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| Content Provider | Springer Nature Link |
|---|---|
| Author | Chen, Guo Xin Chen, Sheng Chang Wang, Han Chuang Zhang, Bo |
| Copyright Year | 2013 |
| Abstract | Missing data are a problem in geophysical surveys, and interpolation and reconstruction of missing data is part of the data processing and interpretation. Based on the sparseness of the geophysical data or the transform domain, we can improve the accuracy and stability of the reconstruction by transforming it to a sparse optimization problem. In this paper, we propose a mathematical model for the sparse reconstruction of data based on the L0-norm minimization. Furthermore, we discuss two types of the approximation algorithm for the L0-norm minimization according to the size and characteristics of the geophysical data: namely, the iteratively reweighted least-squares algorithm and the fast iterative hard thresholding algorithm. Theoretical and numerical analysis showed that applying the iteratively reweighted least-squares algorithm to the reconstruction of potential field data exploits its fast convergence rate, short calculation time, and high precision, whereas the fast iterative hard thresholding algorithm is more suitable for processing seismic data, moreover, its computational efficiency is better than that of the traditional iterative hard thresholding algorithm. |
| Starting Page | 181 |
| Ending Page | 190 |
| Page Count | 10 |
| File Format | |
| ISSN | 16727975 |
| Journal | Applied Geophysics |
| Volume Number | 10 |
| Issue Number | 2 |
| e-ISSN | 19930658 |
| Language | English |
| Publisher | Springer Berlin Heidelberg |
| Publisher Date | 2013-07-02 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Geophysical data sparse reconstruction L0-norm minimization iteratively reweighted least squares fast iterative hard thresholding Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences |
| Content Type | Text |
| Resource Type | Article |
| Subject | Geophysics |
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