Large-Scale Ambient Noise Cross-Correlation Across California using Cloud Computing

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

Submitted September 7, 2025, SCEC Contribution #14872, 2025 SCEC Annual Meeting Poster #TBD

Seismic interferometry has been widely used to construct three-dimensional surface wave velocity models and to evaluate ground motion amplification. By utilizing continuous seismic recordings, this method has proven valuable for time-lapse monitoring of fault zone structures, natural resources, and groundwater recharge systems through ambient noise cross-correlation functions (CCFs). However, ambient noise processing is computationally intensive and involves complex and variable preprocessing workflows. Long- term monitoring spanning over a decade of seismic data and encompassing millions of station pairs poses significant computational challenges.

In this work, we process ambient seismic noise recorded by over one thousand stations across California from 2008 to 2025, collected by the California Integrated Seismic Network, generating CCFs for more than a billion station pairs across broad spatial and temporal scales. To meet the computational demands of this large-scale effort, we leverage cloud computing to process over one hundred terabyte data within days. To ensure consistency across diverse instrumentation settings, all data are resampled to velocity at 20 Hz. The resulting CCF database contains 300-second lag-time windows for all station pairs within a 300 km interstation distance. This comprehensive database supports high-resolution seismic imaging in time and space, accelerating our understanding of the Earth’s dynamic processes, and it would also facilitate other studies requiring high-quality, large-scale CCFs.

Key Words
Ambient Noise Interferometry, Large-scale seismic data processing

Citation
Lin, C. D., Zhu, W., & Taira, T. (2025, 09). Large-Scale Ambient Noise Cross-Correlation Across California using Cloud Computing. Poster Presentation at 2025 SCEC Annual Meeting.

Related Projects & Working Groups
Research Computing (RC)