We use teleseismic receiver functions computed from an $∼35‐day$ nodal dataset recorded along three profiles in the northern basins of Los Angeles, California, to map the depth and shape of the sediment–basement interface and to identify possible deep fault offsets. The results show the Moho discontinuity, the bottom of the basement, and intermediary sedimentary layers. There are also indications of midcrustal offsets along strike of the Red Hill and Raymond faults. The results are compared with receiver functions from nearby permanent broadband stations and the 1993 Los Angeles Region Seismic Experiment (LARSE) profile. The images show that dense deployments of node‐type sensors can be used to characterize basin structure in a noisy urban environment.

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.

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

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.

The Santa Barbara Channel represents the offshore portion of the Ventura Basin in Southern California. Ongoing transpression related to a regional left step in the San Andreas Fault has led to the formation of E‐W trending en‐echelon fault systems that accommodate localized shortening across the basin. Recent studies have suggested that faults within the channel could be capable of a multisegment rupture and producing a M_w 7.7–8.1 tsunamigenic earthquake. However, dynamic rupture models producing these results do not account for stress heterogeneity. With only sparse information available on the stress field in this region, further borehole‐derived stress constraints are essential for obtaining a more comprehensive understanding of the hazards related to the complex fault systems. We used caliper logs from 19 wells obtained from industry to identify stress‐induced borehole breakouts beneath the Holly and Gail oil platforms in the channel. Our newly developed forward modeling technique provides constraints on the orientations and relative magnitudes of the three principal stresses. At Gail, we determine a reverse faulting stress regime (S_Hmax = 1.7; S_hmin = 1.6; S_V = 1.0) and an S_Hmax azimuth of N45°E. Our results are consistent with local structures, which reflect deeper regional scale trends, and with similar studies onshore nearby. At Holly, an S_Hmax rotation from ~N36°W to ~N57°E occurs across ~100 m depth in a single well and differs from nearby results, indicating that short‐length scale (<10 km laterally and <1 km in depth) stress heterogeneity is associated with complex changes in fault geometry.

The San Fernando Valley, 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 networks 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 San Fernando Valley 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 finetuned 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, including 36 new events that were undetected by the regional network, with magnitudes ranging from ML 0.13 to 4. One event cluster reveals a previously unidentified (5.3 km by 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.