Generation of 3‐D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error

Christensen, Nikolaj Kruse; Minsley, Burke J.; Christensen, Steen

doi:10.1002/2016wr019141

Cited by 29 publications

(21 citation statements)

References 47 publications

(83 reference statements)

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“…Geostatistical simulation methods are most commonly used to create multiple realizations whose variability represents combined (uncertain) information, for instance, in order to estimate uncertainties of structural variability (Feyen and Caers, 2006;He et al, 2013;Poeter and Anderson, 2005;Refsgaard et al, 2012) or to make probability calculations to be used for various forecasts (Stafleu et al, 2011;Christensen et al, 2017). In this study (see Appendix A), only one realization of each of the conditional strategies has been presented.…”

Section: Discussionmentioning

confidence: 99%

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Høyer

Vignoli

Hansen

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km 2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km 2 . We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modelling.

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Section: Discussionmentioning

confidence: 99%

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Høyer

Vignoli

Hansen

et al. 2017

Hydrol. Earth Syst. Sci.

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show abstract

“…The AEM method has been used throughout the world for groundwater exploration and aquifer mapping (e.g., Sattel and Kgotlhang 2004;Podgorski et al 2013). The various methods that have been used to develop the hydrostratigraphic model are described in a recent paper by Christensen et al (2017) and include both knowledge-driven cognitive approaches (e.g., Jørgensen et al 2013) and geostatistical approaches (e.g., Marker et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Mapping Aquifer Systems with Airborne Electromagnetics in the Central Valley of California

Knight¹,

Smith

Asch³

et al. 2018

Groundwater

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The passage of the Sustainable Groundwater Management Act in California has highlighted a need for cost-effective ways to acquire the data used in building conceptual models of the aquifer systems in the Central Valley of California. One approach would be the regional implementation of the airborne electromagnetic (AEM) method. We acquired 104 line-kilometers of data in the Tulare Irrigation District, in the Central Valley, to determine the depth of investigation (DOI) of the AEM method, given the abundance of electrically conductive clays, and to assess the usefulness of the method for mapping the hydrostratigraphy. The data were high quality providing, through inversion of the data, models displaying the variation in electrical resistivity to a depth of approximately 500 m. In order to transform the resistivity models to interpreted sections displaying lithology, we established the relationship between resistivity and lithology using collocated lithology logs (from drillers' logs) and AEM data. We modeled the AEM response and employed a bootstrapping approach to solve for the range of values in the resistivity model corresponding to sand and gravel, mixed coarse and fine, and clay in the unsaturated and saturated regions. The comparison between the resulting interpretation and an existing cross section demonstrates that AEM can be an effective method for mapping the large-scale hydrostratigraphy of aquifer systems in the Central Valley. The methods employed and developed in this study have widespread application in the use of the AEM method for groundwater management in similar geologic settings.

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Section: Discussionmentioning

confidence: 99%

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Vignoli¹

2017

View full text Add to dashboard Cite

Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km 2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km 2 . We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modelling.

show abstract

Generation of 3‐D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error

Cited by 29 publications

References 47 publications

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Mapping Aquifer Systems with Airborne Electromagnetics in the Central Valley of California

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