Multistage deep clustering of urban ambient noise for seismic imaging—a case study for train-induced seismic noise

SUMMARY Passive surface wave method is increasingly being applied to urban subsurface exploration due to its non-invasiveness, low cost and high efficiency. However, its imaging quality is often influenced by limited data acquisition time and the heterogeneous distribution of seismic ambient fields in complex urban environments. To extract coherent surface wave signals for seismic imaging in such challenging setting, we developed a multistage urban ambient noise deep clustering framework based on a convolutional autoencoder and deep embedded clustering algorithm. The initial clustering characterizes the distribution patterns of urban noise sources, which informs a secondary, finer clustering to select noise sources optimized for urban seismic imaging. Real-world experiment on the urban train noise field demonstrates our urban noise cluster framework effectively identifies and elucidates the temporal evolution patterns of moving train sources. Compared to traditional data selection methods, our approach yields superior dispersion measurements and significantly attenuates artifacts from the fundamental mode. Furthermore, by employing mode-specific clustering, we successfully capture the refined first overtone, enhancing the accuracy and depth resolution of seismic imaging. This study presents a new perspective to analysing and selecting complex noise sources, significantly advancing seismic imaging and monitoring in alignment with emerging Artificial Intelligence trends.