Earthquake researchers hope AI will lead to a breakthrough in forecasting

We always hear it from seismologists: There is no way to predict when the next big earthquake will occur.

But can technological progress in the twenty-first century change that?

Some researchers, including those at Los Alamos National Laboratory in New Mexico, are hopeful that tools like artificial intelligence and machine learning could lead to a seismic breakout.

Dr. Paul Johnson, a scientist with the Geophysics Group at Los Alamos, says researchers aren’t trying to do real earthquake prediction, which deals with location and time. Instead, they are just looking for the timing of the earthquake, which he says is “hard enough”.

Using computers and an earthquake simulator at Penn State, researchers applied machine learning to document millions of data points generated by seismic waves from a lab-created fault. Then they ran experiments to see if some of the patterns the computers found could help predict the next rupture.

“It worked almost immediately,” Johnson said. “And we were amazed at how quickly it worked and how easy it was to solve a problem we had had for so long.”

The researchers then tested the theory on a fairly recently discovered type of real-world earthquake seen in the Pacific Northwest.

It is an earthquake that no one feels. They’re called “slow-slide earthquakes,” in which one tectonic plate slowly slides over the course of a month or so under the other.

Johnson said the theory worked for those hard-to-detect earthquakes, too. He said computers could warn of the next slow-slip earthquake “immediately after” the previous one.

But Johnson cautions that scientists are still far from demonstrating that models can predict other types of earthquakes, such as the 1994 Northridge earthquake.

“For the earthquakes that cause earthquakes, the ones that we really care about as citizens, we’re just not there yet,” Johnson said.

Lucy Jones, founder of the Dr. Lucy Jones Center for Science and Society, says she’s heard every possible theory related to earthquake prediction.

“Among the general public, we have a lot of thinking about the connection to the lunar cycle and tides, hot weather, early mornings, all of those,” Jones said. “We remember earthquakes that fit the pattern and we forget earthquakes that don’t because we want the pattern to be right.”

So far, Jones said, no theories have been proven true, and she believes that eventually the research – like previous attempts – will only strengthen the theory that large earthquakes are random and unpredictable.

“It’s important to do this study because we have this puzzle of ‘Is it random?'” Jones said. “Is it really random or is it random because it’s so complicated? And machine learning has a chance to help us separate that. “

Jones is not part of the Los Alamos research team, but says she is interested in how the research is going and expects there will be a lot of controversy on the topic for a very long time.

“At this point, we still don’t know. It’s that point in the science that makes it interesting,” Jones said.

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