Magnetic Anomaly Detection Using One-Dimensional Convolutional Neural Network With Multi-Feature Fusion

In order to improve the detection performance of magnetic anomaly signal with low signal-to-noise ratio (SNR), we develop an effective method using one-dimensional convolutional neural network (1D CNN) model with multi-feature fusion. In the method, the magnetic signal is processed by Hilbert-Huang transform and discrete wavelet transform to obtain its information as pre-feature in different dimensions. The 1D CNN model with three processing blocks is used to further extract features from pre-features and identified whether the anomaly signal exists or not based on multi-feature fusion. To train the model, the positive sample set is generated by simulated signals and the measured magnetic noise, while the negative sample set is only the measured magnetic noise. Simulation results show that the proposed method has high accuracies in training and test set. A field experiment is conducted to examine the detection performance of proposed method using real data. Results show that the proposed method has good detection performances in low SNR.