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 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) show magma migration from 7–8 km depth to the surface, drawing a mechanical “tomography” of the magma pathway. Our results also suggest source geometries compatible with observed eruptive fissures and seismicity distribution. In case of finite magma volume involved in final dike injection, source volume estimates from this method allow forecasting volumes of erupted lava.