Magnetic Anomaly Detection Using One-Dimensional Convolutional Neural Network With Multi-Feature Fusion
Magnetic Anomaly Detection Using One-Dimensional Convolutional Neural Network With Multi-Feature Fusion
复制标题
使用具有多特征融合的一维卷积神经网络进行磁异常检测
DOI：
10.1109/jsen.2022.3175447
复制
发表时间：
2022-06
影响因子：
4.3
通讯作者：
Chong Kang
中科院分区：
文献类型：
--
作者：
Fan Liming;Hao Hu;Zhang Xiaojun;Huigang Wang;Chong Kang
为了提高低信噪比下磁异常信号的检测性能，提出了一种基于一维卷积神经网络（1D CNN）模型的多特征融合检测方法。该方法对磁信号进行Hilbert-Huang变换和离散小波变换，提取磁信号在不同维度上的信息作为预特征。该方法利用具有三个处理模块的一维CNN模型进一步从预特征中提取特征，并基于多特征融合识别异常信号是否存在。为了训练模型，正样本集由模拟信号和测量的磁噪声产生，而负样本集仅是测量的磁噪声。仿真结果表明，该方法在训练集和测试集上都具有较高的准确率。利用真实的数据对该方法的检测性能进行了现场实验。实验结果表明，该方法在低信噪比下具有良好的检测性能。
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.