  High rate Global Navigation Satellite System (HR GNSS) data can be highly
useful for earthquake analysis as it provides continuous high-rate measurements
of ground motion. This data can be used to estimate the magnitude, to assess
the potential of an earthquake for generating tsunamis, and to analyze diverse
parameters related to the seismic source. Particularly, in this work, we
present the first results of a deep learning model based on a convolutional
neural network for earthquake magnitude estimation, using HR GNSS displacement
time series. The influence of different dataset configurations, such as station
numbers, epicentral distances, signal duration, and earthquake size, were
analyzed to figure out how the model can be adapted to various scenarios. We
explored the potential of the model for global application and compared its
performance using both synthetic and real data from different seismogenic
regions. The performance of our model at this stage was satisfactory in
estimating earthquake magnitude from synthetic data. Comparable results were
observed in tests using synthetic data from a different tectonic region than
the training data. Furthermore, the model was tested using real data from
different regions and magnitudes, resulting in a good accuracy, provided that
the data from a particular group of stations had similar epicentral distance
constraints to those used during the model training. The robustness of the DL
model can be improved to work independently from the window size of the time
series and the number of stations, enabling faster estimation by the model
using only near field data. Overall, this study provides insights for the
development of future DL approaches for earthquake magnitude estimation with
HR-GNSS data, emphasizing the importance of proper handling and careful data
selection for further model improvements.
