A Review of Airborne Electromagnetic Methods With Focus on Geotechnical and Hydrological Applications From 2007 to 2017

Auken, Esben; Boesen, Tue; Christiansen, A.V.

doi:10.1016/bs.agph.2017.10.002

Cited by 68 publications

(35 citation statements)

References 106 publications

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“…High-precision forward modeling is essential for TEM data interpretation. Owing to the complexity of two-and three-dimensional forward modeling theory and the huge amount of calculations required [1], the more mature one-dimensional layered model forward modeling method is still widely used. Given that the forward analytical expression involves a Bessel function, the frequency-domain solution is generally obtained by Hankel transformation, and the time-domain solution is then calculated using frequency-time transformation [2,3].…”

Section: Introductionmentioning

confidence: 99%

Applicability of Transient Electromagnetic Fast Forward Modeling Algorithm With Small Loop

Chen¹,

Yan²,

Sun³

et al. 2020

PIER M

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Section: Introductionmentioning

confidence: 99%

Applicability of Transient Electromagnetic Fast Forward Modeling Algorithm With Small Loop

Chen¹,

Yan²,

Sun³

et al. 2020

PIER M

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“…Multiple AEM methods exist that can be used to measure an EM response, sensitive to near-surface resistivity (down to hundreds of meters depending on the method) in both frequency-domain, for example, RESOLVE (CGG) and time-domain, for example, TEM-PEST (Lane et al 2000;Worrall et al 2001), SkyTEM (Sørensen & Auken 2004) and VTEM (Witherly et al 2004). For a more detailed overview of AEM methods and the difference between time-and frequency-domain systems see, for example, McCracken et al (1986), Siemon et al (2009), Legault (2015) and Auken et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Inversion of airborne EM data with an explicit choice of prior model

Hansen

Minsley

2019

Geophysical Journal International

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Inversion of airborne electromagnetic (AEM) data is an under-determined inverse problem, in that infinitely many resistivity models exist that will be able to explain the observed data, within measurement errors. Therefore, additional information or constraints must be taken into account to solve the inverse problem. In deterministic approaches, the goal is to locate one optimal model that can be obtained by using some form of smoothness constraints implied through a number of regularization choices. This model, however, will not necessarily represent realistic geological features. Probabilistic methods offer an alternative in which the solution is not one model, but a collection of models, whose variability represents the uncertainty. The probabilistic approach can also rely on implicit model assumptions, representing prior information (a type of regularization information) that may or may not be consistent with the actual available information. Here, we present an approach for AEM inversion in which the prior model is explicitly chosen by a user, preferably selected based on actual prior information available and then integrated with AEM data using a general Monte Carlo based sampling approach. This approach leads to a new workflow to AEM inversion in which geological prior information is independently and explicitly chosen before inversion is carried out. The main benefit of this approach is that each model obtained will, by construction, be consistent with prior (geological) information as well as geophysical data. Through examples based on synthetic and real AEM data, we will demonstrate the methodology, not least that the choice of prior information cannot be avoided: Either it is done explicitly, or it will be chosen implicitly by the choice of method used to invert the AEM data.

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“…Geophysical techniques can provide spatially and temporally distributed information on the subsurface and related processes in a non-invasive way thanks to measurements taken on the ground surface or from the sky [1]. That's one of the reasons for the emergence of hydrogeophysics for the improved understanding of subsurface processes over multiple scales [2].…”

Section: Introductionmentioning

confidence: 99%

Heat as a Proxy to Image Dynamic Processes with 4D Electrical Resistivity Tomography

et al. 2019

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Since salt cannot always be used as a geophysical tracer (because it may pollute the aquifer with the mass that is necessary to induce a geophysical contrast), and since in many contaminated aquifer salts (e.g., chloride) already constitute the main contaminants, another geophysical tracer is needed to force a contrast in the subsurface that can be detected from surface geophysical measurements. In this context, we used heat as a proxy to image and monitor groundwater flow and solute transport in a shallow alluvial aquifer (< 10 m deep) with the help of electrical resistivity tomography (ERT). The goal of our study is to demonstrate the feasibility of such methodology in the context of the validation of the efficiency of a hydraulic barrier that confines a chloride contamination to its source. To do so, we combined a heat tracer push/pull test with time-lapse 3D ERT and classical hydrogeological measurements in wells and piezometers. Our results show that heat can be an excellent salt substitution tracer for geophysical monitoring studies, both qualitatively and semi-quantitatively. Our methodology, based on 3D surface ERT, allows to visually prove that a hydraulic barrier works efficiently and could be used as an assessment of such installations.

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A Review of Airborne Electromagnetic Methods With Focus on Geotechnical and Hydrological Applications From 2007 to 2017

Cited by 68 publications

References 106 publications

Applicability of Transient Electromagnetic Fast Forward Modeling Algorithm With Small Loop

Applicability of Transient Electromagnetic Fast Forward Modeling Algorithm With Small Loop

Inversion of airborne EM data with an explicit choice of prior model

Heat as a Proxy to Image Dynamic Processes with 4D Electrical Resistivity Tomography

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