3-D resistivity forward modeling and inversion using conjugate gradients
We have developed rapid 3-D dc resistivity forward modeling and inversion algorithms that use conjugate gradient relaxation techniques. In the forward network modeling calculation, an incomplete Cholesky decomposition for preconditioning and sparse matrix routines combine to produce a fast and effic...
| Autores principales: | , , |
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| Formato: | Artículo |
| Lenguaje: | en_US |
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Society of Exploration Geophysicists
2017
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| Acceso en línea: | http://hdl.handle.net/1721.1/108498 |
| _version_ | 1826213066164928512 |
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| author | Zhang, Jie Mackie, Randall L. Madden, Theodore R. |
| author2 | Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Zhang, Jie Mackie, Randall L. Madden, Theodore R. |
| author_sort | Zhang, Jie |
| collection | MIT |
| description | We have developed rapid 3-D dc resistivity forward modeling and inversion algorithms that use conjugate gradient relaxation techniques. In the forward network modeling calculation, an incomplete Cholesky decomposition for preconditioning and sparse matrix routines combine to produce a fast and efficient algorithm (approximately 2 minutes CPU time on a Sun SPARC‐station 2 for 50 × 50 × 20 blocks). The side and bottom boundary conditions are scaled impedance conditions that take into account the local current flow at the boundaries as a result of any configuration of current sources. For the inversion, conjugate gradient relaxation is used to solve the maximum likelihood inverse equations. Since conjugate gradient techniques only require the results of the sensitivity matrix [tilde under A] or its transpose [tilde under A][superscript T] multiplying a vector, we are able to bypass the actual computation of the sensitivity matrix and the inversion of [tilde under A][superscript T] [tilde under A], thus greatly decreasing the time needed to do 3-D inversions. We demonstrate 3-D resistivity tomographic imaging using pole‐pole resistivity data collected during an experiment for a leakage monitoring system near evaporation ponds at the Mojave Generating Station in Laughlin, Nevada. |
| first_indexed | 2024-09-23T15:42:48Z |
| format | Article |
| id | mit-1721.1/108498 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:42:48Z |
| publishDate | 2017 |
| publisher | Society of Exploration Geophysicists |
| record_format | dspace |
| spelling | mit-1721.1/1084982022-10-02T03:36:04Z 3-D resistivity forward modeling and inversion using conjugate gradients Zhang, Jie Mackie, Randall L. Madden, Theodore R. Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Zhang, Jie Mackie, Randall L. Madden, Theodore R. We have developed rapid 3-D dc resistivity forward modeling and inversion algorithms that use conjugate gradient relaxation techniques. In the forward network modeling calculation, an incomplete Cholesky decomposition for preconditioning and sparse matrix routines combine to produce a fast and efficient algorithm (approximately 2 minutes CPU time on a Sun SPARC‐station 2 for 50 × 50 × 20 blocks). The side and bottom boundary conditions are scaled impedance conditions that take into account the local current flow at the boundaries as a result of any configuration of current sources. For the inversion, conjugate gradient relaxation is used to solve the maximum likelihood inverse equations. Since conjugate gradient techniques only require the results of the sensitivity matrix [tilde under A] or its transpose [tilde under A][superscript T] multiplying a vector, we are able to bypass the actual computation of the sensitivity matrix and the inversion of [tilde under A][superscript T] [tilde under A], thus greatly decreasing the time needed to do 3-D inversions. We demonstrate 3-D resistivity tomographic imaging using pole‐pole resistivity data collected during an experiment for a leakage monitoring system near evaporation ponds at the Mojave Generating Station in Laughlin, Nevada. United States. Environmental Protection Agency (grant #CR-821516) 2017-04-28T17:45:04Z 2017-04-28T17:45:04Z 1995-09 1994-12 Article http://purl.org/eprint/type/JournalArticle 0016-8033 1942-2156 http://hdl.handle.net/1721.1/108498 Zhang, Jie, Randall L. Mackie, and Theodore R. Madden. “3-D Resistivity Forward Modeling and Inversion Using Conjugate Gradients.” GEOPHYSICS 60.5 (1995): 1313–1325. © 1995 Society of Exploration Geophysicists en_US http://dx.doi.org/10.1190/1.1443868 Geophysics Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Society of Exploration Geophysicists Society of Exploration Geophysicists |
| spellingShingle | Zhang, Jie Mackie, Randall L. Madden, Theodore R. 3-D resistivity forward modeling and inversion using conjugate gradients |
| title | 3-D resistivity forward modeling and inversion using conjugate gradients |
| title_full | 3-D resistivity forward modeling and inversion using conjugate gradients |
| title_fullStr | 3-D resistivity forward modeling and inversion using conjugate gradients |
| title_full_unstemmed | 3-D resistivity forward modeling and inversion using conjugate gradients |
| title_short | 3-D resistivity forward modeling and inversion using conjugate gradients |
| title_sort | 3 d resistivity forward modeling and inversion using conjugate gradients |
| url | http://hdl.handle.net/1721.1/108498 |
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