Summary
Volcanic unrest can give warning of impending eruptions, thus monitoring and appropriate emergency management saves lives. However, the ability to accurately forecast the future behaviour of individual volcanoes relies on interpreting changes in the underlying magmatic system. The conceptual understanding of magmatic systems has evolved rapidly and there is now ample geophysical and petrological evidence that a fluid-dominated ‘magma chamber’ is only one component of a much larger system with a heterogeneous distribution of melts, crystals and gases. The opportunity exists to use these advances to interpret monitoring signals, to improve forecasting skills and in turn contribute to the paradigm shift in understanding. In particular, satellite technology has revolutionised the coverage, resolution and frequency of deformation measurements and is increasingly used for volcano monitoring. Dense time-series of high-resolution images reveal complexity and diversity than was not apparent when only infrequent point measurements were available. The latest images are more compatible with the paradigm of extensive multiphase, magmatic systems, but even the most recent models still rely on spheroidal chamber geometries.
The aims of MAST are thus 1) to analyse and model volcano deformation independent of constraints on geometry or rheology and 2) to link the long-term evolution of the temperature and melt fraction to patterns of surface deformation. These aims capitalise on the rise of satellite data, and recent advances in machine learning, strain imaging and the modelling of multiphase systems. The outputs will provide a scientific basis for observatories to interpret signals observed during unrest and to forecast future activity. Most importantly, the outputs will be consistent with – and contribute to - the latest understanding of magmatic systems. This multidisciplinary proposal is inherently high risk, but builds on my experience leading a broad team of investigators.
The aims of MAST are thus 1) to analyse and model volcano deformation independent of constraints on geometry or rheology and 2) to link the long-term evolution of the temperature and melt fraction to patterns of surface deformation. These aims capitalise on the rise of satellite data, and recent advances in machine learning, strain imaging and the modelling of multiphase systems. The outputs will provide a scientific basis for observatories to interpret signals observed during unrest and to forecast future activity. Most importantly, the outputs will be consistent with – and contribute to - the latest understanding of magmatic systems. This multidisciplinary proposal is inherently high risk, but builds on my experience leading a broad team of investigators.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101003173 |
| Start date: | 01-07-2022 |
| End date: | 30-06-2027 |
| Total budget - Public funding: | 1 995 365,00 Euro - 1 995 365,00 Euro |
Cordis data
Original description
Volcanic unrest can give warning of impending eruptions, thus monitoring and appropriate emergency management saves lives. However, the ability to accurately forecast the future behaviour of individual volcanoes relies on interpreting changes in the underlying magmatic system. The conceptual understanding of magmatic systems has evolved rapidly and there is now ample geophysical and petrological evidence that a fluid-dominated ‘magma chamber’ is only one component of a much larger system with a heterogeneous distribution of melts, crystals and gases. The opportunity exists to use these advances to interpret monitoring signals, to improve forecasting skills and in turn contribute to the paradigm shift in understanding. In particular, satellite technology has revolutionised the coverage, resolution and frequency of deformation measurements and is increasingly used for volcano monitoring. Dense time-series of high-resolution images reveal complexity and diversity than was not apparent when only infrequent point measurements were available. The latest images are more compatible with the paradigm of extensive multiphase, magmatic systems, but even the most recent models still rely on spheroidal chamber geometries.The aims of MAST are thus 1) to analyse and model volcano deformation independent of constraints on geometry or rheology and 2) to link the long-term evolution of the temperature and melt fraction to patterns of surface deformation. These aims capitalise on the rise of satellite data, and recent advances in machine learning, strain imaging and the modelling of multiphase systems. The outputs will provide a scientific basis for observatories to interpret signals observed during unrest and to forecast future activity. Most importantly, the outputs will be consistent with – and contribute to - the latest understanding of magmatic systems. This multidisciplinary proposal is inherently high risk, but builds on my experience leading a broad team of investigators.
Status
SIGNEDCall topic
ERC-2020-COGUpdate Date
27-04-2024
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