Analyzing Shear-Wave Splitting in the Permian Basin and Evaluating the Measurements with Deep Learning

dc.contributor.advisorLi, Aibing
dc.contributor.committeeMemberMann, Paul
dc.contributor.committeeMemberSun, Jiajia
dc.contributor.committeeMemberSavvaidis, Alexandros
dc.creatorGuzman, Veronica Valentina
dc.creator.orcid0000-0002-3711-9051
dc.date.accessioned2024-01-26T23:35:21Z
dc.date.createdDecember 2023
dc.date.issued2023-12
dc.date.updated2024-01-26T23:35:21Z
dc.description.abstractThe Permian Basin, the largest oil-producing basin in the United States, has experienced increasing seismic activity since 2009, including a few large earthquakes with ML5.0 and above in the Delaware and Midland basins. We have conducted shear-wave splitting (SWS) analysis from local earthquakes at ten TexNet stations to understand the increasing and intensifying seismicity in the area. In the Delaware Basin, the fast orientations from individual events vary in a broad range for all stations, indicating a complex fracture system in the upper crust even though the averages are consistent with the local fault strikes or the maximum horizontal stress. Fast orientations with large angles from the local stress appeared after the 2020 ML5.0 earthquake, that I interpret as slip on less favorable fracture planes due to increased pore pressure. SWS orientations in the Midland Basin are parallel with geologic fault strikes to the southwest of Midland City and parallel with the regional SHmax direction to the northeast of Midland SWS analysis requires processing a large quantity of seismograms and inspecting the measurements for quality control, which is laborious and time-consuming. I have developed a new automated procedure to categorize SWS results using the method of Convolutional Neural Networks and Support Vector Machines. The existing SWS dataset in the Delaware Basin is used to test the reliability and scalability of this automated method. The new method significantly reduces the processing time for SWS parameters and shows a high degree of accuracy. The method is also successfully applied to new SWS data in the Delaware Basin. I expect a broad usage of this automation method in other SWS studies from local earthquakes.
dc.description.departmentEarth and Atmospheric Sciences, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions of this document appear in: Guzman, V., Li, A., & Savvaidis, A. (2022). Stress variations in the Delaware Basin from shear-wave splitting analysis. Seismological Research Letters, 93(6), 3433-3443.
dc.identifier.urihttps://hdl.handle.net/10657/16215
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectShear-wave splitting
dc.titleAnalyzing Shear-Wave Splitting in the Permian Basin and Evaluating the Measurements with Deep Learning
dc.type.dcmitext
dc.type.genreThesis
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.lift2025-12-01
local.embargo.terms2025-12-01
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentEarth and Atmospheric Sciences, Department of
thesis.degree.disciplineGeophysics
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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