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Semi-automatic feature detection in volume data through anomalies in local histograms

  • In scientific data visualization huge amounts of data are generated, which implies the task of analyzing these in an efficient way. This includes the reliable detection of important parts and a low expenditure of time and effort. This is especially important for the big-sized seismic volume datasets, that are required for the exploration of oil and gas deposits. Since the generated data is complex and a manual analysis is very time-intensive, a semi-automatic approach could on one hand reduce the time required for the analysis and on the other hand offer more flexibility, than a fully automatic approach. This master's thesis introduces an algorithm, which is capable of locating regions of interest in seismic volume data automatically by detecting anomalies in local histograms. Furthermore the results are visualized and a variety of tools for the exploration and interpretation of the detected regions are developed. The approach is evaluated by experiments with synthetic data and in interviews with domain experts on the basis of real-world data. Conclusively further improvements to integrate the algorithm into the seismic interpretation workflow are suggested.

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Author:Jan Beutgen
Referee: Müller, Stefan, Stefan Rilling
Document Type:Master's Thesis
Date of completion:2017/09/14
Date of publication:2017/09/14
Publishing institution:Universität Koblenz, Universitätsbibliothek
Granting institution:Universität Koblenz, Fachbereich 4
Date of final exam:2017/09/14
Release Date:2017/09/14
Number of pages:90
Institutes:Fachbereich 4 / Institut für Computervisualistik
Licence (German):License LogoEs gilt das deutsche Urheberrecht: § 53 UrhG