For low viscosity magmas such as basalts, rapid and unpredictable transitions between effusive and explosive activity may occur. These transitions dramatically alter the impact of an eruption and pose a real challenge to policymakers tasked with mitigating the risks associated with basaltic eruptions. Mechanisms controlling these transitions, however, are not well understood, mainly due to the lack of a clear understanding of basaltic magma fragmentation. The ENDGAME project proposes to investigate transitions in eruptive styles at basaltic volcanoes by studying fragmentation of basaltic magmas through a combination of targeted cutting-edge fluid dynamics experiments, new holistic numerical modelling of magma ascent and brand new field observations collected during a basaltic eruption. ENDGAME will aim to: 1) define new constitutive equations for basaltic magma fragmentation by implementing and performing jet flow and shock-tube experiments with a bubble- and particle-bearing analogue material in combination with high-speed Schlieren shadow photography; 2) extend a state-of-the-art 3D transient model of magma ascent to model the evolution of the particle-size distribution resulting from fragmentation through time by using a numerical technique which has been recently applied in volcanology, the “Method of Moments”; 3) use the new 3D magma ascent model to investigate the transitions in eruptive style by comparing numerical results with laboratory experiments and field observations that will be collected during an eruption at Piton de la Fournaise. The interdisciplinary approach that characterizes ENDGAME, e.g. linking cutting-edge fluid-dynamics experiments with state-of-the-art 3D magma ascent modelling and field observations of an active eruption, will allow us to shed light on one of the biggest challenges in volcanic hazard assessment: what parameters and how they control the transition in eruptive style at basaltic volcanoes?

Comprehensive seismic programs undertaken in the past few years, combined with emerging new numerical technologies now provide the potential, for the first time, to explore in detail the Earth’s interior. However, such an integrated approach is currently not contemplated, which produces physical inconsistencies among the different studies that strongly bias our understanding of the Earth’s internal structure and dynamics. Of particular concern are nowadays apparent thermo-petrological anomalies in tomographic images that are generated by the unaccounted-for anisotropic structure of the mantle and that are commonly confused with real thermo-petrological features. Given the diffuse mantle seismic anisotropy, apparent thermo-petrological anomalies contaminate most tomographic models against which tectono-magmatic models are validated, representing a critical issue for the present-day window. Here we aim to develop a new methodology that combines state-of-the-art geodynamic modelling and seismological methods. The new methodology will allow building robust anisotropic tomographic models that will exploit anisotropy predictions from petrological-thermomechanical modelling to decompose velocity anomalies into isotropic (true thermo-petrological) and anisotropic (mechanically-induced) components. As a major outcome, we expect to provide a new, geodynamically and seismologically constrained perspective of the current deep structure and tectono-magmatic evolution of different tectonic settings. This new methodology will be applied to the Mediterranean and the Cascadia subduction zone where, despite the abundant seismological observations, large uncertainties about the subsurface structure and tectono-magmatic evolution persist. Furthermore, we plan to develop a new inversion technique for seismic anisotropy, and release an open source, sophisticated code for mantle fabric modelling, which will allow coupling geodynamic and seismological modelling in other tectonic settings.

Earthquakes represent one of our greatest natural hazards. Even a modest improvement in the ability to forecast devastating events like the 2016 sequence that destroyed the villages of Amatrice and Norcia, Italy would save thousands of lives and billions of euros. Current efforts to forecast earthquakes are hampered by a lack of reliable lab or field observations. Moreover, even when changes in rock properties prior to failure (precursors) have been found, we have not known enough about the physics to rationally extrapolate lab results to tectonic faults and account for tectonic history, local plate motion, hydrogeology, or the local P/T/chemical environment. However, recent advances show: 1) clear and consistent precursors prior to earthquake-like failure in the lab and 2) that lab earthquakes can be predicted using machine learning (ML). These works show that stick-slip failure events –the lab equivalent of earthquakes– are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Remarkably, ML predicts the failure time and in some cases the magnitude of lab earthquakes. Here, I propose to connect these results with field observations and use ML to search for earthquake precursors and build predictive models for tectonic faulting. This proposal will support acquisition and analysis of seismic and geodetic data and construction of new lab equipment to unravel earthquake physics, precursors and forecasts. I will use my background in earthquake source theory, ML, fault rheology, and geodesy to address the physics of earthquake precursors, the conditions under which they can be observed for tectonic faults and the extent to which ML can forecast the spectrum of fault slip modes. My multidisciplinary team will train the next generation of researchers in earthquake science and foster a new level of broad community collaboration.