Irregular sampling: from aliasing to noise
Seismic data is often irregularly and/or sparsely sampled along spatial coordinates. We show that these acquisition geometries are not necessarily a source of adversity in order to accurately reconstruct adequately-sampled data. We use two examples to illustrate that it may actually be better than e...
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2008
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ndltd-LACETR-oai-collectionscanada.gc.ca-BVAU.2429-5462014-03-14T15:36:37Z Irregular sampling: from aliasing to noise Hennenfent, Gilles Herrmann, Felix J. irregular sampling aliasing interpolation noise Curvelet Recovery Sparsity-Promoting Inversion ground roll Seismic data is often irregularly and/or sparsely sampled along spatial coordinates. We show that these acquisition geometries are not necessarily a source of adversity in order to accurately reconstruct adequately-sampled data. We use two examples to illustrate that it may actually be better than equivalent regularly subsampled data. This comment was already made in earlier works by other authors. We explain this behavior by two key observations. Firstly, a noise-free underdetermined problem can be seen as a noisy well-determined problem. Secondly, regularly subsampling creates strong coherent acquisition noise (aliasing) difficult to remove unlike the noise created by irregularly subsampling that is typically weaker and Gaussian-like 2008-03-08 2008-03-08 2007 text Hennenfent, Gilles, Herrmann, Felix J. 2007. Irregular sampling: from aliasing to noise. EAGE 69th Conference & Exhibition. http://hdl.handle.net/2429/546 eng Herrmann, Felix J. European Association of Geoscientists & Engineers |
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English |
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irregular sampling aliasing interpolation noise Curvelet Recovery Sparsity-Promoting Inversion ground roll |
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irregular sampling aliasing interpolation noise Curvelet Recovery Sparsity-Promoting Inversion ground roll Hennenfent, Gilles Herrmann, Felix J. Irregular sampling: from aliasing to noise |
description |
Seismic data is often irregularly and/or sparsely sampled along spatial coordinates. We show that these acquisition geometries are not necessarily a source of adversity in order to accurately reconstruct adequately-sampled data. We use two examples to illustrate that it may actually be better than equivalent regularly subsampled data. This comment was already made in earlier works by other authors. We explain this behavior by two key observations. Firstly, a noise-free underdetermined problem can be seen as a noisy well-determined problem. Secondly, regularly subsampling creates strong coherent acquisition noise (aliasing) difficult to remove unlike the noise created by irregularly subsampling that is typically weaker and Gaussian-like |
author |
Hennenfent, Gilles Herrmann, Felix J. |
author_facet |
Hennenfent, Gilles Herrmann, Felix J. |
author_sort |
Hennenfent, Gilles |
title |
Irregular sampling: from aliasing to noise |
title_short |
Irregular sampling: from aliasing to noise |
title_full |
Irregular sampling: from aliasing to noise |
title_fullStr |
Irregular sampling: from aliasing to noise |
title_full_unstemmed |
Irregular sampling: from aliasing to noise |
title_sort |
irregular sampling: from aliasing to noise |
publisher |
European Association of Geoscientists & Engineers |
publishDate |
2008 |
url |
http://hdl.handle.net/2429/546 |
work_keys_str_mv |
AT hennenfentgilles irregularsamplingfromaliasingtonoise AT herrmannfelixj irregularsamplingfromaliasingtonoise |
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1716649291975491584 |