Choosing the method for inputting site conditions is critical in reducing the uncertainty of empirical ground‐motion models (GMMs). We apply a neural network (NN) to construct a GMM of peak ground acceleration that extracts site properties from ground‐motion data instead of referring to ground condition variables given for each site. A key structure of the model is one‐hot representations of the site ID, that is, specifying the collection site of each ground‐motion record by preparing input variables corresponding to all observation sites. This representation makes the best use of the flexibility of NN to obtain site‐specific properties while avoiding overfitting at sites where a small number of strong motions have been recorded. The proposed model exhibits accurate and robust estimations among several compared models in different aspects, including data‐poor sites and strong motions from large earthquakes. This model is expected to derive a single‐station sigma that evaluates the residual uncertainty under the specification of estimation sites. The proposed NN structure of one‐hot representations would serve as a standard ingredient for constructing site‐specific GMMs in general regions.

中文翻译：

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基于神经网络的地震动预测模型从观测记录中提取场地属性

选择输入场地条件的方法对于减少经验地震动模型 (GMM) 的不确定性至关重要。我们应用神经网络 (NN) 来构建峰值地面加速度的 GMM，该 GMM 从地面运动数据中提取站点属性，而不是参考为每个站点给出的地面条件变量。该模型的一个关键结构是站点 ID 的单热表示，即通过准备与所有观测站点对应的输入变量来指定每个地面运动记录的收集站点。这种表示充分利用了 NN 的灵活性来获得特定于站点的属性，同时避免在记录了少量强烈运动的站点上过度拟合。所提出的模型在不同方面的几个比较模型中表现出准确和稳健的估计，包括数据贫乏的站点和大地震引起的强烈运动。预计该模型将推导出单站 sigma，用于评估在估计站点规范下的剩余不确定性。提出的 one-hot 表示的 NN 结构将作为在一般区域构建站点特定 GMM 的标准成分。