Vilalta, Ricardo2016-08-282016-08-28August 2012014-08http://hdl.handle.net/10657/1461The 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.application/pdfengThe 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).Machine learningData miningK-means algorithmClusteringExpectation Maximization algorithmMin-cut algorithmEuclidean ClusteringSeismic dataFeature extraction2016-08-28Thesisborn digital