New study reveals the most effective way to forecast volcanic eruptions. An eruption of magma (hot, liquid, and semi-liquid rock), ash, and gases from a volcano occurs when the earth’s crust breaks free or ruptures.
Volcanic hotspots may cause them to form in the midst of plates as well as at the meeting or separation points of tectonic plates. It’s common knowledge that lava and gas are emitted during a volcanic eruption.
Magma that has just erupted is known as a “glowing avalanche” as it slides down the volcano’s flanks. Temperatures as high as 1,200 degrees Fahrenheit may be reached in a few seconds. Ashfall and lahars are two more potential dangers (mud or debris flows).
Volcanoes are known for displacing people and causing food shortages. Magma, or molten rock, rises because it is lighter than the surrounding rock.
However, this magma resides kilometers under the surface in magma chambers. A volcanic eruption is often the result of a magma chamber being overburdened, which causes the magma to push its way to the surface and then explode.
New study reveals the most effective way to forecast volcanic eruptions
A new volcano forecasting model has recently been installed on the Blue Waters and iForge supercomputers in the autumn of 2017 by geology professor Patricia Gregg and her colleagues. During the same time period, a second crew was monitoring the Sierra Negra volcano in Ecuador.
As a result of Gregg’s communication with Dennis Geist of Colgate University, one of the scientists on the Ecuador project, Gregg was able to accurately predict the June 2018 Sierra Negra eruption five months in advance.
The new modeling method was first made on an iMac computer and has been praised for its ability to recreate the Okmok volcanic explosion in Alaska in 2008. To see whether the model’s high-performance computing update worked, Gregg’s team turned to the National Center for Supercomputing Applications and University of Illinois Urbana-Champaign geologists at the National Center for Supercomputing Applications.
According to Gregg, the report’s principal author, “Sierra Negra is a well-behaved volcano.” This means that, prior to eruptions, this volcano has shown all the telltale symptoms of an eruption, such as earth movement, gas release and increased seismic activity.
Because of this, Sierra Negra was a great test subject for our new, better way of doing things. Volcanoes, on the other hand, don’t always follow known patterns, according to experts. Developing quantitative models to better predict eruptions is Gregg and her team’s primary goal.
During the 2017–18 winter break, Gregg and her coworkers used a new supercomputing-powered model. But even though the January 2018 run was meant to be a test, it ended up giving information about Sierra Negra’s eruption cycles and the likelihood and timing of future events, which no one had expected.
A mechanical breakdown and subsequent eruption were predicted by Gregg, who is also an NCSA faculty member, if the rocks containing Sierra Negra’s magma chamber became unstable between June 25 and July 5.
“During a scientific meeting in March of this year, we came to this conclusion. I hadn’t looked at our models since Dennis contacted me on June 26 asking if I wanted to check the date we had predicted. Unfortunately, Sierra Negra erupted exactly one day after our most optimistic estimate of its first mechanical breakdown had been issued. We were shocked.”
But even if it’s an ideal situation, high-performance supercomputing in real research is powerful, according to the study’s authors. Yan Zhan, a former doctoral student at the University of Illinois and co-author of the paper said, “The benefit of this improved model is that it can continuously take in real-time, cross-disciplinary data and quickly analyze it to make a daily prediction.”
To do this, volcano forecasters need a lot of processing power that hasn’t been available to them before. As a result of collaborating with NCSA, Gregg’s team had access to the interdisciplinary expertise they needed to build modeling software of this calibre.
To predict mechanical failure, “we all speak the same language when it comes to numerical multiphysics analysis and high-performance computing needed to forecast mechanical failure,” said Seid Koric, who is the technical assistant director at NCSA and a research professor of mechanical sciences and engineering.
They intend to use Koric’s expertise to integrate AI and machine learning into their forecasting model, so that researchers using normal laptops and desktop computers may take advantage of this computational capacity.
Published in Science Advances, the findings of this research have been made public.
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