Scientific American] Predicting earthquakes is the holy grail of seismology. After all, quakes are deadly precisely because they’re erratic—striking without warning, triggering fires and tsunamis, and sometimes killing hundreds of thousands of people. If scientists could warn the public weeks or months in advance that a large temblor is coming, evacuation and other preparations could save countless lives.
So far, no one has found a reliable way to forecast earthquakes, even
though many scientists have tried. Some experts consider it a hopeless
endeavor. “You’re viewed as a nutcase if you say you think you’re going
to make progress on predicting earthquakes,” says Paul Johnson, a
geophysicist at Los Alamos National Laboratory. But he is trying anyway,
using a powerful tool he thinks could potentially solve this impossible
puzzle: artificial intelligence.
Researchers around the world have spent decades studying various
phenomena they thought might reliably predict earthquakes: foreshocks,
electromagnetic disturbances, changes in groundwater chemistry—even
unusual animal behavior. But none of these has consistently worked.
Mathematicians and physicists even tried applying machine learning to
quake prediction in the 1980s and ’90s, to no avail. “The whole topic is
kind of in limbo,” says Chris Scholz, a seismologist at Columbia
University’s Lamont–Doherty Earth Observatory.
But advances in technology—improved machine-learning algorithms and
supercomputers as well as the ability to store and work with vastly
greater amounts of data—may now give Johnson’s team a new edge in using
artificial intelligence. “If we had tried this 10 years ago, we would
not have been able to do it,” says Johnson, who is collaborating with
researchers from several institutions. Along with more sophisticated
computing, he and his team are trying something in the lab no one else
has done before: They are feeding machinesraw data—massive sets of
measurements taken continuously before, during and after lab-simulated
earthquake events. They then allow the algorithm to sift through the
data to look for patterns that reliably signal when an artificial quake
will happen. In addition to lab simulations, the team has also begun
doing the same type of machine-learning analysis using raw seismic data
from real temblors.
This is different from how scientists have attempted quake prediction
in the past—they typically used processed seismic data, called
“earthquake catalogues,” to look for predictive clues. These data sets
contain only earthquake magnitudes, locations and times, and leave out
the rest of the information. By using raw data instead, Johnson’s
machine algorithm may be able to pick up on important predictive
markers. Read More