Latest Geodetic Changes of Austre Lovénbreen and Pedersenbreen, Svalbard

Ai, Songtao; Ding, Xi; Tölle, Florian; Wang, Zemin; Zhao, Xi

doi:10.3390/rs11242890

Cited by 6 publications

(2 citation statements)

References 51 publications

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“…The basic idea of the Kriging algorithm is that a certain weight is assigned to each sample value, according to the spatial correlation between sample points, especially the spatial position, and the unknowns in the estimation region that were estimated using the weighted average method [14].…”

Section: Principle Of Interpolationmentioning

confidence: 99%

Accounting for Geometric Anisotropy in Sparse Magnetic Data Using a Modified Interpolation Algorithm

Li,

Zhang,

Pan

et al. 2024

Remote Sensing

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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.

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Section: Principle Of Interpolationmentioning

confidence: 99%

Accounting for Geometric Anisotropy in Sparse Magnetic Data Using a Modified Interpolation Algorithm

Li,

Zhang,

Pan

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The basic idea of the Kriging algorithm is that a certain weight is assigned to each sample value according to the spatial correlation between sample points, especially the spatial position, and the unknowns in the estimation region were estimated using the weighted average method [25].…”

Section: Principle Of Interpolationmentioning

confidence: 99%

Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data

Li,

Zhang,

Pan

et al. 2024

Applied Sciences

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The high-precision magnetic anomaly reference map is a prerequisite for magnetic navigation and magnetic target detection. However, it is difficult to reflect the detailed characteristics of magnetic anomaly changes by using conventional data interpolation and reconstruction in the areas where magnetic anomaly gradients vary drastically and the distribution of magnetic survey lines is sparse. To solve the problem, an improved variogram of the Kriging interpolation method is proposed to improve the spatial resolution of magnetic anomaly feature. This method selects the spherical variogram model and uses the third power of the lag distance to fit the trend of magnetic anomalies. Meanwhile, the second power of the lag distance is introduced to solve the problem of under-fitting between the lag distance and the value of the variation function near the origin of the sparse variogram graph of measured data. Hyperparameter λ is introduced to compensate for the unbalance caused by the introduction of quadratic lag in the spherical variogram model. The results of several sets of simulated and measured data show that the interpolation accuracy of the proposed method is improved by 30–50% compared with the traditional Gaussian, spherical, and exponential models in the region where the magnetic anomaly gradient changes drastically, and the proposed model provides an effective way to build a high-precision magnetic anomaly reference map of the complex magnetic background under the condition of sparse survey lines.

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