  We present a novel approach for data-driven modeling of the time-domain
induced polarization (IP) phenomenon using variational autoencoders (VAE). VAEs
are Bayesian neural networks that aim to learn a latent statistical
distribution to encode extensive data sets as lower dimension representations.
We collected 1 600 319 IP decay curves in various regions of Canada, the United
States and Kazakhstan, and compiled them to train a deep VAE. The proposed deep
learning approach is strictly unsupervised and data-driven: it does not require
manual processing or ground truth labeling of IP data. Moreover, our VAE
approach avoids the pitfalls of IP parametrization with the empirical Cole-Cole
and Debye decomposition models, simple power-law models, or other sophisticated
mechanistic models. We demonstrate four applications of VAEs to model and
process IP data: (1) representative synthetic data generation, (2) unsupervised
Bayesian denoising and data uncertainty estimation, (3) quantitative evaluation
of the signal-to-noise ratio, and (4) automated outlier detection. We also
interpret the IP compilation's latent representation and reveal a strong
correlation between its first dimension and the average chargeability of IP
decays. Finally, we experiment with varying VAE latent space dimensions and
demonstrate that a single real-valued scalar parameter contains sufficient
information to encode our extensive IP data compilation. This new finding
suggests that modeling time-domain IP data using mathematical models governed
by more than one free parameter is ambiguous, whereas modeling only the average
chargeability is justified. A pre-trained implementation of our model --
readily applicable to new IP data from any geolocation -- is available as
open-source Python code for the applied geophysics community.
