  Reliable earthquake forecasting methods have long been sought after, and so
the rise of modern data science techniques raises a new question: does deep
learning have the potential to learn this pattern? In this study, we leverage
the large amount of earthquakes reported via good seismic station coverage in
the subduction zone of Japan. We pose earthquake forecasting as a
classification problem and train a Deep Learning Network to decide, whether a
timeseries of length greater than 2 years will end in an earthquake on the
following day with magnitude greater than 5 or not. Our method is based on
spatiotemporal b value data, on which we train an autoencoder to learn the
normal seismic behaviour. We then take the pixel by pixel reconstruction error
as input for a Convolutional Dilated Network classifier, whose model output
could serve for earthquake forecasting. We develop a special progressive
training method for this model to mimic real life use. The trained network is
then evaluated over the actual dataseries of Japan from 2002 to 2020 to
simulate a real life application scenario. The overall accuracy of the model is
72.3 percent. The accuracy of this classification is significantly above the
baseline and can likely be improved with more data in the future
