Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows

Amir Adler; Mauricio Araya-Polo; Tomaso Poggio

doi:10.1109/MSP.2020.3037429

Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows

Amir Adler
, Mauricio Araya-Polo
, Tomaso Poggio

Research output: Contribution to journal › Article › peer-review

146 Scopus citations

Abstract

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.

Original language	English
Article number	9363496
Pages (from-to)	89-119
Number of pages	31
Journal	IEEE Signal Processing Magazine
Volume	38
Issue number	2
DOIs	https://doi.org/10.1109/MSP.2020.3037429
State	Published - Mar 2021
Externally published	Yes

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10.1109/MSP.2020.3037429

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title = "Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows",

abstract = "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.",

author = "Amir Adler and Mauricio Araya-Polo and Tomaso Poggio",

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year = "2021",

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AU - Araya-Polo, Mauricio

AU - Poggio, Tomaso

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AB - 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.

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Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows

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