Penn State Researchers Eye Volcano Movements with AI
October 28, 2020
RADAR satellites can collect massive amounts of remote sensing data that
can detect ground movements — surface deformations — at volcanoes in
near real time. These ground movements could signal impending volcanic
activity and unrest; however, clouds and other atmospheric and
instrumental disturbances can introduce significant errors in those
ground movement measurements.
Mauna Loa in Hawaii
Now, Penn State researchers have used artificial intelligence (AI) to
clear up that noise, drastically facilitating and improving near
real-time observation of volcanic movements and the detection of
volcanic activity and unrest.
“The shape of volcanoes is constantly changing and much of that change
is due to underground magma movements in the magma plumbing system made
of magma reservoirs and conduits,” said Christelle Wauthier, associate
professor of geosciences and Institute for Data and Computational
Sciences (ICDS) faculty fellow. “Much of this movement is subtle and
cannot be picked up by the naked eye.”
Geoscientists have used several methods to measure the ground changes
around volcanoes and other areas of seismic activity, but all have
limitations, said Jian Sun, lead author of the paper and a postdoctoral
scholar in geosciences, funded by Dean's Postdoc-Facilitated Innovation
through Collaboration Award from the College of Earth and Mineral
He added that, for example, scientists can use ground stations, such as
GPS or tiltmeters, to monitor possible ground movement due to volcanic
activity. However, there are a few problems with these ground-based
methods. First, the instruments can be expensive and need to be
installed and maintained on site.
“So, it’s hard to put a lot of ground-based stations in a specific area
in the first place, but, let’s say there actually is a volcanic
explosion or an earthquake, that would probably damage a lot of these
very expensive instruments,” said Sun. “Second, those instruments will
only give you ground movement measurements at specific locations where
they are installed, therefore those measurements will have a very
limited spatial coverage.”
On the other hand, satellites and other forms of remote sensing can
gather a lot of important data about volcanic activity for
geoscientists. These devices are also, for the most part, out of harm’s
way from an eruption and the satellite images offer very extended
spatial coverage of ground movement. However, even this method has its
drawbacks, according to Sun.
“We can monitor the movement of the ground caused by earthquakes or
volcanoes using RADAR remote sensors, but while we have access to a lot
of remote sensing data, the RADAR waves must go through the atmosphere
to get recorded at the sensor,” he said. “And the propagation path will
likely be affected by that atmosphere, especially if the climate is
tropical with a lot of water vapor and clouds variations in time and
According to the researchers, who report their findings in a recent
issue of the Journal of Geophysical Research, a deep learning method
they developed acts much like a jigsaw puzzle master. By taking pieces
of data that are clear, the system can begin to fill in the holes of
“noisy” data, holes created by the interference of weather and other
instrumental noises. It can then build a reasonably accurate picture of
the land and its movements.
Using this deep learning method, scientists could gain valuable insights
into the movement of the ground, particularly in areas with active
volcanoes or earthquake zones and faults, said Sun. The program may be
able spot potential warning signs, such as sudden land shifts that might
be a portent of an oncoming volcanic eruption, or earthquake.
“It’s really important for areas close to active volcanoes, or near
where there have been earthquakes, to have as early warning as possible
that something might happen,” said Sun.
Deep learning, as its name suggests, uses training data to teach the
system to recognize features that the programmers want to study. In this
case, the researchers trained the system with synthetic data that was
similar to satellite surface deformation data. The data included signals
of volcanic deformation, both spatially and topographically correlated
atmospheric features and errors in the estimation of satellite orbits.
Future research will focus on refining and expanding our deep learning
algorithm, according to Wauthier.
wish to be able to identify earthquake and fault movements as well as
magmatic sources and include several underground sources generating
surface deformation,” she said. “We will apply this new groundbreaking
method to other active volcanoes thanks to support from NASA.”
Sun and Wauthier also worked with Kirsten Stephens and Machel Higgins,
both doctoral candidate in geosciences; Melissa Gervais, assistant
professor of meteorology and atmospheric science and ICDS co-hire; Guido
Cervone, professor of geography, meteorology and atmospheric Science and
ICDS associate director; and Peter La Femina, associate professor of
The National Aeronautics and Space Administration, The National Science
Foundation, the Penn State College of Earth and Mineral Sciences
supported this work.
Computations were done using the ICDS’s Roar supercomputer.