Pioneering artificial intelligence is used to predict earthquakes in an unprecedented technical pilot

An innovative system uses artificial intelligence (AI) to predict how earthquakes will shake in an unprecedented technology experience.

The tool, called DeepShake, is being developed to give several seconds of advance notice that earthquakes are on the way.

It uses a deep neural network, a form of artificial intelligence, to identify patterns from past earthquakes that can predict how vibration will travel.

The system is similar to an already existing system called ShakeAlert, which is based on the US West Coast and uses computing power, according to LiveScience.

ShakeAlert can detect the first waves of an earthquake’s motion, before calculating when the slow-moving waves that cause the most vibration will descend.

Researchers say the tool can give advance warning of vibration (Photo: ETIENNE LAURENT / EPA-EFE / REX) Read more related articles Read more related articles

“DeepShake is able to capture signals in seismic waveforms across dimensions of space and time,” said Avoy Datta, part of the Stanford University team behind DeepShake.

He said it would be “blazing fast” compared to previous warning systems.

The team developed the system to predict the ground motion of earthquakes near Ridgecrest, California in the United States.

The region was hit by a series of earthquakes in 2019, the largest of which was 7.1, although most of them were small.

The system uses a deep neural network (Photo: Anadolu Agency via Getty Images) Read more related articles

Researchers trained the tool to predict vibration using data from more than 36,000 earthquakes that struck the region from July to September 2019.

After putting 80% of the dataset into it, they kept 10% for modification and switching and 10% for testing if their predictions matched reality.

Scientists have programmed it to give more weight to larger earthquakes in the sequence, so that it can work better as an early warning system.

The tool did not give any information about the type or location of the earthquake.

But researchers said it was able to warn of vibration at earthquake monitoring stations between three and 13 seconds before it happened.

The researchers say it shows that machine learning models have the potential to improve the speed and accuracy of alarm systems.

“When training a machine learning model from start to finish, we really believe that these models can leverage this additional information to improve accuracy,” said team member Daniel J. Wu.

He said that most early warning systems first determine the location and magnitude of the earthquake, before calculating the Earth’s movement for a site.

“Each of these steps could introduce a mistake that could weaken the expectations of the earth’s shaking,” said Mr. Wu.

The results have been submitted to the American Seismological Association.

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