TY - JOUR

T1 - Random noise suppression of magnetic resonance sounding oscillating signal by combining empirical mode decomposition and time-frequency peak filtering

AU - Lin, Tingting

AU - Zhang, Yang

AU - Muller-Petke, Mike

N1 - Funding information: This work was supported in part by the NSFC under Grant 41722405, in part by the key project of the National Instrumentation Project under Grant 2011YQ03113, in part by the National Research and Development Project under Grant 2017YFC0804105, and in part by the Jilin Research Project under Grant 20180201017GX.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Hydrogeophysics

KW - magnetic resonance sounding

KW - random noise

KW - time-frequency filtering

UR - http://www.scopus.com/inward/record.url?scp=85069045982&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2019.2923689

DO - 10.1109/ACCESS.2019.2923689

M3 - Article

AN - SCOPUS:85069045982

VL - 7

SP - 79917

EP - 79926

JO - IEEE ACCESS

JF - IEEE ACCESS

SN - 2169-3536

M1 - 8741009

ER -