Comprehensive California-wide large-scale noise cross correlations

Abstract:

Ambient noise cross-correlations have proven use in crustal tomography, structure, and source monitoring. We combine all available seismic data in the state of California since 2000 until 2020, which accumulates over 2000 asynchronous 3-component stations combining the Northern California Earthquake Data Center, the Southern California Seismic Network, and temporary arrays available at IRIS and nodal data from the BASIN experiment (Clayton et al, 2019). The total original data size is on the order of 200TB. We create three libraries of ambient noise cross-correlations. The first is a low-frequency, 1Hz sampling rate, accounting for all station pairs that are spaced by at least 20 km and cover the entire state. The second, intermediate frequency correlation functions of 20 Hz sampling rate, links all stations within 300 km. The third is all of the station autocorrelations sampled at 100 Hz or below depending on the instrument. Here, we frame a methodology to compute large scale cross correlations by leveraging cloud computing, GPU processing, and performant scripting in the Julia language, presented in a companion abstract (Clements et al.).

We keep daily-stacks of the correlation functions to perform monitoring of shallow structure, such as the impact of groundwater on seismic velocities, or deeper structure over the course of 20 years. The full stacks of the correlation functions can be used for crustal tomography, either with travel-time information or with the full waveforms. The full stacks are also used for ground motion prediction in Los Angeles for future earthquakes in another companion abstract (Denolle et al.).