Sparse graph-regularized dictionary learning for suppressing random seismic noise

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. V215-V231 ◽  
Author(s):  
Lina Liu ◽  
Jianwei Ma ◽  
Gerlind Plonka
Keyword(s):  
Similarity Measure ◽  
Dictionary Learning ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Laplacian Matrix ◽  
Sparse Data ◽  
Data Representation ◽  
Image Patches ◽  
Learned Dictionary ◽  
Value Decomposition

We have developed a new regularization method for the sparse representation and denoising of seismic data. Our approach is based on two components: a sparse data representation in a learned dictionary and a similarity measure for image patches that is evaluated using the Laplacian matrix of a graph. Dictionary-learning (DL) methods aim to find a data-dependent basis or a frame that admits a sparse data representation while capturing the characteristics of the given data. We have developed two algorithms for DL based on clustering and singular-value decomposition, called the first and second dictionary constructions. Besides using an adapted dictionary, we also consider a similarity measure for the local geometric structures of the seismic data using the Laplacian matrix of a graph. Our method achieves better denoising performance than existing denoising methods, in terms of peak signal-to-noise ratio values and visual estimation of weak-event preservation. Comparisons of experimental results on field data using traditional [Formula: see text]-[Formula: see text] deconvolution (FX-Decon) and curvelet thresholding methods are also provided.

Simultaneous dictionary learning and denoising for seismic data

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. A27-A31 ◽  
Author(s):  
Simon Beckouche ◽  
Jianwei Ma
Keyword(s):  
Total Variation ◽  
Dictionary Learning ◽  
Seismic Data ◽  
Patch Size ◽  
Signal To Noise Ratio ◽  
Data Representation ◽  
Feature Preservation ◽  
Nonlocal Total Variation ◽  
Flexible Framework ◽  
Anisotropic Total Variation

We evaluated a dictionary learning (DL) method for seismic-data denoising. The data were divided into smaller patches, and a dictionary of patch-size atoms was learned. The DL method offers a more flexible framework to adaptively construct sparse data representation according to the seismic data themselves. The representation being learned from the data, did not rely on a guess of the data morphology like standard wavelet or curvelet transforms. The method could learn a dictionary and denoise seismic data, whether simultaneously or in two distinctive steps. Empirical study on field data showed promising denoising performance of the presented method in terms of signal-to-noise ratio and weak-feature preservation, in comparison with wavelets, curvelets, anisotropic total variation, and nonlocal total variation.

A dictionary learning method with atom splitting for seismic footprint suppression

Geophysics ◽  
2021 ◽  
pp. 1-97
Author(s):  
Dawei Liu ◽  
Lei Gao ◽  
Xiaokai Wang ◽  
wenchao Chen
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Original Data ◽  
Morphological Features ◽  
Time Slice ◽  
Learning Method ◽  
Seismic Attribute ◽  
Morphological Component Analysis ◽  
Value Decomposition

Acquisition footprint causes serious interference with seismic attribute analysis, which severely hinders accurate reservoir characterization. Therefore, acquisition footprint suppression has become increasingly important in industry and academia. In this work, we assume that the time slice of 3D post-stack migration seismic data mainly comprises two components, i.e., useful signals and acquisition footprint. Useful signals describe the spatial distributions of geological structures with local piecewise smooth morphological features. However, acquisition footprint often behaves as periodic artifacts in the time-slice domain. In particular, the local morphological features of the acquisition footprint in the marine seismic acquisition appear as stripes. As useful signals and acquisition footprint have different morphological features, we can train an adaptive dictionary and divide the atoms of the dictionary into two sub-dictionaries to reconstruct these two components. We propose an adaptive dictionary learning method for acquisition footprint suppression in the time slice of 3D post-stack migration seismic data. To obtain an adaptive dictionary, we use the K-singular value decomposition algorithm to sparsely represent the patches in the time slice of 3D post-stack migration seismic data. Each atom of the trained dictionary represents certain local morphological features of the time slice. According to the difference in the variation level between the horizontal and vertical directions, the atoms of the trained dictionary are divided into two types. One type significantly represents the local morphological features of the acquisition footprint, whereas the other type represents the local morphological features of useful signals. Then, these two components are reconstructed using morphological component analysis based on different types of atoms, respectively. Synthetic and field data examples indicate that the proposed method can effectively suppress the acquisition footprint with fidelity to the original data.

Retrieving the leaked signals from noise using a fast dictionary learning method

Geophysics ◽  
2021 ◽  
pp. 1-86
Author(s):  
Wei Chen ◽  
Omar M. Saad ◽  
Yapo Abolé Serge Innocent Oboué ◽  
Liuqing Yang ◽  
Yangkang Chen
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
State Of The Art ◽  
Computational Cost ◽  
Learning Method ◽  
Global Parameter ◽  
Radius Parameter ◽  
Orthogonalization Method ◽  
Value Decomposition ◽  
Learned Features

Most traditional seismic denoising algorithms will cause damages to useful signals, which are visible from the removed noise profiles and are known as signal leakage. The local signal-and-noise orthogonalization method is an effective method for retrieving the leaked signals from the removed noise. Retrieving leaked signals while rejecting the noise is compromised by the smoothing radius parameter in the local orthogonalization method. It is not convenient to adjust the smoothing radius because it is a global parameter while the seismic data is highly variable locally. To retrieve the leaked signals adaptively, we propose a new dictionary learning method. Because of the patch-based nature of the dictionary learning method, it can adapt to the local feature of seismic data. We train a dictionary of atoms that represent the features of the useful signals from the initially denoised data. Based on the learned features, we retrieve the weak leaked signals from the noise via a sparse co ding step. Considering the large computational cost when training a dictionary from high-dimensional seismic data, we leverage a fast dictionary up dating algorithm, where the singular value decomposition (SVD) is replaced via the algebraic mean to update the dictionary atom. We test the performance of the proposed method on several synthetic and field data examples, and compare it with that from the state-of-the-art local orthogonalization method.

scholarly journals A method of combining coherence-constrained sparse coding and dictionary learning for denoising

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. V137-V148 ◽  
Author(s):  
Pierre Turquais ◽  
Endrias G. Asgedom ◽  
Walter Söllner
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
Random Noise ◽  
A Priori ◽  
Seismic Signal ◽  
Noise Variance ◽  
A Priori Information ◽  
Seismic Section ◽  
Learned Dictionary ◽  
Priori Information

We have addressed the seismic data denoising problem, in which the noise is random and has an unknown spatiotemporally varying variance. In seismic data processing, random noise is often attenuated using transform-based methods. The success of these methods in denoising depends on the ability of the transform to efficiently describe the signal features in the data. Fixed transforms (e.g., wavelets, curvelets) do not adapt to the data and might fail to efficiently describe complex morphologies in the seismic data. Alternatively, dictionary learning methods adapt to the local morphology of the data and provide state-of-the-art denoising results. However, conventional denoising by dictionary learning requires a priori information on the noise variance, and it encounters difficulties when applied for denoising seismic data in which the noise variance is varying in space or time. We have developed a coherence-constrained dictionary learning (CDL) method for denoising that does not require any a priori information related to the signal or noise. To denoise a given window of a seismic section using CDL, overlapping small 2D patches are extracted and a dictionary of patch-sized signals is trained to learn the elementary features embedded in the seismic signal. For each patch, using the learned dictionary, a sparse optimization problem is solved, and a sparse approximation of the patch is computed to attenuate the random noise. Unlike conventional dictionary learning, the sparsity of the approximation is constrained based on coherence such that it does not need a priori noise variance or signal sparsity information and is still optimal to filter out Gaussian random noise. The denoising performance of the CDL method is validated using synthetic and field data examples, and it is compared with the K-SVD and FX-Decon denoising. We found that CDL gives better denoising results than K-SVD and FX-Decon for removing noise when the variance varies in space or time.

A model-based data-driven dictionary learning for seismic data representation

2017 ◽  
Vol 66 (1) ◽  
pp. 98-123 ◽  
Author(s):  
Can Evren Yarman ◽  
Rajiv Kumar ◽  
James Rickett
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
Data Representation ◽  
Data Driven ◽  
Model Based

scholarly journals Locality Preserving and Label-Aware Constraint-Based Hybrid Dictionary Learning for Image Classification

2021 ◽  
Vol 11 (16) ◽  
pp. 7701
Author(s):  
Jianqiang Song ◽  
Lin Wang ◽  
Zuozhi Liu ◽  
Muhua Liu ◽  
Mingchuan Zhang ◽  
...  
Keyword(s):  
Image Classification ◽  
Dictionary Learning ◽  
Laplacian Matrix ◽  
Graph Laplacian ◽  
Data Representation ◽  
Optimization Strategy ◽  
Locality Preserving ◽  
L2 Norm ◽  
Infant Stage ◽  
Locality Information

Dictionary learning has been an important role in the success of data representation. As a complete view of data representation, hybrid dictionary learning (HDL) is still in its infant stage. In previous HDL approaches, the scheme of how to learn an effective hybrid dictionary for image classification has not been well addressed. In this paper, we proposed a locality preserving and label-aware constraint-based hybrid dictionary learning (LPLC-HDL) method, and apply it in image classification effectively. More specifically, the locality information of the data is preserved by using a graph Laplacian matrix based on the shared dictionary for learning the commonality representation, and a label-aware constraint with group regularization is imposed on the coding coefficients corresponding to the class-specific dictionary for learning the particularity representation. Moreover, all the introduced constraints in the proposed LPLC-HDL method are based on the l2-norm regularization, which can be solved efficiently via employing an alternative optimization strategy. The extensive experiments on the benchmark image datasets demonstrate that our method is an improvement over previous competing methods on both the hand-crafted and deep features.

scholarly journals Attenuating seismic noise via incoherent dictionary learning

2018 ◽  
Vol 15 (4) ◽  
pp. 1327-1338
Author(s):  
Juan Wu ◽  
Min Bai
Keyword(s):  
Sparse Representation ◽  
Image Denoising ◽  
Dictionary Learning ◽  
Seismic Data ◽  
Learning Algorithm ◽  
Random Noise ◽  
Learning Technology ◽  
Image Patches ◽  
Seismic Images ◽  
Traditional Image

Abstract We propose to apply an incoherent dictionary learning algorithm for reducing random noise in seismic data. The image denoising algorithm based on incoherent dictionary learning is proposed for solving the problem of losing partial texture information using traditional image denoising methods. The noisy image is firstly divided into different image patches, and those patches are extracted for dictionary learning. Then, we introduce the incoherent dictionary learning technology to update the dictionary. Finally, sparse representation problem is solved to obtain sparse representation coefficients by sparse coding algorithm. The denoised data can be obtained by reconstructing the image using the sparse coefficients. Application of the incoherent dictionary learning method to seismic images presents successful performance and demonstrates its superiority to the state-of-the-art denoising methods.

A Novel Approach of K-SVD-Based Algorithm for Image Denoising

2019 ◽  
pp. 154-180 ◽  
Author(s):  
Madhu Golla ◽  
Sudipta Rudra
Keyword(s):  
Image Denoising ◽  
Dictionary Learning ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Novel Approach ◽  
Image Patches ◽  
Computational Speed ◽  
Novel Strategy ◽  
Value Decomposition ◽  
Application Developers

In recent years, denoising has played an important role in medical image analysis. Image denoising is still accepted as a challenge for researchers and image application developers in medical image applications. The idea is to denoise a microscopic image through over-complete dictionary learning using a k-means algorithm and singular value decomposition (K-SVD) based on pursuit methods. This approach is good in performance on the quality improvement of the medical images, but it has low computational speed with high computational complexity. In view of the above limitations, this chapter proposes a novel strategy for denoising insight phenomena of the K-SVD algorithm. In addition, the authors utilize the technology of improved dictionary learning of the image patches using heap sort mechanism followed by dictionary updating process. The experimental results validate that the proposed approach successfully reduced noise levels on various test image datasets. This has been found to be more accurate than the best in class denoising approaches.

GROUND ROLL ATTENUATION USING ADAPTIVE SINGULAR VALUE DECOMPOSITION FILTERING IN THE F-K DOMAIN

2016 ◽  
Vol 33 (3) ◽  
Author(s):  
Danilo S. Cruz ◽  
Milton J. Porsani
Keyword(s):  
Singular Value Decomposition ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Singular Value ◽  
Signal To Noise ◽  
Coherent Noise ◽  
Ground Roll ◽  
Noise Ratio ◽  
Value Decomposition

ABSTRACT. The land seismic data often have low signal-to-noise ratio due, among other factors, the presence of ground roll. It is a coherent noise present in seismograms that appears as linear events... RESUMO. Os dados sísmicos terrestres geralmente apresentam baixa razão sinal-ruído devido, entre outros fatores, à presença do ground roll . Trata-se de um ruído dominado por altas amplitudes...

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