Hydrostratigraphic analysis of the Darling River valley (Australia) using electromagnetic induction data and a spatially constrained algorithm for quasi-three-dimensional electrical conductivity imaging

2011 ◽  
Vol 19 (5) ◽  
pp. 1053-1063 ◽  
Author(s):  
John Triantafilis ◽  
Fernando Acácio Monteiro Santos
Keyword(s):  
Electrical Conductivity ◽  
Electromagnetic Induction ◽  
Three Dimensional ◽  
River Valley ◽  
Conductivity Imaging

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 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.

Exceptionally high thermal and electrical conductivity of three-dimensional graphene-foam-based polymer composites

RSC Advances ◽  
2016 ◽  
Vol 6 (27) ◽  
pp. 22364-22369 ◽  
Author(s):  
Zhiduo Liu ◽  
Dianyu Shen ◽  
Jinhong Yu ◽  
Wen Dai ◽  
Chaoyang Li ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Electrical Conductivity ◽  
Polymer Composites ◽  
Epoxy Matrix ◽  
Three Dimensional ◽  
Neat Epoxy ◽  
Graphene Foam

Three dimensional graphene foam incorporated into epoxy matrix greatly enhance its thermal conductivity (up to 1.52 W mK−1) at low graphene foam loading (5.0 wt%), over an eight-fold enhancement in comparison with that of neat epoxy.

Electrical conductivity imaging of the Philippine Sea upper mantle using seafloor magnetotelluric data

2010 ◽  
Vol 183 (1-2) ◽  
pp. 44-62 ◽  
Author(s):  
Kiyoshi Baba ◽  
Hisashi Utada ◽  
Tada-nori Goto ◽  
Takafumi Kasaya ◽  
Hisayoshi Shimizu ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Upper Mantle ◽  
Magnetotelluric Data ◽  
Philippine Sea ◽  
Conductivity Imaging
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