Full magnetic gradient tensor from triaxial aeromagnetic gradient measurements: Calculation and application

Luo, Yao; Wu, Meiping; Wang, Ping; Duan, Shuling; Liu, Haojun; Wang, Jinlong; An, Zhanfeng

doi:10.1007/s11770-015-0508-y

Cited by 19 publications

(6 citation statements)

References 8 publications

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“…[ 143 ] It is possible to create and use a fluxgate sensor; if a two‐axis ring core is employed, both axial and transverse gradients can be determined by selecting and comparing the appropriate outputs. [ 144 ] Geophysical exploration [ 142,145 ] and security/military applications both use full‐tensor information [ 8,146 ]…”

Section: Fluxgate Sensor Applicationsmentioning

confidence: 99%

Fabrication Advancements in Integrated Fluxgate Sensors: A Mini Review

2022

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Research on magnetic sensors has been carried out in recent years to enhance sensing capability, enabling us to understand the Earth's magnetic field changes [1] with better sensitivities of 10 À10 to 10 À2 T. [2] Magnetic field sensors have been extensively investigated according to their operating principles and actions in various applications. Sensitivity and selectivity are the two essential properties crucial in the evolution of magnetic field sensors to achieve high fidelity outputs. The former is strengthened by increasing the sensor area and improvements to the planar surface with various Nano-materials. In contrast, the latter is improved by using multiple materials and techniques to enhance interfacial interactions.Magnetic sensors detect disturbances and changes in magnetic fields such as force, direction, and flux. It is a transducer that converts a magnetic field into an electrical voltage. There are several types of magnetic field sensors, each with its own sensitivity range. In addition to sensitivity, the temperature range, the physical size of the sensor, and its on-chip manufacturability must all be considered. Magnetic fields of less than 100 pT are extremely weak and far below the earth's magnetic field. Earth magnetic field sensors have a sensitivity range of 10 μT to a few micro-Tesla, while bias magnet field (high field) sensors have a sensitivity of more than 1 mT.Magnetic fields of less than 100 pT are extremely weak and far below the earth's magnetic field. Earth magnetic field sensors have a sensitivity range of 10 μT to a few micro-Tesla, while bias magnet field (high field) sensors have a sensitivity of more than 1 mT. The most sensitive low-field sensor is the superconducting quantum interference device (SQUID). It is based on Brian J. Josephson's research on a point-contact connection for measuring extremely low currents.The SQUID ring, the radio-frequency coil, and the big antenna loop are all superconducting components of the SQUID sensor. All three must be cooled to the point where they become superconducting. The SQUID itself is small, but the requirement for liquid helium cooling makes the entire apparatus big and hefty. Magnetoresistive sensors (MR) have a high sensitivity, are inexpensive to manufacture, and are small in size. However, they have poor noise performance and linearity, as well as a small dynamic range, making them unappealing and unsuitable for many low-noise applications. Furthermore, as the power usage is reduced, their sensitivity degrades.Search-coil or induction sensors are high-sensitivity, lowpower sensors, but their sensitivity decreases as the cross-section area decreases, making miniaturization ineffective. Because search coils can detect an alternating current magnetic field, they cannot detect a static magnetic field like the Earth's magnetic field. Magnetic sensors based on semiconductors, such as Hall-Effect sensors, magnetotransistors, and magnetodiodes, are very small in size. Because of its low manufacturing cost

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Section: Fluxgate Sensor Applicationsmentioning

confidence: 99%

Fabrication Advancements in Integrated Fluxgate Sensors: A Mini Review

2022

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“…We have 1074 training samples to train the SVM and use 462 test samples to predict the six orientations. Each sample (including training samples and test samples) corresponds to a label (1,2,3,4,5,6). The training sample is to enable the SVM to establish a network of relationships between samples and labels, for example, if input the samples of type 1, the SVM will only output the label 1; while input the samples of type 2, the SVM will output the label 2.…”

Section: Experimental Verificationmentioning

confidence: 99%

“…In the past two decades, the magnetic gradient tensor which is symmetric has received extensive attention from scholars in various countries, providing a new way to detect and explain the shape, size, attitude, and other information of magnetic objects [1][2][3][4][5][6][7]. Currently there is extensive research on magnetic gradient tensor data, including interpretations and applications, magnetic object localization [2,3,8], and other local magnetic anomaly identification methods [6][7][8][9]. All of them require complicated theoretical derivation, formula calculation, algorithm design, and a large amount of magnetic data, and they cannot leave the range of calculation or inversion of magnetic data based on complex theoretical formulas and models.…”

Section: Introductionmentioning

confidence: 99%

Symmetric Magnetic Anomaly Objects’ Orientation Recognition Based on Local Binary Pattern and Support Vector Machine

Zheng

Fan

et al. 2019

Symmetry

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In order to identify the orientation or recognize the attitude of small symmetric magnetic anomaly objects at shallow depth, we propose a method of extracting local binary pattern (LBP) features from denoised magnetic anomaly signals and classifying symmetric magnetic objects that have different orientations based on support vector machine (SVM). First, nine component signals, such as magnetic gradient tensor matrix, total magnetic intensity (TMI), and so forth, are calculated from the original signal detected by the flux gate sensors. The nine component signals are processed by discrete wavelet transform (DWT), which aims to reduce noise and make the signal’s features clear. Then we extract LBP texture features from the denoised nine component signals. From the simulation analysis, we can conclude that the LBP texture features of the nine component signals have good interclass discrimination and intraclass aggregation, which can be used for pattern recognition. Finally, the LBP texture features are constructed into feature vectors. The orientations of symmetric ferromagnetic objects underground are identified by SVM based on the feature vectors. Through experiments, we can conclude that the orientation recognition accuracy rate reaches 90%. This suggests that we can obtain the details of magnetic anomalies through our method.

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“…For this reason, many ground-based survey methods are being altered for integration with UASs. One of these methods is magnetic surveying, which is used widely for geophysical surveys investigating minerals, geologic features, unexploded ordinances (UXOs), or other magnetic subsurface features [2,3]. By integrating with UASs, the benefits from airborne surveys, such as ease of flight and increased spatial accessibility, are combined with very high-resolution data collection that can be produced at a lower altitude and slower flight speed.…”

Section: Introductionmentioning

confidence: 99%

“…Since UASs can fly close to ground level at slow speeds, they are ideal for high-resolution magnetic mapping [7]. The resolution of magnetic maps can also be enhanced further using a magnetic gradiometer on a UAS [2]. Magnetic gradiometers are composed of two magnetometers oriented in the same direction at a vertical or horizontal offset.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Fluxgate Magnetic Gradiometer for Integration with an Unmanned Aircraft System

Luoma

Zhou

2020

Remote Sensing

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The use of unmanned aircraft systems (UASs) for geophysical exploration and environmental monitoring allows for flexible, quick, and effective surveys with high-resolution results. Developing and integrating a magnetic gradiometer with a UAS allows for geophysical exploration of magnetic subsurface features such as geologic structures, metal detection, or locating unexploded ordinances (UXOs). This paper presents the development of a magnetic gradiometer for integration with a UAS. The magnetic gradiometer is composed of two fluxgate magnetometers, two GPS receivers, and a microcontroller-based controlling and data-logging system. The components of the magnetic gradiometer system are lightweight and inexpensive, ideal for use with a UAS. Initial field tests for the magnetic gradiometer are discussed. The initial results demonstrate the magnetic gradiometer’s data coherency along with future improvements that will improve the design of the instrument.

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Full magnetic gradient tensor from triaxial aeromagnetic gradient measurements: Calculation and application

Cited by 19 publications

References 8 publications

Fabrication Advancements in Integrated Fluxgate Sensors: A Mini Review

Fabrication Advancements in Integrated Fluxgate Sensors: A Mini Review

Symmetric Magnetic Anomaly Objects’ Orientation Recognition Based on Local Binary Pattern and Support Vector Machine

Construction of a Fluxgate Magnetic Gradiometer for Integration with an Unmanned Aircraft System

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