Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures
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.