Rank-constrained seismic data interpolation and denoising
Brazilian Journal of Geophysics, 2022 Rank-constrained seismic data interpolation methods have been used to cope with spatial sampling deficiencies, but some fundamental aspects are often neglected. Understanding their underlying features is the first step for developing new solutions to overcome existing limitations.
Quézia Cavalcante, Milton Porsani
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Denoising of Seismic Data Based on Block Dictionary Learning Theory
CT Lilun yu yingyong yanjiu, 2022 With the increasingly complex observation environment of oil and gas exploration, the seismic data collected are often mixed with various noise signals, resulting in the effective weak signal caused by the exploration target is covered, which seriously ...
Junjie ZHOU +3 more
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Multi-scale interactive network in the application of DAS seismic data processing
Frontiers in Earth Science, 2023 Distributed acoustic sensing (DAS) is regarded as a novel acquisition technology for seismic data. Compared with conventional electrical geophones, DAS has a series of obvious advantages including low-cost, high spatial resolution, good coverage, and ...
Hongzhou Wang +5 more
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A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal [PDF]
, 2014 Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in ...
Chaux, Caroline +3 more
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Inpainting of Cyclic Data using First and Second Order Differences [PDF]
, 2014 Cyclic data arise in various image and signal processing applications such as interferometric synthetic aperture radar, electroencephalogram data analysis, and color image restoration in HSV or LCh spaces.
Bergmann, Ronny, Weinmann, Andreas
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Hierarchical Bayesian sparse image reconstruction with application to MRFM [PDF]
, 2008 This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally
Dobigeon, Nicolas +2 more
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Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
Applied Sciences, 2022 As rock fractures caused by micro-seismic events has potential safety hazards to underground workers, it is often necessary to accurately locate the micro-seismic source for hidden danger investigation.
Jianting Chen +5 more
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M-estimate robust PCA for seismic noise attenuation [PDF]
, 2016 The robust principal component analysis (PCA) method has shown very promising results in seismic ambient noise attenuation when dealing with outliers in the data.
Akhondi-Asl, H, Nelson, JDB
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Tomographic inversion using $\ell_1$-norm regularization of wavelet coefficients [PDF]
, 2006 We propose the use of $\ell_1$ regularization in a wavelet basis for the solution of linearized seismic tomography problems $Am=d$, allowing for the possibility of sharp discontinuities superimposed on a smoothly varying background.
Dahlen, F. A. +3 more
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Random Noise Attenuation Based on Residual Convolutional Neural Network in Seismic Datasets
IEEE Access, 2020 Seismic random noise attenuation is a key step in seismic data processing. The random seismic data recorded by the detector tends to have strong noise, and this noisy seismic ratio can be seen as a low signal-to-noise ratio (SNR).
Liuqing Yang +4 more
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