Tsunami simulations require powerful computational resources to be performed efficiently. Although the modern graphics processing units (GPUs) allow the acceleration of this kind of simulations, they can still last many minutes or even hours for simulations which have to deal with very high spatial resolutions or simulation times. In this paper, we propose a method to predict the alert or inundation level of a tsunami generated by an earthquake using deep learning methods. In particular, we train multilayer perceptron (MLP) neural networks for predicting the alert level due to a tsunami at given coastal locations. Ensemble methods are used to improve the predictions of the neural networks. Tsunamis caused by ruptures of several fault segments at different time instants, application to real events, probabilistic forecasting and comparison with other machine learning algorithms are also addressed. Results on realistic scenarios confirm that good accuracies are obtained. The inference times of the trained networks and ensembles are also very low, lasting less than one second to predict the results of thousands of simulations. The proposed method could be used in a tsunami early warning system along with the application of scaling laws.

中文翻译：

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使用深度学习预测海啸警报级别

海啸模拟需要强大的计算资源才能有效执行。尽管现代图形处理单元（GPU）允许加速这种模拟，但对于必须处理非常高的空间分辨率或模拟时间的模拟来说，它们仍然可以持续几分钟甚至几个小时。在本文中，我们提出了一种使用深度学习方法来预测地震引发的海啸的警报或淹没级别的方法。特别是，我们训练多层感知器（MLP）神经网络来预测给定沿海地区海啸的警报级别。集成方法用于改进神经网络的预测。还讨论了由多个断层段在不同时刻破裂引起的海啸、在实际事件中的应用、概率预测以及与其他机器学习算法的比较。现实场景的结果证实获得了良好的准确性。经过训练的网络和集成的推理时间也非常短，只需不到一秒即可预测数千次模拟的结果。所提出的方法可以与缩放定律的应用一起用于海啸预警系统。