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Abstract The San Fernando Valley (SFV), part of the Los Angeles metropolitan area, is a seismically active urban environment. Large-magnitude earthquakes, such as the 1994 Mw 6.7 Northridge event that occurred on a blind fault beneath the valley, caused significant infrastructure damage in the region, underscoring the need for enhanced seismic monitoring to improve the identification of buried faults and hazard evaluation. Currently, the Southern California Earthquake Data Center operates four broadband instruments within the valley; however, the network’s ability to capture small earthquakes beneath the region may be limited. To demonstrate how this data gap can be filled, we use recordings from the SFV array, comprised of 140 nodal instruments with interstation distances ranging from 0.3 to 2.5 km that recorded for one month. High-anthropogenic noise levels in urbanized areas tend to conceal earthquake signals; therefore, we applied a previously developed machine learning model fine-tuned on similar waveforms to detect events and pick seismic phases. In a two-step event association workflow, isolated phase picks were first culled, which eliminated false positive detections and reduced computational runtime. We located 62 events within a 209 km radius of our array with magnitudes ranging from ML 0.13 to 4, including 36 new events that were undetected by the regional network. One event cluster reveals a previously unidentified (5.3 km × 4 km) blind fault zone located ∼5 km beneath the southern part of the valley. Seismicity from this zone is rare in the regional catalog (<3 events per year), despite producing a Mb 4.4 event in 2014. Our results highlight the benefits of detecting small-magnitude seismicity for hazard estimation. Temporary nodal arrays can identify critical gaps in regional monitoring and guide site selection for permanent stations. In addition, our workflow can be applied to complement seismic monitoring in other urban settings. 

Abstract The San Fernando Valley (SFV), a densely populated region in Southern California, has high earthquake hazard due to a complex network of active faults and the amplifying effects of the sedimentary basin. Since the devastating 1994 Mw 6.7 Northridge earthquake, numerous studies have examined its structure using various geological and geophysical datasets. However, current seismic velocity models still lack the resolution to accurately image the near-surface velocity structure and concealed or blind faults, which are critical for high-frequency wavefield simulations and earthquake hazard modeling. To address these challenges, we develop a 3D high-resolution shear-wave velocity model for the SFV using ambient noise data from a dense array of 140 seismic nodes and 10 Southern California Seismic Network stations. We also invert gravity data to map the basin geometry and integrate horizontal-to-vertical spectral ratios and aeromagnetic data to constrain interfaces and map major geological structures. With a lateral resolution of 250 m near the basin center, our model reveals previously unresolved geological features, including the detailed geometry of the basin and previously unmapped structure of faults at depth. The basin deepens from the Santa Monica Mountains in the south to approximately 4 km near its center and 7 km in the Sylmar sub-basin at the basin’s northern margin. Strong velocity contrasts are observed across major faults, at the basin edges, and in the basin’s upper 500 m, for which we measure velocities as low as 200 m/s. Our high-resolution model will enhance ground-motion simulations and earthquake hazard assessments for the SFV and has implications for other urban areas with high seismic risk. 

Abstract Seismicity in the Los Angeles metropolitan area has been primarily attributed to the regional stress loading. Below the urban areas, earthquake sequences have occurred over time showing migration off the faults and providing evidence that secondary processes may be involved in their evolution. Combining high-frequency seismic attenuation with other geophysical observations is a powerful tool for understanding which Earth properties distinguish regions with ongoing seismicity. We develop the first high-resolution 3D seismic attenuation models across the region east of downtown Los Angeles using 5,600 three-component seismograms from local earthquakes recorded by a dense seismic array. We present frequency-dependent peak delay and coda-attenuation tomography as proxies for seismic scattering and absorption, respectively. The scattering models show high sensitivity to the seismicity along some of the major faults, such as the Cucamonga fault and the San Jacinto fault zone, while a channel of low scattering in the basement extends from near the San Andreas fault westward. In the vicinity of the Fontana seismic sequence, high absorption, low scattering, and seismicity migration across a fault network suggest fluid-driven processes. Our attenuation and fault network imaging characterize near-fault zones and rock-fluid properties beneath the study area for future improvements in seismic hazard evaluation. 

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. 

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. 

This study focusses on understanding seismic structure of the San Fernando Valley, Southern California. 

Accurately predicting the seismic wavefield is important for physics-based earthquake hazard studies and is dependent on an accurate source model, a good model of the subsurface geology, and the full physics of wave propagation. Here, we conduct numerical experiments to investigate the effect of different representations of the Southern California Earthquake Center and Harvard community velocity models on seismic waveform predictions in the vicinity of the San Andreas fault in Salton Trough. We test general preconceptions about the importance of topography, near-surface geotechnical layering, and anelastic attenuation up to a maximum frequency of 0.5 Hz. For the Southern California Earthquake Center model developed without topography, we implement 1D and linear model extensions that preserve the geologic structure and a pull-up approach that adapts the original model to topographic variations and distorts the subsurface. The Harvard model includes an elevation model, so we test the squashed topography representation, which flattens it. For both community models, we modify the top 350 m by partially applying the Ely geotechnical layer using a minimum shear wave velocity of 600 m/s and incorporate an Olsen attenuation model using a ratio of 0.05. We evaluate the resulting 24 model representations using the classical waveform misfit and five moderate-magnitude earthquakes. Only the inclusion of attenuation consistently improves the wavefield predictions. It becomes more impactful at higher frequencies, where it significantly improves the performance levels of the crude 1D and linear extension models close to that of the original version. The pull-up topography representation also enhances the waveform prediction ability of the original model. Squashing the topography of the elevation-referenced Harvard model produces better seismogram fits, suggesting that seismic imagers construct community tomographic models without topography to avoid issues related to missing model parameters near the free surface or discrepancies with a different elevation model. Although full implementation of the Ely geotechnical layer that would permit shear wave velocities as low as 90 m/s proves computationally expensive, our partial implementation provides slightly better results in some cases. Our results can serve as recommendations for implementing these community models for future validation or optimization studies. 

Abstract Cost-effective strategies for enhancing seismic velocity models are an active research topic. The recently developed hybridization technique shows promise in improving models used for deterministic earthquake hazard evaluation. We augment the results of Ajala and Persaud (2021) by exploring other hybrid models generated using 13 sets of embedding parameters—taper widths and subvolumes—and summarize their effect on waveform predictions up to a minimum period of 2 s. Our results introduce the notion of compatibility as a consideration by showing that the same basin models embedded into two different regional models can produce notably different outcomes. In contrast to most of our hybrid Harvard models that produce better matching ground motions, only one of the hybrid models generated using the Southern California Earthquake Center model as a regional model gives a closer match to the waveforms. Similar results are obtained at higher frequencies; however, improvements due to hybridization are reduced. A potential explanation for these results may be the limited high spatial frequencies in the travel time tomography basin models and the >5–6 s wavefield-dominated adjoint regional models. Although the strongly tapered compatible hybrid models tend to produce better results, we find instances of improvements even with merging artifacts. 

This dataset contains the shear wave velocity model of northern Los Angeles basins, including San Gabriel, Chino, Raymond, and San Bernardino basin. The model domain is a rectangular box, with longitude between 116.90°W and 118.37°W, latitude between 33.90°N and 34.25°N. Details of the files see README file.