  Understanding the noise in gravitational-wave detectors is central to
detecting and interpreting gravitational-wave signals. Glitches are transient,
non-Gaussian noise features that can have a range of environmental and
instrumental origins. The Gravity Spy project uses a machine-learning algorithm
to classify glitches based upon their time-frequency morphology. The resulting
set of classified glitches can be used as input to detector-characterisation
investigations of how to mitigate glitches, or data-analysis studies of how to
ameliorate the impact of glitches. Here we present the results of the Gravity
Spy analysis of data up to the end of the third observing run of Advanced LIGO.
We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO
Livingston into morphological classes. We find that the distribution of
glitches differs between the two LIGO sites. This highlights the potential need
for studies of data quality to be individually tailored to each
gravitational-wave observatory.
