| Abstrak/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 at Piton de la Fournaise before the June 2014 eruption. Using 7-day moving trends of GNSS data, we compute a total of 15 inversion models of 2.5 million forward problems each, without a priori information. Obtained source shapes are dikes, prolate ellipsoids or pipes that exhibit a global migration from 7-8 km depth to the surface, drawing a “mechanical tomography” of the plumbing system below the summit crater. The last sources have geometries compatible with observed eruptive fissures and seismicity distribution, and associated volume variations consistent with the observed amount of erupted lava. |
