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Deep Learning Applications in EM ImagingNormal access

Authors: J. Byun, S. Oh, K. Noh, D. Yoon and S.J. Seol
Event name: 81st EAGE Conference and Exhibition 2019 Workshop Programme
Session: WS11 Reservoir Characterization and Monitoring from Inversion and Joint Inversion of Seismic and Non-seismic Methods
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201901986
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 596.94Kb )
Price: € 20

Summary:
Deep learning is now one of the most powerful techniques for solving various scientific and engineering problems. These deep learning techniques have recently begun to be applied in the field of subsurface imaging. As a part of the effort, we have applied the deep learning techniques to the imaging of subsurface from electromagnetic (EM) data. This presentation introduces three cases of the application: salt delineation and monitoring of injected CO₂ using towed streamer EM data sets and kimberlite exploration using airborne EM data set. The results with significant qualities open up the possibility of the deep learning as an alternative of the conventional inversion techniques.


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