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A powerful earthquake on 24 August 2016 killed hundreds of people in Amatrice, Italy (pictured) and was followed by destructive aftershocks. New machine learning models hold potential for predicting the number of quake aftershocks.Credit: Stefano Montesi/Corbis via Getty

Seismologists are finally making traction on one of their most tantalizing but challenging goals: using machine learning to improve earthquake forecasts.

Three new papers describe deep-learning models that perform better than a conventional state-of-the-art model for forecasting earthquakes13. The findings are preliminary and apply only to limited situations, such as in assessing the risk of aftershocks after a big one has already hit. But they are a rare advance towards the long-sought goal of harnessing the power of machine learning to reduce seismic risk.

Im really excited that this is finally happening, says Morgan Page, a seismologist at the US Geological Survey (USGS) in Pasadena, California, who was not involved with the studies.

Heres what earthquake forecasts are not: predictions of an event of a particular magnitude happening in a particular location at a particular time the next Tuesday at 3 p.m. scenario. The notion that scientists can make such highly specific predictions has been discredited. Instead, statistical analyses are helping seismologists understand broader trends, such as how many aftershocks might be expected in the days to weeks after a large earthquake. Agencies such as the USGS issue aftershock forecasts to warn people in quake-ravaged areas of what else might be coming.

Algorithms spot millions of Californias tiniest quakes in historical data

At first glance, earthquake forecasts seem to be an obvious application to try to improve using deep learning4. The techniques do well when they ingest and synthesize large amounts of data and use them to predict the next steps in a pattern. And seismology is rich with data from catalogues of earthquakes that occur worldwide. Just as a large language model can train itself on millions of words to predict what word might come next, an earthquake-forecasting model should be able to train itself on earthquake catalogues to forecast the chances of a quake following one that has already occurred.

But researchers have struggled to extract meaningful trends from all the quake data5. Big earthquakes are rare, and working out what to worry about isnt easy.

In the past several years, however, seismologists have used machine learning to uncover small earthquakes that had not been spotted before in seismic records. These quakes have bulked up the existing earthquake catalogues, and provide fresh fodder for a second round of machine-learning analysis.

Current USGS forecasts use a model that relies on basic information about past earthquake magnitudes and locations to predict what might happen next. The three latest papers instead use a neural-network approach, which updates calculations during each step of the analysis to better capture the complex patterns of how earthquakes occur.

In the first1, geophysicist Kelian Dascher-Cousineau at the University of California, Berkeley, and his colleagues tested their model on a catalogue of thousands of quakes that struck southern California between 2008 and 2021. Their model performed better than the standard one at forecasting how many quakes would occur in rolling two-week periods. It was also better at capturing the full magnitude range of possible earthquakes, thus reducing the chance of a surprise big one.

At the University of Bristol, UK, applied statistician Samuel Stockman developed a similar method that performed well when trained2 on a catalogue of earthquakes that shook central Italy in 201617, damaging several towns. When researchers lower the magnitude of quakes included in the training set, the machine-learning model starts to perform better, Stockman says.

Rubble piles still stood in Castro, Italy, almost a year after the village was damaged by the same earthquake that levelled Amatrice.Credit: Amelia Hennighausen/Nature

And at Tel Aviv University in Israel, physicist Yohai Bar-Sinai led a team that developed a third neural-network model3. When tested against 30 years of quake data from Japan, it, too, did better than the standard model. The work might provide insight into fundamental quake physics, Bar-Sinai says. There is hope that we will understand more about the underlying mechanisms about what causes earthquakes to start, what determines their magnitude.

All three models are moderately promising, says Leila Mizrahi, a seismologist at the Swiss Federal Institute of Technology (ETH) in Zurich. They arent breakthroughs in their current form, she says, but they show potential for bringing machine-learning techniques into quake forecasting on an everyday basis.

Its certainly no silver bullet, adds Maximilian Werner, a seismologist at the University of Bristol who works with Stockman. But, he says, machine learning will gradually become part of official earthquake forecasting over the coming years, because it is so well suited to working with the huge earthquake data sets that are becoming more common.

Agencies such as the USGS will probably start to use machine-learning models alongside their standard one, and then transition entirely to the machine-learning approach if it proves to be superior, Page says. That could improve forecasts when aftershocks are rumbling unpredictably and disrupting peoples lives for months, as happened in Italy. The models could also be used to improve forecasts after large rare earthquakes, including the magnitude-6.8 earthquake that hit Morocco in September, killing thousands.

Still, Dascher-Cousineau warns people not to rely on these fancy new models too much. At the end of the day, preparing for quakes is the most important, he says. We dont get to stop making sure our buildings are up to code, we dont get to not have our earthquake kits, [just] because we have a better earthquake-forecasting model.

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AI predicts how many earthquake aftershocks will strike and their ... - Nature.com