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.

中文翻译：

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集合经验模态分解和最小熵特征的自适应磁异常检测方法

由于磁场随着距离的快速衰减，通常由远程磁性目标产生的磁异常被掩埋在磁噪声中。为了提高低信噪比的磁异常检测（MAD）的性能，我们提出了一种具有整体经验模态分解（EEMD）和最小熵（ME）特征的自适应MAD方法。EEMD将磁数据分解为具有不同尺度的多个固有模态函数（IMF）。根据定义的标准，分别基于IMF重建磁噪声和磁信号。基于由重构磁噪声更新的噪声的概率密度函数（PDF），提取重构磁信号的熵特征。与传统的最小熵方法相比，该方法提取的熵特征更为明显。只要熵特征下降到阈值以下，就会检测到磁异常。因此，通过所提出的方法对于揭示弱磁性异常是有效的。测量的磁噪声用于验证所提出方法的性能。结果表明，该方法在低输入信噪比的情况下具有较高的检测概率。