arXiv:2606.07725v1 Announce Type: cross Abstract: Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and investigating the different stages of the earthquake cycle. Machine learning methods have proven promising for GNSS applications; however, most remain fully supervised. This creates a bottleneck as labeled data are scarce, even though large amounts of unlabeled GNSS data are freely available. We present GNSS-FM, a self-supervised foundation model for daily GNSS time serie
The proliferation of unlabeled GNSS data combined with advancements in self-supervised learning techniques makes this an opportune moment for developing foundation models in geosciences.
This development addresses a critical bottleneck in geospatial machine learning by enabling more effective utilization of vast, unlabeled GNSS data, which is essential for climate monitoring and disaster prediction.
The reliance on scarce labeled data for GNSS applications will decrease, allowing for more robust and scalable analysis of crustal deformations and earthquake cycles using self-supervised AI models.
- · Geospatial intelligence platforms
- · Geological survey organizations
- · Earthquake prediction research
- · AI model developers
- · Traditional supervised learning approaches in geophysics
- · Companies reliant solely on expert-labeled GNSS datasets
Improved accuracy and efficiency in monitoring tectonic activity and predicting seismic events through AI.
Enhanced resilience for critical infrastructure in seismically active regions due to better predictive capabilities.
Potential for new global standards in geological monitoring and early warning systems leveraging foundation models.
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Read at arXiv cs.LG