Abstract
The detection of deformation in GNSS time series associated with
(a)seismic events down to a low magnitude is still a challenging issue.
The presence of a considerable amount of noise in the data makes it
difficult to reveal patterns of small ground deformation. Traditional
analyses and methodologies are able to effectively retrieve the
deformation associated to medium to large magnitude events. However, the
automatic detection and characterization of such events is still a
complex task, because traditionally-employed methods often separate the
time series analysis from the source characterization. Here we propose a
first end-to-end framework to characterize seismic sources using
geodetic data by means of deep learning, which can be an efficient
alternative to the traditional workflow, possibly overcoming its
performance. We exploit three different geodetic data representations in
order to leverage the intrinsic spatio-temporal structure of the GNSS
noise and the target signal associated with (slow) earthquake
deformation. We employ time series, images and image time series to
account for the temporal, spatial and spatio-temporal domain,
respectively. Thereafter, we design and develop a specific deep learning
model for each data set. We analyze the performance of the tested models
both on synthetic and real data from North Japan, showing that image
time series of geodetic deformation can be an effective data
representation to embed the spatio-temporal evolution, with the
associated deep learning method outperforming the other two. Therefore,
jointly accounting for the spatial and temporal evolution may be the key
to effectively detect and characterize fast or slow earthquakes.