Cloud-Based Ambient Noise Interferometry for Statewide Seismic Velocity Monitoring Across California

Chris D. Lin, Weiqiang Zhu, & Taka'aki Taira

Submitted August 30, 2026, SCEC Contribution #15486, 2026 SCEC Annual Meeting Poster #TBD

Passive seismic monitoring has become a powerful tool for tracking environmental and subsurface changes using long-term continuous seismic records. However, the computational and storage demands increase rapidly as the number of stations grows. Processing continuous waveforms from thousands of stations over two decades produces datasets exceeding hundreds of terabytes, making data transfer and I/O major computational bottlenecks. To address these challenges, we develop a cloud-computing workflow for large-scale ambient noise cross-correlation of continuous seismic data. We demonstrate the workflow using two decades of continuous seismic recordings from California. We benchmark the resulting seismic velocity changes (dv/v) against published studies in Parkfield, Los Angeles, and Ridgecrest, showing that our workflow reliably reproduces previously reported observations and is suitable for monitoring groundwater variations, active fault zones, and other environmental and tectonic processes throughout California. Our results demonstrate the feasibility of cloud-based ambient noise processing for large-scale continuous seismic datasets and highlight its potential for scalable statewide monitoring. The resulting statewide cross-correlation function dataset provides a valuable resource for future studies and demonstrates a practical framework for processing dense seismic arrays and distributed acoustic sensing (DAS) observations at regional scales.

Key Words
Ambient Noise Interferometry, Large-scale Seismic Data Processing

Citation
Lin, C. D., Zhu, W., & Taira, T. (2026, 08). Cloud-Based Ambient Noise Interferometry for Statewide Seismic Velocity Monitoring Across California. Poster Presentation at 2026 SCEC Annual Meeting.

Related Projects & Working Groups
Seismology