  While deep learning models have seen recent high uptake in the geosciences,
and are appealing in their ability to learn from minimally processed input
data, as black box models they do not provide an easy means to understand how a
decision is reached, which in safety-critical tasks especially can be
problematical. An alternative route is to use simpler, more transparent white
box models, in which task-specific feature construction replaces the more
opaque feature discovery process performed automatically within deep learning
models. Using data from the Groningen Gas Field in the Netherlands, we build on
an existing logistic regression model by the addition of four further features
discovered using elastic net driven data mining within the catch22 time series
analysis package. We then evaluate the performance of the augmented logistic
regression model relative to a deep (CNN) model, pre-trained on the Groningen
data, on progressively increasing noise-to-signal ratios. We discover that, for
each ratio, our logistic regression model correctly detects every earthquake,
while the deep model fails to detect nearly 20 % of seismic events, thus
justifying at least a degree of caution in the application of deep models,
especially to data with higher noise-to-signal ratios.
