PetaScale Data-Driven Seismology: Geohazard Discovery via Large Array Data Mining at the Edge, on Premise, and on the Cloud

The accumulation of decades of continuous seismic observations, combined with the emergence of new sensing technologies (e.g., distributed acoustic sensing-DAS- and nodes) and novel computing infrastructure, presents both outstanding challenges and opportunities for the observational geophysical community to tackle data processing at petabytes scale. Methodologies, open-source software practices, and cyberinfrastructure have advanced to a point where mining petabyte-scale archives can be done within a single day of cloud computation (Ni et al, 2025a,b). This contribution reviews research workflows centered around seismic event monitoring with large-scale seismometers and regional DAS networks. We evaluate strategies for both cloud infrastructure (Ni et al, 2025) and edge DAS units (Shi et al, 2025a,b) for a seismic event monitoring pipeline that leverages deep learning for rapid feature extraction, such as classification of seismic source type and picking of P- and S-wave arrivals. Leveraging and advocating for open-source software, we discuss computational considerations and strategies to improve the performance of pre-trained deep learning models through transfer-learning and model architecture adaptation. We illustrate these findings with the United States NSF-National Geophysics Facility archive operated by the EarthScope Consortium, as well as diverse experiments from the University of Washington FiberLab.