2020
DOI: 10.1029/2020gl089419
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Mechanical Imaging of a Volcano Plumbing System From GNSS Unsupervised Modeling

Abstract: 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 Global Navigation Satellite System (GNSS) data preceding the June 2014 eruption, we compute … Show more

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Cited by 22 publications
(18 citation statements)
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References 28 publications
(43 reference statements)
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“…2c). This small amount of deformation can be modeled in real time using an unsupervised inverse problem and a compound dislocation model source (Nikkhoo et al, 2017;Beauducel, Peltier, et al, 2020). On 1 April, the pre-eruptive inflation was interpreted as being due to pressurization of a single source: an ellipsoid with a volume increase of 450;000 50;000 m 3 located at about 1 km below the Dolomieu crater (Fig.…”
Section: Eruption Precursorsmentioning
confidence: 99%
“…2c). This small amount of deformation can be modeled in real time using an unsupervised inverse problem and a compound dislocation model source (Nikkhoo et al, 2017;Beauducel, Peltier, et al, 2020). On 1 April, the pre-eruptive inflation was interpreted as being due to pressurization of a single source: an ellipsoid with a volume increase of 450;000 50;000 m 3 located at about 1 km below the Dolomieu crater (Fig.…”
Section: Eruption Precursorsmentioning
confidence: 99%
“…Finally, we also suggest further analysis of ground deformation and seismicity datasets or use of Bayesian inversion methods to model the source of deformation during magma migration at Piton de la Fournaise [50]. These data could improve our understanding of the different eruptive trends observed in this study.…”
mentioning
confidence: 76%
“…Beside difficulties in implementing the FEM, such as meshing issues, this powerful method is computationally too demanding to be used for detailed inverse modeling. In contrast, the point CDM is a half‐space model, but has already proven to be suitable for exploring the parameter space in both detailed Bayesian inferences (see Lundgren et al., 2017) and rapid and unsupervised inversions of deformation data (see Beauducel et al., 2020). The gravity change solutions for the point CDM, which we provide here, extend this potential to joint inversions of surface displacements and gravity changes.…”
Section: Discussionmentioning
confidence: 99%