The Los Angeles basin is located within the North America–Pacific plate boundary and contains multiple earthquake faults that threaten greater Los Angeles. Seismic attenuation tomography has the potential to provide important constraints on wave propagation in the basin and to provide supplementary information on structure in the form of the distribution of anelastic properties. On the basis of the amplitude information from seismic interferometry from the linear LASSIE array in the Los Angeles basin, we apply station-triplet attenuation tomography to obtain a 2D depth profile for the attenuation structure of the uppermost 0.6 km. The array crosses four Quaternary faults, three of which are blind. The attenuation tomography resolves strong attenuation (shear attenuation Qs ~ 20) for the fault zones and is consistent with sharp boundaries across them.

A new interpretation of the horizontal to vertical (H/V) spectral ratio in terms of the Diffuse Field Assumption (DFA) has fuelled a resurgence of interest in that approach. The DFA links H/V measurements to Green's function retrieval through autocorrelation of the ambient seismic field. This naturally allows for estimation of layered velocity structure. In this contribution, we further explore the potential of H/V analysis. Our study is facilitated by a distributed array of surface and co-located borehole stations deployed at multiple depths, and by detailed prior information on velocity structure that is available due to development of the Groningen gas field. We use the vertical distribution of H/V spectra recorded at discrete depths inside boreholes to obtain shear wave velocity models of the shallow subsurface. We combine both joint H/V inversion and borehole interferometry to reduce the non-uniqueness of the problem and to allow faster convergence towards a reliable velocity model. The good agreement between our results and velocity models from an independent study validates the methodology, demonstrates the power of the method, but more importantly provides further constraints on the shallow velocity structure, which is an essential component of integrated hazard assessment in the area.

We revisit the finding of widespread deep seismicity in the upper mantle imaged with a dense, temporary nodal seismic array in Long Beach, California using back-projection to detect candidate events and trace randomization to develop a reliable imaging threshold for candidate detections. We find that nearly all detections of small events at depths greater than 20 kilometers in the upper mantle fall below the reliability threshold. We find a modest number of small, shallower events in the crust that appear to align with the active Newport-Inglewood Fault. These events occur primarily at 15- to 20-kilometer depth near the base of the seismogenic zone. Localized seismicity under fault zones suggests that the deep extensions of active faults are localized and deforming, with stress concentration leading to a concentration of small events, near the seismic-aseismic transition.

Earthquake monitoring in urban settings is essential but challenging, due to the strong anthropogenic noise inherent to urban seismic recordings. Here, we develop a deep-learning-based denoising algorithm, UrbanDenoiser, to filter out urban seismological noise. UrbanDenoiser strongly suppresses noise relative to the signals, because it was trained using waveform datasets containing rich noise sources from the urban Long Beach dense array and high signal-to-noise ratio (SNR) earthquake signals from the rural San Jacinto dense array. Application to the dense array data and an earthquake sequence in an urban area shows that UrbanDenoiser can increase signal quality and recover signals at an SNR level down to ~0 dB. Earthquake location using our denoised Long Beach data does not support the presence of mantle seismicity beneath Los Angeles but suggests a fault model featuring shallow creep, intermediate locking, and localized stress concentration at the base of the seismogenic zone.