3. Producción

Browse

Settings

Author: search.filters.author.Philippe Lesage

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    The 2013–2020 Seismic Activity at Sabancaya Volcano (Peru): Long-Lasting Unrest and Eruption
    (Elsevier B.V., 2023-03-01)
    Sabancaya volcano is the youngest and second most active volcano in Peru. It is part of the Ampato-Sabancaya volcanic complex which sits to the south of the ancient Hualca Hualca volcano and several frequently active faults, thus resulting in complex volcano-tectonic interactions. After 15 years of repose, in 2013, a series of 4 earthquakes with magnitude >4.5 occurred within 24 h, marking the beginning of a new episode of unrest. Several additional swarms of earthquakes occurred in the following years until magmatic eruptive activity started on 6 November 2016. This activity is ongoing as of this writing, with an average of 50 explosions per day. In this study, we present results of multiparametric monitoring of Sabancaya's activity observed during 2013–2020. Seismic data are used to create a one-dimensional seismic velocity model, to catalog, locate, and characterize earthquakes, to detect repeating earthquake families, and to monitor seismic velocity variations by ambient noise cross-correlation. These analyses are complemented by visual and remote sensing observations and ground deformation measurements. All monitored parameters showed significant changes on 6 November 2016, the day of eruption onset, thus dividing the eruptive activity into pre-eruptive and eruptive stages. The unrest is characterized by high levels of seismic activity with hundreds of events detected per day. Volcano-tectonic (VT) earthquakes were dominant during the pre-eruptive period while long-period (LP) events and explosions have been most numerous since the eruption onset. Earthquake locations highlight long-lasting seismogenic zones along multiple previously active regional faults, as well as along newly identified faults. This VT seismicity is mainly distributed in a sector from the northwest to the east of the volcanic complex at distances of up to 30 km from the crater. We focus our analysis on two eruptive episodes: the eruption onset and subsequent crater migration from south to north, and the increase of lava dome extrusion rate in 2019. Both episodes are accompanied by seismic velocity decreases of up to 0.2% and are preceded by a few weeks by bursts of distal VT activity, including numerous repeating earthquakes. These repeated events were located on several remote tectonic faults (5–25 km from the vent). We suggest that these phenomena could be due to the injection of a batch of magma in the deep reservoir and/or conduit, which would generate 1) a pressure wave propagating in the hydrothermal system, triggering the bursts of seismic activity and 2) slow rising of magma by melting old material filling the conduit that eventually produced the eruptive and dome growth acceleration events.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Deep Learning and Machine Learning Applied to the Detection and Classification of Volcano-Seismic Events at Piton de la Fournaise Volcano
    (Birkhäuser, 2025-10-01)
    The Piton de la Fournaise volcano (PdlF) on the island of La Réunion is one of the most active and best monitored volcanoes in the world. Its frequent eruptions make it a natural laboratory for developing new methods and evaluating their performance over multiple eruption sequences. In this work, we present a Deep Learning (DL) model for volcanic earthquake detection and two models for classification based on DL and Machine Learning (ML) algorithms. The detection model is based on encoder–decoder layers that extract high-order features in the time domain that are hidden in the seismograms. The first classification model consists of a simple convolutional neural network that uses the short-time Fourier transform of the signals as input data. A second classifier is based on ML approach and uses hand-crafted features. We show that our detection model, trained on ~ 7 000 volcano-seismic events recorded at PdlF between 2014 and 2021, outperforms previous DL-based models in detecting volcano-seismic events, achieving an accuracy of 98.15% on the testing dataset. Seven classes of signals are considered for classification models: volcano-tectonic (VT) events, rockfall, long-period events, volcanic tremors, tectonic events, anthropogenic noise and environmental noise. Both tested classification models achieve an accuracy of 96.55% in the testing dataset. By applying these models to the continuous data recorded at PdlF in 2019, we are able to detect and classify 1.5 times more VT events than the catalog provided by the Observatory. The detection model takes 28 s to process 24 h seismograms and from a few to a maximum of 70 s for classification.
    Scopus© Citations 1  3