Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction

arXiv:2405.03320v1 Announce Type: new
Abstract: 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 …

abstract application arxiv case cs.ai cs.lg data denoising earth eess.sp event extraction geo geospatial graph graph neural networks monitoring networks neural networks physics.geo-ph sampling series spatial temporal time series type