  In Earthquake Early Warning (EEW), every sufficiently impulsive signal is
potentially the first evidence for an unfolding large earthquake. More often
than not, however, impulsive signals are mere nuisance signals. One of the most
fundamental - and difficult - tasks in EEW is to rapidly and reliably
discriminate real local earthquake signals from all other signals. This
discrimination is necessarily based on very little information, typically a few
seconds worth of seismic waveforms from a small number of stations. As a
result, current EEW systems struggle to avoid discrimination errors, and suffer
from false and missed alerts. In this study we show how modern machine learning
classifiers can strongly improve real-time signal/noise discrimination. We
develop and compare a series of non-linear classifiers with variable
architecture depths, including fully connected, convolutional (CNN) and
recurrent neural networks, and a model that combines a generative adversarial
network with a random forest (GAN+RF). We train all classifiers on the same
data set, which includes 374k local earthquake records (M3.0-9.1) and 946k
impulsive noise signals. We find that all classifiers outperform existing
simple linear classifiers, and that complex models trained directly on the raw
signals yield the greatest degree of improvement. Using 3s long waveform
snippets, the CNN and the GAN+RF classifiers both reach 99.5% precision and
99.3% recall on an independent validation data set. Most misclassifications
stem from impulsive teleseismic records, and from incorrectly labeled records
in the data set. Our results suggest that machine learning classifiers can
strongly improve the reliability and speed of EEW alerts.
