Resolving the spatial distribution of the true electrical conductivity with depth using EM38 and EM31 signal data and a laterally constrained inversion model

J. Triantafilis; F. A. Monteiro Santos

doi:10.1071/sr09149

Resolving the spatial distribution of the true electrical conductivity with depth using EM38 and EM31 signal data and a laterally constrained inversion model

Soil Research ◽

10.1071/sr09149 ◽

2010 ◽

Vol 48 (5) ◽

pp. 434 ◽

Cited By ~ 50

Author(s):

J. Triantafilis ◽

F. A. Monteiro Santos

Keyword(s):

Electrical Conductivity ◽

Spatial Distribution ◽

Vadose Zone ◽

Root Zone ◽

Joint Inversion ◽

Geophysical Methods ◽

Exchange Capacity ◽

Saturated Soil ◽

Cotton Field ◽

Inversion Algorithm

The ability to map the spatial distribution of average soil property values using geophysical methods at the field and district level has been well described. This includes the use of electromagnetic (EM) instruments which measure bulk soil electrical conductivity (σa). However, soil is a 3-dimensional medium. In order to better represent the spatial distribution of soil properties with depth, various methods of inverting EM instrument data have been attempted and include Tikhonov regularisation and layered earth models. In this paper we employ a 1-D inversion algorithm with 2-D smoothness constraints to predict the true electrical conductivity (σ) using σa data collected along a transect in an irrigated cotton field in the lower Namoi valley. The primary σa data include the root-zone measuring EM38 and the vadose-zone sensing EM31, in the vertical (v) and horizontal (h) dipole modes and at heights of 0.2 and 1.0 m, respectively. In addition, we collected σa with the EM38 at heights of 0.4 and 0.6 m. In order to compare and contrast the value of the various σa data we carry out individual inversions of EM38v and EM38h collected at heights of 0.2, 0.4, and 0.6 m, and EM31v and EM31h at 1.0 m. In addition, we conduct joint inversions of various combinations of EM38 σa data available at various heights (e.g. 0.2 and 0.4 m). Last we conduct joint inversions of the EM38v and EM38h σa data at 0.2, 0.4, and 0.6 m with the EM31v and EM31h at 1.0 m. We find that the values of σ achieved along the transect studied represent the duplex nature of the soil. In general, the EM38v and EM38h collected at a height of 0.2, 0.4, and 0.6 m assist in resolving solum and root-zone variability of the cation exchange capacity (cmol(+)/kg of soil solids) and the electrical conductivity of a saturated soil paste extract (ECe, dS/m), while the use of the EM31v and EM31h at 1.0 m assists in characterising the vadose zone and the likely location of a shallow perched-water table. In terms of identifying an optimal set of EM σa data for inversion we found that a joint inversion of the EM38 at a height of 0.6 m and EM31 signal data provided the best correlation with electrical conductivity of a saturated soil paste (ECp, dS/m) and ECe (respectively, 0.81 and 0.77) closely followed by a joint inversion of all the EM38 and EM31 σa data available (0.77 and 0.56).

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Modeling the electrical conductivity of hydrogeological strata using joint-inversion of loop-loop electromagnetic data

Geophysics ◽

10.1190/geo2011-0507.1 ◽

2012 ◽

Vol 77 (4) ◽

pp. WB99-WB107 ◽

Cited By ~ 23

Author(s):

J. Triantafilis ◽

V. Wong ◽

F. A. Monteiro Santos ◽

D. Page ◽

R. Wege

Keyword(s):

Electrical Conductivity ◽

Water Table ◽

Saline Water ◽

Joint Inversion ◽

Geophysical Methods ◽

Agricultural Landscapes ◽

Estuarine Sediments ◽

North Coast ◽

Inversion Algorithm ◽

Text Data

In coastal-estuarine agricultural landscapes that are inherently rich in sulfidic sediments and saline water-tables, natural resource management data need to be collected to describe the heterogeneous nature of the soil, underlying regolith, and interactions with groundwater. Geophysical methods, such as electromagnetic (EM) induction instruments, are increasingly being used. This is because they measure apparent soil electrical conductivity [Formula: see text], which has previously been successfully used to map the areal distribution of soil (e.g., salinity) and hydrological (e.g., water-table depth) properties. We explored the potential of a next-generation DUALEM-421 and EM34 to be used independently and in conjunction with each other to provide information we can use to represent the pedological and hydrogeological setting of alluvial and estuarine sediments. A 1D laterally constrained joint-inversion algorithm can account for the nonlinearity of large [Formula: see text] (i.e., [Formula: see text]). We applied this algorithm to develop 2D cross sections of electrical conductivity ([Formula: see text]) from DUALEM-421 and EM34 [Formula: see text] data acquired across an estuarine landscape and situated within Quaternary fluvial sediments adjacent to Rocky Mouth Creek on the far north coast of New South Wales, Australia. We compared this joint-inversion model with inversions of the DUALEM-421 and EM34 [Formula: see text] data independently of each other. For the most part, the general patterns of the inverted models of [Formula: see text] compare favorably with existing pedological and hydrogeological interpretations, based on results achieved during a previous geoelectrical survey. However, the joint-inversion provides a more realistic model of the location and extent of a saline water-table and associated with the location of sulfidic sediments.

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2-Dimensional soil and vadose-zone representation using an EM38 and EM34 and a laterally constrained inversion model

Soil Research ◽

10.1071/sr09013 ◽

2009 ◽

Vol 47 (8) ◽

pp. 809 ◽

Cited By ~ 34

Author(s):

J. Triantafilis ◽

F. A. Monteiro Santos

Keyword(s):

Electrical Conductivity ◽

Vadose Zone ◽

Root Zone ◽

Clay Content ◽

Inversion Algorithm ◽

Near Surface ◽

Particle Size Fractions ◽

Murray Darling Basin ◽

Full Solution ◽

Soil Particle Size Fractions

The network of prior streams and palaeochannels common across the Riverine Plains of the Murray–Darling Basin act as conduits for the redistribution of water and soluble salts beneath the root-zone. To improve scientific understanding of these hydrological processes there is the need to better represent and map the connectivity and spatial extent of these physiographic and stratigraphic features. Groundbased electromagnetic (EM) instruments, which measure bulk soil electrical conductivity (σa), have been used widely to map their areal distribution across the landscape. However, methods to resolve their location with depth have rarely been attempted. In this paper we employ a 1-D inversion algorithm with 2-D smoothness constraints to predict the true electrical conductivity (σ) at discrete depth increments using EM data. The EM data we use include the root-zone measuring EM38 and the deeper sensing EM34. We collected EM38 data in the vertical (EM38v) and horizontal (EM38h) dipole modes and EM34 data in the horizontal mode and coil spacing of 10, 20, and 40 m (respectively, EM34-10, EM34-20, and EM34-40). In order to compare and contrast the value of the various EM data we carried out multiple inversions using different combinations, which include: independent inversions of (i) EM38 (root-zone) and (ii) EM34 data (vadose-zone), and in combination using (iii) EM38v, EM38h, and EM34-10 (near-surface), and (iv) all 5 EM datasets (regolith) available. The general patterns of σ are shown to compare favourably with the known pedoderms, physiographic, and stratigraphic features and soil particle size fractions collected from calibration cores drilled across the lower Macquarie Valley study area. In general we find that the EM38 assists in resolving root-zone variability, specifically duplex soil profiles and physiographic features such as prior streams, while the use of the EM34 assists in resolving the stratigraphic nature of the vadose-zone and specifically the likely location of palaeochannels and subsurface anomalies that may indicate the location of good quality groundwater and/or clay aquitards. In this case, our potential to use σ to predict clay content is limited by the non-linearity of the cumulative functions. In order to improve on the non-linearity of our inversion we need to develop a full solution of the forward problem.

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Mapping cation exchange capacity using a quasi-3d joint-inversion of EM38 and EM31 data

10.5194/egusphere-egu2020-918 ◽

2020 ◽

Author(s):

Dongxue Zhao ◽

John Triantafilis

Keyword(s):

Electrical Conductivity ◽

Cation Exchange ◽

Cation Exchange Capacity ◽

Joint Inversion ◽

Exchange Capacity ◽

Damping Factor ◽

Inversion Algorithm ◽

Good Concordance ◽

Value Add ◽

Leave One Out

The cation exchange capacity (CEC, cmol(+)/kg) is a measure of soil&#8217;s capacity to retain exchangeable cations. However, it is expensive to collect CEC across a heterogenous field and at different depths. To value-add to limited data, proximal sensed electromagnetic (EM) data has been coupled to CEC through linear regression (LR) models, because they measure apparent soil electrical conductivity (ECa, mS/m). However, these LRs have been depth-specific. This approach was compared with one universal LR between estimates of true electrical conductivity (s, mS/m) and CEC from various depths, including topsoil (0-0.3 m), subsurface (0.3-0.6 m), shallow subsoil (0.6-0.9 m) and deeper subsoil (0.9-2.1 m). We estimated s from inversion of EM38 and EM31 ECa either alone or in combination (joint-inversion), in horizontal (ECah) and vertical (ECav) modes, using a quasi-3d (q3-d) inversion software (EM4Soil) and various parameters, including EM38 at two different heights (i.e. 0.2 or 0.4 m). In terms of performance, the LR correlation (R2 > 0.60) was largest between deeper subsoil CEC and EM38 ECah at 0.2 m. However, the LR was unsatisfactory for CEC calibration in the topsoil (0.31), subsurface (0.37) and shallow subsoil (0.52). In comparison, a universal LR between CEC and &#963; was well correlated (0.72), when both EM38 (0.2 m) and EM31 ECa in both modes, were inverted using a forward model (CF), inversion algorithm (S2) and small damping factor (&#955; = 0.03). The calibrations tested using a leave-one-out cross validation, showed CEC prediction was precise (RMSE, 2.35 cmol(+)/kg), unbiased (ME, -0.002 cmol(+)/kg) with good concordance (Lin&#8217;s, 0.83). To improve areal prediction, closer spaced transects need to be collected, while improved vertical resolution of CEC prediction we recommend DUALEM-421 ECa data be acquired.&#160;

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Joint inversion of full-waveform ground-penetrating radar and electrical resistivity data: Part 1

Geophysics ◽

10.1190/geo2019-0754.1 ◽

2020 ◽

Vol 85 (6) ◽

pp. H97-H113 ◽

Cited By ~ 1

Author(s):

Diego Domenzain ◽

John Bradford ◽

Jodi Mead

Keyword(s):

Electrical Conductivity ◽

Electrical Resistivity ◽

Ground Penetrating Radar ◽

Joint Inversion ◽

Geophysical Methods ◽

Data Types ◽

Full Waveform ◽

Electrical Permittivity ◽

Ground Penetrating ◽

Subsurface Geometry

We have developed an algorithm for joint inversion of full-waveform ground-penetrating radar (GPR) and electrical resistivity (ER) data. The GPR data are sensitive to electrical permittivity through reflectivity and velocity, and electrical conductivity through reflectivity and attenuation. The ER data are directly sensitive to the electrical conductivity. The two types of data are inherently linked through Maxwell’s equations, and we jointly invert them. Our results show that the two types of data work cooperatively to effectively regularize each other while honoring the physics of the geophysical methods. We first compute sensitivity updates separately for the GPR and ER data using the adjoint method, and then we sum these updates to account for both types of sensitivities. The sensitivities are added with the paradigm of letting both data types always contribute to our inversion in proportion to how well their respective objective functions are being resolved in each iteration. Our algorithm makes no assumptions of the subsurface geometry nor the structural similarities between the parameters with the caveat of needing a good initial model. We find that our joint inversion outperforms the GPR and ER separate inversions, and we determine that GPR effectively supports ER in regions of low conductivity, whereas ER supports GPR in regions with strong attenuation.

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Pareto Joint Inversion of 2D magnetometric and gravity data- synthetic study

E3S Web of Conferences ◽

10.1051/e3sconf/201913301009 ◽

2019 ◽

Vol 133 ◽

pp. 01009

Author(s):

Tomasz Danek ◽

Andrzej Leśniak ◽

Katarzyna Miernik ◽

Elżbieta Śledź

Keyword(s):

Pareto Front ◽

Joint Inversion ◽

Gravity Data ◽

Group Analysis ◽

Geophysical Methods ◽

Model Parameters ◽

Data Sets ◽

Inversion Algorithm ◽

Promising Tool ◽

Real Model

Pareto joint inversion for two or more data sets is an attractive and promising tool which eliminates target functions weighing and scaling, providing a set of acceptable solutions composing a Pareto front. In former author’s study MARIA (Modular Approach Robust Inversion Algorithm) was created as a flexible software based on global optimization engine (PSO) to obtain model parameters in process of Pareto joint inversion of two geophysical data sets. 2D magnetotelluric and gravity data were used for preliminary tests, but the software is ready to handle data from more than two geophysical methods. In this contribution, the authors’ magnetometric forward solver was implemented and integrated with MARIA. The gravity and magnetometry forward solver was verified on synthetic models. The tests were performed for different models of a dyke and showed, that even when the starting model is a homogeneous area without anomaly, it is possible to recover the shape of a small detail of the real model. Results showed that the group analysis of models on the Pareto front gives more information than the single best model. The final stage of interpretation is the raster map of Pareto front solutions analysis.

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Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software

Sensors ◽

10.3390/s19183936 ◽

2019 ◽

Vol 19 (18) ◽

pp. 3936 ◽

Cited By ~ 4

Author(s):

Tibet Khongnawang ◽

Ehsan Zare ◽

Dongxue Zhao ◽

Pranee Srihabun ◽

John Triantafilis

Keyword(s):

Electrical Conductivity ◽

Soil Properties ◽

Cation Exchange ◽

Cation Exchange Capacity ◽

Three Dimensional ◽

Exchange Capacity ◽

Inversion Algorithm ◽

Three Dimensional Mapping ◽

Em Induction ◽

Leave One Out

Most cultivated upland areas of northeast Thailand are characterized by sandy and infertile soils, which are difficult to improve agriculturally. Information about the clay (%) and cation exchange capacity (CEC—cmol(+)/kg) are required. Because it is expensive to analyse these soil properties, electromagnetic (EM) induction instruments are increasingly being used. This is because the measured apparent soil electrical conductivity (ECa—mS/m), can often be correlated directly with measured topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m) clay and CEC. In this study, we explore the potential to use this approach and considering a linear regression (LR) between EM38 acquired ECa in horizontal (ECah) and vertical (ECav) modes of operation and the soil properties at each of these depths. We compare this approach with a universal LR relationship developed between calculated true electrical conductivity (σ—mS/m) and laboratory measured clay and CEC at various depths. We estimate σ by inverting ECah and ECav data, using a quasi-3D inversion algorithm (EM4Soil). The best LR between ECa and soil properties was between ECah and subsoil clay (R2 = 0.43) and subsoil CEC (R2 = 0.56). We concluded these LR were unsatisfactory to predict clay or CEC at any of the three depths, however. In comparison, we found that a universal LR could be established between σ with clay (R2 = 0.65) and CEC (R2 = 0.68). The LR model validation was tested using a leave-one-out-cross-validation. The results indicated that the universal LR between σ and clay at any depth was precise (RMSE = 2.17), unbiased (ME = 0.27) with good concordance (Lin’s = 0.78). Similarly, satisfactory results were obtained by the LR between σ and CEC (Lin’s = 0.80). We conclude that in a field where a direct LR relationship between clay or CEC and ECa cannot be established, can still potentially be mapped by developing a LR between estimates of σ with clay or CEC if they all vary with depth.

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Joint Inversion of Seismic and Audio Magnetotelluric Data with Structural Constraint for Metallic Deposit

Journal of Environmental and Engineering Geophysics ◽

10.2113/jeeg23.2.159 ◽

2018 ◽

Vol 23 (2) ◽

pp. 159-169

Author(s):

Xuan Feng ◽

Enhedelihai Nilot ◽

Cai Liu ◽

Minghe Zhang ◽

Hailong Yu ◽

...

Keyword(s):

Partial Information ◽

Joint Inversion ◽

Waveform Inversion ◽

Geophysical Methods ◽

P Wave ◽

Real Field ◽

Sulfide Deposit ◽

Inversion Algorithm ◽

Data Types ◽

Magnetotelluric Data

Audio magnetotelluric (AMT) and seismic methods are widely used to detect metallic deposits. However, each geophysical method only provides partial information of the underground target. Besides, individual methods have inherent limitations and ambiguity which leads to non-uniqueness when solving the inverse problem. To obtain a more robust and consistent ore deposit model, it is best to integrate different geophysical methods and data types. Towards this effort, we propose a joint inversion algorithm using cross-gradient constraint to build a connection between seismic and AMT data, and simultaneously invert for a resistivity and P-wave velocity model. Compared with separate AMT Gauss–Newton inversion and seismic Full waveform inversion (FWI) method, we can get more detailed and robust inversion results. In addition, frequency domain FWI with the Limited-Memory-Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm provides an effective way to reduce computer memory usage and improve convergence speed. This joint inversion algorithm has been tested using simple synthetic models with two cross targets. The results obtained with separate inversions were compared with those obtained with joint inversion. Then, we applied the algorithm to geophysical models of the Jinchuan sulfide deposit. The AMT results obtained with joint inversion of seismic data were better than those obtained with separate AMT inversion. The joint inversion approach appears more robust than the traditional separate FWI inversion and it is recommended that the proposed algorithm be considered in future projects of real field data.

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Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

Hydrology and Earth System Sciences ◽

10.5194/hess-22-1509-2018 ◽

2018 ◽

Vol 22 (2) ◽

pp. 1509-1523 ◽

Cited By ~ 8

Author(s):

Giovanna Dragonetti ◽

Alessandro Comegna ◽

Ali Ajeel ◽

Gian Piero Deidda ◽

Nicola Lamaddalena ◽

...

Keyword(s):

Electrical Conductivity ◽

Spatial Distribution ◽

Time Domain ◽

Electromagnetic Induction ◽

Root Zone ◽

Linear Mapping ◽

Time Domain Reflectometry ◽

Data Series ◽

Actual Distribution ◽

Ground Truth Data

Abstract. This paper deals with the issue of monitoring the spatial distribution of bulk electrical conductivity, σb, in the soil root zone by using electromagnetic induction (EMI) sensors under different water and salinity conditions. To deduce the actual distribution of depth-specific σb from EMI apparent electrical conductivity (ECa) measurements, we inverted the data by using a regularized 1-D inversion procedure designed to manage nonlinear multiple EMI-depth responses. The inversion technique is based on the coupling of the damped Gauss–Newton method with truncated generalized singular value decomposition (TGSVD). The ill-posedness of the EMI data inversion is addressed by using a sharp stabilizer term in the objective function. This specific stabilizer promotes the reconstruction of blocky targets, thereby contributing to enhance the spatial resolution of the EMI results in the presence of sharp boundaries (otherwise smeared out after the application of more standard Occam-like regularization strategies searching for smooth solutions). Time-domain reflectometry (TDR) data are used as ground-truth data for calibration of the inversion results. An experimental field was divided into four transects 30 m long and 2.8 m wide, cultivated with green bean, and irrigated with water at two different salinity levels and using two different irrigation volumes. Clearly, this induces different salinity and water contents within the soil profiles. For each transect, 26 regularly spaced monitoring soundings (1 m apart) were selected for the collection of (i) Geonics EM-38 and (ii) Tektronix reflectometer data. Despite the original discrepancies in the EMI and TDR data, we found a significant correlation of the means and standard deviations of the two data series; in particular, after a low-pass spatial filtering of the TDR data. Based on these findings, this paper introduces a novel methodology to calibrate EMI-based electrical conductivities via TDR direct measurements. This calibration strategy consists of a linear mapping of the original inversion results into a new conductivity spatial distribution with the coefficients of the transformation uniquely based on the statistics of the two original measurement datasets (EMI and TDR conductivities).

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Spatial and Temporal Variability of Soil Salinity in the Yangtze River Estuary Using Electromagnetic Induction

Remote Sensing ◽

10.3390/rs13101875 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1875

Author(s):

Wenping Xie ◽

Jingsong Yang ◽

Rongjiang Yao ◽

Xiangping Wang

Keyword(s):

Electrical Conductivity ◽

Spatial Distribution ◽

Soil Salinity ◽

Yangtze River ◽

Laboratory Data ◽

River Estuary ◽

Yangtze River Estuary ◽

Soil Electrical Conductivity ◽

The Yangtze River ◽

The Yangtze River Estuary

Soil salt-water dynamics in the Yangtze River Estuary (YRE) is complex and soil salinity is an obstacle to regional agricultural production and the ecological environment in the YRE. Runoff into the sea is reduced during the impoundment period as the result of the water-storing process of the Three Gorges Reservoir (TGR) in the upper reaches of the Yangtze River, which causes serious seawater intrusion. Soil salinity is a problem due to shallow and saline groundwater under serious seawater intrusion in the YRE. In this research, we focused on the temporal variation and spatial distribution characteristics of soil salinity in the YRE using geostatistics combined with proximally sensed information obtained by an electromagnetic induction (EM) survey method in typical years under the impoundment of the TGR. The EM survey with proximal sensing method was applied to perform soil salinity survey in field in the Yangtze River Estuary, allowing quick determination and quantitative assessment of spatial and temporal variation of soil salinity from 2006 to 2017. We developed regional soil salinity survey and mapping by coupling limited laboratory data with proximal sensed data obtained from EM. We interpreted the soil electrical conductivity by constructing a linear model between the apparent electrical conductivity data measured by an EM 38 device and the soil electrical conductivity (EC) of soil samples measured in laboratory. Then, soil electrical conductivity was converted to soil salt content (soil salinity g kg−1) through established linear regression model based on the laboratory data of soil salinity and soil EC. Semivariograms of regional soil salinity in the survey years were fitted and ordinary kriging interpolation was applied in interpolation and mapping of regional soil salinity. The cross-validation results showed that the prediction results were acceptable. The soil salinity distribution under different survey years was presented and the area of salt affected soil was calculated using geostatistics method. The results of spatial distribution of soil salinity showed that soil salinity near the riverbanks and coastlines was higher than that of inland. The spatial distribution of groundwater depth and salinity revealed that shallow groundwater and high groundwater salinity influenced the spatial distribution characteristics of soil salinity. Under long-term impoundment of the Three Gorges Reservoir, the variation of soil salinity in different hydrological years was analyzed. Results showed that the area affected by soil salinity gradually increased in different hydrological year types under the impoundment of the TGR.

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