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dc.contributor.advisorPeng, Zhigang
dc.contributor.authorLi, Chenyu
dc.date.accessioned2020-09-08T12:49:00Z
dc.date.available2020-09-08T12:49:00Z
dc.date.created2020-08
dc.date.issued2020-08-03
dc.date.submittedAugust 2020
dc.identifier.urihttp://hdl.handle.net/1853/63694
dc.description.abstractAs one of the most common geological phenomena, earthquake occurs in various tectonic settings, such as fault zone along plate boundaries, volcanoes, and oil/gas production sites. The stress changes caused by large earthquake are capable of triggering new seismicity from near-field to far-field. Better understanding of the interaction mechanism among diverse seismic events is significant to learn about the fundamental fault behaviors as well as mitigate potential seismic-related hazards. In order to do so, detailed seismicity documentation and analysis are essential. Traditional earthquake catalogs adopted by analysts and automatic algorithms based on signal-to-noise ratio (SNR) tend to miss weak events buried in the coda wave of large earthquake or noises. In this study, a semi-automatic template-matching earthquake detection method is utilized, which cross-correlates waveform of known events with continuous data for new event recognition. Specifically, we use this method to study dynamic triggered earthquakes in volcanoes (Changbaishan in China and Mt. Erebus in Antarctica) and geothermal regions (Salton Sea Geothermal Field). The behaviors of dynamic triggering in these regions have both similarities and different site-dependent responses. The template matching is also applied to aftershock sequence of the 2015 Mw7.5 Hindu Kush intermediate-depth earthquake, consequently more than 14 times events are detected compared to the listed ones in the standard catalog. This result strongly demonstrates the potential to further expand deep earthquake catalog with template matching method. Finally, we explore two recently-developed seismic event detection methods, one is network based waveform-similarity method for large-N array, and another is based on CNN. They offer more opportunities to automatically detect seismicity in regions with deficient catalog events.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectEarthquake triggering
dc.subjectEarthquake detection
dc.titleImproved understanding of earthquake interaction with waveform matching method
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentEarth and Atmospheric Sciences
thesis.degree.levelDoctoral
dc.contributor.committeeMemberNewman, Andrew V.
dc.contributor.committeeMemberMcClellan, James H.
dc.contributor.committeeMemberHerrmann, Felix
dc.contributor.committeeMemberDai, Sheng
dc.date.updated2020-09-08T12:49:00Z


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