Theses and Dissertations

Fall 2010

Hong, Sahyun

, nonlinear relations and different qualities. Previous approaches rely on a strong Gaussian assumption or the combination of the source-specific probabilities that are individually calibrated from each data source. This dissertation develops different approaches to integrate diverse earth science data

. First approach is based on combining probability. Each of diverse data is calibrated to generate individual conditional probabilities, and they are combined by a combination model. Some existing models are reviewed and a combination model is proposed with a new weighting scheme. Weakness of the

probability combination schemes (PCS) is addressed. Alternative to the PCS, this dissertation develops a multivariate analysis technique. The method models the multivariate distributions without a parametric distribution assumption and without ad-hoc probability combination procedures. The method accounts

Spring 2022

Nwabia, Francis N

(DFN) model offers a viable alternative for explicit representation of multiple fractures in the domain, where the comprising fracture properties are defined in accordance with specific probability distributions. However, even with the successful modelling of a DFN, the relationship between a set of

) interpretations, which are useful for inferring the prior probability distributions of relevant fracture parameters. A pilot point scheme and sequential indicator simulation are employed to update the distributions of fracture intensities which represent the abundance of secondary fractures (NFs) in the entire

), transmissivity of the secondary induced fracture (Tsf) and secondary fracture intensity (Psf32L), secondary fracture aperture (re), length and height (L and H), in a multifractured shale gas well in the Horn River Basin. An initial realization of the DFN model is sampled from the prior probability distributions

Fall 2023

Sanchez Villar, Sebastian

bivariate distribution and shows novel equations for the calculation of probabilities of the internal bivariate distribution. Additionally, it proposes a workflow to use the equations as a tool to aid the indicator variogram modeling process. Third, it proposes a new methodology of MIK that uses the RBF

Quantifying uncertainty is a critical task of resource delineation in the mining industry. Uncertainty is used to assess risk in economic evaluation and for classification in resource reporting. The inference of local distributions from conditioning data is key to quantifying uncertainty. Multiple

indicator Kriging (MIK) is a well-established non-parametric local distribution inference technique that does not assume a prior distribution. The local conditional cumulative distribution functions (CCDF) are estimated directly from indicators defined from thresholds. MIK is flexible since allows the

Fall 2009

Hosseini, Amir Hossein

uncertainty band that meets the requirements of unbiasedness and fairness of the calibrated probabilities. The second development in this thesis is related to a probabilistic model for characterization of uncertainty in the 3D localized distribution of residual NAPL in a real site. A categorical variable is

source geometry and hydraulic conductivity distribution. The central idea in this thesis is to develop a flexible modeling approach for characterization of uncertainty in residual NAPL dissolution rate and first-order biodegradation rate by tailoring the estimation of these parameters to distributions of

defined based on the available CPT-UVIF data, while secondary data based on soil texture and groundwater table elevation are also incorporated into the model. A cross-validation study shows the importance of incorporation of secondary data in improving the prediction of contaminated and uncontaminated