Iterative Deblending of Simultaneous-Source Seismic Data With Structuring Median Constraint

2018 ◽  
Vol 15 (1) ◽  
pp. 58-62 ◽  
Author(s):  
Weilin Huang ◽  
Runqiu Wang ◽  
Xiangbo Gong ◽  
Yangkang Chen
Keyword(s):  
Seismic Data ◽  
Simultaneous Source

scholarly journals Deblending by direct inversion

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. A9-A12 ◽  
Author(s):  
Kees Wapenaar ◽  
Joost van der Neut ◽  
Jan Thorbecke
Keyword(s):  
Inverse Problem ◽  
Seismic Data ◽  
Iterative Procedure ◽  
Inversion Process ◽  
Direct Inversion ◽  
Source Data ◽  
Band Limited ◽  
Blended Data ◽  
Simultaneous Source ◽  
Additional Constraints

Deblending of simultaneous-source data is usually considered to be an underdetermined inverse problem, which can be solved by an iterative procedure, assuming additional constraints like sparsity and coherency. By exploiting the fact that seismic data are spatially band-limited, deblending of densely sampled sources can be carried out as a direct inversion process without imposing these constraints. We applied the method with numerically modeled data and it suppressed the crosstalk well, when the blended data consisted of responses to adjacent, densely sampled sources.

Deblending of Simultaneous-Source Seismic Data Using Bregman Iterative Shaping

2020 ◽  
pp. 1-10
Author(s):  
Jing-Wang Cheng ◽  
Wei Chen ◽  
Li Zhou ◽  
Liuqing Yang ◽  
Qimin Liu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Simultaneous Source

scholarly journals Flawless diamond separation in simultaneous source acquisition by seismic apparition

2017 ◽  
Vol 209 (3) ◽  
pp. 1735-1739 ◽  
Author(s):  
Fredrik Andersson ◽  
Johan O.A. Robertsson ◽  
Dirk-Jan van Manen ◽  
Jens Wittsten ◽  
Kurt Eggenberger ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Seismic Data ◽  
Source Separation ◽  
Periodic Sequence ◽  
Spectral Domain ◽  
Entire Data ◽  
Source Line ◽  
Seismic Data Acquisition ◽  
Simultaneous Source ◽  
Exact Separation

Abstract In this paper we prove that the recently introduced method of signal apparition optimally separates signals from interfering sources recorded during simultaneous source seismic data acquisition. By utilizing a periodic sequence of source signatures along one source line, that wavefield becomes separately partially visible in the spectral domain where it can be isolated from interfering signals, processed, and subtracted from the original recordings, thereby separating the wavefields from each other. Whereas other methods for simultaneous source separation can recover data in a triangle-shaped region in the spectral domain, signal apparition allows for the exact separation of data in a diamond-shaped region that is twice as large thereby enabling superior reconstruction of separated wavefields throughout the entire data bandwidth.

Fast dual-domain reduced-rank algorithm for 3D deblending via randomized QR decomposition

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. V89-V101 ◽  
Author(s):  
Jinkun Cheng ◽  
Mauricio D. Sacchi
Keyword(s):  
Seismic Data ◽  
Seismic Profile ◽  
Qr Decomposition ◽  
Low Rank ◽  
Acquisition Time ◽  
Vertical Seismic Profile ◽  
Rank Constraint ◽  
Truncated Svd ◽  
Dual Domain ◽  
Simultaneous Source

We have developed a fast dual-domain algorithm based on matrix rank reduction for separating simultaneous-source seismic data. Our algorithm operates on 3D common receiver gathers or offset-midpoint gathers. At a given monochromatic frequency slice in the [Formula: see text]-[Formula: see text]-[Formula: see text] domain, the spatial data of the ideal unblended common receiver or offset-midpoint gather could be represented via a low-rank matrix. The interferences from the randomly and closely fired shots increased the rank of the aforementioned matrix. Therefore, we could minimize the misfit between the blended observation and the predicted blended data subject to a low-rank constraint that was applied to the data in the [Formula: see text]-[Formula: see text]-[Formula: see text] domain. The low-rank constraint could be implemented via the classic truncated singular value decomposition (SVD) or via a randomized QR decomposition (rQRd). The rQRd yielded nearly one order of processing time improvement with respect to the truncated SVD. We have also discovered that the rQRd was less stringent on the selection of the rank of the data. The latter was important because we often had no precise knowledge of the optimal rank that was required to represent the data. We adopted a synthetic 3D vertical seismic profile and a real seismic data set acquired at the North Viking Graben to test the performance of the proposed source separation algorithm. The proposed algorithm effectively eliminated the interferences while preserving the desired unblended signal. Especially for the synthetic vertical seismic profile example, experiments were evaluated under different survey time ratios. Our tests indicated that the proposed method could save up to 90% of acquisition time under a self-simultaneous source acquisition scenario.

Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization

Geophysics ◽  
2014 ◽  
Vol 79 (5) ◽  
pp. V179-V189 ◽  
Author(s):  
Yangkang Chen ◽  
Sergey Fomel ◽  
Jingwei Hu
Keyword(s):  
Seismic Data ◽  
Simultaneous Source

Learning the blending spikes using sparse dictionaries

2020 ◽  
Vol 222 (3) ◽  
pp. 1846-1863
Author(s):  
Yangkang Chen ◽  
Shaohuan Zu ◽  
Wei Chen ◽  
Mi Zhang ◽  
Zhe Guan
Keyword(s):  
Seismic Data ◽  
Traditional Method ◽  
Ambient Noise ◽  
State Of The Art ◽  
Random Noise ◽  
New Approach ◽  
Learned Dictionary ◽  
Seismic Acquisition ◽  
Iterative Inversion ◽  
Simultaneous Source

SUMMARY Deblending plays an important role in preparing high-quality seismic data from modern blended simultaneous-source seismic acquisition. State-of-the-art deblending is based on the sparsity-constrained iterative inversion. Inversion-based deblending assumes that the ambient noise level is low and the data misfit during iterative inversion accounts for the random ambient noise. The traditional method becomes problematic when the random ambient noise becomes extremely strong and the inversion iteratively fits the random noise instead of the signal and blending interference. We propose a constrained inversion model that takes the strong random noise into consideration and can achieve satisfactory result even when strong random noise exists. The principle of this new method is that we use sparse dictionaries to learn the blending spikes and thus the learned dictionary atoms are able to distinguish between blending spikes and random noise. The separated signal and blending spikes can then be better fitted by the iterative inversion framework. Synthetic and field data examples are used to demonstrate the performance of the new approach.

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