Search for: All records

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 Underground storage in geologic formations will play a key role in the energy transition by providing low‐cost storage of renewable fuels such as hydrogen. The sealing qualities of caverns leached in salt and availability of domal salt bodies make them ideal for energy storage. However, unstable boundary shear zones of anomalous friable salt can enhance internal shearing and pose a structural hazard to storage operations. Considering the indistinct nature of internal salt heterogeneities when imaged with conventional techniques such as reflection seismic surveys, we develop a method to map shear zones using seismicity patterns in the US Gulf Coast, the region with the world's largest underground crude oil emergency supply. We developed and finetuned a machine learning algorithm using tectonic and local microearthquakes. The finetuned model was applied to detect microearthquakes in a 12‐month long nodal seismic dataset from the Sorrento salt dome. Clustered microearthquake locations reveal the three‐dimensional geometry of two anomalous salt shear zones and their orientations were determined using probabilistic hypocenter imaging. The seismicity pattern, combined with borehole pressure measurements, and cavern sonar surveys, shows the spatiotemporal evolution of cavern shapes within the salt dome. We describe how shear zone seismicity contributed to a cavern well failure and gas release incident that occurred during monitoring. Our findings show that caverns placed close to shear zones are more susceptible to structural damage. We propose a non‐invasive technique for mapping hazards related to internal salt dome deformation that can be employed in high‐noise industrial settings to characterize storage facilities. 

The energy transition to meet net-zero emissions by 2050 has created demand for underground caverns needed to safely store CO2, hydrocarbon, hydrogen, and wastewater. Salt domes are ideal for underground storage needs because of their low permeability and affordable costs, which makes them the preferred choice for large-scale storage projects like the US Strategic Petroleum Reserves. However, the uneven upward movement of salt spines can create drilling problems and breach cavern integrity, releasing harmful gases into overlying aquifers and endangering nearby communities. Here, we present a novel application of data-driven geophysical methods combined with machine learning that improves salt dome characterization during feasibility studies for site selection and potentially advances the effectiveness of current early-warning systems. We utilize long-term, non-invasive seismic monitoring to investigate deformation processes at the Sorrento salt dome in Louisiana. We developed a hybrid autoencoder model and applied it to an 8-month dataset from a nodal array deployed in 2020, to produce a high-fidelity microearthquake catalog. Our hybrid model outperformed traditional event detection techniques and other neural network detectors. Seismic signals from storms, rock bursts, trains, aircraft, and other anthropogenic sources were identified. Clusters of microearthquakes were observed along two N-S trends referred to as Boundary Shear Zones (BSZ), along which we infer that salt spines are moving differentially. Time-lapse sonar surveys were used to confirm variations in propagation rates within salt spines and assess deformation within individual caverns. Seismicity along one BSZ is linked with a well failure incident that created a 30-ft wide crater at the surface in 2021. This study introduces a novel method for mapping spatial and temporal variations in salt shear zones and provides insights into the subsurface processes that can compromise the safety and lifetime of underground storage sites. 

Earthquakes in stable salt domes are few, with a notable increase in the rate of seismicity prior to catastrophic events, such as the collapse of salt caverns used to store hydrocarbons. Cavern collapse, subsequent gas leakage, and the formation of sinkholes pose a significant hazard for local communities, given that they can disrupt normal societal functions, have various socio-economic impacts, and may result in the evacuation of residents. In Louisiana, one such event was the Bayou Corne collapse in 2012. Following reports of unusual ground tremors, we began monitoring seismicity at the Sorrento salt dome in February 2020. The goal of this study is to improve our understanding of the subsurface processes and their impact on the mechanical integrity of salt domes; we do this by examining the spatio-temporal evolution of the seismicity. We deployed an ~5 km x 4 km nodal array of 12-17 stations, with interstation distances of 0.2 km to 1.9 km, across the dome and recorded eight months of data that were sampled at 500 Hz. Sorrento dome events are usually low in magnitude, often with emergent P-wave onsets, as well as P-waves shrouded in the coda of preceding events, during swarms. Such characteristics make the events difficult to identify using standard automatic detection and location procedures. We first evaluate current methods using an STA/LTA algorithm, coincidence event detectors, and pre-trained, deep-learning detectors and pickers. We find that detection of consistent P-wave phases across several stations for the same event is challenging and poses a major problem for event association and location. To address this problem, we initiate a manual review of all initial event associations to eliminate false positives that could incorrectly inflate the number of events in the catalog. We, therefore, developed a custom-trained detector and picker that outperformed other methods, and it identified multiple events that were recorded by >70% of the stations in the array. Our approach is well-suited for identifying events with emergent P-wave onsets and short durations (~2-10 s), and our method correctly identified a spike in seismicity in the days leading up to a well failure at the dome. Our methodology can be easily adapted for similar types of studies, such as volcano, mine and dam monitoring, and geothermal exploration. 

The US Gulf Coast has several massive underground caverns within salt domes. These caverns can store vast amounts of hydrocarbons, including the US Strategic Petroleum Reserve, used to increase energy supplies during emergency shortages. Unstable caverns can collapse, leading to sinkhole formation and the release of gas. Previous studies have identified elevated seismicity and surface deformation as precursors to salt cavern collapse and sinkhole formation. However, identifying sporadic seismicity can be complicated, requiring complex methods for robust detection and characterization of events, especially in high-noise settings. We investigate deformation of the Sorrento salt dome in Louisiana using 8 months of data from seismic arrays first deployed in February 2020. Our arrays are comprised of 12 to 17 SmartSolo 3C seismic nodes, spaced 0.2-1.9 km and installed around the dome. We recorded more than 1.2 Tb of data, sampled at 500 Hz. Waveforms of identified events range from <1 s to over 30 s in length, rendering power detection methods like the STA/LTA inefficient. Building on recent studies that use machine learning methods to identify small magnitude (Mw -2.0 to 2.0) earthquakes, we developed a custom-trained convolutional neural network and applied it over sliding windows of the waveforms to detect earthquakes, pick P-wave arrivals, and reduce false positives. We correlated waveforms across all stations and identified events when they were observed on at least 60% of the array stations. We used spectrograms to infer fluid content around sources and to eliminate anthropogenic signals, including but not limited to, helicopters, trains, and boats from the catalog. Event locations were used to identify microearthquake swarms within the dome. Our preliminary results show elevated seismicity in the days preceding a well failure, suggesting our method can be used to monitor underground caverns and similar settings, such as mines, dams, and geothermal sites.