“Like Putting on Glasses for the First Time”—How AI Improves Earthquake Detection
Freeman writes:
On January 1, 2008, at 1:59 am in Calipatria, California, an earthquake happened. You haven't heard of this earthquake; even if you had been living in Calipatria, you wouldn't have felt anything. It was magnitude -0.53, about the same amount of shaking as a truck passing by. Still, this earthquake is notable, not because it was large but because it was small-and yet we know about it.
Over the past seven years, AI tools based on computer imaging have almost completely automated one of the fundamental tasks of seismology: detecting earthquakes.
[...]
"In the best-case scenario, when you adopt these new techniques, even on the same old data, it's kind of like putting on glasses for the first time, and you can see the leaves on the trees," said Kyle Bradley, co-author of the Earthquake Insights newsletter.
[...]
Less certain is what comes next. Earthquake detection is a fundamental part of seismology, but there are many other data processing tasks that have yet to be disrupted. The biggest potential impacts, all the way to earthquake forecasting, haven't materialized yet."It really was a revolution," said Joe Byrnes, a professor at the University of Texas at Dallas. "But the revolution is ongoing."
[...]
The main tool that scientists traditionally use is a seismometer. These record the movement of the Earth in three directions: up-down, north-south, and east-west. If an earthquake happens, seismometers can measure the shaking in that particular location.
[...]
Before good algorithms, earthquake cataloging had to happen by hand. Byrnes said that "traditionally, something like the lab at the United States Geological Survey would have an army of mostly undergraduate students or interns looking at seismograms."
[...]
"The field of seismology historically has always advanced as computing has advanced," Bradley told me.There's a big challenge with traditional algorithms, though: They can't easily find smaller quakes, especially in noisy environments.
[...]
earthquakes have a characteristic "shape." The magnitude 7.7 earthquake above looks quite different from the helicopter landing, for instance.So one idea scientists had was to make templates from human-labeled datasets. If a new waveform correlates closely with an existing template, it's almost certainly an earthquake.
Read more of this story at SoylentNews.