2-D TFPF based on Contourlet transform for seismic random noise attenuation

Zhao, Xian Ping; Li, Yue; Zhuang, Guanghai; Zhang, Chao; Han, Xue

doi:10.1016/j.jappgeo.2016.03.030

Cited by 26 publications

(6 citation statements)

References 7 publications

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“…Moreover, the same direction of adjacent scales shows a strong correlation; we can determine the signal directions according to the correlation energy between adjacent fine scales (Zhao et al . ), which is described as:

E Cor {normalr}_{j, j + 1} (k) = E (j, k) E (j + 1, k) .

…”

Section: Time Picking Methods Based On Cascading Use Of Shearlet and Smentioning

confidence: 99%

“…When the scale becomes finer, more significant coefficients are generated in signal directions at fine scales. Moreover, the same direction of adjacent scales shows a strong correlation; we can determine the signal directions according to the correlation energy between adjacent fine scales (Zhao et al 2016), which is described as:…”

Section: T I M E P I C K I N G M E T H O D B a S E D O N C A S C A D mentioning

confidence: 99%

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First‐break picking for microseismic data based on cascading use of Shearlet and Stockwell transforms

Cheng

Zhang

2018

Geophysical Prospecting

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First‐break picking of microseismic data is a significant step in microseismic monitoring. There is a great error in conventional first‐break picking methods based on time domain analysis in low signal to noise ratio. S‐transform may provide a novel approach, it can extract the time–frequency features of the signal and reduce the picking error because of its high time–frequency resolution and good time–frequency clustering; however, the S‐transform is not well suited for microseismic data with high noise. For applications to array data where the weak signal has spatial coherency as well as some distinct temporal characteristics, we propose to combine the shearlet transform with a time–frequency transform. In the proposed method, the shearlet transform is used to capture spatial coherency features of the signal. The information of the signal and noise in shearlet domain is represented by shearlet coefficients. We use the correlation of signal coefficients at adjacent fine scales to give prominence to signal features to accurately discriminate the signal from noise. The prominent signal coefficients make the signal better gathered in time–frequency spectrum of the S‐transform. Finally, we can get reliable and accurate first breaks based on the change of energy. The performance of the proposed method was tested on synthetic and field microseismic data. The experimental results indicated that our method is outstanding in terms of both picking precision and adaptability to noise.

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E Cor {normalr}_{j, j + 1} (k) = E (j, k) E (j + 1, k) .

…”

Section: Time Picking Methods Based On Cascading Use Of Shearlet and Smentioning

confidence: 99%

Section: T I M E P I C K I N G M E T H O D B a S E D O N C A S C A D mentioning

confidence: 99%

First‐break picking for microseismic data based on cascading use of Shearlet and Stockwell transforms

Cheng

Zhang

2018

Geophysical Prospecting

Self Cite

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“…The shearlet transform can decompose the data into different scales and directions which yields the shearlet coefficients S f ( j , k, m). The signals energy is concentrated in only a few directions due to their spatial correlation at those dips only [Zhao et al, 2016, Zhang andVan der Baan, 2019]. We define the energy of the shearlet coefficient as:…”

Section: Shearlet Energy Entropymentioning

confidence: 99%

Use of the shearlet energy entropy and of the support vector machine classifier to process weak microseismic and desert seismic signals

Li¹,

Shi-yu²,

Zhang³

et al. 2020

Comptes Rendus. Géoscience

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“…Sparse transform‐based methods can identify signal and noise in sparse transform domain where the signal is sparse but the noise is not. Therefore, it is only necessary to set a soft threshold operator for the coefficients in the transformed sparse domain, and finally transform the sparse coefficients back into the real domain to reconstruct the clear signal, for example, Fourier transform (Naghizadeh, 2012), Radon transform (Durrani and Bisset, 1984; Trad et al ., 2003), wavelet transform (Beenamol et al ., 2012; Mousavi et al ., 2016; Mousavi and Langston, 2016), seislet transform (Fomel and Liu, 2010; Chen and Fomel, 2017), dreamlet transform (Wu et al ., 2013), curvelet transform (Candes et al ., 2006; Herrmann and Hennenfent, 2008; Neelamani et al ., 2008) and contourlets transform (Zhao et al ., 2016). These sparse transforms have the characteristics of close framework and fast numerical implementation, but they lack adaptability to capture the various sparse structures existing in seismic data.…”

Section: Introductionmentioning

confidence: 99%

Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach

Gao

Zhao²,

et al. 2021

Geophysical Prospecting

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Random noise attenuation is an essential step in seismic data processing for improving seismic data quality and signal‐to‐noise ratio. We adopt an unsupervised machine learning approach to attenuate random noise via signal reconstruction strategy. This approach can be accomplished in the following steps: Firstly, we randomly mute a part of the input data of the neural network according to a certain percentage, and then the network outputs the reconstructed data influenced by this randomly mute. The objective function measures the distance between the input data and the reconstructed data. Secondly, we use the adaptive moment estimation algorithm to minimize the distance, and the network adjusts its internal parameters so that sparse representations can be captured by the multiple processing layers of the neural network. Finally, we take the same proportion of random mute on the raw seismic data which are fed to the trained neural network. Through this network, reconstruction of seismic data and attenuation of random noise are completed simultaneously. We use both synthetic and field data to testify the feasibility and applicability of the proposed method. Synthetic data experiment indicates that the proposed method achieves better denoised results than the conventional methods. Field data applications further demonstrate its superiority and practicality.

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2-D TFPF based on Contourlet transform for seismic random noise attenuation

Cited by 26 publications

References 7 publications

First‐break picking for microseismic data based on cascading use of Shearlet and Stockwell transforms

First‐break picking for microseismic data based on cascading use of Shearlet and Stockwell transforms

Use of the shearlet energy entropy and of the support vector machine classifier to process weak microseismic and desert seismic signals

Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach

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