Modeling of High-Precision Sea Surface Geomagnetic Field in the Northern South China Sea Based on PSO-BP Neural Network

In existing regional geomagnetic field modeling, the smoothness of basic functions and the insufficient data constraints in marginal regions lead to the omission of detail features and extrapolation oscillations. To address these limitations and develop a high-precision marine regional geomagnetic field model, we develop a back propagation neural network (BPNN) method enhanced by particle swarm optimization (PSO). The PSO-BPNN method has the ability of adaptive learning and could extract local features. By combining the magnetic field data measured by ships with the previous model data, a high-precision geomagnetic field model of the northern South China Sea (SCS) is developed. The fitting error of the PSO-BPNN model is 18.05 nT, which is 16% and 20.1% lower than those of the traditional Legendre Polynomial (LP) and Taylor Polynomial (TP) models, respectively. The proposed PSO-BPNN model demonstrates superior robustness and higher accuracy, while retaining more magnetic signals of small geological bodies.