Full Tensor Eigenvector Analysis on Air-Borne Magnetic Gradiometer Data for the Detection of Dipole-Like Magnetic Sources

Zuo, Boxin; Wang, Lizhe; Chen, Weitao

doi:10.3390/s17091976

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…Besides the inversion approaches, a key element is the direct inverse algorithm. For example, this topic was covered in [ 18 ], where the eigenvector decomposition of the magnetic gradient tensor was used to locate dipole-like magnetic sources, allowing automatic detection of dipole-like magnetic sources without estimating the magnetic moment direction. A similar approach has also been discussed in [ 19 ], where the authors proposed a new algorithm with a magnetic gradient tensor and singular value decomposition (SVD) to estimate the target position and characterization quickly and accurately.…”

Section: Related Workmentioning

confidence: 99%

On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models

Blachnik,

Przyłucki,

Golak

et al. 2023

Sensors

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Scanning underwater areas using magnetometers in search of unexploded ordnance is a difficult challenge, where machine learning methods can find a significant application. However, this requires the creation of a dataset enabling the training of prediction models. Such a task is difficult and costly due to the limited availability of relevant data. To address this challenge in the article, we propose the use of numerical modeling to solve this task. The conducted experiments allow us to conclude that it is possible to obtain high compliance with the numerical model based on the finite element method with the results of physical tests. Additionally, the paper discusses the methodology of simplifying the computational model, allowing for an almost three times reduction in the calculation time without affecting model quality. The article also presents and discusses the methodology for generating a dataset for the discrimination of UXO/non-UXO objects. According to that methodology, a dataset is generated and described in detail including assumptions on objects considered as UXO and nonUXO.

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Section: Related Workmentioning

confidence: 99%

On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models

Blachnik,

Przyłucki,

Golak

et al. 2023

Sensors

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“…• detection and localization of dipole-like magnetized compact objects such as UXOs using * fitting of measured signals to model functions such as Anderson functions e.g. in [152] or wavelet mother functions [153], * Euler deconvolutions [104,154] including Helbigs method [155] for estimation of the magnetization direction, or * direct calculation using the properties of the MGT and its time derivatives such as [78,79,156,157], * downward continuation algorithms as inverse problems with Tikhonov regularization [158],…”

Section: The Inversion and Interpretation Of Mgt Data Goes Three Main...mentioning

confidence: 99%

Superconducting sensors and methods in geophysical applications

Stolz

Schmelz

Zakosarenko³

et al. 2021

Supercond. Sci. Technol.

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More than 50 years ago superconducting quantum interference devices (SQUIDs) were invented. Since then many applications opened up. Already in a 1980 workshop (Weinstock and Overton 1981 SQUID Applications to Geophysics (Society of Exploration Geophysicists)) the application of SQUIDs in geosciences was reviewed. The fabrication and cooling technologies, electronics and other SQUID system components underwent significant improvement within the past years. Thus, SQUIDs are today better suited, more sensitive and effective as well as robust and reliable in operation for geophysical measurements. Many successful application examples, demonstrations and discoveries of mineral resources have been made using them in laboratory devices for investigation of magnetic properties, magnetic exploration, transient electromagnetics and for superconducting gravimeters as well as gravity gradiometers. Therefore, this article intends to review the past, present, and some future aspects of SQUIDs in geo-scientific applications such as e.g. mineral exploration. Since this field is still very active and quite a number of developments are ongoing, this review cannot be comprehensive.

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“…Different from the iterative algorithm, the magnetic gradient tensor localization algorithm, which can directly calculate the target position, has been effectively used to locate targets in magnetic detection [28][29][30][31][32][33]. The localization accuracy with the magnetic gradient tensor is mainly affected by signal-to-noise ratio (SNR) and calculated error of the magnetic gradient tensor.…”

Section: Introductionmentioning

confidence: 99%

Fast Localization and Characterization of Underground Targets with a Towed Transient Electromagnetic Array System

Wang

Zhang

Chen

et al. 2022

Sensors

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A fast inversion algorithm combined with the transient electromagnetic (TEM) detection system has important significance for improving the detection efficiency of unexploded ordnance. The traditional algorithms, such as differential evolution or Gauss–Newton algorithms, usually require tens to thousands of iterations to locate the underground target. A new algorithm with a magnetic gradient tensor and singular value decomposition (SVD) to estimate the target position and characterization quickly and accurately is proposed in this paper. Two modes of magnetic gradient tensor are constructed to accurately locate shallow and deep targets, respectively. The SVD algorithm is applied to the responses to estimate the electromagnetic characteristics of the target quickly and accurately. To verify the performance of the proposed algorithm, a towed TEM sensor is designed, which is constructed with three transmitting coils and nine three-component receiving coils arranged in a 3 × 3 array. Field experiments in survey and cued modes were taken to verify the performance of the proposed algorithm and the towed system. Results show that the magnetic gradient tensor algorithm proposed in this paper can accurately locate a single target within 2.0 m depth, and the error of depth is no more than 8 cm. Even for overlapping response of multi targets, the error of depth is no more than 12 cm. The underground target can be accurately characterized by the SVD algorithm. For targets with depths over 2.0 m, the signal-to-noise ratio of characteristic response estimated by SVD is higher than that of the traditional method. The proposed method needs approximately 40 ms, only 1% of the traditional one, considerably improving detection efficiency and laying a theoretical and experimental foundation for real-time data processing.

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Full Tensor Eigenvector Analysis on Air-Borne Magnetic Gradiometer Data for the Detection of Dipole-Like Magnetic Sources

Cited by 8 publications

References 30 publications

On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models

On the Development of a Digital Twin for Underwater UXO Detection Using Magnetometer-Based Data in Application for the Training Set Generation for Machine Learning Models

Superconducting sensors and methods in geophysical applications

Fast Localization and Characterization of Underground Targets with a Towed Transient Electromagnetic Array System

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