用注意力模型从稀疏海啸仪数据重建高精度波场
Attention-Based Reconstruction of Full-Field Tsunami Waves from Sparse Tsunameter Networks
- 基于注意力机制的Senseiver模型，从稀疏观测重建海啸波场
- 在无训练覆盖震源情况下仍能准确重建波场，优于传统方法
- 适合海啸预警系统，尤其适用于传感器稀疏区域
我们研究了一种基于注意力机制的神经网络架构——Senseiver，在海啸预测中用于稀疏传感。重点关注海啸数据同化方法，该方法从海啸仪网络生成预报。我们的模型能够从极稀疏观测中重建高分辨率海啸波场，包括训练集未包含震源的情况。此外，我们证明该方法显著优于基于惠更斯-菲涅耳原理的线性插值，在生成密集观测网络方面实现了明显更高的精度。
原文摘要 · Abstract (English)
We investigate the potential of an attention-based neural network architecture, the Senseiver, for sparse sensing in tsunami forecasting. Specifically, we focus on the Tsunami Data Assimilation Method, which generates forecasts from tsunameter networks. Our model is used to reconstruct high-resolution tsunami wavefields from extremely sparse observations, including cases where the tsunami epicenters are not represented in the training set. Furthermore, we demonstrate that our approach significantly outperforms the Linear Interpolation with Huygens-Fresnel Principle in generating dense observation networks, achieving markedly improved accuracy.
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