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This projects focuses on understanding seismic and aseismic deformation at salt domes and high-volume wastewater injection sites. 

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. 

This projects focuses on understanding seismic and aseismic deformation at salt domes and high-volume wastewater injection 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.