Self-Supervised Deep Learning to Reconstruct Seismic Data With Consecutively Missing Traces
Author:
Affiliation:
1. State Key Laboratory of Marine Geology, School of Ocean and Earth Science, Tongji University, Shanghai, China
2. ByteDance Inc., Shanghai, China
3. AI Laboratory, SAIC Motor Corporation Ltd., Shanghai, China
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Strategic Priority Research Program of the Chinese Academy of Science
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9633014/09703292.pdf?arnumber=9703292
Reference64 articles.
1. Seismic data interpolation using deep learning with generative adversarial networks
2. Seismic trace interpolation for irregularly spatial sampled data using convolutional autoencoder
3. Streaming prediction-error filters
4. Accurate data reconstruction through simultaneous application of statistical and physics-based constraints to multiple geophysical data sets
5. Seismic trace interpolation in the f‐x‐y domain
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