Application of Kriging Algorithm Based on ACFPSO in Geomagnetic Data Interpolation

High-precision geomagnetic field model is the key to magnetic anomaly detection and localization technology. The model is usually constructed through Kriging interpolation. Aiming at the problem of insufficient fitting of variogram in the existing Kriging interpolation methods, this paper proposes a particle swarm optimization algorithm with an adaptive compression factor (ACFPSO). The algorithm utilizes the degree of particle aggregation and the number of iterations to dynamically change the compression factor so as to achieve an effective balance between global optimization and local exploration. The cross-validation results show that the ACFPSO algorithm has the same convergence speed as the conventional particle swarm optimization algorithm, but the convergence accuracy is higher. Compared with the commonly used high-efficiency interpolation methods, such as the plain Kriging, the inverse distance weighting, and the radial basis function, the ACFPSO-optimized Kriging method achieves better performance (the mean absolute error is around 0.3 nT).