scholarly journals Nonstretch normal moveout through iterative partial correction and deconvolution

Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. V131-V141 ◽  
Author(s):  
Ettore Biondi ◽  
Eusebio Stucchi ◽  
Alfredo Mazzotti
Keyword(s):  
Seismic Data ◽  
Singular Value ◽  
Long Offset ◽  
Nmo Correction ◽  
Seismic Data Acquisition ◽  
Marine Data ◽  
Value Decomposition ◽  
Nmo Stretch ◽  
Space Variant ◽  
Partial Correction

Source to receiver distances used in seismic data acquisition have been steadily increasing and it is now common to work with data acquired with more than 10 km of offset. Subbasalt exploration and seismic undershooting are just two applications in which long-offset reflections are sought. However, such reflections are often subjected to muting to suppress normal moveout (NMO) stretch artifacts, thus causing a loss of valuable information. To retrieve these portions of the recorded wavefield, we developed a nonstretch NMO correction based on wavelet estimation and on an iterative procedure of partial NMO correction and deconvolution. We evaluated this methodology using fourth-order traveltime curve approximations to increase the offset of usable reflections, but it can be adapted to traveltime curves of any order. Time- and space-variant wavelets, estimated by means of singular value decomposition along the sought traveltimes, were used to build the desired output for the deconvolution that aims at retrieving the original shape of the partially stretched wavelets. We tested our method on a synthetic gather presenting time and offset varying wavelets, on a real-marine line simulating an undershooting pattern and on true undershooting land-marine data. These examples demonstrated that our new algorithm effectively limits the stretching associated with the NMO correction and enables the recovery of those portions of the stacked sections that are typically lost from muting in the standard NMO correction.

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

Optimized 6thorder NMO correction for long‐offset seismic data

2002 ◽  
Author(s):  
Chuanwen Sun ◽  
Hongwei Wang ◽  
Ruben D. Martinez
Keyword(s):  
Seismic Data ◽  
Long Offset ◽  
Nmo Correction

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.

Automatic source localization of diffracted seismic noise in shallow water

Geophysics ◽  
2014 ◽  
Vol 79 (2) ◽  
pp. V23-V31 ◽  
Author(s):  
Wenkai Lu ◽  
Zhang Yingqiang ◽  
Zhen Boran
Keyword(s):  
Source Localization ◽  
Seismic Data ◽  
Seismic Noise ◽  
Clustering Algorithm ◽  
Source Location ◽  
Seismic Line ◽  
Multiple Shot ◽  
Seismic Data Acquisition ◽  
Marine Data ◽  
Localization Approach

We evaluated an automatic source localization approach for diffracted seismic noise (DSN) attenuation based on apex recognition. The potential DSNs in each shot gather were first detected by identifying their apexes. Then, the positions of these detected apexes were used to calculate the source locations of their corresponding DSNs. After that, according to the distribution of the source locations obtained in all shot gathers in one seismic line, we removed some false detected DSNs and further improved the source location estimates of the remaining ones. By assuming that the source location of one DSN is fixed or slowly changed during the seismic data acquisition, for a truly existing DSN, its multiple source location estimates, which are obtained from different streamers in multiple shot gathers, should focus. Therefore, a clustering algorithm was applied to obtain the source location estimates of the final selected DSNs and remove the false recognized DSNs at the same time. To verify the source localization results obtained, we suppressed these DSNs by flattening them along their trajectories and extracting them by multichannel filters, similar to other existing methodologies. A real 3D marine data example demonstrated that the proposed method obtains some promising results for attenuation of the DSNs.

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