Deep learning artificial intelligence keeps an eye on volcano movements


Date:
October 15, 2020
Source:
Penn State
Summary:
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. Now,
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.



FULL STORY ========================================================================== 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.


==========================================================================
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 Sciences.

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 space."
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.

"We 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."

========================================================================== Story Source: Materials provided by Penn_State. Original written by Matt Swayne. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Jian Sun, Christelle Wauthier, Kirsten Stephens, Melissa Gervais,
Guido
Cervone, Peter La Femina, Machel Higgins. Automatic Detection
of Volcanic Surface Deformation Using Deep Learning. Journal
of Geophysical Research: Solid Earth, 2020; 125 (9) DOI:
10.1029/2020JB019840 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201015134215.htm

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