Representing Spatial Uncertainty Using Distances and Kernels

Scheidt, Céline; Caers, Jef

doi:10.1007/s11004-008-9186-0

Cited by 214 publications

(118 citation statements)

References 18 publications

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“…The following statistical and visualisation tools were employed: (1) cross-correlation functions for identifying temporal offsets in hydrological response; (2) a combination of multi-dimensional scaling (MDS) and k-means algorithm for clustering the loggers (Borg & Groenen, 1997;Scheidt & Caers, 2009). …”

Section: Data Treatment and Statistical Methodsmentioning

confidence: 99%

Spatially dense drip hydrological monitoring and infiltration behaviour at the Wellington Caves, South East Australia

Jex¹,

Mariethoz²,

Baker³

et al. 2012

IJS

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Despite the fact that karst regions are recognised as significant groundwater resources, the nature of groundwater flow paths in the unsaturated zone of such fractured rock is at present poorly understood. Many traditional methods for constraining groundwater flow regimes in karst aquifers are focussed on the faster drainage components and are unable to inform on the smaller fracture or matrix-flow components of the system. Caves however, offer a natural inception point to observe both the long term storage and the preferential movement of water through the unsaturated zone of such fractured carbonate rock by monitoring of drip rates of stalactites, soda straws and seepage from fractures/micro fissures that emerge in the cave ceiling. Here we present the largest spatial survey of automated cave drip rate monitoring published to date with the aim of better understanding both karst drip water hydrogeology and the relationship between drip hydrology and surface climate. By the application of cross correlation functions and multi-dimensional scaling, clustered by k-means technique, we demonstrate the nature of the relationships between drip behaviour and initial surface infiltration and similarity amongst the drip rate time series themselves that may be interpreted in terms of flow regimes and cave chamber morphology and lithology.

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Section: Data Treatment and Statistical Methodsmentioning

confidence: 99%

Spatially dense drip hydrological monitoring and infiltration behaviour at the Wellington Caves, South East Australia

Jex¹,

Mariethoz²,

Baker³

et al. 2012

IJS

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“…A natural question to address is how to choose the number of scenarios to consider. This is a well-studied topic in stochastic optimization (Dupacova et al, 2003) and other fields (Scheidt and Caers, 2009). The case studies presented in this paper use 20 scenarios because past work, such as the work in Albor and Dimitrakopoulos (2009), indicates that after about 15 simulated representations of an orebody, stochastic schedules converge to a stable final physical schedule as well as stable forecasts of production performance.…”

Section: Mathematical Formulationmentioning

confidence: 98%

“…The scenarios are equiprobable (ie, π s = 1/S ∀s = 1, …, S), and they were generated from a limited amount of drilling information using the geostatistical techniques of conditional simulation (Goovaerts, 1997;Scheidt and Caers, 2009;Boucher and Dimitrakopoulos, 2012). These techniques can be seen as a complex Monte Carlo simulation framework.…”

Section: Current Solution Xmentioning

confidence: 99%

A variable neighbourhood descent algorithm for the open-pit mine production scheduling problem with metal uncertainty

Lamghari

Dimitrakopoulos

Ferland

2014

Journal of the Operational Research Society

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Uncertainty is an inherent aspect of the open-pit mine production scheduling problem (MPSP); however, little is reported in the literature about solution methods for the stochastic versions of the problem. In this paper, two variants of a variable neighbourhood descent algorithm are proposed for solving the MPSP with metal uncertainty. The proposed methods are tested and compared on actual large-scale instances, and very good solutions, with an average deviation of less than 3% from optimality, are obtained within a few minutes up to a few hours.

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“…It enables transformation of data points from the input space R Na to some (possibly) higher dimensional space Y, where inner products are used to extract features in the data which are not available in R Na . This has been exploited in many branches of science, for example machine learning (see, e.g., [36][37][38][39]) and reservoir geostatistics (see, e.g., [40,41]). …”

Section: Shape Prior Regularization Termmentioning

confidence: 99%

Identification of subsurface structures using electromagnetic data and shape priors

Tveit

Bakr

Lien

et al. 2015

Journal of Computational Physics

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Representing Spatial Uncertainty Using Distances and Kernels

Cited by 214 publications

References 18 publications

Spatially dense drip hydrological monitoring and infiltration behaviour at the Wellington Caves, South East Australia

Spatially dense drip hydrological monitoring and infiltration behaviour at the Wellington Caves, South East Australia

A variable neighbourhood descent algorithm for the open-pit mine production scheduling problem with metal uncertainty

Identification of subsurface structures using electromagnetic data and shape priors

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