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Multi-scale seismic imaging of the Ridgecrest, CA, region with full-waveform inversions of regional and dense array data

Guoliang Li, & Yehuda Ben-Zion

Under Review 2023, SCEC Contribution #13339

We develop a methodology for deriving multi-scale seismic velocity models with waveform inversions of earthquake and ambient noise data recorded by regional and dense sensor configurations. The method is applied for the area around the M6.4 and M7.1 rupture zones of the 2019 Ridgecrest earthquake sequence in California, utilizing data recorded by regional stations and dense 2D and 1D arrays with station spacings of ~5 km and ~100 m, respectively. Starting with regional Vp, Vs models and locations of Ridgecrest aftershocks, the velocity models and event locations are improved iteratively by inversions of waveforms recorded by regional stations and the 2D array, using a minimum Gauss-Lobato-Legendre (GLL) distance of ~150 m. Waveforms from local events recorded by dense 1D arrays across the M7.1 rupture zone with high SNR for frequencies of 10 Hz or more are used to resolve with waveform inversions and a local GLL point distance of 20 m small-scale features of the rupture zone and shallow crust. The refined Vp and Vs models provide self-consistent descriptions of the rupture zone and the shallow crust embedded in the regional structures. The results reveal pronounced low Vs values and high Vp/Vs ratios in the M6.4 and M7.1 rupture zones coinciding with concentration of seismicity, and also around the Garlock fault and in several local basins. We also observe clear velocity contrasts across the Garlock fault with polarity reversals along strike and with depth. The obtained multi-scale velocity models can be used to improve derivations of earthquake source properties, simulations of dynamic ruptures and ground motions, and the understanding of fault and tectonic processes in the region.

Citation
Li, G., & Ben-Zion, Y. (2023). Multi-scale seismic imaging of the Ridgecrest, CA, region with full-waveform inversions of regional and dense array data. Journal of Geophysical Research, (under review).