Researchers test new method in volcanic eruption forecasting


A research team has tested a new technique that could possibly forecast how a volcanic eruption will happen accurately. The method combined physics and statistics to capture the probability of past eruption patterns.

The risk of volcanic eruptions is unpredictable especially if they show zero signals beforehand. With this, geologists developed a method to get a better and more precise prediction of volcanic eruptions by testing if the models can capture the likelihood of previous eruptions. The scientists studied the history of the eruption of the Okmok Volcano located in Alaska.

An ash plume from the massive explosion extended estimated 1.6 km (1 mile) into the sky which then posed a hazard to aircraft engines. Jack Albright, the author of the study and a graduate of the University of Illinois explained, "The 2008 eruption of Okmok came as a bit of surprise. After an eruption that occurred in 1997, there were periods of slight unrest, but very little seismicity or other eruption precursors."

He continued, "in order to develop better forecasting, it is crucial to understand volcanic eruptions that deviate from the norm." Eruptions are commonly predicted by studying earthquake activity, groundswell and gas release, and other established patterns of pre-eruption unrest. However, the Okmok Volcano did not display any of the patterns.

The research team used a statistical data analysis technique called Kalman filtering, which was improved after World War II. Geology professor and research co-author Patricia Gregg said, "the version of Kalman filtering that we used for our study was updated in 1996 and has continued to be used in weather and climate forecasting, as well as physical oceanography."

She further explained that the research team is the first group to use the updated method in volcanology, and it worked as well for Okmok's eruption study.

The researchers said that there is a lack of increased seismicity before the eruption. A hypothesis explains that the reservoir under the volcano remains the same size as it fills with hot gases and magma. This occurrence results in pressure in the chamber that triggers surrounding rocks to move, eventually leading to earthquakes.

Albright stated, "In the 2008 eruption, it appears that the magma chamber grew larger to accommodate the increasing pressure, so we did not see the precursor seismic activity we would expect."

"By looking back in time with our models, or hindcasting, we can now observe that stress had been building up in the rocks around the chamber for weeks, and the growth of the magma system ultimately led to its failure and eruption," he added.

The backward and forward modeling enables scientists to observe the evolution of the volcanic system. Gregg expressed that the team is now able to propagate the new model forward in time and predict where Okmok eruption is heading afterward.

The team clarified that since every volcano is different, a model must be specifically made for each.


"Hindcasting magma reservoir stability preceding the 2008 eruption of Okmok, Alaska" – J.A. Albright et al. – AGU / Geophysical Research Letters – DOI: 10.1029/2019GL083395


Volcanic eruptions pose a significant and sometimes unpredictable hazard, especially at systems that display little to no precursory signals. For example, the 2008 eruption of Okmok volcano in Alaska notably lacked observable short‐term precursors despite years of low‐level unrest. This unpredictability highlights that direct monitoring alone is not always enough to reliably forecast eruptions. In this study, we use the Ensemble Kalman Filter (EnKF) to produce a successful hindcast of the Okmok magma system in the lead up to its 2008 eruption. By assimilating geodetic observations of ground deformation, finite element models track the evolving stress state of the magma system and evaluate its stability using mechanical failure criteria. The hindcast successfully indicates an increased eruption likelihood due to tensile failure weeks in advance of the 2008 eruption. The effectiveness of this hindcast illustrates that EnKF‐based forecasting methods may provide critical information on eruption probability in systems lacking obvious precursors.

Plain Language Summary

Volcano monitoring agencies routinely use increases in volcanic unrest as indicators of the potential for eruption. However, for some eruptions, such as the 2008 eruption of Okmok volcano in Alaska, these behaviors can be subtle or missing altogether. In this study, a new statistics‐based volcano forecasting approach is used to test whether computer models are able to capture an increase in eruption likelihood leading up to the 2008 event. The models indicate that Okmok was trending toward eruption weeks in advance due to the increased probability of failure of the magma chamber. This successful test indicates that stress around the magma chamber is a strong predictor of volcano stability and that this method could apply to active volcanic systems and improve hazard mitigation efforts.

Featured image: Sabancaya volcano, 2017. Credit: Galeria del Ministerio de Defensa del Perú

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