Untitled

Leuangthong, Oy

doi:10.1023/a:1023235505120

Cited by 93 publications

(9 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4) to Gaussian random field does not ensure that the multivariate distributions are also Gaussian (Leuangthong and Deutsch 2003) which is a critical assumption for implementing TBCOSIM. One important specification is to examine the multivariate Gaussianity by checking the homoscedasticity and linearity among the cross-correlated variables (Johnson and Wichern 1998).…”

Section: Checking the Multivariate Gaussianitymentioning

confidence: 99%

Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

2018

View full text Add to dashboard Cite

With a mining-driven economy, Botswana has experienced increased geochemical exploration of minerals around existing mining towns. The mining and smelting of copper and nickel around Selibe-Phikwe in the Central Province are capable of releasing heavy metals including Pb, Fe, Mn, Co, Ni and Cu into the soil environments, thereby exposing humans, plants and animals to health risks. In this study, turning bands co-simulation, a multivariate geostatistical algorithm, was presented as a tool for spatial uncertainty quantification and probability mapping of cross-correlated heavy metals (Co, Mn, Fe and Pb) risk assessment in a semiarid Cu-Ni exploration field of Botswana. A total of 1050 soil samples were collected across the field at a depth of $ 10 cm in a grid sampling design. Rapid elemental concentration analysis was done using an Olympus Delta Sigma portable X-ray fluorescence device. Enrichment factor, geoaccumulation index and pollution load index were used to assess the potential risk of heavy metals contamination in soils. The partially heterotopic nature of the dataset and strong correlations among the heavy metals favors the use of co-simulation instead of independent simulation in the probability mapping of heavy metal risks in the study area. The strong correlation of Co and Mn to iron infers they are of lithogenic origin, unlike Pb which had weak correlation pointing to its source in the area being of anthropogenic source. Manganese, Co and Fe show low enrichment, whereas Pb had high enrichment suggesting possible lead pollution. We, however, recommend that speciation of Pb in the soils rather than total concentration should be ascertained to infer chances of possible bioaccumulation, and subsequent health risk to human by chronic exposure.

show abstract

Section: Checking the Multivariate Gaussianitymentioning

confidence: 99%

Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

2018

View full text Add to dashboard Cite

show abstract

“…Existing spatial models for multivariate random fields include coregionalisation kriging models (Cressie and Wikle, 2011), which ignore nonlinear spatial dependence and fail to reproduce the individual responses across locations (Leuangthong and Deutsch, 2003). Also, Gnann et al (2018) proposed a method to model multivariate spatial dependence using Gaussian copula, which was not capable of modelling non-Gaussian spatial random fields.…”

Section: Introductionmentioning

confidence: 99%

Spatial Mixture Copula Model For Multiple Correlated Responses With An Environmental Application

Abraj

Thompson

Wang

2021

Preprint

View full text Add to dashboard Cite

In environmental monitoring, multiple measurements are often collected at many locations and these measurements depend on each other in complex ways, such as nonlinear dependence. In this research, a novel copula-based geostatistical modelling approach was developed to model multivariate continuous spatial random fields using mixture copulas that captures both spatial and joint dependence of multiple responses over two-dimensional locations. In a bivariate context, the mixture copulas were used to capture the joint spatial dependence of a bivariate random field and the spatial copula of the bivariate random field was constructed as the convex combination of mixture copulas. The proposed model was applied to real forest data and simulated nonlinear data. The performance of the novel method was compared with existing spatial methods, which included a univariate spatial pair-copula model, a multivariate spatial pair-copula model that utilises nonlinear principal component analysis (NLPCA), and conventional kriging. The results show that the proposed model outperforms the existing methods in the interpolation of individual responses and reproduction of their bivariate dependence.

show abstract

“…In this respect, conventional co-simulation approaches are not sufficient to reproduce such a crucial condition. To overcome this impediment, several avenues have been suggested [7,[13][14][15]. Emery [16] proposed change to the variables free of inequality constraint integrating the conventional paradigm of co-simulation.…”

Section: Introductionmentioning

confidence: 99%

“…Such an inference can be implemented by linear model of coregionalization [17]. Fitting a proper function to the experimental direct and cross-variograms is somehow demanding [14,18]. One alternative is based on transformation of converted cross-correlated variables into the factors that have no spatial interrelationship continuity.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Joint Simulation of Geometallurgical Variables with Inequality Constraint

2019

View full text Add to dashboard Cite

Geometallurgical variables have a significant impact on downstream activities of mining projects. Reliable 3D spatial modelling of these variables plays an important role in mine planning and mineral processing, in which it can improve the overall viability of the mining projects. This interdisciplinary paradigm involves geology, geostatistics, mineral processing and metallurgy that creates a need for enhanced techniques of modelling. In some circumstances, the geometallurgical responses demonstrate a decent intrinsic correlation that motivates one to use co-estimation or co-simulation approaches rather than independent estimation or simulation. The latter approach allows us to reproduce that dependency characteristic in the final model. In this paper, two problems have been addressed, one is concerning the inequality constraint that might exist among geometallurgical variables, and the second is dealing with difficulty in variogram analysis. To alleviate the first problem, the variables can be converted to new variables free of inequality constraint. The second problem can also be solved by taking into account the minimum/maximum autocorrelation factors (MAF) transformation technique which allows defining a hybrid approach of joint simulation rather than conventional method of co-simulation. A case study was carried out for the total and acid soluble copper grades obtained from an oxide copper deposit. Firstly, these two geometallurgical variables are transferred to the new variables without inequality constraint and then MAF analysis is used for joint simulation and modelling. After back transformation of the results, they are compared with traditional approaches of co-simulation, for which they showed that the MAF methodology is able to reproduce the spatial correlation between the variables without loss of generality while the inequality constraint is honored. The results are then post processed to support probabilistic domaining of geometallurgical zones.

show abstract

Untitled

Cited by 93 publications

References 16 publications

Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

Spatial Mixture Copula Model For Multiple Correlated Responses With An Environmental Application

A Hybrid Approach for Joint Simulation of Geometallurgical Variables with Inequality Constraint

Contact Info

Product

Resources

About