arXiv subjects

Piero Poli

Publications and source records attributed to Piero Poli.

3 recordsLinked to original sources

arXiv2026

Enhanced Seismicity Monitoring in the Rapid Scientific Response to the 2025 Santorini Crisis

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.

physics.geo-ph↗

arXiv2026

Earthquakes and cluster dynamics during Interseismic phases between the Northern and Central Apennines (Italy)

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.

physics.geo-ph↗

arXiv2023

Deep learning detects uncataloged low-frequency earthquakes across regions

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.

physics.geo-ph↗