Data integration from multiprobe sensor networks to assess risk scenario’s at volcanic hydrothermal ecosystems – DIRE

1. In-situ thermal sensors and weather station has been installed on site, and by January 2023 three years of quasi-continuous data will have been collected.

2. The infrastructure of the first muon telescope (container, mechanical structure, detector control system, power) was installed in situ. The detection units were built at LPC.

3. An improved design was finalized for the second muon telescope; the mechanical constituents were produced and will be assembled beginning of 2023.

4. The algorithms for 2D muographic inversion were developed and tested on data previously acquired on puy de Dôme; studies of the systematic uncertainties affecting the density reconstruction were performed; algorithms for 2D density mapping were develop to optimize, at choice, the spatial resolution or the density resolution.

5. The time series of ASTER data for the calibration period (2020-2022) has been built, and targeted overpasses completed simultaneous with field work on eight occasions.

1. By January 2023, two one-year cycles of annual change in heat flux and vegetation health will have been recorded, and ground-truthed for each of the four seasons.

2. CO2 flux data have been collected and processed for the period 2022-2022.

3. The deep learning algorithm has been prepared and tested.

4. The expert system is now being implemented on the 2020-2022 data sets to search for anomalous system behavior.

5. A science, arts and dance festival was held on Vulcano during June 2021 involving local groups to aid in building resilience

1. Full implementation of the muon system; generating 2D and 3D density maps of the inner structure of Vulcano and tracking in real time changes in the inner structure

2. Workup of the data sets to produce the heat flux times series, and tie this to other geophysical metrics;

3. Implementation of deep learning on all geophysical, thermal and gas flux data sets;

4. Identifying correlations between the surface activity of the volcano and changes in the internal structure;

5. Analysis of the satellite based time series back to the 1980s for heat flux and vegetation stress;

6. Completion of a second on-island science, arts and the environment festival;

7. Generation of guided walks and implementation of eco-walks, with accompanying guide book;

8. Work-up of the Nyrisos data, and extension to all global hydrothermal systems through mining the global remote sensing archive.

Highlights:

Diliberto IS ; Pailot Bonnètat S; Harris AJL; Bani P; Rafflin V; Boudoire G ; Gattuso A ; Grassa F ; Van Wyk de Vries B; Bilotta G ; Cappello A ; Ganci G (2022) The 2021 unrest at Vulcano: insights from ground-based and satellites observations. EGU22, the 24th EGU General Assembly, held 23-27 May, 2022 in Vienna, Austria and Online. Online at egu22.eu, id.EGU22-11576. 10.5194/egusphere-egu22-11576

Pailot – Bonnétat S, Giannoulis M, Harris A, Barra V, Diliberto IS, Grassa F, Gattuso A, Ramsey M (2022) Thermal Remote Sensing of Crises at Hydrothermal Systems: ASTER and La Fossa di Vulcano. Cities on Volcanoes 11, held 12-17 June 2022 in Heraklion Crete, Greece and Online.

Rafflin V, Boudoire G, Harris A, Gailler L, Massaro S, Costa A, Stocchi M, Grassa F, Giuffrida G, Liuzzo M, Cluzel N, Gald F, Planche C, Pasdeloup G, Bansone S (2022) Multi-hazards assessment in a quiescent volcanic area: the case study of Lake Pavin (France) and its surroundings. Cities on Volcanoes 11, held 12-17 June 2022 in Heraklion Crete, Greece and Online.

Volcanic hydrothermal systems transmit heat and mass to the surface, causing damage on resident socio-ecosystems. The time–space emission distributions and hence the environmental impacts are controlled by complex interplays between fluid flow through the system and opening/closure of pathways. We will thus build a data-driven, multiparametric fluid flow surveillance model to alert to the timing and location of environmental crises. The model will use data from various sensor networks (thermal, seismic and deformation arrays) analyzed within a robust structural model derived from a new imaging technique, muography. The structural density variations, correlated to the evolution of sub-surface seismicity and surface heating, can reveal system change in all three spatial dimensions plus time. We will use deep learning algorithms to predict crises from precursors and to set hazard levels. The real-time evolution of an environmental system will be tracked during and after a crisis.