# Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data  
[PERSON], Qi Zhang  
College of Intelligence Science and Technology, National University of Defense Technology,  
Changsha 410073, China; [EMAIL_ADDRESS] (H.L.) Mengchun Pan  
College of Intelligence Science and Technology, National University of Defense Technology,  
Changsha 410073, China; [EMAIL_ADDRESS] (H.L.) Dixiang Chen  
College of Intelligence Science and Technology, National University of Defense Technology,  
Changsha 410073, China; [EMAIL_ADDRESS] (H.L.) Ziqiang Yu  
College of Intelligence Science and Technology, National University of Defense Technology,  
Changsha 410073, China; [EMAIL_ADDRESS] (H.L.) Yujing Xu  
College of Intelligence Science and Technology, National University of Defense Technology,  
Changsha 41007  
algorithms and found that the method of Kriging and radial basis function were better in generating accurate grid data. [PERSON] et al. [14] compared the methods of Shepard surface fitting, weighted moving trend surface fitting, and others to be interpolated for gridding and verified that the accuracy of the Kriging interpolation method was better than other methods in the process of magnetic mapping. [PERSON] et al. [15] used the Kriging algorithm to interpolate magnetic data obtained by satellites to create an instantaneous interpolation map of the magnetic field inside a tetrahedron and point out that the Kriging interpolation algorithm is worth paying close attention to for multi-point space measurements. Therefore, the Kriging algorithm can better represent the structural components of the magnetic field and accurately reflect the magnetic information in local areas, which has great advantages in complex magnetic regions [16]. By collecting the published literature on interpolation methods of magnetic data using Kriging, and considering the external usage conditions, internal fusion methods, the selection of core variogram, and other factors, it is found that relevant algorithms can be divided into the following three categories:  
(1) Interpolation processing based on different Kriging algorithms. This kind of algorithm depends on the external conditions of the Kriging interpolation algorithm. Currently, the common models include the ordinary Kriging (OK) algorithm, universal Kriging (UK) algorithm, co-Kriging (CK) algorithm, and so on. [PERSON] et al. [17] studied the basic principle of ordinary Kriging interpolation algorithm, appropriately selected the semi-variance function model, and tested via experiment that the magnetic grid data obtained by the ordinary Kriging method were more accurate. [PERSON] et al. [18] used cross-validation to verify that the universal Kriging algorithm has a better interpolation effect than the ordinary Kriging method. [PERSON] et al. [19] applied the co-Kriging interpolation algorithm to indoor navigation and positioning based on magnetic field. After dividing the sample into two parts, the ordinary Kriging algorithm was carried out and then fusion processing was carried out, which can result in a small root-mean-square error and good interpolation effect. (2) Interpolation processing based on the fusion of multiple algorithms. In this kind of processing, other model algorithms are introduced, whose advantages are used to make up for the shortcomings of Kriging algorithm. There are many articles published on this subject. For example, [PERSON] et al. [20] proposed a step-by-step interpolation correction method based on the multifractal Kriging to improve the low-pass filtering characteristics of the Kriging method and reduce the loss of fractal characteristics when interpolating with sparse magnetic anomaly field data via multifractal Kriging. On the basis of this research, the step-by-step interpolation and correction method (SSICM) was proposed in [21], which conducts a singularity correction according to scale invariance in the small-scale range of the magnetic anomaly field, thereby stepwise building a reference map in gridding format. [PERSON] et al.