Artificial intelligence will soon predict where earthquake aftershocks might hit. Aftershocks can be more destructive than the quakes they follow, making it all the more important for experts to be able to predict them.
A group of researchers trained a "deep learning" programme with data about tens of thousands of earthquakes and aftershocks to see if they improve predictions. ALSO READ | Mars Opportunity rover has 45 days to contact home: NASAUsing deep learning algorithms, the pair analysed a database of earthquakes from around the world to try to predict where aftershocks might occur, and developed a system that was able to forecast aftershocks significantly better than random assignment according to the paper published in Nature.
"The previous baseline for aftershock forecasting has a precision of around three percent across the testing data set. Our neural network approach has a precision of around six percent," said Phoebe DeVries, co-author of the study and a post-doctoral fellow at Harvard.
"This approach is more accurate because it was developed without a strongly held prior belief about where aftershocks ought to occur," said DeVries. The notion of using artificial intelligent neural networks to try to predict aftershocks first came up several years ago, during the first of Meade's two sabbaticals at Google in Cambridge.
The programme allowed the researchers to map relationships "between the characteristics of a large earthquake -- the shape of the fault, how much did it slip, and how did it stress the earth -- and where aftershocks occurred," said Brendan Meade, professor of earth and planetary science at Harvard, and a study co-author.
In an article published in Nature alongside the study, he said the research had focused on only one set of changes caused by earthquakes that can affect where aftershocks occur.
"Another reason for caution is that the authors' analysis relies on factors that are fraught with uncertainty," wrote Gregory Beroza a professor of geophysics at Stanford University. ALSO READ | Moon mission now won’t require big budgets: NASA Chief
Beroza said the research had established a "beachhead" for additional study into how artificial intelligence could help forecasting.
"The application of machine-learning methods has the potential to extract meaning from these large and complex sources of information, but we are still in the early stages of this process" he said.