Abstract
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).
Highlights
Geomagnetic data provide important information for nearsurface detection [1]
In order to improve the precision of the compression factor particle swarm optimization (CFPSO) algorithm, this paper proposes an ACFPSO algorithm, which adaptively adjusts the compression factor according to the search progress of the algorithm, improving the ability of the algorithm to search for the global optimal solution
Aggregation affects the results of optimization in the process of optimization. erefore, this paper proposes to combine the influence of the number of iterations with the degree of aggregation to realize the dynamic adjustment of the compression factor. e optimization scheme proposed in this paper utilizes average aggregation distance and maximum aggregation distance [33], which formula is as follows:
Summary
Geomagnetic data provide important information for nearsurface detection [1]. existing measurement methods cannot realize fine-grained measurements on the geomagnetic field. e spatial interpolation algorithm can effectively increase the density of measurements [2]. When traditional interpolation methods are applied to continuous geomagnetic field modelling, limited or sparse geomagnetic data can lead to bias and inconsistent spatial estimation in spatial prediction results. Kriging interpolation as a geostatistical method is one of the widely used interpolation methods in the field of precipitation prediction [3, 4], soil property estimation [5, 6], groundwater statistics [7], and digital terrain modelling [8]. Kriging interpolation may overcome the limitations encountered with current interpolation methods and enhance the quality of geomagnetic data. The least-squares method [10] often falls into local optimum, and the genetic algorithm [11] is of a complicated structure and has more parameters to tune, even though its accuracy is relatively high
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