The region along and off the San Jacinto Fault Zone is a prolific area of seismic activity. Between 2012 and 2022, there were around five magnitude 4 earthquakes and numerous earthquake swarms. Several of the swarms are assumed to have an aseismic driving process that may relate to crustal fluid migration. Here we study the time-varying seismicity background rates of independently triggered events from a 2012-2022 catalog built from deep learning algorithms. We apply the newly developed method called deep Gaussian process – ETAS to probabilistically model the time-variable background rate of aftershock sequences and swarms in the area. The aftershock sequences show a nearly constant background rate while the swarms exhibit a rich time- varying seismicity background rate. In these latter cases, the swarms' background rate evolution also demonstrates some degree of aftershock triggering. The need for a time-varying background to explain the swarm sequences continues to suggest a strong role of aseismic forcing in this area. We are also able to better quantify the aseismic factor with unprecedented detail of these not well- understood sequences.

Recent observations of widespread ultra-slow diffusive swarms in southern California provide an opportunity to study the mechanical properties along these faults at depth. In particular, we focus on a four-year-long (2016-19) earthquake swarm, which delineated an approximately 4-km by 4-km fault near Cahuilla, Southern California. The swarm exhibits very slow diffusive migration consistent with natural fluid injections into a fault.

The sedimentary basin beneath the city of Los Angeles and surrounding area intensifies seismic ground motions and prolongs the duration of shaking experienced by major population centers and infrastructure in Southern California. Characterizing the basin's shape, which in this case, has been altered over the course of its complex tectonic evolution, is necessary for improving predictions of the strong motions of large earthquakes such as the potential Mw 8 on the southern San Andreas fault. Using a dataset acquired in fall 2022, when 273 seismic nodes were installed in the Los Angeles Basin (LAB), we applied a novel algorithm to identify the P-to-S conversion and related phases from the sediment-basement interface, intrasedimentary layers, and the Moho discontinuity in receiver functions. The survey included two linear arrays and a distributed set of shotgun stations, a configuration that typically challenges receiver function analysis due to phase incoherency among sparsely spaced stations. We addressed this limitation by integrating gravity measurements, allowing us to jointly interpret the multi-layer crustal structure. Our results reveal a narrow, oblong basin with a maximum depth approaching 10 km. Assuming isostatic equilibrium, we find that the Moho is uplifted beneath the basin by up to 45% when compared to the surrounding crust.

The structures of the northern Los Angeles basins are important to quantify the seismic hazard of the surrounding megacity, and in particular, the depth of basement is a critical factor in determining the ground shaking levels. In addition, the northern basins provide a mechanism to channel energy from a potential southern San Andreas earthquake into downtown Los Angeles. The Basin Amplification Seismic Investigation (BASIN) project conducted passive seismic surveys of the San Gabriel, Chino, and San Bernardino basins.

Despite their physical proximity, the subsurface structure of the northern basins is quite different in depth and shape, which provides insights about their formation. The San Gabriel Basin is the deepest basin with a maximum modeled depth of 3500 kilometers at the center. Another zone of significant depth is on the eastern edge of the basin. The Chino Basin, generally known as a groundwater basin, is characterized with shallow depths of about 1-1.5 km, and deepen to the north. The San Bernardino Basin, bounded between the San Jacinto fault (SJF) and San Andreas fault, is a semi-circular shaped basin with its deepest section edged closer to the SJF. Other notable depocenter of interest is north of the San Bernardino Basin and east of Chino Basin, which has a significant depth of 3.5 km. The fault locations with the basins have also affected their shape showing possible interactions with their formation.

The San Fernando Valley (SFV) in Southern California is a complex sedimentary basin whose shape strongly influences ground shaking. We develop a fully quantitative, probabilistic graph-regularized inference model that integrates both gravity and receiver function (RF) constraints and evaluate its ability to determine the basin's shape. The sediment-basement interface in single-station RFs is often difficult to interpret due to scattering and noise, which can render isolated stations unusable. By using RFs from a dense seismic array and incorporating gravity, we address the issue of non-uniqueness in converting the times of RF phases to layer thickness by comparing the predicted gravity to observations at each station. In areas where the density contrast may change, Bayesian inference with a graph Laplacian allows us to determine the effective density contrast by taking into account its neighbors' picks and densities. This method promotes spatial smoothness between neighboring stations, while preserving sharp contrasts in locations supported by the RF and gravity data. We applied this method to a dataset that was acquired in fall 2023, when 140 nodes were installed in the SFV. Our results show the deep Sylmar sub-basin, the San Fernando sub-basin, and the Leadwell high found in a previous study (Juárez-Zúñiga and Persaud, 2025), and our results also show good agreement with the industry seismic reflection profiles across the valley. This method demonstrates how to incorporate gravity with lateral density variations into receiver function interpretation to better map interfaces in the subsurface.