UC San Diego

Ambient seismic noise consists of emergent and impulsive signals containing distinguishing features from natural and anthropogenic activities. Developing techniques to identify and utilize specific cultural noise signals will benefit studies performing seismic imaging from continuous records. We examine time and frequency spectral features in urban cultural noise from a spatially dense seismic array located in Long Beach, California. The spectral features are used to develop self-supervised clustering models that differentiate cultural noise into separable classes of signal. We use 161 hours of seismic data from 5200 geophones that contain impulsive signals originating from human activity. The model uses convolutional autoencoders, a self-supervised machine learning technique, to learn latent features from spectrograms produced from the data. The latent space features are evaluated using a deep clustering algorithm to separate the noise signals into different classes. We evaluate the separation of data and analyze the classes to identify the likely sources of the signals present in the data. To interpret the model performance we examine the time-frequency domain features of the signals and the spatiotemporal evolution observed for each class. We demonstrate autoencoder feature extraction used with probabilistic clustering algorithms is a useful approach to characterize seismic noise and identify signals in the data with a low signal-to-noise ratio.