Discrete fracture network modeling of bedrock in the Hyvinkää area

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http://urn.fi/URN:NBN:fi-fe201804208688
Title: Discrete fracture network modeling of bedrock in the Hyvinkää area
Author: Kallanranta, Antti
Contributor: University of Helsinki, Faculty of Science, Department of Geosciences and Geography
Publisher: Helsingin yliopisto
Date: 2018
Language: eng
URI: http://urn.fi/URN:NBN:fi-fe201804208688
http://hdl.handle.net/10138/273524
Thesis level: master's thesis
Discipline: Geography
Maantiede
Geografi
Abstract: The geological Discrete Fracture Network (DFN) model is a statistical model for stochastically simulating rock fractures and minor faults (Fox et al. 2007). Unlike the continuum model approaches, DFN model geometries explicitly represent populations of individual or equivalent fractures (Wilson et al. 2011). Model construction typically involves stochastic approaches that create multiple deterministic realizations of the fracture network (Gringarten 1998). This study was made as a part of a broader Salpausselkä project to gain deeper understanding of the brittle structures in the study area.This thesis can be broken down to three steps: literaturereview of the DFN methodology, parameterization of the model variables, and DFN modeling itself. For the purposes of the DFN modeling one-dimensionalfracture intensities measured in the field (P10) had to be converted into their volumetric counterpart (P32). Wang’s (2005) C13 conversion factor was decided tobe the most appropriatemethod. Calculation of the angles between the scanlines and fracture normals (α), conversion factor C13, and P32 were done in Python by applying the methods presented by Wang (2005) and Fox et al. (2007, 2012). Fracture setswere weighted by their P10 intensities to get clearer picture of the dominant fracturing orientations. For better and automated classification clustering of the fracture poles into desired number of mean vectorswas conducted byusing kmeansfunctionof Python module MPLstereonet. The function finds centers of multi-modal clusters of data by using a numpy einsummodified for spherical measurements. Fracture set poles were divided into populationsby finding the mean vector with the smallest angular distance from each pole. C13 calculation was done by integrating over the probability distribution function(PDF)of each population.C13 values produced by the script fall within the expected range quoted by the reference literature(Wang 2005, Fox et al. 2007, 2012). In the final modeling phase the clustered groups were modeled in MOVE as finite surfaces and the resulting DFN model was compared to the Local anisotropy interpolator (LAI) model created by Ruuska (2018).Fracture populations were modeled on an outcrop level as well as interpolated over the whole study area, producing two different interpretations of the most dominant fracturing orientations.Based on the results, fracture set pole clustering with open source methods (MPLStereonet K-means) is a feasible approach. K-means clustering algorithm was superior to the expert approach on every level, though more studies are needed to ascertain the soundness of the methodology. Statements made at this point are merely tentative due to the quality and amount of the available data. Taking into account the results of the parallel MSc thesis (Ruuska, 2018) thesis, the DFN and clustered fracture populations constructed using aforementioned methods can be used as a tentative approximation of the preferred fracturing orientations within the boundaries of the study area. Outcrop level model shows the true, measured values and could be used as ground truth in future modeling efforts. Efficient production of large-scale brittle models could be possible with the added flexibility of the implicit modeling methods and automated clustering.


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