Transformer model SeismoGPT forecasts seismic waveforms with high accuracy

Researchers have developed SeismoGPT, a transformer-based model designed to forecast seismic waveforms. This model operates autoregressively in the time domain, continuing waveform data beyond observed seismic arrivals. SeismoGPT achieved high accuracy, with median normalized cross-correlation above 0.93, demonstrating its ability to maintain phase coherence and spectral energy distribution. The findings suggest that foundation models can be applied to physics-driven time-series forecasting, with potential uses in seismic warning and hazard mitigation, particularly for advanced gravitational-wave observatories. AI

IMPACT Demonstrates potential for foundation models in physics-driven time-series forecasting, with applications in seismic warning systems.

RANK_REASON The cluster contains an academic paper detailing a new methodology and model for seismic waveform forecasting. [lever_c_demoted from research: ic=1 ai=1.0]

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