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TitleThe dynamics of large silicic systems from satellite remote sensing observations: the intriguing case of Domuyo volcano, Argentina
AuthorLundgren, PORCID logo; Girona, T; Bato, M G; Realmuto, V J; Samsonov, SORCID logo; Cardona, C; Franco, LORCID logo; Gurrola, E; Aivazis, M
SourceScientific Reports vol. 10, issue 1, 11642, 2020 p. 1-15, Open Access logo Open Access
Alt SeriesNatural Resources Canada, Contribution Series 20200297
PublisherNature Research
Mediapaper; on-line; digital
File formatpdf; html
Lat/Long WENS -72.0000 -69.0000 -35.0000 -38.0000
Subjectstectonics; geophysics; Nature and Environment; Science and Technology; Health and Safety; magmatism; volcanism; volcanoes; remote sensing; satellite imagery; radar methods; infrared surveys; thermal imagery; thermal analyses; tectonic evolution; deformation; modelling; Domuyo Volcano; Methodology
Illustrationslocation maps; geoscientific sketch maps; satellite images; time series; profiles; schematic models
Released2020 07 15
AbstractSilicic magmatic systems are the most dangerous volcanoes on Earth, capable of large and catastrophic eruptions, yet their low eruptive frequency makes it challenging to interpret their short-term unrest. Here we present a decade-plus analysis that integrates, for the first time, time series of satellite interferometric synthetic aperture radar (InSAR) surface deformation and satellite thermal infrared edifice-scale surface warming at a large silicic system: Domuyo volcano, in Argentina. We find that deformation and warming are highly correlated, and depending on the sign and lag between the time series, either shallow sealing or magma influx could drive Domuyo's ongoing inflation (~ 0.15 m/year; from an InSAR-derived tabular source, ~ 11 × 8 × 1 km; ~ 6.5 km depth; ~ 0.037 km3/year volume-change rate) and warming (0.3-0.4 °C/year). This study shows the potential that combined satellite surface deformation and edifice-scale surface warming time series have on assessing the physical mechanisms of silicic volcanic systems and for constraining deterministic models.

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