Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows
© 1991-2012 IEEE. Seismic inversion is a fundamental tool in geophysical analysis, providing a window into Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for hydrocarbon exploration, mining, earthquake analysis, shallow hazard assessment, and other geophys...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/138408 |
| _version_ | 1811077522795790336 |
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| author | Adler, A Araya-Polo, M Poggio, T |
| author_facet | Adler, A Araya-Polo, M Poggio, T |
| author_sort | Adler, A |
| collection | MIT |
| description | © 1991-2012 IEEE. Seismic inversion is a fundamental tool in geophysical analysis, providing a window into Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for hydrocarbon exploration, mining, earthquake analysis, shallow hazard assessment, and other geophysical tasks. |
| first_indexed | 2024-09-23T10:44:22Z |
| format | Article |
| id | mit-1721.1/138408 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:44:22Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1384082021-12-10T03:26:53Z Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows Adler, A Araya-Polo, M Poggio, T © 1991-2012 IEEE. Seismic inversion is a fundamental tool in geophysical analysis, providing a window into Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for hydrocarbon exploration, mining, earthquake analysis, shallow hazard assessment, and other geophysical tasks. 2021-12-09T19:30:50Z 2021-12-09T19:30:50Z 2021-03-01 2021-12-09T19:15:49Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/138408 Adler, A, Araya-Polo, M and Poggio, T. 2021. "Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows." IEEE Signal Processing Magazine, 38 (2). en 10.1109/MSP.2020.3037429 IEEE Signal Processing Magazine Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Prof. Poggio |
| spellingShingle | Adler, A Araya-Polo, M Poggio, T Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows |
| title | Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows |
| title_full | Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows |
| title_fullStr | Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows |
| title_full_unstemmed | Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows |
| title_short | Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows |
| title_sort | deep learning for seismic inverse problems toward the acceleration of geophysical analysis workflows |
| url | https://hdl.handle.net/1721.1/138408 |
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