  In a recent study (Jozinovi\'c et al, 2020) we showed that convolutional
neural networks (CNNs) applied to network seismic traces can be used for rapid
prediction of earthquake peak ground motion intensity measures (IMs) at distant
stations using only recordings from stations near the epicenter. The
predictions are made without any previous knowledge concerning the earthquake
location and magnitude. This approach differs from the standard procedure
adopted by earthquake early warning systems (EEWSs) that rely on location and
magnitude information. In the previous study, we used 10 s, raw, multistation
waveforms for the 2016 earthquake sequence in central Italy for 915 events (CI
dataset). The CI dataset has a large number of spatially concentrated
earthquakes and a dense station network. In this work, we applied the CNN model
to an area around the VIRGO gravitational waves observatory sited near Pisa,
Italy. In our initial application of the technique, we used a dataset
consisting of 266 earthquakes recorded by 39 stations. We found that the CNN
model trained using this smaller dataset performed worse compared to the
results presented in the original study by Jozinovi\'c et al. (2020). To
counter the lack of data, we adopted transfer learning (TL) using two
approaches: first, by using a pre-trained model built on the CI dataset and,
next, by using a pre-trained model built on a different (seismological) problem
that has a larger dataset available for training. We show that the use of TL
improves the results in terms of outliers, bias, and variability of the
residuals between predicted and true IMs values. We also demonstrate that
adding knowledge of station positions as an additional layer in the neural
network improves the results. The possible use for EEW is demonstrated by the
times for the warnings that would be received at the station PII.
