It’s hard to detect smaller earthquakes in areas that have few seismic stations. And the less data you have, the harder it can be. Now, with a convolutional neural network developed by Harvard and MIT researchers, seismologists can better sift through the data to find earthquakes. By feeding the network training sets from seismically inactive regions, the network can identify and disregard regular activity while parsing the data, allowing it to clearly identify tremors.
What are the implications? We can better identify earthquakes and tremors with less data.
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