用Transformer模型预测爱因斯坦望远镜的地震波形，助力降噪与实时控制。
Forecasting Seismic Waveforms: A Deep Learning Approach for Einstein Telescope
- 基于Transformer的自回归建模，可处理单站和阵列输入
- 短时预测准确，随时间推移性能逐步下降
- 为引力波探测器的地震噪声抑制提供数据驱动方案
我们提出SeismoGPT，一种基于Transformer的模型，用于未来引力波探测器（如爱因斯坦望远镜）场景下的三通道地震波形预测。该模型在自回归设定下训练，可处理单站和阵列输入。通过直接从波形数据中学习时空依赖关系，SeismoGPT能够捕捉真实的地面运动模式，并提供高精度的短期预测。结果表明，模型在即时预测窗口内表现良好，但随着预测时长增加性能逐渐下降，这符合自回归系统的预期。该方法为数据驱动的地震预报奠定了基础，有望支持牛顿噪声抑制与实时观测站控制。
原文摘要 · Abstract (English)
We introduce \textit{SeismoGPT}, a transformer-based model for forecasting three-component seismic waveforms in the context of future gravitational wave detectors like the Einstein Telescope. The model is trained in an autoregressive setting and can operate on both single-station and array-based inputs. By learning temporal and spatial dependencies directly from waveform data, SeismoGPT captures realistic ground motion patterns and provides accurate short-term forecasts. Our results show that the model performs well within the immediate prediction window and gradually degrades further ahead, as expected in autoregressive systems. This approach lays the groundwork for data-driven seismic forecasting that could support Newtonian noise mitigation and real-time observatory control.
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