Artificial intelligence helps seismologists find the next monster earthquake

“We had huge amounts of data available,” Ross says. He explains that because California is so geologically active and completely covered by seismometers, there was more data than human experts alone could reasonably handle. In addition, most fault slips create small, imperceptible earthquakes. These seismic waves emanate from such a small volume that even the most skilled seismologists may have difficulty detecting them in seismic data, especially when they resemble noise from human sources, such as traffic.

In 2017, Ross had an epiphany. He saw machine learning programs handling huge collections of images, identifying and classifying objects in them with precision and speed that humans couldn't match. So, think, why not apply a similar approach to seismology?

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Ross's first target: those small, abundant earthquakes. They may be harmless, but that doesn't make them unimportant – their waves can light up every fault they pass through, including the most dangerous, pressure-laden fault that may one day explode and lead to disaster.

Ross and his colleagues took seismic waveforms from around Southern California, which human scientists identified as real earthquakes. Then he made models of them, snapshots of the seismic wave pattern of each earthquake. Finally, he put an algorithm on the seismic record, an algorithm that looks for elusive earthquakes that match those templates, those snapshots.

The algorithm quickly identified nearly two million previously hidden small earthquakes from 2008 to 2017, which in turn highlighted a complex network of faults and fault characteristics that previous earthquake searches could not see.

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