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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.
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Searching for Hidden Microearthquakes using Data-based, Physics-based, and Hybrid Models: Implications for Salt Dome Monitoring
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.
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- Award ID(s):
- 2045983
- PAR ID:
- 10323694
- Date Published:
- Journal Name:
- Southern California Earthquake Center 2021 Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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