Data-Driven Forecasting of three-Component Seismograms Using Transformer Architectures
Forecasting seismic waveforms beyond observed data remains challenging due to the nonlinear, dispersive, and multi-scale nature of seismic wave propagation. In this work, we introduce \textsc{SeismoGPT}, a transformer-based autoregressive model designed to forecast three-component seismic waveforms directly in the time domain. Forecasting is formulated as a physically constrained continuation problem in which the model receives waveform context beginning at the P-wave arrival and extending a defined time beyond the S-wave arrival, after which future motion is generated recursively without access to ground-truth samples. Evaluation is performed on synthetic seismograms spanning source dept...
| Authors | Waleed Esmail, Stuart Russell, Jana Klinge et al. |
|---|---|
| Primary category | astro-ph.IM |
| Published | 1 June 2026 |
| Last updated at source | 8 July 2026 |
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