2018
DOI: 10.1139/cjes-2016-0112
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Using a multiple variogram approach to improve the accuracy of subsurface geological models

Abstract: Subsurface geological models are often used to visualize and analyze the nature, geometry, and variability of geologic and hydrogeologic units in the context of groundwater resource studies. The development of three-dimensional (3D) subsurface geological models covering increasingly larger model domains has steadily increased in recent years, in step with the rapid development of computing technology and software, and the increasing need to understand and manage groundwater resources at the regional scale. The… Show more

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Cited by 10 publications
(6 citation statements)
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“…The former approach is more suitable in mountainous and upland terrain where bedrock elevation is typically highly correlated with land surface elevation, while the latter is preferred in regions where the bedrock topography is uncorrelated from land surface topography, typical of many lowland regions. This is problematic in regions of mixed physiography, and most studies tend to either generate multiple separate models [ 9 , 13 , 14 ] or resort to an iterative process where the initial results are repeatedly re-interpolated with synthetic data that has been generated to resolve problematic areas [ 14 , 51 ]. In either case, this makes the modelling process cumbersome and impossible to automate.…”
Section: Discussionmentioning
confidence: 99%
“…The former approach is more suitable in mountainous and upland terrain where bedrock elevation is typically highly correlated with land surface elevation, while the latter is preferred in regions where the bedrock topography is uncorrelated from land surface topography, typical of many lowland regions. This is problematic in regions of mixed physiography, and most studies tend to either generate multiple separate models [ 9 , 13 , 14 ] or resort to an iterative process where the initial results are repeatedly re-interpolated with synthetic data that has been generated to resolve problematic areas [ 14 , 51 ]. In either case, this makes the modelling process cumbersome and impossible to automate.…”
Section: Discussionmentioning
confidence: 99%
“…When analysing the RMSE statistics, a small RMSE value indicates that the interpolated values for the output model are more similar to the observed data point values, whereas a large RMSE value suggests that the interpolated model values are less similar to the observed data points. Thus, RMSE was used here to determine how well the model fits the observed data values, with low RMSE values indicting a high degree of model accuracy [50,51].…”
Section: Validation Proceduresmentioning
confidence: 99%
“…Constrained interpolation is applied with non-uniform discrete geological data points to construct a high-precision geological model (Le et al 2014;MacCormack et al 2018). The discrete smooth interpolation (DSI) algorithm creates a grid of interconnected nodes by constructing a discretized natural body model.…”
Section: Model Interpolation Algorithmmentioning
confidence: 99%