De-noising methods for NMR logging echo signals based on wavelet transform

Xie, Ranhong; Wu, Youbin; Liu, Kang; Mi, Liu; Xiao, Lizhi

doi:10.1088/1742-2132/11/3/035003

Cited by 24 publications

(9 citation statements)

References 22 publications

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“…To improve SNR per unit time several other techniques have been used i.e. FIR filter [8], wavelet transform [9], and adaptive filter [10]. FIR filter is generally based on prior knowledge of signal.…”

Section: Post Processing Of Nqr Signalsmentioning

confidence: 99%

The Building of Pulsed NQR/NMR Spectrometer

Hemnani¹,

Rajarajan²,

Joshi³

et al. 2018

IJECE

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<p>NQR spectrometer designed is composed of four modules; Transmitter, Probe, Receiver and computer controlled (FPGA & Software) module containing frequency synthesizer, synchronous demodulator, pulse programmer and display. The function of the Transmitter module is to amplify the RF pulse sequence to about 200 W power level into the probe (50 Ohm) which is a parallel resonance circuit with a tapped capacitor. The probe excites the nucleus and picks-up the signal emitted from the nuclei. The nuclear signal at the same frequency as the excitation, which is typically in the range of a few microvolts is amplified, demodulated and filtered (1 kHz to 100 kHz) by receiver module. 14N NQR, 1H and 2H NMR signals are observed from the spectrometer.As the SNR of NQR signal is very low, NQR signal processing based on Adaptive Line Enhancement is presented.</p>

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Section: Post Processing Of Nqr Signalsmentioning

confidence: 99%

The Building of Pulsed NQR/NMR Spectrometer

Hemnani¹,

Rajarajan²,

Joshi³

et al. 2018

IJECE

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“…; Xie et al . ). In order to improve the effectiveness of NMR logs in tight gas sands, it is necessary to improve the SNR of NMR data.…”

Section: Introductionmentioning

confidence: 97%

“…However, the amplitude of the echo trains detected by NMR logs in tight gas sands is usually low, which reduces the * E-mail: xieranhong@cup.edu.cn signal-to-noise ratio (SNR) of the NMR data. Consequently, the results of transverse relaxation time T 2 spectrum inversion poorly describe the real situation, which reduces the effectiveness of NMR logs in these gas formations (Winkler et al 2006;Wen et al 2011;Xie et al 2014). In order to improve the effectiveness of NMR logs in tight gas sands, it is necessary to improve the SNR of NMR data.…”

Section: Introductionmentioning

confidence: 99%

Using wavelet‐domain adaptive filtering to improve signal‐to‐noise ratio of nuclear magnetic resonance log data from tight gas sands

Xie

Wu²,

Liu

et al. 2015

Geophysical Prospecting

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A B S T R A C TIn tight gas sands, the signal-to-noise ratio of nuclear magnetic resonance log data is usually low, which limits the application of nuclear magnetic resonance logs in this type of reservoir. This project uses the method of wavelet-domain adaptive filtering to denoise the nuclear magnetic resonance log data from tight gas sands. The principles of the maximum correlation coefficient and the minimum root mean square error are used to decide on the optimal basis function for wavelet transformation. The feasibility and the effectiveness of this method are verified by analysing the numerical simulation results and core experimental data. Compared with the wavelet thresholding denoise method, this adaptive filtering method is more effective in noise filtering, which can improve the signal-to-noise ratio of nuclear magnetic resonance data and the inversion precision of transverse relaxation time T 2 spectrum. The application of this method to nuclear magnetic resonance logs shows that this method not only can improve the accuracy of nuclear magnetic resonance porosity but also can enhance the recognition ability of tight gas sands in nuclear magnetic resonance logs.

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“…The existing NMR signal de-noising methods generally assume that the noise is white noise subject to Gaussian distribution, but in fact it is much more complicated. For the de-noising of NMR echo string, many scholars adopt wavelet threshold de-noising method, and put forward many improved methods based on it [14,15]. However, the wavelet threshold de-noising method is based on the underlying white noise model, which cannot eliminate the colored noise.…”

Section: Introductionmentioning

confidence: 99%

Research on NMR Noise Reduction Method Based on Improved CEEMD

Zhu

Liu

2020

IEEE Access

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Low-field NMR technology has been widely used in many fields, among which the most representative method is the use of CPMG sequence for T2 measurement. Important parameters such as permeability, saturation and fluid type of porous media can be obtained by T2 spectrum inversion calculation of NMR echo sequence signal. However, T2 spectrum inversion calculation is easily affected by noise signals. Improving the signal-to-noise ratio (SNR) of echo signal is one of the key problems with the application of low-field NMR technology. Therefore, in order to obtain more accurate analysis results, it is necessary to develop corresponding noise reduction methods. The past researches on NMR signal noise reduction mainly focus on echo string. In this paper, a complete NMR signal model is constructed, and the problem of the echo and echo string de-noising is discussed. FIR filter and orthogonal modulation filter (OMF) are studied for noise reduction of echo. To solve the problem of echo string de-noising, an improved complementary ensemble empirical mode decomposition (CEEMD) threshold de-noising method is proposed and compared with the heuristic threshold de-noising method based on sym4 wavelet and the previous empirical mode decomposition (EMD) threshold de-noising method. Based on the construction model and practical verification, it is proved that the proposed method can invert the more accurate T2 spectrum while improving the signal-to-noise ratio. The research results of this paper provide a strong support for the application of low-field NMR technology in the environment of strong noise.

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De-noising methods for NMR logging echo signals based on wavelet transform

Cited by 24 publications

References 22 publications

The Building of Pulsed NQR/NMR Spectrometer

The Building of Pulsed NQR/NMR Spectrometer

Using wavelet‐domain adaptive filtering to improve signal‐to‐noise ratio of nuclear magnetic resonance log data from tight gas sands

Research on NMR Noise Reduction Method Based on Improved CEEMD

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