  A network of three or more gravitational wave detectors simultaneously taking
data is required to generate a well-localized sky map for gravitational wave
sources, such as GW170817. Local seismic disturbances often cause the LIGO and
Virgo detectors to lose light resonance in one or more of their component optic
cavities, and the affected detector is unable to take data until resonance is
recovered. In this paper, we use machine learning techniques to gain insight
into the predictive behavior of the LIGO detector optic cavities during the
second LIGO-Virgo observing run. We identify a minimal set of optic cavity
control signals and data features which capture interferometer behavior leading
to a loss of light resonance, or lockloss. We use these channels to accurately
distinguish between lockloss events and quiet interferometer operating times
via both supervised and unsupervised machine learning methods. This analysis
yields new insights into how components of the LIGO detectors contribute to
lockloss events, which could inform detector commissioning efforts to mitigate
the associated loss of uptime. Particularly, we find that the state of the
component optical cavities is a better predictor of loss of lock than ground
motion trends. We report prediction accuracies of 98% for times just prior to
lock loss, and 90% for times up to 30 seconds prior to lockloss, which shows
promise for this method to be applied in near-real time to trigger preventative
detector state changes. This method can be extended to target other auxiliary
subsystems or times of interest, such as transient noise or loss in detector
sensitivity. Application of these techniques during the third LIGO-Virgo
observing run and beyond would maximize the potential of the global detector
network for multi-messenger astronomy with gravitational waves.
