When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms
arXiv:2606.10868v1 Announce Type: new Abstract: Long-horizon autoregressive forecasting of oscillatory physical signals, such as seismograms, gravitational-wave strain, and similar wavefields is limited by error accumulation: as a causal model is fed its own outputs over hundreds of steps, small per-step errors compound into phase drift that pointwise metrics fail to detect. We ask when such rollout stays stable, using synthetic three-component seismograms as a physically structured testbed and the \textsc{SeismoGPT} autoregressive forecaster as the model under study. Through controlled, intra
The proliferation of advanced autoregressive models capable of handling complex temporal data is driving research into their limitations and stability, especially in scientific forecasting applications where accuracy is paramount.
Improving the long-term forecasting stability of autoregressive models for physical phenomena like seismic activity is crucial for applications ranging from disaster prediction to resource exploration, impacting critical infrastructure and economic sectors.
This study clarifies the conditions under which autoregressive models can reliably forecast complex wavefields, providing a framework for developing more stable and accurate AI models for scientific applications.
- · Geophysics researchers
- · AI model developers
- · Energy exploration companies
- · Disaster preparedness agencies
- · Traditional physics-based simulation
- · Undifferentiated AI forecasting tools
More robust and reliable AI tools become available for forecasting complex physical systems, especially those with oscillatory behavior.
Increased adoption of AI in fields like seismology and astronomy for predictive modeling, leading to new scientific discoveries or improved risk assessments.
Potential for new AI-driven platforms that integrate multiple geophysical data streams for holistic planetary monitoring and predictive capabilities, impacting resource management and urban planning.
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Read at arXiv cs.LG