Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data

In recent years, there has been a growing interest in the detection, location, and classification (DLC) of multiple dipole-like magnetic sources based on magnetic gradient tensor (MGT) data. In these applications, the tilt angle is usually used to detect the number of sources. We found that the tilt...
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Autor*in:	Lin Ge [verfasserIn] Qi Han [verfasserIn] Xiaojun Tong [verfasserIn] Yizhen Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	unknown dipole quantity multiple magnetic dipole detection magnetic gradient tensor nonlinear optimization normalized source strength

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 23(2023), 9, p 4440
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:9, p 4440

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s23094440

Katalog-ID:	DOAJ09033566X

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520			\|a In recent years, there has been a growing interest in the detection, location, and classification (DLC) of multiple dipole-like magnetic sources based on magnetic gradient tensor (MGT) data. In these applications, the tilt angle is usually used to detect the number of sources. We found that the tilt angle is only suitable for the scenario where the positive and negative signs of the magnetic sources’ inclination are the same. Therefore, we map the L2 norm of the vertical magnetic gradient tensor on the arctan function, denoted as the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle, to detect the number of sources. Then we use the normalized source strength (NSS) to narrow the parameters’ search space and combine the differential evolution (DE) algorithm with the Levenberg–Marquardt (LM) algorithm to solve the sources’ locations and magnetic moments. Simulation experiments and a field demonstration show that the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle is insensitive to the sign of inclination and more accurate in detecting the number of magnetic sources than the tilt angle. Meanwhile, our method can quickly locate and classify magnetic sources with high precision.
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allfields	10.3390/s23094440 doi (DE-627)DOAJ09033566X (DE-599)DOAJcf7976ab8af54d28875a630b96c73e4a DE-627 ger DE-627 rakwb eng TP1-1185 Lin Ge verfasserin aut Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, there has been a growing interest in the detection, location, and classification (DLC) of multiple dipole-like magnetic sources based on magnetic gradient tensor (MGT) data. In these applications, the tilt angle is usually used to detect the number of sources. We found that the tilt angle is only suitable for the scenario where the positive and negative signs of the magnetic sources’ inclination are the same. Therefore, we map the L2 norm of the vertical magnetic gradient tensor on the arctan function, denoted as the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle, to detect the number of sources. Then we use the normalized source strength (NSS) to narrow the parameters’ search space and combine the differential evolution (DE) algorithm with the Levenberg–Marquardt (LM) algorithm to solve the sources’ locations and magnetic moments. Simulation experiments and a field demonstration show that the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle is insensitive to the sign of inclination and more accurate in detecting the number of magnetic sources than the tilt angle. Meanwhile, our method can quickly locate and classify magnetic sources with high precision. unknown dipole quantity multiple magnetic dipole detection magnetic gradient tensor nonlinear optimization normalized source strength Chemical technology Qi Han verfasserin aut Xiaojun Tong verfasserin aut Yizhen Wang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 9, p 4440 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:9, p 4440 https://doi.org/10.3390/s23094440 kostenfrei https://doaj.org/article/cf7976ab8af54d28875a630b96c73e4a kostenfrei https://www.mdpi.com/1424-8220/23/9/4440 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 9, p 4440
spelling	10.3390/s23094440 doi (DE-627)DOAJ09033566X (DE-599)DOAJcf7976ab8af54d28875a630b96c73e4a DE-627 ger DE-627 rakwb eng TP1-1185 Lin Ge verfasserin aut Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, there has been a growing interest in the detection, location, and classification (DLC) of multiple dipole-like magnetic sources based on magnetic gradient tensor (MGT) data. In these applications, the tilt angle is usually used to detect the number of sources. We found that the tilt angle is only suitable for the scenario where the positive and negative signs of the magnetic sources’ inclination are the same. Therefore, we map the L2 norm of the vertical magnetic gradient tensor on the arctan function, denoted as the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle, to detect the number of sources. Then we use the normalized source strength (NSS) to narrow the parameters’ search space and combine the differential evolution (DE) algorithm with the Levenberg–Marquardt (LM) algorithm to solve the sources’ locations and magnetic moments. Simulation experiments and a field demonstration show that the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle is insensitive to the sign of inclination and more accurate in detecting the number of magnetic sources than the tilt angle. Meanwhile, our method can quickly locate and classify magnetic sources with high precision. unknown dipole quantity multiple magnetic dipole detection magnetic gradient tensor nonlinear optimization normalized source strength Chemical technology Qi Han verfasserin aut Xiaojun Tong verfasserin aut Yizhen Wang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 9, p 4440 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:9, p 4440 https://doi.org/10.3390/s23094440 kostenfrei https://doaj.org/article/cf7976ab8af54d28875a630b96c73e4a kostenfrei https://www.mdpi.com/1424-8220/23/9/4440 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 9, p 4440
allfields_unstemmed	10.3390/s23094440 doi (DE-627)DOAJ09033566X (DE-599)DOAJcf7976ab8af54d28875a630b96c73e4a DE-627 ger DE-627 rakwb eng TP1-1185 Lin Ge verfasserin aut Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, there has been a growing interest in the detection, location, and classification (DLC) of multiple dipole-like magnetic sources based on magnetic gradient tensor (MGT) data. In these applications, the tilt angle is usually used to detect the number of sources. We found that the tilt angle is only suitable for the scenario where the positive and negative signs of the magnetic sources’ inclination are the same. Therefore, we map the L2 norm of the vertical magnetic gradient tensor on the arctan function, denoted as the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle, to detect the number of sources. Then we use the normalized source strength (NSS) to narrow the parameters’ search space and combine the differential evolution (DE) algorithm with the Levenberg–Marquardt (LM) algorithm to solve the sources’ locations and magnetic moments. Simulation experiments and a field demonstration show that the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle is insensitive to the sign of inclination and more accurate in detecting the number of magnetic sources than the tilt angle. Meanwhile, our method can quickly locate and classify magnetic sources with high precision. unknown dipole quantity multiple magnetic dipole detection magnetic gradient tensor nonlinear optimization normalized source strength Chemical technology Qi Han verfasserin aut Xiaojun Tong verfasserin aut Yizhen Wang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 9, p 4440 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:9, p 4440 https://doi.org/10.3390/s23094440 kostenfrei https://doaj.org/article/cf7976ab8af54d28875a630b96c73e4a kostenfrei https://www.mdpi.com/1424-8220/23/9/4440 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 9, p 4440
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allfieldsSound	10.3390/s23094440 doi (DE-627)DOAJ09033566X (DE-599)DOAJcf7976ab8af54d28875a630b96c73e4a DE-627 ger DE-627 rakwb eng TP1-1185 Lin Ge verfasserin aut Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, there has been a growing interest in the detection, location, and classification (DLC) of multiple dipole-like magnetic sources based on magnetic gradient tensor (MGT) data. In these applications, the tilt angle is usually used to detect the number of sources. We found that the tilt angle is only suitable for the scenario where the positive and negative signs of the magnetic sources’ inclination are the same. Therefore, we map the L2 norm of the vertical magnetic gradient tensor on the arctan function, denoted as the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle, to detect the number of sources. Then we use the normalized source strength (NSS) to narrow the parameters’ search space and combine the differential evolution (DE) algorithm with the Levenberg–Marquardt (LM) algorithm to solve the sources’ locations and magnetic moments. Simulation experiments and a field demonstration show that the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle is insensitive to the sign of inclination and more accurate in detecting the number of magnetic sources than the tilt angle. Meanwhile, our method can quickly locate and classify magnetic sources with high precision. unknown dipole quantity multiple magnetic dipole detection magnetic gradient tensor nonlinear optimization normalized source strength Chemical technology Qi Han verfasserin aut Xiaojun Tong verfasserin aut Yizhen Wang verfasserin aut In Sensors MDPI AG, 2003 23(2023), 9, p 4440 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:9, p 4440 https://doi.org/10.3390/s23094440 kostenfrei https://doaj.org/article/cf7976ab8af54d28875a630b96c73e4a kostenfrei https://www.mdpi.com/1424-8220/23/9/4440 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 9, p 4440
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callnumber-first	T - Technology
author	Lin Ge
spellingShingle	Lin Ge misc TP1-1185 misc unknown dipole quantity misc multiple magnetic dipole detection misc magnetic gradient tensor misc nonlinear optimization misc normalized source strength misc Chemical technology Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data
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topic_title	TP1-1185 Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data unknown dipole quantity multiple magnetic dipole detection magnetic gradient tensor nonlinear optimization normalized source strength
topic	misc TP1-1185 misc unknown dipole quantity misc multiple magnetic dipole detection misc magnetic gradient tensor misc nonlinear optimization misc normalized source strength misc Chemical technology
topic_unstemmed	misc TP1-1185 misc unknown dipole quantity misc multiple magnetic dipole detection misc magnetic gradient tensor misc nonlinear optimization misc normalized source strength misc Chemical technology
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title	Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data
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title_full	Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data
author_sort	Lin Ge
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title_sort	detection, location, and classification of multiple dipole-like magnetic sources based on l2 norm of the vertical magnetic gradient tensor data
callnumber	TP1-1185
title_auth	Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data
abstract	In recent years, there has been a growing interest in the detection, location, and classification (DLC) of multiple dipole-like magnetic sources based on magnetic gradient tensor (MGT) data. In these applications, the tilt angle is usually used to detect the number of sources. We found that the tilt angle is only suitable for the scenario where the positive and negative signs of the magnetic sources’ inclination are the same. Therefore, we map the L2 norm of the vertical magnetic gradient tensor on the arctan function, denoted as the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle, to detect the number of sources. Then we use the normalized source strength (NSS) to narrow the parameters’ search space and combine the differential evolution (DE) algorithm with the Levenberg–Marquardt (LM) algorithm to solve the sources’ locations and magnetic moments. Simulation experiments and a field demonstration show that the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle is insensitive to the sign of inclination and more accurate in detecting the number of magnetic sources than the tilt angle. Meanwhile, our method can quickly locate and classify magnetic sources with high precision.
abstractGer	In recent years, there has been a growing interest in the detection, location, and classification (DLC) of multiple dipole-like magnetic sources based on magnetic gradient tensor (MGT) data. In these applications, the tilt angle is usually used to detect the number of sources. We found that the tilt angle is only suitable for the scenario where the positive and negative signs of the magnetic sources’ inclination are the same. Therefore, we map the L2 norm of the vertical magnetic gradient tensor on the arctan function, denoted as the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle, to detect the number of sources. Then we use the normalized source strength (NSS) to narrow the parameters’ search space and combine the differential evolution (DE) algorithm with the Levenberg–Marquardt (LM) algorithm to solve the sources’ locations and magnetic moments. Simulation experiments and a field demonstration show that the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle is insensitive to the sign of inclination and more accurate in detecting the number of magnetic sources than the tilt angle. Meanwhile, our method can quickly locate and classify magnetic sources with high precision.
abstract_unstemmed	In recent years, there has been a growing interest in the detection, location, and classification (DLC) of multiple dipole-like magnetic sources based on magnetic gradient tensor (MGT) data. In these applications, the tilt angle is usually used to detect the number of sources. We found that the tilt angle is only suitable for the scenario where the positive and negative signs of the magnetic sources’ inclination are the same. Therefore, we map the L2 norm of the vertical magnetic gradient tensor on the arctan function, denoted as the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle, to detect the number of sources. Then we use the normalized source strength (NSS) to narrow the parameters’ search space and combine the differential evolution (DE) algorithm with the Levenberg–Marquardt (LM) algorithm to solve the sources’ locations and magnetic moments. Simulation experiments and a field demonstration show that the VMGT<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msub<<mrow<</mrow<<mn<2</mn<</msub<</semantics<</math<</inline-formula< angle is insensitive to the sign of inclination and more accurate in detecting the number of magnetic sources than the tilt angle. Meanwhile, our method can quickly locate and classify magnetic sources with high precision.
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container_issue	9, p 4440
title_short	Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data
url	https://doi.org/10.3390/s23094440 https://doaj.org/article/cf7976ab8af54d28875a630b96c73e4a https://www.mdpi.com/1424-8220/23/9/4440 https://doaj.org/toc/1424-8220
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up_date	2024-07-03T14:09:45.496Z
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Detection, Location, and Classification of Multiple Dipole-like Magnetic Sources Based on L2 Norm of the Vertical Magnetic Gradient Tensor Data

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