• Login
    View Item 
    •   SMARTech Home
    • Georgia Tech Theses and Dissertations
    • Georgia Tech Theses and Dissertations
    • View Item
    •   SMARTech Home
    • Georgia Tech Theses and Dissertations
    • Georgia Tech Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Improved understanding of earthquake interaction with waveform matching method

    Thumbnail
    View/Open
    LI-DISSERTATION-2020.pdf (37.41Mb)
    Date
    2020-08-03
    Author
    Li, Chenyu
    Metadata
    Show full item record
    Abstract
    As 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.
    URI
    http://hdl.handle.net/1853/63694
    Collections
    • Georgia Tech Theses and Dissertations [23877]
    • School of Earth and Atmospheric Sciences Theses and Dissertations [543]

    Browse

    All of SMARTechCommunities & CollectionsDatesAuthorsTitlesSubjectsTypesThis CollectionDatesAuthorsTitlesSubjectsTypes

    My SMARTech

    Login

    Statistics

    View Usage StatisticsView Google Analytics Statistics
    facebook instagram twitter youtube
    • My Account
    • Contact us
    • Directory
    • Campus Map
    • Support/Give
    • Library Accessibility
      • About SMARTech
      • SMARTech Terms of Use
    Georgia Tech Library266 4th Street NW, Atlanta, GA 30332
    404.894.4500
    • Emergency Information
    • Legal and Privacy Information
    • Human Trafficking Notice
    • Accessibility
    • Accountability
    • Accreditation
    • Employment
    © 2020 Georgia Institute of Technology