Data assimilation is a powerful tool for directly forecasting tsunami wavefields from the waveforms recorded at dense observational stations like S-Net without the need to know the earthquake source parameters. However, this method requires a high computational load and a quick warning is essential when a tsunami threat is near. We propose a new approach based on a deep predictive coding network for forecasting spatiotemporal tsunami wavefields. Unlike the previous data assimilation method, which continuously computes the wavefield when observed data are available, we use only a short sequence from previously assimilated wavefields to forecast the future wavefield. Since the predictions are computed through matrix multiplication, the future wavefield can be estimated in seconds. We apply the proposed method to simple bathymetry and the 2011 Tohoku tsunami. The results show that our proposed method is very fast (1.6 s for 32 frames of prediction with 1-min interval) and comparable to the previous data assimilation. Therefore, the proposed method is promising for integration with data assimilation to reduce the computational cost.

中文翻译：

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深度预测编码网络在时空海啸波场预测中的潜力

数据同化是一种强大的工具，可以从诸如S-Net的密集观测站记录的波形直接预测海啸波场，而无需知道地震源参数。但是，这种方法需要较高的计算量，当海啸威胁临近时，快速警告至关重要。我们提出了一种基于深度预测编码网络的新方法，用于预测时空海啸波场。与以前的数据同化方法不同（在观测数据可用时连续计算波场），我们仅使用来自先前同化波场的短序列来预测未来的波场。由于预测是通过矩阵乘法计算的，因此可以以秒为单位估算未来的波场。我们将建议的方法应用于简单的测深和2011年的东北海啸。结果表明，我们提出的方法非常快（对于32帧预测，每1分钟间隔为1.6 s），并且可以与以前的数据同化相比。因此，该方法有望与数据同化集成以降低计算成本。