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ABSTRACT The 1989 Mw 6.9 Loma Prieta earthquake is the first major event to occur along the San Andreas fault (SAF) zone in central California since the 1906 M 7.9 San Francisco earthquake. Given the complexity of this event, uncertainty has persisted as to whether this earthquake ruptured the SAF itself or a secondary fault. Recent work on the SAF in the Coachella Valley in southern California has revealed similar complexity, arising from a nonplanar, nonvertical fault geometry, and has led us to reexamine the Loma Prieta event. We have compiled data sets and data analyses in the vicinity of the Loma Prieta earthquake, including the 3D seismic velocity model and aftershock relocations of Lin and Thurber (2012), potential field data collected by the U.S. Geological Survey following the earthquake, and seismic refraction and reflection data from the 1991 profile of Catchings et al. (2004). The velocity model and aftershock relocations of Lin and Thurber (2012) reveal a geometry for the SAF that appears similar to that in the Coachella Valley (although rotated 180°): at Loma Prieta the fault dips steeply near the surface and curves with depth to join the moderately southwest-dipping main rupture below 6 km depth, itself also nonplanar. The SAF is a clear velocity boundary, with higher velocities on the northeast, attributable to Mesozoic accretionary and other rocks, and lower velocities on the southwest, attributable to Cenozoic sedimentary and volcanic rocks of the La Honda block. Rocks of the La Honda block have been offset right-laterally hundreds of kilometers from similar rocks in the southern San Joaquin Valley and vicinity, providing evidence that the curved northeast fault boundary of this block is the plate boundary. Thus, we interpret that the Loma Prieta earthquake occurred on the SAF and not on a secondary fault. 

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. 

Bio-derived isobutanol has been approved as a gasoline additive in the US, but our understanding of its combustion chemistry still has significant uncertainties. Detailed quantum calculations could improve model accuracy leading to better estimation of isobutanol's combustion properties and its environmental impacts. This work examines 47 molecules and 38 reactions involved in the first oxygen addition to isobutanol's three alkyl radicals located α, β, and γ to the hydroxide. Quantum calculations are mostly done at CCSD(T)-F12/cc-pVTZ-F12//B3LYP/CBSB7, with 1-D hindered rotor corrections obtained at B3LYP/6-31G(d). The resulting potential energy surfaces are the most comprehensive isobutanol peroxy networks published to date. Canonical transition state theory and a 1-D microcanonical master equation are used to derive high-pressure-limit and pressure-dependent rate coefficients, respectively. At all conditions studied, the recombination of γ-isobutanol radical with O 2 forms HO 2 + isobutanal. The recombination of β-isobutanol radical with O 2 forms a stabilized hydroperoxy alkyl radical below 400 K, water + an alkoxy radical at higher temperatures, and HO 2 + an alkene above 1200 K. The recombination of β-isobutanol radical with O 2 results in a mixture of products between 700–1100 K, forming acetone + formaldehyde + OH at lower temperatures and forming HO 2 + alkenes at higher temperatures. The barrier heights, high-pressure-limit rates, and pressure-dependent kinetics generally agree with the results from previous quantum chemistry calculations. Six reaction rates in this work deviate by over three orders of magnitude from kinetics in detailed models of isobutanol combustion, suggesting the rates calculated here can help improve modeling of isobutanol combustion and its environmental fate.