Flawed Processing of Airborne EM Data Affecting Hydrogeological Interpretation

Viezzoli, Andrea; Jørgensen, Flemming; Sørensen, Camilla

doi:10.1111/j.1745-6584.2012.00958.x

Cited by 21 publications

(18 citation statements)

References 18 publications

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“…Previously, geophysical data have been used in mapping the underground geological structure (Jørgensen et al, 2012;Jørgensen et al, 2013;Viezzoli et al, 2013). Several studies have tried to use geophysical data when generating stochastic geological models with various geostatistical techniques, such as indicator geostatistics, multiple-point geostatistics and object-based methods (Deutsch and Wen, 2000;Parra et al, 2006;Mariethoz et al, 2009;Engdahl et al, 2010).…”

Section: Resultsmentioning

confidence: 99%

Assessing hydrological model predictive uncertainty using stochastically generated geological models

Højberg

Jørgensen

et al. 2015

Hydrol. Process.

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Abstract:In distributed and coupled surface water-groundwater modelling, the uncertainty from the geological structure is unaccounted for if only one deterministic geological model is used. In the present study, the geological structural uncertainty is represented by multiple, stochastically generated geological models, which are used to develop hydrological model ensembles for the Norsminde catchment in Denmark. The geological models have been constructed using two types of field data, airborne geophysical data and borehole well log data. The use of airborne geophysical data in constructing stochastic geological models and followed by the application of such models to assess hydrological simulation uncertainty for both surface water and groundwater have not been previously studied. The results show that the hydrological ensemble based on geophysical data has a lower level of simulation uncertainty, but the ensemble based on borehole data is able to encapsulate more observation points for stream discharge simulation. The groundwater simulations are in general more sensitive to the changes in the geological structure than the stream discharge simulations, and in the deeper groundwater layers, there are larger variations between simulations within an ensemble than in the upper layers. The relationship between hydrological prediction uncertainties measured as the spread within the hydrological ensembles and the spatial aggregation scale of simulation results has been analysed using a representative elementary scale concept. The results show a clear increase of prediction uncertainty as the spatial scale decreases.

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Section: Resultsmentioning

confidence: 99%

Assessing hydrological model predictive uncertainty using stochastically generated geological models

Højberg

Jørgensen

et al. 2015

Hydrol. Process.

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“…Nevertheless, the use of multiple data sets, such as geochemistry, temperature, and hydraulic heads, can shed light on past and current groundwater dynamics in regions impacted by pumping (Gumm et al ). However, interpreting variations in hydraulic conductivity and geological structures from some methods can be challenging (e.g., Viezzoli et al ). Therefore, simple approaches to understand the impact of impermeable barriers on predictions of future groundwater levels can be very useful.…”

Section: Resultsmentioning

confidence: 99%

The Effect of Undetected Barriers on Groundwater Drawdown and Recovery

Marshall¹,

Cook

Miller

et al. 2019

Groundwater

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In large‐scale pumping projects, such as mine dewatering, predictions are often made about the rate of groundwater level recovery after pumping has ceased. However, these predictions may be impacted by geological uncertainty—including the presence of undetected impermeable barriers. During pumping, an impermeable barrier may be undetected if it is located beyond the maximum extent of the cone of depression; yet it may still control drawdown during the recovery phase. This has implications for regional‐scale modeling and monitoring of groundwater level recovery. In this article, non‐dimensional solutions are developed to show the conditions under which a barrier may be undetected during pumping but still significantly impact groundwater level recovery. The magnitude of the impact from an undetected barrier will increase as the ratio of pumping rate to aquifer transmissivity increases. The results are exemplified for a hypothetical aquifer with an unknown barrier 3 km from a pumping well. The difference in drawdown between a model with and without a barrier may be <1 m in the 10 years while pumping is occurring, but up to 50 m after pumping has ceased.

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“…AEM data is especially sensitive to coupling and noise from the influence of man-made infrastructure such as powerlines, pipes, buildings, and other objects [48,65]. Data affected by such interference needs to be culled from the dataset.…”

Section: Discussionmentioning

confidence: 99%

“…Resistivity-depth profiles are generated through numerical inversion, providing 2D and 3D visualization of the conductivity structure of the subsurface. AEM is highly sensitive to human infrastructure such as pipelines, transmission lines, fences, and buildings, so proper data filtering and processing is essential for correct interpretation [48]. Numerical inversion is non-unique; many solutions can equally fit the data within the range of error.…”

Section: Airborne Electromagnetic (Aem) Methodsmentioning

confidence: 99%

Combining Hydraulic Head Analysis with Airborne Electromagnetics to Detect and Map Impermeable Aquifer Boundaries

Korus

2018

Water

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Impermeable aquifer boundaries affect the flow of groundwater, transport of contaminants, and the drawdown of water levels in response to pumping. Hydraulic methods can detect the presence of such boundaries, but these methods are not suited for mapping complex, 3D geological bodies. Airborne electromagnetic (AEM) methods produce 3D geophysical images of the subsurface at depths relevant to most groundwater investigations. Interpreting a geophysical model requires supporting information, and hydraulic heads offer the most direct means of assessing the hydrostratigraphic function of interpreted geological units. This paper presents three examples of combined hydraulic and AEM analysis of impermeable boundaries in glacial deposits of eastern Nebraska, USA. Impermeable boundaries were detected in a long-term hydrograph from an observation well, a short-duration pumping test, and a water table map. AEM methods, including frequency-domain and time-domain AEM, successfully imaged the impermeable boundaries, providing additional details about the lateral extent of the geological bodies. Hydraulic head analysis can be used to verify the hydrostratigraphic interpretation of AEM, aid in the correlation of boundaries through areas of noisy AEM data, and inform the design of AEM surveys at local to regional scales.

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Flawed Processing of Airborne EM Data Affecting Hydrogeological Interpretation

Cited by 21 publications

References 18 publications

Assessing hydrological model predictive uncertainty using stochastically generated geological models

Assessing hydrological model predictive uncertainty using stochastically generated geological models

The Effect of Undetected Barriers on Groundwater Drawdown and Recovery

Combining Hydraulic Head Analysis with Airborne Electromagnetics to Detect and Map Impermeable Aquifer Boundaries

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