Machine Learning in Large and Small Earthquakes: from Rapid Large Earthquake Characterization to Slow Fault Zone Processes
| dc.contributor.advisor | Thomas, Amanda | |
| dc.contributor.author | Lin, Jiun-Ting | |
| dc.date.accessioned | 2023-03-24T18:59:27Z | |
| dc.date.available | 2023-03-24T18:59:27Z | |
| dc.date.issued | 2023-03-24 | |
| dc.description.abstract | This dissertation summarizes the work of integrating machine-learning and traditional seismic analysis techniques into large and small earthquake problems. Earthquake early warning for large magnitude earthquakes is one of the most challenging problems in seismology. Here I develop an algorithm, called M-LARGE, that harnesses machine-learning, rupture simulations, and GNSS data to rapidly predict magnitude without saturation issue with an accuracy of 99%, outperforming other similar methods. I then show how M-LARGE can predict finite fault parameters and their evolution when rupture unfolds for fast and accurate ground motion forecasting.This dissertation will demonstrate how machine-learning can be used as a data mining tool to detect small magnitude seismicity buried in noisy waveforms. I will show its application to detect LFEs, a special class of small earthquakes typically occur down-dip of the seismogenic zone. The model detects more than five times the number of events than the original catalog in Vancouver Island and can apply to unseen stations, which provides a more flexible way to refine the temporal resolution of subduction zone processes. Finally, I will show how do small and slow earthquakes link to large and fast events and their implication on earthquake hazard assessment. With jointly inverted GNSS, strong motion, and tsunami data of the 2018 M7.1 Hawaii earthquake, I find that fast slip ruptures into the area previously hosts slow slip. The result is further validated by rupture simulations, where we find that the effective stress can be a factor that exerts a dominant control on the rupture extent. This reinforces the idea that an individual section of fault can host a variety of distinct slip behaviors, and slow slip should be considered as rupture extent for a more accurate hazard assessment. This dissertation includes previously published and unpublished co-authored material. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1794/28103 | |
| dc.language.iso | en_US | |
| dc.publisher | University of Oregon | |
| dc.rights | All Rights Reserved. | |
| dc.subject | Earthquake Early Warning | en_US |
| dc.subject | Event Detection | en_US |
| dc.subject | Joint Inversion | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Rupture Simulation | en_US |
| dc.subject | Seismology | en_US |
| dc.title | Machine Learning in Large and Small Earthquakes: from Rapid Large Earthquake Characterization to Slow Fault Zone Processes | |
| dc.type | Electronic Thesis or Dissertation | |
| thesis.degree.discipline | Department of Geological Sciences | |
| thesis.degree.grantor | University of Oregon | |
| thesis.degree.level | doctoral | |
| thesis.degree.name | Ph.D. |
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