Controlled Study Identifies Key Design Factors for Stable Autoregressive Forecasting of Seismic Wavefields
Researchers published a controlled study on arXiv examining when autoregressive sequence models can reliably forecast oscillatory physical signals like seismograms over long time horizons. Using synthetic three-component seismograms and a model called SeismoGPT, they isolated which architectural design choices most reduce error accumulation during extended rollout. The findings matter because long-horizon forecasting of physical wavefields is a persistent challenge, and identifying the dominant stabilizing factors could guide future model design in seismology and related fields.
The study, submitted to arXiv on June 9, 2026, investigates the conditions under which autoregressive models can stably forecast oscillatory physical signals without compounding per-step errors into phase drift — a known failure mode that standard pointwise metrics often miss. Using synthetic three-component seismograms as a controlled testbed and the SeismoGPT architecture, the authors conducted intra-architecture ablations with paired significance tests to isolate individual design contributions. Multi-token prediction emerged as the dominant stabilizing factor, accounting for nearly all of the improvement over a single-token baseline (a median normalized cross-correlation gain of +0.040). A horizon-embedding hybrid prediction head and a cross-horizon STFT-magnitude coherence loss each provided smaller but consistent additional gains. The study also found a sharp performance threshold tied to context length: rollout generalization collapses when the context ratio falls below approximately one, roughly corresponding to the full P-S wave interval. A key residual failure identified is polarity inversion, which magnitude-based spectral losses cannot penalize by construction, pointing to phase-aware training objectives as a necessary next step. The authors explicitly frame the work as a controlled rollout stability study rather than a broad architectural benchmark.
What's missing
The study relies entirely on synthetic seismograms and does not evaluate whether the identified design principles generalize to real-world seismic recordings. The authors do not report computational cost or scalability of multi-token prediction relative to single-token baselines, nor do they test across multiple distinct architectures, limiting external validity.
What different sources said
- arXiv cs.LGCenter
When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms
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