Google and Harvard use AI to predict earthquake aftershocks

Researchers from Google's AI division and Harvard University have created an AI model capable of predicting the location of aftershocks up to one year after a major earthquake. The model was trained with 199 major earthquake events in recent decades followed by 130,000 aftershocks, and was found to be more accurate than a method used to predict aftershocks today. Aftershocks included in the dataset used to train the neural network took place in a perimeter that stretches 50 kilometers vertically and 100 kilometers horizontally from each earthquake epicenter. "We found that after feeding these model stress changes into the neural network, the neural network could sort of predict aftershock locations in the testing dataset more accurately that the sort of baseline Coulomb failure stress change criterion that's used a lot in studies of aftershock locations," Phoebe DeVries of the Department of Earth and Planetary Sciences at Harvard University told VentureBeat in a phone interview. Data used to train the model came from noteworthy earthquakes such as the 2004 Sumatra earthquake, the 2011 earthquake in Japan, the 1989 Loma Prieta earthquake in the San Francisco Bay Area, and the 1994 Northride earthquake near Los Angeles.