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Enhanced Seismicity Monitoring in the Rapid Scientific Response to the 2025 Santorini Crisis
Authors:
Margarita Segou,
Foteini Dervisi,
Xing Tan,
Rajat Choudhary,
Patricia Martínez-Garzón,
Francesco Scotto di Uccio,
Gregory Beroza,
Genny Giacomuzzi,
Claudio Chiarabba,
Wayne Shelley,
Stephanie Prejean,
Jeremy Pesicek,
John J. Wellik,
Marco Bohnhoff,
David Pyle,
Costas Synolakis,
Tom Parsons,
Athanassios Ganas,
William Ellsworth,
Brian Baptie,
Gaetano Festa,
Piero Poli,
Warner Marzocchi
Abstract:
We used a deep learning workflow to enhance earthquake detection during the 2025 seismic unrest between Santorini and Amorgos islands to track the evolution of the crisis in near real-time. We analysed the continuous seismic waveforms daily (1/2 - 3/3/25) as the crisis unfolded. Our analysis enhanced the earthquake catalogue from around 4,000 to 80,000 earthquakes. The enhanced catalogue allowed t…
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We used a deep learning workflow to enhance earthquake detection during the 2025 seismic unrest between Santorini and Amorgos islands to track the evolution of the crisis in near real-time. We analysed the continuous seismic waveforms daily (1/2 - 3/3/25) as the crisis unfolded. Our analysis enhanced the earthquake catalogue from around 4,000 to 80,000 earthquakes. The enhanced catalogue allowed this international expert group to identify the volcanic-tectonic character, clearly revealing burst-like, spasmodic seismicity swarms, which is a pattern associated with fluid-driven processes from early stages of the crisis. Detailed moment tensor inversions in early events characterised by a significant non-double couple component indicated the involvement of magmatic or high-pressure hydrothermal fluids driving the unrest. Concurrent DL-enhanced tomography efforts identified a third, deep magmatic reservoir beneath Anydros Islet, consistent with pressure-driven processes. To date, volcanic-tectonic swarms in which >200 earthquakes of ML > 4 occurred within only a few weeks, largely within episodic bursts of seismicity, have not been observed elsewhere.
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Submitted 11 March, 2026;
originally announced March 2026.
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Earthquakes and cluster dynamics during Interseismic phases between the Northern and Central Apennines (Italy)
Authors:
Marion Baques,
Piero Poli,
Michele Fondriest
Abstract:
In the last thirty years, the Northern and Central Apennines (Italy) have been affected by three main destructive seismic sequences: the 1997 Colfiorito (three events $M_L > 5.5$), the 2009 L'Aquila (one event $M_L > 5.5$), and the 2016--2017 Amatrice--Visso--Norcia (three events $M_L > 5.5$). Several studies have analysed the spatio-temporal evolution and processes driving each sequence, focusing…
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In the last thirty years, the Northern and Central Apennines (Italy) have been affected by three main destructive seismic sequences: the 1997 Colfiorito (three events $M_L > 5.5$), the 2009 L'Aquila (one event $M_L > 5.5$), and the 2016--2017 Amatrice--Visso--Norcia (three events $M_L > 5.5$). Several studies have analysed the spatio-temporal evolution and processes driving each sequence, focusing mainly on the foreshock--mainshock--aftershock periods. Here, we focus on the 2018--2024 interseismic phase, aiming to unravel the long-term seismogenic behaviour of this region. We first relocated the earthquake catalogue and identified clusters through a declustering algorithm. During this phase, background seismicity and most clusters were arranged in a 2--3 km thick low-angle layer. We found that (i) most clusters were driven by aseismic processes, (ii) the depth of both clusters and the seismicity layer increased toward the southeast, (iii) the volume of clusters decreased toward the southeast, and (iv) the low-angle layer almost disappeared in the L'Aquila area. Comparing two interseismic phases (2011--2016 and 2018--2024), we found striking similarities, with events occurring at the same sites and characterized by both foreshock--mainshock--aftershock and swarm-like behaviour. In addition, the L'Aquila area was seismically more silent compared to the northern sites during both interseismic phases. We propose that these different long-term seismogenetic behaviours reflect variations in the structure and rheology of the upper crust from the Northern to the Central Apennines. This highlights the important role of structural inheritance in controlling how active deformation affects the interseismic period.
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Submitted 8 January, 2026;
originally announced January 2026.
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Deep learning detects uncataloged low-frequency earthquakes across regions
Authors:
Jannes Münchmeyer,
Sophie Giffard-Roisin,
Marielle Malfante,
William Frank,
Piero Poli,
David Marsan,
Anne Socquet
Abstract:
Documenting the interplay between slow deformation and seismic ruptures is essential to understand the physics of earthquakes nucleation. However, slow deformation is often difficult to detect and characterize. The most pervasive seismic markers of slow slip are low-frequency earthquakes (LFEs) that allow resolving deformations at minute-scale. Detecting LFEs is hard, due to their emergent onsets…
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Documenting the interplay between slow deformation and seismic ruptures is essential to understand the physics of earthquakes nucleation. However, slow deformation is often difficult to detect and characterize. The most pervasive seismic markers of slow slip are low-frequency earthquakes (LFEs) that allow resolving deformations at minute-scale. Detecting LFEs is hard, due to their emergent onsets and low signal-to-noise ratios, usually requiring region-specific template matching approaches. These approaches suffer from low flexibility and might miss LFEs as they are constrained to sources identified a priori. Here, we develop a deep learning-based workflow for LFE detection, modeled after classical earthquake detection with phase picking, phase association, and location. Across three regions with known LFE activity, we detect LFEs from both previously cataloged sources and newly identified sources. Furthermore, the approach is transferable across regions, enabling systematic studies of LFEs in regions without known LFE activity.
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Submitted 23 November, 2023;
originally announced November 2023.
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GIGJ: a crustal gravity model of the Guangdong Province for predicting the geoneutrino signal at the JUNO experiment
Authors:
M. Reguzzoni,
L. Rossi,
M. Baldoncini,
I. Callegari,
P. Poli,
D. Sampietro,
V. Strati,
F. Mantovani,
G. Andronico,
V. Antonelli,
M. Bellato,
E. Bernieri,
A. Brigatti,
R. Brugnera,
A. Budano,
M. Buscemi,
S. Bussino,
R. Caruso,
D. Chiesa,
D. Corti,
F. Dal Corso,
X. F. Ding,
S. Dusini,
A. Fabbri,
G. Fiorentini
, et al. (44 additional authors not shown)
Abstract:
Gravimetric methods are expected to play a decisive role in geophysical modeling of the regional crustal structure applied to geoneutrino studies. GIGJ (GOCE Inversion for Geoneutrinos at JUNO) is a 3D numerical model constituted by ~46 x 10$^{3}$ voxels of 50 x 50 x 0.1 km, built by inverting gravimetric data over the 6° x 4° area centered at the Jiangmen Underground Neutrino Observatory (JUNO) e…
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Gravimetric methods are expected to play a decisive role in geophysical modeling of the regional crustal structure applied to geoneutrino studies. GIGJ (GOCE Inversion for Geoneutrinos at JUNO) is a 3D numerical model constituted by ~46 x 10$^{3}$ voxels of 50 x 50 x 0.1 km, built by inverting gravimetric data over the 6° x 4° area centered at the Jiangmen Underground Neutrino Observatory (JUNO) experiment, currently under construction in the Guangdong Province (China). The a-priori modeling is based on the adoption of deep seismic sounding profiles, receiver functions, teleseismic P-wave velocity models and Moho depth maps, according to their own accuracy and spatial resolution. The inversion method allowed for integrating GOCE data with the a-priori information and regularization conditions through a Bayesian approach and a stochastic optimization. GIGJ fits the homogeneously distributed GOCE gravity data, characterized by high accuracy, with a ~1 mGal standard deviation of the residuals, compatible with the observation accuracy. Conversely to existing global models, GIGJ provides a site-specific subdivision of the crustal layers masses which uncertainties include estimation errors, associated to the gravimetric solution, and systematic uncertainties, related to the adoption of a fixed sedimentary layer. A consequence of this local rearrangement of the crustal layer thicknesses is a ~21% reduction and a ~24% increase of the middle and lower crust expected geoneutrino signal, respectively. Finally, the geophysical uncertainties of geoneutrino signals at JUNO produced by unitary uranium and thorium abundances distributed in the upper, middle and lower crust are reduced by 77%, 55% and 78%, respectively. The numerical model is available at http://www.fe.infn.it/u/radioactivity/GIGJ
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Submitted 7 January, 2019;
originally announced January 2019.
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Cooperative robustness to dephasing: single-exciton Superradiance in a nanoscale ring to model natural light-harvesting systems
Authors:
G. L. Celardo,
P. Poli,
L. Lussardi,
F. Borgonovi
Abstract:
We analyze a 1-d ring structure composed of many two-levels systems, in the limit where only one excitation is present. The two-levels systems are coupled to a common environment, where the excitation can be lost, which induces super and subradiant behavior. Moreover, each two-levels system is coupled to another independent environment, modeled by a classical white noise, simulating a dephasing ba…
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We analyze a 1-d ring structure composed of many two-levels systems, in the limit where only one excitation is present. The two-levels systems are coupled to a common environment, where the excitation can be lost, which induces super and subradiant behavior. Moreover, each two-levels system is coupled to another independent environment, modeled by a classical white noise, simulating a dephasing bath and described by the Haken-Strobl master equation. Single exciton Superradiance, an example of cooperative quantum coherent effect, is destroyed at a critical dephasing strength proportional to the system size, showing robustness of cooperativity to the action of the dephasing environment. We also show that the coupling to a common decay channel contrasts the action of dephasing, driving the entanglement decay to slow down on increasing the system size. Moreover, after a projective measurement which finds the excitation in the system, the entanglement reaches a stationary value, independent of the initial conditions.
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Submitted 20 August, 2014; v1 submitted 22 March, 2014;
originally announced March 2014.