Forecasting Eruptions at Volcanoes after Extended Repose (FEVER)

Principal Investigator: Professor CRJ Kilburn, University College London, Earth Sciences
Co-Investigator: Dr PM Benson, University College London, Earth Sciences
Grant held at: University College London, Earth Sciences

Volcanic eruptions threaten one in ten people on Earth. The threat is under-estimated by exposed communities because most major eruptions occur from volcanoes that have been quiescent for 100 years or more. The repose intervals are long enough for volcanoes to remain unmonitored. When unrest returns, forecasts of eruption rely on data from hastily-installed monitoring networks. As a result, current forecasts contain large uncertainties, which hinder hazard mitigation and diminish the trust of vulnerable communities. A compelling social need therefore exists for reliable forecasts of eruptions at long-quiescent volcanoes, using emergency data obtained after the start of unrest.

An important weakness in current forecasting strategies is the popular starting assumption that volcanic systems are too complex to follow a shared set of pre-eruptive trends. Statistical methods are applied on a case-by-case basis to identify combinations of precursory behaviour that may signal an eruption. The results are empirical and cannot be transferred from one volcano to another. We challenge the validity of this starting assumption, especially at volcanoes that have remained quiet for generations. In such cases, magma must break open a pathway through the crust before it erupts. The fundamental mechanics of crustal rupture occurs under restricted ranges of physical conditions and these, in turn, promote repeatable and quantifiable patterns of deformation and fracture.

We propose that deformation and fracture can be used to determine the pre-eruptive stability of a volcano and, also, to permit deterministic forecasts of eruption far enough in advance to be of practical value. We have supporting evidence from a new model that we have developed for elastic-brittle deformation in the crust. Pilot applications have successfully forecast real-time changes in precursory regime before eruptions. Our goal is to transform the model into a robust and practical forecasting tool that will enhance existing statistical methods. To achieve this goal, we have assembled a multidisciplinary team to bridge gaps in knowledge of the full spectrum of eruption forecasting, from model development and testing to operational implementation. We will test our hypothesis by validating patterns of precursory behaviour observed in the field against novel laboratory experiments that recreate deformation within a volcano. We will use the results to embed new, deterministic criteria from rock physics into current statistical forecasting methodologies and so advance the real-world ambition to protect communities that are threatened by volcanic activity.