Analyses and Applications of Seismic Surface Waves Using Machine Learning Algorithm

dc.contributor.advisorLi, Aibing
dc.contributor.committeeMemberHilterman, Fred J.
dc.contributor.committeeMemberZheng, Yingcai
dc.contributor.committeeMemberKhan, Shuhab D.
dc.contributor.committeeMemberVerm, Richard
dc.creatorXia, Keyao 1990-
dc.date.accessioned2019-09-10T15:16:15Z
dc.date.createdDecember 2018
dc.date.issued2018-12
dc.date.submittedDecember 2018
dc.date.updated2019-09-10T15:16:15Z
dc.description.abstractSurface waves are energetic signal components in the seismic inversion for near-surface elastic properties. However, they are noise components in reflection seismology in hydrocarbon exploration. Detecting and outlining the region of surface waves in seismic gathers facilitates either their inversion for sub-surface structures or their noise removal. I propose an unsupervised machine-learning algorithm using K-means clustering to automatically identify surface waves in the raw seismic data according to their common features, including low frequency, low velocity, and high amplitude when compared to body waves. The local attributes of frequency, amplitude, and velocity, which are obtained by the Gabor frequency and structure tensor, are used in K-means analyses to detect and outline surface waves on seismic shot gathers. The performance of this new algorithm is evaluated on three datasets, including two synthetic datasets with random noise and missing traces, and a field dataset. In these examples, the proposed algorithm is stable and can successfully outline the surface waves. Then I propose to integrate this approach into a procedure to automatically suppress and/or invert surface waves from raw seismic shot gathers. In the first stage, K-means analysis detects and outlines surface waves on raw shot gathers by using the seismic properties of local frequency, amplitude, and velocity. Then I calculate a high-resolution dispersion image from the outlined surface waves via nonlinear signal comparison. From the high-resolution image, the dispersion curves are obtained by an auto-picking algorithm. The auto-picked dispersion curves are used for both surface-wave suppression and inversion. v Surface waves are predicted and then subtracted from the raw shot gather to enhance the signal-to-noise ratio of body waves. Moreover, the picked dispersion curves can be used to invert for the shallow S-wave velocity model. We applied our approach on a ten-layered synthetic shot gather and field data. The results indicate that our approach is capable to automatically suppress and invert surface waves on raw seismic shot gathers.
dc.description.departmentEarth and Atmospheric Sciences, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/4416
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. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectMachine learning
dc.subjectSurface Wave Detection
dc.subjectSurface Wave Suppression
dc.subjectSurface Wave Inversion
dc.titleAnalyses and Applications of Seismic Surface Waves Using Machine Learning Algorithm
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2020-12-01
local.embargo.terms2020-12-01
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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