  We review previous approaches to nowcasting earthquakes and introduce new
approaches based on deep learning using three distinct models based on
recurrent neural networks and transformers. We discuss different choices for
observables and measures presenting promising initial results for a region of
Southern California from 1950-2020. Earthquake activity is predicted as a
function of 0.1-degree spatial bins for time periods varying from two weeks to
four years. The overall quality is measured by the Nash Sutcliffe Efficiency
comparing the deviation of nowcast and observation with the variance over time
in each spatial region. The software is available as open-source together with
the preprocessed data from the USGS.
