Deep-learning based ocean bottom seismic wavefield recovery
- Ali Siahkoohi (School of Computational Science and Engineering, Georgia Institute of Technology) | Rajiv Kumar (Georgia Institute of Technology, and DownUnder GeoSolutions, Perth, Australia) | Felix J. Herrmann (School of Computational Science and Engineering, Georgia Institute of Technology)
- Document ID
- Society of Exploration Geophysicists
- SEG International Exposition and Annual Meeting, 15-20 September, San Antonio, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Exploration Geophysicists
- Machine learning, Reconstruction, Reciprocity, Ocean-bottom node
- 1 in the last 30 days
- 13 since 2007
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Ocean bottom surveys usually suffer from having very sparse receivers. Assuming a desirable source sampling, achievable by existing methods such as (simultaneous-source) randomized marine acquisition, we propose a deep-learning based scheme to bring the receivers to the same spatial grid as sources using a convolutional neural network. By exploiting source-receiver reciprocity, we construct training pairs by artificially subsampling the fully-sampled single-receiver frequency slices using a random training mask and later, we deploy the trained neural network to fill-in the gaps in single-source frequency slices. Our experiments show that a random training mask is essential for successful wavefield recovery, even when receivers are on a periodic gird. No external training data is required and experiments on a 3D synthetic data set demonstrate that we are able to recover receivers for up to 90% missing receivers, missing either randomly or periodically, with a better recovery for random case, at low to midrange frequencies.
Presentation Date: Monday, September 16, 2019
Session Start Time: 1:50 PM
Presentation Time: 3:05 PM
Presentation Type: Oral
|File Size||7 MB||Number of Pages||6|
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