Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows
Name
IEEE_SPM_Deep_Learning_for_Seismic_Inverse_Problems.pdf
Description
Accepted version
Size
12.71 MB
Format
Unknown
Checksum (MD5)
3cea9c36f892975753a0126bd138f44c
Author(s)
Adler, Amir
Araya-Polo, Mauricio
Poggio, Tomaso
Date Issued
March 2021
Journal
IEEE Signal Processing Magazine
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
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).
Version
Author's final manuscript
Abstract
© 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.
MIT Department
McGovern Institute for Brain Research at MIT
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/msp.2020.3037429
