Iterative deblending of simultaneous-source data using smoothed singular spectrum analysis

Wu, Juan; Zhang, Hua

doi:10.1016/j.jappgeo.2018.10.015

Cited by 11 publications

(2 citation statements)

References 77 publications

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“…Because the synthetic data shown in Figure 5(a) are clean, the accurate slope for this example can be obtained by PWD and displayed in Figure 5(c). Given the clean synthetic data, the signal‐to‐noise ratio (SNR) is selected to quantitatively evaluate the performance (Zhang et al ., 2018; Bai et al ., 2019):

\begin{equation} {\rm {SNR}} = \frac{\Vert \mathbf {s}\Vert ^2_2}{\Vert \mathbf {s}-\hat{\mathbf {s}}\Vert ^2_2}, \end{equation}

in which

\Vert \mathbf {s}\Vert _2^2

denotes the energy of the useful signal and

\Vert \mathbf {s}-\hat{\mathbf {s}}\Vert _2^2

denotes the energy of noise or error. The higher the SNR is, the closer

\mathbf {s}

and

\hat{\mathbf {s}}

are.…”

Section: Examplesmentioning

confidence: 99%

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Robust local slope estimation by deep learning

Cao

Fomel

et al. 2022

Geophysical Prospecting

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In the seismic community, the local slope is essential for various applications, including structure prediction, seislet transform, trace interpolation and denoising. The most popular method to calculate slope is the plane-wave destruction, which assumes that the seismic data can be represented by local plane waves. However, the plane-wave destruction method fails when the seismic data become very noisy. Taking random noise into consideration, we adopt the deep learning method to calculate the local slope. In the deep learning architecture, the input is noisy data and the target is an accurate slope estimated from the clean data using plane-wave destruction. The deep learning architecture includes a convolutional layer, deconvolutional layer, normalization layer and activation layer which are suitable to suppress noise and learn the nonlinear relationship between the input and the target. After training, the network is applied to other test examples to calculate local slopes. In addition, we implement the seislet transform and structure prediction applications based on the estimated slope. Both the estimated slope and the related applications indicate that the proposed method can robustly obtain the local slope from noisy seismic data, compared with the planewave destruction method.

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\begin{equation} {\rm {SNR}} = \frac{\Vert \mathbf {s}\Vert ^2_2}{\Vert \mathbf {s}-\hat{\mathbf {s}}\Vert ^2_2}, \end{equation}

in which

\Vert \mathbf {s}\Vert _2^2

denotes the energy of the useful signal and

\Vert \mathbf {s}-\hat{\mathbf {s}}\Vert _2^2

denotes the energy of noise or error. The higher the SNR is, the closer

\mathbf {s}

and

\hat{\mathbf {s}}

are.…”

Section: Examplesmentioning

confidence: 99%

“…Because the synthetic data shown in are clean, the accurate slope for this example can be obtained by PWD and displayed in Figure5(c). Given the clean synthetic data, the signal-to-noise ratio (SNR) is selected to quantitatively evaluate the performance(Zhang et al, 2018;Bai et al, 2019):…”

mentioning

confidence: 99%