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. The grounded electrical source airborne transientelectromagnetic (GREATEM) system is an important method for obtainingsubsurface conductivity distribution as well as outstanding detectionefficiency and easy flight control. However, there are the superposition ofdesired signals and various noises for the GREATEM signal. The baseline wandercaused by the receiving coil motion always exists in the process of dataacquisition and affects measurement results. The baseline wander is one ofthe 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 adaptivefiltering (EEMD-AF) based on EEMD tosuppress baseline wander. Firstly, the EEMD-AF method will decompose theelectromagnetic signal into multi-stage intrinsic mode function (IMF)components. Subsequently, the adaptive filter will process higher-index IMFcomponents containing the baseline wander. Lastly, the de-noised signal willbe reconstructed. To examine the performance of our introduced method, weprocessed the simulated and field signal containing the baseline wander bydifferent methods. Through the evaluation of the signal-to-noise ratio (SNR)and mean-square error (MSE), the result indicates that the signal usingthe EEMD-AF method can get a higher SNR and lower MSE. Comparing correctional datausing the EEMD-AF and the wavelet-based method in the anomaly curve profileimages of the response signal, it is proved that the EEMD-AF method is practical and effective for the suppression of the baseline wander inthe GREATEM signal.

geoscientific instrumentation, methods and data systems

The baseline wander correction based on the improved ensemble empirical mode decomposition (EEMD) algorithm for grounded electrical source airborne transient electromagnetic signals