“If this was a volcano in places where people lived, they would be evacuated.” The words of University of Washington professor of oceanography William Wilcock ring with urgency as Axial Seamount, a colossal submarine volcano 300 miles off the coast of Oregon, grumbles with scores of earthquakes per day. But this is no ordinary volcano its eruptions are hidden a mile beneath the Pacific, monitored not for threat to humans in the immediate sense, but for the scientific data it yields on the dynamic Earth.
A decade plus ago, Axial Seamount became one of nature’s own laboratories for geoscientists, who have made the site one of the world’s most heavily monitored submarine volcanoes. At the heart of the activity is a sophisticated system of ocean-floor seismometers and pressure sensors with GPS, deployed as part of the National Science Foundation’s Ocean Observatories Initiative Regional Cabled Array. They send real-time data from the bottom of the ocean to shore-dwelling scientists in a continuous monitoring of every tremor and small ground movement since the past ten years.
The past few months have seen the volcano unrest become more vigorous, with 100 to 300 earthquakes per day, and even exceeding 1,000 during intense swarms. These earthquakes, typically too shallow to be detected on land, are the telling signature of magma rising, pressurizing the magma chamber below the caldera. “The summit of the volcano has already reached the depth it was at when it erupted in 1998 and 2011 and is approaching that of the 2015 eruption,” reports Deborah Kelley, University of Washington School of Oceanography Director in a recent expedition summary.
Its rate of inflation of about eight inches a year has been erratic, making it difficult to predict. Since the 2015 eruption, the volcano surface rapidly grew, subsided, only to grow again towards the end of 2023. The unpredictability makes it challenging to forecast eruptions, as does the unpredictable path of the ascent of the magma. Scientists have mentioned that eruptions of Axial are often “inflation predictable” when the caldera floor is at a point of critical uplift, but it is not possible to forecast the exact timing because inflation rates and seismicity vary as noted by Bill Chadwick.
Machine learning has started being employed to sift through extensive seismic databases. Wang et al.’s 2024 research indicated an unusual peak of mixed-frequency earthquake signals that rose 15 hours before the 2015 eruption to an all-time high only one hour before lava emerged into the seafloor. The signals, thought to reflect brittle failure resulting from magma migration and volatile exsolution, may offer a novel forecast technique not only for Axial but also for active volcanoes worldwide with unsupervised algorithms.
The engineering feat of viewing Axial is impressive enough in itself. The cabled array receives and serves more than 150 instruments, including high-definition cameras, bottom pressure recorders, and chemical sensors, all of which operate in the blistering pressure and corrosive depths of the deep ocean. Data travels at the speed of light, allowing researchers to watch inflation, seismic swarms, and even hydrothermal vent community alterations in near real time with live video and sensor observations.
Axial’s eruptions are normally effusive, yielding voluminous flows of pillow lava and spectacular seafloor alterations, but not explosive risks as occur at subaerial volcanoes. Nonetheless, every occurrence generates thousands of earthquakes up to 10,000 in one day during the 2015 eruption and lowers the caldera floor several meters as magma is released per Smithsonian’s Global Volcanism Program.
It is understanding magma dynamics in such submarine settings as Axial that is critical. The interaction of faulting, tectonic extension, and magmatic overpressure governs not only the timing of eruptions but also lava flow style and pattern. Results from recent simulations suggest eruptions occur when pressure in the magma reservoir surpasses a threshold of 12–14 MPa, a finding that makes scientists better at interpreting deformation and seismic proxies as eruption precursors in advanced numerical models.
The knowledge gained from Axial’s restless cycles is already shaping how scientists approach eruption forecasting at other, more hazardous volcanoes. As Valerio Acocella, volcanologist at Roma Tre University, puts it: “We’ll understand it better and that will help us understand other volcanoes, too.” The lessons learned from Axial’s deep, data-rich laboratory may one day provide earlier warnings for communities living in the shadow of far more dangerous peaks.
