Supervulkaan Yellowstone #2

quote:

Hidden magma cap discovered at Yellowstone National Park

Geoscientists have discovered a magma cap at Yellowstone National Park that is likely playing a critical role in preventing a massive eruption in one of the largest active volcanic systems in the world.

The cap is made of molten silicate materials and supercritical water -- a liquid-like gas that forms after water exceeds its critical point of 374 degrees Celsius -- and porous rock. It is located about 2.4 miles below the Earth's surface and essentially acts as a lid, trapping pressure and heat below it, according to the team of researchers who uncovered it.

The scientists found the cap by using a 53,000-pound vibroseis truck, a device capable of injecting low-frequency vibrations into the Earth to study the geology of the volcanic system. By generating tiny earthquakes that send seismic waves into the ground, the researchers were able to measure how the waves reflected off subsurface layers.

The scientists were surprised to see "something physically happening" at that depth, said Brandon Schmandt, professor of earth, environmental and planetary sciences at Rice University and co-author of the study, in a statement.

The stability of hazardous volcanic systems is "strongly influenced" by the uppermost magma storage depth, according to researchers.

"For decades, we’ve known there’s magma beneath Yellowstone, but the exact depth and structure of its upper boundary has been a big question," Schmandt said. "What we’ve found is that this reservoir hasn’t shut down -- it’s been sitting there for a couple million years, but it’s still dynamic."

In 2022, researchers discovered that Yellowstone's supervolcano has substantially more magma reservoir under the caldera than previously thought. The lava is also flowing at shallow depths that fueled prior eruption.

Through modeling, the researchers determined the magma cap consists of molten silicate materials and supercritical water bubbles within porous rock. The bubbles are formed as the magma rises and decompresses, causing gases like water and carbon dioxide to separate from the melt.

Volcanic eruptions can then occur as the bubbles accumulate and increase in buoyancy, driving an explosion.

An eruption at Yellowstone, however, is likely not imminent, the researchers said.

Data from seismic imaging and advanced computer modeling indicates that the magma reservoir is actively releasing gas and remains in a stable state. The system was described by Schmandt as "steady breathing," with the bubbles rising and releasing through the porous rock of the magma cap.

"Although we detected a volatile-rich layer, its bubble and melt contents are below the levels typically associated with imminent eruption," Schmandt said. "Instead, it looks like the system is efficiently venting gas through cracks and channels between mineral crystals."

quote:

Study finds 10 times more quakes in Yellowstone than earlier records showed

Machine learning algorithms applied to waveform data from 2008 to 2022 have revealed 86 276 earthquakes beneath the Yellowstone caldera, U.S., approximately 10 times more than previously recorded. The revised catalogue, published in Science Advances on July 18, 2025, was created by researchers from Western University, Universidad Industrial de Santander, and the U.S. Geological Survey.

A newly compiled seismic catalogue based on 15 years of waveform data shows that the Yellowstone caldera, a large volcanic depression spanning parts of Wyoming, Idaho, and Montana, experienced 86 276 earthquakes between 2008 and 2022.

This marks a tenfold increase compared to the previously known number of events and was made possible through the application of advanced machine learning techniques and a region-specific 3D velocity model.

The study, published in Science Advances on July 18, was led by Bing Li of Western University in collaboration with Universidad Industrial de Santander (Colombia) and the United States Geological Survey (USGS).

It demonstrates how artificial intelligence can radically improve detection rates and characterization of microseismic activity in complex volcanic regions.

Prior to this effort, earthquake detection relied heavily on manual inspections and traditional algorithms, limiting the scale and granularity of the seismic record. To overcome these limitations, researchers trained a separate AI model for each seismic station in the Yellowstone network.

This approach allowed accurate magnitude assignment, even during periods of overlapping swarm events. In validation tests, the model recovered 83% of previously documented earthquakes and identified 855 new events over just a 10-day window, with over 99% of those confirmed as real earthquakes.

More than half of the earthquakes were found to occur in swarms, sequences of small earthquakes clustered in time and space, typically lacking a dominant mainshock.

These swarms migrated through immature, high-roughness fault segments rather than mature fault zones, unlike seismic regions such as southern California.

The roughness was quantified using fractal geometry, a method that measures the self-similarity and spatial irregularity of seismic activity.