Digital Twin Components in Volcanology
Folch, Arnau,Papale, Paolo,Costa, Antonio,Barsotti, Sara,Mingari, Leonardo,Garg, Deepak,Macedonio, Giovanni,Cannavò, Flavio,Currenti, Gilda
- Published
- 2024-04-02
- Language
- en
Abstract
Interdisciplinary digital twins are becoming able to mimic the different Earth system domains with unrivalled precision, providing analyses, forecasts, uncertainty quantification, and ¿what if¿ scenarios for natural and anthropogenic hazards from their genesis to propagation and impacts. The EU DT-GEO project (2022-2025) is deploying a prototype digital twin on geophysical extremes consisting of interrelated Digital Twin Components (DTCs), intended as self-contained containerised entities embedding simulation codes, Artificial Intelligence (AI) layers, large volumes of nearly-real-time data streams, data assimilation methodologies, and overarching workflows for deployment and execution of single or coupled DTCs in centralised High Performance Computing (HPC) and virtual cloud computing Research Infrastructures (RIs). These DTCs, actually a first step towards a digital twin on Geophysical Extremes integrated in the Destination Earth (DestinE) initiative, will deal with geohazards from earthquakes, volcanoes, and tsunamis by harnessing world-class computational (EuroHPC) and data (EPOS) Research Infrastructures, operational monitoring networks, and leading-edge research and academia partnerships. In particular, 4 DTCs of the 12 in DT-GEO will address different volcanic hazards. DTC-V1 will merge multi-parametric data from ground- based and remote observation systems (on-site monitoring networks and satellites) with global modelling of magma and rock dynamics and with AI approach. DTC-V2 will merge real-time geostationary satellite observations with the FALL3D model using the on-line data assimilation PDAF system to generate deterministic and ensemble-based probabilistic forecast products. DTC-V3 will merge real-time multi- parametric data from ground-based and remote observation systems with deterministic modelling of lava flow propagation and inundation areas including Bayesian modelling of vent opening. Finally, DTC-V4 will consider air-quality data and AI in a gas dispersal forecast context to improve our operational Early Warning Systems
Full text
Page | 329 426 Digital Twin Components in Volcanology Professor Arnau Folch1, Paolo Papale2, Antonio Costa3, Sara Barsotti4, Leonardo Mingari5, Deepak Garg2, Giovanni Macedonio6, Flavio Cannavò7, Gilda Currenti7 1Geociencias Barcelona (GEO3BCN-CSIC), Barcelona, Spain, 2Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Pisa, Pisa, Italy, 3Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Bologna, Bologna, Italy, 4Icelandic Meteorological Office, , Iceland, 5Barcelona Supercomputing Center, Barcelona, Spain, 6Istituto Nazionale di Geofisica e Vulcanologia (INGV), Osservatorio Vesuviano, , Italy, 7Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Catania, , Italy Interdisciplinary digital twins are becoming able to mimic the different Earth system domains with unrivalled precision, providing analyses, forecasts, uncertainty quantification, and “what if” scenarios for natural and anthropogenic hazards from their genesis to propagation and impacts. The EU DT-GEO project (2022-2025) is deploying a prototype digital twin on geophysical extremes consisting of interrelated Digital Twin Components (DTCs), intended as self-contained containerised entities embedding simulation codes, Artificial Intelligence (AI) layers, large volumes of nearly-real-time data streams, data assimilation methodologies, and overarching workflows for deployment and execution of single or coupled DTCs in centralised High Performance Computing (HPC) and virtual cloud computing Research Infrastructures (RIs). These DTCs, actually a first step towards a digital twin on Geophysical Extremes integrated in the Destination Earth (DestinE) initiative, will deal with geohazards from earthquakes, volcanoes, and tsunamis by harnessing world-class computational (EuroHPC) and data (EPOS) Research Infrastructures, operational monitoring networks, and leading-edge research and academia partnerships. In particular, 4 DTCs of the 12 in DT-GEO will address different volcanic hazards. DTC-V1 will merge multi-parametric data from groundbased and remote observation systems (on-site monitoring networks and satellites) with global modelling of magma and rock dynamics and with AI approach. DTC-V2 will merge real-time geostationary satellite observations with the FALL3D model using the on-line data assimilation PDAF system to generate deterministic and ensemble-based probabilistic forecast products. DTC-V3 will merge real-time multiparametric data from ground-based and remote observation systems with deterministic modelling of lava flow propagation and inundation areas including Bayesian modelling of vent opening. Finally, DTC-V4 will consider air-quality data and AI in a gas dispersal forecast context to improve our operational Early Warning Systems.
