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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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2022
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| Online Access: | https://hdl.handle.net/1721.1/138408.2 |
| _version_ | 1811091133474799616 |
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| author | Adler, Amir Araya-Polo, Mauricio Poggio, Tomaso |
| author2 | McGovern Institute for Brain Research at MIT |
| author_facet | McGovern Institute for Brain Research at MIT Adler, Amir Araya-Polo, Mauricio Poggio, Tomaso |
| author_sort | Adler, Amir |
| 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 | 2024-09-23T14:57:32Z |
| format | Article |
| id | mit-1721.1/138408.2 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:57:32Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/138408.22024-06-14T16:06:03Z Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows Adler, Amir Araya-Polo, Mauricio Poggio, Tomaso McGovern Institute for Brain Research at MIT Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences © 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. 2022-03-21T13:20:58Z 2021-12-09T19:30:50Z 2022-03-21T13:20:58Z 2021-03 2021-12-09T19:15:49Z Article http://purl.org/eprint/type/JournalArticle 1053-5888 1558-0792 https://hdl.handle.net/1721.1/138408.2 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 http://dx.doi.org/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/octet-stream Institute of Electrical and Electronics Engineers (IEEE) Prof. Poggio |
| spellingShingle | Adler, Amir Araya-Polo, Mauricio Poggio, Tomaso 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/1721.1/138408.2 |
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