The aftershocks that follow an earthquake can be even more dangerous and damaging than the main temblor, for example by collapsing already structurally weakened buildings. With deep learning emerging as something of a panacea in the world of science, AI researchers and seismologists alike are leveraging the tech in pursuit of better aftershock forecast solutions. A major breakthrough seemed to occur in 2018 when a Harvard University and Google research team published the paper Deep learning of aftershock patterns following large earthquakes in Nature. The paper proposed a deep learning model that significantly improved aftershock location forecasts compared to previous methods. It went viral on social media and garnered global mainstream media coverage.

The problem as far as I can see is that far too few papers use a basic and simple baseline to compare against as a control. They are always comparing against the state of the art and previous DL techniques, but rarely to do they include basic correlation analysis, linear / logistic regressions, etc., as a basis for comparison. In statistics one doesn't just say "we got X performance which was better than Y performance", one says "we show that the effect size is better than control by X amount, and confirm that this actually represents an improvement and is not likely a bias induced by random sampling of the data with 95% confidence." But DL papers often just include final test set performance and traces of loss function per iteration, and say, look X learns faster than Y and Z and ends up with less error. Often this is even done without confidence intervals, which, for methods that depend on random initial conditions, is a sin.