  Earthquake forecasting and prediction have long and in some cases sordid
histories but recent work has rekindled interest based on advances in early
warning, hazard assessment for induced seismicity and successful prediction of
laboratory earthquakes. In the lab, frictional stick-slip events provide an
analog for earthquakes and the seismic cycle. Labquakes are ideal targets for
machine learning (ML) because they can be produced in long sequences under
controlled conditions. Recent works show that ML can predict several aspects of
labquakes using fault zone acoustic emissions. Here, we generalize these
results and explore deep learning (DL) methods for labquake prediction and
autoregressive (AR) forecasting. DL improves existing ML methods of labquake
prediction. AR methods allow forecasting at future horizons via iterative
predictions. We demonstrate that DL models based on Long-Short Term Memory
(LSTM) and Convolution Neural Networks predict labquakes under several
conditions, and that fault zone stress can be predicted with fidelity,
confirming that acoustic energy is a fingerprint of fault zone stress. We
predict also time to start of failure (TTsF) and time to the end of Failure
(TTeF) for labquakes. Interestingly, TTeF is successfully predicted in all
seismic cycles, while the TTsF prediction varies with the amount of preseismic
fault creep. We report AR methods to forecast the evolution of fault stress
using three sequence modeling frameworks: LSTM, Temporal Convolution Network
and Transformer Network. AR forecasting is distinct from existing predictive
models, which predict only a target variable at a specific time. The results
for forecasting beyond a single seismic cycle are limited but encouraging. Our
ML/DL models outperform the state-of-the-art and our autoregressive model
represents a novel framework that could enhance current methods of earthquake
forecasting.
