Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Skoumal.Dissertation.pdf (4.1 MB)

ETD Abstract Container

Abstract Header

Characterizing induced and natural earthquake swarms using correlation algorithms

Skoumal, Robert J
http://orcid.org/0000-0002-5627-6239
http://rave.ohiolink.edu/etdc/view?acc_num=miami1460552844

Abstract Details

2016, Doctor of Philosophy, Miami University, Geology and Environmental Earth Science.
Relationships between earthquakes are observed by the clustering of seismic events in space and time. This clustering commonly occurs as mainshock-aftershock sequences, which are generally interpreted to contain the initial rupture of a fault (the mainshock) and a decaying cascade of smaller ruptures on or very near to the initial rupture plane (aftershocks). Clustering of earthquakes in space and time can also occur as earthquake swarms, which are empirically defined as an increase in seismicity rate above the background rate without a clear triggering mainshock earthquake. Earthquake swarms are often associated with volcanic regions and are studied because of their relationship to eruptions. Earthquake swarms have also been correlated with subduction zone slow slip events, including a case that led into the 2011 Tohoku earthquake. Earthquake swarms are also well associated with many induced (“human influenced”) earthquake sequences. Understanding the mechanisms that lead to earthquake swarms and the rapid detection of these events are key factor in reducing the hazard posed by these events. Here, we present four chapters that seek to detect and better characterize earthquake swarms with an emphasis on induced seismicity. We develop an efficient template matching algorithm that can be used to improve an earthquake catalog completeness by more than an order of magnitude and apply it throughout the state of Ohio. We also develop a new method, referred to as a Repeating Signal Detector (RSD), that uses agglomerative clustering to group signals of interest according to their temporal and frequency domain characteristics. Resulting signal families can be stacked, improving the signal-to-noise ratio of the recorded signals, and then the signal stack can the used in template matching. We apply the technique to detect earthquake swarms in volcanic, subduction, and induced seismicity settings throughout North America. In each case, RSD duplicates or improves upon existing catalogs in rapid, computationally-efficient manner. As more observations typically lead to improved interpretations, techniques like optimized template matching and RSD are important tools for the future understanding of earthquake swarms in a variety of settings.
Michael Brudzinski, PhD (Advisor)
Brian Currie, PhD (Committee Member)
Jens Mueller, PhD (Committee Member)
Jonathan Levy, PhD (Committee Member)
Jacob Walter, PhD (Committee Member)
115 p.
Geology; Geophysics
induced seismicity; earthquake swarm; template matching; repeating signal detector; hydraulic fracturing; wastewater disposal

Recommended Citations

Citations

  • Skoumal, R. J. (2016). Characterizing induced and natural earthquake swarms using correlation algorithms [Doctoral dissertation, Miami University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=miami1460552844

    APA Style (7th edition)

  • Skoumal, Robert. Characterizing induced and natural earthquake swarms using correlation algorithms. 2016. Miami University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=miami1460552844.

    MLA Style (8th edition)

  • Skoumal, Robert. "Characterizing induced and natural earthquake swarms using correlation algorithms." Doctoral dissertation, Miami University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=miami1460552844

    Chicago Manual of Style (17th edition)

miami1460552844
689
© 2016, all rights reserved.
This open access ETD is published by Miami University and OhioLINK.