  Precise real time estimates of earthquake magnitude and location are
essential for early warning and rapid response. While recently multiple deep
learning approaches for fast assessment of earthquakes have been proposed, they
usually rely on either seismic records from a single station or from a fixed
set of seismic stations. Here we introduce a new model for real-time magnitude
and location estimation using the attention based transformer networks. Our
approach incorporates waveforms from a dynamically varying set of stations and
outperforms deep learning baselines in both magnitude and location estimation
performance. Furthermore, it outperforms a classical magnitude estimation
algorithm considerably and shows promising performance in comparison to a
classical localization algorithm. In this work, we furthermore conduct a
comprehensive study of the requirements on training data, the training
procedures and the typical failure modes using three diverse and large scale
data sets. Our analysis gives several key insights. First, we can precisely
pinpoint the effect of large training data; for example, a four times larger
training set reduces the required time for real time assessment by a factor of
four. Second, the basic model systematically underestimates large magnitude
events. This issue can be mitigated by incorporating events from other regions
into the training through transfer learning. Third, location estimation is
highly precise in areas with sufficient training data, but is strongly degraded
for events outside the training distribution. Our analysis suggests that these
characteristics are not only present for our model, but for most deep learning
models for fast assessment published so far. They result from the black box
modeling and their mitigation will likely require imposing physics derived
constraints on the neural network.
