AI News, Machine-Learning Algorithm Predicts Laboratory Earthquakes
- On Thursday, October 4, 2018
- By Read More
Machine-Learning Algorithm Predicts Laboratory Earthquakes
They've trained a machine-learning algorithm to spot the tell-tale signs that a laboratory earthquake is about to give way using only the sounds it emits under strain.
The team is cautious about the new technique’s utility for real earthquakes, but the work opens up new avenues of research in this area.
Earthquakes occurred here in 1857, 1881, 1901, 1922, 1934, and 1966, suggesting a pattern in which quakes occur every 22 years give or takea few years.
There has been little evidence that this type of prediction will ever be possible, even though there is much anecdotal evidence to suggest that animals can somehow sense the imminent onset of a quake.
At the interface between the blocks, they packed a mixture of rocky material, called gouge material, to simulate the properties of real faults.
Geologists know that as a quake approaches, the gouge material begins to fail, emitting groans and cracks as it shears—a kind of seismic chatter.
It generates lots of small slips and just a few large ones—a distribution that follows the well-known Gutenberg-Richter relation, just as a real earthquakes do.
The question these guys ask is whether the sound emitted by the fault can be used to predict the time of next slip.
The researchers fed the algorithm a sliding window of acoustic emissions, asking it to make a prediction at each instant of the likelihood of an earthquake.
“We show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with great accuracy,” they say.
Rouet-Leduc and co hypothesize that seismic precursors can be much smaller than had previously been thought and so are not usually recorded in the real world.
The machine seems to have spotted an entirely new signal that geologists had previously dismissed as noise in the laboratory quakes.
- On Tuesday, September 17, 2019
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