Costantino G., Giffard-Roisin Sophie, Dalla Mura M., Socquet A. (2024). Denoising of geodetic time series using spatiotemporal graph neural networks : application to slow slip event extraction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 17567-17579. ISSN 1939-1404.
Titre du document
Denoising of geodetic time series using spatiotemporal graph neural networks : application to slow slip event extraction
Costantino G., Giffard-Roisin Sophie, Dalla Mura M., Socquet A.
Source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024,
17, 17567-17579 ISSN 1939-1404
Geospatial data have 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 sources, including both environmental signals and instrumental artifacts, which can be 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 global navigation satellite system (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 multistation spatiotemporal graph-based attentive denoiser that learns latent characteristics of GNSS noise to reveal SSE-related displacement with submillimeter 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.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Géophysique interne [066]
;
Télédétection [126]
