A deep learning algorithm can remove the sound of city noise from earthquake monitoring tools, potentially making it easier to pinpoint when and where a tremor occurs.
A deep learning algorithm can remove city noise from earthquake monitoring tools, potentially making it easier to pinpoint when and where a tremor occurs.
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.
But he does highlight one drawback: the neural network was trained on data labelled by humans, a method called supervised learning, and the readings were all from one area. The fact that the model was supervised specifically to remove noise from sounds in California means it is less likely to be successful when presented with noise from elsewhere.
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