A new case study done by a team of international scientists led by UC Santa Barbara geophysicist Robin Matoza examined data from the 2015 eruption of the Calbuco volcano in Chile using a network from the United Nations’ International Monitoring System (IMS), to track volcanic eruptions in remote locations.

The researchers chose this event because they could compare long-range data with local readings, enabling study of the large volcanic explosion using infrasound sensors.

As part of the United Nations’ Comprehensive Nuclear-Test- Ban Treaty, an IMS was built to detect any nuclear explosion on Earth, including underground, underwater or in the atmosphere. The system is a network to detect atmospheric infrasound, sound waves with frequencies below the lower limit of human audibility.

Calbuco volcano in Chile erupted April 2015 in two sub-plinian phases. Plinian eruptions are volcanic eruptions marked by their similarity to the eruption of Mount Vesuvius in 79 AD. This was Calbuco’s first activity since 1972. Courtesy of wiki commons

“We want to be able to monitor regions in the world where many volcanoes do not have local monitoring stations like Calbuco does,” Matoza, an assistant professor in UCSB’s Department of Earth Science, said. “In some places — for example, the Aleutian Islands in Alaska — it’s challenging to maintain observation networks on the volcanoes themselves due to harsh weather and their remote locations. Consequently, many Aleutian volcanoes are not instrumented. We want to be able to detect, locate and characterize remote explosive volcanic activity because eruptions can release ash clouds into the atmosphere, which are hazardous to aircraft.”

The well-documented April 2015 eruption of Calbuco represented a unique opportunity to test and evaluate remote infrasonic detection, location and characterization capabilities. The researchers evaluated the ability of remote IMS infrasound stations to detect volcanic eruption signals in the presence of wind noise and interfering infrasound signals, provide fast and reliable location solutions using automated procedures and provide constraints on the timing of explosive eruptive activity.

Previous work has demonstrated that remote infrasound arrays can be used to detect, locate and provide detailed chronologies of remote explosive volcanism, with the potential to provide source parameters for ash transport and dispersal models.

“What’s nice about infrasound is that we are able to gather information farther from the source than with traditional ground-based monitoring methods. Typically, seismic signals from eruptions don’t propagate more than a few hundred kilometers from the source,” Matoza said. “With Calbuco, for example, you can see the eruption very clearly on the local monitoring stations and out to about 250 kilometers on regional seismic networks, but with infrasound, the signal propagates in the atmosphere for more than 5,000 kilometers. What’s more, infrasound delivers different information than seismic data alone.”

The April 2015 Calbuco eruption was a useful case study to test and validate remote infrasound signal association and source location methodologies using the IMS network. The researchers applied Matoza’s method to detect and locate the eruption signals using a brute-force, grid-search cross-bearings approach. The method involves determining location trajectories from multiple arrays that intersect.

“One of the recommendations from this study is that more seismic networks should also have infrasound sensors,” Matoza said. “It’s one extra channel of data to record that provides very useful information for improving volcano monitoring.”

The remote IMS infrasound arrays provide an accurate explosion chronology consistent with the regional and local seismo-acoustic data, increasing confidence in the use of remote infrasound observations for automated detection, location and characterization of explosive volcanism. Augmenting the IMS in regions of dense volcanism, even with relatively sparse regional seismo-acoustic networks, will dramatically enhance volcanic signal detection, reduce latency and improve discrimination capability.

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