A deep learning algorithm can remove the sound of city noise from earthquake monitoring tools, so it is easy to identify tremors.
This algorithm strips out city noise to improve earthquake monitoring systems as the sounds of cities can make it tough to discern the underground signals that indicate an earthquake is happening, but deep learning algorithms could filter out this noise.
“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.”
To try to improve the ability to identify and locate earthquakes, Baroza and his colleagues introduced a deep neural network to distinguish between earthquake signals and other noise sources.
The sounds of cities – from cars, aircraft, helicopters, and general hustle and bustle add noise that makes it difficult to discern the underground signals that indicate an earthquake is happening.
However, there is one drawback: the neural network was trained on data labeled 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.
“The holy grail in this field is unsupervised learning,” says de Hoop. “If I go to one of the major cities in Japan, the chances this would work directly are pretty small, because it is supervised.”
Baroza is also unsure about how well the model would work in places other than California. “Depending on the environment, noise signatures are probably going to be different than the ones it’s trained on,” he says.
Around 80,000 samples of urban noise and 33,751 samples of earthquake signals were combined in different forms to train, validate and test the neural network. The noise samples came from audio recorded in Long Beach, California, while the earthquake signals were taken from the rural area around San Jacinto, also in California. “We made many millions of combinations of the two to train the neural network,” says Baroza.
The research is very useful for the field, says Maarten de Hoop at Rice University in Houston, Texas. “It’s very well done, and I think beautiful work,” he says.
The goal of identifying earthquakes early is to give warning of potentially damaging earthquakes early enough to allow appropriate response to the disaster, enabling people to minimize loss of life and property. The U.S. Geological Survey conducts and supports research on the likelihood of future earthquakes to save lives.
