Grazia De Landro

We present a large seismological dataset (https://geofon.gfz.de/doi/network/ZK/2021, last access: 14 October 2026) composed of continuous recordings from 200 temporary stations (seismic network ZK, https://doi.org/10.14470/MX7576871994, Bindi et al., 2021), which operated continuously for approximately one year in the Southern Apennines (Southern Italy) as part of the DETECT experiment. The dataset is compliant with the seismological standards for the archiving of data and metadata, and with standardized tools for disseminating them, e.g., the International Federation of Digital Seismograph Networks (FDSN) web services. The data set was collected during the DETECT experiment in the Irpinia area (southern Italy), which is one of the regions with the highest seismic hazard in Italy. From August 2021 to August 2022, a constellation of 20 seismic arrays, with a total of 200 seismic stations, was installed above the fault segments responsible for the M _w 6.8, 1980 Irpinia earthquake, the strongest and most destructive seismic event in Italy in the last century. Each seismic array had a maximum aperture of ∼2 km and was composed of one broad-band sensor, one short- period sensor with 1 Hz and eight 4.5 Hz natural frequency geophones. Data and metadata were managed in accordance with the GIPP/GEOFON policy using SeisComP (Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences and GEMPA GmbH, 2008), and the final archive size is approximately 5.2 TB. In this contribution, we provide details on how to download waveform data and station metadata, as well as information on data availability and quality.

The characterization of seismic sequences provides key constraints on fault geometry, rupture processes, and seismic hazard. In this study we investigate seismicity recorded between 2015 and 2024 in the south-eastern Alps and Western Dinarides (SEAWD), one of the most tectonically complex regions of the Alpine–Adriatic domain. Starting from the bulletin seismic catalogue of the Northeastern Italy Seismometer Network, we implement a multiscale relocation workflow. The catalogue is first relocated at regional scale using a multi-iterative NonLinLoc Source-Specific Station Term (NLL-SSST) approach, progressively refining residual grids to account for velocity-model uncertainties. Seismic sequences are then identified through nearest-neighbour clustering. Selected sequences are finally relocated at local scale using waveform-coherence–based NonLinLoc (NLL-coherence), enforcing spatial consistency among similar events. This workflow yields locations with ~hundreds-of-meters resolution and resolves fine-scale fault structures. We identify 71 sequences, mainly foreshock–mainshock–aftershock and mainshock–aftershock types, with limited swarm-like activity. Principal component analysis quantifies sequence volume and orientation of seismicity distributions. Most clusters define tabular structures: larger volumes correspond to planar fault zones, whereas compact clusters involve low-magnitude and low seismic activity. These patterns highlight the structural complexity of the Alpine–Dinaric transition zone and provide new constraints on active seismogenic structures and seismic hazard in northeastern Italy and adjacent regions.

The Southern Apennines in Italy represent a geologically complex and seismically active region shaped by the ongoing convergence between the African and Eurasian plates. In this study, we present a high-resolution 3D crustal model of the Southern Apennines, parameterized on a 5 × 5 × 1 km³ grid and constructed through the systematic integration of geological, geophysical, and petrophysical data. The considered dataset includes laboratory velocity measurements (vp) under different pressure and temperature conditions, stratigraphic well logs, seismic tomography (vp), and geophysical models from inversion of gravimetric and magnetic data. Pressure- and temperature-dependent corrections were applied to P-wave velocities derived from regional seismic tomography, enabling us to delineate key intra-crustal boundaries, including the top of the Mesozoic limestones, the base of the Mesozoic sedimentary succession, the onset of the felsic crystalline basement, and the deeper transition to mafic, gabbroic lower-crustal units, within a 3D volume extending down to the Moho discontinuity. This modelling strategy ensures full internal consistency across spatial scales and tectonic domains, and is validated using independent borehole data, receiver functions, and other geophysical interpretations. The resulting 3D volume offers a robust framework for investigating crustal architecture and supports applications in geophysical modeling and seismic hazard assessments. All datasets are openly accessible under FAIR principles, promoting transparency, reproducibility, and interdisciplinary reuse of the results.

We evaluate the performance of two real-time earthquake location algorithms, RTLoc and MParLoc, implemented in the Earthquake Early Warning System (EEWS) QuakeUp. Specifically, we assess whether the recently developed MParLoc algorithm—which incorporates P-wave arrival times, P-peak amplitude ratios, and back-azimuth estimates—improves earthquake localization accuracy in the Ibero-Maghrebian Region (IMR). Analyzing M_w ≥ 4 earthquakes recorded in the IMR between 2011 and 2026, we find that MParLoc generally outperforms RTLoc. It yields hypocentral locations significantly closer to reference solutions, particularly during the earliest stages after earthquake detection, when only a few stations and short P-wave windows are available. Although both methods converge as more P-wave picks accumulate, MParLoc’s early-stage precision is critical. Hypocentral accuracy strongly influences the distance term in the ground motion prediction equations used to generate real-time shakemaps, which define the spatial distribution of expected ground shaking and delineate Potential Damage Zones (PDZs). The results indicate that implementing MParLoc in PDZ-based EEWS like QuakeUp would significantly improve warning performance in the IMR, especially during the initial stages of an earthquake, by reducing the time to first alert, increasing lead-time for emergency safety actions, and providing valuable scientific and operational information for emergency response.

We evaluate the performance of two real-time earthquake location algorithms, RTLoc and MParLoc, implemented in the Earthquake Early Warning System (EEWS) QuakeUp. Specifically, we assess whether the recently developed MParLoc algorithm—which incorporates P-wave arrival times, P-peak amplitude ratios, and back-azimuth estimates—improves earthquake localization accuracy in the Ibero-Maghrebian Region (IMR). Analyzing Mw ≥ 4 earthquakes recorded in the IMR between 2011 and 2026, we find that MParLoc generally outperforms RTLoc. It yields hypocentral locations significantly closer to reference solutions, particularly during the earliest stages after earthquake detection, when only a few stations and short P-wave windows are available. Although both methods converge as more P-wave picks accumulate, MParLoc’s early-stage precision is critical. Hypocentral accuracy strongly influences the distance term in the ground motion prediction equations used to generate real-time shakemaps, which define the spatial distribution of expected ground shaking and delineate Potential Damage Zones (PDZs). The results indicate that implementing MParLoc in PDZ-based EEWS like QuakeUp would significantly improve warning performance in the IMR, especially during the initial stages of an earthquake, by reducing the time to first alert, increasing lead-time for emergency safety actions, and providing valuable scientific and operational information for emergency response.

We present a large seismological dataset (https://geofon.gfz.de/doi/network/ZK/2021) composed by continuous recordings from 200 temporary stations (seismic network ZK, 10.14470/MX7576871994), which operated continuously for approximately one year in the Southern Apennines (Southern Italy) as part of the DETECT experiment. The dataset is compliant with the seismological standards for the archiving of data and metadata, and with standardized tools for disseminating them, e.g., the International Federation of Digital Seismograph Networks (FDSN) web services. The data set was collected during the DETECT experiment in the Irpinia area (southern Italy), which is one of the regions with the highest seismic hazard in Italy. From August 2021 to August 2022, a constellation of 20 seismic arrays, with a total of 200 seismic stations, was installed above the fault segments responsible for the M 6.9, 1980 Irpinia earthquake, the strongest and most destructive seismic event in Italy in the last fifty years. Each seismic array had a maximum aperture of ~2 km and was composed of one broad-band sensor, one short- period sensor with 1 Hz and eight 4.5 Hz natural frequency geophones. Data and metadata were managed in accordance with the GIPP/GEOFON policy using SeisComP (Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences and GEMPA GmbH, 2008) and the final archive size is approximately 5.2 TB. In this contribution, we provide details on how to download waveform data and station metadata, as well as information on data availability and quality.

The study of the seismicity distribution in space and time is a key element for assessing seismic hazard, as earthquake occurrence is controlled by variations in crustal stress and fault loading. The identification and characterization of seismic sequences therefore represent an effective approach to investigate earthquake interaction and the activation of complex fault systems.We analyze ten years of seismicity (2015–2024) in the South-Eastern Alps and the Western Dinarides (SEAWD), a tectonically active region characterized by moderate to high seismic hazard and by the occurrence of large historical earthquakes, including the Mw 6.5 Friuli event of 1976. The study is based on the seismic catalog of the Northeastern Italy Seismometer Network, managed by the Seismological Research Center (CRS) of the National Institute of Oceanography and Experimental Geophysics – OGS.Seismic sequences are first detected using the Zaliapin and Ben-Zion nearest-neighbor clustering technique and subsequently analyzed with the NLL-SSST Coherence algorithm, which allows a detailed reconstruction of their spatio-temporal evolution and source coherence. A total of 75 seismic sequences are identified and classified: 49 Foreshock-Mainshock-Aftershock sequences (65%), 25 Mainshock-Aftershock sequences (32%), and one swarm-type sequence. The preliminary results indicate that Mainshock-Aftershock sequences are, on average, associated with larger mainshock magnitudes compared to Foreshock-Mainshock-Aftershock sequences.Further analyses are currently underway to refine the characterization of the detected sequences and to explore their implications for fault interaction processes.