An official website of the United States government
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.
more »
« less
This content will become publicly available on August 22, 2026
Detecting Urban Earthquakes with the San Fernando Valley Nodal Array and Machine Learning
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.
more »
« less
- PAR ID:
- 10645845
- Publisher / Repository:
- Seismological Research Letters
- Date Published:
- Journal Name:
- Seismological Research Letters
- ISSN:
- 0895-0695
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
Abstract We develop an automated processing procedure to derive a new catalog of earthquake locations, magnitudes, and potencies and analyze 9 years of data between 2008 and 2016 in the San Jacinto fault‐zone region. Our procedure accounts for detailed 3‐D velocity structure using a probabilistic global‐search location inversion and obtains high‐precision relative event locations using differential travel times measured by cross‐correlating waveforms. The obtained catalog illuminates spatiotemporal seismicity patterns in the fault zone with observations for 108,800 earthquakes in the magnitude range −1.8 to 5.4. Inside a focus region consisting of an 80‐km by 50‐km rectangle oriented parallel to the main fault trace, we estimate a 99% detection rate of earthquakes with magnitude 0.6 and greater and detect and locate about 60% more events than those present in the Southern California Seismic Network catalog. The results provide the most complete catalog available for the focused study region during the analyzed period and include both deeper events and very shallow patches of seismicity not present in the regional catalog. The seismicity exhibits a variety of complex patterns that contain important information on deformation processes in the region. The fraction of event pairs with waveforms having cross‐correlation coefficients ≥0.95 is only about 3%, indicating diverse processes operating in the fault zone.more » « less
-
Abstract Accurate and (near) real-time earthquake monitoring provides the spatial and temporal behaviors of earthquakes for understanding the nature of earthquakes, and also helps in regional seismic hazard assessments and mitigations. Because of the increase in both the quality and quantity of seismic data, an automated earthquake monitoring system is needed. Most of the traditional methods for detecting earthquake signals and picking phases are based on analyses of features in recordings of an individual earthquake and/or their differences from background noises. When seismicity is high, the seismograms are complicated, and, therefore, traditional analysis methods often fail. With the development of machine learning algorithms, earthquake signal detection and seismic phase picking can be more accurate using the features obtained from a large amount of earthquake recordings. We have developed an attention recurrent residual U-Net algorithm, and used data augmentation techniques to improve the accuracy of earthquake detection and seismic phase picking on complex seismograms that record multiple earthquakes. The use of probability functions of P and S arrivals and potential P and S arrival pairs of earthquakes can increase the computational efficiency and accuracy of backprojection for earthquake monitoring in large areas. We applied our workflow to monitor the earthquake activity in southern California during the 2019 Ridgecrest sequence. The distribution of earthquakes determined by our method is consistent with that in the Southern California Earthquake Data Center (SCEDC) catalog. In addition, the number of earthquakes in our catalog is more than three times that of the SCEDC catalog. Our method identifies additional earthquakes that are close in origin times and/or locations, and are not included in the SCEDC catalog. Our algorithm avoids misidentification of seismic phases for earthquake location. In general, our algorithm can provide reliable earthquake monitoring on a large area, even during a high seismicity period.more » « less
-
Predicting the onset and timing of fault failure is one of the ultimate goals of seismology. However, our current understanding of the earthquake preparation and nucleation process is limited. One direction towards understanding this process is looking at precursory signals preceding large earthquakes. Previous laboratory experiments have studied robust precursory signals, observed as temporal changes in pressure and shear wave velocities during the seismic cycle. The effects of such precursory velocity changes on the seismic cycle are not well understood. We use numerical models to simulate fully-dynamic earthquake cycles in 2D strike-slip fault systems with antiplane geometry, surrounded by a narrow fault-parallel damage zone. By imposing shear wave velocity changes inside fault damage zones, we investigate the effects of these precursors on multiple stages of the seismic cycle, including nucleation, coseismic, postseismic, and interseismic stages. Our modeling results show a wide spectrum of fault-slip behaviors including fast earthquakes, slow-slip events, and variable creep. One primary effect of the imposed velocity precursor is the facilitation of the otherwise slow-slip event to grow into a fully dynamic earthquake. Furthermore, the onset time of these precursors have significant effects on the nucleation phase of the earthquakes, and earlier onset of precursors causes the earthquakes to nucleate earlier with a smaller nucleation size. Our results highlight the importance of short and long-term monitoring of fault zone structures for better assessment of regional seismic hazard.more » « less
