Closed-loop surface-related multiple elimination and its application to simultaneous data reconstruction

Geophysics ◽  
2015 ◽  
Vol 80 (6) ◽  
pp. V189-V199 ◽  
Author(s):  
Gabriel A. Lopez ◽  
D. J. Verschuur
Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. T133-T141 ◽  
Author(s):  
Jan-Willem Vrolijk ◽  
Eric Verschuur ◽  
Gabriel Lopez

Accurate surface-related multiple removal is an important step in conventional seismic processing, and more recently, primaries and surface multiples are separated such that each of them is available for imaging algorithms. Current developments in the field of surface-multiple removal aim at estimating primaries in a large-scale inversion process. Using such a so-called closed-loop process, in each iteration primaries and surface multiples will be updated until they fit the measured data. The advantage of redefining surface-multiple removal as a closed-loop process is that certain preprocessing steps can be included, which can lead to an improved multiple removal. In principle, the surface-related multiple elimination process requires deghosted data as input; thus, the source and receiver ghost must be removed. We have focused on the receiver ghost effect and assume that the source is towed close to the sea surface, such that the source ghost effect is well-represented by a dipole source. The receiver ghost effect is integrated within the closed-loop primary estimation process. Thus, primaries are directly estimated without the receiver ghost effect. After receiver deghosting, the upgoing wavefield is defined at zero depth, which is the surface. We have successfully validated our method on a 2D simulated data and on a 2D subset from 3D broadband field data with a slanted cable.


2015 ◽  
Vol 213 ◽  
pp. 566-573 ◽  
Author(s):  
Daniel Kopiec ◽  
Piotr Pałetko ◽  
Konrad Nieradka ◽  
Wojciech Majstrzyk ◽  
Piotr Kunicki ◽  
...  

2017 ◽  
Vol 114 ◽  
pp. 4811-4821 ◽  
Author(s):  
Yun Wu ◽  
Steven L. Bryant ◽  
Larry W. Lake

Geophysics ◽  
2021 ◽  
pp. 1-72
Author(s):  
Dong Zhang ◽  
Dirk Jacob (Eric) Verschuur

Reliably separating primary and multiple reflections in a shallow water environment (i.e., 50 m to 200 m water depth) still remains a challenge. The success of previously published closed-loop surface-related multiple estimation (CL-SRME) depends heavily on the data coverage, i.e., the near-offset reconstruction. Therefore, we propose the integrated framework of CL-SRME and full-wavefield migration (FWM). Multiples recorded in the data are capable of helping infill the acquisition imprint of the FWM image. With this image as a strong constraint, we are able to reconstruct the data at near-offsets, which is essential for better primary and multiple estimation during CL-SRME. FWM applied in a non-linear way can avoid the negative influences from the missing data, and at the same time bring in more physics between primaries and multiples. The FWM image of the top part of the subsurface is also used to back-project the information from multiples to primaries with the physical constraint of all this information belongs to the same earth model, provided that a good description of the source wavefield and a reasonable velocity model are available. The proposed integrated framework first reconstructs near-offsets via the closed-loop imaging process of FWM and then feeds the complete reconstructed data to CL-SRME for better primary and multiple estimation. A good performance is demonstrated on both 2D synthetic and field data examples in a challenging shallow water environment.


2015 ◽  
Vol 44 (2) ◽  
pp. 228001
Author(s):  
付丽辉 FU Li-hui ◽  
尹文庆 YIN Wen-qing ◽  
王马华 WANG Ma-hua ◽  
季仁东 JI Ren-dong ◽  
居勇峰 JU Yong-feng

Geophysics ◽  
2009 ◽  
Vol 74 (3) ◽  
pp. A23-A28 ◽  
Author(s):  
G. J. van Groenestijn ◽  
D. J. Verschuur

Accurate removal of surface-related multiples remains a challenge in many cases. To overcome typical inaccuracies in current multiple-removal techniques, we have developed a new primary-estimation method: estimation of primaries by sparse inversion (EPSI). EPSI is based on the same primary-multiple model as surface-related multiple elimination (SRME) and also requires no subsurface model. Unlike SRME, EPSI estimates the primaries as unknowns in a multidimensional inversion process rather than in a subtraction process. Furthermore, it does not depend on interpolated missing near-offset data because it can reconstruct missing data simultaneously. Sparseness plays a key role in the new primary-estimation procedure. The method was tested on 2D synthetic data.


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