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.

Implementation of 2D explicit depth extrapolation FIR digital filters for 3D seismic volumes using singular value decomposition

Geophysics ◽  
2010 ◽  
Vol 75 (1) ◽  
pp. V1-V12 ◽  
Author(s):  
Wail A. Mousa ◽  
Said Boussakta ◽  
Desmond C. McLernon ◽  
Mirko Van der Baan
Keyword(s):  
Singular Value Decomposition ◽  
Impulse Response ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
Fir Filter ◽  
Singular Value ◽  
3D Seismic ◽  
Fir Digital Filters ◽  
Value Decomposition ◽  
Seismic Volumes

We propose a new scheme for implementing predesigned 2D complex-valued wavefield extrapolation finite impulse response (FIR) digital filters, which are used for extrapolating 3D seismic wavefields. The implementation is based on singular value decomposition (SVD) of quadrantally symmetric 2D FIR filters (extrapolators). To simplify the SVD computations for such a filter impulse response structure, we apply a special matrix transformation on the extrapolation FIR filter impulse responses where we guarantee the retention of their wavenumber phase response. Unlike the existing 2D FIR filter implementation methods that are used for this geophysical application such as the McClellan transformation or its improved version, this implementation via SVD results in perfect circularly symmetrical magnitude and phase wavenumber responses. In this paper, we also demonstrate that the SVD method can save (depending on the filter size) more than 23% of the number of multiplications per output sample and approximately 62% of the number of additions per output sample when compared to direct implementation with quadrantal symmetry via true 2D convolution. Finally, an application to extrapolation of a seismic impulse is shown to prove our theoretical conclusions.

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

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.

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