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Mechanical tomography of a volcano plumbing system from GNSS unsupervised modeling
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  • François Beauducel,
  • Aline Peltier,
  • Antoine Villié,
  • Wiwit Suryanto
François Beauducel
Institut de Physique du Globe de Paris - Université de Paris

Corresponding Author:beauducel@ipgp.fr

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Aline Peltier
Institut De Physique Du Globe De Paris
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Antoine Villié
Laboratoire de biométrie et biologie évolutive, CNRS, France
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Wiwit Suryanto
Geophysics Research Group, Universitas Gadjah Mada, Indonesia
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Identification of internal structures in an active volcano is mandatory to quantify the physical processes preceding eruptions. We propose a fully unsupervised Bayesian inversion method that uses the point compound dislocation model as a complex source of deformation, to dynamically identify the substructures activated during magma migration. We applied this method at Piton de la Fournaise. Using 7-day moving trends of GNSS data preceding the June 2014 eruption, we compute a total of 15 inversion models of 2.5 million forward problems each, without a priori information. Obtained source shapes (dikes, prolate ellipsoids or pipes) exhibit a global migration from 7-8 km depth to the surface, drawing a “mechanical tomography”? of the plumbing system. Our results allow retrieving geometries compatible with observed eruptive fissures and seismicity distribution, and the retrieved source volume variations made this method a good proxy to anticipate erupted lava in case of no co-eruptive refilling.
16 Sep 2020Published in Geophysical Research Letters volume 47 issue 17. 10.1029/2020GL089419