Tube Wave Suppression in High Frequency Mine Seismic Data by Singular Value Decomposition

1995 ◽  
Vol 26 (2-3) ◽  
pp. 512-517 ◽  
Author(s):  
Geraldine Teakle ◽  
Shunhua Coa ◽  
Stewart Greenhalgh
Keyword(s):  
Singular Value Decomposition ◽  
Seismic Data ◽  
High Frequency ◽  
Singular Value ◽  
Tube Wave ◽  
Value Decomposition

scholarly journals An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Min Wang ◽  
Zhen Li ◽  
Xiangjun Duan ◽  
Wei Li
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Value ◽  
Denoising Method ◽  
Diagonal Part ◽  
Value Decomposition ◽  
Image Part

This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.

An Improved Seismic Data Completion Algorithm using Low-Rank Tensor Optimization: Cost Reduction and Optimal Data Orientation

Geophysics ◽  
2021 ◽  
pp. 1-88
Author(s):  
Jonathan Popa ◽  
Susan E. Minkoff ◽  
Yifei Lou
Keyword(s):  
Fourier Transform ◽  
Singular Value Decomposition ◽  
Seismic Data ◽  
Convex Sets ◽  
Singular Spectrum Analysis ◽  
Singular Value ◽  
Low Rank ◽  
Traditional Methods ◽  
Value Decomposition ◽  
Conjugate Symmetry

Seismic data are often incomplete due to equipment malfunction, limited source and receiver placement at near and far offsets, and missing cross-line data. Seismic data contain redundancies as they are repeatedly recorded over the same or adjacent subsurface regions, causing the data to have a low-rank structure. To recover missing data one can organize the data into a multidimensional array or tensor and apply a tensor completion method. We can increase the effectiveness and efficiency of low-rank data reconstruction based on the tensor singular value decomposition (tSVD) by analyzing the effect of tensor orientation and exploiting the conjugate symmetry of the multidimensional Fourier transform. In fact, these results can be generalized to any order tensor. Relating the singular values of the tSVD to those of a matrix leads to a simplified analysis, revealing that the most square orientation gives the best data structure for low-rank reconstruction. After the first step of the tSVD, a multidimensional Fourier transform, frontal slices of the tensor form conjugate pairs. For each pair a singular value decomposition can be replaced with a much cheaper conjugate calculation, allowing for faster computation of the tSVD. Using conjugate symmetry in our improved tSVD algorithm reduces the runtime of the inner loop by 35% to 50%. We consider synthetic and real seismic datasets from the Viking Graben Region and the Northwest Shelf of Australia arranged as high-dimensional tensors. We compare tSVD based reconstruction to traditional methods, projection onto convex sets and multichannel singular spectrum analysis, and see that the tSVD based method gives similar or better accuracy and is more efficient, converging with runtimes that are an order of magnitude faster than the traditional methods. Additionally, we verify the most square orientation improves recovery for these examples by 10-20% compared to the other orientations.

scholarly journals The acoustic diversity of the seabed based on the similarity index computed from Chirp seismic data

2008 ◽  
Vol 66 (2) ◽  
pp. 227-236 ◽  
Author(s):  
Gwang H. Lee ◽  
Han J. Kim ◽  
Dae C. Kim ◽  
Bo Y. Yi ◽  
Seong M. Nam ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Real Time ◽  
Seismic Data ◽  
Similarity Index ◽  
Singular Value ◽  
Coarse Grained ◽  
Marine Science ◽  
Rocky Bottom ◽  
Acoustic Diversity ◽  
Value Decomposition

Abstract Lee, G. H., Kim, H. J., Kim, D. C., Yi, B. Y., Nam, S. M., Khim, B. K., and Lim, M. S. 2009. The acoustic diversity of the seabed based on the similarity index computed from Chirp seismic data. – ICES Journal of Marine Science, 66: 227–236. The similarity index (SI), computed from the singular value decomposition of seabed-echo envelopes recorded in Chirp seismic data, was tested in mapping the acoustic diversity of the seabed in Suyong Bay, Busan, Korea. Rocky bottom is characterized by low SI values, indicating acoustic heterogeneity, and sedimentary seabed by high SI values, also indicating acoustic homogeneity. Isolated areas of low SI values, not identified as rocky bottom in Chirp profiles, may suggest a shallow basement. The gradual seaward change of the substratum from coarse-grained to relatively poorly sorted, finer-grained sediments also corresponds to an overall seaward decrease in the SI value. The straightforward and quick computation of the SI makes it possible to assess the gross acoustic diversity of the seabed in almost real time.

scholarly journals FILTRAGEM SVD APLICADA À MELHORIA DO RASTREAMENTO DE HORIZONTES SÍSMICOS

2011 ◽  
Vol 29 (3) ◽  
Author(s):  
Milton J. Porsani ◽  
Fredy A.V. Artola ◽  
Michelângelo G. da Silva ◽  
Paulo E.M. de Melo
Keyword(s):  
Singular Value Decomposition ◽  
Seismic Data ◽  
Automatic Detection ◽  
Singular Value ◽  
Seismic Events ◽  
Computationally Efficient ◽  
Automatic Mapping ◽  
Multichannel Filtering ◽  
Value Decomposition ◽  
Filtering Approach

No presente artigo apresentamos uma aplicação da filtragem SVD (Singular Value Decomposition) para o mapeamento automático de horizontes sísmicos. A filtragem SVD pode ser vista como um método de filtragem multicanal onde cada traço filtrado guarda certo grau de coerência com os traços imediatamente vizinhos. Esta filtragem preserva as relações de amplitude, fase e correlação espacial dos eventos sísmicos, ao tempo em que permite eliminar o ruído incoerente, normalmente associado aos últimos autovalores. A decomposição SVD é realizada sobre o subconjunto de traços vizinhos a cada traço da linha sísmica 2D ou de um volume 3D. O traço filtrado é obtido utilizando apenas alguns dos autovetores e autovalores associados. Ilustramos a aplicação do método sobre dados sísmicos terrestres. A melhoria da coerência dos eventos sísmicos permitiu maior robustez ao autotracking no mapeamento e interpretação automática dos horizontes sísmicos. A filtragem SVD é computacionalmente eficiente e tem o mérito de melhorar significativamente a coerência, a consistência e a continuidade dos eventos de reflexão facilitando muito o "trabalho", do tracker na busca de padrões no processo de autotracking.Keywords : mapeamento automático de horizontes; processamento sísmico; filtragem SVD; rastreamento de horizontes sísmicos.ABSTRACTWe present an application of a singular value decomposition (SVD) filtering approach to the automatic detection of seismic horizons. The SVD filtering approach may be seen as a multichannel filtering method where each filtered seismic trace retains the coherence of the neighbouring seismic traces. The SVD filtering preserves the amplitude and phase relations and reinforces the spacial correlation between seismic events, and at the same time it reduces the incoherent noise in data, which normally is associated to the last eigenvalues. The SVD decomposition is performed on each subset of traces around each trace of the original 2D or 3D seismic data. The filtered trace is obtained from the most important eigenvalues and eigenvectors. We illustrate the application of the new approach on 3D post-stack land seismic data. The improvement of the resultant coherence in the seismic reflected events allows for greater autotracking robustness during the automatic interpretation of the seismic horizons. The SVD filtering approach is computationally efficient and improves significantly the coherence, the consistency and the spacial continuity of the seismic events making easier the automatic detection of the commercial software in the search for patterns along the autotracking process.Keywords : automatic mapping of horizons; seismic processing; SVD filtering; tracking horizons seismic.

scholarly journals ADAPTIVE SINGULAR VALUE DECOMPOSITION FILTERING TO ENHANCE REFLECTORS AND GEOLOGICAL STRUCTURES IN 3D SEISMIC DATA

2016 ◽  
Vol 34 (2) ◽  
Author(s):  
Washington Oliveira Martins ◽  
Milton José Porsani ◽  
Michelângelo G. da Silva
Keyword(s):  
Singular Value Decomposition ◽  
Adaptive Filtering ◽  
Seismic Data ◽  
Singular Value ◽  
3D Seismic ◽  
Data Filtering ◽  
Geological Structures ◽  
Value Decomposition ◽  
Seismic Volumes ◽  
3D Seismic Data

ABSTRACT. We applied an adaptive seismic data filtering method, based on the singular value decomposition (SVD) to improve the identification of reflectors and geological structures in 3D stacked seismic volumes...Keywords: seismic data processing, SVD filtering, 3D pos-stacked filtering, adaptive filtering. RESUMO. Nós aplicamos um método de filtragem adaptativa de dados sísmicos, baseado na decomposição em valores singulares (SVD), para melhorar a identificação de refletores e estruturas geológicas em volumes sísmicos empilhados 3D...Palavras-chave: processamento sísmico, filtragem SVD, filtragem pós-stack 3D, filtragem adaptativa.

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