Detailed clustering of continuous seismic waveforms with deep scattering networks: a case study on the Ridgecrest earthquake sequence

Clustering techniques facilitate the exploration of extensive seismogram datasets, uncovering a variety of distinct seismic signatures. This study employs deep scattering networks (Seydoux et al. 2020), a novel approach in deep convolutional neural networks using fixed wavelet filters, to analyze continuous multichannel seismic time-series data spanning four months before the 2019 Ridgecrest earthquake sequence in California. By extracting robust physical features known as scattering coefficients and disentangling them via independent component analysis, we cluster different seismic signals, including those from foreshock events and anthropogenic noises. We investigate the variability of intracluster (dispersion within each cluster) and examine how it correlates with waveform properties and feature space. The methodology allows us to measure this variability, either through distance to cluster centroids or 2D manifold mapping. Our findings reveal distinct patterns in the occurrence rate, daily frequency, and waveform characteristics of these clusters, providing new insights into the behavior of seismic events versus anthropogenic noises.