  Active faults release tectonic stress imposed by plate motion through a
spectrum of slip modes, from slow, aseismic slip, to dynamic, seismic events.
Slow earthquakes are often associated with tectonic tremor, non-impulsive
signals that can easily be buried in seismic noise and go undetected.
  We present a new methodology aimed at improving the detection and location of
tremors hidden within seismic noise. After identifying tremors with a classic
convolutional neural network, we rely on neural network attribution to extract
core tremor signatures and denoise input waveforms. We then use these cleaned
waveforms to locate tremors with standard array-based techniques.
  We apply this method to the Cascadia subduction zone, where we identify
tremor patches consistent with existing catalogs. In particular, we show that
the cleaned signals resulting from the neural network attribution analysis
correspond to a waveform traveling in the Earth's crust and mantle at
wavespeeds consistent with local estimates. This approach allows us to extract
small signals hidden within the noise, and therefore to locate more tremors
than in existing catalogs.
