Ren C X, Peltier A, Ferrazzini V, Rouet-Leduc B, Johnson P A, Brenguier F
Space Data Science and Systems Group Los Alamos National Laboratory Los Alamos NM USA.
Geophysics Group Los Alamos National Laboratory Los Alamos NM USA.
Geophys Res Lett. 2020 Feb 16;47(3):e2019GL085523. doi: 10.1029/2019GL085523. Epub 2020 Feb 7.
Volcanic tremor is key to our understanding of active magmatic systems, but due to its complexity, there is still a debate concerning its origins and how it can be used to characterize eruptive dynamics. In this study we leverage machine learning techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La Réunion island) to describe specific patterns of seismic signals recorded during eruptions. These results unveil what we interpret as signals associated with various eruptive dynamics of the volcano, including the effusion of a large volume of lava during the August-October 2015 eruption as well as the closing of the eruptive vent during the September-November 2018 eruption. The machine learning workflow we describe can easily be applied to other active volcanoes, potentially leading to an enhanced understanding of the temporal and spatial evolution of volcanic eruptions.
火山震颤是我们理解活跃岩浆系统的关键，但由于其复杂性，关于其起源以及如何用于表征喷发动力学仍存在争议。在本研究中，我们利用机器学习技术，使用来自富尔奈斯火山（留尼汪岛）6年的连续地震数据，来描述喷发期间记录的地震信号的特定模式。这些结果揭示了我们所认为的与该火山各种喷发动力学相关的信号，包括2015年8月至10月喷发期间大量熔岩的涌出，以及2018年9月至11月喷发期间喷发口的关闭。我们所描述的机器学习工作流程可以很容易地应用于其他活火山，有可能增进对火山喷发时空演化的理解。
