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De-noising magnetotelluric (MT) data has become a key research topic closely related to the application of the MT method itself. Variational mode decomposition (VMD), a newly developed non-recursive signal decomposition method, has been applied to the denoising of MT data. However, some high-frequency noise may remain after VMD denoising. Moreover, the useful low-frequency signal and large-scale noise cannot always be easily separated, resulting in the loss of useful low-frequency signal components during signal reconstruction. To overcome this problem, a new time series editing strategy is proposed, which combines wavelet thresholding (WT) and mathematical morphology filtering (MMF) for further processing of the VMD output. WT is used to remove the remaining high-frequency noise from the retained signal after VMD, while MMF is utilized to extract the useful low-frequency signal components from the signal rejected by VMD. Finally, the WT and MMF outputs are merged to obtain the final denoising results. The proposed method was evaluated using a simulated dataset and two real datasets collected in the Hami Basin of East Tianshan, China. The simulated data tests showed that the proposed method has superior denoising ability compared to other time-domain methods, while the real data study showed that the quality of MT responses obtained from the proposed method is superior to that from conventional methods, especially for the low-frequency band. • A new time series editing strategy for MT data denoising is developed. • WT is used to remove the residual high-frequency noise from the retained signal after VMD. • MMF is utilized to extract the useful low-frequency components from the signal rejected by VMD. • The proposed method is verified using simulated and real datasets. [ABSTRACT FROM AUTHOR]

Independent component analysis (ICA) is one of the most effective approaches in extracting independent signals from a global navigation satellite system (GNSS) regional station network. However, ICA requires the involved time series to be complete, thereby the missing data of incomplete time series should be interpolated beforehand. In this contribution, a modified ICA is proposed, by which the missing data are first recovered based on the reversible property between the original time series and decomposed principal components, then the complete time series are further processed with FastICA. To evaluate the performance of the modified ICA for extracting independent components, 24 regional GNSS network stations located in North China from 2011 to 2019 were selected. After the trend, annual and semiannual terms were removed from the GNSS time series, the first two independent components captured 17.42, 18.44 and 17.38% of the total energy for the North, East and Up coordinate components, more than those derived by the iterative ICA that accounted for 16.21%, 17.72% and 16.93%, respectively. Therefore, modified ICA can extract more independent signals than iterative ICA. Subsequently, selecting the 7 stations with less missing data from the network, we repeatedly process the time series after randomly deleting parts of the data and compute the root mean square error (RMSE) from the differences of reconstructed signals before and after deleting data. All RMSEs of modified ICA are smaller than those of iterative ICA, indicating that modified ICA can extract more exact signals than iterative ICA. [ABSTRACT FROM AUTHOR]

In this project, a novel noise reduction method based on empirical mode decomposition (EMD), referred to as EMD-AdaptiveP, is proposed and investigated with the expectation of providing an effective enhancement for the full waveform of nonlinear and nonstationary signals obtained by space-borne laser altimetry and the forthcoming GaoFen-7 (GF-7) mapping satellite in China. This method combines EMD with a similar strategy of wavelet filtering to adapt to the characteristics of each waveform. The reconstruction of an effective waveform signal is implemented through reverse superimposition of its intrinsic mode functions (IMFs) and the residual, which is thresholded by the three-sigma principle of background noise of the waveform signal and an additional check mechanism in the lower or higher signal of noise levels, after the waveform is decomposed by EMD. This method is qualitatively and quantitatively examined with simulated waveform data, real ice, cloud and land elevation satellite (ICESat)/Geoscience Laser Altimeter System (GLAS) waveforms and GF-7 proto-waveform data, in contrast with other IMF selection schemes, like EMD-soft, EMD-hard and EMD-Wavelet, or the traditional filtering methods, such as Gaussian, μ\λ and wavelet. The results show that the method of EMD-AdaptiveP (1) is sufficiently robust to denoise the waveform signal with different levels of signal-to-noise ratio, (2) has the adaptive ability to distinguish effective signal IMFs from noise IMFs in signal reconstruction, and (3) performs noise reduction more effectively than traditional methods. [ABSTRACT FROM AUTHOR]