Near-real-time detection of dike-intrusion-indiced unrest: a Digital Twinfor Mount Etna volcano, Italy

We present a Digital Twin that tracks the evolution of unrest caused by dike intrusion at volcanoes, leveraging HPC computational models and Artificial Intelligence algorithms to combine real-time monitoring data and physics-based predictions.

The Digital Twin includes three main components. A preliminary, offline scenario database is produced by simulating ground deformation due to dike
intrusion using the finite element HPC software GALES. The model calculates the three-dimensional elastostatic response induced by overpressurized  dikes within a spectrum of geometries, positions and orien. The computational domain can include DEM topography and heterogeneous rock properties, e.g. from seismic tomography surveys. Scenarios are used to train a machine learning module that reconstructs the source of observed deformation patterns. The source is identified in terms of dike size, position, orientation and intensity (dike opening). An auto-encoder, trained on multi-parametric observational time series, detects unrest by identifying variations from the long-term trends at multiple stations. As unrest is detected, inversion of the observed deformation is performed by the trained ML module, providing the location and size of dike intrusion. The geodetic dataset is updated in near-real-time, providing the ability to model dike evolution as it rises towards the surface.

The Digital Twin has been applied restrospectively to the December 2018 dike intrusion at Mount Etna, tailoring ground deformation simulations to the specifics of the volcano, including observed distribution of dike properties. Results show the ability of the Digital Twin to identify unrest and track the evolution of the dike towards the surface to the eruptive vent.

The Digital Twin is available through a dedicated GitLab repository for the EU-funded DT-GEO project, including the case study application.