Two Active Magma Reservoirs Detected at Masaya Volcano

Pennsylvania State University

Masaya volcano in Nicaragua sits within 13 miles of 2 million people and is a popular tourist destination due to its lava lake. Penn State researchers recently found evidence of two new magma sources under the active volcano, highlighting what they called the critical importance of understanding transitions in volcanic behavior and shallow magma processes, especially when they potentially pose hazards to local communities.

The team used satellite data to investigate how the ground around Masaya volcano changed over six years. They identified a period of subsidence - when the ground moves down - and then uplifting - when the ground moves up. They interpreted this change as an inflation of the main reservoir, possibly from a new deep magma source. They also saw the area around the volcano's primary crater, called Santiago crater, and lava lake deflate over the entire study period, suggesting a more complex plumbing system. The researchers published their findings in Geophysical Research Letters.

"These observations help scientists better understand how magma moves beneath Masaya and may improve monitoring and eruption forecasting in the future," said Lizzie Johnson, who graduated from Penn State with a bachelor's degree in geosciences in May and is the lead author of the paper.

Masaya volcano is a complex volcano with a nested series of craters and calderas - sinkholes formed from empty magma chambers - near Nicaragua's capital city Managua. Masaya's magma systems are complex and dynamic, so they are still not well understood by unclear and therefore of interest to researchers, Johnson said.

"Having a thorough understanding of Masaya's volcanic activity and its related hazards is critical as around 2 million people live near Masaya and the caldera is a national park and popular tourist destination," Johnson said. "Although Masaya last produced a major lava flow in 1772, its persistent degassing often produces dangerously high concentrations of sulfur dioxide volcanic gas."

Young Cheol Kim, who graduated from Penn State this summer with his doctorate in geosciences, explained that the goal of the study was to understand the volcano to improve forecasting and communicating potential risks.

"Doing continuous monitoring allows us to understand the baseline activity and identify see if things are changing," he said, noting that specific changes can indicate higher levels of activity and potential eruption events. "And then if there is a need to warn people, we can do that in a timely manner."

To identify meaningful changes, they used geospatial geodetic data - many data points of ground deformation over a long period of time by a satellite. The satellite, in this case the Sentinel-1 satellite, makes a pass over the site about once every 12 days, according to the researchers.

Sentinel-1 provided the data the researchers used in their interferometric synthetic aperture radar (InSAR) remote sensing approach. InSAR has two parts: synthetic aperture radar, which enables the satellite's sensor to penetrate clouds with long microwave radiation wavelengths to monitor the planet's surface; and interferometry, a way to measure distance by seeing how light waves interfere with each other and the surface.

"Cloud penetration is important for volcanoes because many of them, like Masaya, are in tropical areas where there is often cloud cover," Kim explained. "And when we look at all the different satellite images across multiple years, we can see how the distance between the satellite and the ground changes, where it is uplifting or subsiding."

During the study period of 2018 to 2024, the researchers found that the ground of the caldera first receded and then rose again.

"When it's subsiding, that means that there's probably magma draining away from the volcano, but once the ground starts uplifting again, that can mean that there's potentially new magma moving in," Johnson said. "At the same time, the area directly around the active Santiago crater deflated over the entire study period independently of the transition to inflation of the main reservoir. This indicates that the plumbing system is more complex and could be due to decreasing magma in a second, shallower reservoir."

Knowing how the ground moves is crucial to assessing volcanic hazards accurately and timely, the researchers said, because that movement provides information about the magma supply and transport mechanisms between the reservoirs and surface.

The researchers said there are they did not identify any no immediate dangers from Masaya to the surrounding community, and that continued monitoring of changes to the magma system understanding the magma system better makes it easier to identify hazards. with continued monitoring.

"Further long-term geodetic and modeling studies will provide a more thorough understanding and allow for effective hazard monitoring and mitigation at Masaya and other lava-lake hosting basaltic volcanoes," Johnson said.

Johnson joined the research team after hearing a talk by Christelle Wauthier, professor of geosciences and computational sciences hub director in the Institute for Computational and Data Sciences at Penn State, and conducted this research as part of her Schreyer's Honors College thesis. She worked with graduate students like Kim to understand the data and learn how to conduct academic research.

"I appreciated learning how a research group works," said Johnson, who has recently started a doctoral program in Earth sciences at the University of Hawaii at Manoa, where she will continue studying volcanoes using seismic waves to image below the surface of Kilauea volcano. "I had never had any experience with research, so it was great to be able to join in the group meetings and get to hear about everybody's work. And learning coding was also useful as I continue with my Ph.D."

This work was supported by the U.S. National Science Foundation under award numbers 1945417 and 2523375, as well as by the computational research infrastructure of the Institute for Computational and Data Sciences at Penn State. This content is solely the responsibility of the authors and does not necessarily reflect the views of the funders.

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