Forecasting volcanic activity using deep learning (DEEPVOLC)
- Start date: 1 June 2020
- End date: 31 January 2026
- Funder: European Research Council (ERC)
- Partners and collaborators: University of Bristol
- Primary investigator: Professor Andy Hooper
- Co-investigators: Professor David Hogg, Dr Susanna Ebmeier
- External co-investigators: Professor Juliet Biggs
- Postgraduate students: Dr Camila Novoa, Dr Lin Shen, Dr Matt Gaddes
DEEPVOLC will help to forecast activity at volcanoes by applying advances in artificial intelligence to transformative new geodetic datasets.
Some 200 million people live within 30 km of a volcano but accurate forecasting of volcanic activity is difficult. It relies on human expertise at individual volcanoes, but volcanoes often behave in unexpected ways not previously observed at that location.
An additional complication is that most volcanoes are not instrumented. A key indicator of volcanic activity is deformation of a volcano's surface due to magma migrating beneath and recent advances in satellite monitoring now allow us to monitor this deformation worldwide.
DEEPVOLC will apply the latest deep learning algorithms to the satellite data to combine knowledge from all volcanoes that have been active in the satellite-monitoring era. This will enable us to use knowledge of how volcanoes behave globally to identify deformation at volcanoes locally, and forecast how it will evolve.
Through working with volcano observatories throughout the project we will deliver tools that can be used to aid in the forecasting of volcanic activity.