| Summary: | 2018 Fall. Includes bibliographical references. Event detection is a crucial part of the data-driven science of seismology. With decades of continuous seismic data recorded across thousands of networks and tens of thousands of stations, and an ever-accelerating rate of data acquisition, automated methods of event detection, as opposed to manual/visual inspection, allow scientists to rapidly sift through enormous data sets extracting event information from background noise for further analysis. Automation naturally increases the numbers of detected events and lowers the minimum magnitude of detectable events. Increasing numbers and decreasing magnitudes of detected events, particularly with respect to earthquakes, enables the construction of more complete event catalogs and more detailed analysis of spatiotemporal trends in earthquake sequences. These more complete catalogs allow for enhanced knowledge of Earth structure, earthquake processes, and have potential for informing hazard mitigation. This study utilizes automated event detection techniques, namely matched filter and subspace detection, and applies them to three different types of continental intraplate earthquake sequences: a tectonic aftershock sequences in Montana, an induced aftershock sequence in Oklahoma, and a magmatic swarm sequence in Antarctica. In Montana, the combination of matched filtering and multiple-event relocation techniques provided a more complete picture of the spatiotemporal evolution of the aftershock sequence of the large intraplate earthquake that occurred near Lincoln, Montana in 2017. The study reveals movement along an unmapped fault that is antithetical to the main fault system trend in the region and demonstrates the hazards associated with a highly faulted and seismically active region encompassing complex and hidden structures. In Oklahoma, subspace detection methodology is used in combination with multiple-event relocation techniques to reveal movement along three different faults associated with the 2011 Prague, ...
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