mapping and monitoring of morphological features, capturing changes over an extended historical period (1907‑2025) by integrating regional geological maps and archival topographical data. In-situ measurements of methane (CH4) and carbon dioxide (CO2) fluxes revealed localized methane emissions associated with vents characterized by high soil permeability, while CO2 fluxes likely reflect biogenic soil respiration near mud deposits. However, geochemical signatures, including δ13C-CH4 values and the presence of ethane, suggest a thermogenic component, highlighting the complex interplay between biological and geological processes governing gas emissions in the area. Complementary satellite imagery and spatial analyses additionally elucidated the spatial distribution of these processes. This multidisciplinary approach not only advances the understanding of mud volcano dynamics in this geologically active region, but also establishes a practical and scalable methodological framework. The proposed workflow, incorporating targeted geophysical surveys such as geomagnetic and passive seismic measurements, aims to enhance the characterization of subsurface structures. As a preliminary study, this contribution provides a valuable foundation for subsequent monitoring and risk assessment efforts of mud volcanic systems in similar geological contexts. In this view, comparing present-day observations with historical data may also offer critical insights for assessing long-term hazard potential