  Robust estimation of ground motions generated by scenario earthquakes is
critical for many engineering applications. We leverage recent advances in
Generative Adversarial Networks (GANs) to develop a new framework for
synthesizing earthquake acceleration time histories. Our approach extends the
Wasserstein GAN formulation to allow for the generation of ground-motions
conditioned on a set of continuous physical variables. Our model is trained to
approximate the intrinsic probability distribution of a massive set of
strong-motion recordings from Japan. We show that the trained generator model
can synthesize realistic 3-Component accelerograms conditioned on magnitude,
distance, and $V_{s30}$. Our model captures the expected statistical features
of the acceleration spectra and waveform envelopes. The output seismograms
display clear P and S-wave arrivals with the appropriate energy content and
relative onset timing. The synthesized Peak Ground Acceleration (PGA) estimates
are also consistent with observations. We develop a set of metrics that allow
us to assess the training process's stability and tune model hyperparameters.
We further show that the trained generator network can interpolate to
conditions where no earthquake ground motion recordings exist. Our approach
allows the on-demand synthesis of accelerograms for engineering purposes.
