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dc.contributor.advisorAlRegib, Ghassan
dc.contributor.authorShafiq, Muhammad Amir
dc.date.accessioned2018-05-31T18:15:03Z
dc.date.available2018-05-31T18:15:03Z
dc.date.created2018-05
dc.date.issued2018-04-06
dc.date.submittedMay 2018
dc.identifier.urihttp://hdl.handle.net/1853/59889
dc.description.abstractThe exploration of oil and gas is a vital part of today's increasing power demands to meet the energy we need to power our homes, businesses, and transportation. Oil and gas explorers use seismic surveys, both onshore and offshore, to produce detailed images of the various rock types, layers, and their locations beneath the Earth's subsurface. The acquired data undergo a series of processing steps, which require powerful computing hardware, sophisticated software, and specialized manpower. To extract useful information from seismic data, interpreters manually delineate important geological structures, which contain hints about petroleum and gas reservoirs such as salt domes, faults, channels, fractures, and horizons. These structures typically span over several square kilometers and are delineated based on correlation, changes in illumination, intensity, contrast, and texture of seismic data. There are limited tools available for automatic detection and manual interpretation is becoming extremely time consuming and labor intensive. In this dissertation, we propose novel seismic attributes based on texture dissimilarity, visual-attention theory, the modeling of human visual system, and machine learning to quantify changes and highlight geological features in a three-dimensional space. To automate the process of seismic interpretation, we develop interpreter-assisted, fully-, and semi-automated workflows that are interactive and easy-to-use for the delineation of important geological structures within seismic volumes. Experimental results on real and synthetic datasets show that our proposed algorithms outperform the state-of-the-art methods for seismic interpretation. In a nutshell, this dissertation introduces novel seismic attributes and automated, interactive, and interpreter-assisted workflows, which have a very promising future in effective seismic interpretation. The proposed research is computationally inexpensive and is expected to not only reduce the time for seismic interpretation but also become a handy tool in the interpreter's toolbox for detecting and delineating important geological structures.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectSeismic interpretation
dc.subjectAutomation
dc.subjectGradient of texture
dc.subjectVisual saliency
dc.subjectSalsi
dc.subjectSeisSal
dc.subjectUnsupervised learning
dc.subjectAutoencoder
dc.subject3D FFT
dc.subjectMultispectral projections
dc.subjectDirectional center-surround
dc.subjectFeature maps
dc.subjectHuman visual system
dc.subjectTexture dissimilarity
dc.subjectF3 block
dc.subjectNetherlands
dc.subjectNorth Sea
dc.subjectSEAM
dc.subjectGreat South Basin
dc.subjectNew Zealand
dc.subjectStratton
dc.subjectListric faults
dc.subjectTexas Gulf Coast
dc.subjectSparse classification
dc.titleComputational seismic interpretation using attention models, texture dissimilarity, and learning
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberMcClellan, James H.
dc.contributor.committeeMemberAnderson, David V.
dc.contributor.committeeMemberStuber, Gordon L.
dc.contributor.committeeMemberPeng, Zhigang
dc.date.updated2018-05-31T18:15:03Z


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