Comparison of the projection onto convex sets and iterative hard thresholding methods for seismic data interpolation and denoising

2018 ◽  
Vol 49 (6) ◽  
pp. 825-832 ◽  
Author(s):  
Benfeng Wang ◽  
Chenglong Gu
Keyword(s):  
Seismic Data ◽  
Convex Sets ◽  
Data Interpolation ◽  
Hard Thresholding ◽  
Projection Onto Convex Sets ◽  
Iterative Hard Thresholding

An Improved Weighted Projection Onto Convex Sets Method for Seismic Data Interpolation and Denoising

2016 ◽  
Vol 9 (1) ◽  
pp. 228-235 ◽  
Author(s):  
Benfeng Wang ◽  
Xiaohong Chen ◽  
Jingye Li ◽  
Jingjie Cao
Keyword(s):  
Seismic Data ◽  
Convex Sets ◽  
Data Interpolation ◽  
Projection Onto Convex Sets

Simultaneous potential field data interpolation, border padding, and denoising via projection onto convex sets algorithm

2020 ◽  
Vol 175 ◽  
pp. 103983
Author(s):  
Xiaoniu Zeng ◽  
Xihai Li ◽  
Jihao Liu ◽  
Chao Niu
Keyword(s):  
Potential Field ◽  
Field Data ◽  
Convex Sets ◽  
Data Interpolation ◽  
Potential Field Data ◽  
Projection Onto Convex Sets

Simultaneous Reconstruction and Denoising of 5-D Seismic Data Using Damped Sparse Representation Based Projection Onto Convex Sets

2017 ◽  
Author(s):  
W. Huang ; University of California ◽  
Santa Cruz ◽  
R. Wang ◽  
H. Li ◽  
S. Zu ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Seismic Data ◽  
Convex Sets ◽  
Projection Onto Convex Sets ◽  
Simultaneous Reconstruction

Seismic data interpolation without iteration using t-x-y streaming prediction filter with varying smoothness

Geophysics ◽  
2021 ◽  
pp. 1-57
Author(s):  
Yang Liu ◽  
Geng WU ◽  
Zhisheng Zheng
Keyword(s):  
Iterative Methods ◽  
Seismic Data ◽  
Convex Sets ◽  
Interpolation Method ◽  
Computational Cost ◽  
Economic Factor ◽  
High Dimensional ◽  
Data Interpolation ◽  
Time Space ◽  
Nonstationary Field

Although there is an increase in the amount of seismic data acquired with wide-azimuth geometry, it is difficult to achieve regular data distributions in spatial directions owing to limitations imposed by the surface environment and economic factor. To address this issue, interpolation is an economical solution. The current state of the art methods for seismic data interpolation are iterative methods. However, iterative methods tend to incur high computational cost which restricts their application in cases of large, high-dimensional datasets. Hence, we developed a two-step non-iterative method to interpolate nonstationary seismic data based on streaming prediction filters (SPFs) with varying smoothness in the time-space domain; and we extended these filters to two spatial dimensions. Streaming computation, which is the kernel of the method, directly calculates the coefficients of nonstationary SPF in the overdetermined equation with local smoothness constraints. In addition to the traditional streaming prediction-error filter (PEF), we proposed a similarity matrix to improve the constraint condition where the smoothness characteristics of the adjacent filter coefficient change with the varying data. We also designed non-causal in space filters for interpolation by using several neighboring traces around the target traces to predict the signal; this was performed to obtain more accurate interpolated results than those from the causal in space version. Compared with Fourier Projection onto a Convex Sets (POCS) interpolation method, the proposed method has the advantages such as fast computational speed and nonstationary event reconstruction. The application of the proposed method on synthetic and nonstationary field data showed that it can successfully interpolate high-dimensional data with low computational cost and reasonable accuracy even in the presence of aliased and conflicting events.

3D interpolation of irregular data with a POCS algorithm

Geophysics ◽  
2006 ◽  
Vol 71 (6) ◽  
pp. E91-E97 ◽  
Author(s):  
Ray Abma ◽  
Nurul Kabir
Keyword(s):  
Seismic Data ◽  
Convex Sets ◽  
Fourier Transforms ◽  
Higher Dimensions ◽  
Seismic Surveys ◽  
Projection Onto Convex Sets ◽  
Irregular Data ◽  
Restoration Method ◽  
The Cost

Seismic surveys generally have irregular areas where data cannot be acquired. These data should often be interpolated. A projection onto convex sets (POCS) algorithm using Fourier transforms allows interpolation of irregularly populated grids of seismic data with a simple iterative method that produces high-quality results. The original 2D image restoration method, the Gerchberg-Saxton algorithm, is extended easily to higher dimensions, and the 3D version of the process used here produces much better interpolations than typical 2D methods. The only parameter that makes a substantial difference in the results is the number of iterations used, and this number can be overestimated without degrading the quality of the results. This simplicity is a significant advantage because it relieves the user of extensive parameter testing. Although the cost of the algorithm is several times the cost of typical 2D methods, the method is easily parallelized and still completely practical.

Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform

2016 ◽  
Vol 130 ◽  
pp. 194-208 ◽  
Author(s):  
Shuwei Gan ◽  
Shoudong Wang ◽  
Yangkang Chen ◽  
Xiaohong Chen ◽  
Weiling Huang ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Seismic Data ◽  
Convex Sets ◽  
Data Reconstruction ◽  
Projection Onto Convex Sets ◽  
Seismic Data Reconstruction

Seismic data reconstruction via fast projection onto convex sets in the seislet transform domain

2015 ◽  
Author(s):  
Shuwei Gan* ◽  
Shoudong Wang ◽  
Yangkang Chen ◽  
Xiaohong Chen
Keyword(s):  
Seismic Data ◽  
Convex Sets ◽  
Data Reconstruction ◽  
Transform Domain ◽  
Projection Onto Convex Sets ◽  
Seismic Data Reconstruction

Remote Sensing Images Upsampling Based on Projection onto Convex Sets and Complex Wavelet Packet Transfer

2011 ◽  
Vol 34 (3) ◽  
pp. 482-488 ◽  
Author(s):  
Yan ZHANG ◽  
Xian-Ying LI ◽  
Yi-Yun MAN
Keyword(s):  
Remote Sensing ◽  
Convex Sets ◽  
Wavelet Packet ◽  
Remote Sensing Images ◽  
Projection Onto Convex Sets ◽  
Complex Wavelet

scholarly journals Research on Seismic Data Interpolation and Reconstruction

2020 ◽  
Vol 1631 ◽  
pp. 012110
Author(s):  
Xiaoguo Xie ◽  
Shuling Pan ◽  
Bing Luo ◽  
Cailing Chen ◽  
Kai Chen
Keyword(s):  
Seismic Data ◽  
Data Interpolation
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