Magnetization Vector Inversion Based on Amplitude and Gradient Constraints
Magnetization vector inversion has been developed since it can increase inversion accuracy due to the unknown magnetization direction caused by remanence. However, the three components of total magnetizations vector are simultaneously inverted and then synthesized into the magnetization magnitude an...
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/21/5497 |
| _version_ | 1797466624171704320 |
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| author | Xiaoqing Shi Hua Geng Shuang Liu |
| author_facet | Xiaoqing Shi Hua Geng Shuang Liu |
| author_sort | Xiaoqing Shi |
| collection | DOAJ |
| description | Magnetization vector inversion has been developed since it can increase inversion accuracy due to the unknown magnetization direction caused by remanence. However, the three components of total magnetizations vector are simultaneously inverted and then synthesized into the magnetization magnitude and direction, which increases the inherent non-uniqueness of the inversion. The positions of the three components of the magnetization vector are originally consistent. If there is a lack of constraints between them during the inversion process, they may be misaligned, resulting in a large deviation between the synthesized vector model and the ground truth. To address this issue and at the same time increase the accuracy of the edges of the inversion models, this paper proposes a magnetization vector inversion scheme with model and its gradients’ constraints by sparse Lp norm functions based on the amplitude of the three components of the magnetization vector instead of a single component to improve the accuracy of the inversion result. To evaluate the inversion accuracy performance, an improved evaluation index is also proposed in this paper, which can better evaluate the accuracy of the shape, position and magnetization amplitude of the inversion model. The proposed inversion method can recover the models with higher accuracy compared with traditional methods, indicated by the inverted model and the evaluation indexes. Simulation results based on the open-source SimPEG software and inversion on actual measured Galinge iron ore deposit (China) data verified the effectiveness and advantages of the proposed method. |
| first_indexed | 2024-03-09T18:42:19Z |
| format | Article |
| id | doaj.art-af2617753d92465a9ea75a8ebbe7872a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:42:19Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-af2617753d92465a9ea75a8ebbe7872a2023-11-24T06:39:59ZengMDPI AGRemote Sensing2072-42922022-10-011421549710.3390/rs14215497Magnetization Vector Inversion Based on Amplitude and Gradient ConstraintsXiaoqing Shi0Hua Geng1Shuang Liu2Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, ChinaDepartment of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, ChinaHubei Subsurface Multi-Scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaMagnetization vector inversion has been developed since it can increase inversion accuracy due to the unknown magnetization direction caused by remanence. However, the three components of total magnetizations vector are simultaneously inverted and then synthesized into the magnetization magnitude and direction, which increases the inherent non-uniqueness of the inversion. The positions of the three components of the magnetization vector are originally consistent. If there is a lack of constraints between them during the inversion process, they may be misaligned, resulting in a large deviation between the synthesized vector model and the ground truth. To address this issue and at the same time increase the accuracy of the edges of the inversion models, this paper proposes a magnetization vector inversion scheme with model and its gradients’ constraints by sparse Lp norm functions based on the amplitude of the three components of the magnetization vector instead of a single component to improve the accuracy of the inversion result. To evaluate the inversion accuracy performance, an improved evaluation index is also proposed in this paper, which can better evaluate the accuracy of the shape, position and magnetization amplitude of the inversion model. The proposed inversion method can recover the models with higher accuracy compared with traditional methods, indicated by the inverted model and the evaluation indexes. Simulation results based on the open-source SimPEG software and inversion on actual measured Galinge iron ore deposit (China) data verified the effectiveness and advantages of the proposed method.https://www.mdpi.com/2072-4292/14/21/5497magnetization vector inversionsparse Lp constraintremanent magnetizationevaluation index |
| spellingShingle | Xiaoqing Shi Hua Geng Shuang Liu Magnetization Vector Inversion Based on Amplitude and Gradient Constraints Remote Sensing magnetization vector inversion sparse Lp constraint remanent magnetization evaluation index |
| title | Magnetization Vector Inversion Based on Amplitude and Gradient Constraints |
| title_full | Magnetization Vector Inversion Based on Amplitude and Gradient Constraints |
| title_fullStr | Magnetization Vector Inversion Based on Amplitude and Gradient Constraints |
| title_full_unstemmed | Magnetization Vector Inversion Based on Amplitude and Gradient Constraints |
| title_short | Magnetization Vector Inversion Based on Amplitude and Gradient Constraints |
| title_sort | magnetization vector inversion based on amplitude and gradient constraints |
| topic | magnetization vector inversion sparse Lp constraint remanent magnetization evaluation index |
| url | https://www.mdpi.com/2072-4292/14/21/5497 |
| work_keys_str_mv | AT xiaoqingshi magnetizationvectorinversionbasedonamplitudeandgradientconstraints AT huageng magnetizationvectorinversionbasedonamplitudeandgradientconstraints AT shuangliu magnetizationvectorinversionbasedonamplitudeandgradientconstraints |