Estimation of multiple scattering by iterative inversion, Part II: Practical aspects and examples

Geophysics ◽  
1997 ◽  
Vol 62 (5) ◽  
pp. 1596-1611 ◽  
Author(s):  
D. J. Verschuur ◽  
A. J. Berkhout
Keyword(s):  
Iteration Step ◽  
Linear Least Squares ◽  
Data Set ◽  
Elimination Method ◽  
Elimination Process ◽  
Free Data ◽  
Iterative Inversion ◽  
Multiple Elimination ◽  
Directivity Effects ◽  
Generating System

A surface‐related multiple‐elimination method can be formulated as an iterative procedure: the output of one iteration step is used as input for the next iteration step (part I of this paper). In this paper (part II) it is shown that the procedure can be made very efficient if a good initial estimate of the multiple‐free data set can be provided in the first iteration, and in many situations, the Radon‐based multiple‐elimination method may provide such an estimate. It is also shown that for each iteration, the inverse source wavelet can be accurately estimated by a linear (least‐squares) inversion process. Optionally, source and detector variations and directivity effects can be included, although the examples are given without these options. The iterative multiple elimination process, together with the source wavelet estimation, are illustrated with numerical experiments as well as with field data examples. The results show that the surface‐related multiple‐elimination process is very effective in time gates where the moveout properties of primaries and multiples are very similar (generally deep data), as well as for situations with a complex multiple‐generating system.

Adaptive surface‐related multiple elimination

Geophysics ◽  
1992 ◽  
Vol 57 (9) ◽  
pp. 1166-1177 ◽  
Author(s):  
D. J. Verschuur ◽  
A. J. Berkhout ◽  
C. P. A. Wapenaar
Keyword(s):  
Seismic Data ◽  
Field Data ◽  
Simulated Data ◽  
Seismic Inversion ◽  
The Other ◽  
Inversion Process ◽  
Elimination Process ◽  
Free Data ◽  
Surface Reflectivity ◽  
Multiple Elimination

The major amount of multiple energy in seismic data is related to the large reflectivity of the surface. A method is proposed for the elimination of all surface‐related multiples by means of a process that removes the influence of the surface reflectivity from the data. An important property of the proposed multiple elimination process is that no knowledge of the subsurface is required. On the other hand, the source signature and the surface reflectivity do need to be provided. As a consequence, the proposed process has been implemented adaptively, meaning that multiple elimination is designed as an inversion process where the source and surface reflectivity properties are estimated and where the multiple‐free data equals the inversion residue. Results on simulated data and field data show that the proposed multiple elimination process should be considered as one of the key inversion steps in stepwise seismic inversion.

Migration with reduced artifacts from internal multiple reflections

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. A25-A29
Author(s):  
Lele Zhang
Keyword(s):  
Seismic Reflection ◽  
Computational Cost ◽  
Data Sets ◽  
Square Root ◽  
Multiple Reflections ◽  
Data Set ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Recent Developments ◽  
Multiple Elimination

Migration of seismic reflection data leads to artifacts due to the presence of internal multiple reflections. Recent developments have shown that these artifacts can be avoided using Marchenko redatuming or Marchenko multiple elimination. These are powerful concepts, but their implementation comes at a considerable computational cost. We have derived a scheme to image the subsurface of the medium with significantly reduced computational cost and artifacts. This scheme is based on the projected Marchenko equations. The measured reflection response is required as input, and a data set with primary reflections and nonphysical primary reflections is created. Original and retrieved data sets are migrated, and the migration images are multiplied with each other, after which the square root is taken to give the artifact-reduced image. We showed the underlying theory and introduced the effectiveness of this scheme with a 2D numerical example.

scholarly journals Data-driven internal multiple elimination and its consequences for imaging: A comparison of strategies

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. S365-S372 ◽  
Author(s):  
Lele Zhang ◽  
Jan Thorbecke ◽  
Kees Wapenaar ◽  
Evert Slob
Keyword(s):  
Inverse Scattering ◽  
Computational Cost ◽  
Multiple Reflection ◽  
Data Driven ◽  
Transmission Losses ◽  
Multiple Reflections ◽  
Data Set ◽  
Numerical Examples ◽  
Inverse Scattering Series ◽  
Multiple Elimination

We have compared three data-driven internal multiple reflection elimination schemes derived from the Marchenko equations and inverse scattering series (ISS). The two schemes derived from Marchenko equations are similar but use different truncation operators. The first scheme creates a new data set without internal multiple reflections. The second scheme does the same and compensates for transmission losses in the primary reflections. The scheme derived from ISS is equal to the result after the first iteration of the first Marchenko-based scheme. It can attenuate internal multiple reflections with residuals. We evaluate the success of these schemes with 2D numerical examples. It is shown that Marchenko-based data-driven schemes are relatively more robust for internal multiple reflection elimination at a higher computational cost.

Optimizing surface‐related multiple elimination on a synthetic subsalt data set

2001 ◽  
Author(s):  
Monica P. Miley ◽  
Josef Paffenholz ◽  
Kent Hall ◽  
Scott Michell
Keyword(s):  
Data Set ◽  
Multiple Elimination

Multiscale seismic waveform inversion

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1457-1473 ◽  
Author(s):  
Carey Bunks ◽  
Fatimetou M. Saleck ◽  
S. Zaleski ◽  
G. Chavent
Keyword(s):  
Iterative Methods ◽  
Global Minimum ◽  
Multigrid Method ◽  
Waveform Inversion ◽  
Seismic Inversion ◽  
Local Minima ◽  
Data Set ◽  
Seismic Waveform ◽  
Inversion Methods ◽  
Iterative Inversion

Iterative inversion methods have been unsuccessful at inverting seismic data obtained from complicated earth models (e.g. the Marmousi model), the primary difficulty being the presence of numerous local minima in the objective function. The presence of local minima at all scales in the seismic inversion problem prevent iterative methods of inversion from attaining a reasonable degree of convergence to the neighborhood of the global minimum. The multigrid method is a technique that improves the performance of iterative inversion by decomposing the problem by scale. At long scales there are fewer local minima and those that remain are further apart from each other. Thus, at long scales iterative methods can get closer to the neighborhood of the global minimum. We apply the multigrid method to a subsampled, low‐frequency version of the Marmousi data set. Although issues of source estimation, source bandwidth, and noise are not treated, results show that iterative inversion methods perform much better when employed with a decomposition by scale. Furthermore, the method greatly reduces the computational burden of the inversion that will be of importance for 3-D extensions to the method.

Efficient Laplace-domain full waveform inversion using a cyclic shot subsampling method

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. R37-R46 ◽  
Author(s):  
Wansoo Ha ◽  
Changsoo Shin
Keyword(s):  
Waveform Inversion ◽  
Computation Time ◽  
Velocity Model ◽  
Divide And Conquer ◽  
Iteration Step ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Laplace Domain ◽  
Full Waveform ◽  
Subsampling Method

Full waveform inversion is a method used to recover subsurface parameters, and it requires heavy computational resources. We present a cyclic shot subsampling method to make the full waveform inversion efficient while maintaining the quality of the inversion results. The cyclic method subsamples the shots at a regular interval and changes the shot subset at each iteration step. Using this method, we can suppress the aliasing noise present in regular-interval subsampling. We compared the cyclic method with divide-and-conquer, random, and random-in-each-subgroup subsampling methods using the Laplace-domain full waveform inversion. We found examples of a 2D marine field data set from the Gulf of Mexico and a 3D synthetic salt velocity model. In the inversion examples using the subsampling methods, we could reduce the computation time and obtain results comparable to that without a subsampling technique. The cyclic method and two random subsampling methods yielded similar results; however, the cyclic method generated the best results, especially when the number of shot subsamples was small, as expected. We also examined the effect of subsample updating frequency. The updating frequency does not have a significant effect on the results when the number of subsamples is large. In contrast, frequent subsample updating becomes important when the number of subsamples is small. The random-in-each-subgroup scheme showed the best results if we did not update the subsamples frequently, while the cyclic method suffers from aliasing. The results suggested that the cyclic subsampling scheme can be an alternative to the random schemes and the distributed subsampling schemes with a frequently changing subset are better than lumped subsampling schemes.

Learning Catalyst Design Based on Bias-Free Data Set for Oxidative Coupling of Methane

ACS Catalysis ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 1797-1809
Author(s):  
Thanh Nhat Nguyen ◽  
Sunao Nakanowatari ◽  
Thuy Phuong Nhat Tran ◽  
Ashutosh Thakur ◽  
Lauren Takahashi ◽  
...  
Keyword(s):  
Oxidative Coupling ◽  
Oxidative Coupling Of Methane ◽  
Catalyst Design ◽  
Data Set ◽  
Free Data
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