  A small fraction of the gravitational-wave (GW) signals that will be detected
by second and third generation detectors are expected to be strongly lensed by
galaxies and clusters, producing multiple observable copies. While optimal
Bayesian model selection methods are developed to identify lensed signals,
processing tens of thousands (billions) of possible pairs of events detected
with second (third) generation detectors is both computationally intensive and
time consuming. To mitigate this problem, we propose to use machine learning to
rapidly rule out a vast majority of candidate lensed pairs. As a proof of
principle, we simulate non-spinning binary black hole events added to Gaussian
noise, and train the machine on their time-frequency maps (Q-transforms) and
localisation skymaps (using Bayestar), both of which can be generated in
seconds. We show that the trained machine is able to accurately identify lensed
pairs with efficiencies comparable to existing Bayesian methods.
