  Seismology from the past few decades has utilized the most advanced
technologies and equipment to monitor seismic events globally. However,
forecasting disasters like earthquakes is still an underdeveloped topic from
the history. Recent researches in spatiotemporal forecasting have revealed some
possibilities of successful predictions, which becomes an important topic in
many scientific research fields. Most studies of them have many successful
applications of using deep neural networks. In the geoscience study, earthquake
prediction is one of the world's most challenging problems, about which cutting
edge deep learning technologies may help to discover some useful patterns. In
this project, we propose a joint deep learning modeling method for earthquake
forecasting, namely TSEQPREDICTOR. In TSEQPREDICTOR, we use comprehensive deep
learning technologies with domain knowledge in seismology and exploit the
prediction problem using encoder-decoder and temporal convolutional neural
networks. Comparing to some state-of-art recurrent neural networks, our
experiments show our method is promising in terms of predicting major shocks
for earthquakes in Southern California.
