An absurdly successful prediction! [2]
Predicting volcano eruptions is hard. It’s a freakin’ mountain, man! It does what it wants, when it wants!
But sensors are getting better and cheaper, and computer models are improving.
Down the street at my Alma Mater researchers are taking a victory lap [1].
In 2018, Professor Patricia M. Gregg and colleagues ported their prediction model to the local supercomputer (Blue Waters at the time), and used data from the active and well-studied Sierra Negra Volcano in the Galapagos as a test. The forecast predicted an eruption beginning between 25 June 2018 and 5 July 2018, as reported at a workshop in December 2018.
Five months later, on June 26, Sierra Negra erupted.
(Good volcano! Who’s a good volcano! What good volcano you are! )
They analysis uses an ensemble of 240 models, fed Synthetic Aperture Radar data from ESA’s Sentinel-1 satellite. This data is particularly useful for measuring the altitude of the surface, and multiple measurements reveals uplift and subsidence. These data were analyzed based on records of earlier events.
The model flagged the June 25 time, though the model wasn’t clear whether it would be an earthquake or an eruption. It predicted the geological failure that would produce something big.
“It is unclear whether the through-going failure flagged by the EnKF forecast was forecasting the potential of the 26 June earthquake or the eruption. We posit that the more important outcome is the success of the EnKF to quantify deformation, stress, and failure as indicators to track the evolution of the system.” (
([1], p. 6)
Cool!
It is remarkable to me that the orbital sensors actually have enough resolution to detect the evolution of the system this precisely. The satellite passes over every 6 days, and has limited spatial resolution. Even the limited coverage was more data than could be processed in real time by the model. Still, the model predicted when the rock would collapse, releasing the chamber underground.
I’ll note that the report discusses the tricky issues of interpreting the ensemble of models, as well as reporting on “hindcasts”, analyses of the data after the prediction. It’s all pretty neat, though I don’t necessarily grok the details.
The researchers indicate that this cool result was possible only because of the availability of oodles of computing power. The model was originally developed on laptops, but real time prediction needs a lot more oomph than that.
The researchers are thinking of adding in machine learning to improve the forecasting. If it works this well without ML, think what may be possible with a big enough training set?
- Patricia M. Gregg, Yan Zhan, Falk Amelung, Dennis Geist, Patricia Mothes, Seid Koric, and Zhang Yunjun, Forecasting mechanical failure and the 26 June 2018 eruption of Sierra Negra Volcano, Galápagos, Ecuador. Science Advances, 8 (22):eabm4261, https://doi.org/10.1126/sciadv.abm4261
- Lois Yoksoulian, Great timing, supercomputer upgrade lead to successful forecast of volcanic eruption, in Illinois News Bureau – Research News, June 3, 2022. https://news.illinois.edu/view/6367/913924091
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