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

Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning