Accounting for Geometric Anisotropy in Sparse Magnetic Data Using a Modified Interpolation Algorithm
The construction of a high-precision geomagnetic map is a prerequisite for geomagnetic navigation and magnetic target-detection technology. The Kriging interpolation algorithm makes use of the variogram to perform linear unbiased and optimal estimation of unknown sample points. It has strong spatial...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/5/883 |
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| author | Haibin Li Qi Zhang Mengchun Pan Dixiang Chen Zhongyan Liu Liang Yan Yujing Xu Zengquan Ding Ziqiang Yu Xu Liu Ke Wan Weiji Dai |
| author_facet | Haibin Li Qi Zhang Mengchun Pan Dixiang Chen Zhongyan Liu Liang Yan Yujing Xu Zengquan Ding Ziqiang Yu Xu Liu Ke Wan Weiji Dai |
| author_sort | Haibin Li |
| collection | DOAJ |
| description | The construction of a high-precision geomagnetic map is a prerequisite for geomagnetic navigation and magnetic target-detection technology. The Kriging interpolation algorithm makes use of the variogram to perform linear unbiased and optimal estimation of unknown sample points. It has strong spatial autocorrelation and is one of the important methods for geomagnetic map construction. However, in a region with a complex geomagnetic field, the sparse geomagnetic survey lines make the ratio of line-spacing resolution to in-line resolution larger, and the survey line direction differs from the geomagnetic trend, which leads to a serious effect of geometric anisotropy and thus, reduces the interpolation accuracy of the geomagnetic maps. Therefore, this paper focuses on the problem of geometric anisotropy in the process of constructing a geomagnetic map with sparse data, analyzes the influence of sparse data on geometric anisotropy, deduces the formula of geometric anisotropy correction, and proposes a modified interpolation algorithm accounting for geometric anisotropy correction of variogram for sparse geomagnetic data. The results of several sets of simulations and measured data show that the proposed method has higher interpolation accuracy than the conventional spherical variogram model in a region where the geomagnetic anomaly gradient changes sharply, which provides an effective way to build a high-precision magnetic map of the complex geomagnetic field under the condition of sparse survey lines. |
| first_indexed | 2024-04-25T00:21:39Z |
| format | Article |
| id | doaj.art-ec9c5b27642a442cb07e4317df8d65da |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-25T00:21:39Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ec9c5b27642a442cb07e4317df8d65da2024-03-12T16:54:19ZengMDPI AGRemote Sensing2072-42922024-03-0116588310.3390/rs16050883Accounting for Geometric Anisotropy in Sparse Magnetic Data Using a Modified Interpolation AlgorithmHaibin Li0Qi Zhang1Mengchun Pan2Dixiang Chen3Zhongyan Liu4Liang Yan5Yujing Xu6Zengquan Ding7Ziqiang Yu8Xu Liu9Ke Wan10Weiji Dai11College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCenter for Applied Mathematics, College of Science, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, ChinaThe construction of a high-precision geomagnetic map is a prerequisite for geomagnetic navigation and magnetic target-detection technology. The Kriging interpolation algorithm makes use of the variogram to perform linear unbiased and optimal estimation of unknown sample points. It has strong spatial autocorrelation and is one of the important methods for geomagnetic map construction. However, in a region with a complex geomagnetic field, the sparse geomagnetic survey lines make the ratio of line-spacing resolution to in-line resolution larger, and the survey line direction differs from the geomagnetic trend, which leads to a serious effect of geometric anisotropy and thus, reduces the interpolation accuracy of the geomagnetic maps. Therefore, this paper focuses on the problem of geometric anisotropy in the process of constructing a geomagnetic map with sparse data, analyzes the influence of sparse data on geometric anisotropy, deduces the formula of geometric anisotropy correction, and proposes a modified interpolation algorithm accounting for geometric anisotropy correction of variogram for sparse geomagnetic data. The results of several sets of simulations and measured data show that the proposed method has higher interpolation accuracy than the conventional spherical variogram model in a region where the geomagnetic anomaly gradient changes sharply, which provides an effective way to build a high-precision magnetic map of the complex geomagnetic field under the condition of sparse survey lines.https://www.mdpi.com/2072-4292/16/5/883geomagnetic mapKriging interpolation algorithmvariogramgeometric anisotropy |
| spellingShingle | Haibin Li Qi Zhang Mengchun Pan Dixiang Chen Zhongyan Liu Liang Yan Yujing Xu Zengquan Ding Ziqiang Yu Xu Liu Ke Wan Weiji Dai Accounting for Geometric Anisotropy in Sparse Magnetic Data Using a Modified Interpolation Algorithm Remote Sensing geomagnetic map Kriging interpolation algorithm variogram geometric anisotropy |
| title | Accounting for Geometric Anisotropy in Sparse Magnetic Data Using a Modified Interpolation Algorithm |
| title_full | Accounting for Geometric Anisotropy in Sparse Magnetic Data Using a Modified Interpolation Algorithm |
| title_fullStr | Accounting for Geometric Anisotropy in Sparse Magnetic Data Using a Modified Interpolation Algorithm |
| title_full_unstemmed | Accounting for Geometric Anisotropy in Sparse Magnetic Data Using a Modified Interpolation Algorithm |
| title_short | Accounting for Geometric Anisotropy in Sparse Magnetic Data Using a Modified Interpolation Algorithm |
| title_sort | accounting for geometric anisotropy in sparse magnetic data using a modified interpolation algorithm |
| topic | geomagnetic map Kriging interpolation algorithm variogram geometric anisotropy |
| url | https://www.mdpi.com/2072-4292/16/5/883 |
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