GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series
Researchers have developed GNSS-FM, a novel self-supervised foundation model designed for analyzing daily Global Navigation Satellite System (GNSS) displacement time series. This model utilizes a dual-stream input combining displacement and velocity data, pre-trained with a masked latent prediction objective. After pre-training on data from over 17,000 GNSS stations, GNSS-FM demonstrated strong performance when fine-tuned for displacement forecasting and seismic step localization, outperforming existing task-specific baselines. AI
IMPACT This self-supervised approach could enable more widespread use of AI in geophysics by overcoming data labeling limitations.