Broadband soil susceptibility measurements for EMI applications

McFadden, Michael J.; Scott, Waymond R.

doi:10.1016/j.jappgeo.2013.01.009

Cited by 14 publications

(3 citation statements)

References 16 publications

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“…It is then possible to use the acquired EMI maps to locate appropriately further prospections – geophysical or not – for example at zones with high contrasts in the EMI measurements. Another advantage of the EMI method, particularly used in archaeology studies, is its sensitivity to additional subsurface properties that complete the electrical resistivity mapping: the complex magnetic susceptibility

\kappa \ = {\kappa }_{{\rm{ph}}}\ - i{\kappa }_{\rm{q}}\

comprising the in‐phase magnetic susceptibility ( κ ph ) and the quadrature magnetic viscosity ( κ q ); and the complex dielectric permittivity

\varepsilon \ = \varepsilon ^{\prime}\ - i\varepsilon ^{\prime\prime}

with

i\ = \sqrt { - 1}

(Benech et al., 2016; McFadden & Scott, 2013; Tabbagh et al., 2021). In the measured EM fields, the contributions of the dielectric losses

\varepsilon ^{\prime\prime}

cannot be separated from the conductivity response; hereafter, we will only consider the real part of the effective relative dielectric permittivity ε r where

\varepsilon ^{\prime} = {\varepsilon }_0\ {\varepsilon }_{\rm{r}}

(with ε 0 the permittivity in vacuum:

{\varepsilon }_0 = \ 8.854 \times {10}^{ - 12}{\rm{\ F\ m}}^{ - 1}).

…”

Section: Introductionmentioning

confidence: 99%

Calibration of near‐surface multi‐frequency electromagnetic induction data

Finco

Réjiba

Schamper

et al. 2023

Geophysical Prospecting

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The frequency-domain electromagnetic induction method is a very efficient and convenient method to investigate a site of interest and for a large range of applications including hydrogeology and archaeology. Besides an easy infield acquisition, the data can be used to estimate the electrical, magnetic and, in certain cases, dielectric properties of the ground. It does, however, require calibration to get reliable quantitative values of these properties. We propose here an original and reliable self-calibration method, specific to multi-frequency measurements in the range of (5-100 kHz)-based only on the consistency between the data at different acquisition frequencies. Contrary to other existing cross-calibration procedures, no additional measurements with other methods are necessary. This self-calibration (quadrature and in-phase components of the electromagnetic induction signal) procedure is tested at a test site located at the centre for studies on risks, the environment, mobility and urban planning Normandie-Centre facilities (Rouen, France). The developed quadrature self-calibration has been successfully compared with the usual electrical resistivity tomography -electromagnetic induction cross-calibration procedure. The main results reveal the potential of systematic assessment of all electromagnetic parameters from multi-frequency and multi-configuration electromagnetic induction data: The electrical resistivity and the magnetic susceptibility are estimated from the low-frequency range, whereas the highest part of the frequency range is used for the dielectric permittivity.

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\kappa \ = {\kappa }_{{\rm{ph}}}\ - i{\kappa }_{\rm{q}}\

comprising the in‐phase magnetic susceptibility ( κ ph ) and the quadrature magnetic viscosity ( κ q ); and the complex dielectric permittivity

\varepsilon \ = \varepsilon ^{\prime}\ - i\varepsilon ^{\prime\prime}

with

i\ = \sqrt { - 1}

(Benech et al., 2016; McFadden & Scott, 2013; Tabbagh et al., 2021). In the measured EM fields, the contributions of the dielectric losses

\varepsilon ^{\prime\prime}

cannot be separated from the conductivity response; hereafter, we will only consider the real part of the effective relative dielectric permittivity ε r where

\varepsilon ^{\prime} = {\varepsilon }_0\ {\varepsilon }_{\rm{r}}

(with ε 0 the permittivity in vacuum:

{\varepsilon }_0 = \ 8.854 \times {10}^{ - 12}{\rm{\ F\ m}}^{ - 1}).

…”

Section: Introductionmentioning

confidence: 99%

Calibration of near‐surface multi‐frequency electromagnetic induction data

Finco

Réjiba

Schamper

et al. 2023

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…However, new geotechnologies have emerged in recent decades, allowing the acquisition of data at shorter times, with non-invasive and accurate methods such as reflectance spectroscopy, satellite imagery, and geophysical techniques Demattê et al, 2017Demattê et al, , 2007Fioriob, 2013;Fongaro et al, 2018;Mello et al, 2021;Terra et al, 2018). Among these technologies, geophysical sensors have been recently used in pedology to understand pedogenesis and the relationship between these processes and soil attributes (Son et al, 2010;Schuler et al, 2011;Beamish, 2013;McFadden and Scott, 2013;Sarmast et al, 2017;Reinhardt and Herrmann, 2019). Among these geophysical techniques used, we highlight gamma-ray spectrometry, magnetic susceptibility (κ), and apparent electrical conductivity (ECa).…”

Section: Introductionmentioning

confidence: 99%

A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes

Mello¹,

Veloso²,

Lana³

et al. 2022

Geosci. Model Dev.

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Abstract. Geophysical sensors combined with machine learning algorithms were used to understand the pedosphere system and landscape processes and to model soil attributes. In this research, we used parent material, terrain attributes, and data from geophysical sensors in different combinations to test and compare different and novel machine learning algorithms to model soil attributes. We also analyzed the importance of pedoenvironmental variables in predictive models. For that, we collected soil physicochemical and geophysical data (gamma-ray emission from uranium, thorium, and potassium; magnetic susceptibility and apparent electric conductivity) by three sensors (gamma-ray spectrometer, RS 230; susceptibilimeter KT10, Terraplus; and conductivimeter, EM38 Geonics) at 75 points and analyzed the data. The models with the best performance (R2 0.48, 0.36, 0.44, 0.36, 0.25, and 0.31) varied for clay, sand, Fe2O3, TiO2, SiO2, and cation exchange capacity prediction, respectively. Modeling with the selection of covariates at three phases (variance close to zero, removal by correction, and removal by importance) was adequate to increase the parsimony. The results were validated using the method “nested leave-one-out cross-validation”. The prediction of soil attributes by machine learning algorithms yielded adequate values for field-collected data, without any sample preparation, for most of the tested predictors (R2 values ranging from 0.20 to 0.50). Also, the use of four regression algorithms proved to be important since at least one of the predictors used one of the tested algorithms. The performance values of the best algorithms for each predictor were higher than those obtained with the use of a mean value for the entire area comparing the values of root mean square error (RMSE) and mean absolute error (MAE). The best combination of sensors that reached the highest model performance was that of the gamma-ray spectrometer and the susceptibilimeter. The most important variables for most predictions were parent material, digital elevation, standardized height, and magnetic susceptibility. We concluded that soil attributes can be efficiently modeled by geophysical data using machine learning techniques and geophysical sensor combinations. This approach can facilitate future soil mapping in a more time-efficient and environmentally friendly manner.

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“…However, new geotechnologies have emerged in the last decades, allowing the acquisition of data at shorter times, with non-invasive and accurate methods, such as reflectance spectroscopy, satellite imagery and geophysical techniques (Mello et al, 2020;Demattê et al, 2017Demattê et al, , 2007Fioriob, 2013;Fongaro et al, 2018;Mello et al, 2021;Terra et al, 2018aTerra et al, , 2018b. Among these technologies, geophysical sensors have been recently used in pedology to understand pedogenesis and the relationship between these processes and soil attributes (Son et al, 2010;Schuler et al, 2011;Beamish, 2013;McFadden and Scott, 2013;Sarmast et al, 2017;Reinhardt and Herrmann, 2019). Among these geophysical techniques used, we highlight the gamma-spectrometry, magnetic susceptibility (κ) and apparent electrical conductivity (ECa).…”

Section: Introductionmentioning

confidence: 99%

A new methodological framework by geophysical sensors combinations associated with machine learning algorithms to understand soil attributes

Mello

Veloso

Lana

et al. 2021

Preprint

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Abstract. Geophysical sensors combined with machine learning algorithms have been used to understand the pedosphere system, landscape processes and to model soil attributes. In this research, we used parent material, terrain attributes and data from geophysical sensors in different combinations, to test and compare different and novel machine learning algorithms to model soil attributes. Also, we analyzed the importance of pedoenvironmental variables in predictive models. For that, we collected soil physico-chemical and geophysical data (gamma-ray emission from uranium, thorium and potassium, magnetic susceptibility and apparent electric conductivity) by three sensors, gamma-ray spectrometer – RS 230, susceptibilimeter KT10 – Terraplus and Conductivimeter – EM38 Geonics) at 75 points and, we performed soil analysis afterwards. The results showed varying models with the best performance (R2 > 0.2) for clay, sand, Fe2O3, TiO2, SiO2 and Cation Exchange Capacity prediction. Modeling with selection of covariates at three phases (variance close to zero, removal by correction and removal by importance), demonstrated to be adequate to increase the parsimony. The prediction of soil attributes by machine learning algorithms demonstrated adequate values for field collected data, without any sample preparation, for most of the tested predictors (R2 ranging from 0.20 to 0.50). Also, the use of four regression algorithms proved important, since at least one of the predictors used one of the tested algorithms. The performances of the best algorithms for each predictor were higher than the use of a mean value for the entire area comparing the values of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The best combination of sensors that reached the best model performance to predict soil attributes were gamma-ray spectrometer and susceptibilimeter. The most important variables were parent material, digital elevation model, standardized height and magnetic susceptibility for most predictions. We concluded that soil attributes can be efficiently modelled by geophysical data using machine learning techniques and geophysical sensors combinations. The technique can bring light for future soil mapping with gain of time and environment friendly.

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Broadband soil susceptibility measurements for EMI applications

Cited by 14 publications

References 16 publications

Calibration of near‐surface multi‐frequency electromagnetic induction data

Calibration of near‐surface multi‐frequency electromagnetic induction data

A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes

A new methodological framework by geophysical sensors combinations associated with machine learning algorithms to understand soil attributes

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