Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand

D. E. Dempsey (), S. J. Cronin, S. Mei and A. W. Kempa-Liehr
Additional contact information
D. E. Dempsey: University of Auckland
S. J. Cronin: University of Auckland
S. Mei: University of Auckland
A. W. Kempa-Liehr: University of Auckland

Nature Communications, 2020, vol. 11, issue 1, 1-8

Abstract: Abstract Sudden steam-driven eruptions strike without warning and are a leading cause of fatalities at touristic volcanoes. Recent deaths following the 2019 Whakaari eruption in New Zealand expose a need for accurate, short-term forecasting. However, current volcano alert systems are heuristic and too slowly updated with human input. Here, we show that a structured machine learning approach can detect eruption precursors in real-time seismic data streamed from Whakaari. We identify four-hour energy bursts that occur hours to days before most eruptions and suggest these indicate charging of the vent hydrothermal system by hot magmatic fluids. We developed a model to issue short-term alerts of elevated eruption likelihood and show that, under cross-validation testing, it could provide advanced warning of an unseen eruption in four out of five instances, including at least four hours warning for the 2019 eruption. This makes a strong case to adopt real-time forecasting models at active volcanoes.

Date: 2020
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DOI: 10.1038/s41467-020-17375-2

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