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

פרסום מחקרי: פרסום בכתב עת › מאמר › ביקורת עמיתים

146 ציטוטים ‏(Scopus)

תקציר

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.

שפה מקורית	אנגלית
מספר המאמר	9363496
עמודים (מ-עד)	89-119
מספר עמודים	31
כתב עת	IEEE Signal Processing Magazine
כרך	38
מספר גיליון	2
מזהי עצם דיגיטלי (DOIs)	https://doi.org/10.1109/MSP.2020.3037429
סטטוס פרסום	פורסם - מרץ 2021
פורסם באופן חיצוני	כן

גישה למסמך

10.1109/MSP.2020.3037429

קבצים וקישורים אחרים

Link to publication in Scopus

פורמט ציטוט ביבליוגרפי

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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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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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