Random Noise Reduction in Seismic Data by Using Bidimensional Empirical Mode Decomposition and Shearlet Transform

Hou, Wenlong; Jia, Rui-Sheng; Sun, Hong-Mei; Zhang, Xingli; Deng, Meng-Di; Tian, Yu

doi:10.1109/access.2019.2920021

Cited by 17 publications

(6 citation statements)

References 28 publications

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“…Moreover, the original signal has at least two extrema. Bidimensional empirical mode decomposition (BEMD), which has been applied to target recognition (Chang et al., 2019) and random noise reduction in seismic data (Hou et al., 2019), is based on EMD, which can be employed in multiscale data decomposition. Since IMFs are waveforms with different characteristic scales after the decomposition of the original signal, BEMD is suitable not only for linear data decomposition but also for nonlinear discrete data decomposition.…”

Section: Proposed Methodsmentioning

confidence: 99%

Improved Tucker decomposition algorithm for noise suppression of 3D GPR data in road detection

Yan

Zhang

2023

Near Surface Geophysics

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The echo data of ground‐penetrating radar (GPR) have a low signal‐to‐noise ratio. Denoising and interference suppression are important for improving the accuracy of underground target recognition and detection. In this paper, a new method of noise analysis and suppression of 3D GPR is proposed, transforming the problem of noise reduction into an optimization problem regarding a third‐order tensor. This method is improved with the following features; a bidimensional empirical mode decomposition (BEMD) algorithm is employed to decompose the 3D GPR noise. The main source of the noise is determined by computing the standard deviation of each component decomposed. For the shortcoming of the existing GPR denoising methods that reduce the dimension of 3D GPR data, the proposed approach can denoise 3D GPR data directly to avoid the loss of data caused by reducing the dimension. This preserves the desired signal and extracts noise through the Tucker decomposition of 3D GPR data. An improved high‐order orthogonal iteration algorithm is utilized to optimize the decomposition. The peak signal‐to‐noise ratio (PSNR) of the signal for each survey line is calculated to evaluate the effectiveness of noise reduction. Simulations and real data sets are provided to compare the performance of algorithms. The results show that the mean PSNR after noise suppression with our method is 8.915 dB and 16.458 dB higher than wavelet transform and singular value decomposition algorithms, respectively. This demonstrates that the proposed approach can better suppress 3D GPR noise and provides technical support for improving 3D GPR data quality and road detection accuracy.

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Section: Proposed Methodsmentioning

confidence: 99%

Improved Tucker decomposition algorithm for noise suppression of 3D GPR data in road detection

Yan

Zhang

2023

Near Surface Geophysics

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“…The Hilbert-Huang transform (HHT) has been widely studied in seismic exploration [23]. The HHT is a timefrequency analysis method suitable for non-stationary signals, such as seismic signals [24]. The empirical mode decomposition (EMD) algorithm, the core concept of the HHT, decomposes noisy seismic data into a series of intrinsic mode functions (IMF), which can describe the signal in different scales.…”

Section: A Ceemdmentioning

confidence: 99%

A Noise Attenuation Method for Weak Seismic Signals Based on Compressed Sensing and CEEMD

Sun

Li³

et al. 2020

IEEE Access

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The exploration of deep and subtle oil and gas reservoirs is currently an important means of increasing production in older oil fields. How to effectively identify weak signals with noise is a common problem faced in such reservoirs. Especially for deep seismic reflection data, the application of traditional denoising methods is limited due to the weak energy and small difference frequency band between the effective signals and the noise. A novel method of noise attenuation for weak seismic signals based on compressed sensing and CEEMD (complementary ensemble empirical mode decomposition) was proposed in this work. This method consists of three steps: First, the CEEMD algorithm, a time-frequency analysis method, was introduced into the classic CS (compressed sensing) denoising method to overcome the nonadaptability of CS. CEEMD decomposed the raw seismic signals into sets of IMFs (finite intrinsic mode functions). The IMFs with noise were reconstructed and denoised by CS. In the second step, an enhancement operator was introduced into the penalty term to ensure that the effective signals could be extracted. Finally, the OMP algorithm is adopted to reconstruct the seismic weak signal to prevent the iterative threshold method from damaging weak effective signals. It was demonstrated through the synthetic seismic record and the field seismic data that (a) the CS method can identify the weak signals submerged in the noise by selecting the basic function that is most similar to the effective signals, but it cannot adaptively suppress the high frequency and high wavenumber noise of the complex seismic records; (b) the proposed method overcome the nonadaptability of CS and enhance the edge information described by the curved wave rather than the noises, as a result, the high frequency noise is suppressed while the middle/low frequency noise and weak effective signals are also effectively separated. INDEX TERMS Deep seismic reflection data, CEEMD, CS, weak signal denoising, OMP. I. INTRODUCTION Deep petroliferous basins have become important areas in current and future exploration [1], [2]. In particular, deep water exploration has seen major breakthroughs in development over several decades [3], [4]. However, deep exploration targets are typically located a region with complex geological structures, and the quality of their seismic data is poor. The associate editor coordinating the review of this manuscript and approving it for publication was Jun Shi. Especially in the slope break zones of deep water and the middle-deep layers, the effective signals are basically submerged in noise [5]. To obtain high-quality imaging of middeep layers, the extraction technology of weak signals has attracted great interest in recent years. Various methods, e.g., the random resonance theory [6], the high-order statistics method [7], the wavelet transform method [8], [9], the SVD (singular value decomposition) method [10], [11], or the EMD (empirical mode decomposition) method [12], can be used to extract weak seismic

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“…In equations ( 18), (19), and (20), y i is the original signal, x i is the signal after noise suppression, and N is the number of samples. The SNR, RMSE, and E sn before and after the signal f noise suppression are calculated using equations (18), (19), and (20), respectively.…”

Section: Simulation Experiments and Analysismentioning

confidence: 99%

Noise Suppression Method of Microseismic Signal Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Packet Threshold

et al. 2019

Self Cite

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Aiming at the situation that complementary ensemble empirical mode decomposition (CEEMD) noise suppression method may produce redundant noise and wavelet transform easily loses high-frequency detail information, considering wavelet packet transform can be used to perform better time-frequency localization analysis on signals containing a large amount of medium and high frequency information, according to the noise and useful signal components of both the characteristic of self-correlation function is different, the CEEMD and wavelet packet threshold jointed method is proposed. The method uses the energy concentration ratio to find noise and useful signal component demarcation point to denoise the microseismic signals. Firstly, we utilize adaptively decompose the signal from high frequency to low frequency by the CEEMD; Secondly, using the self-correlation method to select the intrinsic mode function (IMF) that needs noise suppression, the wavelet suppression method is used to suppress the noise of several high-frequency components whose self-correlation coefficient is below the critical value K; Finally, the IMF component after the wavelet packet threshold noise suppression is reconstructed with the noisefree IMF component. In order to verify the effectiveness of the proposed method on the noise suppression of microseismic signal, we added a Gaussian white noise to the Ricker wavelet signal similar to the microseismic signal. The experimental results show that the signal-to-noise ratio (SNR) of the signal is raised more than 10dB. The energy percentage is higher than 92%. In practical engineering, our proposal achieves an effective noise suppression effect on the microseismic signal.INDEX TERMS Complementary ensemble empirical mode decomposition, wavelet packet threshold, selfcorrelation, noise suppression.

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Random Noise Reduction in Seismic Data by Using Bidimensional Empirical Mode Decomposition and Shearlet Transform

Cited by 17 publications

References 28 publications

Improved Tucker decomposition algorithm for noise suppression of 3D GPR data in road detection

Improved Tucker decomposition algorithm for noise suppression of 3D GPR data in road detection

A Noise Attenuation Method for Weak Seismic Signals Based on Compressed Sensing and CEEMD

Noise Suppression Method of Microseismic Signal Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Packet Threshold

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