  This study describes a deep convolutional neural network (CNN) based
technique for the prediction of intensity measurements (IMs) of ground shaking.
The input data to the CNN model consists of multistation 3C broadband and
accelerometric waveforms recorded during the 2016 Central Italy earthquake
sequence for M $\ge$ 3.0. We find that the CNN is capable of predicting
accurately the IMs at stations far from the epicenter and that have not yet
recorded the maximum ground shaking when using a 10 s window starting at the
earthquake origin time. The CNN IM predictions do not require previous
knowledge of the earthquake source (location and magnitude). Comparison between
the CNN model predictions and the predictions obtained with Bindi et al. (2011)
GMPE (which require location and magnitude) has shown that the CNN model
features similar error variance but smaller bias. Although the technique is not
strictly designed for earthquake early warning, we found that it can provide
useful estimates of ground motions within 15-20 sec after earthquake origin
time depending on various setup elements (e.g., times for data transmission,
computation, latencies). The technique has been tested on raw data without any
initial data pre-selection in order to closely replicate real-time data
streaming. When noise examples were included with the earthquake data, the CNN
was found to be stable predicting accurately the ground shaking intensity
corresponding to the noise amplitude.
