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Permanent link (DOI): https://doi.org/10.7939/R3S10B

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Inference of Locally Varying Anisotropy Fields Open Access

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Other title
Subject/Keyword
anisotropy
geostatistics
nonstationary
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Lillah, Maksuda
Supervisor and department
Boisvert, Jeff (Dept of Civil & Environmental Engineering)
Examining committee member and department
Pourrahimian, Yashar (Dept of Civil & Environmental Engineering)
Deutsch, Clayton (Dept of Civil & Environmental Engineering)
Chalaturnyk, Rick (Dept of Civil & Environmental Engineering)
Department
Department of Civil and Environmental Engineering
Specialization
Mining Engineering
Date accepted
2014-09-29T09:49:07Z
Graduation date
2014-11
Degree
Master of Science
Degree level
Master's
Abstract
Considering nonstationary features in geostatistical modeling is important. Recent developments in nonlinear estimation provide a practical way to infuse geological realism into numerical modeling by incorporating complex spatial features. Second order nonstationarity when present must be considered for more accurate geo-models. A critical step when considering second order non-stationarity is a parametric representation of the underlying anisotropy field quantifying the orientation and magnitude of anisotropy exhaustively. The inference of a locally varying anisotropy (LVA) field is deposit and data specific and this work presents a suit of methodologies for field generation in both 2D and 3D cases. Three different data types are explored: (1) 3D point data from direct angle measures from down bore hole formation etc, (2) exhaustive data from outcrop images, (3) compact geologic bodies typically inferred by geologists. Proposed methodologies for the generation of LVA field include reorienting point data by angle variance criteria and estimation by kriging, interpolating orientation in 3D by quaternion averaging, identifying local structures from exhaustive data by moving window technique and centerline extraction of geo bodies by thinning. Features of interest can occur at a coarser or finer scale than the grid size of the final model. Local structures of LVA can be better captured by allowing search areas to adjust to the range of the relevant features. In deposits characterized by varying scales of anisotropy, adaptive moving windows are used to assess the local orientation. Usually several different data types may need to be consolidated for the generation of a single LVA field and a tensor based combination scheme is discussed whereby multiple LVA fields can be merged. In the last section, data from a copper porphyry deposit is considered for LVA generation and results show higher correlation and lower mean squared error in cross validation analysis when kriging with LVA compared to traditional kriging.
Language
English
DOI
doi:10.7939/R3S10B
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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