Adaptive Magnetic Anomaly Detection Method with Ensemble Empirical Mode Decomposition and Minimum Entropy Feature
Adaptive Magnetic Anomaly Detection Method with Ensemble Empirical Mode Decomposition and Minimum Entropy Feature
复制标题
集成经验模态分解和最小熵特征的自适应磁异常检测方法
DOI：
10.1155/2020/8856577
复制
发表时间：
2020-08
影响因子：
1.9
通讯作者：
Zou Mingliang
中科院分区：
文献类型：
--
作者：
Fan Liming;Kang Chong;Wang Huigang;Hu Hao;Zou Mingliang
由于磁场随距离的快速衰减，远距离磁目标产生的磁异常通常被掩埋在磁噪声中。为了提高低信噪比磁异常检测(MAD)的性能，提出了一种集成经验模式分解(EEMD)和最小熵(ME)特征的自适应磁异常检测方法。EEMD将地磁数据分解为不同尺度的多个固有模态函数(IMF)。根据定义的准则，分别基于IMF重建磁噪声和磁信号。基于重构磁噪声更新后的噪声概率密度函数，提取重构磁信号的熵特征。与传统的最小熵方法相比，该方法提取的特征更加明显。每当熵特征降到阈值以下时，就会检测到磁异常。因此，该方法对于揭示弱磁异常是有效的。测量的磁噪声被用来验证该方法的性能。结果表明，在输入信噪比较低的情况下，该方法具有较高的检测概率。
Due to the fast attenuation of the magnetic field along with the distance, the magnetic anomaly generated by the remote magnetic target is usually buried in the magnetic noise. In order to improve the performance of magnetic anomaly detection (MAD) with low SNR, we propose an adaptive method of MAD with ensemble empirical mode decomposition (EEMD) and minimum entropy (ME) feature. The magnetic data is decomposed into the multiple intrinsic modal functions (IMFs) with different scales by EEMD. According to a defined criterion, the magnetic noise and magnetic signal are reconstructed based on IMFs, respectively. Entropy feature of reconstructed magnetic signal is extracted based on the probability density function (PDF) of the noise which is updated by the reconstructed magnetic noise. Compared to the traditional minimum entropy method, the entropy feature extracted by the proposed method is more obvious. The magnetic anomaly is detected whenever the entropy feature drops below the threshold. Thus, it is effective for revealing the weak magnetic anomaly by the proposed method. The measured magnetic noise is used to validate the performance of the proposed method. The results show that the detection probability of the proposed method is higher with low input SNR.
登录
查看更多内容
影响因子：
4.8
作者：
S. Tantum;Yongli Yu;L. Collins
通讯作者：
S. Tantum;Yongli Yu;L. Collins
基于主成分分析和广义学习系统的故障诊断方法
影响因子：
3.9
作者：
Huimin Zhao;Jianjie Zheng;Junjie Xu;Wu Deng
通讯作者：
Wu Deng
使用 Karhunen-Loeve 展开改进磁异常探测器的性能
影响因子：
1.4
作者：
Wan Chengbiao;Pan Mengchun;Zhang Qi;Chen Dixiang;Pang Hongfeng;Zhu Xuejun
通讯作者：
Zhu Xuejun
一种改进的深度信念网络量子启发差分进化算法
DOI：
10.1109/tim.2020.2983233
发表时间：
2020-10-01
影响因子：
5.6
作者：
Deng, Wu;Liu, Hailong;Song, Yingjie
通讯作者：
Song, Yingjie
影响因子：
1.4
作者：
Nalband, Saif;Prince, Amalin;Agrawal, Anita
通讯作者：
Agrawal, Anita