The Campi Flegrei caldera is currently experiencing significant ground deformation and bradyseismic activity, yet the subsurface processes driving this unrest remain debated. Competing interpretations invoke magmatic intrusion, hydrothermal circulation, or regional tectonic stress, with no consensus on the role of magma at depth.Here, we present results from a new probabilistic high-resolution seismic tomography of the Campi Flegrei caldera, imaging both isotropic velocity structure and seismic anisotropy using P-wave arrival times from a recently published, machine-learning–derived seismic catalog (Tan et al., 2025). The model reveals a pronounced low-velocity volume extending from shallow levels down to approximately 7 km depth. Within this region, anisotropy patterns are characterized by vertically oriented fast axes and predominantly horizontal slow axes, consistent with aligned vertical cracks or dike-like structures. The magnitude and spatial coherence of the inferred anisotropy, together with the observed low velocities, are difficult to reconcile with purely hydrothermal or tectonic processes. Instead, they suggest a stress regime compatible with upward pressurization from depth, potentially associated with magmatic intrusion. These interpretations are further supported by numerical modeling that tests alternative source configurations and reproduces the observed anisotropy orientations only when deep magmatic pressurization is included. While alternative mechanisms cannot be fully excluded, our results indicate that magma may play an active role in driving the ongoing deformation of the Campi Flegrei caldera.These findings provide new constraints on the physical processes underlying caldera unrest and have important implications for hazard assessment in one of the most densely populated volcanic regions in the world. Tan, X., Tramelli, A., Gammaldi, S., Beroza, G. C., Ellsworth, W. L., & Marzocchi, W. (2025). A clearer view of the current phase of unrest at Campi Flegrei caldera. Science, 390(6768), 70-75.

Abstract. Understanding river dynamics during flood events is critical for effective hazard mitigation and water resource management, especially as extreme weather events become increasingly frequent. Environmental seismology, which consists in monitoring natural surface processes with seismic instruments, has gained considerable attention over the past two decades. During floods events continuous seismic signals, also called seismic noise in this context, are generated by the turbulent flow and the transported bedload at the riverbed. If recorded at nearby seismic stations (i.e. from the riverbank to a few hundred meters), these seismic data become an important source of information complementing traditional methods (e.g., stream gauge, bedload basket sampler) to improve models and early warning systems. Despite the increasing number of case studies worldwide, the potential of seismic monitoring to capture flood-induced natural river processes in the Alps remains underexplored, particularly regarding the opportunistic use of existing stations from permanent network(s) originally deployed for earthquake monitoring. This study investigates the potential of records from permanent seismic stations relatively far from the river (up to ∼3 km) to assess bedload discharge and river flow dynamics during flood events in one of the rare morphologically preserved alpine rivers, the Tagliamento River in northern Italy. Seismic data from three selected stations at the subwatershed scale (i.e., spaced by about 20 km at maximum) were analysed together with hydrological and meteorological measurements such as water height, rain rate, and wind velocity, hence allowing to identify specific frequency bands for which seismic amplitude timeseries correlate with weather and river components. For particular frequencies, we notably observe a hysteresis behaviour between seismic amplitudes and the rising and falling phases of flood event, suggesting seismic source mechanisms related to turbulent flow and/or the movement of coarse sediments. The study demonstrates that even stations not specifically positioned close to the riverbed can capture valuable information on flood dynamics, thereby providing an early indication of flood propagation. These findings highlight the potential for incorporating seismic monitoring into flood forecasting and river management strategies, contributing to enhanced hazard mitigation efforts in the context of increasingly frequent extreme meteorological events. More specifically, the present study also helps in gaining information about the Tagliamento catchment response and relative seismic signatures during flood events for further investigations in developing early warning systems based on seismic data.

The assessment of seismic hazard and risk requires a catalogue of earthquakes (both historical and recorded during the instrumental era), where the location and magnitude of events affecting the study area are reported. Mw is commonly used for this aim, but it is a static measure of earthquake size and it cannot capture the full extent of earthquake source complexity. Other magnitude scales connected to the rupture kinematics and dynamics should complement Mw in hazard studies, but this required efforts in re-analyzing catalogs of intensity data. To this aim, we developed a high-frequency magnitude (m3Hz) estimated using a random or log-averaged horizontal component of ground motion from frequencies above the highest corner frequency of the earthquake source spectrum. Here, we apply the novel procedure to the intensity data from the Parametric Catalogue of Italian Earthquakes (CPTI15). We derive m3Hz for 1,951 earthquakes (from 1117 to 2020). The new earthquake catalog is defined by the acronym ICEM. For all earthquakes a quality index based on the variance-to-mean ratio (VMR) has been assigned.

The understanding of the magma system beneath intracontinental volcanic fields depends critically on our ability to resolve small-sized anomalies distributed over large areas of hundreds of kilometres. Magmatic reservoirs co-exist at different depths in the upper mantle and crust and may consist of extensive zones of crystal mush, swarms of sills and dikes of different ages and states, pore space saturated by volatiles or melt, or larger-volume, differentiated magma. Passive seismological experiments with a large number of sensors deployed with small interstation spacings, combining different types of sensors and fibre-optic sensor technology, have great promise for addressing the resolution to capture the distributed magmatic system. We report on a one-year, large-N experiment in the Quaternary volcanic fields of the Eifel, Germany, where more than 494 seismic stations were deployed and combined with a 64-km-long DAS cable and permanent stations. A cloud-based, open-source GIS system was implemented to address logistical challenges and ensure data quality combined with seismological analysis and visualisation tools. We present initial results to test the potential of such an extensive waveform database and automated processing for locating small earthquakes and imaging crustal and upper mantle anomalies using techniques such as ambient noise cross-correlation, receiver functions, and SKS splitting.

GITpy is an open-source object-oriented Python software package implementing the well-established Generalized Inversion Technique (GIT), a spectral decomposition approach to isolate the source, propagation, and site contributions from S-phase Fourier amplitude spectra (FAS) (Andrews, 1986; Castro et al., 1990; Boatwright et al., 1991; Drouet et al., 2008; Edwards et al., 2008; Oth et al., 2011; Bindi et al., 2020). GITpy applies a nonparametric (i.e., without imposing any a priori parametric models on the different terms), one-step inversion procedure. GITpy offers the possibility to: (1) simplify the attenuation and source modeling process by providing configuration files and interactive procedures allowing for rapid testing of different models; (2) choose between different levels of attenuation modeling complexity (geometrical spreading and anelastic attenuation); (3) select among different source spectrum modeling options, including the use of a homogeneous or heterogeneous crustal model, as well as the ability to define the frequency range for the model fitting; (4) calculate station-specific apparent source spectra by correcting the input FAS for site amplification and nonparametric attenuation obtained from the inversion, and then fit them. This can be particularly useful for directivity studies. Furthermore, this module can be used independently for a rapid estimation of source parameters in case of a strong event; (5) provide several source parameters including radiated energy, apparent stress, and radiation efficiency alongside seismic moment, corner frequency, and stress drop. Here, we present the versatility of GITpy by applying it to the well-documented 2016–2017 seismic sequence in central Italy, showcasing the software’s capabilities through specific modules for source and attenuation modeling, as well as for calculating apparent source spectra. To achieve this, a comprehensive dataset including 355 stations and 8534 events was assembled, allowing for the evaluation of the software’s performance in handling large-scale datasets.

We estimate the stress drop ∆σ for 551 earthquakes from the 2019 Ridgecrest sequence in Southern California using a spectral decomposition. To assess the impact of propagation model assumptions, we apply a 2D cell-based approach that accounts for lateral attenuation variations and compare results with previous models using distance and depth-dependent attenuation. The 95% confidence interval for azimuthal-dependent attenuation over an 80 km radius is 0.290 at 2 Hz and 0.473 at 14 Hz (log10 units). While the 2D model reveals significant azimuthal variations, the overall ∆σ distribution remains similar to that from a simple distance-dependent model, at least for the analyzed data set. High ∆σ is observed near the M7.1 and M6.4 events, while lower values appear at shallower depths, especially toward the Coso region and near the left-lateral fault junction of the M6.4 sequence. All models consistently identify a high-∆σ region at 4-8 km depth between stations CLC and WRC2, north of the M7.1 hypocenter, where the main fault bends. While spatial comparisons reveal more localized differences, the most pronounced impact arises when the attenuation model incorporates depth dependence.

Extracting source parameters from recorded spectra requires correction for attenuation effects. In consideration of the trade-off between source and propagation effects, various strategies have been proposed to constrain the inverse problem with a priori assumptions. The objective of this study is to assess the impact of constraining the source spectra of reference earthquakes in an attempt to remove a common unknown term from the spectral decomposition results. We perform numerical analyses to simulate the outcomes of the decomposition by generating source spectra for different stress drop versus seismic moment scaling, considering a large population of earthquakes with moment magnitudes between 1.8 and 6.5. Following the strategy of constraining the corner frequency of reference events, we evaluate the error of the retrieved source parameters when the applied constraint shows different levels of discrepancy with respect to the assumption used to generate the synthetics. The numerical tests show that an assumption that differs from the correct one can introduce a magnitude-dependent bias that could alter the scaling of the corner frequency with earthquake size. Furthermore, the source spectral shape for large events is also influenced by the constraints applied to the reference earthquakes. As a consequence, inferences about the self-similarity of the rupture process across the scales, or the selection of the most appropriate source model based on the goodness of the spectral fit, may be strongly biased by the constraint imposed on the reference earthquakes.

Obtaining a size estimate of an earthquake that well represents its potential impact is of primary importance in seismology and earthquake engineering. The magnitude scales currently used in earthquake catalogs, mainly Ms and Mw, are not designed to represent the variability of shaking due to variations in high-frequency radiation. Conversely, ML, which best describes the seismic-wave energy released by an earthquake, is unable to provide an accurate representation of events with magnitudes exceeding approximately 6.5. Therefore, building on and extending the concept of high-frequency magnitude (Atkinson and Hanks, 1995), we propose in this article a new high-frequency magnitude scale m3Hz. This magnitude is estimated by also considering correction factors for the crustal model and site effects. We apply the new magnitude scale m3Hz to two data sets (Central Italy and Japan) and validate its ability to capture high-frequency radiation effects through comparison with available source parameters. Finally, a procedure for estimating m3Hz is also proposed for preinstrumental earthquakes. This procedure addresses a limitation of previous magnitude estimation techniques, such as Me, and is validated for four events for which both macroseismic and instrumental intensity data are available.

We present the dynamic landscape of EPOS Seismology, a Thematic Core Service consortium at the foundation of the European Plate Observing System (EPOS) infrastructure. Cultivated over the past decade through partnerships with prominent pan-European seismological entities, the ORFEUS (Observatories and Research Facilities for European Seismology), EMSC (Euro-Mediterranean Seismological Center), and EFEHR (European Facilities for Earthquake Hazard and Risk), EPOS Seismology stands out as a collaborative governance framework. Facilitating the harmonized interaction between seismological community services, EPOS, and its associated bodies, endeavors to widen the collaboration to include data management, product provision, and the evolution of new seismological services. Within the EPOS Delivery Framework, EPOS Seismology pioneers a diverse array of services, fostering open access to a wealth of seismological data and products while unwaveringly adhering to the FAIR principles and promoting open data and science. These services encompass the archival and dissemination of seismic waveforms of 24,000 seismic stations, access to pertinent station and data quality information, parametric earthquake data spanning recent and historical events, as well as advanced event-specific products such as moment tensors and source models together with reference seismic hazard and risk for the Euro-Mediterranean region. The seismological services are seamlessly integrated into the interoperable centralized EPOS data infrastructure and are openly accessible through established domain-specific platforms and websites. Collaboratively orchestrated by EPOS Seismology and its participating organizations, this integration provides a cohesive framework for the ongoing and future development of these services within the extensive EPOS network. The products and services support the transformative role of seismological research infrastructures, showcasing their pivotal contributions to the evolving narrative of solid Earth science within the broader context of EPOS.

We investigate the dependence of interevent residuals on the choice of source parameters used in ground-motion prediction models calibrated for peak ground acceleration, peak ground velocity, and peak ground displacement. Using a dataset of 877,566 recordings from 1586 earthquakes with magnitudes ranging from 1 to 6.5 in central-southern Italy, we perform multiple mixed-effects regressions, exploring different approaches for the source scaling component. We compare the interevent standard deviation of models based on various source parameters, including moment magnitude, local magnitude, radiated energy, source spectral value at 3 Hz, moment magnitude with stress drop, and moment magnitude with apparent stress. Our results show that combining moment magnitude with either stress drop or apparent stress yields the lowest variability across all peak parameters, as expected. In addition, using local magnitude effectively captures the stress-drop-related component of variability. For the analyzed magnitude range, the source spectral amplitude at 3 Hz performs similarly to local magnitude in this regard, without saturating for large magnitudes. These findings suggest that source parameter choices complementary or alternative to moment magnitude can help reduce interevent variability. However, the suitability of models based on parameters other than moment magnitude depends on the specific application.

We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress-drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude-spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress-drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two-week period of the 2019 Ridgecrest sequence, recorded within a 1 degrees radius. In this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite-fault modeling, ground-motion-based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between-method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress-drop values.

Eruptions at continental basaltic volcanoes can take and combine various forms, including lava lakes, lava flows and fountaining, explosions or structural collapses. Aside from a few well-instrumented cases worldwide, accurately reconstructing eruptive scenarios is hampered by the lack of detailed visual observations. However, volcanoes also have their own acoustic signatures composed of low-pitched inaudible sounds, called infrasounds. Here we analyze infrasound records close to Nyiragongo volcano (D.R. Congo) ( <20 km) up to Kenya ( ~800 km), which are converted using acoustic numerical modeling into time-lapse observations of the catastrophic drainage of the world’s largest lava lake on 22 May 2021. The emitted infrasounds also enable tracking of fissure openings and lava eruptions along the flank, occurring simultaneously with the lava lake drainage. This striking example supports the growing role of infrasound as a key component of volcano monitoring and early-warning systems, as it provides unique information inaccessible to other ground-based instruments. Nyiragongo volcano emits infrasound, a low-pitched, inaudible sound that enables tracking of fissure openings and lava eruptions along the flank, co-occurring with the lava lake drainage, according to an experiment that uses acoustic modeling and source scanning.

For an ω 2 -source model, moment-based estimates of the stress drop are obtained by combining corner frequency and seismic moment source parameters. Therefore, the moment-based estimates of the stress drop are informative about the amount of energy radiated at high frequencies by dynamic rupture processes. This study aims to systematically estimate such stress drop from the harmonized dataset at the European scale and to characterize the distributions of the stress drop for application in future stochastic simulations. We analyze the seismological records associated with shallow crustal seismic events that occurred in Western Europe between January 1990 and May 2020. We processed 220,000 high-quality records and isolated the contributions of the source, site, and path contributions using the Generalized Inversion Technique. The source parameters, including the corner frequency, moment magnitude, and stress drop, of 6135 seismic events are calculated. The events processed are mainly tectonic events (e.g., earthquakes of the central Italy 2009–2016 sequence), although non-tectonic events associated with the Groningen gas field and mining activities in Western Europe are also included in the analysis. The impact of different attenuation models and reference site choices are evaluated. Most of the obtained source spectra follow the standard ω 2 -model except for a few events where the data sampling considered does not allow an effective spectral decomposition. The resulting stress drop shows a positive correlation with moment magnitude between 3 and 4, and a self-similarity for magnitudes greater than 4 with a mean stress drop of 13.8 MPa.

As part of the community stress-drop validation study, we evaluate the uncertainties of seismic moment M0 and corner frequency fc for earthquakes of the 2019 Ridgecrest sequence. Source spectra were obtained in the companion article by applying the spectral decomposition approach with alternative processing and model assumptions. The objective of the present study is twofold: first, to quantify the impact of different assumptions on the source parameters; and second, to use the distribution of values obtained with different assumptions to estimate an epistemic contribution to the uncertainties. Regarding the first objective, we find that the choice of the attenuation model has a strong impact on fc results: by introducing a depth-dependent attenuation model, fc estimates of events shallower than 6 km increase of about 10%. Also, the duration of the window used to compute the Fourier spectra show an impact on fc: the average ratio between the estimates for 20 s duration to those for 5 s decreases from 1.1 for Mw<3 to 0.66 for Mw>4.5. For the second objective, we use a mixed-effect regression to partition the intraevent variability into duration, propagation, and site contributions. The standard deviation ϕ of the intraevent residuals for log(fc) is 0.0635, corresponding to a corner frequency ratio 102ϕ=1.33. When the intraevent variability is compared to uncertainties on log(fc), we observe that 2ϕ is generally larger than the 95% confidence interval of log(fc), suggesting that the uncertainty of the source parameters provided by the fitting procedure might underestimate the model-related (epistemic) uncertainty. Finally, although we observe an increase of log(Δσ) with log(M0) regardless of the model assumptions, the increase of Δσ with depth depends on the assumptions, and no significant trends are detected when depth-dependent attenuation and velocity values are considered.

As part of the community stress-drop validation study initiative, we apply a spectral decomposition approach to isolate the source spectra of 556 events occurred during the 2019 Ridgecrest sequence (Southern California). We perform multiple decompositions by introducing alternative choices for some processing and model assumptions, namely: three different S-wave window durations (i.e., 5 s, 20 s, and variable between 5 and 20 s); two attenuation models that account differently for depth dependencies; and two different site amplification constraints applied to restore uniqueness of the solution. Seismic moment and corner frequency are estimated for the Brune and Boatwright source models, and an extensive archive including source spectra, site amplifications, attenuation models, and tables with source parameters is disseminated as the main product of the present study. We also compare different approaches to measure the precision of the parameters expressed in terms of 95% confidence intervals (CIs). The CIs estimated from the asymptotic standard errors and from Monte Carlo resampling of the residual distribution show an almost one-to-one correspondence; the approach based on model selection by setting a threshold for misfit chosen with an F-ratio test is conservative compared to the approach based on the asymptotic standard errors. The uncertainty analysis is completed in the companion article in which the outcomes from this work are used to compare epistemic uncertainty with precision of the source parameters.

Abstract During eruptions, volcanoes produce air‐pressure waves inaudible for the human ear called infrasound, which are very helpful for detecting early signs of magma at the surface. Compared to violent ash‐rich explosions, recording more discrete atmospheric disturbances from effusive eruptions remains a practical challenge depending on the distance to the source. At Nyiragongo volcano (D.R. Congo), towering above a 1‐million urban area, we analyzed local infrasonic records between January 2018 and April 2022. An acoustic signature from this open‐vent volcano is detected up to the volcano observatory facilities in Goma city center about 17 km from its crater. We compared infrasound signals with space‐based observations of the intra‐crater activity (SO2 emissions, thermal anomalies, crater depth/radius). We thus obtain a comprehensive picture of Nyiragongo's eruptive activity during this period, encompassing the drainage of its lava lake during its third known flank eruption on 22 May 2021.

Earthquake early warning (EEW) systems can serve as a viable solution to protect specific hazard‐prone targets (major cities or critical infrastructure) against harmful seismic events. Using the example of the Lower Rhine Embayment (western Germany), we present a novel approach for evaluating and optimizing seismic networks for EEW purposes. The network optimization is applied to simulated earthquake scenarios from a hazard-compatible stochastic catalog, which represents a realization of the seismicity in the target area over a given period of time. We propose a densification of the existing network in the area by pre-selecting a number of potential sites with an optimal station configuration using a microgenetic algorithm, minimizing an appropriate cost function associated with the network layout. We show that the new decentralized network significantly improves the warning time and the accuracy of the warnings for levels of shaking for threshold levels of at least 0.02 g. Although the accuracy of the alerts for other cities outside the target area varies depending on their location, we demonstrate that the updated network layout will also improve the warning times for neighboring cities.

Volcano monitoring requires simple techniques to rapidly identify the cause of volcanic unrest. The so-called RSAM (real-time seismic amplitude measurements) technique, used in many observatories, is a good example of extracting information from seismograms with minimal processing. Built on a similar principle, the more recent seismic amplitude ratio analysis (SARA) technique allows locating migrating seismicity at high frequency (> 2 Hz, e.g., due to dike intrusions) under certain assumptions. However, such analysis generally requires a dense distribution of stations close to the seismic sources (depending on the magnitude) and/or station sites undisturbed by human activity. In a more straightforward and qualitative approach, computing amplitude ratios between station pairs can also allow for the detection of temporal and (2D) spatial changes of volcanic activity. In this work, we adopt such a simplified approach of SARA in order to characterize seismic tremors originating from two open-vent neighboring volcanoes, Nyiragongo and Nyamulagira, in the Virunga Volcanic Province (VVP) in the Democratic Republic of the Congo (DRC). In contrast with previous studies, we focus here on the low-frequency band (0.3–1 Hz), free from anthropogenic noise and sensitive to shallow volcanic tremors linked to intermittent or permanent intra-crater eruptive activity recorded through the large-aperture local network. We apply for the first time the SARA methodology for volcanic sources predominantly generating surface waves and propagating over long distances. The analysis is performed on more than two years of continuous seismic data. Seismic amplitude analysis in this frequency band is strongly influenced by the short-period microseisms originating from nearby Lake Kivu. Despite this diurnal to seasonal amplitude variability, SARA successfully detects continuous volcanic tremor activity and its arrest at both volcanoes. In light of these findings, we discuss the applicability of the method to the continuous, real-time detection, and characterization of long-period shallow volcanic tremor sources in this region.