One neuron versus deep learning in aftershock prediction

Forecasting the spatial distribution of aftershocks in the aftermath of large seismic events is of great importance for improving both our understanding of earthquake triggering and post-disaster management. Recently, DeVries et al. attempted to solve this scientific problem by deep learning…

Here we first clarify that similar performances had already been obtained (by the same authors, in 2017)…

Second, we reformulate the 2017 results using two-parameter logistic regression (that is, one neuron) and obtain the same performance as that of the 13,451-parameter DNN…

This demonstrates that so far the proposed deep learning strategy does not provide any new insight (predictive or inferential) in this domain.