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

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3936 ◽  
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.

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 A new uncertainty estimation approach with multiple datasets and implementation for various precipitation products

2020 ◽  
Vol 24 (4) ◽  
pp. 2061-2081 ◽  
Author(s):  
Xudong Zhou ◽  
Jan Polcher ◽  
Tao Yang ◽  
Ching-Sheng Huang
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Scales ◽  
Three Dimensional ◽  
Variance Partitioning ◽  
Uncertainty Estimation ◽  
The Other ◽  
New Approach ◽  
Multiple Datasets ◽  
Uncertainty Estimates ◽  
Spatiotemporal Scales

Abstract. Ensemble estimates based on multiple datasets are frequently applied once many datasets are available for the same climatic variable. An uncertainty estimate based on the difference between the ensemble datasets is always provided along with the ensemble mean estimate to show to what extent the ensemble members are consistent with each other. However, one fundamental flaw of classic uncertainty estimates is that only the uncertainty in one dimension (either the temporal variability or the spatial heterogeneity) can be considered, whereas the variation along the other dimension is dismissed due to limitations in algorithms for classic uncertainty estimates, resulting in an incomplete assessment of the uncertainties. This study introduces a three-dimensional variance partitioning approach and proposes a new uncertainty estimation (Ue) that includes the data uncertainties in both spatiotemporal scales. The new approach avoids pre-averaging in either of the spatiotemporal dimensions and, as a result, the Ue estimate is around 20 % higher than the classic uncertainty metrics. The deviation of Ue from the classic metrics is apparent for regions with strong spatial heterogeneity and where the variations significantly differ in temporal and spatial scales. This shows that classic metrics underestimate the uncertainty through averaging, which means a loss of information in the variations across spatiotemporal scales. Decomposing the formula for Ue shows that Ue has integrated four different variations across the ensemble dataset members, while only two of the components are represented in the classic uncertainty estimates. This analysis of the decomposition explains the correlation as well as the differences between the newly proposed Ue and the two classic uncertainty metrics. The new approach is implemented and analysed with multiple precipitation products of different types (e.g. gauge-based products, merged products and GCMs) which contain different sources of uncertainties with different magnitudes. Ue of the gauge-based precipitation products is the smallest, while Ue of the other products is generally larger because other uncertainty sources are included and the constraints of the observations are not as strong as in gauge-based products. This new three-dimensional approach is flexible in its structure and particularly suitable for a comprehensive assessment of multiple datasets over large regions within any given period.

scholarly journals Three-Dimensional Holographic Electromagnetic Imaging for Accessing Brain Stroke

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3852
Author(s):  
Lulu Wang
Keyword(s):  
Electromagnetic Induction ◽  
Sensor Array ◽  
Neurological Diseases ◽  
Three Dimensional ◽  
Object A ◽  
Imaging Tool ◽  
Brain Stroke ◽  
Preliminary Study ◽  
Small Stroke ◽  
Two Inclusions

The authors recently developed a two-dimensional (2D) holographic electromagnetic induction imaging (HEI) for biomedical imaging applications. However, this method was unable to detect small inclusions accurately. For example, only one of two inclusions can be detected in the reconstructed image if the two inclusions were located at the same XY plane but in different Z-directions. This paper provides a theoretical framework of three-dimensional (3D) HEI to accurately and effectively detect inclusions embedded in a biological object. A numerical system, including a realistic head phantom, a 16-element excitation sensor array, a 16-element receiving sensor array, and image processing model has been developed to evaluate the effectiveness of the proposed method for detecting small stroke. The achieved 3D HEI images have been compared with 2D HEI images. Simulation results show that the 3D HEI method can accurately and effectively identify small inclusions even when two inclusions are located at the same XY plane but in different Z-directions. This preliminary study shows that the proposed method has the potential to develop a useful imaging tool for the diagnosis of neurological diseases and injuries in the future.

scholarly journals Perbandingan Pengaruh Beberapa Jenis Pupuk Mengandung Fosfat Terhadap Kehilangan Hara Melalui Pelindian Pada Tanah Gambut

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
IGM Subiksa
Keyword(s):  
Slow Release ◽  
Block Design ◽  
Peat Soil ◽  
Mineral Soil ◽  
Free Water ◽  
Nutrient Loss ◽  
Control Treatment ◽  
Leaching Rate ◽  
Complete Control ◽  
Leachate Water

Subiksa et al, 2018. Comparison Effect of Several Phosphate Contain Fertilizers to Nutrient Loss Trough Leaching on Peat Soil. JLSO 7(1): Peat soil have specific nutrient adsorption characteristics which are affected by soil pH dependent charge. Therefore, nutrient management on such soil should be done using different approach compared to mineral soil. Research on the comparison effects of several types of phosphate containing fertilizers to nutrient loss through leaching on peat soil has been carried out in greenhouse using a coulom experiment. The objective of study was to evaluate the rate of primary macro nutrient loss and look for fertilization technology which can reduced leaching rate.  The study used a randomized block design of 14 treatments with 3 replications. The treatments were complete control treatment, partial control and 4 types of P contain fertilizer, namely SP-36, NPK compound, Chrismast Island Phosphate Rock (CIRP), and Pugam each of them with 3 levels dose. The peat soil used was ombrogenous peat with hemic maturity level taken from OKI Regency, South Sumatra. Watering was done every 2 days with 350 ml ion-free water/pot. The results showed that N and K nutrients leaching, mostly was due to application rate of those nutrient, whereas type of fertilizer was not revealed significantly different. Meanwhile, P concentration in leachate water was significantly different among treatments. Leaching of P in the control treatment was very low because of P content of peat soil was low. The highest loss of P trough leaching rate was shown by the NPK treatment because NPK compound is belong to fast nutrient release fertilizer. CIRP and Pugam treatments showed low P loss trough leaching rates due to the slow release of P on CIRP and Pugam. The low leaching rates of CIRP and Pugam are also because of high content of Al and Fe as polyvalent cation that can promote new soil positive charges as site adsorption of P. It can be concluded that fertilization with a slow release type of phosphate fertilizer and contain sesquioxide as source of polyvalent cations such as CIRP and Pugam can reduced the rate of phosphate loss trough leaching.

scholarly journals Three-Dimensional Inversion of Borehole-Surface Resistivity Method Based on the Unstructured Finite Element

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhi Wang ◽  
Sinan Fang
Keyword(s):  
Numerical Solution ◽  
Surface Topography ◽  
Current Source ◽  
Three Dimensional ◽  
Surface Resistivity ◽  
Normal Equation ◽  
Inversion Algorithm ◽  
Field Source ◽  
Resistivity Method ◽  
Borehole Surface

The electromagnetic wave signal from the electromagnetic field source generates induction signals after reaching the target geological body through the underground medium. The time and spatial distribution rules of the artificial or the natural electromagnetic fields are obtained for the exploration of mineral resources of the subsurface and determining the geological structure of the subsurface to solve the geological problems. The goal of electromagnetic data processing is to suppress the noise and improve the signal-to-noise ratio and the inversion of resistivity data. Inversion has always been the focus of research in the field of electromagnetic methods. In this paper, the three-dimensional borehole-surface resistivity method is explored based on the principle of geometric sounding, and the three-dimensional inversion algorithm of the borehole-surface resistivity method in arbitrary surface topography is proposed. The forward simulation and calculation start from the partial differential equation and the boundary conditions of the total potential of the three-dimensional point current source field are satisfied. Then the unstructured tetrahedral grids are used to discretely subdivide the calculation area that can well fit the complex structure of subsurface and undulating surface topography. The accuracy of the numerical solution is low due to the rapid attenuation of the electric field at the point current source and the nearby positions and sharply varying potential gradients. Therefore, the mesh density is defined at the local area, that is, the vicinity of the source electrode and the measuring electrode. The mesh refinement can effectively reduce the influence of the source point and its vicinity and improve the accuracy of the numerical solution. The stiffness matrix is stored with Compressed Row Storage (CSR) format, and the final large linear equations are solved using the Super Symmetric Over Relaxation Preconditioned Conjugate Gradient (SSOR-PCG) method. The quasi-Newton method with limited memory (L_BFGS) is used to optimize the objective function in the inversion calculation, and a double-loop recursive method is used to solve the normal equation obtained at each iteration in order to avoid computing and storing the sensitivity matrix explicitly and reduce the amount of calculation. The comprehensive application of the above methods makes the 3D inversion algorithm efficient, accurate, and stable. The three-dimensional inversion test is performed on the synthetic data of multiple theoretical geoelectric models with topography (a single anomaly model under valley and a single anomaly model under mountain) to verify the effectiveness of the proposed algorithm.

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