Automated stratigraphic classification and feature detection from images of borehole cores

dc.contributor.advisorCloete, J. H.
dc.contributor.advisorHerbst, B. M.
dc.contributor.authorVan der Walt, Stefan Johannen_ZA
dc.contributor.otherUniversity of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
dc.descriptionThesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2005.
dc.description.abstractThis thesis describes techniques proposed for analysing images of borehole cores. We address two problems: firstly, the automated stratigraphic classification of cores based on texture and secondly, the location of thin chromitite layers hidden in pyroxenite cores. Texture features of different rock types are extracted using wavelets, the theory of which provides an intuitive and powerful tool for this purpose. A Bayesian classifier is trained and used to discriminate between different samples. Thin, planar chromitite layers are located using a shortest path algorithm. In order to estimate the physical orientation of any layer found, a sinusoidal curve is fitted. The proposed algorithms were implemented and tested on samples taken from photographed cores. A high success rate was obtained in rock classification, and thin planar layers were located and characterised.en_ZA
dc.format.extent3775840 bytesen_ZA
dc.publisherStellenbosch : University of Stellenbosch
dc.subjectImage processingen_ZA
dc.subjectRocks -- Analysisen_ZA
dc.subjectDissertations -- Electronic engineeringen_ZA
dc.subjectTheses -- Electronic engineeringen_ZA
dc.subject.otherElectrical and Electronic Engineeringen_ZA
dc.titleAutomated stratigraphic classification and feature detection from images of borehole coresen_ZA
dc.rights.holderUniversity of Stellenbosch

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