GNSS-FM is a self-supervised foundation model for GNSS displacement time series that outperforms task-specific baselines on 90-day forecasting and seismic step localization after pretraining on global station data.
SeisLM: a foundation model for seismic waveforms,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
SeismoGPT is a transformer autoregressive model achieving median normalized cross-correlation above 0.93 when forecasting synthetic three-component seismograms up to 240 s ahead from P- and S-wave context.
citing papers explorer
-
Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures
SeismoGPT is a transformer autoregressive model achieving median normalized cross-correlation above 0.93 when forecasting synthetic three-component seismograms up to 240 s ahead from P- and S-wave context.