Geometric modeling of underground ferromagnetic pipelines for magnetic dipole reconstruction-based magnetic anomaly detection

Zhao, Dandan; Guo, Zhiyong; Du, Jiuyu; Liu, Zhongxiang; Xu, Wei; Liu, Gaofei

doi:10.1016/j.petlm.2019.06.001

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…It is a common practice to map P i into {0, 1} 2 through rounding. According to (3), if the rounded P i FIGURE 3 Samples and their features extracted by the trained network FIGURE 4 The ambient magnetic field collection system equals to [1, 0] T , there is no target in X i , and if the rounded P i equals to [0, 1] T , there is a target in X i .…”

Section: Training and Decisionmentioning

confidence: 99%

“…In some applications, such as detecting urban pipelines [4,5] and underground unexploded ordnances (UXOs) [6,7], benefiting from the close detection distances, targets can cause strong magnetic anomalies. However, in some other applications, magnetometers are very far from targets, which leads to very low signal-to-noise ratios (SNRs) and make it very hard to determine the existences of targets.…”

Section: Introductionmentioning

confidence: 99%

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A deep neural network based method for magnetic anomaly detection

Wang

Han

Zhao

et al. 2021

IET Science Measure & Tech

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Magnetic anomaly detection (MAD) is a technique to find ferromagnets hiding in strong and complicated magnetic background. In many practical cases, the targets are very far from the detection sensor, which leads to low signal-to-noise ratio (SNR) and high detection difficulty. Most of the current methods determine the existence of target by some approaches based on signal analysis, such as the orthogonal basis function (OBF) and the minimum entropy (ME). However, although these methods consume low resources, the detection performances are not satisfactory enough. In recent years, due to the increase of computer capability, complex methods become applicable in MAD. In this study, a deep neural network (DNN) is adopted to detect the magnetic anomalies. The DNN has shown its better ability to represent natural data in many applications. A feature automatically learned by a DNN from data in the raw form is more effective for detecting target signals and suppressing irrelevant variations. Herein, a convolutional network with residual structure to implement the feature extraction is designed and an MAD method based on it is proposed. Through the semi-real tests, the proposed method exhibits a strong capability to extract features and shows excellent performances on detection.

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Section: Training and Decisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep neural network based method for magnetic anomaly detection

Wang

Han

Zhao

et al. 2021

IET Science Measure & Tech

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“…He eliminates the influence of jitter on the magnetic stress detection results by establishing a magnetic dipole model [14]. Zhao developed positioning methods for different pipeline constructions by establishing a magnetic dipole calculation model [15].…”

Section: Introductionmentioning

confidence: 99%

Study on the Strengthening Effect of Excitation Magnetic Field on Stress-Induced Magnetic Signal of X80 Steels

He,

Liao,

Liao

et al. 2024

J. Phys.: Conf. Ser.

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Weak magnetic excitation stress detection has good engineering application prospects in the pipeline non-destructive testing field. To obtain the quantitative variation law between the excited stress-induced magnetic signal and the axial stress value of X80 steel, an axial tensile experimental test of X80 steel under weak magnetic excitation is conducted in this paper. The enhancing effect of the excitation magnetic field on the sensitivity of stress-induced magnetic signals has been quantitatively analyzed. The results indicate that the value of normal magnetic flux density B z increases by 24 times when the excitation magnetic field reaches 600 A/m. Within the scope of 400 A/m to 700 A/m, the excitation magnetic field of 400 A/m has the best excitation effect on X80 steel.

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“…Wu and Wei [18] established the magnetic anomaly model of non-coplanar cross pipelines based on the principle of magnetic dipole reconstruction and analyzed the influence of pipeline parameters, geomagnetic parameters, cross angle and other factors on the magnetic anomaly of non-coplanar pipelines. Zhao et al [19] also used the magnetic dipole reconstruction method to establish and analyze magnetic anomaly models of pipes with different shapes, such as straight pipes, elbows and tee joints. These models are very accurate in the ideal case without any external magnetic interference or geomagnetic parameter variance.…”

Section: Introductionmentioning

confidence: 99%

A localization method for subsea pipeline based on active magnetization

Huang¹,

Wang²,

Ma³

et al. 2022

Meas. Sci. Technol.

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Accurate localization of subsea pipelines is the prerequisite of real-time tracking and elaborate inspection by underwater robots. Magnetic anomaly generated by the ferromagnetic pipeline can be used to locate both uncover and buried pipelines. However, due to low signal ratio and model inconstancy under weak and variable ambient magnetization, there is still missing an intuitive and reliable pipeline localization method in order to realize pipeline tracking. This paper proposes a method capable of immediately and accurately locating pipelines via active magnetization and vertical magnetic measurement. Finite element simulation shows that magnet array can significantly enhance the magnetic anomaly, and the vertical magnetic component alone can accurately indicate pipeline position, avoiding the inconvenience of magnetic three-component alignment in field. It is experimentally demonstrated that magnetic detection SNR can be significantly increased by 5dB~20dB for a Φ219mm steel pipe by using the magnet array, and the max lateral positioning error is 0.03m and much smaller than that without magnet.

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Geometric modeling of underground ferromagnetic pipelines for magnetic dipole reconstruction-based magnetic anomaly detection

Cited by 10 publications

References 18 publications

A deep neural network based method for magnetic anomaly detection

A deep neural network based method for magnetic anomaly detection

Study on the Strengthening Effect of Excitation Magnetic Field on Stress-Induced Magnetic Signal of X80 Steels

A localization method for subsea pipeline based on active magnetization

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