An efficient calibration procedure for correction of drift in EMI survey data

Delefortrie, Samuël; Smedt, Philippe De; Saey, Timothy; Vijver, Ellen Van De; Meirvenne, Marc Van

doi:10.1016/j.jappgeo.2014.09.004

Cited by 54 publications

(50 citation statements)

References 17 publications

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“…As a consequence, several factors influence EC (Friedman, 2005). A higher clay content and/or higher organic matter content usually correspond to a higher content of adsorbed water (i.e., higher θ ), higher EC s , and higher cation exchange capacity (CEC), thus potentially leading to higher EC (e.g., Hudson, 1994;Dingman, 2002;Lal and Shukla, 2004;Roth, 2012). Moreover, of particular importance is EC w , which often increases with higher CEC: as soil water interacts with the soil minerals (especially clay) and with soil organic matter (SOM), ions from the soil minerals can be released into the soil solution and, conversely, free ions can be adsorbed to equilibrate the mineral surface charges.…”

Section: Introductionmentioning

confidence: 99%

Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring network

Martini

Werban

Zacharias

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Electromagnetic induction (EMI) measurements are widely used for soil mapping, as they allow fast and relatively low-cost surveys of soil apparent electrical conductivity (ECa). Although the use of non-invasive EMI for imaging spatial soil properties is very attractive, the dependence of ECa on several factors challenges any interpretation with respect to individual soil properties or states such as soil moisture (θ ). The major aim of this study was to further investigate the potential of repeated EMI measurements to map θ , with particular focus on the temporal variability of the spatial patterns of ECa and θ . To this end, we compared repeated EMI measurements with high-resolution θ data from a wireless soil moisture and soil temperature monitoring network for an extensively managed hillslope area for which soil properties and θ dynamics are known. For the investigated site, (i) ECa showed small temporal variations whereas θ varied from very dry to almost saturation, (ii) temporal changes of the spatial pattern of ECa differed from those of the spatial pattern of θ , and (iii) the ECa-θ relationship varied with time. Results suggest that (i) depending upon site characteristics, stable soil properties can be the major control of ECa measured with EMI, and (ii) for soils with low clay content, the influence of θ on ECa may be confounded by changes of the electrical conductivity of the soil solution. Further, this study discusses the complex interplay between factors controlling ECa and θ , and the use of EMI-based ECa data with respect to hydrological applications.

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Section: Introductionmentioning

confidence: 99%

Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring network

Martini

Werban

Zacharias

et al. 2017

Hydrol. Earth Syst. Sci.

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“…; Delefortrie et al . ; Thiesson et al . ), it is feasible to perform a quantitative analysis of multi‐channel EMI data.…”

Section: Introductionmentioning

confidence: 99%

1D sequential inversion of portable multi‐configuration electromagnetic induction data

Guillemoteau

Simon

Lück

et al. 2016

Near Surface Geophysics

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We present an algorithm that performs sequentially one‐dimensional inversion of subsurface magnetic permeability and electrical conductivity by using multi‐configuration electromagnetic induction sensor data. The presented method is based on the conversion of the in‐phase and out‐of‐phase data into effective magnetic permeability and electrical conductivity of the equivalent homogeneous half‐space. In the case of small‐offset systems, such as portable electromagnetic induction sensors, for which in‐phase and out‐of‐phase data are moderately coupled, the effective half‐space magnetic permeability and electrical conductivity can be inverted sequentially within an iterative scheme. We test and evaluate the proposed inversion strategy using synthetic and field examples. First, we apply it to synthetic data for some highly magnetic environments. Then, the method is tested on real field data acquired in a basaltic environment to image a formation of archaeological interest. These examples demonstrate that a joint interpretation of in‐phase and out‐of‐phase data leads to a better characterisation of the subsurface in magnetic environments such as volcanic areas.

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“…[34]. Then, the data were drift corrected using a tie line approach, as described in [35] and [36]. Subsequently, the data were interpolated to a regular grid (cells of 0.3 m by 0.3 m) using a natural neighbour interpolation.…”

Section: Field Data Setmentioning

confidence: 99%

Probabilistic 1-D Inversion of Frequency-Domain Electromagnetic Data Using a Kalman Ensemble Generator

Bobe

Vijver

Keller

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Frequency-domain electromagnetic (FDEM) data of the subsurface are determined by electrical conductivity and magnetic susceptibility. We apply a Kalman Ensemble generator (KEG) to one-dimensional probabilistic multi-layer inversion of FDEM data to derive conductivity and susceptibility simultaneously. The KEG provides an efficient alternative to an exhaustive Bayesian framework for FDEM inversion, including a measure for the uncertainty of the inversion result. Additionally, the method provides a measure for the depth below which the measurement is insensitive to the parameters of the subsurface. This so-called depth of investigation is derived from ensemble covariances. A synthetic and a field data example reveal how the KEG approach can be applied to FDEM data and how FDEM calibration data and prior beliefs can be combined in the inversion procedure. For the field data set, many inversions for one-dimensional subsurface models are performed at neighbouring measurement locations. Assuming identical prior models for these inversions, we save computational time by re-using the initial KEG ensemble across all measurement locations.[7] describe how including the IP component can avoid systematic underestimation of EC when a significant IP shift is recorded.The aforementioned publications apply classical, deterministic inversion approaches yielding a single model parameter realisation [11]. We propose a pragmatic approach to probabilistic FDEM inversion by applying the ensemble-based KEG. The KEG method can be seen as a trade-off between two extreme approaches to inverse problems: (1) using a deterministic inversion that quickly finds a single model satisfying the data, and (2) computing a large number of possible parameter models in search techniques aiming to be exhaustive (e.g., Markov-chain Monte-Carlo methods [12]).Ensemble-based inversions have been proven to be efficient and robust [6], but at the same time comprehensive enough to characterize the uncertainty of the result. In the context of Kalman methods, uncertainty is characterized by standard deviations (STD) of model parameters, which are a measure for the parameter spread of the models that match the field measurements. The KEG method uses an equation equal to the update step of the Ensemble Kalman Filter [13], but it performs updates exclusively in the model parameters [6].The novelty of our work lies in the application of the KEG to the inversion of FDEM data. We update prior EC and MS ensembles simultaneously, based on the measurements of the IP and OP component of the secondary electromagnetic field. Additionally, it is shown how correlations computed in the KEG can be used as a proxy for the measurement sensitivity. Using this sensitivity proxy, we determine a depth of negligible sensitivity, the so-called depth of investigation of a particular measurement setup. The KEG provides an interesting inversion method for geophysical data, especially when moving towards large and multi-dimensional forward models.The application of the KEG is demonstrated ...

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An efficient calibration procedure for correction of drift in EMI survey data

Cited by 54 publications

References 17 publications

Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring network

Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring network

1D sequential inversion of portable multi‐configuration electromagnetic induction data

Probabilistic 1-D Inversion of Frequency-Domain Electromagnetic Data Using a Kalman Ensemble Generator

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