Improved Bucking Coil Design in Helicopter Transient Electromagnetic System

Xiao, Pei; Shi, Zongyang; Wu, Xiaofeng; Fang, and Guangyou

doi:10.2528/pierm17071902

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…Meanwhile, the mutual inductance problem arises with the traditional central-loop device, as multiturn wires are twined tightly with each other [21]. To address these issues, weak-coupling TEM devices have recently been developed [22,23]. And in this paper, TEM responses of both types of device are calculated to further confirm the feasibility of tunnel advanced prediction using the TEM method.…”

Section: Introductionmentioning

confidence: 93%

Feasibility of Tunnel TEM Advanced Prediction: A 3D Forward Modeling Study

Fu,

Yang,

Zhou

et al. 2023

Geofluids

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The transient electromagnetic (TEM) method has long been applied in tunnel advanced prediction. However, it remains questionable to what extent a geologic anomaly body will influence the induced electromagnetic response in front of the heading face. The dilemma is partly because observed TEM data are frequently interpreted by empirical formulas or proportional relationships, and a quantitative measurement has not been established. In this paper, we strive to understand the TEM characteristics from a 3D finite-element time-domain (FETD) modeling aspect. The modeling algorithm is based on unstructured space meshing and unconditional stable time discretization, which ensures its accuracy and stability. The modeling algorithm is verified by a half-space model, in which the misfit of late-time channels that we are concerned with is generally below 1%. The algorithm has also been utilized to carry out the TEM response of tunnel models with different types of TEM devices. Through model studies, we find that both the traditional central-loop device and the recently developed weak-coupling opposing-coil device are feasible in tunnel advanced detection. Nevertheless, the latter type of device better distinguishes low-resistivity anomalies at 30 m ahead of the heading face with a relative difference (between models with and without the anomaly) of more than 1000% at certain time channels, compared with only a 10% difference of the central-loop device. Also, we conclude that the vertical electromagnetic field component should be recorded and interpreted together with the horizontal field to provide more convincing results.

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Section: Introductionmentioning

confidence: 93%

Feasibility of Tunnel TEM Advanced Prediction: A 3D Forward Modeling Study

Fu,

Yang,

Zhou

et al. 2023

Geofluids

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“…Another solution used by the VTEM systems is to cancel the primary fields in a receiver by injecting the same pulse of current but with reverse polarity into a more proximal transmitter bucking coil (Kuzmin and Morison, 2013). The disadvantage of this solution is that 𝑚 will be slightly lower, compared to the situation where there is no bucking coil, due to the field of the bucking coil, opposing that of the main transmitter therefore lowering the overall system moment (Xiao et al 2017). Yet another solution is to null couple the primary field with the receiver, for example above the transmitter wire where the field is horizontal and a receiver loop is horizontal.…”

Section: Effect Of Bucking/shielding Mechanismsmentioning

confidence: 99%

On the time decay constant of AEM systems: a semi-heuristic algorithm to validate calculations.

Martı́nez¹,

Smith

Díaz

2019

Exploration Geophysics

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The time decay constant or "tau" of airborne EM systems (AEM) is commonly used to indicate the presence and the relative conductivity or conductance of conductors in the survey area. In fact, it is not a constant since it depends on: the system; the survey design and the method of calculation. The system dependence is a consequence of parameters relating to the acquisition and pre-and post-processing of the signal. Here we propose a method for calculating tau, which is simply the time at which the transient voltage decays to 37%, or V37, of some initial value. The model utilises a semi-heuristic algorithm that estimates V37 for each transient in the database and then it calculates the delay time that voltage is measures, which is the estimates tau value. No calculation is involved with the data, instead tau is given by a weighted average of the delay times associated with the windows either side of the V37 value. We illustrate how this algorithm works using data collected by MEGATEM II in the Reid-Mahaffy test site.Results shown a good agreement between tau-grids reported in previous studies and those calculated with our V37method. To account for all effects coming from the acquisition and processing of EM data, the algorithm allows the emphasis to be shifted from early to late time parts of the transient. It is envisage that since this method does not apply any mathematical operation to the data it may serve as a robust means of for validating other methods.

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“…Cover meters are based on electromagnetic induction (EMI). Under the excitation of time‐varying current, a conductive object induces a secondary magnetic field, whereas concrete, as a nonferromagnetic background material, does not induce eddy current and thus contributes nothing to the secondary magnetic field (Xiao et al., 2017). The intensity distributions of the received secondary fields are dependent on the geometric properties and the compositions of the conductive objects.…”

Section: Introductionmentioning

confidence: 99%

Deep learning–based nondestructive evaluation of reinforcement bars using ground‐penetrating radar and electromagnetic induction data

Liu

Zhou

et al. 2021

Computer aided Civil Eng

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This paper proposes a nondestructive evaluation method based on deep learning using combined ground‐penetrating radar (GPR) and electromagnetic induction (EMI) data for autonomic and accurate estimation of the cover thickness and diameter of reinforcement bars. A real‐time object detection algorithm—You Only Look Once–version 3 (YOLO v3)—is adopted to automatically identify the reinforcement bar reflected signals from radargrams, with which the range of the cover thickness is roughly predicted. Subsequently, EMI data, accompanied with the cover thickness range, are imported to a one‐dimensional convolutional neural network (1D CNN), pretrained by calibrated EMI and GPR data, to simultaneously estimate the cover thickness and reinforcement bar diameter. Testing with the on‐site GPR data shows that YOLO v3 is superior to Single Shot Multibox Detector method in GPR hyperbolic signal identification. Testing of 1D CNN with the EMI and GPR data collected in an in‐house sand pit experiment shows that the estimation accuracy of the cover thickness and reinforcement bar diameter is, respectively, 96.8% and 90.3% with a permissible error of 1 mm. Further, an experiment with concrete specimens demonstrates that among the 22 estimated values (including the reinforcement bar diameter and cover thickness), there are 17 values accurately estimated, while the inaccurately estimated values have an error up to 2 mm. The experimental results show that the proposed method can autonomically evaluate the reinforcement bar diameter and cover thickness with a high accuracy.

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Improved Bucking Coil Design in Helicopter Transient Electromagnetic System

Cited by 11 publications

References 6 publications

Feasibility of Tunnel TEM Advanced Prediction: A 3D Forward Modeling Study

Feasibility of Tunnel TEM Advanced Prediction: A 3D Forward Modeling Study

On the time decay constant of AEM systems: a semi-heuristic algorithm to validate calculations.

Deep learning–based nondestructive evaluation of reinforcement bars using ground‐penetrating radar and electromagnetic induction data

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