Estimation of channelized features in geological media using sparsity constraint
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2008
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| Online Access: | http://hdl.handle.net/1721.1/43040 |
| _version_ | 1811084083985383424 |
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| author | Jafarpour, Behnam |
| author2 | William T. Freeman and Vivek K. Goyal. |
| author_facet | William T. Freeman and Vivek K. Goyal. Jafarpour, Behnam |
| author_sort | Jafarpour, Behnam |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. |
| first_indexed | 2024-09-23T12:44:29Z |
| format | Thesis |
| id | mit-1721.1/43040 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:44:29Z |
| publishDate | 2008 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/430402019-04-12T09:48:32Z Estimation of channelized features in geological media using sparsity constraint Jafarpour, Behnam William T. Freeman and Vivek K. Goyal. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. Includes bibliographical references (p. 86-89). In this thesis, a new approach is studied for inverse modeling of ill-posed problems with spatially continuous parameters that exhibit sparseness in an incoherent basis (e.g. a Fourier basis). The solution is constrained to be sparse in the transform domain and the dimension of the search space is effectively reduced to a low frequency subspace to improve estimation efficiency. The solution subspace is spanned by a subset of a discrete cosine transform (DCT) basis containing low-frequency elements. The methodology is related to compressive sensing, which is a recently introduced paradigm for estimation and perfect reconstruction of sparse signals from partial linear observations in an incoherent basis. The sparsity constraint is applied in the DCT domain and reconstruction of unknown DCT coefficients is carried out through incorporation of point measurements and prior knowledge in the spatial domain. The approach appears to be generally applicable for estimating spatially distributed parameters that are approximately sparse in a transformed domain such as DCT. The suitability of the proposed inversion framework is demonstrated through synthetic examples in characterization of hydrocarbon reservoirs. by Behnam Jafarpour. S.M. 2008-11-07T18:55:11Z 2008-11-07T18:55:11Z 2008 2008 Thesis http://hdl.handle.net/1721.1/43040 243610475 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 89 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Jafarpour, Behnam Estimation of channelized features in geological media using sparsity constraint |
| title | Estimation of channelized features in geological media using sparsity constraint |
| title_full | Estimation of channelized features in geological media using sparsity constraint |
| title_fullStr | Estimation of channelized features in geological media using sparsity constraint |
| title_full_unstemmed | Estimation of channelized features in geological media using sparsity constraint |
| title_short | Estimation of channelized features in geological media using sparsity constraint |
| title_sort | estimation of channelized features in geological media using sparsity constraint |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/43040 |
| work_keys_str_mv | AT jafarpourbehnam estimationofchannelizedfeaturesingeologicalmediausingsparsityconstraint |