Akhondi-Asl, H;
Nelson, JDB;
(2016)
M-estimate robust PCA for seismic noise attenuation.
In: Karam, L and Katsaggelos, A, (eds.)
2016 IEEE International Conference on Image Processing: Proceedings.
(pp. pp. 1853-1857).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
The robust principal component analysis (PCA) method has shown very promising results in seismic ambient noise attenuation when dealing with outliers in the data. However, the model assumes a general Gaussian distribution plus sparse outliers for the noise. In seismic data however, the noise standard variation could vary from one place to another leading to a more heavy-tailed noise distribution. In this paper, we present a new method which solves a convex minimisation problem of the robust PCA method with an M-estimate penalty function. Our empirical results show that the proposed method can outperform the robust PCA method.
Type: | Proceedings paper |
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Title: | M-estimate robust PCA for seismic noise attenuation |
Event: | 2016 IEEE International Conference on Image Processing (ICIP), 25-28 September 2016, Phoenix, Arizona, USA |
Location: | Phoenix, AZ |
Dates: | 25 September 2016 - 28 September 2016 |
ISBN-13: | 9781467399616 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/ICIP.2016.7532679 |
Publisher version: | https://doi.org/10.1109/ICIP.2016.7532679 |
Language: | English |
Additional information: | Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Science & Technology, Technology, Engineering, Electrical & Electronic, Imaging Science & Photographic Technology, Engineering, Robust PCA, M-Estimate, Seismic Noise Attenuation, Convex Optimisation, ADMM |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences |
URI: | https://discovery.ucl.ac.uk/id/eprint/1539367 |
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2. | China | 4 |
3. | Indonesia | 2 |
4. | Russian Federation | 2 |
5. | Israel | 1 |
6. | Saudi Arabia | 1 |
7. | France | 1 |
8. | Iran, Islamic Republic of | 1 |
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