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Over the last few years, because of huge cost of sample analysis in mining science and environmental studies the use of multivariate analysis has increased exponentially and the interpretation have become more important, subsequently. Determination of geochemical concentrations and spatial distribution of elements regarding geological features using different statistical methods is an essential analysis for mineral exploration. This book deals with the distribution of ore elements and their corresponding association in an orebody, have tried to use C-N multi bi-fractal models, the…mehr

Produktbeschreibung
Over the last few years, because of huge cost of sample analysis in mining science and environmental studies the use of multivariate analysis has increased exponentially and the interpretation have become more important, subsequently. Determination of geochemical concentrations and spatial distribution of elements regarding geological features using different statistical methods is an essential analysis for mineral exploration. This book deals with the distribution of ore elements and their corresponding association in an orebody, have tried to use C-N multi bi-fractal models, the correspondence analysis, cluster and PCA while opened using log-ratio transformations. The aim of this book is to present a methodology (combination of fractal geometry and CA) for analyzing elemental distribution, clustering the elemental rank of mineralization, and spatial association between elements and samples. From a theoretical point of view, the extraction of qualitative and quantitative properties of mineralization in different epithermal and porphyry deposits can be improved by these methods.
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Autorenporträt
Farshad Darabi-Golestan has a PhD in Mining Exploration Engineering from the Amirkabir University of Technology, Iran. He published a book entitled ¿Exploration Modeling and Evaluation of Geochemistry Datä especially focused on Copper and Gold deposits. His current research interests include geochemical exploration, geostatistic, and data mining.