Poster Presentation

We perform ambient noise tomography (ANT) using data recorded on 342 seismographs within a 50x50 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 directly from measurements. 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, up to 5 km wide and at least 5 km deep low velocity zone (LVZ) around the causative faults for the M7.1 and M6.4 events, with a 40% reduction of Rayleigh wave velocity. The extent of the LVZ is consistent with the observed complex and distributed active faulting observed along the 2019 Ridgecrest ruptures and ambient noise imaging in other regions.