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1. New Insights into the Crustal Structure of the San Fernando Valley, California, from a Dense Nodal Seismic Array

   https://doi.org/10.1785/0220240473  

   Juárez-Zúñiga, Alan; Persaud, Patricia (May 2025, Seismological Research Letters)

   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. 

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2. Three‐Dimensional Basin Depth Map of the Northern Los Angeles Basins From Gravity and Seismic Measurements

   https://doi.org/10.1029/2022JB025425  

   Villa, Valeria; Li, Yida; Clayton, Robert W.; Persaud, Patricia (July 2023, Journal of Geophysical Research: Solid Earth)

   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. 

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3. Detecting Urban Earthquakes with the San Fernando Valley Nodal Array and Machine Learning

   https://doi.org/10.1785/0220250124  

   Omojola, Joses; Persaud, Patricia (August 2025, Seismological Research Letters)

   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. 

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4. A Deep Learning-Aided Workflow for Decoding the Stress Regime of Southern Nevada

   https://doi.org/10.1785/0220240443  

   Chatterjee, Avigyan; Pennington, Colin N; Trugman, Daniel T; Walter, William R (March 2025, Seismological Research Letters)

   Abstract The Rock Valley fault zone in southern Nevada has a notable history of seismic activity and is the site of a future direct comparison experiment of explosion and earthquake sources. This study aims to gain insight into regional tectonic processes by leveraging recent advances in seismic monitoring capabilities to elucidate the local stress regime. A crucial step in this investigation is the accurate determination of P-wave first-motion polarities, which play a vital role in resolving earthquake focal mechanisms of small earthquakes. We deploy a deep learning-based method for automatic determination of first-motion polarities to vastly expand the polarity dataset beyond what has been reviewed by human analysts. By the integrating P-wave polarities with new measurements of S/P amplitude ratios, we obtain robust focal mechanism estimates for 1306 earthquakes with a local magnitude of 1 and above occurring between 2010 and 2023 in southern Nevada. We then use the focal mechanism catalog to examine the regional stress orientation, confirming an overall trans-tensional stress regime with smaller scale complexities illuminated by individual earthquake sequences. These findings demonstrate how detailed analyses of small earthquakes can provide fundamental information for understanding earthquake processes in the region and inform future experiments at the Nevada National Security Site. 

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5. Coherent Spatial Variations in the Productivity of Earthquake Sequences in California and Nevada

   https://doi.org/10.1785/0320230039  

   Trugman, Daniel T.; Ben-Zion, Yehuda (October 2023, The Seismic Record)

   Abstract Earthquakes are clustered in space and time, with individual sequences composed of events linked by stress transfer and triggering mechanisms. On a global scale, variations in the productivity of earthquake sequences—a normalized measure of the number of triggered events—have been observed and associated with regional variations in tectonic setting. Here, we focus on resolving systematic variations in the productivity of crustal earthquake sequences in California and Nevada—the two most seismically active states in the western United States. We apply a well-tested nearest-neighbor algorithm to automatically extract earthquake sequence statistics from a unified 40 yr compilation of regional earthquake catalogs that is complete to M ∼ 2.5. We then compare earthquake sequence productivity to geophysical parameters that may influence earthquake processes, including heat flow, temperature at seismogenic depth, complexity of quaternary faulting, geodetic strain rates, depth to crystalline basement, and faulting style. We observe coherent spatial variations in sequence productivity, with higher values in the Walker Lane of eastern California and Nevada than along the San Andreas fault system in western California. The results illuminate significant correlations between productivity and heat flow, temperature, and faulting that contribute to the understanding and ability to forecast crustal earthquake sequences in the area. 

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