“We are at a point where the technology can do as well as — or better than — human experts,” Dr. Ross said.
Driving that guarded optimism is the belief that as sensors get smaller and cheaper, scientists will be able to gather larger amounts of seismic data. With help from neural networks and similar A.I. techniques, they hope to glean new insights from all this data.
Dr. Ross and other Caltech researchers are using these techniques to build systems that can more accurately recognize earthquakes as they are happening and anticipate where the epicenter is and where the shaking will spread.
Japan and Mexico have early warning systems, and California just rolled out its own. But scientists say artificial intelligence could greatly improve their accuracy, helping predict the direction and intensity of a rupture in the earth’s crust and providing earlier warnings to hospitals and other institutions that could benefit from a few extra seconds of preparation.
“The more detail you have, the better your forecasts will be,” Dr. Ross said.
Scientists working on these projects said neural networks have their limits. Though they are good at finding familiar signals in data, they are not necessarily suited to finding new kinds of signals — like the sounds tectonic plates make as they grind together.
But at Los Alamos, Dr. Johnson and his colleagues have shown that a machine-learning technique called “random forests” can identify previously unknown signals in a simulated fault created inside a lab. In one case, their system showed that a particular sound made by the fault, which scientists previously thought was meaningless, was actually an indication of when an earthquake would arrive.
Some scientists, like Robert Geller, a seismologist at the University of Tokyo, are unconvinced that A.I. will improve earthquake forecasts. He questions the very premise that past earthquakes can predict future ones. And ultimately, he said, we would only know the effectiveness of A.I. forecasting when earthquakes can be predicted beyond random chance.
“There are no shortcuts,” Dr. Geller said. “If you cannot predict the future, then your hypothesis is wrong.”