Airborne electromagnetic mapping of the base of aquifer in areas of western Nebraska

Abraham, Jared D.; Cannia, James C.; Bedrosian, Paul A.; Johnson, Michaela R.; Ball, Lyndsay B.; Sibray, Steven S.

doi:10.3133/sir20115219

Cited by 31 publications

(40 citation statements)

References 17 publications

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“…Ground-based and airborne electromagnetic methods have shown great potential for mapping geological structures (Jør-gensen et al, 2003;Thomsen et al, 2004;Abraham et al, 2012;Oldenborger et al, 2013;He et al, 2014;Munday et al, 2015). For large-scale mapping, the airborne electromagnetic method (AEM) is efficient and cost-effective, supplementing traditional data with dense estimates of electrical resistivity which, in some environments, inform about the lithology and thereby about the structure (Robinson et al, 2008;Binley et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…Figure 1 shows a 19-layer resistivity model obtained by inverting a profile of electromagnetic SkyTEM data recorded in Nebraska (Abraham et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Localized Smart Interpretation - a data driven semi-automatic geological modelling method

Lundh

Torben²,

Bach

et al. 2015

ASEG Extended Abstracts

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INTRODUCTIONWhen setting up geological models today, an enormous amount of information may be available, e.g. airborne electromagnetic data (AEM-data), borehole data, radiometric data, seismic data, geological information, etc. However, it is only possible to incorporate a limited amount of the accessible information in the geological modelling process due to the very time demanding task of manual interpretation work. We suggest a methodology to target this problem, which infers a linear model that describes the relation between geological interpretation and the information available to the geologist making the interpretation. Once such a model has been inferred, it can be used, in a semi-automatic way, to perform new (i.e., predict) geological interpretation based on and consistent with the interpretation made by the geological expert. METHODIdeally the method constructed will build a statistical model f (d,M), describing the relation between what the geologist interprets, d=(d 1, d 2, …, d M ) T , and the quantifiable information (i.e., attributes) available to the geologist, M = (m 1, m 2, …, m N ). Each vector m i contains the i'th attribute, which can be any available information that can be quantified, such as geophysical data, the result of geophysical inversion, elevation maps etc. The vector d contains actual interpretations, such as for example the depth to the base of a ground water reservoir. First, the statistical model f(d,M) should be inferred by examining sets of actual interpretations made by a geological expert d and the attributes used to perform the interpretation M. This makes it possible to formulate a probability distribution f(d,M) that describes the relation between attributes and the geological interpretation. As the geological expert proceeds interpreting, the number of interpreted data points from which the statistical model is inferred, increases and, consequently, the accuracy of the statistical model increases. When a model f(d,M) has been successfully inferred, it is possible to predict how the geological expert would perform an interpretation d pred given some external information M ext through f(d pred |M ext ).In this paper we assume a linear relation between d pred and M ext such that:and seek to find the d pred , which gives the highest probability value of the conditional probability distribution (d pred |M ext ). In equation (1) SUMMARYLocalised Smart interpretation (LSI) is a method that infers a statistical model, which describes a relation between the knowledge of a geologist (as quantified by geological interpretation) and the available information (such as geophysical data, well log data, etc.) that a geologist uses when he/she interprets. This model is then used to perform semi-automatic geological interpretation wherever the same kinds of attributes, as used for the initial interpretation, are available. The statistical model is inferred using a combination of a regularized least squares method and cross validation. In this study, we demonstrate the applicability of...

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“…Conventional boring programs involving evenly-spaced borings can often miss localized anomalous features that require special foundation treatment, and noncontact geophysical methods are preferred to acquire continuous data. Airborne surveys of large floodplain extent include studies by Abraham et al (2011), Dunbar et al (20032004), and URS (2008) for purposes of aquifer characterization and levee assessment purposes. Additionally, ground-based geophysical surveys are routinely employed to assess conditions along long smaller-scale levee reaches where airborne methods are not economical because of the reduced aerial extents involved (Burton and Cania 2011;Dunbar and Llopis 2005).…”

Section: Sources Of Geophysical Datamentioning

confidence: 99%

Remote sensing and monitoring of earthen flood-control structures

Dunbar¹,

Galan-Comas²,

Walshire³

et al. 2017

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The purpose of this study was to identify and review technologies that are applicable in locating weaknesses and poor performance within floodcontrol structures from extreme loading events. The focus of this study was to assess current technologies and state-of-practice techniques involving remote sensing, testing, and real-time monitoring of earthen structures. Advancements in satellite and sensor technology combined with high-speed Internet and telecommunication capabilities and smart decision-making software permit real-time monitoring of earthen floodcontrol structures such as dams and levees.Technologies evaluated included both active and passive sensing methods. These technologies included satellite, airborne, and ground-based sensor systems to identify surface and subsurface characteristics of the watershed, as well as point sensors typically embedded in hydraulic structures to monitor the health of the structure. Point sensors typically record water loading, soil pore pressures, soil movements, and other important properties to evaluate global stability of the water control structure. Geophysical-based methods are typically used in mapping, monitoring, and detection of subsurface stratigraphy, seepage, and any changes in subsurface conditions through time within flood-control structures and their foundations.

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Airborne electromagnetic mapping of the base of aquifer in areas of western Nebraska

Cited by 31 publications

References 17 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

Localized Smart Interpretation - a data driven semi-automatic geological modelling method

Remote sensing and monitoring of earthen flood-control structures

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