  Slow earthquakes may trigger failure on neighboring locked faults that are
stressed enough to break, and slow slip patterns may evolve before a nearby
great earthquake. However, even in the clearest cases such as Cascadia, slow
earthquakes and associated tremor have only been observed in intermittent and
discrete bursts. By training a convolutional neural network to detect known
tremor on a single seismic station in Cascadia, we isolate and identify tremor
and slip preceding and following known larger slow events. The deep neural
network can be used for the detection of quasi-continuous tremor, providing a
proxy that quantifies the slow slip rate. Furthermore, the model trained in
Cascadia recognizes tremor in other subduction zones and also along the San
Andreas Fault at Parkfield, suggesting a universality of waveform
characteristics and source processes, as posited from experiments and theory.
