EXTRACTION OF UNDERLYING GEOLOGICAL STRUCTURE FROM SEISMIC DATA USING DATA MINING TECHNIQUES

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dc.contributor.advisorVilalta, Ricardo
dc.contributor.committeeMemberTsekos, Nikolaos V.
dc.contributor.committeeMemberHolley, Thomas K.
dc.creatorGhosh, Ushasi 1985-
dc.date.accessioned2016-08-28T18:26:37Z
dc.date.available2016-08-28T18:26:37Z
dc.date.createdAugust 2014
dc.date.issued2014-08
dc.date.updated2016-08-28T18:26:37Z
dc.description.abstractThe development of seismic-imaging technology has substantially improved the exploration of subsurface deposits of crude oil, natural gas and minerals. Recent advances in data capture, processing power and storage capabilities have enabled us to analyze large volumes of seismic data. In this study we report on the implementation of machine learning and data mining techniques for analysis of seismic data to reveal salt deposits underneath the soil. Several seismic attributes have been extracted from these datasets. Using information gain, the best six attributes (homogeneity, contrast, energy, median, peaks and average energy) have been selected for further classification. Finally we compared the results obtained using four different clustering techniques: k-means algorithm, expectation maximization algorithm, min-cut algorithm and Euclidean clustering.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10657/1461
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.subjectData mining
dc.subjectK-means algorithm
dc.subjectClustering
dc.subjectExpectation Maximization algorithm
dc.subjectMin-cut algorithm
dc.subjectEuclidean Clustering
dc.subjectSeismic data
dc.subjectFeature extraction
dc.titleEXTRACTION OF UNDERLYING GEOLOGICAL STRUCTURE FROM SEISMIC DATA USING DATA MINING TECHNIQUES
dc.type.dcmiText
dc.type.genreThesis
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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