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Abstract We show reflectivity cross sections for the San Gabriel, Chino, and San Bernardino basins north of Los Angeles (LA), California, determined from autocorrelations of ambient noise and teleseismic earthquake waves. These basins are thought to channel the seismic energy from earthquakes on the San Andreas fault to LA, and a more accurate model of their depth is important for hazard mitigation. We use the causal side of the autocorrelation function (ACF) to determine the zero-offset reflection response. To minimize the smoothing effect of the source time function, we remove the common mode from the autocorrelation to reveal the zero-offset reflection response. We apply this to 10 temporary nodal lines consisting of a total of 758 geophones with an intraline spacing of 250–300 m. We also show that the ACF from teleseismic events can provide illumination on the subsurface that is consistent with ambient noise. Both autocorrelation results compare favorably to receiver functions. 

We constrained sedimentary basin structure using a nodal seismic array consisting of ten dense lines that overlie multiple basins in the northern Los Angeles area. The dense array consists of 758 seismic nodes, spaced ~250–300 m apart along linear transects, that recorded ground motions for 30–35 days. We applied the receiver function (RF) technique to 16 teleseismic events to investigate basin structure. Primary basin-converted phases were identified in the RFs. A shear wave velocity model produced in a separate study using the same dataset was incorporated to convert the basin time arrivals to depth. The deepest part of the San Bernardino basin was identified near the Loma Linda fault at a depth of 2.4 km. Basin depths identified at pierce points for separate events reveal lateral changes in basin depth across distances of ~2–3 km near individual stations. A significant change in basin depth was identified within a small distance of ~4 km near the San Jacinto fault. The San Gabriel basin exhibited the largest basin depths of all three basins, with a maximum depth of 4.2 km. The high lateral resolution from the dense array helped to reveal more continuous structures and reduce uncertainties in the RFs interpretation. We discovered a more complex basin structure than previously identified. Our findings show that the basins’ core areas are not the deepest, and significant changes in basin depth were observed near some faults, including the San Jacinto fault, Fontana fault, Red Hill fault and Indian Hill fault. 

Abstract The San Gabriel, Chino, and San Bernardino sedimentary basins in Southern California amplify earthquake ground motions and prolong the duration of shaking due to the basins' shape and low seismic velocities. In the event of a major earthquake rupture along the southern segment of the San Andreas fault, their connection and physical proximity to Los Angeles (LA) can produce a waveguide effect and amplify strong ground motions. Improved estimates of the shape and depth of the sediment‐basement interface are needed for more accurate ground‐shaking models. We obtain a three‐dimensional basement map of the basins by integrating gravity and seismic measurements. The travel time of the sediment‐basementP‐to‐Sconversion, and the Bouguer gravity along 10 seismic lines, are combined to produce a linear relationship that is used to extend the 2D profiles to a 3D basin map. Basement depth is calculated using the predicted travel time constrained by gravity with anS‐wave velocity model of the area. The model is further constrained by the basement depths from 17 boreholes. The basement map shows the south‐central part of the San Gabriel basin is the deepest part and a significant gravity signature is associated with our interpretation of the Raymond fault. The Chino basin deepens toward the south and shallows northeastward. The San Bernardino basin deepens eastward along the edge of the San Jacinto Fault Zone. In addition, we demonstrate the benefit of using gravity data to aid in the interpretation of the sediment‐basement interface in receiver functions. 

Abstract We construct a new shear velocity model for the San Gabriel, Chino and San Bernardino basins located in the northern Los Angeles area using ambient noise correlation between dense linear nodal arrays, broadband stations, and accelerometers. We observe Rayleigh and Love waves in the correlation of vertical (Z) and transverse (T) components, respectively. By combining Hilbert and Wavelet transforms, we obtain the separated fundamental and first higher mode of the Rayleigh wave dispersion curves based on their distinct particle motion polarization. Basin depths constrained by receiver functions, gravity, and borehole data are incorporated into the prior model. Our 3D shear wave velocity model covers the upper 3–5 km of the crust in the San Gabriel, Chino and San Bernardino basin area. The Vs model is in agreement with the geological and geophysical cross‐sections from other studies, but discrepancies exist between our model and a Southern California Earthquake Center community velocity model. Our shear wave velocity model shows good consistency with the CVMS 4.26 in the San Gabriel basin, but predicts a deeper and slower sedimentary basin in the San Bernardino and Chino basins than the community model. 

Buoyancy anomalies within Earth’s mantle create large convective currents that are thought to control the evolution of the lithosphere. While tectonic plate motions provide evidence for this relation, the mechanism by which mantle processes influence near-surface tectonics remains elusive. Here, we present an azimuthal anisotropy model for the Pacific Northwest crust that strongly correlates with high-velocity structures in the underlying mantle but shows no association with the regional mantle flow field. We suggest that the crustal anisotropy is decoupled from horizontal basal tractions and, instead, created by upper mantle vertical loading, which generates pressure gradients that drive channelized flow in the mid-lower crust. We then demonstrate the interplay between mantle heterogeneities and lithosphere dynamics by predicting the viscous crustal flow that is driven by local buoyancy sources within the upper mantle. Our findings reveal how mantle vertical load distribution can actively control crustal deformation on a scale of several hundred kilometers. 

Abstract The proliferation of dense arrays promises to improve our ability to image geological structures at the scales necessary for accurate assessment of seismic hazard. However, combining the resulting local high‐resolution tomography with existing regional models presents an ongoing challenge. We developed a framework based on the level‐set method that infers where local data provide meaningful constraints beyond those found in regional models ‐ for example the Community Velocity Models (CVMs) of southern California. This technique defines a volume within which updates are made to a reference CVM, with the boundary of the volume being part of the inversion rather than explicitly defined. By penalizing the complexity of the boundary, a minimal update that sufficiently explains the data is achieved. To test this framework, we use data from the Community Seismic Network, a dense permanent urban deployment. We inverted Love wave dispersion and amplification data, from the Mw 6.4 and 7.1 2019 Ridgecrest earthquakes. We invert for an update to CVM‐S4.26 using the Tikhonov Ensemble Sampling scheme, a highly efficient derivative‐free approximate Bayesian method. We find the data are best explained by a deepening of the Los Angeles Basin with its deepest part south of downtown Los Angeles, along with a steeper northeastern basin wall. This result offers new progress toward the parsimonious incorporation of detailed local basin models within regional reference models utilizing an objective framework and highlights the importance of accurate basin models when accounting for the amplification of surface waves in the high‐rise building response band. 

Abstract Urban basin investigation is crucial for seismic hazard assessment and mitigation. Recent advances in robust nodal‐type sensors facilitate the deployment of large‐N arrays in urban areas for high‐resolution basin imaging. However, arrays typically operate for only one month due to the instruments' battery life, and hence, only record a few teleseismic events. This limits the number of available teleseismic events for traditional receiver function (RF) analysis‐the primary method used in sediment‐basement interface imaging in passive source seismology. Insufficient stacking of RFs from a limited number of earthquakes could, however, introduce significant biases to the results. In this study, we present a novel Bayesian array‐based Coherent Receiver Function (CRF) method that can leverage datasets from short‐term dense arrays to constrain basin geometry. We cast the RF deconvolution as a sparsity‐promoted inverse problem, in which the deconvolution at a single‐station involves the constraints from neighboring stations and multiple events. We solve the inverse problem using a trans‐dimensional Markov chain Monte Carlo Bayesian algorithm to find an ensemble of RF solutions, which provides a quantitative way of deciding which features are well resolved and warrant geological interpretation. An application in the northern Los Angeles basin demonstrates the ability of our method to produce reliable and easy‐to‐interpret RF images. The use of dense seismic networks and the state‐of‐the‐art Bayesian array‐based CRF method can provide a robust approach for subsurface structure imaging.