Random Noise Suppression of Magnetic Resonance Sounding Oscillating Signal by Combining Empirical Mode Decomposition and Time-Frequency Peak Filtering

Abstract

Magnetic resonance sounding (MRS) signals are always corrupted by random noise. Although time-frequency peak filtering (TFPF) has been proven to be an effective method to suppress the random noise, it shows shortcomings when processing the oscillating high-frequency MRS signal at about 2 kHz. In this study, a new method combining empirical mode decomposition (EMD) and TFPF is proposed to overcome the TFPF limitation when processing the MRS oscillating signal. With the help of EMD decomposition characteristics, the random-noise-corrupted MRS oscillating signal is first decomposed into several different components which contain frequencies ranging from the highest to the lowest ones. Then, the components which do not have signal frequency are discarded to bring down the level of random noise. The residual components are further processed by TFPF, respectively, based on the theory of instantaneous frequency estimation and the property of noise accumulation. Finally, the de-noised result is obtained by reconstructing the processed components. The numerical simulations on synthetic signals embedded in both artificial noise and real noise show the combined method can improve the signal-to-noise ratios and reduce the uncertainties of signal parameters. In addition, the combined method is applied following a standard processing scheme in field data, and better results are also obtained. INDEX TERMS Hydrogeophysics, magnetic resonance sounding, random noise, time-frequency filtering.

Novel Multi-Scale Filter Profile-Based Framework for VHR Remote Sensing Image Classification

“…(1) MMF [23] is first extended with multi-scale parameters to construct MFPs. Experimental comparison has demonstrated that this extension is helpful and effective for improving classification performance [20]. In the present study, the proposed MFPs not only improve the homogeneity of a target but also the separability of the ground target under consideration.…”

“…(1) The proposed MFPF provided competitive accuracies in land cover classification of VHR remote sensing images. As an extension of MMF [20], the proposed MFPF achieved the best accuracy compared with that of the MMF, MF, MedF, and the raw image without any filter processing Furthermore, the results of the second experiment indicated that the proposed MFPF is more robust for the different classifiers compared with that of the MF, MedF, and MMF. In addition, the classification results achieved by the proposed MFPF clearly demonstrated its effectiveness and superiority in terms of visual performance and quantitative accuracies compared with those based on the classical spatial-spectral feature extraction approaches, including EPFs [26], RFs [27], M_EMPs [25], and RGF [28].…”

“…Filtering is an intuitive yet simple way of smoothing the noise of a VHR image. The classical methods, such as mean filtering (MF) [17] and median filtering (MedF) [18], have been applied to reduce the noise in images and signals [19,20]. Several spatial filters have been developed for VHR remote sensing image classification [21,22].…”

A noise reduction method of rolling bearing based on empirical wavelet transform and adaptive time frequency peak filtering

“…Time-frequency peak filtering (TFPF) proposed by Bouake and Mostefa [4], is an effective method for suppressing Gaussian noise in non-stationary deterministic limited-band signals, which has the advantages of wide applicability and requiring less additional information. Therefore, this method has been successfully applied in the fields of seismic monitoring [5], Electro Encephalo Graphy (EEG) signal enhancement [6], frequency hopping signal detection [7] and mechanical fault diagnosis [8], showing a good suppression effect on strong random noise. It should be noted that the window length of TFPF plays an important role in balancing noise suppression ability and signal fidelity.…”

Electromagnetic Noise Suppression of Magnetic Resonance Sounding Combined with Data Acquisition and Multi-Frame Spectral Subtraction in the Frequency Domain

“…The model-based method [16], the wavelet-based method [17] and the non-linear energy operator [14] have been proposed in dealing with the spike noise. For random noise attenuation, the stacking method [13], the time-frequency peak filtering method [7,18] and intensive sampling sparse reconstruction and kernel regression estimation [19] are investigated. Although all these methods have obtained good results in MRS noise suppression, they still have some disadvantages.…”