High-Resolution Imaging of Complex Shallow Fault Zones Along the July 2019 Ridgecrest Ruptures

We perform ambient noise tomography using data recorded on 342 seismographs within a 50 x 50 km area inside which the July 2019 M7.1 and M6.4 Ridgecrest earthquakes occurred. We used the locally sparse tomography (LST) method, an unsupervised machine learning approach that learns to represent small-scale geophysical structures using only data from the immediate study. The Rayleigh group speed obtained from LST better predicts travel times than conventional regularized least squares inversion. The 3D shear velocity model of the area obtained from the surface wave dispersion maps reveals a highly heterogeneous low-velocity zone (with the primary velocity reduction in the upper 2-3 km) around the causative faults for the M7.1 and M6.4 events, with a 40% reduction of the shear wave velocity. Further, correlation of other imaged LVZs in the model area with parts of the Little Lake Fault System without recent activity may indicate long-lasting damage zones.

Automated summary

In this study, we conducted ambient noise tomography using data recorded from 342 seismographs within a 50 x 50 km area affected by the July 2019 M7.1 and M6.4 Ridgecrest earthquakes. The locally sparse tomography (LST) method, an unsupervised machine learning approach, was used to construct a 3D shear velocity model of the area. The results revealed a highly heterogeneous low-velocity zone around the causative faults, with a significant reduction in shear wave velocity, and suggested the presence of long-lasting damage zones associated with inactive fault systems.