  The seismically excited motion of high-Q pendula in gravitational-wave
observatories sets a sensitivity limit to sub-audio gravitational-wave
frequencies. Here, we report on the use of machine learning to predict the
motion of a high-Q pendulum with a resonance frequency of 1.4Hz that is driven
by natural seismic activity. We achieve a reduction of the displacement power
spectral density of 40dB at the resonant frequency 1.4Hz and 6dB at 11Hz. Our
result suggests that machine learning is able to significantly reduce
seismically induced test mass motion in gravitational-wave detectors in
combination with corrective feed-forward techniques.
