Testing alternative uses of electromagnetic data to reduce the prediction  error of groundwater models

Christensen, Nikolaj Kruse; Christensen, Steen; Ferré, T. P. A.

doi:10.5194/hess-20-1925-2016

Cited by 11 publications

(15 citation statements)

References 84 publications

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“…For illustrative purposes, we use a 3-D synthetic system very similar to that presented by Christensen et al (2016). The only difference is that the active part of the groundwater system only consists of 5 layers, whereas Christensen et al (2016) used a 20-layer model.…”

Section: Synthetic Examplementioning

confidence: 99%

“…The only difference is that the active part of the groundwater system only consists of 5 layers, whereas Christensen et al (2016) used a 20-layer model.…”

Section: Synthetic Examplementioning

confidence: 99%

“…In SHI the geophysical data are inverted first and independently from the later inversion of the hydrological data. For large-scale groundwater modeling, Herckenrath et al (2013) and Christensen et al (2016) used both SHI and joint hydrogeophysical inversion approaches (JHI; as defined by Ferré et al, 2009). In JHI, the geophysical and hydrological data are inverted jointly by linking the geophysical and hydrological models directly through some of their parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The studies by Herckenrath et al (2013) and Christensen et al (2016) used a fixed petrophysical relationship throughout the model domain. Better results can be obtained by using a spatially variable relationship, which allows for local translation between hydraulic conductivity and electrical resistivity, and by including the spatially dependent petrophysical parameters in the optimization process (Linde et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

Christensen

Ferré

Fiandaca

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. We present a workflow for efficient construction and calibration of large-scale groundwater models that includes the integration of airborne electromagnetic (AEM) data and hydrological data. In the first step, the AEM data are inverted to form a 3-D geophysical model. In the second step, the 3-D geophysical model is translated, using a spatially dependent petrophysical relationship, to form a 3-D hydraulic conductivity distribution. The geophysical models and the hydrological data are used to estimate spatially distributed petrophysical shape factors. The shape factors primarily work as translators between resistivity and hydraulic conductivity, but they can also compensate for structural defects in the geophysical model.The method is demonstrated for a synthetic case study with sharp transitions among various types of deposits. Besides demonstrating the methodology, we demonstrate the importance of using geophysical regularization constraints that conform well to the depositional environment. This is done by inverting the AEM data using either smoothness (smooth) constraints or minimum gradient support (sharp) constraints, where the use of sharp constraints conforms best to the environment. The dependency on AEM data quality is also tested by inverting the geophysical model using data corrupted with four different levels of background noise. Subsequently, the geophysical models are used to construct competing groundwater models for which the shape factors are calibrated. The performance of each groundwater model is tested with respect to four types of prediction that are beyond the calibration base: a pumping well's recharge area and groundwater age, respectively, are predicted by applying the same stress as for the hydrologic model calibration; and head and stream discharge are predicted for a different stress situation.As expected, in this case the predictive capability of a groundwater model is better when it is based on a sharp geophysical model instead of a smoothness constraint. This is true for predictions of recharge area, head change, and stream discharge, while we find no improvement for prediction of groundwater age. Furthermore, we show that the model prediction accuracy improves with AEM data quality for predictions of recharge area, head change, and stream discharge, while there appears to be no accuracy improvement for the prediction of groundwater age.

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Section: Synthetic Examplementioning

confidence: 99%

“…The only difference is that the active part of the groundwater system only consists of 5 layers, whereas Christensen et al (2016) used a 20-layer model.…”

Section: Synthetic Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

Christensen

Ferré

Fiandaca

et al. 2017

Hydrol. Earth Syst. Sci.

Self Cite

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“…However, in this case (as in all real cases) the model has structural errors that make the misfit between hydraulic head data and equivalent simulated values much larger than what can be explained by measurement error. In accordance with common groundwater modeling practice (e.g., Christensen et al, 1998), we therefore conducted residual analysis and a few experiments to estimate the magnitude of the total head error (which is the sum of observation error and structural error). This indicated that the standard deviation for the total error on the hydraulic head is approximately 10 • σ h , while the total error for the river discharge is totally dominated by measurement error.…”

Section: Groundwater Model Parameterization and Calibrationmentioning

confidence: 99%

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

Christensen¹,

Ferré²,

Fiandaca³

et al. 2016

Preprint

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Abstract. We present a workflow for automated construction and calibration of large-scale groundwater models that includes the integration of airborne electromagnetic (AEM) data and hydrological data. The AEM data are inverted to form a 3D geophysical model. The parameter of interest is the hydraulic conductivity, which can be determined by translating the 3D geophysical model, using a petrophysical relationship, to form a 3D hydraulic conductivity distribution. We use the geophysical models and hydrological data to determine the optimum spatially distributed petrophysical relationship. The two shape factors of the petrophysical relationship primarily work as translators between resistivity and hydraulic conductivity, but the shape factors can also compensate for structural defects in the model. The method is demonstrated for a synthetic case study. The AEM data are inverted with both smoothness (smooth) and minimum gradient support (sharp) constraints, resulting in two competitive geophysical models. The value of the AEM data quality is tested by inverting the alternative geophysical models using data corrupted with four different levels of background noise. Subsequently, the geophysical models are used to construct two competing groundwater models. The performance of the flow model was tested for four types of prediction. All predictions occurred beyond the calibration base. Predictions of a pumping well's recharge area and groundwater age were applying the same stress situation as applied during hydrologic model calibration, while predictions of head and stream discharge was done for a stress situation changed from those applied during hydrologic model calibration. The results show that geophysical models inverted with sharp constraints improve the predictive capability of the groundwater models compared to geophysical models inverted with smooth constraints. It was found that the use of sharp models improves the prediction of recharge area, while prediction of groundwater age does not improve significantly. When the stress situation is changed the prediction of head change and stream discharge improves significantly for sharp models compared to smooth models. This is especially true for predictions of head change made in the vicinity of the pumping well and far-away from hydrologic boundaries. Furthermore, the geophysical data quality has variable influence on different model predictions. Prediction accuracy improves with AEM data quality for predictions of recharge area, head change and stream discharge, while the accuracy appears to not improve for prediction of groundwater age.

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Direct prediction of spatially and temporally varying physical properties from time‐lapse electrical resistance data

Hermans

Oware

Caers

2016

Water Resources Research

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Time‐lapse applications of electrical methods have grown significantly over the last decade. However, the quantitative interpretation of tomograms in terms of physical properties, such as salinity, temperature or saturation, remains difficult. In many applications, geophysical models are transformed into hydrological models, but this transformation suffers from spatially and temporally varying resolution resulting from the regularization used by the deterministic inversion. In this study, we investigate a prediction‐focused approach (PFA) to directly estimate subsurface physical properties with electrical resistance data, circumventing the need for classic tomographic inversions. First, we generate a prior set of resistance data and physical property forecast through hydrogeological and geophysical simulations mimicking the field experiment. We reduce the dimension of both the data and the forecast through principal component analysis in order to keep the most informative part of both sets in a reduced dimension space. Then, we apply canonical correlation analysis to explore the relationship between the data and the forecast in their reduced dimension space. If a linear relationship can be established, the posterior distribution of the forecast can be directly sampled using a Gaussian process regression where the field data scores are the conditioning data. In this paper, we demonstrate PFA for various physical property distributions. We also develop a framework to propagate the estimated noise level in the reduced dimension space. We validate the results by a Monte Carlo study on the posterior distribution and demonstrate that PFA yields accurate uncertainty for the cases studied.

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Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

Cited by 11 publications

References 84 publications

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

Direct prediction of spatially and temporally varying physical properties from time‐lapse electrical resistance data

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