  The observation of gravitational waves is hindered by the presence of
transient noise (glitches). We study data from the third observing run of the
Advanced LIGO detectors, and identify new glitch classes. Using training sets
assembled by monitoring of the state of the detector, and by citizen-science
volunteers, we update the Gravity Spy machine-learning algorithm for glitch
classification. We find that a new glitch class linked to ground motion at the
detector sites is especially prevalent, and identify two subclasses of this
linked to different types of ground motion. Reclassification of data based on
the updated model finds that 27 % of all transient noise at LIGO Livingston
belongs to the new glitch class, making it the most frequent source of
transient noise at that site. Our results demonstrate both how glitch
classification can reveal potential improvements to gravitational-wave
detectors, and how, given an appropriate framework, citizen-science volunteers
may make discoveries in large data sets.
