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Gravitational wave observatories have always been affected by tele-seismicearthquakes leading to a decrease in duty cycle and coincident observationtime. In this analysis, we leverage the power of machine learning algorithmsand archival seismic data to predict the ground motion and the state of thegravitational wave interferometer during the event of an earthquake. Wedemonstrate improvement from a factor of 5 to a factor of 2.5 in scatter of theerror in the predicted ground velocity over a previous model fitting basedapproach. The level of accuracy achieved with this scheme makes it possible toswitch control configuration during periods of excessive ground motion thuspreventing the interferometer from losing lock. To further assess the accuracyand utility of our approach, we use IRIS seismic network data and obtainsimilar levels of agreement between the estimates and the measured amplitudes.The performance indicates that such an archival or prediction scheme can beextended beyond the realm of gravitational wave detector sites for hazard-basedearly warning alerts.

Ground motion prediction at gravitational wave observatories using archival seismic data