  29 August 2018: "Artificial intelligence nails predictions of earthquake
aftershocks". This Nature News headline is based on the results of DeVries et
al. (2018) who forecasted the spatial distribution of aftershocks using Deep
Learning (DL) and static stress feature engineering. Using receiver operating
characteristic (ROC) curves and the area under the curve (AUC) metric, the
authors found that a deep neural network (DNN) yields AUC = 0.85 compared to
AUC = 0.58 for classical Coulomb stress. They further showed that this result
was physically interpretable, with various stress metrics (e.g. sum of absolute
stress components, maximum shear stress, von Mises yield criterion) explaining
most of the DNN result. We here clarify that AUC c. 0.85 had already been
obtained using ROC curves for the same scalar metrics and by the same authors
in 2017. This suggests that DL - in fact - does not improve prediction compared
to simpler baseline models. We reformulate the 2017 results in probabilistic
terms using logistic regression (i.e., one neural network node) and obtain AUC
= 0.85 using 2 free parameters versus the 13,451 parameters used by DeVries et
al. (2018). We further show that measured distance and mainshock average slip
can be used instead of stress, yielding an improved AUC = 0.86, again with a
simple logistic regression. This demonstrates that the proposed DNN so far does
not provide any new insight (predictive or inferential) in this domain.
