  Gravitational wave observatories have always been affected by tele-seismic
earthquakes leading to a decrease in duty cycle and coincident observation
time. In this analysis, we leverage the power of machine learning algorithms
and archival seismic data to predict the ground motion and the state of the
gravitational wave interferometer during the event of an earthquake. We
demonstrate improvement from a factor of 5 to a factor of 2.5 in scatter of the
error in the predicted ground velocity over a previous model fitting based
approach. The level of accuracy achieved with this scheme makes it possible to
switch control configuration during periods of excessive ground motion thus
preventing the interferometer from losing lock. To further assess the accuracy
and utility of our approach, we use IRIS seismic network data and obtain
similar levels of agreement between the estimates and the measured amplitudes.
The performance indicates that such an archival or prediction scheme can be
extended beyond the realm of gravitational wave detector sites for hazard-based
early warning alerts.
