Tremor Waveform Denoising and Automatic Location with Neural Network Interpretation

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 detecting tremors with a classic convolutional neural network, we rely on neural network attribution to extract core tremor signatures. By identifying and extracting tremor characteristics, in particular in the frequency domain, the attribution analysis allows us to uncover structure in the data and denoise input waveforms. In particular, we show that these cleaned signals correspond to a waveform traveling in the Earth's crust and mantle at wavespeeds consistent with local estimates. We then use these cleaned waveforms to locate tremors with standard array-based techniques. 

We apply this method to the Cascadia subduction zone. We analyze a slow slip event that occurred in 2018 below the southern end of the Vancouver Island, Canada, where we identify tremor patches consistent with existing catalogs. Having validated our new methodology in a well-studied area, we further apply it to various tectonic contexts and discuss the implications of tremor occurrences in the scope of exploring the interplay between seismic and aseismic slip.