Using Seismic Ambient Noise to Monitor Environmental Changes

dc.contributor.advisorDabkowski, Mieczyslaw K.
dc.contributor.advisorZhu, Hejun
dc.contributor.committeeMemberMinkoff, Susan E.
dc.contributor.committeeMemberLumley, David
dc.contributor.committeeMemberChen, Feng
dc.contributor.committeeMemberBrikowski, Thomas H.
dc.creatorLuo, Bingxu 1995-
dc.creator.orcid0000-0001-7075-1529
dc.date.accessioned2023-10-23T21:44:26Z
dc.date.available2023-10-23T21:44:26Z
dc.date.created2023-08
dc.date.issuedAugust 2023
dc.date.submittedAugust 2023
dc.date.updated2023-10-23T21:44:27Z
dc.description.abstractOver the past decades, the Earth has suffered from a variety of environmental hazards, such as global warming, floods, drought, ice sheets melting and sea level rise. Scientists would like to monitor, interpret and predict these hazards by using different techniques. As a seismologist, I would like to generalize seismological techniques and apply them to explore these severe hazards. One of my research directions is to measure near-surface seismic velocity changes (dv/v) from ambient noise records, and then correlate them with independent environmental variables. The first topic is to measure decadal dv/v by auto- correlating noise records at each seismographic station deployed in Greenland. Our results demonstrate that dv/v for most stations have less than 3 months lag times in comparison to the surface ice mass change. These various lag times may provide us with constraints for the thickness of the subglacial till layer over different regions in Greenland. Moreover, we also observe a change in the long-term trend of dv/v in southwest Greenland, which is consistent with the mass change rate during the “2012-2013 warm-cold transition”. The second topic is to explore the potential of using regional averaged dv/v records to predict incoming flood events in the Yellowstone National Park (YNP). Over the past ten years, we find that the annual peaks of dv/v variations always have one to two months lead-time in comparison to anomalous water discharges in summers. In particular, there was a short- term high velocity perturbation around 62 days ahead of the 2022 historic flood in the YNP. We therefore suggest that the annual peaks of dv/v in springs are mainly driven by snow accumulation during winters, which might be the major contributor to the flood events in the YNP. This study demonstrates the potential of using seismic observables to predict incoming flood events one to two months in advance. The third topic is to use machine learning (convolutional neural networks) to build a complete aftershock catalog for the 2020 MW 6.5 Stanley, Idaho earthquake. This new catalog has over seven times more events and 0.9 lower completeness magnitude than the current USGS-NEIC catalog. The distribution and expansion of these aftershocks improve the resolution of two north-northwest-trending faults with different dip angles, providing us further support for a central stepover region that changed the earthquake rupture trajectory and induced sustained seismicity.
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/10735.1/9956
dc.language.isoEnglish
dc.subjectGeophysics
dc.titleUsing Seismic Ambient Noise to Monitor Environmental Changes
dc.typeThesis
dc.type.materialtext
thesis.degree.collegeSchool of Natural Sciences and Mathematics
thesis.degree.departmentGeosciences
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.namePHD

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
LUO-PRIMARY-2023.pdf
Size:
34.46 MB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
proquest_license.txt
Size:
6.36 KB
Format:
Plain Text
Description:
No Thumbnail Available
Name:
license.txt
Size:
1.98 KB
Format:
Plain Text
Description: