Velocity model building by deep learning: From general synthetics to field data application

Vladimir Kazei, Oleg Ovcharenko, Tariq Ali Alkhalifah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations


Velocity model building is not straightforward in geologically complex environments. We train a convolutional neural network (CNN) to map full wavefields to smooth subsurface parameter distributions to address the problem. Specifically, cubes of neighboring CMP gathers are mapped into in 1D vertical profiles to simplify the training phase and to make it easier to utilize well logs in future applications. We train the CNN using a total of one hundred thousand random subsurface models generated on-the-fly and the corresponding synthetic data. The application of the trained CNN on synthetic and real data admitted reasonably accurate models representing mostly the low wavenumber features of the true models.
Original languageEnglish (US)
Title of host publicationSEG Technical Program Expanded Abstracts 2020
PublisherSociety of Exploration Geophysicists
StatePublished - Oct 1 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-08
Acknowledgements: We thank Saudi Aramco for its support and CGG for sharingthe broadseis field data. The research reported in this publica-tion was supported by funding from King Abdullah Universityof Science and Technology (KAUST), Thuwal, 23955-6900,Saudi Arabia


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