Unsupervised Single-Station Analysis of Weak Seismicity Preceding the 2019 Ridgecrest Sequence

ABSTRACT

We analyze four months of continuous seismic data recorded at two stations in the area of the 2019 Ridgecrest earthquake sequence. We identify seismic signal patterns using hierarchical clustering applied to deep scattering coefficients computed from continuous seismograms. To address the strong class imbalance between seismic events and noise, we propose three complementary refinement strategies. Two strategies operate at the data or feature level by reducing the influence of anthropogenic noise, whereas the third defines the latent-space geometry using nighttime data and applies it to the full dataset. Our analysis identifies several episodes of localized swarm-like activity preceding the Mw 6.4 foreshock on 4 July 2019. Comparison with the reference earthquake catalog shows no corresponding increase in cataloged seismicity, suggesting that the detected signals correspond to weak, localized, and possibly off-fault seismic activity. These results highlight the potential of unsupervised single-station analyses as an efficient complement to network-derived earthquake catalogs.