Clustering analysis of sequence data continues to address many applications in engineering design, aided with the rapid growth of machine learning in applied science. This paper presents an unsupervised machine learning algorithm to extract defining characteristics of earthquake ground-motion spectra, also called latent features, to aid in ground-motion selection (GMS). In this context, a latent feature is a low-dimensional machine-discovered spectral characteristic learned through nonlinear relationships of a neural network autoencoder. Machine discovered latent features can be combined with traditionally defined intensity measures and clustering can be performed to select a representative subgroup from a large ground-motion suite. The objective of efficient GMS is to choose characteristic records representative of what the structure will probabilistically experience in its lifetime. Three examples are presented to validate this approach, including the use of synthetic and field recorded ground-motion datasets. The presented deep embedding clustering of ground-motion spectra has three main advantages: 1. defining characteristics the represent the sparse spectral content of ground-motions are discovered efficiently through training of the autoencoder, 2. domain knowledge is incorporated into the machine learning framework with conditional variables in the deep embedding scheme, and 3. method exhibits excellent performance when compared to a benchmark seismic hazard analysis.

翻译：序列数据的聚类分析持续解决工程设计中的众多应用问题，并借助机器学习在应用科学中的快速发展而不断演进。本文提出一种无监督机器学习算法，用于提取地震动谱的定义性特征（亦称潜在特征），以辅助地震动选取（GMS）。在此语境下，潜在特征是通过神经网络自编码器的非线性关系学习得到的低维机器发现型谱特性。机器发现的潜在特征可与传统定义的强度测量指标相结合，通过聚类从大规模地震动记录库中选取代表性子集。高效GMS的目标是选取能概率化表征结构在其服役期内可能经历事件的特征记录。通过三个实例验证该方法，包括使用合成地震动数据集和实测地震动数据集。本文提出的地震动谱深度嵌入聚类具有三大优势：1.通过自编码器训练高效发现表征地震动稀疏谱特征的定义性属性；2.通过深度嵌入方案中的条件变量将领域知识融入机器学习框架；3.与基准地震危险性分析方法相比展现出优异性能。