Internal multiple prediction: an application on synthetic data, physical modeling data and field data

atmire.migration.oldid456
dc.contributor.advisorInnanen, Kristopher
dc.contributor.authorHernandez, Melissa
dc.date.accessioned2012-11-27T23:00:42Z
dc.date.available2013-06-15T07:01:35Z
dc.date.issued2012-11-27
dc.date.submitted2012en
dc.description.abstractIn this work we examined and applied a method of internal multiple prediction based on the inverse scattering series. The internal multiple prediction algorithm predicts and then suppresses all order of internal multiples independent of the subsurface reflectors that generate them. In this thesis we promote a stepped approach to predicting multiples in a given field data set: first, by carrying out synthetic/numerical examples; second by carrying out tests on laboratory physical modeling data; and finally by testing prediction of a field data set suspected to be strongly contaminated with internal multiples. In the synthetic examples we draw conclusions about the central frequency of the seismic wavelet and the optimum choice for parameter epsilon (є). The physical modelling study, in which internal multiples are deliberately generated in order to be predicted, is the first of this kind. The results confirm the synthetic’s study conclusions regarding the estimation of epsilon (є), and motivated the development of a method for optimum estimation of epsilon (є) based on autocorrelation. In the land data study, the prediction allows us to confirm and precisely predict the presence of internal multiples in regions where they were expected.en_US
dc.identifier.citationHernandez, M. (2012). Internal multiple prediction: an application on synthetic data, physical modeling data and field data (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26615en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/26615
dc.identifier.urihttp://hdl.handle.net/11023/327
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectGeophysics
dc.subject.classificationmultiplesen_US
dc.titleInternal multiple prediction: an application on synthetic data, physical modeling data and field data
dc.typemaster thesis
thesis.degree.disciplineGeoscience
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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