AI strips out city noise to improve earthquake monitoring systems

A deep learning algorithm can remove city noise from earthquake monitoring tools, potentially making it easier to pinpoint when and where a tremor occurs. "Earthquake monitoring in urban settings is important because it helps us understand the fault systems that underlie vulnerable cities," says Gregory Baroza at Stanford University in California. "By seeing where the faults go, we can better anticipate earthquake events." However, the sounds of cities – from cars, aircraft, helicopters and general hustle and bustle – adds noise that makes it difficult to discern the underground signals that indicate an earthquake is happening. To try to improve our ability to identify and locate earthquakes, Baroza and his colleagues trained a deep neural network to distinguish between earthquake signals and other noise sources.