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Self-supervised model GNSS-FM advances seismic displacement analysis

By PulseAugur Editorial · [1 sources] ·

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.

RANK_REASON This is a research paper describing a new model and its performance on specific tasks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Nick Teutschmann (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland), Laura Crocetti (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland), Fanny Lehmann (ETH AI Center, Switzerland), Leonardo Trentini (Institute of Geodesy an… ·

    GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series

    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 c…

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