GFZ German research centre for geo sciences

MagmaPropagator: a forecasting tool for location and time of volcanic eruptions due to off-conduit magma propagation

Time Frame:   1 July 2019 - 31 January 2023

Funding:  DFG

Principal Investigators:  Dr. Eleonora Rivalta

Personnel GFZ:  Prof. Torsten Dahm, Lorenzo Mantiloni, Timothy Davis, Jeane Dagoy,  Ayleen Gaete

Project collaborators:  Dr. Francesco Maccaferri, Dr. Luigi Passarelli, Dr. Fabio Corbi, Dr. Valerio Acocella

Partner:  ISTerre, Grenoble, FR

Methods & Instruments:  Numerical simulations, analogue models (air injections into gelatine).

This project aims at developing a physics-based tool to forecast the location and time of a fissure eruption following magma propagation below the surface. Often magma avoids the central conduit and propagates through tortuous pathways, eventually opening a new fissure on the volcano flank or within a caldera. Similar old eruptive fissures are found in many areas now densely populated. The related hazard has so far been estimated purely based on the spatial distribution of previous events. In the proposed research, we will combine the physics of magma propagation with advanced statistical tools to create a physics-based method to forecast location and time of an eruption following rupture of a magma chamber. We will taylor our approach on three well-monitored cases: Campi Flegrei (Italy) is extremely high-risk and motivates development of near-real time forecasting methods; Etna, Italy, and Piton de la Fournaise, La Reunion have had frequent fissure eruptions and offer data-rich environment to test our models. The outcomes of this project will be: 1) A better understanding of how the state of stress of volcanoes of different shapes evolve with time and how the edifice history controls the migration of surface volcanism, 2) A long-term forecasting tool for the future distribution of eruptive vents, useful for land planning, 3) A short-term tool to update the forecast scenarios according to intrusion parameters progressively determined by assimilating monitoring data.

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