  Rocks under stress deform by creep mechanisms that include formation and slip
on small-scale internal cracks. Intragranular cracks and slip along grain
contacts release energy as elastic waves termed acoustic emissions (AE). AEs
are thought to contain predictive information that can be used for fault
failure forecasting. Here we present a method using unsupervised classification
and an attention network to forecast labquakes using AE waveform features. Our
data were generated in a laboratory setting using a biaxial shearing device
with granular fault gouge intended to mimic the conditions of tectonic faults.
Here we analyzed the temporal evolution of AEs generated throughout several
hundred laboratory earthquake cycles. We used a Conscience Self-Organizing Map
(CSOM) to perform topologically ordered vector quantization based on waveform
properties. The resulting map was used to interactively cluster AEs. We
examined the clusters over time to identify those with predictive ability.
Finally, we used a variety of LSTM and attention-based networks to test the
predictive power of the AE clusters. By tracking cumulative waveform features
over the seismic cycle, the network is able to forecast the time-to-failure
(TTF) of lab earthquakes. Our results show that analyzing the data to isolate
predictive signals and using a more sophisticated network architecture are key
to robustly forecasting labquakes. In the future, this method could be applied
on tectonic faults monitor earthquakes and augment current early warning
systems.
