Forecasting Seismic Waveforms: A Deep Learning Approach for Einstein Telescope
Waleed Esmail, Alexander Kappes, Stuart Russell and Christine Thomas
我们介绍了SeismamoGPT,这是一种基于变压器的模型,用于预测未来引力波探测器(如爱因斯坦望远镜)的三种组分地震波形。 该模型在自动回归设置中训练,可以在单站和基于数组的输入上运行。 通过直接从波形数据中学习时间和空间依赖性,SeismmoGPT捕获了逼真的地面运动模式,并提供准确的短期预测。 我们的结果表明,该模型在即时预测窗口中表现良好,并逐渐进一步下降,正如在自动回归系统中所期望的那样。 这种方法为数据驱动的地震预测奠定了基础,可以支持牛顿噪声缓解和实时天文台控制。
We introduce 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 p...