  Machine learning models using seismic emissions can predict instantaneous
fault characteristics such as displacement in laboratory experiments and slow
slip in Earth. Here, we address whether the acoustic emission (AE) from
laboratory experiments contains information about near-future frictional
behavior. The approach uses a convolutional encoder-decoder containing a
transformer layer. We use as input progressively larger AE input time windows
and progressively larger output friction time windows. The attention map from
the transformer is used to interpret which regions of the AE contain hidden
information corresponding to future frictional behavior. We find that very
near-term predictive information is indeed contained in the AE signal, but
farther into the future the predictions are progressively worse. Notably,
information for predicting near future frictional failure and recovery are
found to be contained in the AE signal. This first effort predicting future
fault frictional behavior with machine learning will guide efforts for
applications in Earth.
