  Geospatial data has been transformative for the monitoring of the Earth, yet,
as in the case of (geo)physical monitoring, the measurements can have variable
spatial and temporal sampling and may be associated with a significant level of
perturbations degrading the signal quality. Denoising geospatial data is,
therefore, essential, yet often challenging because the observations may
comprise noise coming from different origins, including both environmental
signals and instrumental artifacts, which are spatially and temporally
correlated, thus hard to disentangle. This study addresses the denoising of
multivariate time series acquired by irregularly distributed networks of
sensors, requiring specific methods to handle the spatiotemporal correlation of
the noise and the signal of interest. Specifically, our method focuses on the
denoising of geodetic position time series, used to monitor ground displacement
worldwide with centimeter- to-millimeter precision. Among the signals affecting
GNSS data, slow slip events (SSEs) are of interest to seismologists. These are
transients of deformation that are weakly emerging compared to other signals.
Here, we design SSEdenoiser, a multi-station spatiotemporal graph-based
attentive denoiser that learns latent characteristics of GNSS noise to reveal
SSE-related displacement with sub-millimeter precision. It is based on the key
combination of graph recurrent networks and spatiotemporal Transformers. The
proposed method is applied to the Cascadia subduction zone, where SSEs occur
along with bursts of tectonic tremors, a seismic rumbling identified from
independent seismic recordings. The extracted events match the spatiotemporal
evolution of tremors. This good space-time correlation of the denoised GNSS
signals with the tremors validates the proposed denoising procedure.
