In this article, we address the question of how observed ground‐motion data can most effectively be modeled for engineering seismological purposes. Toward this goal, we use a data‐driven method, based on a deep‐learning autoencoder with a variable number of nodes in the bottleneck layer, to determine how many parameters are needed to reconstruct synthetic and observed ground‐motion data in terms of their median values and scatter. The reconstruction error as a function of the number of nodes in the bottleneck is used as an indicator of the underlying dimensionality of ground‐motion data, that is, the minimum number of predictor variables needed in a ground‐motion model. Two synthetic and one observed datasets are studied to prove the performance of the proposed method. We find that mapping ground‐motion data to a 2D manifold primarily captures magnitude and distance information and is suited for an approximate data reconstruction. The data reconstruction improves with an increasing number of bottleneck nodes of up to three and four, but it saturates if more nodes are added to the bottleneck.

中文翻译：

---

通过应用自动编码器技术探索地面运动数据的维数

在本文中，我们解决了如何为工程地震学目的最有效地模拟观测到的地面运动数据的问题。为了实现这个目标，我们基于瓶颈层中具有可变数量节点的深度学习自动编码器，使用了一种数据驱动方法，以确定需要多少参数来重建合成的和观测到的地面运动数据中值和散点。作为瓶颈中节点数量的函数的重建误差被用作地震动数据潜在维度的指标，即地震动模型中所需的最小预测变量数。研究了两个合成数据集和一个观察数据集，以证明所提出方法的性能。我们发现将地面运动数据映射到 2D 流形主要捕获幅度和距离信息，并且适用于近似数据重建。随着瓶颈节点数量的增加（最多三个和四个），数据重建得到改善，但如果向瓶颈添加更多节点，它就会饱和。