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dc.contributor.advisorLi, Yaoguo
dc.contributor.authorMelo, Aline Tavares
dc.date.accessioned2018-05-25T20:14:38Z
dc.date.accessioned2022-02-03T13:11:07Z
dc.date.available2018-05-25T20:14:38Z
dc.date.available2022-02-03T13:11:07Z
dc.date.issued2018
dc.identifierMelo_mines_0052E_11499.pdf
dc.identifierT 8497
dc.identifier.urihttps://hdl.handle.net/11124/172327
dc.descriptionIncludes bibliographical references.
dc.description2018 Spring.
dc.description.abstractThe future of mineral exploration depends on innovative methods of data integration and interpretation because new discoveries are becoming scarcer over the years. As brownfield exploration areas reach maturity and greenfield exploration faces increasingly deeper targets and targets hidden under cover, geophysics is becoming the primary exploration tool. When little a priori geological information is available, such as in greenfield exploration, multiple geophysical methods are necessary to improve interpretation and decrease exploration risk. However, it is challenging to deal with multiple geophysical methods in geologically complex areas. For this reason the main motivation of my thesis is to develop integrated quantitative interpretation methods of multiple geophysical data for geology differentiation. Multiphysics is fundamental for identifying geological units instead of just identifying isolated geophysical anomalies in different physical property models. It also allows uncertainties to be minimized if all the available data are properly integrated. Therefore, I first develop a method for geology differentiation based on spatially limited geological information and general relations of physical properties that can be applied to geophysical data over a large area. Then, in the absence of geological information, I incorporate more geophysical data and develop a method of geology differentiation by applying unsupervised machine learning (correlation-based clustering) for the construction of a quasi-geology model. Additionally, I develop a novel method to improve the construction of susceptibility models, the magnetic on-time transient electromagnetic (MoTEM) method. The use of more accurate physical property models improve the geology differentiation. The research I have developed contributes to solving practical challenges of greenfield mineral exploration by providing effective unbiased integrated interpretation methods that produce directly interpretable quasi-geology models.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2018 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectIOCG
dc.subjectmining
dc.subjectmachine learning
dc.subjectinversion
dc.titleIntegrated quantitative interpretation of multiple geophysical data for geology differentiation
dc.typeText
dc.contributor.committeememberHitzman, Murray Walter
dc.contributor.committeememberDagdelen, Kadri
dc.contributor.committeememberSava, Paul C.
dc.contributor.committeememberSwidinsky, Andrei
thesis.degree.nameDoctor of Philosophy (Ph.D.)
thesis.degree.levelDoctoral
thesis.degree.disciplineGeophysics
thesis.degree.grantorColorado School of Mines


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