An Unsupervised Machine Learning Approach for Ground Motion Clustering and Selection

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 records, also called latent features, to aid in ground-motion clustering and selection. In this context, a latent feature is a low dimensional machine-discovered spectral characteristic learned through nonlinear relationships of a neural network autoencoder. Clustering can be performed on the latent features and used to select a representative archetypal subgroup from a large ground-motion suite. The objective of efficient ground-motion selection is to choose records representative of what the structure will probabilistically experience in its lifetime. Three examples are presented to validate this approach, including a synthetic spectral dataset and spectra from field recorded ground-motion records. Deep embedding clustering of ground motion spectra improves on the results of static feature extraction, utilizing characteristics that represent the sparse spectral content of ground motions.