A modified empirical mode decomposition method for multiperiod time-series detrending and the application in full-waveform induced polarization data

Abstract

, A modified empirical mode decomposition method for multiperiod time-series detrending and the application in full-waveform induced polarization data,

Magnetotelluric Signal-Noise Separation Using IE-LZC and MP

“…Based on the essential characteristics of the MT signal and noise, the collected MT data is often due to factors such as topographical structure and the human electromagnetic environment, and various electromagnetic noises inevitably interfere with it. However, the complex electromagnetic interference sources cause some disturbances to show robust features in the time-series and frequency spectrum, while other useful signals do not show any features in the time-series and frequency spectrum [ 22 ]. Therefore, the proposed method was processed for time-series and it was composed of three steps: characteristic extraction (IE and LZC), clustering analysis (FCM), and the de-noising algorithm (MP).…”

EMD decomposition-based signal denoising method for DC electrical methods

“…High signal-to-noise ratio data is a prerequisite for good exploration results, in the seismic method and electromagnetic method of exploration to improve the signal to noise ratio of multiple superposition and gain control and other techniques, in the DC method is mainly used to supply electrodes positive and negative power conversion and arithmetic averaging to eliminate noise interference in the signal of the DC method. The DC resistivity method requires the electrodes to be arranged in an array according to a certain arrangement, which will inevitably encounter earth currents, astronomical interference, strong industrial interference, Gaussian random noise and other interference effects on the supply electric field [2]. In actual exploration, the external interference field sources are complex and variable, and the interference signal is a time-varying signal with different characteristics in different spatial locations.…”

Denoising of magnetotelluric data using K‐SVD dictionary training

“…Therefore, using modern data processing technology to remove the strong interference existing in measured MT signals has become an important research topic in the field of electromagnetic exploration, and the improvement of MT data quality in the strong interference area will provide strong technical support for the subsequent inversion interpretation (Ren et al ., 2013; Qi et al ., 2020). A wide variety of methods have been proposed for solving this problem, such as short‐time Fourier transform (Vozoff, 1972; Kao and Rankin, 1977; Griffin and Lim, 1984), remote reference (RR) method (Goubau et al ., 1978; Gamble et al ., 1979; Clarke et al ., 1983; Kappler, 2012), robust estimation (Egbert and Booker, 1986; Larsen, 1989; Chave and Thomson, 1989, 2004; Larsen et al ., 1996; Egbert, 1997), wavelet transform (Trad and Travassos, 2000; He et al ., 2009; Carbonari et al ., 2017), Hilbert–Huang transform (HHT) and empirical mode decomposition (EMD; Chen et al ., 2012; Cai, 2014; Chen and Fomel, 2018; Liu et al ., 2019), mathematical morphological filtering (MMF; Tang et al ., 2012b), inter‐station transfer functions (Wang et al ., 2017), Self‐organizing Map (SOM) neural networks (Carbonari et al ., 2018), multifractal spectrum and matching pursuit (MP; Li et al ., 2019), Mahalanobis distance and magnetic field constraints (Platz and Weckmann, 2019), shift‐invariant sparse coding (Li et al ., 2020) etc. These methods have certain advantages and promote the development of MT signal–noise separation research to a certain extent, but there are still some shortcomings.…”