Simultaneous calibration and inversion algorithm for multiconfiguration electromagnetic induction data acquired at multiple elevations

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. EN1-EN14 ◽  
Author(s):  
Xihe Tan ◽  
Achim Mester ◽  
Christian von Hebel ◽  
Egon Zimmermann ◽  
Harry Vereecken ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Electromagnetic Induction ◽  
Synthetic Data ◽  
Ground Surface ◽  
Acquisition Time ◽  
Inversion Algorithm ◽  
Transect Line ◽  
Promising Tool ◽  
Reference Measurements ◽  
And Inversion

Electromagnetic induction (EMI) is a contactless and fast geophysical measurement technique. Frequency-domain EMI systems are available as portable rigid booms with fixed separations up to approximately 4 m between the transmitter and the receivers. These EMI systems are often used for high-resolution characterization of the upper subsurface meters (up to depths of approximately 1.5 times the maximum coil separation). The availability of multiconfiguration EMI systems, which measure multiple apparent electrical conductivity ([Formula: see text]) values of different but overlapping soil volumes, enables EMI data inversions to estimate electrical conductivity ([Formula: see text]) changes with depth. However, most EMI systems currently do not provide absolute [Formula: see text] values, but erroneous shifts occur due to calibration problems, which hinder a reliable inversion of the data. Instead of using physical soil data or additional methods to calibrate the EMI data, we have used an efficient and accurate simultaneous calibration and inversion approach to avoid a possible bias of other methods while reducing the acquisition time for the calibration. By measuring at multiple elevations above the ground surface using a multiconfiguration EMI system, we simultaneously obtain multiplicative and additive calibration factors for each coil configuration plus an inverted layered subsurface electrical conductivity model at the measuring location. Using synthetic data, we verify our approach. Experimental data from five different calibration positions along a transect line showed similar calibration results as the data obtained by more elaborate vertical electrical sounding reference measurements. The synthetic and experimental results demonstrate that the multielevation calibration and inversion approach is a promising tool for quantitative electrical conductivity analyses.

scholarly journals Magnetometric resistivity tomography using chaos polynomial expansion

2020 ◽  
Vol 221 (3) ◽  
pp. 1469-1483
Author(s):  
M T Vu ◽  
A Jardani ◽  
A Revil ◽  
M Jessop
Keyword(s):  
Magnetic Field ◽  
Electrical Conductivity ◽  
Current Source ◽  
Electrical Potential ◽  
Ground Surface ◽  
Theoretical Background ◽  
Polynomial Expansion ◽  
Model Parameters ◽  
Inversion Algorithm ◽  
Heterogeneous Distribution

SUMMARY We present an inversion algorithm to reconstruct the spatial distribution of the electrical conductivity from the analysis of magnetometric resistivity (MMR) data acquired at the ground surface. We first review the theoretical background of MMR connecting the generation of a magnetic field in response to the injection of a low-frequency current source and sink in the ground given a known distribution of electrical conductivity in the subsurface of the Earth. The forward modelling is based on sequentially solving the Poisson equation for the electrical potential distribution and the magnetostatic (Biot and Savart) equation for the magnetic field. Then, we introduce a Gauss–Newton inversion algorithm in which the logarithm of the electrical conductivity field is parametrized by using the chaos polynomial expansion in order to reduce the number of model parameters. To illustrate how the method works, the algorithm is successfully applied on four synthetic models with 3-D heterogeneous distribution of the electrical conductivity. Finally, we apply our algorithm to a field case study in which seepage was known to be occurring along an embankment of a headrace channel to a power station.

Toward subsurface magnetic permeability imaging with electromagnetic induction sensors: Sensitivity computation and reconstruction of measured data

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. E335-E345 ◽  
Author(s):  
Tim Klose ◽  
Julien Guillemoteau ◽  
François-Xavier Simon ◽  
Jens Tronicke
Keyword(s):  
Electrical Conductivity ◽  
Magnetic Permeability ◽  
Electromagnetic Induction ◽  
Synthetic Data ◽  
Forward Modeling ◽  
Measured Data ◽  
Modeling Method ◽  
Near Surface ◽  
Sensitivity Functions ◽  
Modeling Approach

In near-surface geophysics, small portable loop-loop electromagnetic induction (EMI) sensors using harmonic sources with a constant and rather small frequency are increasingly used to investigate the electrical properties of the subsurface. For such sensors, the influence of electrical conductivity and magnetic permeability on the EMI response is well-understood. Typically, data analysis focuses on reconstructing an electrical conductivity model by inverting the out-of-phase response. However, in a variety of near-surface applications, magnetic permeability (or susceptibility) models derived from the in-phase (IP) response may provide important additional information. In view of developing a fast 3D inversion procedure of the IP response for a dense grid of measurement points, we first analyze the 3D sensitivity functions associated with a homogeneous permeable half-space. Then, we compare synthetic data computed using a linear forward-modeling method based on these sensitivity functions with synthetic data computed using full nonlinear forward-modeling methods. The results indicate the correctness and applicability of our linear forward-modeling approach. Furthermore, we determine the advantages of converting IP data into apparent permeability, which, for example, allows us to extend the applicability of the linear forward-modeling method to high-magnetic environments. Finally, we compute synthetic data with the linear theory for a model consisting of a controlled magnetic target and compare the results with field data collected with a four-configuration loop-loop EMI sensor. With this field-scale experiment, we determine that our linear forward-modeling approach can reproduce measured data with sufficiently small error, and, thus, it represents the basis for developing efficient inversion approaches.

Detection of soil pipe network by electromagnetic induction (EMI) in relation to the high resolution UAV data

2021 ◽  
Author(s):  
Anita Bernatek-Jakiel ◽  
Marta Kondracka ◽  
Maciej Liro
Keyword(s):  
Electrical Conductivity ◽  
High Resolution ◽  
Electromagnetic Induction ◽  
Geophysical Methods ◽  
Ground Surface ◽  
Degradation Process ◽  
Receiver Coil ◽  
Small Catchment ◽  
Eastern Carpathians ◽  
Soil Piping

<p>Subsurface erosion by soil piping is a widespread land degradation process that occurs in different soil types around the world. Recent studies have shown that piping erosion may lead to the significant soil loss and disturbances of ground surface. This process accelerates also gully erosion. However, it is still omitted in hydrological models of a catchment, as well as in soil and water erosion models. It seems that the main problem in soil piping studies lies on the basic issue, i.e., the detection of subsurface tunnels (soil pipes). As geophysical methods enable the exploration below the ground surface, they are promising in soil piping studies.</p><p> </p><p>This study aims to evaluate the suitability of the electromagnetic induction (EMI) to detect subsurface network of soil pipes. The detailed study was conducted in the small catchment (Cisowiec) in the Bieszczady Mts. (the Eastern Carpathians, SE Poland), where pipes develop in Cambisols. The measurements were carried out using a conductivity meter EM38-MK2 (Geonics) in both vertical and horizontal measuring dipole orientations. The EM38-MK2 provided simultaneous measurements of apparent electrical conductivity with two transmitter receiver coil separation (0.5 m and 1 m). In order to compare subsurface data with the surface response (i.e., depressions and collapses), the high resolution DEM and orthophotos have been produced. These data have been prepared using Structure from Motion (SfM) technique based on the images taken from the low altitude by an Unmanned Aerial Vehicle (UAV; DJI Phantom-4 equipped with a 1' camera). The UAV-derived products (orthophotos and DEM) have the resolution of 0.014 x 0.014 m and point density of 9240 per 1 m<sup>2</sup>.</p><p> </p><p>The EMI results are presented on the maps that gathered data at three depths (0.4 m, 0.75 m, 1.5 m). The results revealed the soil pipes as areas characterized by higher electrical conductivity than the surroundings. The spatial distribution of subsurface tunnels corresponds with the ground depressions and collapses detected in the field and seen on the high resolution DEM and orthophoto. The use of EMI in piping research has been evaluated.</p><p> </p><p>The study is supported by the National Science Centre, Poland within the first author’s project SONATINA 1 (2017/24/C/ST10/00114).</p>

AVO detectability against tuning and stretching artifacts

Geophysics ◽  
1999 ◽  
Vol 64 (2) ◽  
pp. 494-503 ◽  
Author(s):  
Wenjie Dong
Keyword(s):  
Seismic Data ◽  
Synthetic Data ◽  
Seismic Exploration ◽  
Inversion Algorithm ◽  
Amplitude Variation With Offset ◽  
Logical Point ◽  
Anomalous Reflection ◽  
Ricker Wavelet ◽  
And Migration ◽  
And Inversion

The [Formula: see text] of hydrocarbon‐bearing sediments normally deviates from the [Formula: see text] trend of the background rocks. This causes anomalous reflection amplitude variation with offset (AVO) in the seismic data. The estimation of these AVOs is inevitably affected by wave propagation effects and inversion algorithm limitations, such as thin‐bed tuning and migration stretch. A logical point is to determine the minimum [Formula: see text] change required for an anomalous AVO to be detectable beyond the background tuning and stretching effects. Assuming Ricker wavelet for the seismic data, this study addresses this point by quantifying the errors in the intercept/slope estimate. Using these results, two detectability conditions are derived. Denoting the background [Formula: see text] by γ and its variation by δγ, the thin‐bed parameter (thickness/wavelength) by ξ, the maximum background intercept closest to the AVO by |A|max, and the thin‐bed intercept value by |A|thin the two conditions are [Formula: see text] [Formula: see text] for detectability against stretching and tuning plus stretching, respectively. Tests on synthetic data confirm their validity and accuracy. These conditions provide a quantitative guideline for evaluating AVO applicability and effectiveness in seismic exploration. They can eliminate some of the subjectivity when interpreting AVO results in different attribute spaces. To improve AVO detectability, a procedure is suggested for removing the tuning and stretching effects.

scholarly journals Mapping of Peat Thickness Using a Multi-Receiver Electromagnetic Induction Instrument

2020 ◽  
Vol 12 (15) ◽  
pp. 2458
Author(s):  
Amélie Beucher ◽  
Triven Koganti ◽  
Bo V. Iversen ◽  
Mogens H. Greve
Keyword(s):  
Electrical Conductivity ◽  
Electromagnetic Induction ◽  
Peat Soil ◽  
Mineral Soil ◽  
Three Dimensional ◽  
Inversion Algorithm ◽  
Terrain Attributes ◽  
Precise Calculation ◽  
Single Coil ◽  
Uncertainty Estimates

Peatlands constitute extremely valuable areas because of their ability to store large amounts of soil organic carbon (SOC). Investigating different key peat soil properties, such as the extent, thickness (or depth to mineral soil) and bulk density, is highly relevant for the precise calculation of the amount of stored SOC at the field scale. However, conventional peat coring surveys are both labor-intensive and time-consuming, and indirect mapping methods based on proximal sensors appear as a powerful supplement to traditional surveys. The aim of the present study was to assess the use of a non-invasive electromagnetic induction (EMI) technique as an augmentation to a traditional peat coring survey that provides localized and discrete measurements. In particular, a DUALEM-421S instrument was used to measure the apparent electrical conductivity (ECa) over a 10-ha field located in Jutland, Denmark. In the study area, the peat thickness varied notably from north to south, with a range from 3 to 730 cm. Simple and multiple linear regressions with soil observations from 110 sites were used to predict peat thickness from (a) raw ECa measurements (i.e., single and multiple-coil predictions), (b) true electrical conductivity (σ) estimates calculated using a quasi-three-dimensional inversion algorithm and (c) different combinations of ECa data with environmental covariates (i.e., light detection and ranging (LiDAR)-based elevation and derived terrain attributes). The results indicated that raw ECa data can already constitute relevant predictors for peat thickness in the study area, with single-coil predictions yielding substantial accuracies with coefficients of determination (R2) ranging from 0.63 to 0.86 and root mean square error (RMSE) values between 74 and 122 cm, depending on the measuring DUALEM-421S coil configuration. While the combinations of ECa data (both single and multiple-coil) with elevation generally provided slightly higher accuracies, the uncertainty estimates for single-coil predictions were smaller (i.e., smaller 95% confidence intervals). The present study demonstrates a high potential for EMI data to be used for peat thickness mapping.

scholarly journals Calibration, Conversion, and Quantitative Multi-Layer Inversion of Multi-Coil Rigid-Boom Electromagnetic Induction Data

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4753 ◽  
Author(s):  
von Hebel ◽  
van der Kruk ◽  
Huisman ◽  
Mester ◽  
Altdorff ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Electrical Conductivity ◽  
Electromagnetic Induction ◽  
Depth Range ◽  
Electrode Arrays ◽  
Average Value ◽  
Bulk Electrical Conductivity ◽  
Electrical Conductivities ◽  
Electrical Soundings ◽  
And Inversion

Multi-coil electromagnetic induction (EMI) systems induce magnetic fields below and above the subsurface. The resulting magnetic field is measured at multiple coils increasingly separated from the transmitter in a rigid boom. This field relates to the subsurface apparent electrical conductivity (σa), and σa represents an average value for the depth range investigated with a specific coil separation and orientation. Multi-coil EMI data can be inverted to obtain layered bulk electrical conductivity models. However, above-ground stationary influences alter the signal and the inversion results can be unreliable. This study proposes an improved data processing chain, including EMI data calibration, conversion, and inversion. For the calibration of σa, three direct current resistivity techniques are compared: Electrical resistivity tomography with Dipole-Dipole and Schlumberger electrode arrays and vertical electrical soundings. All three methods obtained robust calibration results. The Dipole-Dipole-based calibration proved stable upon testing on different soil types. To further improve accuracy, we propose a non-linear exact EMI conversion to convert the magnetic field to σa. The complete processing workflow provides accurate and quantitative EMI data and the inversions reliable estimates of the intrinsic electrical conductivities. This improves the ability to combine EMI with, e.g., remote sensing, and the use of EMI for monitoring purposes.

scholarly journals Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

2021 ◽  
Vol 13 (7) ◽  
pp. 1238
Author(s):  
Jere Kaivosoja ◽  
Juho Hautsalo ◽  
Jaakko Heikkinen ◽  
Lea Hiltunen ◽  
Pentti Ruuttunen ◽  
...  
Keyword(s):  
Reference Data ◽  
Measurement Techniques ◽  
Synthetic Data ◽  
Ground Truth ◽  
Precision Farming ◽  
Aerial Imaging ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Reference Measurements

The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

scholarly journals Simple low dose radiography allows precise lung volume assessment in mice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Amara Khan ◽  
Andrea Markus ◽  
Thomas Rittmann ◽  
Jonas Albers ◽  
Frauke Alves ◽  
...  
Keyword(s):  
Lung Function ◽  
Whole Body ◽  
Acquisition Time ◽  
Micro Ct ◽  
Lung Volumes ◽  
X Ray ◽  
Promising Tool ◽  
Volume Assessment ◽  
Set Up ◽  
Time Required

AbstractX-ray based lung function (XLF) as a planar method uses dramatically less X-ray dose than computed tomography (CT) but so far lacked the ability to relate its parameters to pulmonary air volume. The purpose of this study was to calibrate the functional constituents of XLF that are biomedically decipherable and directly comparable to that of micro-CT and whole-body plethysmography (WBP). Here, we developed a unique set-up for simultaneous assessment of lung function and volume using XLF, micro-CT and WBP on healthy mice. Our results reveal a strong correlation of lung volumes obtained from radiographic XLF and micro-CT and demonstrate that XLF is superior to WBP in sensitivity and precision to assess lung volumes. Importantly, XLF measurement uses only a fraction of the radiation dose and acquisition time required for CT. Therefore, the redefined XLF approach is a promising tool for preclinical longitudinal studies with a substantial potential of clinical translation.

Quasi-2D inversion of DCR and TDEM data for shallow investigations

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. F239-F250 ◽  
Author(s):  
Fernando A. Monteiro Santos ◽  
Hesham M. El-Kaliouby
Keyword(s):  
Joint Inversion ◽  
Synthetic Data ◽  
Data Sets ◽  
Complex Environments ◽  
Inversion Algorithm ◽  
2D Inversion ◽  
New Approach ◽  
Earth Models ◽  
Time Domain Electromagnetic ◽  
Inversion Techniques

Joint or sequential inversion of direct current resistivity (DCR) and time-domain electromagnetic (TDEM) data commonly are performed for individual soundings assuming layered earth models. DCR and TDEM have different and complementary sensitivity to resistive and conductive structures, making them suitable methods for the application of joint inversion techniques. This potential joint inversion of DCR and TDEM methods has been used by several authors to reduce the ambiguities of the models calculated from each method separately. A new approach for joint inversion of these data sets, based on a laterally constrained algorithm, was found. The method was developed for the interpretation of soundings collected along a line over a 1D or 2D geology. The inversion algorithm was tested on two synthetic data sets, as well as on field data from Saudi Arabia. The results show that the algorithm is efficient and stable in producing quasi-2D models from DCR and TDEM data acquired in relatively complex environments.

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