Abstract
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.