  In this study we develop a single-station deep-learning approach for fast and
reliable estimation of earthquake magnitude directly from raw waveforms. We
design a regressor composed of convolutional and recurrent neural networks that
is not sensitive to the data normalization, hence waveform amplitude
information can be utilized during the training. Our network can predict
earthquake magnitudes with an average error close to zero and standard
deviation of ~0.2 based on single-station waveforms without instrument response
correction. We test the network for both local and duration magnitude scales
and show a station-based learning can be an effective approach for improving
the performance. The proposed approach has a variety of potential applications
from routine earthquake monitoring to early warning systems.
