Mechanical tomography 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 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.