Simultaneous Seismic Data Interpolation and Denoising Based on Nonsubsampled Contourlet Transform Integrating With Two-Step Iterative Log Thresholding Algorithm

Li, Chao; Xu, Wen; Liu, Xingye; Zu, Shaohuan

doi:10.1109/tgrs.2022.3192531

Cited by 9 publications

(2 citation statements)

References 57 publications

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“…This pioneering approach showcased remarkable results, particularly validated through robust field data experiments. Additionally, Li et al [23] optimized the nonsubsampled contourlet transform by refining iterative functions, significantly expediting convergence rates. The efficacy of this refined method was substantiated through comprehensive experiments demonstrating seismic signal interpolation and denoising.…”

Section: Introductionmentioning

confidence: 99%

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

Ding,

Zhou,

Chi

2024

Remote Sensing

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In light of the challenging conditions of exploration environments coupled with escalating exploration expenses, seismic data acquisition frequently entails the capturing of signals entangled amidst diverse noise interferences and instances of data loss. The unprocessed state of these seismic signals significantly jeopardizes the interpretative phase. Evidently, the integration of attention mechanisms and the utilization of generative adversarial networks (GANs) have emerged as prominent techniques within signal processing owing to their adeptness in discerning intricate global dependencies. Our research introduces a pioneering approach for reconstructing and denoising seismic signals, amalgamating the principles of self-attention and generative adversarial networks—hereafter referred to as SAGAN. Notably, the incorporation of the self-attention mechanism into the GAN framework facilitates an enhanced capacity for both the generator and discriminator to emulate meaningful spatial interactions. Subsequently, leveraging the feature map generated by the self-attention mechanism within the GAN structure enables the interpolation and denoising of seismic signals. Rigorous experimentation substantiates the efficacy of SAGAN in simultaneous signal interpolation and denoising. Initially, we benchmarked SAGAN against prominent methods such as UNet, CNN, and Wavelet for the concurrent interpolation and denoising of two-dimensional seismic signals manifesting varying levels of damage. Subsequently, this methodology was extended to encompass three-dimensional seismic data. Notably, performance metrics reveal SAGAN’s superiority over comparative methods. Specifically, the quantitative tables exhibit SAGAN’s pronounced advantage, with a 3.46% increase in PSNR value over UNet and an impressive 11.90% surge compared to Wavelet. Moreover, the RMSE values affirm SAGAN’s robust performance, showcasing an 11.54% reduction in comparison to UNet and an impressive 29.27% decrement relative to Wavelet, hence unequivocally establishing the SAGAN method as a preeminent choice for seismic signal recovery.

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Section: Introductionmentioning

confidence: 99%

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

Ding,

Zhou,

Chi

2024

Remote Sensing

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“…Mautz et al [ 29 ] proposed the so-called B-spline wavelets to represent the spatial time series of the GPS-derived global ionosphere maps of the vertical total electron content of the Earth’s surface to the mean altitudes of GPS satellites over Japan. Herrmann and Hennenfent [ 30 ] proposed a recovery method based on non-parametric transformation, utilizing recently developed curvelet frames to compress seismic data [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

FFT-Based Probability Density Imaging of Euler Solutions

Cao,

Chen,

et al. 2024

Entropy

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When using traditional Euler deconvolution optimization strategies, it is difficult to distinguish between anomalies and their corresponding Euler tails (those solutions are often distributed outside the anomaly source, forming “tail”-shaped spurious solutions, i.e., misplaced Euler solutions, which must be removed or marked) with only the structural index. The nonparametric estimation method based on the normalized B-spline probability density (BSS) is used to separate the Euler solution clusters and mark different anomaly sources according to the similarity and density characteristics of the Euler solutions. For display purposes, the BSS needs to map the samples onto the estimation grid at the points where density will be estimated in order to obtain the probability density distribution. However, if the size of the samples or the estimation grid is too large, this process can lead to high levels of memory consumption and excessive computation times. To address this issue, a fast linear binning approximation algorithm is introduced in the BSS to speed up the computation process and save time. Subsequently, the sample data are quickly projected onto the estimation grid to facilitate the discrete convolution between the grid and the density function using a fast Fourier transform. A method involving multivariate B-spline probability density estimation based on the FFT (BSSFFT), in conjunction with fast linear binning appropriation, is proposed in this paper. The results of two random normal distributions show the correctness of the BSS and BSSFFT algorithms, which is verified via a comparison with the true probability density function (pdf) and Gaussian kernel smoothing estimation algorithms. Then, the Euler solutions of the two synthetic models are analyzed using the BSS and BSSFFT algorithms. The results are consistent with their theoretical values, which verify their correctness regarding Euler solutions. Finally, the BSSFFT is applied to Bishop 5X data, and the numerical results show that the comprehensive analysis of the 3D probability density distributions using the BSSFFT algorithm, derived from the Euler solution subset of x0,y0,z0, can effectively separate and locate adjacent anomaly sources, demonstrating strong adaptability.

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Orthogonal dictionary learning based on l ₄ -Norm maximisation for seismic data interpolation

Yue

Niu

et al. 2023

Exploration Geophysics

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Simultaneous Seismic Data Interpolation and Denoising Based on Nonsubsampled Contourlet Transform Integrating With Two-Step Iterative Log Thresholding Algorithm

Cited by 9 publications

References 57 publications

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

FFT-Based Probability Density Imaging of Euler Solutions

Orthogonal dictionary learning based on l ₄ -Norm maximisation for seismic data interpolation

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Simultaneous Seismic Data Interpolation and Denoising Based on Nonsubsampled Contourlet Transform Integrating With Two-Step Iterative Log Thresholding Algorithm

Cited by 9 publications

References 57 publications

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

Self-Attention Generative Adversarial Network Interpolating and Denoising Seismic Signals Simultaneously

FFT-Based Probability Density Imaging of Euler Solutions

Orthogonal dictionary learning based on l 4 -Norm maximisation for seismic data interpolation

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Product

Resources

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Orthogonal dictionary learning based on l ₄ -Norm maximisation for seismic data interpolation