Automatic First Break Detection by Spectral Decomposition Using Minimum Uncertainty Wavelets

dc.contributor.advisorKouri, Donald J.
dc.contributor.committeeMemberRao, Jagannatha R.
dc.contributor.committeeMemberArdebili, Haleh
dc.creatorKapur, Sunil 1988-
dc.date.accessioned2018-02-15T20:06:37Z
dc.date.available2018-02-15T20:06:37Z
dc.date.createdDecember 2012
dc.date.issued2012-12
dc.date.submittedDecember 2012
dc.date.updated2018-02-15T20:06:37Z
dc.description.abstractSeismic Signal Processing can be effectively utilized to determine micro- seismic events. With the advances in hydraulic fracturing techniques, first break detection has become really important in locating micro-seismic events. The measured data collected gathers far more information than can be extracted by human operators and whose interpretation can consume a lot of time. The transforma- tion in the computational efficiency suggests the involvement of computers in interpreting the measured data. We suggest a new method of first break detec- tion that is based on time-frequency spectral decomposition method and utilizes the Cn Transform and the Super-Gaussian μ wavelets. We tested our method on lab data with various signals and first arrival time was determined. The results were compared to the manual detection and our method had an accuracy of 0.6 μ seconds. The results indicate that our method is robust and is successful in detecting the first arrival time automatically.
dc.description.departmentMechanical Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10657/2184
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.subjectSuper Gaussian Mu-Wavelets
dc.subjectFirst Break Detection
dc.subjectSpectral decomposition
dc.subjectHeisenberg Uncertainty Principle
dc.subjectSingular Value Decomposition Method
dc.subjectCn transform
dc.titleAutomatic First Break Detection by Spectral Decomposition Using Minimum Uncertainty Wavelets
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentMechanical Engineering, Department of
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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