  The computational resources required to solve the full 3D inversion of
time-domain electromagnetic data are immense. To overcome the time-consuming 3D
simulations, we construct a surrogate model, more precisely, a data-driven
statistical model to replace the 3D simulations. It is trained on 3D data and
predicts the approximate output much faster. We construct a surrogate model
that predicts the discrepancy between a 1D subsurface model and a deviation of
the 1D assumption. The latter response is fastly computable with a
semi-analytical 1D forward model. We exemplify the approach on a two-layered
case. The results are encouraging even with few training samples. Given the
computational cost related to the 3D simulations, there are limitations in the
number of training samples that can be generated. In addition, certain
applications require a wide range of parameters to be sampled, such as the
electrical conductivity parameters in a saltwater intrusion case. The challenge
of this work is achieving the best possible accuracy with only a few thousand
samples. We propose to view the performance in terms of learning gain,
representing the gain from the surrogate model whilst still acknowledging a
residual discrepancy. Our works open new avenues for effectively simulating 3D
TDEM data.
