Stumpp Douglas Sami, Cabrera-Pérez Iván, Savard Geneviève, Ricci Tullio, Palano Mimmo, Alparone Salvatore, Ursino Andrea, Sparacino Federica, Finizola Anthony, Muñoz Burbano Francisco, Reyes Hardy María-Paz, Ruch Joël, Bonadonna Costanza, Lupi Matteo
Department of Earth Sciences, University of Geneva, Geneva, Switzerland.
Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma 1, Italy.
Nat Commun. 2025 Aug 27;16(1):7687. doi: 10.1038/s41467-025-62846-z.
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.
火山活动不稳定期间，火山风险会显著升级。2021年末，意大利的武尔卡诺岛进入了一个不稳定阶段，其特征是出现了极长周期地震事件，这些事件被认为是岩浆和气体在火山管道系统中流动的标志。在此，我们展示了神经网络节点环境噪声层析成像如何生成一个高分辨率剪切波速度模型，以研究武尔卡诺岛不稳定的成因。我们使用一个深度学习模型，从在不稳定初期阶段部署的密集节点地震网络中获取地震频散数据。反演得到的三维模型揭示了一个处于不稳定状态的火山系统浅层部分的高分辨率层析成像。如果在不稳定期间（近）实时部署并快速处理，神经网络节点环境噪声层析成像可以促成动态且适应性强的疏散计划。这些进展将有助于在火山地区制定更有效的、基于源头的风险缓解方案， potentially saving lives.（原文最后这个短语有误，正确应该是potentially saving lives，意思是“有可能挽救生命”）
