Spatial distribution of seismic intensity plays an important role in emergency response during and immediately after an earthquake. In this study, we propose a deep learning model to predict the seismic intensity based on only the observation records at the seismic stations in a surrounding area. The deep learning model is trained using the observation records at both the input and target stations, and no geological information is used. Once the model is developed, for example, using the data from a temporal seismic array, the model can spatially interpolate the seismic intensity from the sparse layout of the seismic stations. The model consists of long short‐term memory cells, which are well‐established neural network components for time series analysis. We used observed seismograms in 1996 through 2019 at the Kyoshin Network (K‐NET) and Kiban–Kyoshin Network (KiK‐net) stations located in the northeastern part of Japan. In our deep learning model, approximately 85% of validation data is successfully classified into seismic intensity scales, which is better than adopting either the maximum or weighted average of the input data. We also apply the deep learning model to earthquake early warning (EEW). The model can predict the seismic intensity accurately and provides a long warning time. We concluded that our approach is a possible future solution for increasing the accuracy of EEW.

中文翻译：

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实时地震烈度空间插值的深度学习模型

地震烈度的空间分布在地震发生期间和发生后的应急响应中起着重要作用。在这项研究中，我们提出了一种仅基于周围地震台站的观测记录来预测地震烈度的深度学习模型。使用输入站和目标站的观测记录对深度学习模型进行训练，并且不使用任何地质信息。例如，一旦使用时间地震阵列中的数据开发了模型，该模型就可以根据地震台的稀疏布局在空间上插入地震强度。该模型由长短期记忆单元组成，这是用于时间序列分析的公认的神经网络组件。我们使用了1996年至2019年在日本东北部的Kyoshin网络（K-NET）和Kiban-Kyoshin网络（KiK-net）站观测到的地震图。在我们的深度学习模型中，约有85％的验证数据已成功分类为地震烈度等级，这比采用输入数据的最大值或加权平均值更好。我们还将深度学习模型应用于地震预警（EEW）。该模型可以准确预测地震烈度，并提供较长的预警时间。我们得出结论，我们的方法是提高EEW准确性的未来可能的解决方案。这比采用输入数据的最大值或加权平均值更好。我们还将深度学习模型应用于地震预警（EEW）。该模型可以准确预测地震烈度，并提供较长的预警时间。我们得出结论，我们的方法是提高EEW准确性的未来可能的解决方案。这比采用输入数据的最大值或加权平均值更好。我们还将深度学习模型应用于地震预警（EEW）。该模型可以准确预测地震烈度，并提供较长的预警时间。我们得出结论，我们的方法是提高EEW准确性的未来可能解决方案。