The orthogonal basis functions (OBFs) detector is a detection method widely used in the aerial magnetic measurement. However, OBFs detector works ineffectively under non-Gaussian noise and colored noise. This paper proposes an OBFs detector based on fast convergence wavelet artificial neural network (FC-W-ANN), which can detect abnormal magnetic signals under low SNR. First, the magnetic anomaly signal is modelled. Then, the learning rate is corrected by the iterative error convergence rate under the stability of the network. Finally, the improved network is combined with the OBFs detector to detect magnetic abnormal signals. From the simulation and experimental results, the reconstructed signal of the new method has a higher SNR (SNR = 10.01) compared with OBFs (SNR = -0.21) and OBFs based on the wavelet artificial neural network (W-ANN; SNR = 9.59). Furthermore, the statistical methods are used to analyze FC-W-ANN and W-ANN, showing that FC-W-ANN has higher training accuracy and better stability.

正交基函数（OBF）检测器是一种广泛用于航空磁测量中的检测方法。但是，OBF检测器在非高斯噪声和有色噪声下无法有效工作。本文提出了一种基于快速收敛小波人工神经网络（FC-W-ANN）的OBFs检测器，该检测器可以在低信噪比下检测异常磁信号。首先，对磁异常信号进行建模。然后，在网络的稳定性下，通过迭代误差收敛率来校正学习率。最后，将改进的网络与OBF检测器结合起来，以检测磁异常信号。从仿真和实验结果来看，与基于小波人工神经网络（W-ANN; SNR = 9）的OBF（SNR = -0.21）和OBF相比，新方法的重构信号具有更高的SNR（SNR = 10.01）。59）。此外，通过统计方法对FC-W-ANN和W-ANN进行分析，表明FC-W-ANN具有较高的训练精度和较好的稳定性。