Summary
The geological record shows that volcanic flank collapses and their associated tsunamis are among the largest and most disastrous natural processes on Earth, because of the huge energies involved. The potential impact of such rare but devastating natural disasters is largely ignored by society, leaving us totally unprepared even to detect the precursors of impending catastrophe. Geodetic monitoring documents gradual (cm/yr) down-sliding of individual flanks at many volcanoes worldwide. Such movements express structural instability, and the majority of volcanoes exhibiting slow flank sliding today have experienced flank collapses in the geological past. There is mounting evidence that such collapses were preceded by slow sliding, leading to the hypothesis that gradual flank movement at some point transitions into collapse. This link, however, lacks a physical explanation, and so identifying which slow-sliding precursors might indicate imminent collapse (and therefore what indicators might be used for hazard mitigation measures) is presently impossible. There appear to be two testable mechanisms by which slow sliding could turn into collapse: (i) it results from decrease in the flank’s shearing resistance, or (ii) enhanced activity in the magma system leads to a run-away feedback situation between sliding and depressurization. PRE-COLLAPSE will test these mechanisms on four volcanoes (Anak-Krakatau, Ritter, Etna, Kilauea) by employing advanced computer models capable of simulating both flank sliding and its interaction with the magma system, incorporating rock mechanical behaviours at shear velocities matching those of slowly sliding flanks, and detailed shoreline-crossing interior structures of the volcanoes based on observational data. The outcome will revolutionize how we assess volcanic flank collapse risk, a Gaussian improbability but a societal catastrophe. It will enable us to develop monitoring strategies to detect precursory signals to catastrophic collapse.
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| Web resources: | https://cordis.europa.eu/project/id/948797 |
| Start date: | 01-02-2021 |
| End date: | 31-01-2026 |
| Total budget - Public funding: | 1 500 000,00 Euro - 1 500 000,00 Euro |
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Original description
The geological record shows that volcanic flank collapses and their associated tsunamis are among the largest and most disastrous natural processes on Earth, because of the huge energies involved. The potential impact of such rare but devastating natural disasters is largely ignored by society, leaving us totally unprepared even to detect the precursors of impending catastrophe. Geodetic monitoring documents gradual (cm/yr) down-sliding of individual flanks at many volcanoes worldwide. Such movements express structural instability, and the majority of volcanoes exhibiting slow flank sliding today have experienced flank collapses in the geological past. There is mounting evidence that such collapses were preceded by slow sliding, leading to the hypothesis that gradual flank movement at some point transitions into collapse. This link, however, lacks a physical explanation, and so identifying which slow-sliding precursors might indicate imminent collapse (and therefore what indicators might be used for hazard mitigation measures) is presently impossible. There appear to be two testable mechanisms by which slow sliding could turn into collapse: (i) it results from decrease in the flank’s shearing resistance, or (ii) enhanced activity in the magma system leads to a run-away feedback situation between sliding and depressurization. PRE-COLLAPSE will test these mechanisms on four volcanoes (Anak-Krakatau, Ritter, Etna, Kilauea) by employing advanced computer models capable of simulating both flank sliding and its interaction with the magma system, incorporating rock mechanical behaviours at shear velocities matching those of slowly sliding flanks, and detailed shoreline-crossing interior structures of the volcanoes based on observational data. The outcome will revolutionize how we assess volcanic flank collapse risk, a Gaussian improbability but a societal catastrophe. It will enable us to develop monitoring strategies to detect precursory signals to catastrophic collapse.Status
SIGNEDCall topic
ERC-2020-STGUpdate Date
27-04-2024
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