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.

The extremely-low-frequency (ELF) magnetic fields of submarines serve as key characteristics for target detection, with their formation mechanisms being complex and diverse. Effectively manipulating a submarine to reduce its magnetic signature is crucial for enhancing its magnetic stealth capabilities. However, current research on the impact of various causative factors is insufficient. This study proposes a contribution rate assessment method based on the Analytic Hierarchy Process (AHP) model, aiming to provide a theoretical basis for effective manipulation. Initially, a thorough analysis of the threat causes of a submarine’s ELF magnetic fields is conducted, and a corresponding hierarchical threat structure model is established. Subsequently, magnetic field signal characteristics generated by different causes are obtained through simulation, and threat matrices and characteristic matrices are constructed. Finally, the contribution rates of different causative magnetic fields to the total magnetic field are calculated, and the simulation results validate the effectiveness of the method. At the stern detection line, the contribution rate of the wake magnetic field is the highest, reaching 0.7649. Along the radial detection line, the contribution rate of the shaft frequency magnetic field is the highest and gradually decays, eventually falling below the wake magnetic field at 150 m and remaining at an approximately 0.5 contribution rate. This study calculates the contribution rates under different operational conditions and detection scenarios, laying a technical foundation for research on the comprehensive active control strategies of submarine ELF magnetic fields in different scenarios.