Neural Network Nodal Ambient Noise Tomography of a transient plumbing system under unrest, Vulcano, Italy

Douglas Sami Stumpp (), Iván Cabrera-Pérez, Geneviève Savard, Tullio Ricci, Mimmo Palano, Salvatore Alparone, Andrea Ursino, Federica Sparacino, Anthony Finizola, Francisco Muñoz Burbano, María-Paz Reyes Hardy, Joël Ruch, Costanza Bonadonna and Matteo Lupi ()
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Douglas Sami Stumpp: University of Geneva
Iván Cabrera-Pérez: University of Geneva
Geneviève Savard: University of Geneva
Tullio Ricci: Istituto Nazionale di Geofisica e Vulcanologia
Mimmo Palano: Università degli Studi di Palermo
Salvatore Alparone: Osservatorio Etneo
Andrea Ursino: Osservatorio Etneo
Federica Sparacino: Osservatorio Etneo
Anthony Finizola: Laboratoire GéoSciences Réunion
Francisco Muñoz Burbano: University of Geneva
María-Paz Reyes Hardy: University of Geneva
Joël Ruch: University of Geneva
Costanza Bonadonna: University of Geneva
Matteo Lupi: University of Geneva

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract Volcanic risk escalates significantly during unrest. In late 2021, the Italian island of Vulcano entered into a phase of unrest featuring Very Long Period seismic events, which are considered to be markers of magma and gas flowing across the volcanic plumbing system. Here we show how Neural Network Nodal Ambient Noise Tomography generates a high-resolution shear-wave velocity model for investigating the causative drivers of Vulcano’s unrest. Using a deep learning model we harvest seismic dispersion data from a dense nodal seismic network deployed during the early unrest’s phase. The inverted 3-D model reveals a high-resolution tomography of the shallow part of a volcanic system in unrest. If deployed and rapidly processed in (near) real-time during periods of unrest, Neural Network Nodal Ambient Noise Tomography can lead to dynamic and adaptive evacuation plans. Such advances would contribute to more effective, source-dependent risk mitigation schemes in volcanic regions, potentially saving lives.

Date: 2025
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DOI: 10.1038/s41467-025-62846-z

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