To optimally monitor earthquake-generating processes, seismologists have
sought to lower detection sensitivities ever since instrumental seismic
networks were started about a century ago. Recently, it has become possible to
search continuous waveform archives for replicas of previously recorded events
(template matching), which has led to at least an order of magnitude increase
in the number of detected earthquakes and greatly sharpened our view of
geological structures. Earthquake catalogs produced in this fashion, however,
are heavily biased in that they are completely blind to events for which no
templates are available, such as in previously quiet regions or for very large
magnitude events. Here we show that with deep learning we can overcome such
biases without sacrificing detection sensitivity. We trained a convolutional
neural network (ConvNet) on the vast hand-labeled data archives of the Southern
California Seismic Network to detect seismic body wave phases. We show that the
ConvNet is extremely sensitive and robust in detecting phases, even when masked
by high background noise, and when the ConvNet is applied to new data that is
not represented in the training set (in particular, very large magnitude
events). This generalized phase detection (GPD) framework will significantly
improve earthquake monitoring and catalogs, which form the underlying basis for
a wide range of basic and applied seismological research