The Experts below are selected from a list of 184005 Experts worldwide ranked by ideXlab platform
Yangkang Chen - One of the best experts on this subject based on the ideXlab platform.
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simultaneous denoising and interpolation of 2d Seismic Data using Data driven non negative dictionary learning
Signal Processing, 2017Co-Authors: Mohammad Amir Nazari Siahsar, Saman Gholtashi, Vahid Abolghasemi, Yangkang ChenAbstract:VNMF combines dictionary learning and sparse coding to find atoms of basis matrix.Using non-negativity constraint to induce sparsity and reduce the solution space.Utilizing all patches of the Data to learn a dictionary for precise performance.The algorithm uses lower number of atoms in learning.Simultaneous denoising and interpolation using one minimization problem. As a major concern, the existence of unwanted energy and missing traces in Seismic Data acquisition can degrade interpretation of such Data after processing. Instead of analytical dictionaries, Data-driven dictionary learning (DDL) methods as a flexible framework for sparse representation, are dedicated to the problem of denoising and interpolation. Due to their meaningful geometric repetitive structures, Seismic Data are intrinsically low-rank in the time-space domain. On the other hand, noise and missing traces increase the rank of the noisy Data. Therefore, the clean Data, unlike noise and missing traces, can be modeled as a linear combination of a few elements from a learned dictionary. In this paper, a parts-based 2D DDL scheme is introduced and evaluated for simultaneous denoising and interpolation of Seismic Data. A special case of versatile non-negative matrix factorization (VNMF) is used to learn a dictionary. In VNMF, smoothness constraint can improve interpolation, and sparse coding helps improving denoising. The proposed method is tested on synthetic and real-field Seismic Data for simultaneous denoising and interpolation. Through experimental results, the proposed method is determined to be an effective and robust tool that preserves significant components of the signal. Comparison with four state-of-the-art methods further verifies its superior performance.
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Data driven multitask sparse dictionary learning for noise attenuation of 3d Seismic Data
Geophysics, 2017Co-Authors: Mohammad Amir Nazari Siahsar, Wei Chen, Saman Gholtashi, Amin Roshandel Kahoo, Yangkang ChenAbstract:ABSTRACTRepresentation of a signal in a sparse way is a useful and popular methodology in signal-processing applications. Among several widely used sparse transforms, dictionary learning (DL) algorithms achieve most attention due to their ability in making Data-driven nonanalytical (nonfixed) atoms. Various DL methods are well-established in Seismic Data processing due to the inherent low-rank property of this kind of Data. We have introduced a novel Data-driven 3D DL algorithm that is extended from the 2D nonnegative DL scheme via the multitasking strategy for random noise attenuation of Seismic Data. In addition to providing parts-based learning, we exploit nonnegativity constraint to induce sparsity on the Data transformation and reduce the space of the solution and, consequently, the computational cost. In 3D Data, we consider each slice as a task. Whereas 3D Seismic Data exhibit high correlation between slices, a multitask learning approach is used to enhance the performance of the method by sharing ...
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an open source matlab code package for improved rank reduction 3d Seismic Data denoising and reconstruction
Computers & Geosciences, 2016Co-Authors: Yangkang Chen, Weilin Huang, Dong Zhang, Wei ChenAbstract:Simultaneous Seismic Data denoising and reconstruction is a currently popular research subject in modern reflection seismology. Traditional rank-reduction based 3D Seismic Data denoising and reconstruction algorithm will cause strong residual noise in the reconstructed Data and thus affect the following processing and interpretation tasks. In this paper, we propose an improved rank-reduction method by modifying the truncated singular value decomposition (TSVD) formula used in the traditional method. The proposed approach can help us obtain nearly perfect reconstruction performance even in the case of low signal-to-noise ratio (SNR). The proposed algorithm is tested via one synthetic and field Data examples. Considering that Seismic Data interpolation and denoising source packages are seldom in the public domain, we also provide a program template for the rank-reduction based simultaneous denoising and reconstruction algorithm by providing an open-source Matlab package. HighlightsTraditional rank-reduction based algorithm will cause residual noise.An improved rank reduction method by slightly modifying the TSVD formula.Synthetic and field Data examples show very encouraging results.An open-source Matlab code package for Seismic processing is introduced.
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dealiased Seismic Data interpolation using seislet transform with low frequency constraint
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Shuwei Gan, Yangkang Chen, Shoudong Wang, Yizhuo Zhang, Zhaoyu JinAbstract:Interpolating regularly missing traces in Seismic Data is thought to be much harder than interpolating irregularly missing Seismic traces, because many sparsity-based approaches cannot be used due to the strong aliasing noise in the sparse domain. We propose to use the seislet transform to perform a sparsity-based approach to interpolate highly undersampled Seismic Data based on the classic projection onto convex sets (POCS) framework. Many numerical tests show that the local slope is the main factor that will affect the sparsity and antialiasing ability of seislet transform. By low-pass filtering the undersampled Seismic Data with a very low bound frequency, we can get a precise dip estimation, which will make the seislet transform capable for interpolating the aliased Seismic Data. In order to prepare the optimum local slope during iterations, we update the slope field every several iterations. We also use a percentile thresholding approach to better control the reconstruction performance. Both synthetic and field examples show better performance using the proposed approach than the traditional prediction based and the $F\mbox{--}K$ -based POCS approaches.
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structure oriented singular value decomposition for random noise attenuation of Seismic Data
Journal of Geophysics and Engineering, 2015Co-Authors: Shuwei Gan, Yangkang Chen, Wei ZhongAbstract:Singular value decomposition (SVD) can be used both globally and locally to remove random noise in order to improve the signal-to-noise ratio (SNR) of Seismic Data. However, it can only be applied to Seismic Data with simple structure such that there is only one dip component in each processing window. We introduce a novel denoising approach that utilizes a structure-oriented SVD, and this approach can enhance Seismic reflections with continuous slopes. We create a third dimension for a 2D Seismic profile by using the plane-wave prediction operator to predict each trace from its neighbour traces and apply SVD along this dimension. The added dimension is equivalent to flattening the Seismic reflections within a neighbouring window. The third dimension is then averaged to decrease the dimension. We use two synthetic examples with different complexities and one field Data example to demonstrate the performance of the proposed structure-oriented SVD. Compared with global and local SVDs, and f–x deconvolution, the structure-oriented SVD can obtain much clearer reflections and preserve more useful energy.
Zhiguo Wang - One of the best experts on this subject based on the ideXlab platform.
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adaptive variable time fractional anisotropic diffusion filtering for Seismic Data noise attenuation
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Qingbao Zhou, Jinghuai Gao, Zhiguo WangAbstract:Seismic records are often contaminated with various kinds of noise, which makes it very difficult to distinguish the expected geological features. In this paper, we introduce a novel adaptive variable time fractional-order anisotropic diffusion equation for Seismic Data noise removal and strongly oriented structure enhancement. Since the time fractional-order differential equation interpolates a parabolic equation and a hyperbolic equation, the solution benefits both of these approaches. The presented differential equation can be written as a Volterra integral equation, and its well-posedness can be guaranteed for all time. We employ a structure tensor to analyze the flow-like texture characteristic which is typical in Seismic Data. Then, the diffusion process is guided reasonably by a diffusion tensor based on the structure tensor analysis which allows real anisotropic behavior comparing to the classical scalar diffusion approach. In reference to the numerical implementation, we utilize the predictor–corrector algorithm to solve the Volterra integral equation which provides high-order numerical precision jointly with good stability property. Finally, numerical experiments involved with synthetic and prestacked real Seismic Data are presented. The obtained results demonstrate that the noise is effectively removed, and the coherent Seismic events that express some important geological structures are not only preserved but significantly enhanced.
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time frequency analysis of Seismic Data using synchrosqueezing transform
IEEE Geoscience and Remote Sensing Letters, 2014Co-Authors: Ping Wang, Jinghuai Gao, Zhiguo WangAbstract:Time-frequency analysis can provide useful information in Seismic Data processing and interpretation. An accurate time-frequency representation is important in highlighting subtle geologic structures and in detecting anomalies associated with hydrocarbon reservoirs. The popular methods, like short-time Fourier transform and wavelet analysis, have limitations in dealing with fast varying instantaneous frequencies, which is often the characteristic of Seismic Data. The synchrosqueezing transform (SST) is a promising tool to provide a detailed time-frequency representation. We apply the SST to Seismic Data and show its potential to Seismic signal processing applications.
Sergey Fomel - One of the best experts on this subject based on the ideXlab platform.
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signal and noise separation in prestack Seismic Data using velocity dependent seislet transform
Geophysics, 2015Co-Authors: Yang Liu, Sergey Fomel, Cai LiuAbstract:ABSTRACTThe seislet transform is a waveletlike transform that analyzes Seismic Data by following varying slopes of Seismic events across different scales and provides a multiscale orthogonal basis for Seismic Data. It generalizes the discrete wavelet transform (DWT) in the sense that the DWT in the lateral direction is simply the seislet transform with a zero slope. Our earlier work used plane-wave destruction (PWD) to estimate smoothly varying slopes. However, the PWD operator can be sensitive to strong noise interference, which makes the seislet transform based on PWD (PWD-seislet transform) occasionally fail in providing a sparse multiscale representation for Seismic field Data. We adopted a new velocity-dependent (VD) formulation of the seislet transform, in which the normal moveout equation served as a bridge between local slope patterns and conventional moveout parameters in the common-midpoint domain. The VD slope has better resistance to strong random noise, which indicated the potential of VD sei...
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Seismic Data decomposition into spectral components using regularized nonstationary autoregression
Geophysics, 2013Co-Authors: Sergey FomelAbstract:Summary Seismic Data can be decomposed into nonstationary spectral components with smoothly variable frequencies and smoothly variable amplitudes. To estimate local frequencies, I use a nonstationary version of Prony’s spectral analysis method defined with the help of regularized nonstationary autoregression (RNAR). To estimate local amplitudes of different components, I fit their sum to the Data using regularized nonstationary regression (RNR). Shaping regularization ensures stability of the estimation process and provides controls on smoothness. Potential applications of the proposed technique include noise attenuation, Seismic Data compression, and Seismic Data regularization.
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Seismic Data analysis using local time frequency decomposition
Geophysical Prospecting, 2013Co-Authors: Yang Liu, Sergey FomelAbstract:Many natural phenomena, including geologic events and geophysical Data, are fundamentally nonstationary - exhibiting statistical variation that changes in space and time. Time-frequency characterization is useful for analysing such Data, Seismic traces in particular. We present a novel time-frequency decomposition, which aims at depicting the nonstationary character of Seismic Data. The proposed decomposition uses a Fourier basis to match the target signal using regularized least-squares inversion. The decomposition is invertible, which makes it suitable for analysing nonstationary Data. The proposed method can provide more flexible time-frequency representation than the classical S transform. Results of applying the method to both synthetic and field Data examples demonstrate that the local time-frequency decomposition can characterize nonstationary variation of Seismic Data and be used in practical applications, such as Seismic ground-roll noise attenuation and multicomponent Data registration.
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Seismic Data interpolation beyond aliasing using regularized nonstationary autoregression
Geophysics, 2011Co-Authors: Yang Liu, Sergey FomelAbstract:Seismic Data are often inadequately or irregularly sampled along spatial axes. Irregular sampling can produce artifacts in Seismic imaging results. We present a new approach to interpolate aliased Seismic Data based on adaptive predictionerror ltering (PEF) and regularized nonstationary autoregression. Instead of cutting Data into overlapping windows (patching), a popular method for handling nonstationarity, we obtain smoothly nonstationary PEF coecients by solving a global regularized least-squares problem. We employ shaping regularization to control the smoothness of adaptive PEFs. Finding the interpolated traces can be treated as another linear least-squares problem, which solves for Data values rather than lter coecients. Compared with existing methods, the advantages of the proposed method include an intuitive selection of regularization parameters and fast iteration convergence. Benchmark synthetic and eld Data examples show that the proposed technique can successfully reconstruct Data with decimated or missing traces.
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stacking Seismic Data using local correlation
Geophysics, 2009Co-Authors: Guochang Liu, Sergey Fomel, Long Jin, Xiaohong ChenAbstract:Stacking plays an important role in improving signal-to-noise ratio and imaging quality of Seismic Data. However, for low-fold-coverage Seismic profiles, the result of conventional stacking is not always satisfactory. To address this problem, we have developed a method of stacking in which we use local correlation as a weight for stacking common-midpoint gathers after NMO processing or common-image-point gathers after prestack migration. Application of the method to synthetic and field Data showed that stacking using local correlation can be more effective in suppressing random noise and artifacts than other stacking methods.
Yang Liu - One of the best experts on this subject based on the ideXlab platform.
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signal and noise separation in prestack Seismic Data using velocity dependent seislet transform
Geophysics, 2015Co-Authors: Yang Liu, Sergey Fomel, Cai LiuAbstract:ABSTRACTThe seislet transform is a waveletlike transform that analyzes Seismic Data by following varying slopes of Seismic events across different scales and provides a multiscale orthogonal basis for Seismic Data. It generalizes the discrete wavelet transform (DWT) in the sense that the DWT in the lateral direction is simply the seislet transform with a zero slope. Our earlier work used plane-wave destruction (PWD) to estimate smoothly varying slopes. However, the PWD operator can be sensitive to strong noise interference, which makes the seislet transform based on PWD (PWD-seislet transform) occasionally fail in providing a sparse multiscale representation for Seismic field Data. We adopted a new velocity-dependent (VD) formulation of the seislet transform, in which the normal moveout equation served as a bridge between local slope patterns and conventional moveout parameters in the common-midpoint domain. The VD slope has better resistance to strong random noise, which indicated the potential of VD sei...
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time frequency domain snr estimation and its application in Seismic Data processing
Journal of Applied Geophysics, 2014Co-Authors: Yan Zhao, Yang Liu, Nansen JiangAbstract:Abstract Based on an approach estimating frequency domain signal-to-noise ratio (FSNR), we propose a method to evaluate time–frequency domain signal-to-noise ratio (TFSNR). This method adopts short-time Fourier transform (STFT) to estimate instantaneous power spectrum of signal and noise, and thus uses their ratio to compute TFSNR. Unlike FSNR describing the variation of SNR with frequency only, TFSNR depicts the variation of SNR with time and frequency, and thus better handles non-stationary Seismic Data. By considering TFSNR, we develop methods to improve the effects of inverse Q filtering and high frequency noise attenuation in Seismic Data processing. Inverse Q filtering considering TFSNR can better solve the problem of amplitude amplification of noise. The high frequency noise attenuation method considering TFSNR, different from other de-noising methods, distinguishes and suppresses noise using an explicit criterion. Examples of synthetic and real Seismic Data illustrate the correctness and effectiveness of the proposed methods.
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Seismic Data analysis using local time frequency decomposition
Geophysical Prospecting, 2013Co-Authors: Yang Liu, Sergey FomelAbstract:Many natural phenomena, including geologic events and geophysical Data, are fundamentally nonstationary - exhibiting statistical variation that changes in space and time. Time-frequency characterization is useful for analysing such Data, Seismic traces in particular. We present a novel time-frequency decomposition, which aims at depicting the nonstationary character of Seismic Data. The proposed decomposition uses a Fourier basis to match the target signal using regularized least-squares inversion. The decomposition is invertible, which makes it suitable for analysing nonstationary Data. The proposed method can provide more flexible time-frequency representation than the classical S transform. Results of applying the method to both synthetic and field Data examples demonstrate that the local time-frequency decomposition can characterize nonstationary variation of Seismic Data and be used in practical applications, such as Seismic ground-roll noise attenuation and multicomponent Data registration.
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Seismic Data interpolation beyond aliasing using regularized nonstationary autoregression
Geophysics, 2011Co-Authors: Yang Liu, Sergey FomelAbstract:Seismic Data are often inadequately or irregularly sampled along spatial axes. Irregular sampling can produce artifacts in Seismic imaging results. We present a new approach to interpolate aliased Seismic Data based on adaptive predictionerror ltering (PEF) and regularized nonstationary autoregression. Instead of cutting Data into overlapping windows (patching), a popular method for handling nonstationarity, we obtain smoothly nonstationary PEF coecients by solving a global regularized least-squares problem. We employ shaping regularization to control the smoothness of adaptive PEFs. Finding the interpolated traces can be treated as another linear least-squares problem, which solves for Data values rather than lter coecients. Compared with existing methods, the advantages of the proposed method include an intuitive selection of regularization parameters and fast iteration convergence. Benchmark synthetic and eld Data examples show that the proposed technique can successfully reconstruct Data with decimated or missing traces.
Zhaoyu Jin - One of the best experts on this subject based on the ideXlab platform.
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dealiased Seismic Data interpolation using seislet transform with low frequency constraint
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Shuwei Gan, Yangkang Chen, Shoudong Wang, Yizhuo Zhang, Zhaoyu JinAbstract:Interpolating regularly missing traces in Seismic Data is thought to be much harder than interpolating irregularly missing Seismic traces, because many sparsity-based approaches cannot be used due to the strong aliasing noise in the sparse domain. We propose to use the seislet transform to perform a sparsity-based approach to interpolate highly undersampled Seismic Data based on the classic projection onto convex sets (POCS) framework. Many numerical tests show that the local slope is the main factor that will affect the sparsity and antialiasing ability of seislet transform. By low-pass filtering the undersampled Seismic Data with a very low bound frequency, we can get a precise dip estimation, which will make the seislet transform capable for interpolating the aliased Seismic Data. In order to prepare the optimum local slope during iterations, we update the slope field every several iterations. We also use a percentile thresholding approach to better control the reconstruction performance. Both synthetic and field examples show better performance using the proposed approach than the traditional prediction based and the $F\mbox{--}K$ -based POCS approaches.
