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

TL;DR

This study employed deep learning to significantly improve earthquake detection during the 2025 Santorini seismic crisis, enabling detailed analysis of volcanic activity and fluid-driven processes in near real-time.

Contribution

The paper introduces a deep learning workflow that enhanced earthquake detection from 4,000 to 80,000 events, revealing new insights into the volcanic-tectonic activity during the crisis.

Findings

Enhanced earthquake catalog enabled detailed seismicity analysis.

Identified burst-like seismic swarms associated with fluid-driven processes.

Detected a deep magmatic reservoir beneath Anydros Islet.

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 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.