The grounded electrical source airborne transient electromagnetic (GREATEM) system is an important method for obtaining subsurface conductivity distribution as well as outstanding detection efficiency and easy flight control. However, there are the superposition of desired signals and various noises for the GREATEM signal. The baseline wander caused by the receiving coil motion always exists in the process of data acquisition and affects measurement results. The baseline wander is one of the main noise sources, which has its own characteristics such as being low frequency, large amplitude, non-periodic, and non-stationary and so on. Consequently, it is important to correct the GREATEM signal for an inversion explanation. In this paper, we propose improving the method of ensemble empirical mode decomposition (EEMD) by adaptive filtering (EEMD-AF) based on EEMD to suppress baseline wander. Firstly, the EEMD-AF method will decompose the electromagnetic signal into multi-stage intrinsic mode function (IMF) components. Subsequently, the adaptive filter will process higher-index IMF components containing the baseline wander. Lastly, the de-noised signal will be reconstructed. To examine the performance of our introduced method, we processed the simulated and field signal containing the baseline wander by different methods. Through the evaluation of the signal-to-noise ratio (SNR) and mean-square error (MSE), the result indicates that the signal using the EEMD-AF method can get a higher SNR and lower MSE. Comparing correctional data using the EEMD-AF and the wavelet-based method in the anomaly curve profile images of the response signal, it is proved that the EEMD-AF method is practical and effective for the suppression of the baseline wander in the GREATEM signal.

中文翻译：

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基于改进的集成经验模式分解（EEMD）算法的接地电源机载瞬变电磁信号基线漂移校正

接地电源机载瞬变电磁（GREATEM）系统是获得地下电导率分布以及出色的检测效率和易于飞行控制的重要方法。但是，GREATEM信号存在所需信号和各种噪声的叠加。由接收线圈运动引起的基线漂移始终存在于数据采集过程中，并影响测量结果。基线漂移是主要的噪声源之一，具有其自身的特点，如低频，大幅度，非周期性和非平稳等。因此，为解释反演，校正GREATEM信号很重要。在本文中，我们提出通过基于EEMD的自适应滤波（EEMD-AF）来改进整体经验模式分解（EEMD）的方法，以抑制基线漂移。首先，EEMD-AF方法会将电磁信号分解为多级本征模式函数（IMF）分量。随后，自适应滤波器将处理包含基线漂移的较高索引的IMF分量。最后，去噪信号将被重建。为了检查引入的方法的性能，我们使用不同的方法处理了包含基线漂移的模拟和现场信号。通过评估信噪比（SNR）和均方误差（MSE），结果表明，使用EEMD-AF方法的信号可以获得更高的SNR和更低的MSE。